Econstudentlog

Developmental Biology (I)

On goodreads I called the book “[a]n excellent introduction to the field of developmental biology” and I gave it five stars.

Below I have included some sample observations from the first third of the book or so, as well as some supplementary links.

“The major processes involved in development are: pattern formation; morphogenesis or change in form; cell differentiation by which different types of cell develop; and growth. These processes involve cell activities, which are determined by the proteins present in the cells. Genes control cell behaviour by controlling where and when proteins are synthesized, and cell behaviour provides the link between gene action and developmental processes. What a cell does is determined very largely by the proteins it contains. The hemoglobin in red blood cells enables them to transport oxygen; the cells lining the vertebrate gut secrete specialized digestive enzymes. These activities require specialized proteins […] In development we are concerned primarily with those proteins that make cells different from one another and make them carry out the activities required for development of the embryo. Developmental genes typically code for proteins involved in the regulation of cell behaviour. […] An intriguing question is how many genes out of the total genome are developmental genes – that is, genes specifically required for embryonic development. This is not easy to estimate. […] Some studies suggest that in an organism with 20,000 genes, about 10% of the genes may be directly involved in development.”

“The fate of a group of cells in the early embryo can be determined by signals from other cells. Few signals actually enter the cells. Most signals are transmitted through the space outside of cells (the extracellular space) in the form of proteins secreted by one cell and detected by another. Cells may interact directly with each other by means of molecules located on their surfaces. In both these cases, the signal is generally received by receptor proteins in the cell membrane and is subsequently relayed through other signalling proteins inside the cell to produce the cellular response, usually by turning genes on or off. This process is known as signal transduction. These pathways can be very complex. […] The complexity of the signal transduction pathway means that it can be altered as the cell develops so the same signal can have a different effect on different cells. How a cell responds to a particular signal depends on its internal state and this state can reflect the cell’s developmental history — cells have good memories. Thus, different cells can respond to the same signal in very different ways. So the same signal can be used again and again in the developing embryo. There are thus rather few signalling proteins.”

“All vertebrates, despite their many outward differences, have a similar basic body plan — the segmented backbone or vertebral column surrounding the spinal cord, with the brain at the head end enclosed in a bony or cartilaginous skull. These prominent structures mark the antero-posterior axis with the head at the anterior end. The vertebrate body also has a distinct dorso-ventral axis running from the back to the belly, with the spinal cord running along the dorsal side and the mouth defining the ventral side. The antero-posterior and dorso-ventral axes together define the left and right sides of the animal. Vertebrates have a general bilateral symmetry around the dorsal midline so that outwardly the right and left sides are mirror images of each other though some internal organs such as the heart and liver are arranged asymmetrically. How these axes are specified in the embryo is a key issue. All vertebrate embryos pass through a broadly similar set of developmental stages and the differences are partly related to how and when the axes are set up, and how the embryo is nourished. […] A quite rare but nevertheless important event before gastrulation in mammalian embryos, including humans, is the splitting of the embryo into two, and identical twins can then develop. This shows the remarkable ability of the early embryo to regulate [in this context, regulation refers to ‘the ability of an embryo to restore normal development even if some portions are removed or rearranged very early in development’ – US] and develop normally when half the normal size […] In mammals, there is no sign of axes or polarity in the fertilized egg or during early development, and it only occurs later by an as yet unknown mechanism.”

“How is left–right established? Vertebrates are bilaterally symmetric about the midline of the body for many structures, such as eyes, ears, and limbs, but most internal organs are asymmetric. In mice and humans, for example, the heart is on the left side, the right lung has more lobes than the left, the stomach and spleen lie towards the left, and the bulk of the liver is towards the right. This handedness of organs is remarkably consistent […] Specification of left and right is fundamentally different from specifying the other axes of the embryo, as left and right have meaning only after the antero-posterior and dorso-ventral axes have been established. If one of these axes were reversed, then so too would be the left–right axis and this is the reason that handedness is reversed when you look in a mirror—your dorsoventral axis is reversed, and so left becomes right and vice versa. The mechanisms by which left–right symmetry is initially broken are still not fully understood, but the subsequent cascade of events that leads to organ asymmetry is better understood. The ‘leftward’ flow of extracellular fluid across the embryonic midline by a population of ciliated cells has been shown to be critical in mouse embryos in inducing asymmetric expression of genes involved in establishing left versus right. The antero-posterior patterning of the mesoderm is most clearly seen in the differences in the somites that form vertebrae: each individual vertebra has well defined anatomical characteristics depending on its location along the axis. Patterning of the skeleton along the body axis is based on the somite cells acquiring a positional value that reflects their position along the axis and so determines their subsequent development. […] It is the Hox genes that define positional identity along the antero-posterior axis […]. The Hox genes are members of the large family of homeobox genes that are involved in many aspects of development and are the most striking example of a widespread conservation of developmental genes in animals. The name homeobox comes from their ability to bring about a homeotic transformation, converting one region into another. Most vertebrates have clusters of Hox genes on four different chromosomes. A very special feature of Hox gene expression in both insects and vertebrates is that the genes in the clusters are expressed in the developing embryo in a temporal and spatial order that reflects their order on the chromosome. Genes at one end of the cluster are expressed in the head region, while those at the other end are expressed in the tail region. This is a unique feature in development, as it is the only known case where a spatial arrangement of genes on a chromosome corresponds to a spatial pattern in the embryo. The Hox genes provide the somites and adjacent mesoderm with positional values that determine their subsequent development.”

“Many of the genes that control the development of flies are similar to those controlling development in vertebrates, and indeed in many other animals. it seems that once evolution finds a satisfactory way of developing animal bodies, it tends to use the same mechanisms and molecules over and over again with, of course, some important modifications. […] The insect body is bilaterally symmetrical and has two distinct and largely independent axes: the antero-posterior and dorso-ventral axes, which are at right angles to each other. These axes are already partly set up in the fly egg, and become fully established and patterned in the very early embryo. Along the antero-posterior axis the embryo becomes divided into a number of segments, which will become the head, thorax, and abdomen of the larva. A series of evenly spaced grooves forms more or less simultaneously and these demarcate parasegments, which later give rise to the segments of the larva and adult. Of the fourteen larval parasegments, three contribute to mouthparts of the head, three to the thoracic region, and eight to the abdomen. […] Development is initiated by a gradient of the protein Bicoid, along the axis running from anterior to posterior in the egg; this provides the positional information required for further patterning along this axis. Bicoid is a transcription factor and acts as a morphogen—a graded concentration of a molecule that switches on particular genes at different threshold concentrations, thereby initiating a new pattern of gene expression along the axis. Bicoid activates anterior expression of the gene hunchback […]. The hunchback gene is switched on only when Bicoid is present above a certain threshold concentration. The protein of the hunchback gene, in turn, is instrumental in switching on the expression of the other genes, along the antero-posterior axis. […] The dorso-ventral axis is specified by a different set of maternal genes from those that specify the anterior-posterior axis, but by a similar mechanism. […] Once each parasegment is delimited, it behaves as an independent developmental unit, under the control of a particular set of genes. The parasegments are initially similar but each will soon acquire its own unique identity mainly due to Hox genes.”

“Because plant cells have rigid cell walls and, unlike animal cells, cannot move, a plant’s development is very much the result of patterns of oriented cell divisions and increase in cell size. Despite this difference, cell fate in plant development is largely determined by similar means as in animals – by a combination of positional signals and intercellular communication. […] The logic behind the spatial layouts of gene expression that pattern a developing flower is similar to that of Hox gene action in patterning the body axis in animals, but the genes involved are completely different. One general difference between plant and animal development is that most of the development occurs not in the embryo but in the growing plant. Unlike an animal embryo, the mature plant embryo inside a seed is not simply a smaller version of the organism it will become. All the ‘adult’ structures of the plant – shoots, roots, stalks, leaves, and flowers – are produced in the adult plant from localized groups of undifferentiated cells known as meristems. […] Another important difference between plant and animal cells is that a complete, fertile plant can develop from a single differentiated somatic cell and not just from a fertilized egg. This suggests that, unlike the differentiated cells of adult animals, some differentiated cells of the adult plant may retain totipotency and so behave like animal embryonic stem cells. […] The small organic molecule auxin is one of the most important and ubiquitous chemical signals in plant development and plant growth.”

“All animal embryos undergo a dramatic change in shape during their early development. This occurs primarily during gastrulation, the process that transforms a two-dimensional sheet of cells into the complex three-dimensional animal body, and involves extensive rearrangements of cell layers and the directed movement of cells from one location to another. […] Change in form is largely a problem in cell mechanics and requires forces to bring about changes in cell shape and cell migration. Two key cellular properties involved in changes in animal embryonic form are cell contraction and cell adhesiveness. Contraction in one part of a cell can change the cell’s shape. Changes in cell shape are generated by forces produced by the cytoskeleton, an internal protein framework of filaments. Animal cells stick to one another, and to the external support tissue that surrounds them (the extracellular matrix), through interactions involving cell-surface proteins. Changes in the adhesion proteins at the cell surface can therefore determine the strength of cell–cell adhesion and its specificity. These adhesive interactions affect the surface tension at the cell membrane, a property that contributes to the mechanics of the cell behaviour. Cells can also migrate, with contraction again playing a key role. An additional force that operates during morphogenesis, particularly in plants but also in a few aspects of animal embryogenesis, is hydrostatic pressure, which causes cells to expand. In plants there is no cell movement or change in shape, and changes in form are generated by oriented cell division and cell expansion. […] Localized contraction can change the shape of the cells as well as the sheet they are in. For example, folding of a cell sheet—a very common feature in embryonic development—is caused by localized changes in cell shape […]. Contraction on one side of a cell results in it acquiring a wedge-like form; when this occurs among a few cells locally in a sheet, a bend occurs at the site, deforming the sheet.”

“The integrity of tissues in the embryo is maintained by adhesive interactions between cells and between cells and the extracellular matrix; differences in cell adhesiveness also help maintain the boundaries between different tissues and structures. Cells stick to each other by means of cell adhesion molecules, such as cadherins, which are proteins on the cell surface that can bind strongly to proteins on other cell surfaces. About 30 different types of cadherins have been identified in vertebrates. […] Adhesion of a cell to the extracellular matrix, which contains proteins such as collagen, is by the binding of integrins in the cell membrane to these matrix molecules. […] Convergent extension plays a key role in gastrulation of [some] animals and […] morphogenetic processes. It is a mechanism for elongating a sheet of cells in one direction while narrowing its width, and occurs by rearrangement of cells within the sheet, rather than by cell migration or cell division. […] For convergent extension to take place, the axes along which the cells will intercalate and extend must already have been defined. […] Gastrulation in vertebrates involves a much more dramatic and complex rearrangement of tissues than in sea urchins […] But the outcome is the same: the transformation of a two-dimensional sheet of cells into a three-dimensional embryo, with ectoderm, mesoderm, and endoderm in the correct positions for further development of body structure. […] Directed dilation is an important force in plants, and results from an increase in hydrostatic pressure inside a cell. Cell enlargement is a major process in plant growth and morphogenesis, providing up to a fiftyfold increase in the volume of a tissue. The driving force for expansion is the hydrostatic pressure exerted on the cell wall as a result of the entry of water into cell vacuoles by osmosis. Plant-cell expansion involves synthesis and deposition of new cell-wall material, and is an example of directed dilation. The direction of cell growth is determined by the orientation of the cellulose fibrils in the cell wall.”

Links:

Developmental biology.
August Weismann. Hans Driesch. Hans Spemann. Hilde Mangold. Spemann-Mangold organizer.
Induction. Cleavage.
Developmental model organisms.
Blastula. Embryo. Ectoderm. Mesoderm. Endoderm.
Gastrulation.
Xenopus laevis.
Notochord.
Neurulation.
Organogenesis.
DNA. Gene. Protein. Transcription factor. RNA polymerase.
Epiblast. Trophoblast/trophectoderm. Inner cell mass.
Pluripotency.
Polarity in embryogenesis/animal-vegetal axis.
Primitive streak.
Hensen’s node.
Neural tube. Neural fold. Neural crest cells.
Situs inversus.
Gene silencing. Morpholino.
Drosophila embryogenesis.
Pair-rule gene.
Cell polarity.
Mosaic vs regulative development.
Caenorhabditis elegans.
Fate mapping.
Plasmodesmata.
Arabidopsis thaliana.
Apical-basal axis.
Hypocotyl.
Phyllotaxis.
Primordium.
Quiescent centre.
Filopodia.
Radial cleavage. Spiral cleavage.

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June 11, 2018 Posted by | Biology, Books, Botany, Genetics, Molecular biology | Leave a comment

Molecular biology (III)

Below I have added a few quotes and links related to the last few chapters of the book‘s coverage.

“Normal ageing results in part from exhaustion of stem cells, the cells that reside in most organs to replenish damaged tissue. As we age DNA damage accumulates and this eventually causes the cells to enter a permanent non-dividing state called senescence. This protective ploy however has its downside as it limits our lifespan. When too many stem cells are senescent the body is compromised in its capacity to renew worn-out tissue, causing the effects of ageing. This has a knock-on effect of poor intercellular communication, mitochondrial dysfunction, and loss of protein balance (proteostasis). Low levels of chronic inflammation also increase with ageing and could be the trigger for changes associated with many age-related disorders.”

“There has been a dramatic increase in ageing research using yeast and invertebrates, leading to the discovery of more ‘ageing genes’ and their pathways. These findings can be extrapolated to humans since longevity pathways are conserved between species. The major pathways known to influence ageing have a common theme, that of sensing and metabolizing nutrients. […] The field was advanced by identification of the mammalian Target Of Rapamycin, aptly named mTOR. mTOR acts as a molecular sensor that integrates growth stimuli with nutrient and oxygen availability. Small molecules such as rapamycin that reduce mTOR signalling act in a similar way to severe dietary restriction in slowing the ageing process in organisms such as yeast and worms. […] Rapamycin and its derivatives (rapalogs) have been involved in clinical trials on reducing age-related pathologies […] Another major ageing pathway is telomere maintenance. […] Telomere attrition is a hallmark of ageing and studies have established an association between shorter telomere length (TL) and the risk of various common age-related ailments […] Telomere loss is accelerated by known determinants of ill health […] The relationship between TL and cancer appears complex.”

“Cancer is not a single disease but a range of diseases caused by abnormal growth and survival of cells that have the capacity to spread. […] One of the early stages in the acquisition of an invasive phenotype is epithelial-mesenchymal transition (EMT). Epithelial cells form skin and membranes and for this they have a strict polarity (a top and a bottom) and are bound in position by close connections with adjacent cells. Mesenchymal cells on the other hand are loosely associated, have motility, and lack polarization. The transition between epithelial and mesenchymal cells is a normal process during embryogenesis and wound healing but is deregulated in cancer cells. EMT involves transcriptional reprogramming in which epithelial structural proteins are lost and mesenchymal ones acquired. This facilitates invasion of a tumour into surrounding tissues. […] Cancer is a genetic disease but mostly not inherited from the parents. Normal cells evolve to become cancer cells by acquiring successive mutations in cancer-related genes. There are two main classes of cancer genes, the proto-oncogenes and the tumour suppressor genes. The proto-oncogenes code for protein products that promote cell proliferation. […] A mutation in a proto-oncogene changes it to an ‘oncogene’ […] One gene above all others is associated with cancer suppression and that is TP53. […] approximately half of all human cancers carry a mutated TP53 and in many more, p53 is deregulated. […] p53 plays a key role in eliminating cells that have either acquired activating oncogenes or excessive genomic damage. Thus mutations in the TP53 gene allows cancer cells to survive and divide further by escaping cell death […] A mutant p53 not only lacks the tumour suppressor functions of the normal or wild type protein but in many cases it also takes on the role of an oncogene. […] Overall 5-10 per cent of cancers occur due to inherited or germ line mutations that are passed from parents to offspring. Many of these genes code for DNA repair enzymes […] The vast majority of cancer mutations are not inherited; instead they are sporadic with mutations arising in somatic cells. […] At least 15 per cent of cancers are attributable to infectious agents, examples being HPV and cervical cancer, H. pylori and gastric cancer, and also hepatitis B or C and liver cancer.”

“There are about 10 million different sites at which people can vary in their DNA sequence withing the 3 billion bases in our DNA. […] A few, but highly variable sequences or minisatellites are chosen for DNA profiling. These give a highly sensitive procedure suitable for use with small amounts of body fluids […] even shorter sequences called microsatellite repeats [are also] used. Each marker or microsatellite is a short tandem repeat (STR) of two to five base pairs of DNA sequence. A single STR will be shared by up to 20 per cent of the population but by using a dozen or so identification markers in profile, the error is miniscule. […] Microsatellites are extremely useful for analysing low-quality or degraded DNA left at a crime scene as their short sequences are usually preserved. However, DNA in specimens that have not been optimally preserved persists in exceedingly small amounts and is also highly fragmented. It is probably also riddled by contamination and chemical damage. Such sources of DNA sources of DNA are too degraded to obtain a profile using genomic STRs and in these cases mitochondrial DNA, being more abundant, is more useful than nuclear DNA for DNA profiling. […]  Mitochondrial DNA profiling is the method of choice for determining the identities of missing or unknown people when a maternally linked relative can be found. Molecular biologists can amplify hypervariable regions of mitochondrial DNA by PCR to obtain enough material for analysis. The DNA products are sequenced and single nucleotide differences are sought with a reference DNA from a maternal relative. […] It has now become possible for […] ancient DNA to reveal much more than genotype matches. […] Pigmentation characteristics can now be determined from ancient DNA since skin, hair, and eye colour are some of the easiest characteristics to predict. This is due to the limited number of base differences or SNPs required to explain most of the variability.”

“A broad range of debilitating and fatal conditions, non of which can be cured, are associated with mitochondrial DNA mutations. […] [M]itochondrial DNA mutates ten to thirty times faster than nuclear DNA […] Mitochondrial DNA mutates at a higher rate than nuclear DNA due to higher numbers of DNA molecules and reduced efficiency in controlling DNA replication errors. […] Over 100,000 copies of mitochondrial DNA are present in the cytoplasm of the human egg or oocyte. After fertilization, only maternal mitochondria survive; the small numbers of the father’s mitochondria in the zygote are targeted for destruction. Thus all mitochondrial DNA for all cell types in the resulting embryo is maternal-derived. […] Patients affected by mitochondrial disease usually have a mixture of wild type (normal) and mutant mitochondrial DNA and the disease severity depends on the ratio of the two. Importantly the actual level of mutant DNA in a mother’s heteroplas[m]y […curiously the authors throughout the coverage insist on spelling this ‘heteroplasty’, which according to google is something quite different – I decided to correct the spelling error (?) here – US] is not inherited and offspring can be better or worse off than the mother. This also causes uncertainty since the ratio of wild type to mutant mitochondria may change during development. […] Over 700 mutations in mitochondrial DNA have been found leading to myopathies, neurodegeneration, diabetes, cancer, and infertility.”

Links:

Dementia. Alzheimer’s disease. Amyloid hypothesis. Tau protein. Proteopathy. Parkinson’s disease. TP53-inducible glycolysis and apoptosis regulator (TIGAR).
Progeria. Progerin. Werner’s syndrome. Xeroderma pigmentosum. Cockayne syndrome.
Shelterin.
Telomerase.
Alternative lengthening of telomeres: models, mechanisms and implications (Nature).
Coats plus syndrome.
Neoplasia. Tumor angiogenesis. Inhibitor protein MDM2.
Li–Fraumeni syndrome.
Non-coding RNA networks in cancer (Nature).
Cancer stem cell. (“The reason why current cancer therapies often fail to eradicate the disease is that the CSCs survive current DNA damaging treatments and repopulate the tumour.” See also this IAS lecture which covers closely related topics – US.)
Imatinib.
Restriction fragment length polymorphism (RFLP).
CODIS.
MC1R.
Archaic human admixture with modern humans.
El Tor strain.
DNA barcoding.
Hybrid breakdown/-inviability.
Trastuzumab.
Digital PCR.
Pearson’s syndrome.
Mitochondrial replacement therapy.
Synthetic biology.
Artemisinin.
Craig Venter.
Genome editing.
Indel.
CRISPR.
Tyrosinemia.

June 3, 2018 Posted by | Biology, Books, Cancer/oncology, Genetics, Medicine, Molecular biology | Leave a comment

A few diabetes papers of interest

i. Reevaluating the Evidence for Blood Pressure Targets in Type 2 Diabetes.

“There is general consensus that treating adults with type 2 diabetes mellitus (T2DM) and hypertension to a target blood pressure (BP) of <140/90 mmHg helps prevent cardiovascular disease (CVD). Whether more intensive BP control should be routinely targeted remains a matter of debate. While the American Diabetes Association (ADA) BP guidelines recommend an individualized assessment to consider different treatment goals, the American College of Cardiology/American Heart Association BP guidelines recommend a BP target of <130/80 mmHg for most individuals with hypertension, including those with T2DM (13).

In large part, these discrepant recommendations reflect the divergent results of the Action to Control Cardiovascular Risk in Diabetes-BP trial (ACCORD-BP) among people with T2DM and the Systolic Blood Pressure Intervention Trial (SPRINT), which excluded people with diabetes (4,5). Both trials evaluated the effect of intensive compared with standard BP treatment targets (<120 vs. <140 mmHg systolic) on a composite CVD end point of nonfatal myocardial infarction or stroke or death from cardiovascular causes. SPRINT also included unstable angina and acute heart failure in its composite end point. While ACCORD-BP did not show a significant benefit from the intervention (hazard ratio [HR] 0.88; 95% CI 0.73–1.06), SPRINT found a significant 25% relative risk reduction on the primary end point favoring intensive therapy (0.75; 0.64–0.89).”

“To some extent, CVD mechanisms and causes of death differ in T2DM patients compared with the general population. Microvascular disease (particularly kidney disease), accelerated vascular calcification, and diabetic cardiomyopathy are common in T2DM (1315). Moreover, the rate of sudden cardiac arrest is markedly increased in T2DM and related, in part, to diabetes-specific factors other than ischemic heart disease (16). Hypoglycemia is a potential cause of CVD mortality that is specific to diabetes (17). In addition, polypharmacy is common and may increase CVD risk (18). Furthermore, nonvascular causes of death account for approximately 40% of the premature mortality burden experienced by T2DM patients (19). Whether these disease processes may render patients with T2DM less amenable to derive a mortality benefit from intensive BP control, however, is not known and should be the focus of future research.

In conclusion, the divergent results between ACCORD-BP and SPRINT are most readily explained by the apparent lack of benefit of intensive BP control on CVD and all-cause mortality in ACCORD-BP, rather than differences in the design, population characteristics, or interventions between the trials. This difference in effects on mortality may be attributable to differential mechanisms underlying CVD mortality in T2DM, to chance, or to both. These observations suggest that caution should be exercised extrapolating the results of SPRINT to patients with T2DM and support current ADA recommendations to individualize BP targets, targeting a BP of <140/90 mmHg in the majority of patients with T2DM and considering lower BP targets when it is anticipated that individual benefits outweigh risks.”

ii. Modelling incremental benefits on complications rates when targeting lower HbA1c levels in people with Type 2 diabetes and cardiovascular disease.

“Glucose‐lowering interventions in Type 2 diabetes mellitus have demonstrated reductions in microvascular complications and modest reductions in macrovascular complications. However, the degree to which targeting different HbA1c reductions might reduce risk is unclear. […] Participant‐level data for Trial Evaluating Cardiovascular Outcomes with Sitagliptin (TECOS) participants with established cardiovascular disease were used in a Type 2 diabetes‐specific simulation model to quantify the likely impact of different HbA1c decrements on complication rates. […] The use of the TECOS data limits our findings to people with Type 2 diabetes and established cardiovascular disease. […] Ten‐year micro‐ and macrovascular rates were estimated with HbA1c levels fixed at 86, 75, 64, 53 and 42 mmol/mol (10%, 9%, 8%, 7% and 6%) while holding other risk factors constant at their baseline levels. Cumulative relative risk reductions for each outcome were derived for each HbA1c decrement. […] Of 5717 participants studied, 72.0% were men and 74.2% White European, with a mean (sd) age of 66.2 (7.9) years, systolic blood pressure 134 (16.9) mmHg, LDL‐cholesterol 2.3 (0.9) mmol/l, HDL‐cholesterol 1.13 (0.3) mmol/l and median Type 2 diabetes duration 9.6 (5.1–15.6) years. Ten‐year cumulative relative risk reductions for modelled HbA1c values of 75, 64, 53 and 42 mmol/mol, relative to 86 mmol/mol, were 4.6%, 9.3%, 15.1% and 20.2% for myocardial infarction; 6.0%, 12.8%, 19.6% and 25.8% for stroke; 14.4%, 26.6%, 37.1% and 46.4% for diabetes‐related ulcer; 21.5%, 39.0%, 52.3% and 63.1% for amputation; and 13.6%, 25.4%, 36.0% and 44.7 for single‐eye blindness. […] We did not investigate outcomes for renal failure or chronic heart failure as previous research conducted to create the model did not find HbA1c to be a statistically significant independent risk factor for either condition, therefore no clinically meaningful differences would be expected from modelling different HbA1c levels 11.”

“For microvascular complications, the absolute median estimates tended to be lower than for macrovascular complications at the same HbA1c level, but cumulative relative risk reductions were greater. For amputation the 10‐year absolute median estimate for a modelled constant HbA1c of 86 mmol/mol (10%) was 3.8% (3.7, 3.9), with successively lower values for each modelled 1% HbA1c decrement. Compared with the 86 mmol/mol (10%) HbA1c level, median relative risk reductions for amputation were 21.5% (21.1, 21.9) at 75 mmol/mol (9%) increasing to 52.3% (52.0, 52.6) at 53 mmol/mol (7%). […] Relative risk reductions in micro‐ and macrovascular complications for each 1% HbA1c reduction were similar for each decrement. The exception was all‐cause mortality, where the relative risk reductions for 1% HbA1c decrements were greater at higher baseline HbA1c levels. These simulated outcomes differ from the Diabetes Control and Complications Trial outcome in people with Type 1 diabetes, where lowering HbA1c from higher baseline levels had a greater impact on microvascular risk reduction 18.”

iii. Laser photocoagulation for proliferative diabetic retinopathy (Cochrane review).

“Diabetic retinopathy is a complication of diabetes in which high blood sugar levels damage the blood vessels in the retina. Sometimes new blood vessels grow in the retina, and these can have harmful effects; this is known as proliferative diabetic retinopathy. Laserphotocoagulation is an intervention that is commonly used to treat diabetic retinopathy, in which light energy is applied to the retinawith the aim of stopping the growth and development of new blood vessels, and thereby preserving vision. […] The aim of laser photocoagulation is to slow down the growth of new blood vessels in the retina and thereby prevent the progression of visual loss (Ockrim 2010). Focal laser photocoagulation uses the heat of light to seal or destroy abnormal blood vessels in the retina. Individual vessels are treated with a small number of laser burns.

PRP [panretinal photocoagulation, US] aims to slow down the growth of new blood vessels in a wider area of the retina. Many hundreds of laser burns are placed on the peripheral parts of the retina to stop blood vessels from growing (RCOphth 2012). It is thought that the anatomic and functional changes that result from photocoagulation may improve the oxygen supply to the retina, and so reduce the stimulus for neovascularisation (Stefansson 2001). Again the exact mechanisms are unclear, but it is possible that the decreased area of retinal tissue leads to improved oxygenation and a reduction in the levels of anti-vascular endothelial growth factor. A reduction in levels of anti-vascular endothelial growth factor may be important in reducing the risk of harmful new vessels forming. […] Laser photocoagulation is a well-established common treatment for DR and there are many different potential strategies for delivery of laser treatment that are likely to have different effects. A systematic review of the evidence for laser photocoagulation will provide important information on benefits and harms to guide treatment choices. […] This is the first in a series of planned reviews on laser photocoagulation. Future reviews will compare different photocoagulation techniques.”

“We identified a large number of trials of laser photocoagulation of diabetic retinopathy (n = 83) but only five of these studies were eligible for inclusion in the review, i.e. they compared laser photocoagulation with currently available lasers to no (or deferred) treatment. Three studies were conducted in the USA, one study in the UK and one study in Japan. A total of 4786 people (9503 eyes) were included in these studies. The majority of participants in four of these trials were people with proliferative diabetic retinopathy; one trial recruited mainly people with non-proliferative retinopathy.”

“At 12 months there was little difference between eyes that received laser photocoagulation and those allocated to no treatment (or deferred treatment), in terms of loss of 15 or more letters of visual acuity (risk ratio (RR) 0.99, 95% confidence interval (CI) 0.89 to1.11; 8926 eyes; 2 RCTs, low quality evidence). Longer term follow-up did not show a consistent pattern, but one study found a 20% reduction in risk of loss of 15 or more letters of visual acuity at five years with laser treatment. Treatment with laser reduced the risk of severe visual loss by over 50% at 12 months (RR 0.46, 95% CI 0.24to 0.86; 9276 eyes; 4 RCTs, moderate quality evidence). There was a beneficial effect on progression of diabetic retinopathy with treated eyes experiencing a 50% reduction in risk of progression of diabetic retinopathy (RR 0.49, 95% CI 0.37 to 0.64; 8331 eyes; 4 RCTs, low quality evidence) and a similar reduction in risk of vitreous haemorrhage (RR 0.56, 95% CI 0.37 to 0.85; 224 eyes; 2RCTs, low quality evidence).”

“Overall there is not a large amount of evidence from RCTs on the effects of laser photocoagulation compared to no treatment or deferred treatment. The evidence is dominated by two large studies conducted in the US population (DRS 1978; ETDRS 1991). These two studies were generally judged to be at low or unclear risk of bias, with the exception of inevitable unmasking of patients due to differences between intervention and control. […] In current clinical guidelines, e.g. RCOphth 2012, PRP is recommended in high-risk PDR. The recommendation is that “as retinopathy approaches the proliferative stage, laser scatter treatment (PRP) should be increasingly considered to prevent progression to high risk PDR” based on other factors such as patients’ compliance or planned cataract surgery.

These recommendations need to be interpreted while considering the risk of visual loss associated with different levels of severity of DR, as well as the risk of progression. Since PRP reduces the risk of severe visual loss, but not moderate visual loss that is more related to diabetic maculopathy, most ophthalmologists judge that there is little benefit in treating non-proliferative DR at low risk of severe visual damage, as patients would incur the known adverse effects of PRP, which, although mild, include pain and peripheral visual field loss and transient DMO [diabetic macular oedema, US]. […] This review provides evidence that laser photocoagulation is beneficial in treating diabetic retinopathy. […] based on the baseline risk of progression of the disease, and risk of visual loss, the current approach of caution in treating non-proliferative DR with laser would appear to be justified.

By current standards the quality of the evidence is not high, however, the effects on risk of progression and risk of severe visual loss are reasonably large (50% relative risk reduction).”

iv. Immune Recognition of β-Cells: Neoepitopes as Key Players in the Loss of Tolerance.

I should probably warn beforehand that this one is rather technical. It relates reasonably closely to topics covered in the molecular biology book I recently covered here on the blog, and if I had not read that book quite recently I almost certainly would not have been able to read the paper – so the coverage below is more ‘for me’ than ‘for you’. Anyway, some quotes:

“Prior to the onset of type 1 diabetes, there is progressive loss of immune self-tolerance, evidenced by the accumulation of islet autoantibodies and emergence of autoreactive T cells. Continued autoimmune activity leads to the destruction of pancreatic β-cells and loss of insulin secretion. Studies of samples from patients with type 1 diabetes and of murine disease models have generated important insights about genetic and environmental factors that contribute to susceptibility and immune pathways that are important for pathogenesis. However, important unanswered questions remain regarding the events that surround the initial loss of tolerance and subsequent failure of regulatory mechanisms to arrest autoimmunity and preserve functional β-cells. In this Perspective, we discuss various processes that lead to the generation of neoepitopes in pancreatic β-cells, their recognition by autoreactive T cells and antibodies, and potential roles for such responses in the pathology of disease. Emerging evidence supports the relevance of neoepitopes generated through processes that are mechanistically linked with β-cell stress. Together, these observations support a paradigm in which neoepitope generation leads to the activation of pathogenic immune cells that initiate a feed-forward loop that can amplify the antigenic repertoire toward pancreatic β-cell proteins.”

“Enzymatic posttranslational processes that have been implicated in neoepitope generation include acetylation (10), citrullination (11), glycosylation (12), hydroxylation (13), methylation (either protein or DNA methylation) (14), phosphorylation (15), and transglutamination (16). Among these, citrullination and transglutamination are most clearly implicated as processes that generate neoantigens in human disease, but evidence suggests that others also play a role in neoepitope formation […] Citrulline, which is among the most studied PTMs in the context of autoimmunity, is a diagnostic biomarker of rheumatoid arthritis (RA). […] Anticitrulline antibodies are among the earliest immune responses that are diagnostic of RA and often correlate with disease severity (18). We have recently documented the biological consequences of citrulline modifications and autoimmunity that arise from pancreatic β-cell proteins in the development of T1D (19). In particular, citrullinated GAD65 and glucose-regulated protein (GRP78) elicit antibody and T-cell responses in human T1D and in NOD diabetes, respectively (20,21).”

Carbonylation is an irreversible, iron-catalyzed oxidative modification of the side chains of lysine, arginine, threonine, or proline. Mitochondrial functions are particularly sensitive to carbonyl modification, which also has detrimental effects on other intracellular enzymatic pathways (30). A number of diseases have been linked with altered carbonylation of self-proteins, including Alzheimer and Parkinson diseases and cancer (27). There is some data to support that carbonyl PTM is a mechanism that directs unstable self-proteins into cellular degradation pathways. It is hypothesized that carbonyl PTM [post-translational modification] self-proteins that fail to be properly degraded in pancreatic β-cells are autoantigens that are targeted in T1D. Recently submitted studies have identified several carbonylated pancreatic β-cell neoantigens in human and murine models of T1D (27). Among these neoantigens are chaperone proteins that are required for the appropriate folding and secretion of insulin. These studies imply that although some PTM self-proteins may be direct targets of autoimmunity, others may alter, interrupt, or disturb downstream metabolic pathways in the β-cell. In particular, these studies indicated that upstream PTMs resulted in misfolding and/or metabolic disruption between proinsulin and insulin production, which provides one explanation for recent observations of increased proinsulin-to-insulin ratios in the progression of T1D (31).”

“Significant hypomethylation of DNA has been linked with several classic autoimmune diseases, such as SLE, multiple sclerosis, RA, Addison disease, Graves disease, and mixed connective tissue disease (36). Therefore, there is rationale to consider the possible influence of epigenetic changes on protein expression and immune recognition in T1D. Relevant to T1D, epigenetic modifications occur in pancreatic β-cells during progression of diabetes in NOD mice (37). […] Consequently, DNMTs [DNA methyltransferases] and protein arginine methyltransferases are likely to play a role in the regulation of β-cell differentiation and insulin gene expression, both of which are pathways that are altered in the presence of inflammatory cytokines. […] Eizirik et al. (38) reported that exposure of human islets to proinflammatory cytokines leads to modulation of transcript levels and increases in alternative splicing for a number of putative candidate genes for T1D. Their findings suggest a mechanism through which alternative splicing may lead to the generation of neoantigens and subsequent presentation of novel β-cell epitopes (39).”

“The phenomenon of neoepitope recognition by autoantibodies has been shown to be relevant in a variety of autoimmune diseases. For example, in RA, antibody responses directed against various citrullinated synovial proteins are remarkably disease-specific and routinely used as a diagnostic test in the clinic (18). Appearance of the first anticitrullinated protein antibodies occurs years prior to disease onset, and accumulation of additional autoantibody specificities correlates closely with the imminent onset of clinical arthritis (44). There is analogous evidence supporting a hierarchical emergence of autoantibody specificities and multiple waves of autoimmune damage in T1D (3,45). Substantial data from longitudinal studies indicate that insulin and GAD65 autoantibodies appear at the earliest time points during progression, followed by additional antibody specificities directed at IA-2 and ZnT8.”

“Multiple autoimmune diseases often cluster within families (or even within one person), implying shared etiology. Consequently, relevant insights can be gleaned from studies of more traditional autoantibody-mediated systemic autoimmune diseases, such as SLE and RA, where inter- and intramolecular epitope spreading are clearly paradigms for disease progression (47). In general, early autoimmunity is marked by restricted B- and T-cell epitopes, followed by an expanded repertoire coinciding with the onset of more significant tissue pathology […] Akin to T1D, other autoimmune syndromes tend to cluster to subcellular tissues or tissue components that share biological or biochemical properties. For example, SLE is marked by autoimmunity to nucleic acid–bearing macromolecules […] Unlike other systemic autoantibody-mediated diseases, such as RA and SLE, there is no clear evidence that T1D-related autoantibodies play a pathogenic role. Autoantibodies against citrulline-containing neoepitopes of proteoglycan are thought to trigger or intensify arthritis by forming immune complexes with this autoantigen in the joints of RA patients with anticitrullinated protein antibodies. In a similar manner, autoantibodies and immune complexes are hallmarks of tissue pathology in SLE. Therefore, it remains likely that autoantibodies or the B cells that produce them contribute to the pathogenesis of T1D.”

“In summation, the existing literature demonstrates that oxidation, citrullination, and deamidation can have a direct impact on T-cell recognition that contributes to loss of tolerance.”

“There is a general consensus that the pathogenesis of T1D is initiated when individuals who possess a high level of genetic risk (e.g., susceptible HLA, insulin VNTR, PTPN22 genotypes) are exposed to environmental factors (e.g., enteroviruses, diet, microbiome) that precipitate a loss of tolerance that manifests through the appearance of insulin and/or GAD autoantibodies. This early autoimmunity is followed by epitope spreading, increasing both the number of antigenic targets and the diversity of epitopes within these targets. These processes create a feed-forward loop antigen release that induces increasing inflammation and increasing numbers of distinct T-cell specificities (64). The formation and recognition of neoepitopes represents one mechanism through which epitope spreading can occur. […] mechanisms related to neoepitope formation and recognition can be envisioned at multiple stages of T1D pathogenesis. At the level of genetic risk, susceptible individuals may exhibit a genetically driven impairment of their stress response, increasing the likelihood of neoepitope formation. At the level of environmental exposure, many of the insults that are thought to initiate T1D are known to cause neoepitope formation. During the window of β-cell destruction that encompasses early autoimmunity through dysglycemia and diagnosis of T1D it remains unclear when neoepitope responses appear in relation to “classic” responses to insulin and GAD65. However, by the time of onset, neoepitope responses are clearly present and remain as part of the ongoing autoimmunity that is present during established T1D. […] The ultimate product of both direct and indirect generation of neoepitopes is an accumulation of robust and diverse autoimmune B- and T-cell responses, accelerating the pathological destruction of pancreatic islets. Clearly, the emergence of sophisticated methods of tissue and single-cell proteomics will identify novel neoepitopes, including some that occur at near the earliest stages of disease. A detailed mechanistic understanding of the pathways that lead to specific classes of neoepitopes will certainly suggest targets of therapeutic manipulation and intervention that would be hoped to impede the progression of disease.”

v. Diabetes technology: improving care, improving patient‐reported outcomes and preventing complications in young people with Type 1 diabetes.

“With the evolution of diabetes technology, those living with Type 1 diabetes are given a wider arsenal of tools with which to achieve glycaemic control and improve patient‐reported outcomes. Furthermore, the use of these technologies may help reduce the risk of acute complications, such as severe hypoglycaemia and diabetic ketoacidosis, as well as long‐term macro‐ and microvascular complications. […] Unfortunately, diabetes goals are often unmet and people with Type 1 diabetes too frequently experience acute and long‐term complications of this condition, in addition to often having less than ideal psychosocial outcomes. Increasing realization of the importance of patient‐reported outcomes is leading to diabetes care delivery becoming more patient‐centred. […] Optimal diabetes management requires both the medical and psychosocial needs of people with Type 1 diabetes and their caregivers to be addressed. […] The aim of this paper was to demonstrate how, by incorporating technology into diabetes care, we can increase patient‐centered care, reduce acute and chronic diabetes complications, and improve clinical outcomes and quality of life.”

[The paper’s Table 2 on page 422 of the pdf-version is awesome, it includes a lot of different Hba1c estimates from various patient populations all across the world. The numbers included in the table are slightly less awesome, as most populations only achieve suboptimal metabolic control.]

“The risks of all forms of complications increase with higher HbA1c concentration, increasing diabetes duration, hypertension, presence of other microvascular complications, obesity, insulin resistance, hyperlipidaemia and smoking 6. Furthermore, the Diabetes Research in Children (DirecNet) study has shown that individuals with Type 1 diabetes have white matter differences in the brain and cognitive differences compared with individuals without Type 1 diabetes. These studies showed that the degree of structural differences in the brain were related to the degree of chronic hyperglycaemia, hypoglycaemia and glucose variability 7. […] In addition to long‐term complications, people with Type 1 diabetes are also at risk of acute complications. Severe hypoglycaemia, a hypoglycaemic event resulting in altered/loss of consciousness or seizures, is a serious complication of insulin therapy. If unnoticed and untreated, severe hypoglycaemia can result in death. […] The incidence of diabetic ketoacidosis, a life‐threatening consequence of diabetes, remains unacceptably high in children with established diabetes (Table 5). The annual incidence of ketoacidosis was 5% in the Prospective Diabetes Follow‐Up Registry (DPV) in Germany and Austria, 6.4% in the National Paediatric Diabetes Audit (NPDA), and 7.1% in the Type 1 Diabetes Exchange (T1DX) registry 10. Psychosocial factors including female gender, non‐white race, lower socio‐economic status, and elevated HbA1c all contribute to increased risk of diabetic ketoacidosis 11.”

“Depression is more common in young people with Type 1 diabetes than in young people without a chronic disease […] Depression can make it more difficult to engage in diabetes self‐management behaviours, and as a result, contributes to suboptimal glycaemic control and lower rates of self‐monitoring of blood glucose (SMBG) in young people with Type 1 diabetes 15. […] Unlike depression, diabetes distress is not a clinical diagnosis but rather emotional distress that comes from the burden of living with and managing diabetes 16. A recent systematic review found that roughly one‐third of young people with Type 1 diabetes (age 10–20 years) have some level of diabetes distress and that diabetes distress was consistently associated with higher HbA1c and worse self‐management 17. […] Eating and weight‐related comorbidities also exist for individuals with Type 1 diabetes. There is a higher incidence of obesity in individuals with Type 1 diabetes on intensive insulin therapy. […] Adolescent girls and young adult women with Type 1 diabetes are more likely to omit insulin for weight loss and have disordered eating habits 20.”

“In addition to screening for and treating depression and diabetes distress to improve overall diabetes management, it is equally important to assess quality of life as well as positive coping factors that may also influence self‐management and well‐being. For example, lower scores on the PROMIS® measure of global health, which assesses social relationships as well as physical and mental well‐being, have been linked to higher depression scores and less frequent blood glucose checks 13. Furthermore, coping strategies such as problem‐solving, emotional expression, and acceptance have been linked to lower HbA1c and enhanced quality of life 21.”

“Self‐monitoring of blood glucose via multiple finger sticks for capillary blood samples per day has been the ‘gold standard’ for glucose monitoring, but SMBG only provides glucose measurements as snapshots in time. Still, the majority of young people with Type 1 diabetes use SMBG as their main method to assess glycaemia. Data from the T1DX registry suggest that an increased frequency of SMBG is associated with lower HbA1c levels 23. The development of continuous glucose monitoring (CGM) provides more values, along with the rate and direction of glucose changes. […] With continued use, CGM has been shown to decrease the incidence of hypoglycaemia and HbA1c levels 26. […] Insulin can be administered via multiple daily injections or continuous subcutaneous insulin infusion (insulin pumps). Over the last 30 years, insulin pumps have become smaller with more features, making them a valuable alternative to multiple daily injections. Insulin pump use in various registries ranges from as low as 5.9% among paediatric patients in the New Zealand national register 28 to as high as 74% in the German/Austrian DPV in children aged <6 years (Table 2) 29. Recent data suggest that consistent use of insulin pumps can result in improved HbA1c values and decreased incidence of severe hypoglycaemia 30, 31. Insulin pumps have been associated with improved quality of life 32. The data on insulin pumps and diabetic ketoacidosis are less clear.”

“The majority of Type 1 diabetes management is carried out outside the clinical setting and in individuals’ daily lives. People with Type 1 diabetes must make complex treatment decisions multiple times daily; thus, diabetes self‐management skills are central to optimal diabetes management. Unfortunately, many people with Type 1 diabetes and their caregivers are not sufficiently familiar with the necessary diabetes self‐management skills. […] Parents are often the first who learn these skills. As children become older, they start receiving more independence over their diabetes care; however, the transition of responsibilities from caregiver to child is often unstructured and haphazard. It is important to ensure that both individuals with diabetes and their caregivers have adequate self‐management skills throughout the diabetes journey.”

“In the developed world (nations with the highest gross domestic product), 87% of the population has access to the internet and 68% report using a smartphone 39. Even in developing countries, 54% of people use the internet and 37% own smartphones 39. In many areas, smartphones are the primary source of internet access and are readily available. […] There are >1000 apps for diabetes on the Apple App Store and the Google Play store. Many of these apps have focused on nutrition, blood glucose logging, and insulin dosing. Given the prevalence of smartphones and the interest in having diabetes apps handy, there is the potential for using a smartphone to deliver education and decision support tools. […] The new psychosocial position statement from the ADA recommends routine psychosocial screening in clinic. These recommendations include screening for: 1) depressive symptoms annually, at diagnosis, or with changes in medical status; 2) anxiety and worry about hypoglycaemia, complications and other diabetes‐specific worries; 3) disordered eating and insulin omission for purposes of weight control; 4) and diabetes distress in children as young as 7 or 8 years old 16. Implementation of in‐clinic screening for depression in young people with Type 1 diabetes has already been shown to be feasible, acceptable and able to identify individuals in need of treatment who may otherwise have gone unnoticed for a longer period of time which would have been having a detrimental impact on physical health and quality of life 13, 40. These programmes typically use tablets […] to administer surveys to streamline the screening process and automatically score measures 13, 40. This automation allows psychologists and social workers to focus on care delivery rather than screening. In addition to depression screening, automated tablet‐based screening for parental depression, distress and anxiety; problem‐solving skills; and resilience/positive coping factors can help the care team understand other psychosocial barriers to care. This approach allows the development of patient‐ and caregiver‐centred interventions to improve these barriers, thereby improving clinical outcomes and complication rates.”

“With the advent of electronic health records, registries and downloadable medical devices, people with Type 1 diabetes have troves of data that can be analysed to provide insights on an individual and population level. Big data analytics for diabetes are still in the early stages, but present great potential for improving diabetes care. IBM Watson Health has partnered with Medtronic to deliver personalized insights to individuals with diabetes based on device data 48. Numerous other systems […] allow people with Type 1 diabetes to access their data, share their data with the healthcare team, and share de‐identified data with the research community. Data analysis and insights such as this can form the basis for the delivery of personalized digital health coaching. For example, historical patterns can be analysed to predict activity and lead to pro‐active insulin adjustment to prevent hypoglycaemia. […] Improvements to diabetes care delivery can occur at both the population level and at the individual level using insights from big data analytics.”

vi. Route to improving Type 1 diabetes mellitus glycaemic outcomes: real‐world evidence taken from the National Diabetes Audit.

“While control of blood glucose levels reduces the risk of diabetes complications, it can be very difficult for people to achieve. There has been no significant improvement in average glycaemic control among people with Type 1 diabetes for at least the last 10 years in many European countries 6.

The National Diabetes Audit (NDA) in England and Wales has shown relatively little change in the levels of HbA1c being achieved in people with Type 1 diabetes over the last 10 years, with >70% of HbA1c results each year being >58 mmol/mol (7.5%) 7.

Data for general practices in England are published by the NDA. NHS Digital publishes annual prescribing data, including British National Formulary (BNF) codes 7, 8. Together, these data provide an opportunity to investigate whether there are systematic associations between HbA1c levels in people with Type 1 diabetes and practice‐level population characteristics, diabetes service levels and use of medication.”

“The Quality and Outcomes Framework (a payment system for general practice performance) provided a baseline list of all general practices in England for each year, the practice list size and number of people (both with Type 1 and Type 2 diabetes) on their diabetes register. General practice‐level data of participating practices were taken from the NDA 2013–2014, 2014–2015 and 2015–2016 (5455 practices in the last year). They include Type 1 diabetes population characteristics, routine review checks and the proportions of people achieving target glycaemic control and/or being at higher glycaemic risk.

Diabetes medication data for all people with diabetes were taken from the general practice prescribing in primary care data for 2013–2014, 2014–2015 and 2015–2016, including insulin and blood glucose monitoring (BGM) […] A total of 20 indicators were created that covered the epidemiological, service, medication, technological, costs and outcomes performance for each practice and year. The variance in these indicators over the 4‐year period and among general practices was also considered. […] The values of the indicators found to be in the 90th percentile were used to quantify the potential of highest performing general practices. […] In total 13 085 practice‐years of data were analysed, covering 437 000 patient‐years of management.”

“There was significant variation among the participating general practices (Fig. 3) in the proportion of people achieving target glycaemic control target [percentage of people with HbA1c ≤58 mmol/mol (7.5%)] and in the proportion at high glycaemic risk [percentage of people with HbA1c >86 mmol/mol (10%)]. […] Our analysis showed that, at general practice level, the median target glycaemic control attainment was 30%, while the 10th percentile was 16%, and the 90th percentile was 45%. The corresponding median for the high glycaemic risk percentage was 16%, while the 10th percentile (corresponding to the best performing practices) was 6% and the 90th percentile (greatest proportion of Type 1 diabetes at high glycaemic risk) was 28%. Practices in the deciles for both lowest target glycaemic control and highest high glycaemic risk had 49% of the results in the 58–86 mmol/mol range. […] A very wide variation was found in the percentage of insulin for presumed pump use (deduced from prescriptions of fast‐acting vial insulin), with a median of 3.8% at general practice level. The 10th percentile was 0% and the 90th percentile was 255% of the median inferred pump usage.”

“[O]ur findings suggest that if all practices optimized service and therapies to the levels achieved by the top decile then 16 100 (7%) more people with Type 1 diabetes would achieve the glycaemic control target of 58 mmol/mol (7.5%) and 11 500 (5%) fewer people would have HbA1c >86 mmol/mol (10%). Put another way, if the results for all practices were at the top decile level, 36% vs 29% of people with Type 1 diabetes would achieve the glycaemic control target of HbA1c ≤ 58 mmol/mol (7.5%), and as few as 10% could have HbA1c levels > 86 mmol/mol (10%) compared with 15% currently (Fig. 6). This has significant implications for the potential to improve the longer‐term outcomes of people with Type 1 diabetes, given the close link between glycaemia and complications in such individuals 5, 10, 11.”

“We found that the significant variation among the participating general practices (Fig. 2) in terms of the proportion of people with HbA1c ≤58 mmol/mol (7.5%) was only partially related to a lower proportion of people with HbA1c >86 mmol/mol (10%). There was only a weak relationship between level of target glycaemia achieved and avoidance of very suboptimal glycaemia. The overall r2 value was 0.6. This suggests that there is a degree of independence between these outcomes, so that success factors at a general practice level differ for people achieving optimal glycaemia vs those factors affecting avoiding a level of at risk glycaemia.”

May 30, 2018 Posted by | Cardiology, Diabetes, Epidemiology, Genetics, Immunology, Medicine, Molecular biology, Ophthalmology, Studies | Leave a comment

Molecular biology (II)

Below I have added some more quotes and links related to the book’s coverage:

“[P]roteins are the most abundant molecules in the body except for water. […] Proteins make up half the dry weight of a cell whereas DNA and RNA make up only 3 per cent and 20 per cent respectively. […] The approximately 20,000 protein-coding genes in the human genome can, by alternative splicing, multiple translation starts, and post-translational modifications, produce over 1,000,000 different proteins, collectively called ‘the proteome‘. It is the size of the proteome and not the genome that defines the complexity of an organism. […] For simple organisms, such as viruses, all the proteins coded by their genome can be deduced from its sequence and these comprise the viral proteome. However for higher organisms the complete proteome is far larger than the genome […] For these organisms not all the proteins coded by the genome are found in any one tissue at any one time and therefore a partial proteome is usually studied. What are of interest are those proteins that are expressed in specific cell types under defined conditions.”

“Enzymes are proteins that catalyze or alter the rate of chemical reactions […] Enzymes can speed up reactions […] but they can also slow some reactions down. Proteins play a number of other critical roles. They are involved in maintaining cell shape and providing structural support to connective tissues like cartilage and bone. Specialized proteins such as actin and myosin are required [for] muscular movement. Other proteins act as ‘messengers’ relaying signals to regulate and coordinate various cell processes, e.g. the hormone insulin. Yet another class of protein is the antibodies, produced in response to foreign agents such as bacteria, fungi, and viruses.”

“Proteins are composed of amino acids. Amino acids are organic compounds with […] an amino group […] and a carboxyl group […] In addition, amino acids carry various side chains that give them their individual functions. The twenty-two amino acids found in proteins are called proteinogenic […] but other amino acids exist that are non-protein functioning. […] A peptide bond is formed between two amino acids by the removal of a water molecule. […] each individual unit in a peptide or protein is known as an amino acid residue. […] Chains of less than 50-70 amino acid residues are known as peptides or polypeptides and >50-70 as proteins, although many proteins are composed of more than one polypeptide chain. […] Proteins are macromolecules consisting of one or more strings of amino acids folded into highly specific 3D-structures. Each amino acid has a different size and carries a different side group. It is the nature of the different side groups that facilitates the correct folding of a polypeptide chain into a functional tertiary protein structure.”

“Atoms scatter the waves of X-rays mainly through their electrons, thus forming secondary or reflected waves. The pattern of X-rays diffracted by the atoms in the protein can be captured on a photographic plate or an image sensor such as a charge coupled device placed behind the crystal. The pattern and relative intensity of the spots on the diffraction image are then used to calculate the arrangement of atoms in the original protein. Complex data processing is required to convert the series of 2D diffraction or scatter patterns into a 3D image of the protein. […] The continued success and significance of this technique for molecular biology is witnessed by the fact that almost 100,000 structures of biological molecules have been determined this way, of which most are proteins.”

“The number of proteins in higher organisms far exceeds the number of known coding genes. The fact that many proteins carry out multiple functions but in a regulated manner is one way a complex proteome arises without increasing the number of genes. Proteins that performed a single role in the ancestral organism have acquired extra and often disparate functions through evolution. […] The active site of an enzyme employed in catalysis is only a small part of the protein, leaving spare capacity for acquiring a second function. […] The glycolytic pathway is involved in the breakdown of sugars such as glucose to release energy. Many of the highly conserved and ancient enzymes from this pathway have developed secondary or ‘moonlighting’ functions. Proteins often change their location in the cell in order to perform a ‘second job’. […] The limited size of the genome may not be the only evolutionary pressure for proteins to moonlight. Combining two functions in one protein can have the advantage of coordinating multiple activities in a cell, enabling it to respond quickly to changes in the environment without the need for lengthy transcription and translational processes.”

Post-translational modifications (PTMs) […] is [a] process that can modify the role of a protein by addition of chemical groups to amino acids in the peptide chain after translation. Addition of phosphate groups (phosphorylation), for example, is a common mechanism for activating or deactivating an enzyme. Other common PTMs include addition of acetyl groups (acetylation), glucose (glucosylation), or methyl groups (methylation). […] Some additions are reversible, facilitating the switching between active and inactive states, and others are irreversible such as marking a protein for destruction by ubiquitin. [The difference between reversible and irreversible modifications can be quite important in pharmacology, and if you’re curious to know more about these topics Coleman’s drug metabolism text provide great coverage of related topics – US.] Diseases caused by malfunction of these modifications highlight the importance of PTMs. […] in diabetes [h]igh blood glucose lead to unwanted glocosylation of proteins. At the high glucose concentrations associated with diabetes, an unwanted irreversible chemical reaction binds the gllucose to amino acid residues such as lysines exposed on the protein surface. The glucosylated proteins then behave badly, cross-linking themselves to the extracellular matrix. This is particularly dangerous in the kidney where it decreases function and can lead to renal failure.”

“Twenty thousand protein-coding genes make up the human genome but for any given cell only about half of these are expressed. […] Many genes get switched off during differentiation and a major mechanism for this is epigenetics. […] an epigenetic trait […] is ‘a stably heritable phenotype resulting from changes in the chromosome without alterations in the DNA sequence’. Epigenetics involves the chemical alteration of DNA by methyl or other small molecular groups to affect the accessibility of a gene by the transcription machinery […] Epigenetics can […] act on gene expression without affecting the stability of the genetic code by modifying the DNA, the histones in chromatin, or a whole chromosome. […] Epigenetic signatures are not only passed on to somatic daughter cells but they can also be transferred through the germline to the offspring. […] At first the evidence appeared circumstantial but more recent studies have provided direct proof of epigenetic changes involving gene methylation being inherited. Rodent models have provided mechanistic evidence. […] the importance of epigenetics in development is highlighted by the fact that low dietary folate, a nutrient essential for methylation, has been linked to higher risk of birth defects in the offspring.” […on the other hand, well…]

The cell cycle is divided into phases […] Transition from G1 into S phase commits the cell to division and is therefore a very tightly controlled restriction point. Withdrawal of growth factors, insufficient nucleotides, or energy to complete DNA replication, or even a damaged template DNA, would compromise the process. Problems are therefore detected and the cell cycle halted by cell cycle inhibitors before the cell has committed to DNA duplication. […] The cell cycle inhibitors inactive the kinases that promote transition through the phases, thus halting the cell cycle. […] The cell cycle can also be paused in S phase to allow time for DNA repairs to be carried out before cell division. The consequences of uncontrolled cell division are so catastrophic that evolution has provided complex checks and balances to maintain fidelity. The price of failure is apoptosis […] 50 to 70 billion cells die every day in a human adult by the controlled molecular process of apoptosis.”

“There are many diseases that arise because a particular protein is either absent or a faulty protein is produced. Administering a correct version of that protein can treat these patients. The first commercially available recombinant protein to be produced for medical use was human insulin to treat diabetes mellitus. […] (FDA) approved the recombinant insulin for clinical use in 1982. Since then over 300 protein-based recombinant pharmaceuticals have been licensed by the FDA and the European Medicines Agency (EMA) […], and many more are undergoing clinical trials. Therapeutic proteins can be produced in bacterial cells but more often mammalian cells such as the Chinese hamster ovary cell line and human fibroblasts are used as these hosts are better able to produce fully functional human protein. However, using mammalian cells is extremely expensive and an alternative is to use live animals or plants. This is called molecular pharming and is an innovative way of producing large amounts of protein relatively cheaply. […] In plant pharming, tobacco, rice, maize, potato, carrots, and tomatoes have all been used to produce therapeutic proteins. […] [One] class of proteins that can be engineered using gene-cloning technology is therapeutic antibodies. […] Therapeutic antibodies are designed to be monoclonal, that is, they are engineered so that they are specific for a particular antigen to which they bind, to block the antigen’s harmful effects. […] Monoclonal antibodies are at the forefront of biological therapeutics as they are highly specific and tend not to induce major side effects.”

“In gene therapy the aim is to restore the function of a faulty gene by introducing a correct version of that gene. […] a cloned gene is transferred into the cells of a patient. Once inside the cell, the protein encoded by the gene is produced and the defect is corrected. […] there are major hurdles to be overcome for gene therapy to be effective. One is the gene construct has to be delivered to the diseased cells or tissues. This can often be difficult […] Mammalian cells […] have complex mechanisms that have evolved to prevent unwanted material such as foreign DNA getting in. Second, introduction of any genetic construct is likely to trigger the patient’s immune response, which can be fatal […] once delivered, expression of the gene product has to be sustained to be effective. One approach to delivering genes to the cells is to use genetically engineered viruses constructed so that most of the viral genome is deleted […] Once inside the cell, some viral vectors such as the retroviruses integrate into the host genome […]. This is an advantage as it provides long-lasting expression of the gene product. However, it also poses a safety risk, as there is little control over where the viral vector will insert into the patient’s genome. If the insertion occurs within a coding gene, this may inactivate gene function. If it integrates close to transcriptional start sites, where promoters and enhancer sequences are located, inappropriate gene expression can occur. This was observed in early gene therapy trials [where some patients who got this type of treatment developed cancer as a result of it. A few more details hereUS] […] Adeno-associated viruses (AAVs) […] are often used in gene therapy applications as they are non-infectious, induce only a minimal immune response, and can be engineered to integrate into the host genome […] However, AAVs can only carry a small gene insert and so are limited to use with genes that are of a small size. […] An alternative delivery system to viruses is to package the DNA into liposomes that are then taken up by the cells. This is safer than using viruses as liposomes do not integrate into the host genome and are not very immunogenic. However, liposome uptake by the cells can be less efficient, resulting in lower expression of the gene.”

Links:

One gene–one enzyme hypothesis.
Molecular chaperone.
Protein turnover.
Isoelectric point.
Gel electrophoresis. Polyacrylamide.
Two-dimensional gel electrophoresis.
Mass spectrometry.
Proteomics.
Peptide mass fingerprinting.
Worldwide Protein Data Bank.
Nuclear magnetic resonance spectroscopy of proteins.
Immunoglobulins. Epitope.
Western blot.
Immunohistochemistry.
Crystallin. β-catenin.
Protein isoform.
Prion.
Gene expression. Transcriptional regulation. Chromatin. Transcription factor. Gene silencing. Histone. NF-κB. Chromatin immunoprecipitation.
The agouti mouse model.
X-inactive specific transcript (Xist).
Cell cycle. Cyclin. Cyclin-dependent kinase.
Retinoblastoma protein pRb.
Cytochrome c. CaspaseBcl-2 family. Bcl-2-associated X protein.
Hybridoma technology. Muromonab-CD3.
Recombinant vaccines and the development of new vaccine strategies.
Knockout mouse.
Adenovirus Vectors for Gene Therapy, Vaccination and Cancer Gene Therapy.
Genetically modified food. Bacillus thuringiensis. Golden rice.

 

May 29, 2018 Posted by | Biology, Books, Chemistry, Diabetes, Engineering, Genetics, Immunology, Medicine, Molecular biology, Pharmacology | Leave a comment

Molecular biology (I?)

“This is a great publication, considering the format. These authors in my opinion managed to get quite close to what I’d consider to be ‘the ideal level of coverage’ for books of this nature.”

The above was what I wrote in my short goodreads review of the book. In this post I’ve added some quotes from the first chapters of the book and some links to topics covered.

Quotes:

“Once the base-pairing double helical structure of DNA was understood it became apparent that by holding and preserving the genetic code DNA is the source of heredity. The heritable material must also be capable of faithful duplication every time a cell divides. The DNA molecule is ideal for this. […] The effort then concentrated on how the instructions held by the DNA were translated into the choice of the twenty different amino acids that make up proteins. […] George Gamov [yes, that George Gamov! – US] made the suggestion that information held in the four bases of DNA (A, T, C, G) must be read as triplets, called codons. Each codon, made up of three nucleotides, codes for one amino acid or a ‘start’ or ‘stop’ signal. This information, which determines an organism’s biochemical makeup, is known as the genetic code. An encryption based on three nucleotides means that there are sixty-four possible three-letter combinations. But there are only twenty amino acids that are universal. […] some amino acids can be coded for by more than one codon.”

“The mechanism of gene expression whereby DNA transfers its information into proteins was determined in the early 1960s by Sydney Brenner, Francois Jacob, and Matthew Meselson. […] Francis Crick proposed in 1958 that information flowed in one direction only: from DNA to RNA to protein. This was called the ‘Central Dogma‘ and describes how DNA is transcribed into RNA, which then acts as a messenger carrying the information to be translated into proteins. Thus the flow of information goes from DNA to RNA to proteins and information can never be transferred back from protein to nucleic acid. DNA can be copied into more DNA (replication) or into RNA (transcription) but only the information in mRNA [messenger RNA] can be translated into protein”.

“The genome is the entire DNA contained within the forty-six chromosomes located in the nucleus of each human somatic (body) cell. […] The complete human genome is composed of over 3 billion bases and contain approximately 20,000 genes that code for proteins. This is much lower than earlier estimates of 80,000 to 140,000 and astonished the scientific community when revealed through human genome sequencing. Equally surprising was the finding that genomes of much simpler organisms sequenced at the same time contained a higher number of protein-coding genes than humans. […] It is now clear that the size of the genome does not correspond with the number of protein-coding genes, and these do not determine the complexity of an organism. Protein-coding genes can be viewed as ‘transcription units’. These are made up of sequences called exons that code for amino acids, and separated by by non-coding sequences called introns. Associated with these are additional sequences termed promoters and enhancers that control the expression of that gene.”

“Some sections of the human genome code for RNA molecules that do not have the capacity to produce proteins. […] it is now becoming apparent that many play a role in controlling gene expression. Despite the importance of proteins, less than 1.5 per cent of the genome is made up of exon sequences. A recent estimate is that about 80 per cent of the genome is transcribed or involved in regulatory functions with the rest mainly composed of repetitive sequences. […] Satellite DNA […] is a short sequence repeated many thousands of times in tandem […] A second type of repetitive DNA is the telomere sequence. […] Their role is to prevent chromosomes from shortening during DNA replication […] Repetitive sequences can also be found distributed or interspersed throughout the genome. These repeats have the ability to move around the genome and are referred to as mobile or transposable DNA. […] Such movements can be harmful sometimes as gene sequences can be disrupted causing disease. […] The vast majority of transposable sequences are no longer able to move around and are considered to be ‘silent’. However, these movements have contributed, over evolutionary time, to the organization and evolution of the genome, by creating new or modified genes leading to the production of proteins with novel functions.”

“A very important property of DNA is that it can make an accurate copy of itself. This is necessary since cells die during the normal wear and tear of tissues and need to be replenished. […] DNA replication is a highly accurate process with an error occurring every 10,000 to 1 million bases in human DNA. This low frequency is because the DNA polymerases carry a proofreading function. If an incorrect nucleotide is incorporated during DNA synthesis, the polymerase detects the error and excises the incorrect base. Following excision, the polymerase reinserts the correct base and replication continues. Any errors that are not corrected through proofreading are repaired by an alternative mismatch repair mechanism. In some instances, proofreading and repair mechanisms fail to correct errors. These become permanent mutations after the next cell division cycle as they are no longer recognized as errors and are therefore propagated each time the DNA replicates.”

DNA sequencing identifies the precise linear order of the nucleotide bases A, C, G, T, in a DNA fragment. It is possible to sequence individual genes, segments of a genome, or whole genomes. Sequencing information is fundamental in helping us understand how our genome is structured and how it functions. […] The Human Genome Project, which used Sanger sequencing, took ten years to sequence and cost 3 billion US dollars. Using high-throughput sequencing, the entire human genome can now be sequenced in a few days at a cost of 3,000 US dollars. These costs are continuing to fall, making it more feasible to sequence whole genomes. The human genome sequence published in 2003 was built from DNA pooled from a number of donors to generate a ‘reference’ or composite genome. However, the genome of each individual is unique and so in 2005 the Personal Genome Project was launched in the USA aiming to sequence and analyse the genomes of 100,000 volunteers across the world. Soon after, similar projects followed in Canada and Korea and, in 2013, in the UK. […] To store and analyze the huge amounts of data, computational systems have developed in parallel. This branch of biology, called bioinformatics, has become an extremely important collaborative research area for molecular biologists drawing on the expertise of computer scientists, mathematicians, and statisticians.”

“[T]he structure of RNA differs from DNA in three fundamental ways. First, the sugar is a ribose, whereas in DNA it is a deoxyribose. Secondly, in RNA the nucleotide bases are A, G, C, and U (uracil) instead of A, G, C, and T. […] Thirdly, RNA is a single-stranded molecule unlike double-stranded DNA. It is not helical in shape but can fold to form a hairpin or stem-loop structure by base-pairing between complementary regions within the same RNA molecule. These two-dimensional secondary structures can further fold to form complex three-dimensional, tertiary structures. An RNA molecule is able to interact not only with itself, but also with other RNAs, with DNA, and with proteins. These interactions, and the variety of conformations that RNAs can adopt, enables them to carry out a wide range of functions. […] RNAs can influence many normal cellular and disease processes by regulating gene expression. RNA interference […] is one of the main ways in which gene expression is regulated.”

“Translation of the mRNA to a protein takes place in the cell cytoplasm on ribosomes. Ribosomes are cellular structures made up primarily of rRNA and proteins. At the ribosomes, the mRNA is decoded to produce a specific protein according to the rules defined by the genetic code. The correct amino acids are brought to the mRNA at the ribosomes by molecules called transfer RNAs (tRNAs). […] At the start of translation, a tRNA binds to the mRNA at the start codon AUG. This is followed by the binding of a second tRNA matching the adjacent mRNA codon. The two neighbouring amino acids linked to the tRNAs are joined together by a chemical bond called the peptide bond. Once the peptide bond forms, the first tRNA detaches leaving its amino acid behind. The ribosome then moves one codon along the mRNA and a third tRNA binds. In this way, tRNAs sequentially bind to the mRNA as the ribosome moves from codon to codon. Each time a tRNA molecule binds, the linked amino acid is transferred to the growing amino acid chain. Thus the mRNA sequence is translated into a chain of amino acids connected by peptide bonds to produce a polypeptide chain. Translation is terminated when the ribosome encounters a stop codon […]. After translation, the chain is folded and very often modified by the addition of sugar or other molecules to produce fully functional proteins.”

“The naturally occurring RNAi pathway is now extensively exploited in the laboratory to study the function of genes. It is possible to design synthetic siRNA molecules with a sequence complementary to the gene under study. These double-stranded RNA molecules are then introduced into the cell by special techniques to temporarily knock down the expression of that gene. By studying the phenotypic effects of this severe reduction of gene expression, the function of that gene can be identified. Synthetic siRNA molecules also have the potential to be used to treat diseases. If a disease is caused or enhanced by a particular gene product, then siRNAs can be designed against that gene to silence its expression. This prevents the protein which drives the disease from being produced. […] One of the major challenges to the use of RNAi as therapy is directing siRNA to the specific cells in which gene silencing is required. If released directly into the bloodstream, enzymes in the bloodstream degrade siRNAs. […] Other problems are that siRNAs can stimulate the body’s immune response and can produce off-target effects by silencing RNA molecules other than those against which they were specifically designed. […] considerable attention is currently focused on designing carrier molecules that can transport siRNA through the bloodstream to the diseased cell.”

“Both Northern blotting and RT-PCR enable the expression of one or a few genes to be measured simultaneously. In contrast, the technique of microarrays allows gene expression to be measured across the full genome of an organism in a single step. This massive scale genome analysis technique is very useful when comparing gene expression profiles between two samples. […] This can identify gene subsets that are under- or over-expressed in one sample relative to the second sample to which it is compared.”

Links:

Molecular biology.
Charles Darwin. Alfred Wallace. Gregor Mendel. Wilhelm Johannsen. Heinrich Waldeyer. Theodor Boveri. Walter Sutton. Friedrich Miescher. Phoebus Levene. Oswald Avery. Colin MacLeod. Maclyn McCarty. James Watson. Francis Crick. Rosalind Franklin. Andrew Fire. Craig Mello.
Gene. Genotype. Phenotype. Chromosome. Nucleotide. DNA. RNA. Protein.
Chargaff’s rules.
Photo 51.
Human Genome Project.
Long interspersed nuclear elements (LINEs). Short interspersed nuclear elements (SINEs).
Histone. Nucleosome.
Chromatin. Euchromatin. Heterochromatin.
Mitochondrial DNA.
DNA replication. Helicase. Origin of replication. DNA polymeraseOkazaki fragments. Leading strand and lagging strand. DNA ligase. Semiconservative replication.
Mutation. Point mutation. Indel. Frameshift mutation.
Genetic polymorphism. Single-nucleotide polymorphism (SNP).
Genome-wide association study (GWAS).
Molecular cloning. Restriction endonuclease. Multiple cloning site (MCS). Bacterial artificial chromosome.
Gel electrophoresis. Southern blot. Polymerase chain reaction (PCR). Reverse transcriptase PCR (RT-PCR). Quantitative PCR (qPCR).
GenBank. European Molecular Biology Laboratory (EMBL). Encyclopedia of DNA Elements (ENCODE).
RNA polymerase II. TATA box. Transcription factor IID. Stop codon.
Protein biosynthesis.
SmRNA (small nuclear RNA).
Untranslated region (/UTR sequences).
Transfer RNA.
Micro RNA (miRNA).
Dicer (enzyme).
RISC (RNA-induced silencing complex).
Argonaute.
Lipid-Based Nanoparticles for siRNA Delivery in Cancer Therapy.
Long non-coding RNA.
Ribozyme/catalytic RNA.
RNA-sequencing (RNA-seq).

May 5, 2018 Posted by | Biology, Books, Chemistry, Genetics, Medicine, Molecular biology | Leave a comment

Networks

I actually think this was a really nice book, considering the format – I gave it four stars on goodreads. One of the things I noticed people didn’t like about it in the reviews is that it ‘jumps’ a bit in terms of topic coverage; it covers a wide variety of applications and analytical settings. I mostly don’t consider this a weakness of the book – even if occasionally it does get a bit excessive – and I can definitely understand the authors’ choice of approach; it’s sort of hard to illustrate the potential the analytical techniques described within this book have if you’re not allowed to talk about all the areas in which they have been – or could be gainfully – applied. A related point is that many people who read the book might be familiar with the application of these tools in specific contexts but have perhaps not thought about the fact that similar methods are applied in many other areas (and they might all of them be a bit annoyed the authors don’t talk more about computer science applications, or foodweb analyses, or infectious disease applications, or perhaps sociometry…). Most of the book is about graph-theory-related stuff, but a very decent amount of the coverage deals with applications, in a broad sense of the word at least, not theory. The discussion of theoretical constructs in the book always felt to me driven to a large degree by their usefulness in specific contexts.

I have covered related topics before here on the blog, also quite recently – e.g. there’s at least some overlap between this book and Holland’s book about complexity theory in the same series (I incidentally think these books probably go well together) – and as I found the book slightly difficult to blog as it was I decided against covering it in as much detail as I sometimes do when covering these texts – this means that I decided to leave out the links I usually include in posts like these.

Below some quotes from the book.

“The network approach focuses all the attention on the global structure of the interactions within a system. The detailed properties of each element on its own are simply ignored. Consequently, systems as different as a computer network, an ecosystem, or a social group are all described by the same tool: a graph, that is, a bare architecture of nodes bounded by connections. […] Representing widely different systems with the same tool can only be done by a high level of abstraction. What is lost in the specific description of the details is gained in the form of universality – that is, thinking about very different systems as if they were different realizations of the same theoretical structure. […] This line of reasoning provides many insights. […] The network approach also sheds light on another important feature: the fact that certain systems that grow without external control are still capable of spontaneously developing an internal order. […] Network models are able to describe in a clear and natural way how self-organization arises in many systems. […] In the study of complex, emergent, and self-organized systems (the modern science of complexity), networks are becoming increasingly important as a universal mathematical framework, especially when massive amounts of data are involved. […] networks are crucial instruments to sort out and organize these data, connecting individuals, products, news, etc. to each other. […] While the network approach eliminates many of the individual features of the phenomenon considered, it still maintains some of its specific features. Namely, it does not alter the size of the system — i.e. the number of its elements — or the pattern of interaction — i.e. the specific set of connections between elements. Such a simplified model is nevertheless enough to capture the properties of the system. […] The network approach [lies] somewhere between the description by individual elements and the description by big groups, bridging the two of them. In a certain sense, networks try to explain how a set of isolated elements are transformed, through a pattern of interactions, into groups and communities.”

“[T]he random graph model is very important because it quantifies the properties of a totally random network. Random graphs can be used as a benchmark, or null case, for any real network. This means that a random graph can be used in comparison to a real-world network, to understand how much chance has shaped the latter, and to what extent other criteria have played a role. The simplest recipe for building a random graph is the following. We take all the possible pair of vertices. For each pair, we toss a coin: if the result is heads, we draw a link; otherwise we pass to the next pair, until all the pairs are finished (this means drawing the link with a probability p = ½, but we may use whatever value of p). […] Nowadays [the random graph model] is a benchmark of comparison for all networks, since any deviations from this model suggests the presence of some kind of structure, order, regularity, and non-randomness in many real-world networks.”

“…in networks, topology is more important than metrics. […] In the network representation, the connections between the elements of a system are much more important than their specific positions in space and their relative distances. The focus on topology is one of its biggest strengths of the network approach, useful whenever topology is more relevant than metrics. […] In social networks, the relevance of topology means that social structure matters. […] Sociology has classified a broad range of possible links between individuals […]. The tendency to have several kinds of relationships in social networks is called multiplexity. But this phenomenon appears in many other networks: for example, two species can be connected by different strategies of predation, two computers by different cables or wireless connections, etc. We can modify a basic graph to take into account this multiplexity, e.g. by attaching specific tags to edges. […] Graph theory [also] allows us to encode in edges more complicated relationships, as when connections are not reciprocal. […] If a direction is attached to the edges, the resulting structure is a directed graph […] In these networks we have both in-degree and out-degree, measuring the number of inbound and outbound links of a node, respectively. […] in most cases, relations display a broad variation or intensity [i.e. they are not binary/dichotomous]. […] Weighted networks may arise, for example, as a result of different frequencies of interactions between individuals or entities.”

“An organism is […] the outcome of several layered networks and not only the deterministic result of the simple sequence of genes. Genomics has been joined by epigenomics, transcriptomics, proteomics, metabolomics, etc., the disciplines that study these layers, in what is commonly called the omics revolution. Networks are at the heart of this revolution. […] The brain is full of networks where various web-like structures provide the integration between specialized areas. In the cerebellum, neurons form modules that are repeated again and again: the interaction between modules is restricted to neighbours, similarly to what happens in a lattice. In other areas of the brain, we find random connections, with a more or less equal probability of connecting local, intermediate, or distant neurons. Finally, the neocortex — the region involved in many of the higher functions of mammals — combines local structures with more random, long-range connections. […] typically, food chains are not isolated, but interwoven in intricate patterns, where a species belongs to several chains at the same time. For example, a specialized species may predate on only one prey […]. If the prey becomes extinct, the population of the specialized species collapses, giving rise to a set of co-extinctions. An even more complicated case is where an omnivore species predates a certain herbivore, and both eat a certain plant. A decrease in the omnivore’s population does not imply that the plant thrives, because the herbivore would benefit from the decrease and consume even more plants. As more species are taken into account, the population dynamics can become more and more complicated. This is why a more appropriate description than ‘foodchains’ for ecosystems is the term foodwebs […]. These are networks in which nodes are species and links represent relations of predation. Links are usually directed (big fishes eat smaller ones, not the other way round). These networks provide the interchange of food, energy, and matter between species, and thus constitute the circulatory system of the biosphere.”

“In the cell, some groups of chemicals interact only with each other and with nothing else. In ecosystems, certain groups of species establish small foodwebs, without any connection to external species. In social systems, certain human groups may be totally separated from others. However, such disconnected groups, or components, are a strikingly small minority. In all networks, almost all the elements of the systems take part in one large connected structure, called a giant connected component. […] In general, the giant connected component includes not less than 90 to 95 per cent of the system in almost all networks. […] In a directed network, the existence of a path from one node to another does not guarantee that the journey can be made in the opposite direction. Wolves eat sheep, and sheep eat grass, but grass does not eat sheep, nor do sheep eat wolves. This restriction creates a complicated architecture within the giant connected component […] according to an estimate made in 1999, more than 90 per cent of the WWW is composed of pages connected to each other, if the direction of edges is ignored. However, if we take direction into account, the proportion of nodes mutually reachable is only 24 per cent, the giant strongly connected component. […] most networks are sparse, i.e. they tend to be quite frugal in connections. Take, for example, the airport network: the personal experience of every frequent traveller shows that direct flights are not that common, and intermediate stops are necessary to reach several destinations; thousands of airports are active, but each city is connected to less than 20 other cities, on average. The same happens in most networks. A measure of this is given by the mean number of connection of their nodes, that is, their average degree.”

“[A] puzzling contradiction — a sparse network can still be very well connected — […] attracted the attention of the Hungarian mathematicians […] Paul Erdős and Alfréd Rényi. They tackled it by producing different realizations of their random graph. In each of them, they changed the density of edges. They started with a very low density: less than one edge per node. It is natural to expect that, as the density increases, more and more nodes will be connected to each other. But what Erdős and Rényi found instead was a quite abrupt transition: several disconnected components coalesced suddenly into a large one, encompassing almost all the nodes. The sudden change happened at one specific critical density: when the average number of links per node (i.e. the average degree) was greater than one, then the giant connected component suddenly appeared. This result implies that networks display a very special kind of economy, intrinsic to their disordered structure: a small number of edges, even randomly distributed between nodes, is enough to generate a large structure that absorbs almost all the elements. […] Social systems seem to be very tightly connected: in a large enough group of strangers, it is not unlikely to find pairs of people with quite short chains of relations connecting them. […] The small-world property consists of the fact that the average distance between any two nodes (measured as the shortest path that connects them) is very small. Given a node in a network […], few nodes are very close to it […] and few are far from it […]: the majority are at the average — and very short — distance. This holds for all networks: starting from one specific node, almost all the nodes are at very few steps from it; the number of nodes within a certain distance increases exponentially fast with the distance. Another way of explaining the same phenomenon […] is the following: even if we add many nodes to a network, the average distance will not increase much; one has to increase the size of a network by several orders of magnitude to notice that the paths to new nodes are (just a little) longer. The small-world property is crucial to many network phenomena. […] The small-world property is something intrinsic to networks. Even the completely random Erdős-Renyi graphs show this feature. By contrast, regular grids do not display it. If the Internet was a chessboard-like lattice, the average distance between two routers would be of the order of 1,000 jumps, and the Net would be much slower [the authors note elsewhere that “The Internet is composed of hundreds of thousands of routers, but just about ten ‘jumps’ are enough to bring an information packet from one of them to any other.”] […] The key ingredient that transforms a structure of connections into a small world is the presence of a little disorder. No real network is an ordered array of elements. On the contrary, there are always connections ‘out of place’. It is precisely thanks to these connections that networks are small worlds. […] Shortcuts are responsible for the small-world property in many […] situations.”

“Body size, IQ, road speed, and other magnitudes have a characteristic scale: that is, an average value that in the large majority of cases is a rough predictor of the actual value that one will find. […] While height is a homogeneous magnitude, the number of social connection[s] is a heterogeneous one. […] A system with this feature is said to be scale-free or scale-invariant, in the sense that it does not have a characteristic scale. This can be rephrased by saying that the individual fluctuations with respect to the average are too large for us to make a correct prediction. […] In general, a network with heterogeneous connectivity has a set of clear hubs. When a graph is small, it is easy to find whether its connectivity is homogeneous or heterogeneous […]. In the first case, all the nodes have more or less the same connectivity, while in the latter it is easy to spot a few hubs. But when the network to be studied is very big […] things are not so easy. […] the distribution of the connectivity of the nodes of the […] network […] is the degree distribution of the graph. […] In homogeneous networks, the degree distribution is a bell curve […] while in heterogeneous networks, it is a power law […]. The power law implies that there are many more hubs (and much more connected) in heterogeneous networks than in homogeneous ones. Moreover, hubs are not isolated exceptions: there is a full hierarchy of nodes, each of them being a hub compared with the less connected ones.”

“Looking at the degree distribution is the best way to check if a network is heterogeneous or not: if the distribution is fat tailed, then the network will have hubs and heterogeneity. A mathematically perfect power law is never found, because this would imply the existence of hubs with an infinite number of connections. […] Nonetheless, a strongly skewed, fat-tailed distribution is a clear signal of heterogeneity, even if it is never a perfect power law. […] While the small-world property is something intrinsic to networked structures, hubs are not present in all kind of networks. For example, power grids usually have very few of them. […] hubs are not present in random networks. A consequence of this is that, while random networks are small worlds, heterogeneous ones are ultra-small worlds. That is, the distance between their vertices is relatively smaller than in their random counterparts. […] Heterogeneity is not equivalent to randomness. On the contrary, it can be the signature of a hidden order, not imposed by a top-down project, but generated by the elements of the system. The presence of this feature in widely different networks suggests that some common underlying mechanism may be at work in many of them. […] the Barabási–Albert model gives an important take-home message. A simple, local behaviour, iterated through many interactions, can give rise to complex structures. This arises without any overall blueprint”.

Homogamy, the tendency of like to marry like, is very strong […] Homogamy is a specific instance of homophily: this consists of a general trend of like to link to like, and is a powerful force in shaping social networks […] assortative mixing [is] a special form of homophily, in which nodes tend to connect with others that are similar to them in the number of connections. By contrast [when] high- and low-degree nodes are more connected to each other [it] is called disassortative mixing. Both cases display a form of correlation in the degrees of neighbouring nodes. When the degrees of neighbours are positively correlated, then the mixing is assortative; when negatively, it is disassortative. […] In random graphs, the neighbours of a given node are chosen completely at random: as a result, there is no clear correlation between the degrees of neighbouring nodes […]. On the contrary, correlations are present in most real-world networks. Although there is no general rule, most natural and technological networks tend to be disassortative, while social networks tend to be assortative. […] Degree assortativity and disassortativity are just an example of the broad range of possible correlations that bias how nodes tie to each other.”

“[N]etworks (neither ordered lattices nor random graphs), can have both large clustering and small average distance at the same time. […] in almost all networks, the clustering of a node depends on the degree of that node. Often, the larger the degree, the smaller the clustering coefficient. Small-degree nodes tend to belong to well-interconnected local communities. Similarly, hubs connect with many nodes that are not directly interconnected. […] Central nodes usually act as bridges or bottlenecks […]. For this reason, centrality is an estimate of the load handled by a node of a network, assuming that most of the traffic passes through the shortest paths (this is not always the case, but it is a good approximation). For the same reason, damaging central nodes […] can impair radically the flow of a network. Depending on the process one wants to study, other definitions of centrality can be introduced. For example, closeness centrality computes the distance of a node to all others, and reach centrality factors in the portion of all nodes that can be reached in one step, two steps, three steps, and so on.”

“Domino effects are not uncommon in foodwebs. Networks in general provide the backdrop for large-scale, sudden, and surprising dynamics. […] most of the real-world networks show a doubled-edged kind of robustness. They are able to function normally even when a large fraction of the network is damaged, but suddenly certain small failures, or targeted attacks, bring them down completely. […] networks are very different from engineered systems. In an airplane, damaging one element is enough to stop the whole machine. In order to make it more resilient, we have to use strategies such as duplicating certain pieces of the plane: this makes it almost 100 per cent safe. In contrast, networks, which are mostly not blueprinted, display a natural resilience to a broad range of errors, but when certain elements fail, they collapse. […] A random graph of the size of most real-world networks is destroyed after the removal of half of the nodes. On the other hand, when the same procedure is performed on a heterogeneous network (either a map of a real network or a scale-free model of a similar size), the giant connected component resists even after removing more than 80 per cent of the nodes, and the distance within it is practically the same as at the beginning. The scene is different when researchers simulate a targeted attack […] In this situation the collapse happens much faster […]. However, now the most vulnerable is the second: while in the homogeneous network it is necessary to remove about one-fifth of its more connected nodes to destroy it, in the heterogeneous one this happens after removing the first few hubs. Highly connected nodes seem to play a crucial role, in both errors and attacks. […] hubs are mainly responsible for the overall cohesion of the graph, and removing a few of them is enough to destroy it.”

“Studies of errors and attacks have shown that hubs keep different parts of a network connected. This implies that they also act as bridges for spreading diseases. Their numerous ties put them in contact with both infected and healthy individuals: so hubs become easily infected, and they infect other nodes easily. […] The vulnerability of heterogeneous networks to epidemics is bad news, but understanding it can provide good ideas for containing diseases. […] if we can immunize just a fraction, it is not a good idea to choose people at random. Most of the times, choosing at random implies selecting individuals with a relatively low number of connections. Even if they block the disease from spreading in their surroundings, hubs will always be there to put it back into circulation. A much better strategy would be to target hubs. Immunizing hubs is like deleting them from the network, and the studies on targeted attacks show that eliminating a small fraction of hubs fragments the network: thus, the disease will be confined to a few isolated components. […] in the epidemic spread of sexually transmitted diseases the timing of the links is crucial. Establishing an unprotected link with a person before they establish an unprotected link with another person who is infected is not the same as doing so afterwards.”

April 3, 2018 Posted by | Biology, Books, Ecology, Engineering, Epidemiology, Genetics, Mathematics, Statistics | Leave a comment

A few (more) diabetes papers of interest

Earlier this week I covered a couple of papers, but the second paper turned out to include a lot of interesting stuff so I decided to cut the post short and postpone my coverage of the other papers I’d intended to cover in that post until a later point in time; this post includes some of those other papers I’d intended to cover in that post.

i. TCF7L2 Genetic Variants Contribute to Phenotypic Heterogeneity of Type 1 Diabetes.

“Although the autoimmune destruction of β-cells has a major role in the development of type 1 diabetes, there is growing evidence that the differences in clinical, metabolic, immunologic, and genetic characteristics among patients (1) likely reflect diverse etiology and pathogenesis (2). Factors that govern this heterogeneity are poorly understood, yet these may have important implications for prognosis, therapy, and prevention.

The transcription factor 7 like 2 (TCF7L2) locus contains the single nucleotide polymorphism (SNP) most strongly associated with type 2 diabetes risk, with an ∼30% increase per risk allele (3). In a U.S. cohort, heterozygous and homozygous carriers of the at-risk alleles comprised 40.6% and 7.9%, respectively, of the control subjects and 44.3% and 18.3%, respectively, of the individuals with type 2 diabetes (3). The locus has no known association with type 1 diabetes overall (48), with conflicting reports in latent autoimmune diabetes in adults (816). […] Our studies in two separate cohorts have shown that the type 2 diabetes–associated TCF7L2 genetic variant is more frequent among specific subsets of individuals with autoimmune type 1 diabetes, specifically those with fewer markers of islet autoimmunity (22,23). These observations support a role of this genetic variant in the pathogenesis of diabetes at least in a subset of individuals with autoimmune diabetes. However, whether individuals with type 1 diabetes and this genetic variant have distinct metabolic abnormalities has not been investigated. We aimed to study the immunologic and metabolic characteristics of individuals with type 1 diabetes who carry a type 2 diabetes–associated allele of the TCF7L2 locus.”

“We studied 810 TrialNet participants with newly diagnosed type 1 diabetes and found that among individuals 12 years and older, the type 2 diabetes–associated TCF7L2 genetic variant is more frequent in those presenting with a single autoantibody than in participants who had multiple autoantibodies. These TCF7L2 variants were also associated with higher mean C-peptide AUC and lower mean glucose AUC levels at the onset of type 1 diabetes. […] These findings suggest that, besides the well-known link with type 2 diabetes, the TCF7L2 locus may play a role in the development of type 1 diabetes. The type 2 diabetes–associated TCF7L2 genetic variant identifies a subset of individuals with autoimmune type 1 diabetes and fewer markers of islet autoimmunity, lower glucose, and higher C-peptide at diagnosis. […] A possible interpretation of these data is that TCF7L2-encoded diabetogenic mechanisms may contribute to diabetes development in individuals with limited autoimmunity […]. Because the risk of progression to type 1 diabetes is lower in individuals with single compared with multiple autoantibodies, it is possible that in the absence of this type 2 diabetes–associated TCF7L2 variant, these individuals may have not manifested diabetes. If that is the case, we would postulate that disease development in these patients may have a type 2 diabetes–like pathogenesis in which islet autoimmunity is a significant component but not necessarily the primary driver.”

“The association between this genetic variant and single autoantibody positivity was present in individuals 12 years or older but not in children younger than 12 years. […] The results in the current study suggest that the type 2 diabetes–associated TCF7L2 genetic variant plays a larger role in older individuals. There is mounting evidence that the pathogenesis of type 1 diabetes varies by age (31). Younger individuals appear to have a more aggressive form of disease, with faster decline of β-cell function before and after onset of disease, higher frequency and severity of diabetic ketoacidosis, which is a clinical correlate of severe insulin deficiency, and lower C-peptide at presentation (3135). Furthermore, older patients are less likely to have type 1 diabetes–associated HLA alleles and islet autoantibodies (28). […] Taken together, we have demonstrated that individuals with autoimmune type 1 diabetes who carry the type 2 diabetes–associated TCF7L2 genetic variant have a distinct phenotype characterized by milder immunologic and metabolic characteristics than noncarriers, closer to those of type 2 diabetes, with an important effect of age.”

ii. Heart Failure: The Most Important, Preventable, and Treatable Cardiovascular Complication of Type 2 Diabetes.

“Concerns about cardiovascular disease in type 2 diabetes have traditionally focused on atherosclerotic vasculo-occlusive events, such as myocardial infarction, stroke, and limb ischemia. However, one of the earliest, most common, and most serious cardiovascular disorders in patients with diabetes is heart failure (1). Following its onset, patients experience a striking deterioration in their clinical course, which is marked by frequent hospitalizations and eventually death. Many sudden deaths in diabetes are related to underlying ventricular dysfunction rather than a new ischemic event. […] Heart failure and diabetes are linked pathophysiologically. Type 2 diabetes and heart failure are each characterized by insulin resistance and are accompanied by the activation of neurohormonal systems (norepinephrine, angiotensin II, aldosterone, and neprilysin) (3). The two disorders overlap; diabetes is present in 35–45% of patients with chronic heart failure, whether they have a reduced or preserved ejection fraction.”

“Treatments that lower blood glucose do not exert any consistently favorable effect on the risk of heart failure in patients with diabetes (6). In contrast, treatments that increase insulin signaling are accompanied by an increased risk of heart failure. Insulin use is independently associated with an enhanced likelihood of heart failure (7). Thiazolidinediones promote insulin signaling and have increased the risk of heart failure in controlled clinical trials (6). With respect to incretin-based secretagogues, liraglutide increases the clinical instability of patients with existing heart failure (8,9), and the dipeptidyl peptidase 4 inhibitors saxagliptin and alogliptin are associated with an increased risk of heart failure in diabetes (10). The likelihood of heart failure with the use of sulfonylureas may be comparable to that with thiazolidinediones (11). Interestingly, the only two classes of drugs that ameliorate hyperinsulinemia (metformin and sodium–glucose cotransporter 2 inhibitors) are also the only two classes of antidiabetes drugs that appear to reduce the risk of heart failure and its adverse consequences (12,13). These findings are consistent with experimental evidence that insulin exerts adverse effects on the heart and kidneys that can contribute to heart failure (14). Therefore, physicians can prevent many cases of heart failure in type 2 diabetes by careful consideration of the choice of agents used to achieve glycemic control. Importantly, these decisions have an immediate effect; changes in risk are seen within the first few months of changes in treatment. This immediacy stands in contrast to the years of therapy required to see a benefit of antidiabetes drugs on microvascular risk.”

“As reported by van den Berge et al. (4), the prognosis of patients with heart failure has improved over the past two decades; heart failure with a reduced ejection fraction is a treatable disease. Inhibitors of the renin-angiotensin system are a cornerstone of the management of both disorders; they prevent the onset of heart failure and the progression of nephropathy in patients with diabetes, and they reduce the risk of cardiovascular death and hospitalization in those with established heart failure (3,15). Diabetes does not influence the magnitude of the relative benefit of ACE inhibitors in patients with heart failure, but patients with diabetes experience a greater absolute benefit from treatment (16).”

“The totality of evidence from randomized trials […] demonstrates that in patients with diabetes, heart failure is not only common and clinically important, but it can also be prevented and treated. This conclusion is particularly significant because physicians have long ignored heart failure in their focus on glycemic control and their concerns about the ischemic macrovascular complications of diabetes (1).”

iii. Closely related to the above study: Mortality Reduction Associated With β-Adrenoceptor Inhibition in Chronic Heart Failure Is Greater in Patients With Diabetes.

“Diabetes increases mortality in patients with chronic heart failure (CHF) and reduced left ventricular ejection fraction. Studies have questioned the safety of β-adrenoceptor blockers (β-blockers) in some patients with diabetes and reduced left ventricular ejection fraction. We examined whether β-blockers and ACE inhibitors (ACEIs) are associated with differential effects on mortality in CHF patients with and without diabetes. […] We conducted a prospective cohort study of 1,797 patients with CHF recruited between 2006 and 2014, with mean follow-up of 4 years.”

RESULTS Patients with diabetes were prescribed larger doses of β-blockers and ACEIs than were patients without diabetes. Increasing β-blocker dose was associated with lower mortality in patients with diabetes (8.9% per mg/day; 95% CI 5–12.6) and without diabetes (3.5% per mg/day; 95% CI 0.7–6.3), although the effect was larger in people with diabetes (interaction P = 0.027). Increasing ACEI dose was associated with lower mortality in patients with diabetes (5.9% per mg/day; 95% CI 2.5–9.2) and without diabetes (5.1% per mg/day; 95% CI 2.6–7.6), with similar effect size in these groups (interaction P = 0.76).”

“Our most important findings are:

  • Higher-dose β-blockers are associated with lower mortality in patients with CHF and LVSD, but patients with diabetes may derive more benefit from higher-dose β-blockers.

  • Higher-dose ACEIs were associated with comparable mortality reduction in people with and without diabetes.

  • The association between higher β-blocker dose and reduced mortality is most pronounced in patients with diabetes who have more severely impaired left ventricular function.

  • Among patients with diabetes, the relationship between β-blocker dose and mortality was not associated with glycemic control or insulin therapy.”

“We make the important observation that patients with diabetes may derive more prognostic benefit from higher β-blocker doses than patients without diabetes. These data should provide reassurance to patients and health care providers and encourage careful but determined uptitration of β-blockers in this high-risk group of patients.”

iv. Diabetes, Prediabetes, and Brain Volumes and Subclinical Cerebrovascular Disease on MRI: The Atherosclerosis Risk in Communities Neurocognitive Study (ARIC-NCS).

“Diabetes and prediabetes are associated with accelerated cognitive decline (1), and diabetes is associated with an approximately twofold increased risk of dementia (2). Subclinical brain pathology, as defined by small vessel disease (lacunar infarcts, white matter hyperintensities [WMH], and microhemorrhages), large vessel disease (cortical infarcts), and smaller brain volumes also are associated with an increased risk of cognitive decline and dementia (37). The mechanisms by which diabetes contributes to accelerated cognitive decline and dementia are not fully understood, but contributions of hyperglycemia to both cerebrovascular disease and primary neurodegenerative disease have been suggested in the literature, although results are inconsistent (2,8). Given that diabetes is a vascular risk factor, brain atrophy among individuals with diabetes may be driven by increased cerebrovascular disease. Brain magnetic resonance imaging (MRI) provides a noninvasive opportunity to study associations of hyperglycemia with small vessel disease (lacunar infarcts, WMH, microhemorrhages), large vessel disease (cortical infarcts), and brain volumes (9).”

“Overall, the mean age of participants [(n = 1,713)] was 75 years, 60% were women, 27% were black, 30% had prediabetes (HbA1c 5.7 to <6.5%), and 35% had diabetes. Compared with participants without diabetes and HbA1c <5.7%, those with prediabetes (HbA1c 5.7 to <6.5%) were of similar age (75.2 vs. 75.0 years; P = 0.551), were more likely to be black (24% vs. 11%; P < 0.001), have less than a high school education (11% vs. 7%; P = 0.017), and have hypertension (71% vs. 63%; P = 0.012) (Table 1). Among participants with diabetes, those with HbA1c <7.0% versus ≥7.0% were of similar age (75.4 vs. 75.1 years; P = 0.481), but those with diabetes and HbA1c ≥7.0% were more likely to be black (39% vs. 28%; P = 0.020) and to have less than a high school education (23% vs. 16%; P = 0.031) and were more likely to have a longer duration of diabetes (12 vs. 8 years; P < 0.001).”

“Compared with participants without diabetes and HbA1c <5.7%, those with diabetes and HbA1c ≥7.0% had smaller total brain volume (β −0.20 SDs; 95% CI −0.31, −0.09) and smaller regional brain volumes, including frontal, temporal, occipital, and parietal lobes; deep gray matter; Alzheimer disease signature region; and hippocampus (all P < 0.05) […]. Compared with participants with diabetes and HbA1c <7.0%, those with diabetes and HbA1c ≥7.0% had smaller total brain volume (P < 0.001), frontal lobe volume (P = 0.012), temporal lobe volume (P = 0.012), occipital lobe volume (P = 0.008), parietal lobe volume (P = 0.015), deep gray matter volume (P < 0.001), Alzheimer disease signature region volume (0.031), and hippocampal volume (P = 0.016). Both participants with diabetes and HbA1c <7.0% and those with prediabetes (HbA1c 5.7 to <6.5%) had similar total and regional brain volumes compared with participants without diabetes and HbA1c <5.7% (all P > 0.05). […] No differences in the presence of lobar microhemorrhages, subcortical microhemorrhages, cortical infarcts, and lacunar infarcts were observed among the diabetes-HbA1c categories (all P > 0.05) […]. Compared with participants without diabetes and HbA1c <5.7%, those with diabetes and HbA1c ≥7.0% had increased WMH volume (P = 0.016). The WMH volume among participants with diabetes and HbA1c ≥7.0% was also significantly greater than among those with diabetes and HbA1c <7.0% (P = 0.017).”

“Those with diabetes duration ≥10 years were older than those with diabetes duration <10 years (75.9 vs. 75.0 years; P = 0.041) but were similar in terms of race and sex […]. Compared with participants with diabetes duration <10 years, those with diabetes duration ≥10 years has smaller adjusted total brain volume (β −0.13 SDs; 95% CI −0.20, −0.05) and smaller temporal lobe (β −0.14 SDs; 95% CI −0.24, −0.03), parietal lobe (β − 0.11 SDs; 95% CI −0.21, −0.01), and hippocampal (β −0.16 SDs; 95% CI −0.30, −0.02) volumes […]. Participants with diabetes duration ≥10 years also had a 2.44 times increased odds (95% CI 1.46, 4.05) of lacunar infarcts compared with those with diabetes duration <10 years”.

Conclusions
In this community-based population, we found that ARIC-NCS participants with diabetes with HbA1c ≥7.0% have smaller total and regional brain volumes and an increased burden of WMH, but those with prediabetes (HbA1c 5.7 to <6.5%) and diabetes with HbA1c <7.0% have brain volumes and markers of subclinical cerebrovascular disease similar to those without diabetes. Furthermore, among participants with diabetes, those with more-severe disease (as measured by higher HbA1c and longer disease duration) had smaller total and regional brain volumes and an increased burden of cerebrovascular disease compared with those with lower HbA1c and shorter disease duration. However, we found no evidence that associations of diabetes with smaller brain volumes are mediated by cerebrovascular disease.

The findings of this study extend the current literature that suggests that diabetes is strongly associated with brain volume loss (11,2527). Global brain volume loss (11,2527) has been consistently reported, but associations of diabetes with smaller specific brain regions have been less robust (27,28). Similar to prior studies, the current results show that compared with individuals without diabetes, those with diabetes have smaller total brain volume (11,2527) and regional brain volumes, including frontal and occipital lobes, deep gray matter, and the hippocampus (25,27). Furthermore, the current study suggests that greater severity of disease (as measured by HbA1c and diabetes duration) is associated with smaller total and regional brain volumes. […] Mechanisms whereby diabetes may contribute to brain volume loss include accelerated amyloid-β and hyperphosphorylated tau deposition as a result of hyperglycemia (29). Another possible mechanism involves pancreatic amyloid (amylin) infiltration of the brain, which then promotes amyloid-β deposition (29). […] Taken together, […] the current results suggest that diabetes is associated with both lower brain volumes and increased cerebrovascular pathology (WMH and lacunes).”

v. Interventions to increase attendance for diabetic retinopathy screening (Cochrane review).

“The primary objective of the review was to assess the effectiveness of quality improvement (QI) interventions that seek to increase attendance for DRS in people with type 1 and type 2 diabetes.

Secondary objectives were:
To use validated taxonomies of QI intervention strategies and behaviour change techniques (BCTs) to code the description of interventions in the included studies and determine whether interventions that include particular QI strategies or component BCTs are more effective in increasing screening attendance;
To explore heterogeneity in effect size within and between studies to identify potential explanatory factors for variability in effect size;
To explore differential effects in subgroups to provide information on how equity of screening attendance could be improved;
To critically appraise and summarise current evidence on the resource use, costs and cost effectiveness.”

“We included 66 RCTs conducted predominantly (62%) in the USA. Overall we judged the trials to be at low or unclear risk of bias. QI strategies were multifaceted and targeted patients, healthcare professionals or healthcare systems. Fifty-six studies (329,164 participants) compared intervention versus usual care (median duration of follow-up 12 months). Overall, DRS [diabetic retinopathy screening] attendance increased by 12% (risk difference (RD) 0.12, 95% confidence interval (CI) 0.10 to 0.14; low-certainty evidence) compared with usual care, with substantial heterogeneity in effect size. Both DRS-targeted (RD 0.17, 95% CI 0.11 to 0.22) and general QI interventions (RD 0.12, 95% CI 0.09 to 0.15) were effective, particularly where baseline DRS attendance was low. All BCT combinations were associated with significant improvements, particularly in those with poor attendance. We found higher effect estimates in subgroup analyses for the BCTs ‘goal setting (outcome)’ (RD 0.26, 95% CI 0.16 to 0.36) and ‘feedback on outcomes of behaviour’ (RD 0.22, 95% CI 0.15 to 0.29) in interventions targeting patients, and ‘restructuring the social environment’ (RD 0.19, 95% CI 0.12 to 0.26) and ‘credible source’ (RD 0.16, 95% CI 0.08 to 0.24) in interventions targeting healthcare professionals.”

“Ten studies (23,715 participants) compared a more intensive (stepped) intervention versus a less intensive intervention. In these studies DRS attendance increased by 5% (RD 0.05, 95% CI 0.02 to 0.09; moderate-certainty evidence).”

“Overall, we found that there is insufficient evidence to draw robust conclusions about the relative cost effectiveness of the interventions compared to each other or against usual care.”

“The results of this review provide evidence that QI interventions targeting patients, healthcare professionals or the healthcare system are associated with meaningful improvements in DRS attendance compared to usual care. There was no statistically significant difference between interventions specifically aimed at DRS and those which were part of a general QI strategy for improving diabetes care.”

vi. Diabetes in China: Epidemiology and Genetic Risk Factors and Their Clinical Utility in Personalized Medication.

“The incidence of type 2 diabetes (T2D) has rapidly increased over recent decades, and T2D has become a leading public health challenge in China. Compared with European descents, Chinese patients with T2D are diagnosed at a relatively young age and low BMI. A better understanding of the factors contributing to the diabetes epidemic is crucial for determining future prevention and intervention programs. In addition to environmental factors, genetic factors contribute substantially to the development of T2D. To date, more than 100 susceptibility loci for T2D have been identified. Individually, most T2D genetic variants have a small effect size (10–20% increased risk for T2D per risk allele); however, a genetic risk score that combines multiple T2D loci could be used to predict the risk of T2D and to identify individuals who are at a high risk. […] In this article, we review the epidemiological trends and recent progress in the understanding of T2D genetic etiology and further discuss personalized medicine involved in the treatment of T2D.”

“Over the past three decades, the prevalence of diabetes in China has sharply increased. The prevalence of diabetes was reported to be less than 1% in 1980 (2), 5.5% in 2001 (3), 9.7% in 2008 (4), and 10.9% in 2013, according to the latest published nationwide survey (5) […]. The prevalence of diabetes was higher in the senior population, men, urban residents, individuals living in economically developed areas, and overweight and obese individuals. The estimated prevalence of prediabetes in 2013 was 35.7%, which was much higher than the estimate of 15.5% in the 2008 survey. Similarly, the prevalence of prediabetes was higher in the senior population, men, and overweight and obese individuals. However, prediabetes was more prevalent in rural residents than in urban residents. […] the 2013 survey also compared the prevalence of diabetes among different races. The crude prevalence of diabetes was 14.7% in the majority group, i.e., Chinese Han, which was higher than that in most minority ethnic groups, including Tibetan, Zhuang, Uyghur, and Muslim. The crude prevalence of prediabetes was also higher in the Chinese Han ethnic group. The Tibetan participants had the lowest prevalence of diabetes and prediabetes (4.3% and 31.3%).”

“[T]he prevalence of diabetes in young people is relatively high and increasing. The prevalence of diabetes in the 20- to 39-year age-group was 3.2%, according to the 2008 national survey (4), and was 5.9%, according to the 2013 national survey (5). The prevalence of prediabetes also increased from 9.0% in 2008 to 28.8% in 2013 […]. Young people suffering from diabetes have a higher risk of chronic complications, which are the major cause of mortality and morbidity in diabetes. According to a study conducted in Asia (6), patients with young-onset diabetes had higher mean concentrations of HbA1c and LDL cholesterol and a higher prevalence of retinopathy (20% vs. 18%, P = 0.011) than those with late-onset diabetes. In the Chinese, patients with early-onset diabetes had a higher risk of nonfatal cardiovascular disease (7) than did patients with late-onset diabetes (odds ratio [OR] 1.91, 95% CI 1.81–2.02).”

“As approximately 95% of patients with diabetes in China have T2D, the rapid increase in the prevalence of diabetes in China may be attributed to the increasing rates of overweight and obesity and the reduction in physical activity, which is driven by economic development, lifestyle changes, and diet (3,11). According to a series of nationwide surveys conducted by the China Physical Fitness Surveillance Center (12), the prevalence of overweight (BMI ≥23.0 to <27.5 kg/m2) in Chinese adults aged 20–59 years increased from 37.4% in 2000 to 39.2% in 2005, 40.7% in 2010, and 41.2% in 2014, with an estimated increase of 0.27% per year. The prevalence of obesity (BMI ≥27.5 kg/m2) increased from 8.6% in 2000 to 10.3% in 2005, 12.2% in 2010, and 12.9% in 2014, with an estimated increase of 0.32% per year […]. The prevalence of central obesity increased from 13.9% in 2000 to 18.3% in 2005, 22.1% in 2010, and 24.9% in 2014, with an estimated increase of 0.78% per year. Notably, T2D develops at a considerably lower BMI in the Chinese population than that in European populations. […] The relatively high risk of diabetes at a lower BMI could be partially attributed to the tendency toward visceral adiposity in East Asian populations, including the Chinese population (13). Moreover, East Asian populations have been found to have a higher insulin sensitivity with a much lower insulin response than European descent and African populations, implying a lower compensatory β-cell function, which increases the risk of progressing to overt diabetes (14).”

“Over the past two decades, linkage analyses, candidate gene approaches, and large-scale GWAS have successfully identified more than 100 genes that confer susceptibility to T2D among the world’s major ethnic populations […], most of which were discovered in European populations. However, less than 50% of these European-derived loci have been successfully confirmed in East Asian populations. […] there is a need to identify specific genes that are associated with T2D in other ethnic populations. […] Although many genetic loci have been shown to confer susceptibility to T2D, the mechanism by which these loci participate in the pathogenesis of T2D remains unknown. Most T2D loci are located near genes that are related to β-cell function […] most single nucleotide polymorphisms (SNPs) contributing to the T2D risk are located in introns, but whether these SNPs directly modify gene expression or are involved in linkage disequilibrium with unknown causal variants remains to be investigated. Furthermore, the loci discovered thus far collectively account for less than 15% of the overall estimated genetic heritability.”

“The areas under the receiver operating characteristic curves (AUCs) are usually used to assess the discriminative accuracy of an approach. The AUC values range from 0.5 to 1.0, where an AUC of 0.5 represents a lack of discrimination and an AUC of 1 represents perfect discrimination. An AUC ≥0.75 is considered clinically useful. The dominant conventional risk factors, including age, sex, BMI, waist circumference, blood pressure, family history of diabetes, physical activity level, smoking status, and alcohol consumption, can be combined to construct conventional risk factor–based models (CRM). Several studies have compared the predictive capacities of models with and without genetic information. The addition of genetic markers to a CRM could slightly improve the predictive performance. For example, one European study showed that the addition of an 11-SNP GRS to a CRM marginally improved the risk prediction (AUC was 0.74 without and 0.75 with the genetic markers, P < 0.001) in a prospective cohort of 16,000 individuals (37). A meta-analysis (38) consisting of 23 studies investigating the predictive performance of T2D risk models also reported that the AUCs only slightly increased with the addition of genetic information to the CRM (median AUC was increased from 0.78 to 0.79). […] Despite great advances in genetic studies, the clinical utility of genetic information in the prediction, early identification, and prevention of T2D remains in its preliminary stage.”

“An increasing number of studies have highlighted that early nutrition has a persistent effect on the risk of diabetes in later life (40,41). China’s Great Famine of 1959–1962 is considered to be the largest and most severe famine of the 20th century […] Li et al. (43) found that offspring of mothers exposed to the Chinese famine have a 3.9-fold increased risk of diabetes or hyperglycemia as adults. A more recent study (the Survey on Prevalence in East China for Metabolic Diseases and Risk Factors [SPECT-China]) conducted in 2014, among 6,897 adults from Shanghai, Jiangxi, and Zhejiang provinces, had the same conclusion that famine exposure during the fetal period (OR 1.53, 95% CI 1.09–2.14) and childhood (OR 1.82, 95% CI 1.21–2.73) was associated with diabetes (44). These findings indicate that undernutrition during early life increases the risk of hyperglycemia in adulthood and this association is markedly exaggerated when facing overnutrition in later life.”

February 23, 2018 Posted by | Cardiology, Diabetes, Epidemiology, Genetics, Health Economics, Immunology, Medicine, Neurology, Ophthalmology, Pharmacology, Studies | Leave a comment

Systems Biology (III)

Some observations from chapter 4 below:

The need to maintain a steady state ensuring homeostasis is an essential concern in nature while negative feedback loop is the fundamental way to ensure that this goal is met. The regulatory system determines the interdependences between individual cells and the organism, subordinating the former to the latter. In trying to maintain homeostasis, the organism may temporarily upset the steady state conditions of its component cells, forcing them to perform work for the benefit of the organism. […] On a cellular level signals are usually transmitted via changes in concentrations of reaction substrates and products. This simple mechanism is made possible due to limited volume of each cell. Such signaling plays a key role in maintaining homeostasis and ensuring cellular activity. On the level of the organism signal transmission is performed by hormones and the nervous system. […] Most intracellular signal pathways work by altering the concentrations of selected substances inside the cell. Signals are registered by forming reversible complexes consisting of a ligand (reaction product) and an allosteric receptor complex. When coupled to the ligand, the receptor inhibits the activity of its corresponding effector, which in turn shuts down the production of the controlled substance ensuring the steady state of the system. Signals coming from outside the cell are usually treated as commands (covalent modifications), forcing the cell to adjust its internal processes […] Such commands can arrive in the form of hormones, produced by the organism to coordinate specialized cell functions in support of general homeostasis (in the organism). These signals act upon cell receptors and are usually amplified before they reach their final destination (the effector).”

“Each concentration-mediated signal must first be registered by a detector. […] Intracellular detectors are typically based on allosteric proteins. Allosteric proteins exhibit a special property: they have two stable structural conformations and can shift from one form to the other as a result of changes in ligand concentrations. […] The concentration of a product (or substrate) which triggers structural realignment in the allosteric protein (such as a regulatory enzyme) depends on the genetically-determined affinity of the active site to its ligand. Low affinity results in high target concentration of the controlled substance while high affinity translates into lower concentration […]. In other words, high concentration of the product is necessary to trigger a low-affinity receptor (and vice versa). Most intracellular regulatory mechanisms rely on noncovalent interactions. Covalent bonding is usually associated with extracellular signals, generated by the organism and capable of overriding the cell’s own regulatory mechanisms by modifying the sensitivity of receptors […]. Noncovalent interactions may be compared to requests while covalent signals are treated as commands. Signals which do not originate in the receptor’s own feedback loop but modify its affinity are known as steering signals […] Hormones which act upon cells are, by their nature, steering signals […] Noncovalent interactions — dependent on substance concentrations — impose spatial restrictions on regulatory mechanisms. Any increase in cell volume requires synthesis of additional products in order to maintain stable concentrations. The volume of a spherical cell is given as V = 4/3 π r3, where r indicates cell radius. Clearly, even a slight increase in r translates into a significant increase in cell volume, diluting any products dispersed in the cytoplasm. This implies that cells cannot expand without incurring great energy costs. It should also be noted that cell expansion reduces the efficiency of intracellular regulatory mechanisms because signals and substrates need to be transported over longer distances. Thus, cells are universally small, regardless of whether they make up a mouse or an elephant.”

An effector is an element of a regulatory loop which counteracts changes in the regulated quantity […] Synthesis and degradation of biological compounds often involves numerous enzymes acting in sequence. The product of one enzyme is a substrate for another enzyme. With the exception of the initial enzyme, each step of this cascade is controlled by the availability of the supplied substrate […] The effector consists of a chain of enzymes, each of which depends on the activity of the initial regulatory enzyme […] as well as on the activity of its immediate predecessor which supplies it with substrates. The function of all enzymes in the effector chain is indirectly dependent on the initial enzyme […]. This coupling between the receptor and the first link in the effector chain is a universal phenomenon. It can therefore be said that the initial enzyme in the effector chain is, in fact, a regulatory enzyme. […] Most cell functions depend on enzymatic activity. […] It seems that a set of enzymes associated with a specific process which involves a negative feedback loop is the most typical form of an intracellular regulatory effector. Such effectors can be controlled through activation or inhibition of their associated enzymes.”

“The organism is a self-contained unit represented by automatic regulatory loops which ensure homeostasis. […] Effector functions are conducted by cells which are usually grouped and organized into tissues and organs. Signal transmission occurs by way of body fluids, hormones or nerve connections. Cells can be treated as automatic and potentially autonomous elements of regulatory loops, however their specific action is dependent on the commands issued by the organism. This coercive property of organic signals is an integral requirement of coordination, allowing the organism to maintain internal homeostasis. […] Activities of the organism are themselves regulated by their own negative feedback loops. Such regulation differs however from the mechanisms observed in individual cells due to its place in the overall hierarchy and differences in signal properties, including in particular:
• Significantly longer travel distances (compared to intracellular signals);
• The need to maintain hierarchical superiority of the organism;
• The relative autonomy of effector cells. […]
The relatively long distance travelled by organism’s signals and their dilution (compared to intracellular ones) calls for amplification. As a consequence, any errors or random distortions in the original signal may be drastically exacerbated. A solution to this problem comes in the form of encoding, which provides the signal with sufficient specificity while enabling it to be selectively amplified. […] a loudspeaker can […] assist in acoustic communication, but due to the lack of signal encoding it cannot compete with radios in terms of communication distance. The same reasoning applies to organism-originated signals, which is why information regarding blood glucose levels is not conveyed directly by glucose but instead by adrenalin, glucagon or insulin. Information encoding is handled by receptors and hormone-producing cells. Target cells are capable of decoding such signals, thus completing the regulatory loop […] Hormonal signals may be effectively amplified because the hormone itself does not directly participate in the reaction it controls — rather, it serves as an information carrier. […] strong amplification invariably requires encoding in order to render the signal sufficiently specific and unambiguous. […] Unlike organisms, cells usually do not require amplification in their internal regulatory loops — even the somewhat rare instances of intracellular amplification only increase signal levels by a small amount. Without the aid of an amplifier, messengers coming from the organism level would need to be highly concentrated at their source, which would result in decreased efficiency […] Most signals originated on organism’s level travel with body fluids; however if a signal has to reach its destination very rapidly (for instance in muscle control) it is sent via the nervous system”.

“Two types of amplifiers are observed in biological systems:
1. cascade amplifier,
2. positive feedback loop. […]
A cascade amplifier is usually a collection of enzymes which perform their action by activation in strict sequence. This mechanism resembles multistage (sequential) synthesis or degradation processes, however instead of exchanging reaction products, amplifier enzymes communicate by sharing activators or by directly activating one another. Cascade amplifiers are usually contained within cells. They often consist of kinases. […] Amplification effects occurring at each stage of the cascade contribute to its final result. […] While the kinase amplification factor is estimated to be on the order of 103, the phosphorylase cascade results in 1010-fold amplification. It is a stunning value, though it should also be noted that the hormones involved in this cascade produce particularly powerful effects. […] A positive feedback loop is somewhat analogous to a negative feedback loop, however in this case the input and output signals work in the same direction — the receptor upregulates the process instead of inhibiting it. Such upregulation persists until the available resources are exhausted.
Positive feedback loops can only work in the presence of a control mechanism which prevents them from spiraling out of control. They cannot be considered self-contained and only play a supportive role in regulation. […] In biological systems positive feedback loops are sometimes encountered in extracellular regulatory processes where there is a need to activate slowly-migrating components and greatly amplify their action in a short amount of time. Examples include blood coagulation and complement factor activation […] Positive feedback loops are often coupled to negative loop-based control mechanisms. Such interplay of loops may impart the signal with desirable properties, for instance by transforming a flat signals into a sharp spike required to overcome the activation threshold for the next stage in a signalling cascade. An example is the ejection of calcium ions from the endoplasmic reticulum in the phospholipase C cascade, itself subject to a negative feedback loop.”

“Strong signal amplification carries an important drawback: it tends to “overshoot” its target activity level, causing wild fluctuations in the process it controls. […] Nature has evolved several means of signal attenuation. The most typical mechanism superimposes two regulatory loops which affect the same parameter but act in opposite directions. An example is the stabilization of blood glucose levels by two contradictory hormones: glucagon and insulin. Similar strategies are exploited in body temperature control and many other biological processes. […] The coercive properties of signals coming from the organism carry risks associated with the possibility of overloading cells. The regulatory loop of an autonomous cell must therefore include an “off switch”, controlled by the cell. An autonomous cell may protect itself against excessive involvement in processes triggered by external signals (which usually incur significant energy expenses). […] The action of such mechanisms is usually timer-based, meaning that they inactivate signals following a set amount of time. […] The ability to interrupt signals protects cells from exhaustion. Uncontrolled hormone-induced activity may have detrimental effects upon the organism as a whole. This is observed e.g. in the case of the vibrio cholerae toxin which causes prolonged activation of intestinal epithelial cells by locking protein G in its active state (resulting in severe diarrhea which can dehydrate the organism).”

“Biological systems in which information transfer is affected by high entropy of the information source and ambiguity of the signal itself must include discriminatory mechanisms. These mechanisms usually work by eliminating weak signals (which are less specific and therefore introduce ambiguities). They create additional obstacles (thresholds) which the signals must overcome. A good example is the mechanism which eliminates the ability of weak, random antigens to activate lymphatic cells. It works by inhibiting blastic transformation of lymphocytes until a so-called receptor cap has accumulated on the surface of the cell […]. Only under such conditions can the activation signal ultimately reach the cell nucleus […] and initiate gene transcription. […] weak, reversible nonspecific interactions do not permit sufficient aggregation to take place. This phenomenon can be described as a form of discrimination against weak signals. […] Discrimination may also be linked to effector activity. […] Cell division is counterbalanced by programmed cell death. The most typical example of this process is apoptosis […] Each cell is prepared to undergo controlled death if required by the organism, however apoptosis is subject to tight control. Cells protect themselves against accidental triggering of the process via IAP proteins. Only strong proapoptotic signals may overcome this threshold and initiate cellular suicide”.

Simply knowing the sequences, structures or even functions of individual proteins does not provide sufficient insight into the biological machinery of living organisms. The complexity of individual cells and entire organisms calls for functional classification of proteins. This task can be accomplished with a proteome — a theoretical construct where individual elements (proteins) are grouped in a way which acknowledges their mutual interactions and interdependencies, characterizing the information pathways in a complex organism.
Most ongoing proteome construction projects focus on individual proteins as the basic building blocks […] [We would instead argue in favour of a model in which] [t]he basic unit of the proteome is one negative feedback loop (rather than a single protein) […]
Due to the relatively large number of proteins (between 25 and 40 thousand in the human organism), presenting them all on a single graph with vertex lengths corresponds to the relative duration of interactions would be unfeasible. This is why proteomes are often subdivided into functional subgroups such as the metabolome (proteins involved in metabolic processes), interactome (complex-forming proteins), kinomes (proteins which belong to the kinase family) etc.”

February 18, 2018 Posted by | Biology, Books, Chemistry, Genetics, Medicine, Molecular biology | Leave a comment

Peripheral Neuropathy (I)

The objective of this book is to update health care professionals on recent advances in the pathogenesis, diagnosis and treatment of peripheral neuropathy. This work was written by a group of clinicians and scientists with large expertise in the field.

The book is not the first book about this topic I’ve read, so a lot of the stuff included was of course review – however it’s a quite decent text, and I decided to blog it in at least some detail anyway. It’s somewhat technical and it’s probably not a very good introduction to this topic if you know next to nothing about neurology – in that case I’m certain Said’s book (see the ‘not’-link above) is a better option.

I have added some observations from the first couple of chapters below. As InTech publications like these explicitly encourage people to share the ideas and observations included in these books, I shall probably cover the book in more detail than I otherwise would have.

“Within the developing world, infectious diseases [2-4] and trauma [5] are the most common sources of neuropathic pain syndromes. The developed world, in contrast, suffers more frequently from diabetic polyneuropathy (DPN) [6, 7], post herpetic neuralgia (PHN) from herpes zoster infections [8], and chemotherapy-induced peripheral neuropathy (CIPN) [9, 10]. There is relatively little epidemiological data regarding the prevalence of neuropathic pain within the general population, but a few estimates suggest it is around 7-8% [11, 12]. Despite the widespread occurrence of neuropathic pain, treatment options are limited and often ineffective […] Neuropathic pain can present as on-going or spontaneous discomfort that occurs in the absence of any observable stimulus or a painful hypersensitivity to temperature and touch. […] people with chronic pain have increased incidence of anxiety and depression and reduced scores in quantitative measures of health related quality of life [15]. Despite significant progress in chronic and neuropathic pain research, which has led to the discovery of several efficacious treatments in rodent models, pain management in humans remains ineffective and insufficient [16]. The lack of translational efficiency may be due to inadequate animal models that do not faithfully recapitulate human disease or from biological differences between rodents and humans […] In an attempt to increase the efficacy of medical treatment for neuropathic pain, clinicians and researchers have been moving away from an etiology based classification towards one that is mechanism based. It is current practice to diagnose a person who presents with neuropathic pain according to the underlying etiology and lesion topography [17]. However, this does not translate to effective patient care as these classification criteria do not suggest efficacious treatment. A more apt diagnosis might include a description of symptoms and the underlying pathophysiology associated with those symptoms.”

Neuropathic pain has been defined […] as “pain arising as the direct consequence of a lesion or disease affecting the somatosensory system” [18]. This is distinct from nociceptive pain – which signals tissue damage through an intact nervous system – in underlying pathophysiology, severity, and associated psychological comorbidities [13]. Individuals who suffer from neuropathic pain syndromes report pain of higher intensity and duration than individuals with non-neuropathic chronic pain and have significantly increased incidence of depression, anxiety, and sleep disorders [13, 19]. […] individuals with seemingly identical diseases who both develop neuropathic pain may experience distinct abnormal sensory phenotypes. This may include a loss of sensory perception in some modalities and increased activity in others. Often a reduction in the perception of vibration and light touch is coupled with positive sensory symptoms such as paresthesia, dysesthesia, and pain[20]. Pain may manifest as either spontaneous, with a burning or shock-like quality, or as a hypersensitivity to mechanical or thermal stimuli [21]. This hypersensitivity takes two forms: allodynia, pain that is evoked from a normally non-painful stimulus, and hyperalgesia, an exaggerated pain response from a moderately painful stimulus. […] Noxious stimuli are perceived by small diameter peripheral neurons whose free nerve endings are distributed throughout the body. These neurons are distinct from, although anatomically proximal to, the low threshold mechanoreceptors responsible for the perception of vibration and light touch.”

In addition to hypersensitivity, individuals with neuropathic pain frequently experience ongoing spontaneous pain as a major source of discomfort and distress. […] In healthy individuals, a quiescent neuron will only generate an action potential when presented with a stimulus of sufficient magnitude to cause membrane depolarization. Following nerve injury, however, significant changes in ion channel expression, distribution, and kinetics lead to disruption of the homeostatic electric potential of the membrane resulting in oscillations and burst firing. This manifests as spontaneous pain that has a shooting or burning quality […] There is reasonable evidence to suggest that individual ion channels contribute to specific neuropathic pain symptoms […] [this observation] provides an intriguing therapeutic possibility: unambiguous pharmacologic ion channel blockers to relieve individual sensory symptoms with minimal unintended effects allowing pain relief without global numbness. […] Central sensitization leads to painful hypersensitivity […] Functional and structural changes of dorsal horn circuitry lead to pain hypersensitivity that is maintained independent of peripheral sensitization [38]. This central sensitization provides a mechanistic explanation for the sensory abnormalities that occur in both acute and chronic pain states, such as the expansion of hypersensitivity beyond the innervation territory of a lesion site, repeated stimulation of a constant magnitude leading to an increasing pain response, and pain outlasting a peripheral stimulus [39-41]. In healthy individuals, acute pain triggers central sensitization, but homeostatic sensitivity returns following clearance of the initial insult. In some individuals who develop neuropathic pain, genotype and environmental factors contribute to maintenance of central sensitization leading to spontaneous pain, hyperalgesia, and allodynia. […] Similarly, facilitation also results in a lowered activation threshold in second order neurons”.

“Chronic pain conditions are associated with vast functional and structural changes of the brain, when compared to healthy controls, but it is currently unclear which comes first: does chronic pain cause distortions of brain circuitry and anatomy or do cerebral abnormalities trigger and/or maintain the perception of chronic pain? […] Brain abnormalities in chronic pain states include modification of brain activity patterns, localized decreases in gray matter volume, and circuitry rerouting [53]. […] Chronic pain conditions are associated with localized reduction in gray matter volume, and the topography of gray matter volume reduction is dictated, at least in part, by the particular pathology. […] These changes appear to represent a form of plasticity as they are reversible when pain is effectively managed [63, 67, 68].”

“By definition, neuropathic pain indicates direct pathology of the nervous system while nociceptive pain is an indication of real or potential tissue damage. Due to the distinction in pathophysiology, conventional treatments prescribed for nociceptive pain are not very effective in treating neuropathic pain and vice versa [78]. Therefore the first step towards meaningful pain relief is an accurate diagnosis. […] Treating neuropathic pain requires a multifaceted approach that aims to eliminate the underlying etiology, when possible, and manage the associated discomforts and emotional distress. Although in some cases it is possible to directly treat the cause of neuropathic pain, for example surgery to alleviate a constricted nerve, it is more likely that the primary cause is untreatable, as is the case with singular traumatic events such as stroke and spinal cord injury and diseases like diabetes. When this is the case, symptom management and pain reduction become the primary focus. Unfortunately, in most cases complete elimination of pain is not a feasible endpoint; a pain reduction of 30% is considered to be efficacious [21]. Additionally, many pharmacological treatments require careful titration and tapering to prevent adverse effects and toxicity. This process may take several weeks to months, and ultimately the drug may be ineffective, necessitating another trial with a different medication. It is therefore necessary that both doctor and patient begin treatment with realistic expectations and goals.”

First-line medications for the treatment of neuropathic pain are those that have proven efficacy in randomized clinical trials (RCTs) and are consistent with pooled clinical observations [81]. These include antidepressants, calcium channel ligands, and topical lidocaine [15]. Tricyclic antidepressants (TCAs) have demonstrated efficacy in treating neuropathic pain with positive results in RCTs for central post-stroke pain, PHN, painful diabetic and non-diabetic polyneuropathy, and post-mastectomy pain syndrome [82]. However they do not seem to be effective in treating painful HIV-neuropathy or CIPN [82]. Duloxetine and venlafaxine, two selective serotonin norepinephrine reuptake inhibitors (SSNRIs), have been found to be effective in DPN and both DPN and painful polyneuropathies, respectively [81]. […] Gabapentin and pregabalin have also demonstrated efficacy in several neuropathic pain conditions including DPN and PHN […] Topical lidocaine (5% patch or gel) has significantly reduced allodynia associated with PHN and other neuropathic pain syndromes in several RCTs [81, 82]. With no reported systemic adverse effects and mild skin irritation as the only concern, lidocaine is an appropriate choice for treating localized peripheral neuropathic pain. In the event that first line medications, alone or in combination, are not effective at achieving adequate pain relief, second line medications may be considered. These include opioid analgesics and tramadol, pharmaceuticals which have proven efficacy in RCTs but are associated with significant adverse effects that warrant cautious prescription [15]. Although opioid analgesics are effective pain relievers in several types of neuropathic pain [81, 82, 84], they are associated with misuse or abuse, hypogonadism, constipation, nausea, and immunological changes […] Careful consideration should be given when prescribing opiates to patients who have a personal or family history of drug or alcohol abuse […] Deep brain stimulation, a neurosurgical technique by which an implanted electrode delivers controlled electrical impulses to targeted brain regions, has demonstrated some efficacy in treating chronic pain but is not routinely employed due to a high risk-to-benefit ratio [91]. […] A major challenge in treating neuropathic pain is the heterogeneity of disease pathogenesis within an individual etiological classification. Patients with seemingly identical diseases may experience completely different neuropathic pain phenotypes […] One of the biggest barriers to successful management of neuropathic pain has been the lack of understanding in the underlying pathophysiology that produces a pain phenotype. To that end, significant progress has been made in basic science research.”

In diabetes mellitus, nerves and their supporting cells are subjected to prolonged hyperglycemia and metabolic disturbances and this culminates in reversible/irreversible nervous system dysfunction and damage, namely diabetic peripheral neuropathy (DPN). Due to the varying compositions and extents of neurological involvements, it is difficult to obtain accurate and thorough prevalence estimates of DPN, rendering this microvascular complication vastly underdiagnosed and undertreated [1-4]. According to American Diabetes Association, DPN occurs to 60-70% of diabetic individuals [5] and represents the leading cause of peripheral neuropathies among all cases [6, 7].”

A quick remark: This number seems really high to me. I won’t rule out that it’s accurate if you go with highly sensitive measures of neuropathy, but the number of patients who will experience significant clinical sequelae as a result of DPN is in my opinion likely to be significantly lower than that. On a peripherally related note, it should however on the other hand also be kept in mind that although diabetes-related neurological complications may display some clustering in patient groups – which will necessarily decrease the magnitude of the problem – no single test will ever completely rule out neurological complications in a diabetic; a patient with a negative Semmes-Weinstein monofilament test may still have autonomic neuropathy. So assessing the full disease burden in the context of diabetes-related neurological complications cannot be done using only a single instrument, and the full disease burden is likely to be higher than individual estimates encountered in the literature (unless a full neurological workup was done, which is unlikely to be the case). They do go into more detail about subgroups, clinical significance, etc. below, but I thought this observation was important to add early on in this part of the coverage.

Because diverse anatomic distributions and fiber types may be differentially affected in patients with diabetes, the disease manifestations, courses and pathologies of clinical and subclinical DPN are rather heterogeneous and encompass a broad spectrum […] Current consensus divides diabetes-associated somatic neuropathic syndromes into the focal/multifocal and diffuse/generalized neuropathies [6, 14]. The first category comprises a group of asymmetrical, acute-in-onset and self-limited single lesion(s) of nerve injury or impairment largely resulting from the increased vulnerability of diabetic nerves to mechanical insults (Carpal Tunnel Syndrome) […]. Such mononeuropathies occur idiopathically and only become a clinical problem in association with aging in 5-10% of those affected. Therefore, focal neuropathies are not extensively covered in this chapter [16]. The rest of the patients frequently develop diffuse neuropathies characterized by symmetrical distribution, insidious onset and chronic progression. In particular, a distal symmetrical sensorimotor polyneuropathy accounts for 90% of all DPN diagnoses in type 1 and type 2 diabetics and affects all types of peripheral sensory and motor fibers in a temporally non-uniform manner [6, 17].
Symptoms begin with prickling, tingling, numbness, paresthesia, dysesthesia and various qualities of pain associated with small sensory fibers at the very distal end (toes) of lower extremities [1, 18]. Presence of the above symptoms together with abnormal nociceptive response of epidermal C and A-δ fibers to pain/temperature (as revealed by clinical examination) constitute the diagnosis of small fiber sensory neuropathy, which produces both painful and insensate phenotypes [19]. Painful diabetic neuropathy is a prominent, distressing and chronic experience in at least 10-30% of DPN populations [20, 21]. Its occurrence does not necessarily correlate with impairment in electrophysiological or quantitative sensory testing (QST). […] Large myelinated sensory fibers that innervate the dermis, such as Aβ, also become involved later on, leading to impaired proprioception, vibration and tactile detection, and mechanical hypoalgesia [19]. Following this “stocking-glove”, length-dependent and dying-back evolvement, neurodegeneration gradually proceeds to proximal muscle sensory and motor nerves. Its presence manifests in neurological testings as reduced nerve impulse conductions, diminished ankle tendon reflex, unsteadiness and muscle weakness [1, 24].
Both the absence of protective sensory response and motor coordination predispose neuropathic foot to impaired wound healing and gangrenous ulceration — often ensued by limb amputation in severe and/or advanced cases […]. Although symptomatic motor deficits only appear in later stages of DPN [25], motor denervation and distal atrophy can increase the rate of fractures by causing repetitive minor trauma or falls [24, 28]. Other unusual but highly disabling late sequelae of DPN include limb ischemia and joint deformity [6]; the latter also being termed Charcot’s neuroarthropathy or Charcot’s joints [1]. In addition to significant morbidities, several separate cohort studies provided evidence that DPN [29], diabetic foot ulcers [30] and increased toe vibration perception threshold (VPT) [31] are all independent risk factors for mortality.”

Unfortunately, current therapy for DPN is far from effective and at best only delays the onset and/or progression of the disease via tight glucose control […] Even with near normoglycemic control, a substantial proportion of patients still suffer the debilitating neurotoxic consequences of diabetes [34]. On the other hand, some with poor glucose control are spared from clinically evident signs and symptoms of neuropathy for a long time after diagnosis [37-39]. Thus, other etiological factors independent of hyperglycemia are likely to be involved in the development of DPN. Data from a number of prospective, observational studies suggested that older age, longer diabetes duration, genetic polymorphism, presence of cardiovascular disease markers, malnutrition, presence of other microvascular complications, alcohol and tobacco consumption, and higher constitutional indexes (e.g. weight and height) interact with diabetes and make for strong predictors of neurological decline [13, 32, 40-42]. Targeting some of these modifiable risk factors in addition to glycemia may improve the management of DPN. […] enormous efforts have been devoted to understanding and intervening with the molecular and biochemical processes linking the metabolic disturbances to sensorimotor deficits by studying diabetic animal models. In return, nearly 2,200 articles were published in PubMed central and at least 100 clinical trials were reported evaluating the efficacy of a number of pharmacological agents; the majority of them are designed to inhibit specific pathogenic mechanisms identified by these experimental approaches. Candidate agents have included aldose reductase inhibitors, AGE inhibitors, γ-linolenic acid, α-lipoic acid, vasodilators, nerve growth factor, protein kinase Cβ inhibitors, and vascular endothelial growth factor. Notwithstanding a fruitful of knowledge and promising results in animals, none has translated into definitive clinical success […] Based on the records published by National Institute of Neurological Disorders and Stroke (NINDS), a main source of DPN research, about 16,488 projects were funded at the expense of over $8 billion for the fiscal years of 2008 through 2012. Of these projects, an estimated 72,200 animals were used annually to understand basic physiology and disease pathology as well as to evaluate potential drugs [255]. As discussed above, however, the usefulness of these pharmaceutical agents developed through such a pipeline in preventing or reducing neuronal damage has been equivocal and usually halted at human trials due to toxicity, lack of efficacy or both […]. Clearly, the pharmacological translation from our decades of experimental modeling to clinical practice with regard to DPN has thus far not even [been] close to satisfactory.”

Whereas a majority of the drugs investigated during preclinical testing executed experimentally desired endpoints without revealing significant toxicity, more than half that entered clinical evaluation for treating DPN were withdrawn as a consequence of moderate to severe adverse events even at a much lower dose. Generally, using other species as surrogates for human population inherently encumbers the accurate prediction of toxic reactions for several reasons […] First of all, it is easy to dismiss drug-induced non-specific effects in animals – especially for laboratory rodents who do not share the same size, anatomy and physical activity with humans. […]  Second, some physiological and behavioral phenotypes observable in humans are impossible for animals to express. In this aspect, photosensitive skin rash and pain serve as two good examples of non-translatable side effects. Rodent skin differs from that of humans in that it has a thinner and hairier epidermis and distinct DNA repair abilities [260]. Therefore, most rodent stains used in diabetes modeling provide poor estimates for the probability of cutaneous hypersensitivity reactions to pharmacological treatments […] Another predicament is to assess pain in rodents. The reason for this is simple: these animals cannot tell us when, where or even whether they are experiencing pain […]. Since there is not any specific type of behavior to which painful reaction can be unequivocally associated, this often leads to underestimation of painful side effects during preclinical drug screening […] The third problem is that animals and humans have different pharmacokinetic and toxicological responses.”

“Genetic or chemical-induced diabetic rats or mice have been a major tool for preclinical pharmacological evaluation of potential DPN treatments. Yet, they do not faithfully reproduce many neuropathological manifestations in human diabetics. The difficulty of such begins with the fact that it is not possible to obtain in rodents a qualitative and quantitative expression of the clinical symptoms that are frequently presented in neuropathic diabetic patients, including spontaneous pain of different characteristics (e.g. prickling, tingling, burning, squeezing), paresthesia and numbness. As symptomatic changes constitute an important parameter of therapeutic outcome, this may well underlie the failure of some aforementioned drugs in clinical trials despite their good performance in experimental tests […] Development of nerve dysfunction in diabetic rodents also does not follow the common natural history of human DPN. […] Besides the lack of anatomical resemblance, the changes in disease severity are often missing in these models. […] importantly, foot ulcers that occur as a late complication to 15% of all individuals with diabetes [14] do not spontaneously develop in hyperglycemic rodents. Superimposed injury by experimental procedure in the foot pads of diabetic rats or mice may lend certain insight in the impaired wound healing in diabetes [278] but is not reflective of the chronic, accumulating pathological changes in diabetic feet of human counterparts. Another salient feature of human DPN that has not been described in animals is the predominant sensory and autonomic nerve damage versus minimal involvement of motor fibers [279]. This should elicit particular caution as the selective susceptibility is critical to our true understanding of the etiopathogenesis underlying distal sensorimotor polyneuropathy in diabetes. In addition to the lack of specificity, most animal models studied only cover a narrow spectrum of clinical DPN and have not successfully duplicated syndromes including proximal motor neuropathy and focal lesions [279].
Morphologically, fiber atrophy and axonal loss exist in STZ-rats and other diabetic rodents but are much milder compared to the marked degeneration and loss of myelinated and unmyelinated nerves readily observed in human specimens [280]. Of significant note, rodents are notoriously resistant to developing some of the histological hallmarks seen in diabetic patients, such as segmental and paranodal demyelination […] the simultaneous presence of degenerating and regenerating fibers that is characteristic of early DPN has not been clearly demonstrated in these animals [44]. Since such dynamic nerve degeneration/regeneration signifies an active state of nerve repair and is most likely to be amenable to therapeutic intervention, absence of this property makes rodent models a poor tool in both deciphering disease pathogenesis and designing treatment approaches […] With particular respect to neuroanatomy, a peripheral axon in humans can reach as long as one meter [296] whereas the maximal length of the axons innervating the hind limb is five centimeters in mice and twelve centimeters in rats. This short length makes it impossible to study in rodents the prominent length dependency and dying-back feature of peripheral nerve dysfunction that characterizes human DPN. […] For decades the cytoarchitecture of human islets was assumed to be just like those in rodents with a clear anatomical subdivision of β-cells and other cell types. By using confocal microscopy and multi-fluorescent labeling, it was finally uncovered that human islets have not only a substantially lower percentage of β-cell population, but also a mixed — rather than compartmentalized — organization of the different cell types [297]. This cellular arrangement was demonstrated to directly alter the functional performance of human islets as opposed to rodent islets. Although it is not known whether such profound disparities in cell composition and association also exist in the PNS, it might as well be anticipated considering the many sophisticated sensory and motor activities that are unique to humans. Considerable species difference also manifest at a molecular level. […] At least 80% of human genes have a counterpart in the mouse and rat genome. However, temporal and spatial expression of these genes can vary remarkably between humans and rodents, in terms of both extent and isoform specificity.”

“Ultimately, a fundamental problem associated with resorting to rodents in DPN research is to study a human disorder that takes decades to develop and progress in organisms with a maximum lifespan of 2-3 years. […] It is […] fair to say that a full clinical spectrum of the maturity-onset DPN likely requires a length of time exceeding the longevity of rodents to present and diabetic rodent models at best only help illustrate the very early aspects of the entire disease syndrome. Since none of the early pathogenetic pathways revealed in diabetic rodents will contribute to DPN in a quantitatively and temporally uniform fashion throughout the prolonged natural history of this disease, it is not surprising that a handful of inhibitors developed against these processes have not benefited patients with relatively long-standing neuropathy. As a matter of fact, any agents targeting single biochemical insults would be too little too late to treat a chronic neurological disorder with established nerve damage and pathogenetic heterogeneity […] It is important to point out that the present review does not argue against the ability of animal models to shed light on basic molecular, cellular and physiological processes that are shared among species. Undoubtedly, animal models of diabetes have provided abundant insights into the disease biology of DPN. Nevertheless, the lack of any meaningful advance in identifying a promising pharmacological target necessitates a reexamination of the validity of current DPN models as well as to offer a plausible alternative methodology to scientific approaches and disease intervention. […] we conclude that the fundamental species differences have led to misinterpretation of rodent data and overall failure of pharmacological investment. As more is being learned, it is becoming prevailing that DPN is a chronic, heterogeneous disease unlikely to benefit from targeting specific and early pathogenetic components revealed by animal studies.”

February 13, 2018 Posted by | Books, Diabetes, Genetics, Medicine, Neurology, Pharmacology | Leave a comment

Systems Biology (II)

Some observations from the book’s chapter 3 below:

“Without regulation biological processes would become progressively more and more chaotic. In living cells the primary source of information is genetic material. Studying the role of information in biology involves signaling (i.e. spatial and temporal transfer of information) and storage (preservation of information). Regarding the role of the genome we can distinguish three specific aspects of biological processes: steady-state genetics, which ensure cell-level and body homeostasis; genetics of development, which controls cell differentiation and genesis of the organism; and evolutionary genetics, which drives speciation. […] The ever growing demand for information, coupled with limited storage capacities, has resulted in a number of strategies for minimizing the quantity of the encoded information that must be preserved by living cells. In addition to combinatorial approaches based on noncontiguous genes structure, self-organization plays an important role in cellular machinery. Nonspecific interactions with the environment give rise to coherent structures despite the lack of any overt information store. These mechanisms, honed by evolution and ubiquitous in living organisms, reduce the need to directly encode large quantities of data by adopting a systemic approach to information management.”

Information is commonly understood as a transferable description of an event or object. Information transfer can be either spatial (communication, messaging or signaling) or temporal (implying storage). […] The larger the set of choices, the lower the likelihood [of] making the correct choice by accident and — correspondingly — the more information is needed to choose correctly. We can therefore state that an increase in the cardinality of a set (the number of its elements) corresponds to an increase in selection indeterminacy. This indeterminacy can be understood as a measure of “a priori ignorance”. […] Entropy determines the uncertainty inherent in a given system and therefore represents the relative difficulty of making the correct choice. For a set of possible events it reaches its maximum value if the relative probabilities of each event are equal. Any information input reduces entropy — we can therefore say that changes in entropy are a quantitative measure of information. […] Physical entropy is highest in a state of equilibrium, i.e. lack of spontaneity (G = 0,0) which effectively terminates the given reaction. Regulatory processes which counteract the tendency of physical systems to reach equilibrium must therefore oppose increases in entropy. It can be said that a steady inflow of information is a prerequisite of continued function in any organism. As selections are typically made at the entry point of a regulatory process, the concept of entropy may also be applied to information sources. This approach is useful in explaining the structure of regulatory systems which must be “designed” in a specific way, reducing uncertainty and enabling accurate, error-free decisions.

The fire ant exudes a pheromone which enables it to mark sources of food and trace its own path back to the colony. In this way, the ant conveys pathing information to other ants. The intensity of the chemical signal is proportional to the abundance of the source. Other ants can sense the pheromone from a distance of several (up to a dozen) centimeters and thus locate the source themselves. […] As can be expected, an increase in the entropy of the information source (i.e. the measure of ignorance) results in further development of regulatory systems — in this case, receptors capable of receiving signals and processing them to enable accurate decisions. Over time, the evolution of regulatory mechanisms increases their performance and precision. The purpose of various structures involved in such mechanisms can be explained on the grounds of information theory. The primary goal is to select the correct input signal, preserve its content and avoid or eliminate any errors.”

Genetic information stored in nucleotide sequences can be expressed and transmitted in two ways:
a. via replication (in cell division);
b. via transcription and translation (also called gene expression […]
)
Both processes act as effectors and can be triggered by certain biological signals transferred on request.
Gene expression can be defined as a sequence of events which lead to the synthesis of proteins or their products required for a particular function. In cell division, the goal of this process is to generate a copy of the entire genetic code (S phase), whereas in gene expression only selected fragments of DNA (those involved in the requested function) are transcribed and translated. […] Transcription calls for exposing a section of the cell’s genetic code and although its product (RNA) is short-lived, it can be recreated on demand, just like a carbon copy of a printed text. On the other hand, replication affects the entire genetic material contained in the cell and must conform to stringent precision requirements, particularly as the size of the genome increases.”

The magnitude of effort involved in replication of genetic code can be visualized by comparing the DNA chain to a zipper […]. Assuming that the zipper consists of three pairs of interlocking teeth per centimeter (300 per meter) and that the human genome is made up of 3 billion […] base pairs, the total length of our uncoiled DNA in “zipper form” would be equal to […] 10,000 km […] If we were to unfasten the zipper at a rate of 1 m per second, the entire unzipping process would take approximately 3 months […]. This comparison should impress upon the reader the length of the DNA chain and the precision with which individual nucleotides must be picked to ensure that the resulting code is an exact copy of the source. It should also be noted that for each base pair the polymerase enzyme needs to select an appropriate matching nucleotide from among four types of nucleotides present in the solution, and attach it to the chain (clearly, no such problem occurs in zippers). The reliability of an average enzyme is on the order of 10-3–10-4, meaning that one error occurs for every 1,000–10,000 interactions between the enzyme and its substrate. Given this figure, replication of 3*109 base pairs would introduce approximately 3 million errors (mutations) per genome, resulting in a highly inaccurate copy. Since the observed reliability of replication is far higher, we may assume that some corrective mechanisms are involved. Really, the remarkable precision of genetic replication is ensured by DNA repair processes, and in particular by the corrective properties of polymerase itself.

Many mutations are caused by the inherent chemical instability of nucleic acids: for example, cytosine may spontaneously convert to uracil. In the human genome such an event occurs approximately 100 times per day; however uracil is not normally encountered in DNA and its presence alerts defensive mechanisms which correct the error. Another type of mutation is spontaneous depurination, which also triggers its own, dedicated error correction procedure. Cells employ a large number of corrective mechanisms […] DNA repair mechanisms may be treated as an “immune system” which protects the genome from loss or corruption of genetic information. The unavoidable mutations which sometimes occur despite the presence of error correction-mechanisms can be masked due to doubled presentation (alleles) of genetic information. Thus, most mutations are recessive and not expressed in the phenotype. As the length of the DNA chain increases, mutations become more probable. It should be noted that the number of nucleotides in DNA is greater than the relative number of aminoacids participating in polypeptide chains. This is due to the fact that each aminoacid is encoded by exactly three nucleotides — a general principle which applies to all living organisms. […] Fidelity is, of course, fundamentally important in DNA replication as any harmful mutations introduced in its course are automatically passed on to all successive generations of cells. In contrast, transcription and translation processes can be more error-prone as their end products are relatively short-lived. Of note is the fact that faulty transcripts appear in relatively low quantities and usually do not affect cell functions, since regulatory processes ensure continued synthesis of the required substances until a suitable level of activity is reached. Nevertheless, it seems that reliable transcription of genetic material is sufficiently significant for cells to have developed appropriate proofreading mechanisms, similar to those which assist replication. […] the entire information pathway — starting with DNA and ending with active proteins — is protected against errors. We can conclude that fallibility is an inherent property of genetic information channels, and that in order to perform their intended function, these channels require error correction mechanisms.”

The discrete nature of genetic material is an important property which distinguishes prokaryotes from eukaryotes. […] The ability to select individual nucleotide fragments and construct sequences from predetermined “building blocks” results in high adaptability to environmental stimuli and is a fundamental aspect of evolution. The discontinuous nature of genes is evidenced by the presence of fragments which do not convey structural information (introns), as opposed to structure-encoding fragments (exons). The initial transcript (pre-mRNA) contains introns as well as exons. In order to provide a template for protein synthesis, it must undergo further processing (also known as splicing): introns must be cleaved and exon fragments attached to one another. […] Recognition of intron-exon boundaries is usually very precise, while the reattachment of adjacent exons is subject to some variability. Under certain conditions, alternative splicing may occur, where the ordering of the final product does not reflect the order in which exon sequences appear in the source chain. This greatly increases the number of potential mRNA combinations and thus the variety of resulting proteins. […] While access to energy sources is not a major problem, sources of information are usually far more difficult to manage — hence the universal tendency to limit the scope of direct (genetic) information storage. Reducing the length of genetic code enables efficient packing and enhances the efficiency of operations while at the same time decreasing the likelihood of errors. […] The number of genes identified in the human genome is lower than the number of distinct proteins by a factor of 4; a difference which can be attributed to alternative splicing. […] This mechanism increases the variety of protein structures without affecting core information storage, i.e. DNA sequences. […] Primitive organisms often possess nearly as many genes as humans, despite the essential differences between both groups. Interspecies diversity is primarily due to the properties of regulatory sequences.”

The discontinuous nature of genes is evolutionarily advantageous but comes at the expense of having to maintain a nucleus where such splicing processes can be safely conducted, in addition to efficient transport channels allowing transcripts to penetrate the nuclear membrane. While it is believed that at early stages of evolution RNA was the primary repository of genetic information, its present function can best be described as an information carrier. Since unguided proteins cannot ensure sufficient specificity of interaction with nucleic acids, protein-RNA complexes are used often in cases where specific fragments of genetic information need to be read. […] The use of RNA in protein complexes is common across all domains of the living world as it bridges the gap between discrete and continuous storage of genetic information.”

Epigenetic differentiation mechanisms are particularly important in embryonic development. […] Unlike the function of mature organisms, embryonic programming refers to structures which do not yet exist but which need to be created through cell proliferation and differentiation. […] Differentiation of cells results in phenotypic changes. This phenomenon is the primary difference between development genetics and steady-state genetics. Functional differences are not, however, associated with genomic changes: instead they are mediated by the transcriptome where certain genes are preferentially selected for transcription while others are suppressed. […] In a mature, specialized cell only a small portion of the transcribable genome is actually expressed. The remainder of the cell’s genetic material is said to be silenced. Gene silencing is a permanent condition. Under normal circumstances mature cells never alter their function, although such changes may be forced in a laboratory setting […] Cells which make up the embryo at a very early stage of development are pluripotent, meaning that their purpose can be freely determined and that all of their genetic information can potentially be expressed (under certain conditions). […] At each stage of the development process the scope of pluripotency is reduced until, ultimately, the cell becomes monopotent. Monopotency implies that the final function of the cell has already been determined, although the cell itself may still be immature. […] functional dissimilarities between specialized cells are not associated with genetic mutations but rather with selective silencing of genes. […] Most genes which determine biological functions have a biallelic representation (i.e. a representation consisting of two alleles). The remainder (approximately 10 % of genes) is inherited from one specific parent, as a result of partial or complete silencing of their sister alleles (called paternal or maternal imprinting) which occurs during gametogenesis. The suppression of a single copy of the X chromosome is a special case of this phenomenon.”

Evolutionary genetics is subject to two somewhat contradictory criteria. On the one hand, there is clear pressure on accurate and consistent preservation of biological functions and structures while on the other hand it is also important to permit gradual but persistent changes. […] the observable progression of adaptive traits which emerge as a result of evolution suggests a mechanism which promotes constructive changes over destructive ones. Mutational diversity cannot be considered truly random if it is limited to certain structures or functions. […] Approximately 50 % of the human genome consists of mobile segments, capable of migrating to various positions in the genome. These segments are called transposons and retrotransposons […] The mobility of genome fragments not only promotes mutations (by increasing the variability of DNA) but also affects the stability and packing of chromatin strands wherever such mobile sections are reintegrated with the genome. Under normal circumstances the activity of mobile sections is tempered by epigenetic mechanisms […]; however in certain situations gene mobility may be upregulated. In particular, it seems that in “prehistoric” (remote evolutionary) times such events occurred at a much faster pace, accelerating the rate of genetic changes and promoting rapid evolution. Cells can actively promote mutations by way of the so-called AID process (activity-dependent cytosine deamination). It is an enzymatic mechanism which converts cytosine into uracil, thereby triggering repair mechanisms and increasing the likelihood of mutations […] The existence of AID proves that cells themselves may trigger evolutionary changes and that the role of mutations in the emergence of new biological structures is not strictly passive.”

Regulatory mechanisms which receive signals characterized by high degrees of uncertainty, must be able to make informed choices to reduce the overall entropy of the system they control. This property is usually associated with development of information channels. Special structures ought to be exposed within information channels connecting systems of different character as for example linking transcription to translation or enabling transduction of signals through the cellular membrane. Examples of structures which convey highly entropic information are receptor systems associated with blood coagulation and immune responses. The regulatory mechanism which triggers an immune response relies on relatively simple effectors (complement factor enzymes, phages and killer cells) coupled to a highly evolved receptor system, represented by specific antibodies and organized set of cells. Compared to such advanced receptors the structures which register the concentration of a given product (e.g. glucose in blood) are rather primitive. Advanced receptors enable the immune system to recognize and verify information characterized by high degrees of uncertainty. […] In sequential processes it is usually the initial stage which poses the most problems and requires the most information to complete successfully. It should come as no surprise that the most advanced control loops are those associated with initial stages of biological pathways.”

February 10, 2018 Posted by | Biology, Books, Chemistry, Evolutionary biology, Genetics, Immunology, Medicine, Molecular biology | Leave a comment

Endocrinology (part 4 – reproductive endocrinology)

Some observations from chapter 4 of the book below.

“*♂. The whole process of spermatogenesis takes approximately 74 days, followed by another 12-21 days for sperm transport through the epididymis. This means that events which may affect spermatogenesis may not be apparent for up to three months, and successful induction of spermatogenesis treatment may take 2 years. *♀. From primordial follicle to primary follicle, it takes about 180 days (a continuous process). It is then another 60 days to form a preantral follicle which then proceeds to ovulation three menstrual cycles later. Only the last 2-3 weeks of this process is under gonadotrophin drive, during which time the follicle grows from 2 to 20mm.”

“Hirsutism (not a diagnosis in itself) is the presence of excess hair growth in ♀ as a result of androgen production and skin sensitivity to androgens. […] In ♀, testosterone is secreted primarily by the ovaries and adrenal glands, although a significant amount is produced by the peripheral conversion of androstenedione and DHEA. Ovarian androgen production is regulated by luteinizing hormone, whereas adrenal production is ACTH-dependent. The predominant androgens produced by the ovaries are testosterone and androstenedione, and the adrenal glands are the main source of DHEA. Circulating testosterone is mainly bound to sex hormone-binding globulin (SHBG), and it is the free testosterone which is biologically active. […] Slowly progressive hirsutism following puberty suggests a benign cause, whereas rapidly progressive hirsutism of recent onset requires further immediate investigation to rule out an androgen-secreting neoplasm. [My italics, US] […] Serum testosterone should be measured in all ♀ presenting with hirsutism. If this is <5nmol/L, then the risk of a sinister cause for her hirsutism is low.”

“Polycystic ovary syndrome (PCOS) *A heterogeneous clinical syndrome characterized by hyperandrogenism, mainly of ovarian origin, menstrual irregularity, and hyperinsulinaemia, in which other causes of androgen excess have been excluded […] *A distinction is made between polycystic ovary morphology on ultrasound (PCO which also occurs in congenital adrenal hyperplasia, acromegaly, Cushing’s syndrome, and testesterone-secreting tumours) and PCOS – the syndrome. […] PCOS is the most common endocrinopathy in ♀ of reproductive age; >95% of ♀ presenting to outpatients with hirsutism have PCOS. *The estimated prevalence of PCOS ranges from 5 to 10% on clinical criteria. Polycystic ovaries on US alone are present in 20-25% of ♀ of reproductive age. […] family history of type 2 diabetes mellitus is […] more common in ♀ with PCOS. […] Approximately 70% of ♀ with PCOS are insulin-resistant, depending on the definition. […] Type 2 diabetes mellitus is 2-4 x more common in ♀ with PCOS. […] Hyperinsulinaemia is exacerbated by obesity but can also be present in lean ♀ with PCOS. […] Insulin […] inhibits SHBG synthesis by the liver, with a consequent rise in free androgen levels. […] Symptoms often begin around puberty, after weight gain, or after stopping the oral contraceptive pill […] Oligo-/amenorrhoea [is present in] 70% […] Hirsutism [is present in] 66% […] Obesity [is present in] 50% […] *Infertility (30%). PCOS accounts for 75% of cases of anovulatory infertility. The risk of spontaneous miscarriage is also thought to be higher than the general population, mainly because of obesity. […] The aims of investigations [of PCOS] are mainly to exclude serious underlying disorders and to screen for complications, as the diagnosis is primarily clinical […] Studies have uniformly shown that weight reduction in obese ♀ with PCOS will improve insulin sensitivity and significantly reduce hyperandrogenaemia. Obese ♀ are less likely to respond to antiandrogens and infertility treatment.”

“Androgen-secreting tumours [are] [r]are tumours of the ovary or adrenal gland which may be benign or malignant, which cause virilization in ♀ through androgen production. […] Virilization […] [i]ndicates severe hyperandrogenism, is associated with clitoromegaly, and is present in 98% of ♀ with androgen-producing tumours. Not usually a feature of PCOS. […] Androgen-secreting ovarian tumours[:] *75% develop before the age of 40 years. *Account for 0.4% of all ovarian tumours; 20% are malignant. *Tumours are 5-25cm in size. The larger they are, the more likely they are to be malignant. They are rarely bilateral. […] Androgen-secreting adrenal tumours[:] *50% develop before the age of 50 years. *Larger tumours […] are more likely to be malignant. *Usually with concomitant cortisol secretion as a variant of Cushing’s syndrome. […] Symptoms and signs of Cushing’s syndrome are present in many of ♀ with adrenal tumours. […] Onset of symptoms. Usually recent onset of rapidly progressive symptoms. […] Malignant ovarian and adrenal androgen-secreting tumours are usually resistant to chemotherapy and radiotherapy. […] *Adrenal tumours. 20% 5-year survival. Most have metastatic disease at the time of surgery. *Ovarian tumours. 30% disease-free survival and 40% overall survival at 5 years. […] Benign tumours. *Prognosis excellent. *Hirsutism improves post-operatively, but clitoromegaly, male pattern balding, and deep voice may persist.”

*Oligomenorrhoea is defined as the reduction in the frequency of menses to <9 periods a year. *1° amenorrhoea is the failure of menarche by the age of 16 years. Prevalence ~0.3% *2° amenorrhoea refers to the cessation of menses for >6 months in ♀ who had previously menstruated. Prevalence ~3%. […] Although the list of causes is long […], the majority of cases of secondary amenorrhoea can be accounted for by four conditions: *Polycystic ovary syndrome. *Hypothalamic amenorrhoea. *Hyperprolactinaemia. *Ovarian failure. […] PCOS is the only common endocrine cause of amenorrhoea with normal oestrogenization – all other causes are oestrogen-deficient. Women with PCOS, therefore, are at risk of endometrial hyperplasia, and all others are at risk of osteoporosis. […] Anosmia may indicate Kallman’s syndrome. […] In routine practice, a common differential diagnosis is between mild version of PCOS and hypothalamic amenorrhoea. The distinction between these conditions may require repeated testing, as a single snapshot may not discriminate. The reason to be precise is that PCOS is oestrogen-replete and will, therefore, respond to clomiphene citrate (an antioestrogen) for fertility. HA will be oestrogen-deficient and will need HRT and ovulation induction with pulsatile GnRH or hMG [human Menopausal Gonadotropins – US]. […] […] 75% of ♀ who develop 2° amenorrhoea report hot flushes, night sweats, mood changes, fatigue, or dyspareunia; symptoms may precede the onset of menstrual disturbances.”

“POI [Premature Ovarian Insufficiency] is a disorder characterized by amenorrhoea, oestrogen deficiency, and elevated gonadotrophins, developing in ♀ <40 years, as a result of loss of ovarian follicular function. […] *Incidence – 0.1% of ♀ <30 years and 1% of those <40 years. *Accounts for 10% of all cases of 2° amenorrhoea. […] POI is the result of accelerated depletion of ovarian germ cells. […] POI is usually permanent and progressive, although a remitting course is also experienced and cannot be fully predicted, so all women must know that pregnancy is possible, even though fertility treatments are not effective (often a difficult paradox to describe). Spontaneous pregnancy has been reported in 5%. […] 80% of [women with Turner’s syndrome] have POI. […] All ♀ presenting with hypergonadotrophic amenorrhoea below age 40 should be karyotyped.”

“The menopause is the permanent cessation of menstruation as a result of ovarian failure and is a retrospective diagnosis made after 12 months of amenorrhoea. The average age of at the time of the menopause is ~50 years, although smokers reach the menopause ~2 years earlier. […] Cycles gradually become increasingly anovulatory and variable in length (often shorter) from about 4 years prior to the menopause. Oligomenorrhoea often precedes permanent amenorrhoea. in 10% of ♀, menses cease abruptly, with no preceding transitional period. […] During the perimenopausal period, there is an accelerated loss of bone mineral density (BMD), rendering post-menopausal more susceptible to osteoporotic fractures. […] Post-menopausal are 2-3 x more likely to develop IHD [ischaemic heart disease] than premenopausal , even after age adjustments. The menopause is associated with an increase in risk factors for atherosclerosis, including less favourable lipid profile, insulin sensitivity, and an ↑ thrombotic tendency. […] ♀ are 2-3 x more likely to develop Alzheimer’s disease than ♂. It is suggested that oestrogen deficiency may play a role in the development of dementia. […] The aim of treatment of perimenopausal ♀ is to alleviate menopausal symptoms and optimize quality of life. The majority of women with mild symptoms require no HRT. […] There is an ↑ risk of breast cancer in HRT users which is related to the duration of use. The risk increases by 35%, following 5 years of use (over the age of 50), and falls to never-used risk 5 years after discontinuing HRT. For ♀ aged 50 not using HRT, about 45 in every 1,000 will have cancer diagnosed over the following 20 years. This number increases to 47/1,000 ♀ using HRT for 5 years, 51/1,000 using HRT for 10 years, and 57/1,000 after 15 years of use. The risk is highest in ♀ on combined HRT compared with oestradiol alone. […] Oral HRT increases the risk [of venous thromboembolism] approximately 3-fold, resulting in an extra two cases/10,000 women-years. This risk is markedly ↑ in ♀ who already have risk factors for DVT, including previous DVT, cardiovascular disease, and within 90 days of hospitalization. […] Data from >30 observational studies suggest that HRT may reduce the risk of developing CVD [cardiovascular disease] by up to 50%. However, randomized placebo-controlled trials […] have failed to show that HRT protects against IHD. Currently, HRT should not be prescribed to prevent cardiovascular disease.”

“Any chronic illness may affect testicular function, in particular chronic renal failure, liver cirrhosis, and haemochromatosis. […] 25% of  who develop mumps after puberty have associated orchitis, and 25-50% of these will develop 1° testicular failure. […] Alcohol excess will also cause 1° testicular failure. […] Cytotoxic drugs, particularly alkylating agents, are gonadotoxic. Infertility occurs in 50% of patients following chemotherapy, and a significant number of  require androgen replacement therapy because of low testosterone levels. […] Testosterone has direct anabolic effects on skeletal muscle and has been shown to increase muscle mass and strength when given to hypogonadal men. Lean body mass is also with a reduction in fat mass. […] Hypogonadism is a risk factor for osteoporosis. Testosterone inhibits bone resorption, thereby reducing bone turnover. Its administration to hypogonadal has been shown to improve bone mineral density and reduce the risk of developing osteoporosis. […] *Androgens stimulate prostatic growth, and testosterone replacement therapy may therefore induce symptoms of bladder outflow obstruction in with prostatic hypertrophy. *It is unlikely that testosterone increases the risk of developing prostrate cancer, but it may promote the growth of an existing cancer. […] Testosterone replacement therapy may cause a fall in both LDL and HDL cholesterol levels, the significance of which remains unclear. The effect of androgen replacement therapy on the risk of developing coronary artery disease is unknown.”

“Erectile dysfunction [is] [t]he consistent inability to achieve or maintain an erect penis sufficient for satisfactory sexual intercourse. Affects approximately 10% of and >50% of >70 years. […] Erectile dysfunction may […] occur as a result of several mechanisms: *Neurological damage. *Arterial insufficiency. *Venous incompetence. *Androgen deficiency. *Penile abnormalities. […] *Abrupt onset of erectile dysfunction which is intermittent is often psychogenic in origin. *Progressive and persistent dysfunction indicates an organic cause. […] Absence of morning erections suggests an organic cause of erectile dysfunction.”

“*Infertility, defined as failure of pregnancy after 1 year of unprotected regular (2 x week) sexual intercourse, affects ~10% of all couples. *Couples who fail to conceive after 1 years of regular unprotected sexual intercourse should be investigated. […] Causes[:] *♀ factors (e.g. PCOS, tubal damage) 35%. *♂ factors (idiopathic gonadal failure in 60%) 25%. *Combined factors 25%. *Unexplained infertility 15%. […] [♀] Fertility declines rapidly after the age of 36 years. […] Each episode of acute PID causes infertility in 10-15% of cases. *Trachomatis is responsible for half the cases of PID in developed countries. […] Unexplained infertility [is] [i]nfertility despite normal sexual intercourse occurring at least twice weakly, normal semen analysis, documentation of ovulation in several cycles, and normal patent tubes (by laparoscopy). […] 30-50% will become pregnant within 3 years of expectant management. If not pregnant by then, chances that spontaneous pregnancy will occur are greatly reduced, and ART should be considered. In ♀>34 years of age, then expectant management is not an option, and up to six cycles of IUI or IVF should be considered.”

February 9, 2018 Posted by | Books, Cancer/oncology, Cardiology, Diabetes, Genetics, Medicine, Pharmacology | Leave a comment

Systems Biology (I)

This book is really dense and is somewhat tough for me to blog. One significant problem is that: “The authors assume that the reader is already familiar with the material covered in a classic biochemistry course.” I know enough biochem to follow most of the stuff in this book, and I was definitely quite happy to have recently read John Finney’s book on the biochemical properties of water and Christopher Hall’s introduction to materials science, as both of those books’ coverage turned out to be highly relevant (these are far from the only relevant books I’ve read semi-recently – Atkins introduction to thermodynamics is another book that springs to mind) – but even so, what do you leave out when writing a post like this? I decided to leave out a lot. Posts covering books like this one are hard to write because it’s so easy for them to blow up in your face because you have to include so many details for the material included in the post to even start to make sense to people who didn’t read the original text. And if you leave out all the details, what’s really left? It’s difficult..

Anyway, some observations from the first chapters of the book below.

“[T]he biological world consists of self-managing and self-organizing systems which owe their existence to a steady supply of energy and information. Thermodynamics introduces a distinction between open and closed systems. Reversible processes occurring in closed systems (i.e. independent of their environment) automatically gravitate toward a state of equilibrium which is reached once the velocity of a given reaction in both directions becomes equal. When this balance is achieved, we can say that the reaction has effectively ceased. In a living cell, a similar condition occurs upon death. Life relies on certain spontaneous processes acting to unbalance the equilibrium. Such processes can only take place when substrates and products of reactions are traded with the environment, i.e. they are only possible in open systems. In turn, achieving a stable level of activity in an open system calls for regulatory mechanisms. When the reaction consumes or produces resources that are exchanged with the outside world at an uneven rate, the stability criterion can only be satisfied via a negative feedback loop […] cells and living organisms are thermodynamically open systems […] all structures which play a role in balanced biological activity may be treated as components of a feedback loop. This observation enables us to link and integrate seemingly unrelated biological processes. […] the biological structures most directly involved in the functions and mechanisms of life can be divided into receptors, effectors, information conduits and elements subject to regulation (reaction products and action results). Exchanging these elements with the environment requires an inflow of energy. Thus, living cells are — by their nature — open systems, requiring an energy source […] A thermodynamically open system lacking equilibrium due to a steady inflow of energy in the presence of automatic regulation is […] a good theoretical model of a living organism. […] Pursuing growth and adapting to changing environmental conditions calls for specialization which comes at the expense of reduced universality. A specialized cell is no longer self-sufficient. As a consequence, a need for higher forms of intercellular organization emerges. The structure which provides cells with suitable protection and ensures continued homeostasis is called an organism.”

“In biology, structure and function are tightly interwoven. This phenomenon is closely associated with the principles of evolution. Evolutionary development has produced structures which enable organisms to develop and maintain its architecture, perform actions and store the resources needed to survive. For this reason we introduce a distinction between support structures (which are akin to construction materials), function-related structures (fulfilling the role of tools and machines), and storage structures (needed to store important substances, achieving a compromise between tight packing and ease of access). […] Biology makes extensive use of small-molecule structures and polymers. The physical properties of polymer chains make them a key building block in biological structures. There are several reasons as to why polymers are indispensable in nature […] Sequestration of resources is subject to two seemingly contradictory criteria: 1. Maximize storage density; 2. Perform sequestration in such a way as to allow easy access to resources. […] In most biological systems, storage applies to energy and information. Other types of resources are only occasionally stored […]. Energy is stored primarily in the form of saccharides and lipids. Saccharides are derivatives of glucose, rendered insoluble (and thus easy to store) via polymerization.Their polymerized forms, stabilized with α-glycosidic bonds, include glycogen (in animals) and starch (in plantlife). […] It should be noted that the somewhat loose packing of polysaccharides […] makes them unsuitable for storing large amounts of energy. In a typical human organism only ca. 600 kcal of energy is stored in the form of glycogen, while (under normal conditions) more than 100,000 kcal exists as lipids. Lipids deposit usually assume the form of triglycerides (triacylglycerols). Their properties can be traced to the similarities between fatty acids and hydrocarbons. Storage efficiency (i.e. the amount of energy stored per unit of mass) is twice that of polysaccharides, while access remains adequate owing to the relatively large surface area and high volume of lipids in the organism.”

“Most living organisms store information in the form of tightly-packed DNA strands. […] It should be noted that only a small percentage of DNA (about few %) conveys biologically relevant information. The purpose of the remaining ballast is to enable suitable packing and exposure of these important fragments. If all of DNA were to consist of useful code, it would be nearly impossible to devise a packing strategy guaranteeing access to all of the stored information.”

“The seemingly endless diversity of biological functions frustrates all but the most persistent attempts at classification. For the purpose of this handbook we assume that each function can be associated either with a single cell or with a living organism. In both cases, biological functions are strictly subordinate to automatic regulation, based — in a stable state — on negative feedback loops, and in processes associated with change (for instance in embryonic development) — on automatic execution of predetermined biological programs. Individual components of a cell cannot perform regulatory functions on their own […]. Thus, each element involved in the biological activity of a cell or organism must necessarily participate in a regulatory loop based on processing information.”

“Proteins are among the most basic active biological structures. Most of the well-known proteins studied thus far perform effector functions: this group includes enzymes, transport proteins, certain immune system components (complement factors) and myofibrils. Their purpose is to maintain biological systems in a steady state. Our knowledge of receptor structures is somewhat poorer […] Simple structures, including individual enzymes and components of multienzyme systems, can be treated as “tools” available to the cell, while advanced systems, consisting of many mechanically-linked tools, resemble machines. […] Machinelike mechanisms are readily encountered in living cells. A classic example is fatty acid synthesis, performed by dedicated machines called synthases. […] Multiunit structures acting as machines can be encountered wherever complex biochemical processes need to be performed in an efficient manner. […] If the purpose of a machine is to generate motion then a thermally powered machine can accurately be called a motor. This type of action is observed e.g. in myocytes, where transmission involves reordering of protein structures using the energy generated by hydrolysis of high-energy bonds.”

“In biology, function is generally understood as specific physiochemical action, almost universally mediated by proteins. Most such actions are reversible which means that a single protein molecule may perform its function many times. […] Since spontaneous noncovalent surface interactions are very infrequent, the shape and structure of active sites — with high concentrations of hydrophobic residues — makes them the preferred area of interaction between functional proteins and their ligands. They alone provide the appropriate conditions for the formation of hydrogen bonds; moreover, their structure may determine the specific nature of interaction. The functional bond between a protein and a ligand is usually noncovalent and therefore reversible.”

“In general terms, we can state that enzymes accelerate reactions by lowering activation energies for processes which would otherwise occur very slowly or not at all. […] The activity of enzymes goes beyond synthesizing a specific protein-ligand complex (as in the case of antibodies or receptors) and involves an independent catalytic attack on a selected bond within the ligand, precipitating its conversion into the final product. The relative independence of both processes (binding of the ligand in the active site and catalysis) is evidenced by the phenomenon of noncompetitive inhibition […] Kinetic studies of enzymes have provided valuable insight into the properties of enzymatic inhibitors — an important field of study in medicine and drug research. Some inhibitors, particularly competitive ones (i.e. inhibitors which outcompete substrates for access to the enzyme), are now commonly used as drugs. […] Physical and chemical processes may only occur spontaneously if they generate energy, or non-spontaneously if they consume it. However, all processes occurring in a cell must have a spontaneous character because only these processes may be catalyzed by enzymes. Enzymes merely accelerate reactions; they do not provide energy. […] The change in enthalpy associated with a chemical process may be calculated as a net difference in the sum of molecular binding energies prior to and following the reaction. Entropy is a measure of the likelihood that a physical system will enter a given state. Since chaotic distribution of elements is considered the most probable, physical systems exhibit a general tendency to gravitate towards chaos. Any form of ordering is thermodynamically disadvantageous.”

“The chemical reactions which power biological processes are characterized by varying degrees of efficiency. In general, they tend to be on the lower end of the efficiency spectrum, compared to energy sources which drive matter transformation processes in our universe. In search for a common criterion to describe the efficiency of various energy sources, we can refer to the net loss of mass associated with a release of energy, according to Einstein’s formula:
E = mc2
The
M/M coefficient (relative loss of mass, given e.g. in %) allows us to compare the efficiency of energy sources. The most efficient processes are those involved in the gravitational collapse of stars. Their efficiency may reach 40 %, which means that 40 % of the stationary mass of the system is converted into energy. In comparison, nuclear reactions have an approximate efficiency of 0.8 %. The efficiency of chemical energy sources available to biological systems is incomparably lower and amounts to approximately 10(-7) % […]. Among chemical reactions, the most potent sources of energy are found in oxidation processes, commonly exploited by biological systems. Oxidation tends  to result in the largest net release of energy per unit of mass, although the efficiency of specific types of oxidation varies. […] given unrestricted access to atmospheric oxygen and to hydrogen atoms derived from hydrocarbons — the combustion of hydrogen (i.e. the synthesis of water; H2 + 1/2O2 = H2O) has become a principal source of energy in nature, next to photosynthesis, which exploits the energy of solar radiation. […] The basic process associated with the release of hydrogen and its subsequent oxidation (called the Krebs cycle) is carried by processes which transfer electrons onto oxygen atoms […]. Oxidation occurs in stages, enabling optimal use of the released energy. An important byproduct of water synthesis is the universal energy carrier known as ATP (synthesized separately). As water synthesis is a highly spontaneous process, it can be exploited to cover the energy debt incurred by endergonic synthesis of ATP, as long as both processes are thermodynamically coupled, enabling spontaneous catalysis of anhydride bonds in ATP. Water synthesis is a universal source of energy in heterotrophic systems. In contrast, autotrophic organisms rely on the energy of light which is exploited in the process of photosynthesis. Both processes yield ATP […] Preparing nutrients (hydrogen carriers) for participation in water synthesis follows different paths for sugars, lipids and proteins. This is perhaps obvious given their relative structural differences; however, in all cases the final form, which acts as a substrate for dehydrogenases, is acetyl-CoA“.

“Photosynthesis is a process which — from the point of view of electron transfer — can be treated as a counterpart of the respiratory chain. In heterotrophic organisms, mitochondria transport electrons from hydrogenated compounds (sugars, lipids, proteins) onto oxygen molecules, synthesizing water in the process, whereas in the course of photosynthesis electrons released by breaking down water molecules are used as a means of reducing oxydised carbon compounds […]. In heterotrophic organisms the respiratory chain has a spontaneous quality (owing to its oxidative properties); however any reverse process requires energy to occur. In the case of photosynthesis this energy is provided by sunlight […] Hydrogen combustion and photosynthesis are the basic sources of energy in the living world. […] For an energy source to become useful, non-spontaneous reactions must be coupled to its operation, resulting in a thermodynamically unified system. Such coupling can be achieved by creating a coherent framework in which the spontaneous and non-spontaneous processes are linked, either physically or chemically, using a bridging component which affects them both. If the properties of both reactions are different, the bridging component must also enable suitable adaptation and mediation. […] Direct exploitation of the energy released via the hydrolysis of ATP is possible usually by introducing an active binding carrier mediating the energy transfer. […] Carriers are considered active as long as their concentration ensures a sufficient release of energy to synthesize a new chemical bond by way of a non-spontaneous process. Active carriers are relatively short-lived […] Any active carrier which performs its function outside of the active site must be sufficiently stable to avoid breaking up prior to participating in the synthesis reaction. Such mobile carriers are usually produced when the required synthesis consists of several stages or cannot be conducted in the active site of the enzyme for sterical reasons. Contrary to ATP, active energy carriers are usually reaction-specific. […] Mobile energy carriers are usually formed as a result of hydrolysis of two high-energy ATP bonds. In many cases this is the minimum amount of energy required to power a reaction which synthesizes a single chemical bond. […] Expelling a mobile or unstable reaction component in order to increase the spontaneity of active energy carrier synthesis is a process which occurs in many biological mechanisms […] The action of active energy carriers may be compared to a ball rolling down a hill. The descending snowball gains sufficient energy to traverse another, smaller mound, adjacent to its starting point. In our case, the smaller hill represents the final synthesis reaction […] Understanding the role of active carriers is essential for the study of metabolic processes.”

“A second category of processes, directly dependent on energy sources, involves structural reconfiguration of proteins, which can be further differentiated into low and high-energy reconfiguration. Low-energy reconfiguration occurs in proteins which form weak, easily reversible bonds with ligands. In such cases, structural changes are powered by the energy released in the creation of the complex. […] Important low-energy reconfiguration processes may occur in proteins which consist of subunits. Structural changes resulting from relative motion of subunits typically do not involve significant expenditures of energy. Of particular note are the so-called allosteric proteins […] whose rearrangement is driven by a weak and reversible bond between the protein and an oxygen molecule. Allosteric proteins are genetically conditioned to possess two stable structural configurations, easily swapped as a result of binding or releasing ligands. Thus, they tend to have two comparable energy minima (separated by a low threshold), each of which may be treated as a global minimum corresponding to the native form of the protein. Given such properties, even a weakly interacting ligand may trigger significant structural reconfiguration. This phenomenon is of critical importance to a variety of regulatory proteins. In many cases, however, the second potential minimum in which the protein may achieve relative stability is separated from the global minimum by a high threshold requiring a significant expenditure of energy to overcome. […] Contrary to low-energy reconfigurations, the relative difference in ligand concentrations is insufficient to cover the cost of a difficult structural change. Such processes are therefore coupled to highly exergonic reactions such as ATP hydrolysis. […]  The link between a biological process and an energy source does not have to be immediate. Indirect coupling occurs when the process is driven by relative changes in the concentration of reaction components. […] In general, high-energy reconfigurations exploit direct coupling mechanisms while indirect coupling is more typical of low-energy processes”.

Muscle action requires a major expenditure of energy. There is a nonlinear dependence between the degree of physical exertion and the corresponding energy requirements. […] Training may improve the power and endurance of muscle tissue. Muscle fibers subjected to regular exertion may improve their glycogen storage capacity, ATP production rate, oxidative metabolism and the use of fatty acids as fuel.

February 4, 2018 Posted by | Biology, Books, Chemistry, Genetics, Molecular biology, Pharmacology, Physics | Leave a comment

A few diabetes papers of interest

i. Mechanisms and Management of Diabetic Painful Distal Symmetrical Polyneuropathy.

“Although a number of the diabetic neuropathies may result in painful symptomatology, this review focuses on the most common: chronic sensorimotor distal symmetrical polyneuropathy (DSPN). It is estimated that 15–20% of diabetic patients may have painful DSPN, but not all of these will require therapy. […] Although the exact pathophysiological processes that result in diabetic neuropathic pain remain enigmatic, both peripheral and central mechanisms have been implicated, and extend from altered channel function in peripheral nerve through enhanced spinal processing and changes in many higher centers. A number of pharmacological agents have proven efficacy in painful DSPN, but all are prone to side effects, and none impact the underlying pathophysiological abnormalities because they are only symptomatic therapy. The two first-line therapies approved by regulatory authorities for painful neuropathy are duloxetine and pregabalin. […] All patients with DSPN are at increased risk of foot ulceration and require foot care, education, and if possible, regular podiatry assessment.”

“The neuropathies are the most common long-term microvascular complications of diabetes and affect those with both type 1 and type 2 diabetes, with up to 50% of older type 2 diabetic patients having evidence of a distal neuropathy (1). These neuropathies are characterized by a progressive loss of nerve fibers affecting both the autonomic and somatic divisions of the nervous system. The clinical features of the diabetic neuropathies vary immensely, and only a minority are associated with pain. The major portion of this review will be dedicated to the most common painful neuropathy, chronic sensorimotor distal symmetrical polyneuropathy (DSPN). This neuropathy has major detrimental effects on its sufferers, confirming an increased risk of foot ulceration and Charcot neuroarthropathy as well as being associated with increased mortality (1).

In addition to DSPN, other rarer neuropathies may also be associated with painful symptoms including acute painful neuropathy that often follows periods of unstable glycemic control, mononeuropathies (e.g., cranial nerve palsies), radiculopathies, and entrapment neuropathies (e.g., carpal tunnel syndrome). By far the most common presentation of diabetic polyneuropathy (over 90%) is typical DSPN or chronic DSPN. […] DSPN results in insensitivity of the feet that predisposes to foot ulceration (1) and/or neuropathic pain (painful DSPN), which can be disabling. […] The onset of DSPN is usually gradual or insidious and is heralded by sensory symptoms that start in the toes and then progress proximally to involve the feet and legs in a stocking distribution. When the disease is well established in the lower limbs in more severe cases, there is upper limb involvement, with a similar progression proximally starting in the fingers. As the disease advances further, motor manifestations, such as wasting of the small muscles of the hands and limb weakness, become apparent. In some cases, there may be sensory loss that the patient may not be aware of, and the first presentation may be a foot ulcer. Approximately 50% of patients with DSPN experience neuropathic symptoms in the lower limbs including uncomfortable tingling (dysesthesia), pain (burning; shooting or “electric-shock like”; lancinating or “knife-like”; “crawling”, or aching etc., in character), evoked pain (allodynia, hyperesthesia), or unusual sensations (such as a feeling of swelling of the feet or severe coldness of the legs when clearly the lower limbs look and feel fine, odd sensations on walking likened to “walking on pebbles” or “walking on hot sand,” etc.). There may be marked pain on walking that may limit exercise and lead to weight gain. Painful DSPN is characteristically more severe at night and often interferes with normal sleep (3). It also has a major impact on the ability to function normally (both mental and physical functioning, e.g., ability to maintain work, mood, and quality of life [QoL]) (3,4). […] The unremitting nature of the pain can be distressing, resulting in mood disorders including depression and anxiety (4). The natural history of painful DSPN has not been well studied […]. However, it is generally believed that painful symptoms may persist over the years (5), occasionally becoming less prominent as the sensory loss worsens (6).”

“There have been relatively few epidemiological studies that have specifically examined the prevalence of painful DSPN, which range from 10–26% (79). In a recent study of a large cohort of diabetic patients receiving community-based health care in northwest England (n = 15,692), painful DSPN assessed using neuropathy symptom and disability scores was found in 21% (7). In one population-based study from Liverpool, U.K., the prevalence of painful DSPN assessed by a structured questionnaire and examination was estimated at 16% (8). Notably, it was found that 12.5% of these patients had never reported their symptoms to their doctor and 39% had never received treatment for their pain (8), indicating that there may be considerable underdiagnosis and undertreatment of painful neuropathic symptoms compared with other aspects of diabetes management such as statin therapy and management of hypertension. Risk factors for DSPN per se have been extensively studied, and it is clear that apart from poor glycemic control, cardiovascular risk factors play a prominent role (10): risk factors for painful DSPN are less well known.”

“A broad spectrum of presentations may occur in patients with DSPN, ranging from one extreme of the patient with very severe painful symptoms but few signs, to the other when patients may present with a foot ulcer having lost all sensation without ever having any painful or uncomfortable symptoms […] it is well recognized that the severity of symptoms may not relate to the severity of the deficit on clinical examination (1). […] Because DSPN is a diagnosis of exclusion, a careful clinical history and a peripheral neurological and vascular examination of the lower limbs are essential to exclude other causes of neuropathic pain and leg/foot pain such as peripheral vascular disease, arthritis, malignancy, alcohol abuse, spinal canal stenosis, etc. […] Patients with asymmetrical symptoms and/or signs (such as loss of an ankle jerk in one leg only), rapid progression of symptoms, or predominance of motor symptoms and signs should be carefully assessed for other causes of the findings.”

“The fact that diabetes induces neuropathy and that in a proportion of patients this is accompanied by pain despite the loss of input and numbness, suggests that marked changes occur in the processes of pain signaling in the peripheral and central nervous system. Neuropathic pain is characterized by ongoing pain together with exaggerated responses to painful and nonpainful stimuli, hyperalgesia, and allodynia. […] the changes seen suggest altered peripheral signaling and central compensatory changes perhaps driven by the loss of input. […] Very clear evidence points to the key role of changes in ion channels as a consequence of nerve damage and their roles in the disordered activity and transduction in damaged and intact fibers (50). Sodium channels depolarize neurons and generate an action potential. Following damage to peripheral nerves, the normal distribution of these channels along a nerve is disrupted by the neuroma and “ectopic” activity results from the accumulation of sodium channels at or around the site of injury. Other changes in the distribution and levels of these channels are seen and impact upon the pattern of neuronal excitability in the nerve. Inherited pain disorders arise from mutated sodium channels […] and polymorphisms in this channel impact on the level of pain in patients, indicating that inherited differences in channel function might explain some of the variability in pain between patients with DSPN (53). […] Where sodium channels act to generate action potentials, potassium channels serve as the molecular brakes of excitable cells, playing an important role in modulating neuronal hyperexcitability. The drug retigabine, a potassium channel opener acting on the channel (KV7, M-current) opener, blunts behavioral hypersensitivity in neuropathic rats (56) and also inhibits C and Aδ-mediated responses in dorsal horn neurons in both naïve and neuropathic rats (57), but has yet to reach the clinic as an analgesic”.

and C fibers terminate primarily in the superficial laminae of the dorsal horn where the large majority of neurons are nociceptive specific […]. Some of these neurons gain low threshold inputs after neuropathy and these cells project predominantly to limbic brain areas […] spinal cord neurons provide parallel outputs to the affective and sensory areas of the brain. Changes induced in these neurons by repeated noxious inputs underpin central sensitization where the resultant hyperexcitability of neurons leads to greater responses to all subsequent inputs — innocuous and noxious — expanded receptive fields and enhanced outputs to higher levels of the brain […] As a consequence of these changes in the sending of nociceptive information within the peripheral nerve and then the spinal cord, the information sent to the brain becomes amplified so that pain ratings become higher. Alongside this, the persistent input into the limbic brain areas such as the amygdala are likely to be causal in the comorbidities that patients often report due to ongoing painful inputs disrupting normal function and generating fear, depression, and sleep problems […]. Of course, many patients report that their pains are worse at night, which may be due to nocturnal changes in these central pain processing areas. […] overall, the mechanisms of pain in diabetic neuropathy extend from altered channel function in peripheral nerves through enhanced spinal processing and finally to changes in many higher centers”.

Pharmacological treatment of painful DSPN is not entirely satisfactory because currently available drugs are often ineffective and complicated by adverse events. Tricyclic compounds (TCAs) have been used as first-line agents for many years, but their use is limited by frequent side effects that may be central or anticholinergic, including dry mouth, constipation, sweating, blurred vision, sedation, and orthostatic hypotension (with the risk of falls particularly in elderly patients). […] Higher doses have been associated with an increased risk of sudden cardiac death, and caution should be taken in any patient with a history of cardiovascular disease (65). […] The selective serotonin noradrenalin reuptake inhibitors (SNRI) duloxetine and venlafaxine have been used for the management of painful DSPN (65). […] there have been several clinical trials involving pregabalin in painful DSPN, and these showed clear efficacy in management of painful DSPN (69). […] The side effects include dizziness, somnolence, peripheral edema, headache, and weight gain.”

A major deficiency in the area of the treatment of neuropathic pain in diabetes is the relative lack of comparative or combination studies. Virtually all previous trials have been of active agents against placebo, whereas there is a need for more studies that compare a given drug with an active comparator and indeed lower-dose combination treatments (64). […] The European Federation of Neurological Societies proposed that first-line treatments might comprise of TCAs, SNRIs, gabapentin, or pregabalin (71). The U.K. National Institute for Health and Care Excellence guidelines on the management of neuropathic pain in nonspecialist settings proposed that duloxetine should be the first-line treatment with amitriptyline as an alternative, and pregabalin as a second-line treatment for painful DSPN (72). […] this recommendation of duloxetine as the first-line therapy was not based on efficacy but rather cost-effectiveness. More recently, the American Academy of Neurology recommended that pregabalin is “established as effective and should be offered for relief of [painful DSPN] (Level A evidence)” (73), whereas venlafaxine, duloxetine, amitriptyline, gabapentin, valproate, opioids, and capsaicin were considered to be “probably effective and should be considered for treatment of painful DSPN (Level B evidence)” (63). […] this recommendation was primarily based on achievement of greater than 80% completion rate of clinical trials, which in turn may be influenced by the length of the trials. […] the International Consensus Panel on Diabetic Neuropathy recommended TCAs, duloxetine, pregabalin, and gabapentin as first-line agents having carefully reviewed all the available literature regarding the pharmacological treatment of painful DSPN (65), the final drug choice tailored to the particular patient based on demographic profile and comorbidities. […] The initial selection of a particular first-line treatment will be influenced by the assessment of contraindications, evaluation of comorbidities […], and cost (65). […] caution is advised to start at lower than recommended doses and titrate gradually.”

ii. Sex Differences in All-Cause and Cardiovascular Mortality, Hospitalization for Individuals With and Without Diabetes, and Patients With Diabetes Diagnosed Early and Late.

“A challenge with type 2 diabetes is the late diagnosis of the disease because many individuals who meet the criteria are often asymptomatic. Approximately 183 million people, or half of those who have diabetes, are unaware they have the disease (1). Furthermore, type 2 diabetes can be present for 9 to 12 years before being diagnosed and, as a result, complications are often present at the time of diagnosis (3). […] Cardiovascular disease (CVD) is the most common comorbidity associated with diabetes, and with 50% of those with diabetes dying of CVD it is the most common cause of death (1). […] Newfoundland and Labrador has the highest age-standardized prevalence of diabetes in Canada (2), and the age-standardized mortality and hospitalization rates for CVD, AMI, and stroke are some of the highest in the country (21,22). A better understanding of mortality and hospitalizations associated with diabetes for males and females is important to support diabetes prevention and management. Therefore, the objectives of this study were to compare the risk of all-cause, CVD, AMI, and stroke mortality and hospitalizations for males and females with and without diabetes and those with early and late diagnoses of diabetes. […] We conducted a population-based retrospective cohort study including 73,783 individuals aged 25 years or older in Newfoundland and Labrador, Canada (15,152 with diabetes; 9,517 with late diagnoses). […] mean age at baseline was 60.1 years (SD, 14.3 years). […] Diabetes was classified as being diagnosed “early” and “late” depending on when diabetes-related comorbidities developed. Individuals early in the disease course would not have any diabetes-related comorbidities at the time of their case dates. On the contrary, a late-diagnosed diabetes patient would have comorbidities related to diabetes at the time of diagnosis.”

“For males, 20.5% (n = 7,751) had diabetes, whereas 20.6% (n = 7,401) of females had diabetes. […] Males and females with diabetes were more likely to die, to be younger at death, to have a shorter survival time, and to be admitted to the hospital than males and females without diabetes (P < 0.01). When admitted to the hospital, individuals with diabetes stayed longer than individuals without diabetes […] Both males and females with late diagnoses were significantly older at the time of diagnosis than those with early diagnoses […]. Males and females with late diagnoses of diabetes were more likely to be deceased at the end of the study period compared with those with early diagnoses […]. Those with early diagnoses were younger at death compared with those with late diagnoses (P < 0.01); however, median survival time for both males and females with early diagnoses was significantly longer than that of those with late diagnoses (P < 0.01). During the study period, males and females with late diabetes diagnoses were more likely to be hospitalized (P < 0.01) and have a longer length of hospital stay compared with those with early diagnoses (P < 0.01).”

“[T]he hospitalization results show that an early diagnosis […] increase the risk of all-cause, CVD, and AMI hospitalizations compared with individuals without diabetes. After adjusting for covariates, males with late diabetes diagnoses had an increased risk of all-cause and CVD mortality and hospitalizations compared with males without diabetes. Similar findings were found for females. A late diabetes diagnosis was positively associated with CVD mortality (HR 6.54 [95% CI 4.80–8.91]) and CVD hospitalizations (5.22 [4.31–6.33]) for females, and the risk was significantly higher compared with their male counterparts (3.44 [2.47–4.79] and 3.33 [2.80–3.95]).”

iii. Effect of Type 1 Diabetes on Carotid Structure and Function in Adolescents and Young Adults.

I may have discussed some of the results of this study before, but a search of the blog told me that I have not covered the study itself. I thought it couldn’t hurt to add a link and a few highlights here.

“Type 1 diabetes mellitus causes increased carotid intima-media thickness (IMT) in adults. We evaluated IMT in young subjects with type 1 diabetes. […] Participants with type 1 diabetes (N = 402) were matched to controls (N = 206) by age, sex, and race or ethnicity. Anthropometric and laboratory values, blood pressure, and IMT were measured.”

“Youth with type 1 diabetes had thicker bulb IMT, which remained significantly different after adjustment for demographics and cardiovascular risk factors. […] Because the rate of progression of IMT in healthy subjects (mean age, 40 years) in the Bogalusa Heart study was 0.017–0.020 mm/year (4), our difference of 0.016 mm suggests that our type 1 diabetic subjects had a vascular age 1 year advanced from their chronological age. […] adjustment for HbA1c ablated the case-control difference in IMT, suggesting that the thicker carotid IMT in the subjects with diabetes could be attributed to diabetes-related hyperglycemia.”

“In the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study, progression of IMT over the course of 6 years was faster in subjects with type 1 diabetes, yielding a thicker final IMT in cases (5). There was no difference in IMT at baseline. However, DCCT/EDIC did not image the bulb, which is likely the earliest site of thickening according to the Bogalusa Heart Study […] Our analyses reinforce the importance of imaging the carotid bulb, often the site of earliest detectible subclinical atherosclerosis in youth. The DCCT/EDIC study demonstrated that the intensive treatment group had a slower progression of IMT (5) and that mean HbA1c levels explained most of the differences in IMT progression between treatment groups (12). One longitudinal study of youth found children with type 1 diabetes who had progression of IMT over the course of 2 years had higher HbA1c (13). Our data emphasize the role of diabetes-related hyperglycemia in increasing IMT in youth with type 1 diabetes. […] In summary, our study provides novel evidence that carotid thickness is increased in youth with type 1 diabetes compared with healthy controls and that this difference is not accounted for by traditional cardiovascular risk factors. Better control of diabetes-related hyperglycemia may be needed to reduce future cardiovascular disease.”

iv. Factors Associated With Microalbuminuria in 7,549 Children and Adolescents With Type 1 Diabetes in the T1D Exchange Clinic Registry.

“Elevated urinary albumin excretion is an early sign of diabetic kidney disease (DKD). The American Diabetes Association (ADA) recommends screening for microalbuminuria (MA) annually in people with type 1 diabetes after 10 years of age and 5 years of diabetes duration, with a diagnosis of MA requiring two of three tests to be abnormal (1). Early diagnosis of MA is important because effective treatments exist to limit the progression of DKD (1). However, although reduced rates of MA have been reported over the past few decades in some (24) but not all (5,6) studies, it has been suggested that the development of proteinuria has not been prevented but, rather, has been delayed by ∼10 years and that further improvements in care are needed (7).

Limited data exist on the frequency of a clinical diagnosis of MA in the pediatric population with type 1 diabetes in the U.S. Our aim was to use the data from the T1D Exchange clinic registry to assess factors associated with MA in 7,549 children and adolescents with type 1 diabetes.”

“The analysis cohort included 7,549 participants, with mean age of 13.8 ± 3.5 years (range 2 to 19), mean age at type 1 diabetes onset of 6.9 ± 3.9 years, and mean diabetes duration of 6.5 ± 3.7 years; 49% were female. The racial/ethnic distribution was 78% non-Hispanic white, 6% non-Hispanic black, 10% Hispanic, and 5% other. The average of all HbA1c levels (for up to the past 13 years) was 8.4 ± 1.3% (69 ± 13.7 mmol/mol) […]. MA was present in 329 of 7,549 (4.4%) participants, with a higher frequency associated with longer diabetes duration, higher mean glycosylated hemoglobin (HbA1c) level, older age, female sex, higher diastolic blood pressure (BP), and lower BMI […] increasing age [was] mainly associated with an increase in the frequency of MA when HbA1c was ≥9.5% (≥80 mmol/mol). […] MA was uncommon (<2%) among participants with HbA1c <7.5% (<58 mmol/mol). Of those with MA, only 36% were receiving ACEI/ARB treatment. […] Our results provide strong support for prior literature in emphasizing the importance of good glycemic and BP control, particularly as diabetes duration increases, in order to reduce the risk of DKD.

v. Secular Changes in the Age-Specific Prevalence of Diabetes Among U.S. Adults: 1988–2010.

“This study included 22,586 adults sampled in three periods of the National Health and Nutrition Examination Survey (1988–1994, 1999–2004, and 2005–2010). Diabetes was defined as having self-reported diagnosed diabetes or having a fasting plasma glucose level ≥126 mg/dL or HbA1c ≥6.5% (48 mmol/mol). […] The number of adults with diabetes increased by 75% from 1988–1994 to 2005–2010. After adjusting for sex, race/ethnicity, and education level, the prevalence of diabetes increased over the two decades across all age-groups. Younger adults (20–34 years of age) had the lowest absolute increase in diabetes prevalence of 1.0%, followed by middle-aged adults (35–64) at 2.7% and older adults (≥65) at 10.0% (all P < 0.001). Comparing 2005–2010 with 1988–1994, the adjusted prevalence ratios (PRs) by age-group were 2.3, 1.3, and 1.5 for younger, middle-aged, and older adults, respectively (all P < 0.05). After additional adjustment for body mass index (BMI), waist-to-height ratio (WHtR), or waist circumference (WC), the adjusted PR remained statistically significant only for adults ≥65 years of age.

CONCLUSIONS During the past two decades, the prevalence of diabetes increased across all age-groups, but adults ≥65 years of age experienced the largest increase in absolute change. Obesity, as measured by BMI, WHtR, or WC, was strongly associated with the increase in diabetes prevalence, especially in adults <65.”

The crude prevalence of diabetes changed from 8.4% (95% CI 7.7–9.1%) in 1988–1994 to 12.1% (11.3–13.1%) in 2005–2010, with a relative increase of 44.8% (28.3–61.3%) between the two survey periods. There was less change of prevalence of undiagnosed diabetes (P = 0.053). […] The estimated number (in millions) of adults with diabetes grew from 14.9 (95% CI 13.3–16.4) in 1988–1994 to 26.1 (23.8–28.3) in 2005–2010, resulting in an increase of 11.2 prevalent cases (a 75.5% [52.1–98.9%] increase). Younger adults contributed 5.5% (2.5–8.4%), middle-aged adults contributed 52.9% (43.4–62.3%), and older adults contributed 41.7% (31.9–51.4%) of the increased number of cases. In each survey time period, the number of adults with diabetes increased with age until ∼60–69 years; thereafter, it decreased […] the largest increase of cases occurred in middle-aged and older adults.”

vi. The Expression of Inflammatory Genes Is Upregulated in Peripheral Blood of Patients With Type 1 Diabetes.

“Although much effort has been devoted toward discoveries with respect to gene expression profiling in human T1D in the last decade (15), previous studies had serious limitations. Microarray-based gene expression profiling is a powerful discovery platform, but the results must be validated by an alternative technique such as real-time RT-PCR. Unfortunately, few of the previous microarray studies on T1D have been followed by a validation study. Furthermore, most previous gene expression studies had small sample sizes (<100 subjects in each group) that are not adequate for the human population given the expectation of large expression variations among individual subjects. Finally, the selection of appropriate reference genes for normalization of quantitative real-time PCR has a major impact on data quality. Most of the previous studies have used only a single reference gene for normalization. Ideally, gene transcription studies using real-time PCR should begin with the selection of an appropriate set of reference genes to obtain more reliable results (68).

We have previously carried out extensive microarray analysis and identified >100 genes with significantly differential expression between T1D patients and control subjects. Most of these genes have important immunological functions and were found to be upregulated in autoantibody-positive subjects, suggesting their potential use as predictive markers and involvement in T1D development (2). In this study, real-time RT-PCR was performed to validate a subset of the differentially expressed genes in a large sample set of 928 T1D patients and 922 control subjects. In addition to the verification of the gene expression associated with T1D, we also identified genes with significant expression changes in T1D patients with diabetes complications.

“Of the 18 genes analyzed here, eight genes […] had higher expression and three genes […] had lower expression in T1D patients compared with control subjects, indicating that genes involved in inflammation, immune regulation, and antigen processing and presentation are significantly altered in PBMCs from T1D patients. Furthermore, one adhesion molecule […] and three inflammatory genes mainly expressed by myeloid cells […] were significantly higher in T1D patients with complications (odds ratio [OR] 1.3–2.6, adjusted P value = 0.005–10−8), especially those patients with neuropathy (OR 4.8–7.9, adjusted P value <0.005). […] These findings suggest that inflammatory mediators secreted mainly by myeloid cells are implicated in T1D and its complications.

vii. Overexpression of Hemopexin in the Diabetic Eye – A new pathogenic candidate for diabetic macular edema.

“Diabetic retinopathy remains the leading cause of preventable blindness among working-age individuals in developed countries (1). Whereas proliferative diabetic retinopathy (PDR) is the commonest sight-threatening lesion in type 1 diabetes, diabetic macular edema (DME) is the primary cause of poor visual acuity in type 2 diabetes. Because of the high prevalence of type 2 diabetes, DME is the main cause of visual impairment in diabetic patients (2). When clinically significant DME appears, laser photocoagulation is currently indicated. However, the optimal period of laser treatment is frequently passed and, moreover, is not uniformly successful in halting visual decline. In addition, photocoagulation is not without side effects, with visual field loss and impairment of either adaptation or color vision being the most frequent. Intravitreal corticosteroids have been successfully used in eyes with persistent DME and loss of vision after the failure of conventional treatment. However, reinjections are commonly needed, and there are substantial adverse effects such as infection, glaucoma, and cataract formation. Intravitreal anti–vascular endothelial growth factor (VEGF) agents have also found an improvement of visual acuity and decrease of retinal thickness in DME, even in nonresponders to conventional treatment (3). However, apart from local side effects such as endophthalmitis and retinal detachment, the response to treatment of DME by VEGF blockade is not prolonged and is subject to significant variability. For all these reasons, new pharmacological treatments based on the understanding of the pathophysiological mechanisms of DME are needed.”

“Vascular leakage due to the breakdown of the blood-retinal barrier (BRB) is the main event involved in the pathogenesis of DME (4). However, little is known regarding the molecules primarily involved in this event. By means of a proteomic analysis, we have found that hemopexin was significantly increased in the vitreous fluid of patients with DME in comparison with PDR and nondiabetic control subjects (5). Hemopexin is the best characterized permeability factor in steroid-sensitive nephrotic syndrome (6,7). […] T cell–associated cytokines like tumor necrosis factor-α are able to enhance hemopexin production in mesangial cells in vitro, and this effect is prevented by corticosteroids (8). However, whether hemopexin also acts as a permeability factor in the BRB and its potential response to corticosteroids remains to be elucidated. […] the aims of the current study were 1) to compare hemopexin and hemopexin receptor (LDL receptor–related protein [LRP1]) levels in retina and in vitreous fluid from diabetic and nondiabetic patients, 2) to evaluate the effect of hemopexin on the permeability of outer and inner BRB in cell cultures, and 3) to determine whether anti-hemopexin antibodies and dexamethasone were able to prevent an eventual hemopexin-induced hyperpermeability.”

“In the current study, we […] confirmed our previous results obtained by a proteomic approach showing that hemopexin is higher in the vitreous fluid of diabetic patients with DME in comparison with diabetic patients with PDR and nondiabetic subjects. In addition, we provide the first evidence that hemopexin is overexpressed in diabetic eye. Furthermore, we have shown that hemopexin leads to the disruption of RPE [retinal pigment epithelium] cells, thus increasing permeability, and that this effect is prevented by dexamethasone. […] Our findings suggest that hemopexin can be considered a new candidate in the pathogenesis of DME and a new therapeutic target.”

viii. Relationship Between Overweight and Obesity With Hospitalization for Heart Failure in 20,985 Patients With Type 1 Diabetes.

“We studied patients with type 1 diabetes included in the Swedish National Diabetes Registry during 1998–2003, and they were followed up until hospitalization for HF, death, or 31 December 2009. Cox regression was used to estimate relative risks. […] Type 1 diabetes is defined in the NDR as receiving treatment with insulin only and onset at age 30 years or younger. These characteristics previously have been validated as accurate in 97% of cases (11). […] In a sample of 20,985 type 1 diabetic patients (mean age, 38.6 years; mean BMI, 25.0 kg/m2), 635 patients […] (3%) were admitted for a primary or secondary diagnosis of HF during a median follow-up of 9 years, with an incidence of 3.38 events per 1,000 patient-years (95% CI, 3.12–3.65). […] Cox regression adjusting for age, sex, diabetes duration, smoking, HbA1c, systolic and diastolic blood pressures, and baseline and intercurrent comorbidities (including myocardial infarction) showed a significant relationship between BMI and hospitalization for HF (P < 0.0001). In reference to patients in the BMI 20–25 kg/m2 category, hazard ratios (HRs) were as follows: HR 1.22 (95% CI, 0.83–1.78) for BMI <20 kg/m2; HR 0.94 (95% CI, 0.78–1.12) for BMI 25–30 kg/m2; HR 1.55 (95% CI, 1.20–1.99) for BMI 30–35 kg/m2; and HR 2.90 (95% CI, 1.92–4.37) for BMI ≥35 kg/m2.

CONCLUSIONS Obesity, particularly severe obesity, is strongly associated with hospitalization for HF in patients with type 1 diabetes, whereas no similar relation was present in overweight and low body weight.”

“In contrast to type 2 diabetes, obesity is not implicated as a causal factor in type 1 diabetes and maintaining normal weight is accordingly less of a focus in clinical practice of patients with type 1 diabetes. Because most patients with type 2 diabetes are overweight or obese and glucose levels can normalize in some patients after weight reduction, this is usually an important part of integrated diabetes care. Our findings indicate that given the substantial risk of cardiovascular disease in type 1 diabetic patients, it is crucial for clinicians to also address weight issues in type 1 diabetes. Because many patients are normal weight when diabetes is diagnosed, careful monitoring of weight with a view to maintaining normal weight is probably more essential than previously thought. Although overweight was not associated with an increased risk of HF, higher BMI levels probably increase the risk of future obesity. Our finding that 71% of patients with BMI >35 kg/m2 were women is potentially important, although this should be tested in other populations given that it could be a random finding. If not random, especially because the proportion was much higher than in the entire cohort (45%), then it may indicate that severe obesity is a greater problem in women than in men with type 1 diabetes.”

November 30, 2017 Posted by | Cardiology, Diabetes, Genetics, Molecular biology, Nephrology, Neurology, Ophthalmology, Pharmacology, Studies | Leave a comment

Organic Chemistry (I)

This book‘s a bit longer than most ‘A very short introduction to…‘ publications, and it’s quite dense at times and included a lot of interesting stuff. It took me a while to finish it as I put it away a while back when I hit some of the more demanding content, but I did pick it up later and I really enjoyed most of the coverage. In the end I decided that I wouldn’t be doing the book justice if I were to limit my coverage of it to just one post, so this will be only the first of two posts of coverage of this book, covering roughly the first half of it.

As usual I have included in my post both some observations from the book (…and added a few links to these quotes where I figured they might be helpful) as well as some wiki links to topics discussed in the book.

“Organic chemistry is a branch of chemistry that studies carbon-based compounds in terms of their structure, properties, and synthesis. In contrast, inorganic chemistry covers the chemistry of all the other elements in the periodic table […] carbon-based compounds are crucial to the chemistry of life. [However] organic chemistry has come to be defined as the chemistry of carbon-based compounds, whether they originate from a living system or not. […] To date, 16 million compounds have been synthesized in organic chemistry laboratories across the world, with novel compounds being synthesized every day. […] The list of commodities that rely on organic chemistry include plastics, synthetic fabrics, perfumes, colourings, sweeteners, synthetic rubbers, and many other items that we use every day.”

“For a neutral carbon atom, there are six electrons occupying the space around the nucleus […] The electrons in the outer shell are defined as the valence electrons and these determine the chemical properties of the atom. The valence electrons are easily ‘accessible’ compared to the two electrons in the first shell. […] There is great significance in carbon being in the middle of the periodic table. Elements which are close to the left-hand side of the periodic table can lose their valence electrons to form positive ions. […] Elements on the right-hand side of the table can gain electrons to form negatively charged ions. […] The impetus for elements to form ions is the stability that is gained by having a full outer shell of electrons. […] Ion formation is feasible for elements situated to the left or the right of the periodic table, but it is less feasible for elements in the middle of the table. For carbon to gain a full outer shell of electrons, it would have to lose or gain four valence electrons, but this would require far too much energy. Therefore, carbon achieves a stable, full outer shell of electrons by another method. It shares electrons with other elements to form bonds. Carbon excels in this and can be considered chemistry’s ultimate elemental socialite. […] Carbon’s ability to form covalent bonds with other carbon atoms is one of the principle reasons why so many organic molecules are possible. Carbon atoms can be linked together in an almost limitless way to form a mind-blowing variety of carbon skeletons. […] carbon can form a bond to hydrogen, but it can also form bonds to atoms such as nitrogen, phosphorus, oxygen, sulphur, fluorine, chlorine, bromine, and iodine. As a result, organic molecules can contain a variety of different elements. Further variety can arise because it is possible for carbon to form double bonds or triple bonds to a variety of other atoms. The most common double bonds are formed between carbon and oxygen, carbon and nitrogen, or between two carbon atoms. […] The most common triple bonds are found between carbon and nitrogen, or between two carbon atoms.”

[C]hirality has huge importance. The two enantiomers of a chiral molecule behave differently when they interact with other chiral molecules, and this has important consequences in the chemistry of life. As an analogy, consider your left and right hands. These are asymmetric in shape and are non-superimposable mirror images. Similarly, a pair of gloves are non-superimposable mirror images. A left hand will fit snugly into a left-hand glove, but not into a right-hand glove. In the molecular world, a similar thing occurs. The proteins in our bodies are chiral molecules which can distinguish between the enantiomers of other molecules. For example, enzymes can distinguish between the two enantiomers of a chiral compound and catalyse a reaction with one of the enantiomers but not the other.”

“A key concept in organic chemistry is the functional group. A functional group is essentially a distinctive arrangement of atoms and bonds. […] Functional groups react in particular ways, and so it is possible to predict how a molecule might react based on the functional groups that are present. […] it is impossible to build a molecule atom by atom. Instead, target molecules are built by linking up smaller molecules. […] The organic chemist needs to have a good understanding of the reactions that are possible between different functional groups when choosing the molecular building blocks to be used for a synthesis. […] There are many […] reasons for carrying out FGTs [functional group transformations], especially when synthesizing complex molecules. For example, a starting material or a synthetic intermediate may lack a functional group at a key position of the molecular structure. Several reactions may then be required to introduce that functional group. On other occasions, a functional group may be added to a particular position then removed at a later stage. One reason for adding such a functional group would be to block an unwanted reaction at that position of the molecule. Another common situation is where a reactive functional group is converted to a less reactive functional group such that it does not interfere with a subsequent reaction. Later on, the original functional group is restored by another functional group transformation. This is known as a protection/deprotection strategy. The more complex the target molecule, the greater the synthetic challenge. Complexity is related to the number of rings, functional groups, substituents, and chiral centres that are present. […] The more reactions that are involved in a synthetic route, the lower the overall yield. […] retrosynthesis is a strategy by which organic chemists design a synthesis before carrying it out in practice. It is called retrosynthesis because the design process involves studying the target structure and working backwards to identify how that molecule could be synthesized from simpler starting materials. […] a key stage in retrosynthesis is identifying a bond that can be ‘disconnected’ to create those simpler molecules.”

“[V]ery few reactions produce the spectacular visual and audible effects observed in chemistry demonstrations. More typically, reactions involve mixing together two colourless solutions to produce another colourless solution. Temperature changes are a bit more informative. […] However, not all reactions generate heat, and monitoring the temperature is not a reliable way of telling whether the reaction has gone to completion or not. A better approach is to take small samples of the reaction solution at various times and to test these by chromatography or spectroscopy. […] If a reaction is taking place very slowly, different reaction conditions could be tried to speed it up. This could involve heating the reaction, carrying out the reaction under pressure, stirring the contents vigorously, ensuring that the reaction is carried out in a dry atmosphere, using a different solvent, using a catalyst, or using one of the reagents in excess. […] There are a large number of variables that can affect how efficiently reactions occur, and organic chemists in industry are often employed to develop the ideal conditions for a specific reaction. This is an area of organic chemistry known as chemical development. […] Once a reaction has been carried out, it is necessary to isolate and purify the reaction product. This often proves more time-consuming than carrying out the reaction itself. Ideally, one would remove the solvent used in the reaction and be left with the product. However, in most reactions this is not possible as other compounds are likely to be present in the reaction mixture. […] it is usually necessary to carry out procedures that will separate and isolate the desired product from these other compounds. This is known as ‘working up’ the reaction.”

“Proteins are large molecules (macromolecules) which serve a myriad of purposes, and are essentially polymers constructed from molecular building blocks called amino acids […]. In humans, there are twenty different amino acids having the same ‘head group’, consisting of a carboxylic acid and an amine attached to the same carbon atom […] The amino acids are linked up by the carboxylic acid of one amino acid reacting with the amine group of another to form an amide link. Since a protein is being produced, the amide bond is called a peptide bond, and the final protein consists of a polypeptide chain (or backbone) with different side chains ‘hanging off’ the chain […]. The sequence of amino acids present in the polypeptide sequence is known as the primary structure. Once formed, a protein folds into a specific 3D shape […] Nucleic acids […] are another form of biopolymer, and are formed from molecular building blocks called nucleotides. These link up to form a polymer chain where the backbone consists of alternating sugar and phosphate groups. There are two forms of nucleic acid — deoxyribonucleic acid (DNA) and ribonucleic acid (RNA). In DNA, the sugar is deoxyribose , whereas the sugar in RNA is ribose. Each sugar ring has a nucleic acid base attached to it. For DNA, there are four different nucleic acid bases called adenine (A), thymine (T), cytosine (C), and guanine (G) […]. These bases play a crucial role in the overall structure and function of nucleic acids. […] DNA is actually made up of two DNA strands […] where the sugar-phosphate backbones are intertwined to form a double helix. The nucleic acid bases point into the centre of the helix, and each nucleic acid base ‘pairs up’ with a nucleic acid base on the opposite strand through hydrogen bonding. The base pairing is specifically between adenine and thymine, or between cytosine and guanine. This means that one polymer strand is complementary to the other, a feature that is crucial to DNA’s function as the storage molecule for genetic information. […]  [E]ach strand […] act as the template for the creation of a new strand to produce two identical ‘daughter’ DNA double helices […] [A] genetic alphabet of four letters (A, T, G, C) […] code for twenty amino acids. […] [A]n amino acid is coded, not by one nucleotide, but by a set of three. The number of possible triplet combinations using four ‘letters’ is more than enough to encode all the amino acids.”

“Proteins have a variety of functions. Some proteins, such as collagen, keratin, and elastin, have a structural role. Others catalyse life’s chemical reactions and are called enzymes. They have a complex 3D shape, which includes a cavity called the active site […]. This is where the enzyme binds the molecules (substrates) that undergo the enzyme-catalysed reaction. […] A substrate has to have the correct shape to fit an enzyme’s active site, but it also needs binding groups to interact with that site […]. These interactions hold the substrate in the active site long enough for a reaction to occur, and typically involve hydrogen bonds, as well as van der Waals and ionic interactions. When a substrate binds, the enzyme normally undergoes an induced fit. In other words, the shape of the active site changes slightly to accommodate the substrate, and to hold it as tightly as possible. […] Once a substrate is bound to the active site, amino acids in the active site catalyse the subsequent reaction.”

“Proteins called receptors are involved in chemical communication between cells and respond to chemical messengers called neurotransmitters if they are released from nerves, or hormones if they are released by glands. Most receptors are embedded in the cell membrane, with part of their structure exposed on the outer surface of the cell membrane, and another part exposed on the inner surface. On the outer surface they contain a binding site that binds the molecular messenger. An induced fit then takes place that activates the receptor. This is very similar to what happens when a substrate binds to an enzyme […] The induced fit is crucial to the mechanism by which a receptor conveys a message into the cell — a process known as signal transduction. By changing shape, the protein initiates a series of molecular events that influences the internal chemistry within the cell. For example, some receptors are part of multiprotein complexes called ion channels. When the receptor changes shape, it causes the overall ion channel to change shape. This opens up a central pore allowing ions to flow across the cell membrane. The ion concentration within the cell is altered, and that affects chemical reactions within the cell, which ultimately lead to observable results such as muscle contraction. Not all receptors are membrane-bound. For example, steroid receptors are located within the cell. This means that steroid hormones need to cross the cell membrane in order to reach their target receptors. Transport proteins are also embedded in cell membranes and are responsible for transporting polar molecules such as amino acids into the cell. They are also important in controlling nerve action since they allow nerves to capture released neurotransmitters, such that they have a limited period of action.”

“RNA […] is crucial to protein synthesis (translation). There are three forms of RNA — messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). mRNA carries the genetic code for a particular protein from DNA to the site of protein production. Essentially, mRNA is a single-strand copy of a specific section of DNA. The process of copying that information is known as transcription. tRNA decodes the triplet code on mRNA by acting as a molecular adaptor. At one end of tRNA, there is a set of three bases (the anticodon) that can base pair to a set of three bases on mRNA (the codon). An amino acid is linked to the other end of the tRNA and the type of amino acid present is related to the anticodon that is present. When tRNA with the correct anticodon base pairs to the codon on mRNA, it brings the amino acid encoded by that codon. rRNA is a major constituent of a structure called a ribosome, which acts as the factory for protein production. The ribosome binds mRNA then coordinates and catalyses the translation process.”

Organic chemistry.
Carbon.
Stereochemistry.
Delocalization.
Hydrogen bond.
Van der Waals forces.
Ionic bonding.
Chemoselectivity.
Coupling reaction.
Chemical polarity.
Crystallization.
Elemental analysis.
NMR spectroscopy.
Polymerization.
Miller–Urey experiment.
Vester-Ulbricht hypothesis.
Oligonucleotide.
RNA world.
Ribozyme.

November 9, 2017 Posted by | Biology, Books, Chemistry, Genetics, Molecular biology | Leave a comment

A few diabetes papers of interest

i. Impact of Sex and Age at Onset of Diabetes on Mortality From Ischemic Heart Disease in Patients With Type 1 Diabetes.

“The study examined long-term IHD-specific mortality in a Finnish population-based cohort of patients with early-onset (0–14 years) and late-onset (15–29 years) T1D (n = 17,306). […] Follow-up started from the time of diagnosis of T1D and ended either at the time of death or at the end of 2011. […] ICD codes used to define patients as having T1D were 2500B–2508B, E10.0–E10.9, or O24.0. […] The median duration of diabetes was 24.4 (interquartile range 17.6–32.2) years. Over a 41-year study period totaling 433,782 person-years of follow-up, IHD accounted for 27.6% of the total 1,729 deaths. Specifically, IHD was identified as the cause of death in 478 patients, in whom IHD was the primary cause of death in 303 and a contributory cause in 175. […] Within the early-onset cohort, the average crude mortality rate in women was 33.3% lower than in men (86.3 [95% CI 65.2–112.1] vs. 128.2 [104.2–156.1] per 100,000 person-years, respectively, P = 0.02). When adjusted for duration of diabetes and the year of diabetes diagnosis, the mortality RR between women and men of 0.64 was only of borderline significance (P = 0.05) […]. In the late-onset cohort, crude mortality in women was, on average, only one-half that of men (117.2 [92.0–147.1] vs. 239.7 [210.9–271.4] per 100,000 person-years, respectively, P < 0.0001) […]. An RR of 0.43 remained highly significant after adjustment for duration of diabetes and year of diabetes diagnosis. Every year of duration of diabetes increased the risk 10–13%”

“The number of deaths from IHD in the patients with T1D were compared with the number of deaths from IHD in the background population, and the SMRs were calculated. For the total cohort (early and late onset pooled), the SMR was 7.2 (95% CI 6.4–8.0) […]. In contrast to the crude mortality rates, the SMRs were higher in women (21.6 [17.2–27.0]) than in men (5.8 [5.1–6.6]). When stratified by the age at onset of diabetes, the SMR was considerably higher in patients with early onset (16.9 [13.5–20.9]) than in those with late onset (5.9 [5.2–6.8]). In both the late- and the early-onset cohorts, there was a striking difference in the SMRs between women and men, and this was especially evident in the early-onset cohort where the SMR for women was 52.8 (36.3–74.5) compared with 12.1 (9.2–15.8) for men. This higher risk of death from IHD compared with the background population was evident in all women, regardless of age. However, the most pronounced effect was seen in women in the early-onset cohort <40 years of age, who were 83 times more likely to die of IHD than the age-matched women in the background population. This compares with a 37 times higher risk of death from IHD in women aged >40 years. The corresponding SMRs for men aged <40 and ≥40 years were 19.4 and 8.5, respectively.”

“Overall, the 40-year cumulative mortality for IHD was 8.8% (95% CI 7.9–9.7%) in all patients […] The 40-year cumulative IHD mortality in the early-onset cohort was 6.3% (4.8–7.8%) for men and 4.5% (3.1–5.9%) for women (P = 0.009 by log-rank test) […]. In the late-onset cohort, the corresponding cumulative mortality rates were 16.6% (14.3–18.7%) in men and 8.5% (6.5–10.4%) in women (P < 0.0001 by log-rank test)”

“The major findings of the current study are that women with early-onset T1D are exceptionally vulnerable to dying from IHD, which is especially evident in those receiving a T1D diagnosis during the prepubertal and pubertal years. Crude mortality rates were similar for women compared with men, highlighting the loss of cardioprotection in women. […] Although men of all ages have greater crude mortality rates than women regardless of the age at onset of T1D, the current study shows that mortality from IHD attributable to diabetes is much more pronounced in women than in men. […] it is conceivable that one of the underlying reasons for the loss of female sex as a protective factor against the development of CVD in the setting of diabetes may be the loss of ovarian hormones. Indeed, women with T1D have been shown to have reduced levels of plasma estradiol compared with age-matched nondiabetic women (23) possibly because of idiopathic ovarian failure or dysregulation of the hypothalamic-pituitary-ovarian axis.”

“One of the novelties of the present study is that the risk of death from IHD highly depends on the age at onset of T1D. The data show that the SMR was considerably higher in early-onset (0–14 years) than in late-onset (15–29 years) T1D in both sexes. […] the risk of dying from IHD is high in both women and men receiving a diagnosis of T1D at a young age.

ii. Microalbuminuria as a Risk Predictor in Diabetes: The Continuing Saga.

“The term “microalbuminuria” (MA) originated in 1964 when Professor Harry Keen first used it to signify a small amount of albumin in the urine of patients with type 1 diabetes (1). […] Whereas early research focused on the relevance of MA as a risk factor for diabetic kidney disease, research over the past 2 decades has shifted to examine whether MA is a true risk factor. To appreciate fully the contribution of MA to overall cardiorenal risk, it is important to distinguish between a risk factor and risk marker. A risk marker is a variable that identifies a pathophysiological state, such as inflammation or infection, and is not necessarily involved, directly or causally, in the genesis of a specified outcome (e.g., association of a cardiovascular [CV] event with fever, high-sensitivity C-reactive protein [hs-CRP], or MA). Conversely, a risk factor is involved clearly and consistently with the cause of a specified event (e.g., a CV event associated with persistently elevated blood pressure or elevated levels of LDL). Both a risk marker and a risk factor can predict an adverse outcome, but only one lies within the causal pathway of a disease. Moreover, a reduction (or alteration in a beneficial direction) of a risk factor (i.e., achievement of blood pressure goal) generally translates into a reduction of adverse outcomes, such as CV events; this is not necessarily true for a risk marker.”

“The data sources included in this article were all PubMed-referenced articles in English-language peer-reviewed journals since 1964. Studies selected had to have a minimum follow-up of 1 year; include at least 100 participants; be either a randomized trial, a systematic review, a meta-analysis, or a large observational cohort study in patients with any type of diabetes; or be trials of high CV risk that included at least 50% of patients with diabetes. All studies had to assess changes in MA tied to CV or CKD outcomes and not purely reflect changes in MA related to blood pressure, unless they were mechanistic studies. On the basis of these inclusion criteria, 31 studies qualified and provide the data used for this review.”

“Early studies in patients with diabetes supported the concept that as MA increases to higher levels, the risk of CKD progression and CV risk also increases […]. Moreover, evidence from epidemiological studies in patients with diabetes suggested that the magnitude of urine albumin excretion should be viewed as a continuum of CV risk, with the lower the albumin excretion, the lower the CV risk (15,16). However, MA values can vary daily up to 100% (11). These large biological variations are a result of a variety of conditions, with a central core tied to inflammation associated with factors ranging from increased blood pressure variability, high blood glucose levels, high LDL cholesterol, and high uric acid levels to high sodium ingestion, smoking, and exercise (17) […]. Additionally, any febrile illness, regardless of etiology, will increase urine albumin excretion (18). Taken together, these data support the concept that MA is highly variable and that values over a short time period (i.e., 3–6 months) are meaningless in predicting any CV or kidney disease outcome.”

“Initial studies to understand the mechanisms of MA examined changes in glomerular membrane permeability as a key determinant in patients with diabetes […]. Many factors affect the genesis and level of MA, most of which are linked to inflammatory conditions […]. A good evidence base, however, supports the concept that MA directly reflects the amount of inflammation and vascular “leakiness” present in patients with diabetes (16,18,19).

More recent studies have found a number of other factors that affect glomerular permeability by modifying cytokines that affect permeability. Increased amounts of glycated albumin reduce glomerular nephrin and increase vascular endothelial growth factor (20). Additionally, increases in sodium intake (21) as well as intraglomerular pressure secondary to high protein intake or poorly controlled blood pressure (22,23) increase glomerular permeability in diabetes and, hence, MA levels.

In individuals with diabetes, albumin is glycated and associated with the generation of reactive oxygen species. In addition, many other factors such as advanced glycation end products, reactive oxygen species, and other cellular toxins contribute to vascular injury. Once such injury occurs, the effect of pressor hormones, such as angiotensin II, is magnified, resulting in a faster progression of vascular injury. The end result is direct injury to the vascular smooth muscle cells, endothelial cells, and visceral epithelial cells (podocytes) of the glomerular capillary wall membrane as well as to the proximal tubular cells and podocyte basement membrane of the nephron (20,24,25). All these contribute to the development of MA. […] better glycemic control is associated with far lower levels of inflammatory markers (31).”

“MA is accepted as a CV risk marker for myocardial infarction and stroke, regardless of diabetes status. […] there is good evidence in those with type 2 diabetes that the presence of MA >100 mg/day is associated with higher CV events and greater likelihood of kidney disease development (6). Evidence for this association comes from many studies and meta-analyses […] a meta-analysis by Perkovic et al. (37) demonstrated a dose-response relationship between the level of albuminuria and CV risk. In this meta-analysis, individuals with MA were at 50% greater risk of coronary heart disease (risk ratio 1.47 [95% CI 1.30–1.66]) than those without. Those with macroalbuminuria (i.e., >300 mg/day) had more than a twofold risk for coronary heart disease (risk ratio 2.17 [95% CI 1.87–2.52]) (37). Despite these data indicating a higher CV risk in patients with MA regardless of diabetes status and other CV risk factors, there is no consensus that the addition of MA to conventional CV risk stratification for the general population (e.g., Framingham or Reynolds scoring systems) is of any clinical value, and that includes patients with diabetes (38).”

“Given that MA was evaluated in a post hoc manner in almost all interventional studies, it is likely that the reduction in MA simply reflects the effects of either renin-angiotensin system (RAS) blockade on endothelial function or significant blood pressure reduction rather than the MA itself being implicated as a CV disease risk factor (18). […] associations of lowering MA with angiotensin-converting enzyme (ACE) inhibitors or angiotensin receptor blockers (ARBs) does not prove a direct benefit on CV event lowering associated with MA reduction in diabetes. […] Four long-term, appropriately powered trials demonstrated an inverse relationship between reductions in MA and primary event rates for CV events […]. Taken together, these studies support the concept that MA is a risk marker in diabetes and is consistent with data of other inflammatory markers, such as hs-CRP [here’s a relevant link – US], such that the higher the level, the higher the risk (15,39,42). The importance of MA as a CV risk marker is exemplified further by another meta-analysis that showed that MA has a similar magnitude of CV risk as hs-CRP and is a better predictor of CV events (43). Thus, the data supporting MA as a risk marker for CV events are relatively consistent, clearly indicate that an association exists, and help to identify the presence of underlying inflammatory states, regardless of etiology.”

“In people with early stage nephropathy (i.e., stage 2 or 3a [GFR 45–89 mL/min/1.73 m2]) and MA, there is no clear benefit on slowing GFR decline by reducing MA with drugs that block the RAS independent of lowering blood pressure (16). This is exemplified by many trials […]. Thus, blood pressure lowering is the key goal for all patients with early stage nephropathy associated with normoalbuminuria or MA. […] When albuminuria levels are in the very high or macroalbuminuria range (i.e., >300 mg/day), it is accepted that the patient has CKD and is likely to progress ultimately to ESRD, unless they die of a CV event (39,52). However, only one prospective randomized trial evaluated the role of early intervention to reduce blood pressure with an ACE inhibitor versus a calcium channel blocker in CKD progression by assessing change in MA and creatinine clearance in people with type 2 diabetes (Appropriate Blood Pressure Control in Diabetes [ABCD] trial) (23). After >7 years of follow-up, there was no relationship between changes in MA and CKD progression. Moreover, there was regression to the mean of MA.”

“Many observational studies used development of MA as indicating the presence of early stage CKD. Early studies by the individual groups of Mogensen and Parving demonstrated a relationship between increases in MA and progression to nephropathy in type 1 diabetes. These groups also showed that use of ACE inhibitors, blood pressure reduction, and glucose control reduced MA (9,58,59). However, more recent studies in both type 1 and type 2 diabetes demonstrated that only a subgroup of patients progress from MA to >300 mg/day albuminuria, and this subgroup accounts for those destined to progress to ESRD (29,32,6063). Thus, the presence of MA alone is not predictive of CKD progression. […] some patients with type 2 diabetes progress to ESRD without ever having developed albuminuria levels of ≥300 mg/day (67). […] Taken together, data from outcome trials, meta-analyses, and observations demonstrate that MA [Micro-Albuminuria] alone is not synonymous with the presence of clearly defined CKD [Chronic Kidney Disease] in diabetes, although it is used as part of the criteria for the diagnosis of CKD in the most recent CKD classification and staging (71). Note that only a subgroup of ∼25–30% of people with diabetes who also have MA will likely progress to more advanced stages of CKD. Predictors of progression to ESRD, apart from family history, and many years of poor glycemic and blood pressure control are still not well defined. Although there are some genetic markers, such as CUBN and APOL1, their use in practice is not well established.”

“In the context of the data presented in this article, MA should be viewed as a risk marker associated with an increase in CV risk and for kidney disease, but its presence alone does not indicate established kidney disease, especially if the eGFR is well above 60 mL/min/1.73 m2. Increases in MA, with blood pressure and other CV risk factors controlled, are likely but not proven to portend a poor prognosis for CKD progression over time. Achieving target blood pressure (<140/80 mmHg) and target HbA1c (<7%) should be priorities in treating patients with MA. Recent guidelines from both the American Diabetes Association and the National Kidney Foundation provide a strong recommendation for using agents that block the RAS, such as ACE inhibitors and ARBs, as part of the regimen for those with albuminuria levels >300 mg/day but not MA (73). […] maximal antialbuminuric effects will [however] not be achieved with these agents unless a low-sodium diet is strictly followed.”

iii. The SEARCH for Diabetes in Youth Study: Rationale, Findings, and Future Directions.

“The SEARCH for Diabetes in Youth (SEARCH) study was initiated in 2000, with funding from the Centers for Disease Control and Prevention and support from the National Institute of Diabetes and Digestive and Kidney Diseases, to address major knowledge gaps in the understanding of childhood diabetes. SEARCH is being conducted at five sites across the U.S. and represents the largest, most diverse study of diabetes among U.S. youth. An active registry of youth diagnosed with diabetes at age <20 years allows the assessment of prevalence (in 2001 and 2009), annual incidence (since 2002), and trends by age, race/ethnicity, sex, and diabetes type. Prevalence increased significantly from 2001 to 2009 for both type 1 and type 2 diabetes in most age, sex, and race/ethnic groups. SEARCH has also established a longitudinal cohort to assess the natural history and risk factors for acute and chronic diabetes-related complications as well as the quality of care and quality of life of persons with diabetes from diagnosis into young adulthood. […] This review summarizes the study methods, describes key registry and cohort findings and their clinical and public health implications, and discusses future directions.”

“SEARCH includes a registry and a cohort study […]. The registry study identifies incident cases each year since 2002 through the present with ∼5.5 million children <20 years of age (∼6% of the U.S. population <20 years) under surveillance annually. Approximately 3.5 million children <20 years of age were under surveillance in 2001 at the six SEARCH recruitment centers, with approximately the same number at the five centers under surveillance in 2009.”

“The prevalence of all types of diabetes was 1.8/1,000 youth in 2001 and was 2.2/1,000 youth in 2009, which translated to at least 154,000 children/youth in the U.S. with diabetes in 2001 (5) and at least 192,000 in 2009 (6). Overall, between 2001 and 2009, prevalence of type 1 diabetes in youth increased by 21.1% (95% CI 15.6–27.0), with similar increases for boys and girls and in most racial/ethnic and age groups (2) […]. The prevalence of type 2 diabetes also increased significantly over the same time period by 30.5% (95% CI 17.3–45.1), with increases observed in both sexes, 10–14- and 15–19-year-olds, and among Hispanic and non-Hispanic white and African American youth (2). These data on changes in type 2 are consistent with smaller U.S. studies (711).”

“The incidence of diabetes […] in 2002 to 2003 was 24.6/100,000/year (12), representing ∼15,000 new patients every year with type 1 diabetes and 3,700 with type 2 diabetes, increasing to 18,436 newly diagnosed type 1 and 5,089 with type 2 diabetes in 2008 to 2009 (13). Among non-Hispanic white youth, the incidence of type 1 diabetes increased by 2.7% (95% CI 1.2–4.3) annually between 2002 and 2009. Significant increases were observed among all age groups except the youngest age group (0–4 years) (14). […] The underlying factors responsible for this increase have not yet been identified.”

Over 50% of youth are hospitalized at diabetes onset, and ∼30% of children newly diagnosed with diabetes present with diabetic ketoacidosis (DKA) (19). Prevalence of DKA at diagnosis was three times higher among youth with type 1 diabetes (29.4%) compared with youth with type 2 diabetes (9.7%) and was lowest in Asian/Pacific Islanders (16.2%) and highest among Hispanics (27.0%).”

“A significant proportion of youth with diabetes, particularly those with type 2 diabetes, have very poor glycemic control […]: 17% of youth with type 1 diabetes and 27% of youth with type 2 diabetes had A1C levels ≥9.5% (≥80 mmol/mol). Minority youth were significantly more likely to have higher A1C levels compared with non-Hispanic white youth, regardless of diabetes type. […] Optimal care is an important component of successful long-term management for youth with diabetes. While there are high levels of adherence for some diabetes care indicators such as blood pressure checks (95%), urinary protein tests (83%), and lipid assessments (88%), approximately one-third of youth had no documentation of eye or A1C values at appropriate intervals and therefore were not meeting the American Diabetes Association (ADA)-recommended screening for diabetic control and complications (40). Participants ≥18 years old, particularly those with type 2 diabetes, and minority youth with type 1 diabetes had fewer tests of all kinds performed. […] Despite current treatment options, the prevalence of poor glycemic control is high, particularly among minority youth. Our initial findings suggest that a substantial number of youth with diabetes will develop serious, debilitating complications early in life, which is likely to have significant implications for their quality of life, as well as economic and health care implications.”

“Because recognition of the broader spectrum of diabetes in children and adolescents is recent, there are no gold-standard definitions for differentiating the types of diabetes in this population, either for research or clinical purposes or for public health surveillance. The ADA classification of diabetes as type 1 and type 2 does not include operational definitions for the specific etiologic markers of diabetes type, such as types and numbers of diabetes autoantibodies or measures of insulin resistance, hallmarks of type 1 and 2 diabetes, respectively (43). Moreover, obese adolescents with a clinical phenotype suggestive of type 2 diabetes can present with ketoacidosis (44) or have evidence of autoimmunity (45).”

“Using the ADA framework (43), we operationalized definitions of two main etiologic markers, autoimmunity and insulin sensitivity, to identify four etiologic subgroups based on the presence or absence of markers. Autoimmunity was based on presence of one or more diabetes autoantibodies (GAD65 and IA2). Insulin sensitivity was estimated using clinical variables (A1C, triglyceride level, and waist circumference) from a formula that was highly associated with estimated insulin sensitivity measured using a euglycemic-hyperinsulinemic clamp among youth with type 1 and 2 and normal control subjects (46). Participants were categorized as insulin resistant […] and insulin sensitive (47). Using this approach, 54.5% of SEARCH cases were classified as typical type 1 (autoimmune, insulin-sensitive) diabetes, while 15.9% were classified as typical type 2 (nonautoimmune, insulin-resistant) diabetes. Cases that were classified as autoimmune and insulin-resistant likely represent individuals with type 1 autoimmune diabetes and concomitant obesity, a phenotype becoming more prevalent as a result of the recent increase in the frequency of obesity, but is unlikely to be a distinct etiologic entity.”

“Ten percent of SEARCH participants had no evidence of either autoimmunity or insulin resistance and thus require additional testing, including additional measurements of diabetes-related autoantibodies (only two antibodies were measured in SEARCH) as well as testing for monogenic forms of diabetes to clarify etiology. Among antibody-negative youth, 8% of those tested had a mutation in one or more of the hepatocyte nuclear factor-1α (HNF-1α), glucokinase, and HNF-4α genes, an estimated monogenic diabetes population prevalence of at least 1.2% (48).”

iv. Does the Prevailing Hypothesis That Small-Fiber Dysfunction Precedes Large-Fiber Dysfunction Apply to Type 1 Diabetic Patients?

The short answer is ‘yes, it does’. Some observations from the paper:

“Diabetic sensorimotor polyneuropathy (DSP) is a common complication of diabetes, affecting 28–55% of patients (1). A prospective Finnish study found evidence of probable or definite neuropathy in 8.3% of diabetic patients at the time of diagnosis, 16.7% after 5 years, and 41.9% after 10 years (2). Diabetes-related peripheral neuropathy results in serious morbidity, including chronic neuropathic pain, leg weakness and falls, sensory loss and foot ulceration, and amputation (3). Health care costs associated with diabetic neuropathy were estimated at $10.9 billion in the U.S. in 2003 (4). However, despite the high prevalence of diabetes and DSP, and the important public health implications, there is a lack of serum- or tissue-based biomarkers to diagnose and follow patients with DSP longitudinally. Moreover, numerous attempts at treatment have yielded negative results.”

“DSP is known to cause injury to both large-diameter, myelinated (Aα and Aβ) fibers and small-diameter, unmyelinated nerve (Aδ and C) fibers; however, the sequence of nerve fiber damage remains uncertain. While earlier reports seemed to indicate simultaneous loss of small- and large-diameter nerve fibers, with preserved small/large ratios (5), more recent studies have suggested the presence of early involvement of small-diameter Aδ and C fibers (611). Some suggest a temporal relationship of small-fiber impairment preceding that of large fibers. For example, impairment in the density of the small intraepidermal nerve fibers in symptomatic patients with impaired glucose tolerance (prediabetes) have been observed in the face of normal large-fiber function, as assessed by nerve conduction studies (NCSs) (9,10). In addition, surveys of patients with DSP have demonstrated an overwhelming predominance of sensory and autonomic symptoms, as compared with motor weakness. Again, this has been interpreted as indicative of preferential small-fiber dysfunction (12). Though longitudinal studies are limited, such studies have lead to the current prevailing hypothesis for the natural history of DSP that measures of small-fiber morphology and function decline prior to those of large fibers. One implication of this hypothesis is that small-fiber testing could serve as an earlier, subclinical primary end point in clinical trials investigating interventions for DSP (13).

The hypothesis described above has been investigated exclusively in type 2 diabetic or prediabetic patients. Through the study of a cohort of healthy volunteers and type 1 diabetic subjects […], we had the opportunity to evaluate in cross-sectional analysis the relationship between measures of large-fiber function and small-fiber structure and function. Under the hypothesis that small-fiber abnormalities precede large-fiber dysfunction in the natural history of DSP, we sought to determine if: 1) the majority of subjects who meet criteria for large-fiber dysfunction have concurrent evidence of small-fiber dysfunction and 2) the subset of patients without DSP includes a spectrum with normal small-fiber tests (indicating lack of initiation of nerve injury) as well as abnormal small-fiber tests (indicating incipient DSP).”

“Overall, 57 of 131 (43.5%) type 1 diabetic patients met DSP criteria, and 74 of 131 (56.5%) did not meet DSP criteria. Abnormality of CCM [link] was present in 30 of 57 (52.6%) DSP patients and 6 of 74 (8.1%) type 1 diabetic patients without DSP. Abnormality of CDT [Cooling Detection Thresholds, relevant link] was present in 47 of 56 (83.9%) DSP patients and 17 of 73 (23.3%) without DSP. Abnormality of LDIflare [laser Doppler imaging of heat-evoked flare] was present in 30 of 57 (52.6%) DSP patients and 20 of 72 (27.8%) without DSP. Abnormality of HRV [Heart Rate Variability] was present in 18 of 45 (40.0%) DSP patients and 6 of 70 (8.6%) without DSP. […] sensitivity analysis […] revealed that abnormality of any one of the four small-fiber measures was present in 55 of 57 (96.5%) DSP patients […] and 39 of 74 (52.7%) type 1 diabetic patients without DSP. Similarly, abnormality of any two of the four small-fiber measures was present in 43 of 57 (75.4%) DSP patients […] and 9 of 74 (12.2%) without DSP. Finally, abnormality of either CDT or CCM (with these two tests selected based on their high reliability) was noted in 53 of 57 (93.0%) DSP patients and 21 of 74 (28.4%) patients without DSP […] When DSP was defined based on symptoms and signs plus abnormal sural SNAP [sensory nerve action potential] amplitude or conduction velocity, there were 68 of 131 patients who met DSP criteria and 63 of 131 who did not. Abnormality of any one of the four small-fiber measures was present in 63 of 68 (92.6%) DSP patients and 31 of 63 (49.2%) type 1 diabetic patients without DSP. […] Finally, if DSP was defined based on clinical symptoms and signs alone, with TCNS ≥5, there were 68 of 131 patients who met DSP criteria and 63 of 131 who did not. Abnormality of any one of the four small-fiber measures was present in 62 of 68 (91.2%) DSP patients and 32 of 63 (50.8%) type 1 diabetic patients without DSP.”

“Qualitative analysis of contingency tables shows that the majority of patients with DSP have concurrent evidence of small-fiber dysfunction, and patients without DSP include a spectrum with normal small-fiber tests (indicating lack of initiation of nerve injury) as well as abnormal small-fiber tests. Evidence of isolated large-fiber injury was much less frequent […]. These findings suggest that small-fiber damage may herald the onset of DSP in type 1 diabetes. In addition, the above findings remained true when alternative definitions of DSP were explored in a sensitivity analysis. […] The second important finding was the linear relationships noted between small-fiber structure and function tests (CDT, CNFL, LDIflare, and HRV) […] and the number of NCS abnormalities (a marker of large-fiber function). This might indicate that once the process of large-fiber nerve injury in DSP has begun, damage to large and small nerve fibers occurs simultaneously.”

v. Long-Term Complications and Mortality in Young-Onset Diabetes.

“Records from the Royal Prince Alfred Hospital Diabetes Clinical Database, established in 1986, were matched with the Australian National Death Index to establish mortality outcomes for all subjects until June 2011. Clinical and mortality outcomes in 354 patients with T2DM, age of onset between 15 and 30 years (T2DM15–30), were compared with T1DM in several ways but primarily with 470 patients with T1DM with a similar age of onset (T1DM15–30) to minimize the confounding effect of age on outcome.

RESULTS For a median observation period of 21.4 (interquartile range 14–30.7) and 23.4 (15.7–32.4) years for the T2DM and T1DM cohorts, respectively, 71 of 824 patients (8.6%) died. A significant mortality excess was noted in T2DM15–30 (11 vs. 6.8%, P = 0.03), with an increased hazard for death (hazard ratio 2.0 [95% CI 1.2–3.2], P = 0.003). Death for T2DM15–30 occurred after a significantly shorter disease duration (26.9 [18.1–36.0] vs. 36.5 [24.4–45.4] years, P = 0.01) and at a relatively young age. There were more cardiovascular deaths in T2DM15–30 (50 vs. 30%, P < 0.05). Despite equivalent glycemic control and shorter disease duration, the prevalence of albuminuria and less favorable cardiovascular risk factors were greater in the T2DM15–30 cohort, even soon after diabetes onset. Neuropathy scores and macrovascular complications were also increased in T2DM15–30 (P < 0.0001).

CONCLUSIONS Young-onset T2DM is the more lethal phenotype of diabetes and is associated with a greater mortality, more diabetes complications, and unfavorable cardiovascular disease risk factors when compared with T1DM.

“Only a few previous studies have looked at comparative mortality in T1DM and T2DM onset in patients <30 years of age. In a Swedish study of patients with diabetes aged 15–34 years compared with a general population, the standardized mortality ratio was higher for the T2DM than for the T1DM cohort (2.9 vs. 1.8) (17). […] Recently, Dart et al. (19) examined survival in youth aged 1–18 years with T2DM versus T1DM. Kaplan-Meier analysis revealed a statistically significant lower survival probability for the youth with T2DM, although the number at risk was low after 10 year’s duration. Taken together, these findings are in keeping with the present observations and are supportive evidence for a higher mortality in young-onset T2DM than in T1DM. The majority of deaths appear to be from cardiovascular causes and significantly more so for young T2DM.”

“Although the age of onset of T1DM diabetes is usually in little doubt because of a more abrupt presentation, it is possible that the age of onset of T2DM was in fact earlier than recognized. With a previously published method for estimating time delay until diagnosis of T2DM (26) by plotting the prevalence of retinopathy against duration and extrapolating to a point of zero retinopathy, we found that there is no difference in the slope and intercept of this relationship between the T2DM and the T1DM cohorts […] delay in diagnosis is unlikely to be an explanation for the differences in observed outcome.”

vi. Cardiovascular Risk Factors Are Associated With Increased Arterial Stiffness in Youth With Type 1 Diabetes.

“Increased arterial stiffness independently predicts all-cause and CVD mortality (3), and higher pulse pressure predicts CVD mortality, incidence, and end-stage renal disease development among adults with type 1 diabetes (1,4,5). Several reports have shown that youth and adults with type 1 diabetes have elevated arterial stiffness, though the mechanisms are largely unknown (6). The etiology of advanced atherosclerosis in type 1 diabetes is likely multifactorial, involving metabolic, behavioral, and diabetes-specific cardiovascular (CV) risk factors. Aging, high blood pressure (BP), obesity, the metabolic syndrome (MetS), and type 2 diabetes are the main contributors of sustained increased arterial stiffness in adults (7,8). However, the natural history, the age-related progression, and the possible determinants of increased arterial stiffness in youth with type 1 diabetes have not been studied systematically. […] There are currently no data examining the impact of CV risk factors and their clustering in youth with type 1 diabetes on subsequent CVD morbidity and mortality […]. Thus, the aims of this report were: 1) to describe the progression of arterial stiffness, as measured by pulse wave velocity (PWV), over time, among youth with type 1 diabetes, and 2) to explore the association of CV risk factors and their clustering as MetS with PWV in this cohort.”

“Youth were age 14.5 years (SD 2.8) and had an average disease duration of 4.8 (3.8) years at baseline, 46.3% were female, and 87.6% were of NHW race/ethnicity. At baseline, 10.0% had high BP, 10.9% had a large waist circumference, 11.6% had HDL-c ≤40 mg/dL, 10.9% had a TG level ≥110 mg/dL, and 7.0% had at least two of the above CV risk factors (MetS). In addition, 10.3% had LDL-c ≥130 mg/dL, 72.0% had an HbA1c ≥7.5% (58 mmol/mol), and 9.2% had ACR ≥30 μg/mL. Follow-up measures were obtained on average at age 19.2 years, when the average duration of diabetes was 10.1 (3.9) years.”

“Over an average follow-up period of ∼5 years, there was a statistically significant increase of 0.7 m/s in PWV (from 5.2 to 5.9 m/s), representing an annual increase of 2.8% or 0.145 m/s. […] Based on our data, if this rate of change is stable over time, the estimated average PWV by the time these youth enter their third decade of life will be 11.3 m/s, which was shown to be associated with a threefold increased hazard for major CV events (26). There are no similar studies in youth to compare these findings. In adults, the rate of change in PWV was 0.081 m/s/year in nondiabetic normotensive patients, although it was higher in hypertensive adults (0.147 m/s/year) (7). We also showed that the presence of central adiposity and elevated BP at baseline, as well as clustering of at least two CV risk factors, was associated with significantly worse PWV over time, although these baseline factors did not significantly influence the rate of change in PWV over this period of time. Changes in CV risk factors, specifically increases in central adiposity, LDL-c levels, and worsening glucose control, were independently associated with worse PWV over time. […] Our inability to detect a difference in the rate of change in PWV in our youth with MetS (vs. those without MetS) may be due to several factors, including a combination of a relatively small sample size, short period of follow-up, and young age of the cohort (thus with lower baseline PWV levels).”

 

November 8, 2017 Posted by | Cardiology, Diabetes, Epidemiology, Genetics, Medicine, Nephrology, Neurology, Studies | Leave a comment

A few diabetes papers of interest

i. Chronic Fatigue in Type 1 Diabetes: Highly Prevalent but Not Explained by Hyperglycemia or Glucose Variability.

“Fatigue is a classical symptom of hyperglycemia, but the relationship between chronic fatigue and diabetes has not been systematically studied. […] glucose control [in diabetics] is often suboptimal with persistent episodes of hyperglycemia that may result in sustained fatigue. Fatigue may also sustain in diabetic patients because it is associated with the presence of a chronic disease, as has been demonstrated in patients with rheumatoid arthritis and various neuromuscular disorders (2,3).

It is important to distinguish between acute and chronic fatigue, because chronic fatigue, defined as severe fatigue that persists for at least 6 months, leads to substantial impairments in patients’ daily functioning (4,5). In contrast, acute fatigue can largely vary during the day and generally does not cause functional impairments.

Literature provides limited evidence for higher levels of fatigue in diabetic patients (6,7), but its chronicity, impact, and determinants are unknown. In various chronic diseases, it has been proven useful to distinguish between precipitating and perpetuating factors of chronic fatigue (3,8). Illness-related factors trigger acute fatigue, while other factors, often cognitions and behaviors, cause fatigue to persist. Sleep disturbances, low self-efficacy concerning fatigue, reduced physical activity, and a strong focus on fatigue are examples of these fatigue-perpetuating factors (810). An episode of hyperglycemia or hypoglycemia could trigger acute fatigue for diabetic patients (11,12). However, variations in blood glucose levels might also contribute to chronic fatigue, because these variations continuously occur.

The current study had two aims. First, we investigated the prevalence and impact of chronic fatigue in a large sample of type 1 diabetic (T1DM) patients and compared the results to a group of age- and sex-matched population-based controls. Secondly, we searched for potential determinants of chronic fatigue in T1DM.”

“A significantly higher percentage of T1DM patients were chronically fatigued (40%; 95% CI 34–47%) than matched controls (7%; 95% CI 3–10%). Mean fatigue severity was also significantly higher in T1DM patients (31 ± 14) compared with matched controls (17 ± 9; P < 0.001). T1DM patients with a comorbidity_mr [a comorbidity affecting patients’ daily functioning, based on medical records – US] or clinically relevant depressive symptoms [based on scores on the Beck Depression Inventory for Primary Care – US] were significantly more often chronically fatigued than patients without a comorbidity_mr (55 vs. 36%; P = 0.014) or without clinically relevant depressive symptoms (88 vs. 31%; P < 0.001). Patients who reported neuropathy, nephropathy, or cardiovascular disease as complications of diabetes were more often chronically fatigued […] Chronically fatigued T1DM patients were significantly more impaired compared with nonchronically fatigued T1DM patients on all aspects of daily functioning […]. Fatigue was the most troublesome symptom of the 34 assessed diabetes-related symptoms. The five most troublesome symptoms were overall sense of fatigue, lack of energy, increasing fatigue in the course of the day, fatigue in the morning when getting up, and sleepiness or drowsiness”.

“This study establishes that chronic fatigue is highly prevalent and clinically relevant in T1DM patients. While current blood glucose level was only weakly associated with chronic fatigue, cognitive behavioral factors were by far the strongest potential determinants.”

“Another study found that type 2 diabetic, but not T1DM, patients had higher levels of fatigue compared with healthy controls (7). This apparent discrepancy may be explained by the relatively small sample size of this latter study, potential selection bias (patients were not randomly selected), and the use of a different fatigue questionnaire.”

“Not only was chronic fatigue highly prevalent, fatigue also had a large impact on T1DM patients. Chronically fatigued T1DM patients had more functional impairments than nonchronically fatigued patients, and T1DM patients considered fatigue as the most burdensome diabetes-related symptom.

Contrary to what was expected, there was at best a weak relationship between blood glucose level and chronic fatigue. Chronically fatigued T1DM patients spent slightly less time in hypoglycemia, but average glucose levels, glucose variability, hyperglycemia, or HbA1c were not related to chronic fatigue. In type 2 diabetes mellitus also, no relationship was found between fatigue and HbA1c (7).”

“Regarding demographic characteristics, current health status, diabetes-related factors, and fatigue-related cognitions and behaviors as potential determinants of chronic fatigue, we found that sleeping problems, physical activity, self-efficacy concerning fatigue, age, depression, and pain were significantly associated with chronic fatigue in T1DM. Although depression was strongly related, it could not completely explain the presence of chronic fatigue (38), as 31% was chronically fatigued without having clinically relevant depressive symptoms.”

Some comments may be worth adding here. It’s important to note to people who may not be aware of this that although chronic fatigue is a weird entity that’s hard to get a handle on (and, to be frank, is somewhat controversial), specific organic causes have been identified that greatly increases the risk. Many survivors of cancer experience chronic fatigue (see e.g. this paper, or wikipedia), and chronic fatigue is also not uncommon in a kidney failure setting (“The silence of renal disease creeps up on us (doctors and patients). Do not dismiss odd chronic symptoms such as fatigue or ‘not being quite with it’ without considering checking renal function” (Oxford Handbook of Clinical Medicine, 9th edition. My italics – US)). As observed above, linkage with RA and some neuromuscular disorders has also been observed. The brief discussion of related topics in Houghton & Grey made it clear to me that some people with chronic fatigue are almost certainly suffering from an organic illness which has not been diagnosed or treated. Here’s a relevant quote from that book’s coverage: “it is unusual to find a definite organic cause for fatigue. However, consider anaemia, thyroid dysfunction, Addison’s disease and hypopituitarism.” It’s sort of neat, if you think about the potential diabetes-fatigue link investigated by the guys above, that some of these diseases are likely to be relevant, as type 1 diabetics are more likely to develop them (anemia is not linked to diabetes, as far as I know, and I believe the relationship between autoimmune hypophysitis – which is a cause of hypopituitarism – and type 1 diabetes is at best unclear, but the others are definitely involved) due to their development being caused by some of the same genetic mutations which cause type 1 diabetes; the combinations of some of these diseases even have fancy names of their own, like ‘Type I Polyglandular Autoimmune Syndrome’ and ‘Schmidt Syndrome’ (if you’re interested here are a couple of medscape links). It’s noteworthy that although most of these diseases are uncommon in the general population, their incidence/prevalence is likely to be greatly increased in type 1 diabetics due to the common genetic pathways at play (variants regulating T-cell function seem to be important, but there’s no need to go into these details here). Sperling et al. note in their book that: “Hypothyroid or hyperthyroid AITD [autoimmune thyroid disease] has been observed in 10–24% of patients with type 1 diabetes”. In one series including 151 patients with APS [/PAS]-2, when they looked at disease combinations they found that: “Of combinations of the component diseases, [type 1] diabetes with thyroid disease was the most common, occurring in 33%. The second, diabetes with adrenal insufficiency, made up 15%” (same source).

It seems from estimates like these likely that a not unsubstantial proportion of type 1 diabetics over time go on to develop other health problems that might if unaddressed/undiagnosed cause fatigue, and this may in my opinion be a potentially much more important cause than direct metabolic effects such as hyperglycemia, or chronic inflammation. If this is the case you’d however expect to see a substantial sex difference, as the autoimmune syndromes are in general much more likely to hit females than males. I’m not completely sure how to interpret a few of the results reported, but to me it doesn’t look like the sex differences in this study are anywhere near ‘large enough’ to support such an explanatory model, though. Another big problem is also that fatigue seems to be more common in young patients, which is weird; most long-term complications display significant (positive) duration dependence, and when diabetes is a component of an autoimmune syndrome diabetes tend to develop first, with other diseases hitting later, usually in middle age. Duration and age are strongly correlated, and a negative duration dependence in a diabetes complication setting is a surprising and unusual finding that needs to be explained, badly; it’s unexpected and may in my opinion be the sign of a poor disease model. It’d make more sense for disease-related fatigue to present late, rather than early, I don’t really know what to make of that negative age gradient. ‘More studies needed’ (preferably by people familiar with those autoimmune syndromes..), etc…

ii. Risk for End-Stage Renal Disease Over 25 Years in the Population-Based WESDR Cohort.

“It is well known that diabetic nephropathy is the leading cause of end-stage renal disease (ESRD) in many regions, including the U.S. (1). Type 1 diabetes accounts for >45,000 cases of ESRD per year (2), and the incidence may be higher than in people with type 2 diabetes (3). Despite this, there are few population-based data available regarding the prevalence and incidence of ESRD in people with type 1 diabetes in the U.S. (4). A declining incidence of ESRD has been suggested by findings of lower incidence with increasing calendar year of diagnosis and in comparison with older reports in some studies in Europe and the U.S. (58). This is consistent with better diabetes management tools becoming available and increased renoprotective efforts, including the greater use of ACE inhibitors and angiotensin type II receptor blockers, over the past two to three decades (9). Conversely, no reduction in the incidence of ESRD across enrollment cohorts was found in a recent clinic-based study (9). Further, an increase in ESRD has been suggested for older but not younger people (9). Recent improvements in diabetes care have been suggested to delay rather than prevent the development of renal disease in people with type 1 diabetes (4).

A decrease in the prevalence of proliferative retinopathy by increasing calendar year of type 1 diabetes diagnosis was previously reported in the Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR) cohort (10); therefore, we sought to determine if a similar pattern of decline in ESRD would be evident over 25 years of follow-up. Further, we investigated factors that may mediate a possible decline in ESRD as well as other factors associated with incident ESRD over time.”

“At baseline, 99% of WESDR cohort members were white and 51% were male. Individuals were 3–79 years of age (mean 29) with diabetes duration of 0–59 years (mean 15), diagnosed between 1922 and 1980. Four percent of individuals used three or more daily insulin injections and none used an insulin pump. Mean HbA1c was 10.1% (87 mmol/mol). Only 16% were using an antihypertensive medication, none was using an ACE inhibitor, and 3% reported a history of renal transplant or dialysis (ESRD). At 25 years, 514 individuals participated (52% of original cohort at baseline, n = 996) and 367 were deceased (37% of baseline). Mean HbA1c was much lower than at baseline (7.5%, 58 mmol/mol), the decline likely due to the improvements in diabetes care, with 80% of participants using intensive insulin management (three or more daily insulin injections or insulin pump). The decline in HbA1c was steady, becoming slightly steeper following the results of the DCCT (25). Overall, at the 25-year follow-up, 47% had proliferative retinopathy, 53% used aspirin daily, and 54% reported taking antihypertensive medications, with the majority (87%) using an ACE inhibitor. Thirteen percent reported a history of ESRD.”

“Prevalence of ESRD was negligible until 15 years of diabetes duration and then steadily increased with 5, 8, 10, 13, and 14% reporting ESRD by 15–19, 20–24, 25–29, 30–34, and 35+ years of diabetes duration, respectively. […] After 15 years of diagnosis, prevalence of ESRD increased with duration in people diagnosed from 1960 to 1980, with the lowest increase in people with the most recent diagnosis. People diagnosed from 1922 to 1959 had consistent rather than increasing levels of ESRD with duration of 20+ years. If not for their greater mortality (at the 25-year follow-up, 48% of the deceased had been diagnosed prior to 1960), an increase with duration may have also been observed.

From baseline, the unadjusted cumulative 25-year incidence of ESRD was 17.9% (95% CI 14.3–21.5) in males, 10.3% (7.4–13.2) in females, and 14.2% (11.9–16.5) overall. For those diagnosed in 1970–1980, the cumulative incidence at 14, 20, and 25 years of follow-up (or ∼15–25, 20–30, and 25–35 years diabetes duration) was 5.2, 7.9, and 9.3%, respectively. At 14, 20, and 25 years of follow-up (or 35, 40, and 45 up to 65+ years diabetes duration), the cumulative incidence in those diagnosed during 1922–1969 was 13.6, 16.3, and 18.8%, respectively, consistent with the greater prevalence observed for these diagnosis periods at longer duration of diabetes.”

“The unadjusted hazard of ESRD was reduced by 70% among those diagnosed in 1970–1980 as compared with those in 1922–1969 (HR 0.29 [95% CI 0.19–0.44]). Duration (by 10%) and HbA1c (by an additional 10%) partially mediated this association […] Blood pressure and antihypertensive medication use each further attenuated the association. When fully adjusted for these and [other risk factors included in the model], period of diagnosis was no longer significant (HR 0.89 [0.55–1.45]). Sensitivity analyses for the hazard of incident ESRD or death due to renal disease showed similar findings […] The most parsimonious model included diabetes duration, HbA1c, age, sex, systolic and diastolic blood pressure, and history of antihypertensive medication […]. A 32% increased risk for incident ESRD was found per increasing year of diabetes duration at 0–15 years (HR 1.32 per year [95% CI 1.16–1.51]). The hazard plateaued (1.01 per year [0.98–1.05]) after 15 years of duration of diabetes. Hazard of ESRD increased with increasing HbA1c (1.28 per 1% or 10.9 mmol/mol increase [1.14–1.45]) and blood pressure (1.51 per 10 mmHg increase in systolic pressure [1.35–1.68]; 1.12 per 5 mmHg increase in diastolic pressure [1.01–1.23]). Use of antihypertensive medications increased the hazard of incident ESRD nearly fivefold [this finding is almost certainly due to confounding by indication, as also noted by the authors later on in the paper – US], and males had approximately two times the risk as compared with females. […] Having proliferative retinopathy was strongly associated with increased risk (HR 5.91 [3.00–11.6]) and attenuated the association between sex and ESRD.”

“The current investigation […] sought to provide much-needed information on the prevalence and incidence of ESRD and associated risk specific to people with type 1 diabetes. Consistent with a few previous studies (5,7,8), we observed decreased prevalence and incidence of ESRD among individuals with type 1 diabetes diagnosed in the 1970s compared with prior to 1970. The Epidemiology of Diabetes Complications (EDC) Study, another large cohort of people with type 1 diabetes followed over a long period of time, reported cumulative incidence rates of 2–6% for those diagnosed after 1970 and with similar duration (7), comparable to our findings. Slightly higher cumulative incidence (7–13%) reported from older studies at slightly lower duration also supports a decrease in incidence of ESRD (2830). Cumulative incidences through 30 years in European cohorts were even lower (3.3% in Sweden [6] and 7.8% in Finland [5]), compared with the 9.3% noted for those diagnosed during 1970–1980 in the WESDR cohort. The lower incidence could be associated with nationally organized care, especially in Sweden where a nationwide intensive diabetes management treatment program was implemented at least a decade earlier than recommendations for intensive care followed from the results of the DCCT in the U.S.”

“We noted an increased risk of incident ESRD in the first 15 years of diabetes not evident at longer durations. This pattern also demonstrated by others could be due to a greater earlier risk among people most genetically susceptible, as only a subset of individuals with type 1 diabetes will develop renal disease (27,28). The risk plateau associated with greater durations of diabetes and lower risk associated with increasing age may also reflect more death at longer durations and older ages. […] Because age and duration are highly correlated, we observed a positive association between age and ESRD only in univariate analyses, without adjustment for duration. The lack of adjustment for diabetes duration may have, in part, explained the increasing incidence of ESRD shown with age for some people in a recent investigation (9). Adjustment for both age and duration was found appropriate after testing for collinearity in the current analysis.”

In conclusion, this U.S. population-based report showed a lower prevalence and incidence of ESRD among those more recently diagnosed, explained by improvements in glycemic and blood pressure control over the last several decades. Even lower rates may be expected for those diagnosed during the current era of diabetes care. Intensive diabetes management, especially for glycemic control, remains important even in long-standing diabetes as potentially delaying the development of ESRD.

iii. Earlier Onset of Complications in Youth With Type 2 Diabetes.

The prevalence of type 2 diabetes in youth is increasing worldwide, coinciding with the rising obesity epidemic (1,2). […] Diabetes is associated with both microvascular and macrovascular complications. The evolution of these complications has been well described in type 1 diabetes (6) and in adult type 2 diabetes (7), wherein significant complications typically manifest 15–20 years after diagnosis (8). Because type 2 diabetes is a relatively new disease in children (first described in the 1980s), long-term outcome data on complications are scant, and risk factors for the development of complications are incompletely understood. The available literature suggests that development of complications in youth with type 2 diabetes may be more rapid than in adults, thus afflicting individuals at the height of their individual and social productivity (9). […] A small but notable proportion of type 2 diabetes is associated with a polymorphism of hepatic nuclear factor (HNF)-1α, a transcription factor expressed in many tissues […] It is not yet known what effect the HNF-1α polymorphism has on the risk of complications associated with diabetes.”

“The main objective of the current study was to describe the time course and risk factors for microvascular complications (nephropathy, retinopathy, and neuropathy) and macrovascular complications (cardiac, cerebrovascular, and peripheral vascular diseases) in a large cohort of youth [diagnosed with type 2 diabetes] who have been carefully followed for >20 years and to compare this evolution with that of youth with type 1 diabetes. We also compared vascular complications in the youth with type 2 diabetes with nondiabetic control youth. Finally, we addressed the impact of HNF-1α G319S on the evolution of complications in young patients with type 2 diabetes.”

“All prevalent cases of type 2 diabetes and type 1 diabetes (control group 1) seen between January 1986 and March 2007 in the DER-CA for youth aged 1–18 years were included. […] The final type 2 diabetes cohort included 342 youth, and the type 1 diabetes control group included 1,011. The no diabetes control cohort comprised 1,710 youth matched to the type 2 diabetes cohort from the repository […] Compared with the youth with type 1 diabetes, the youth with type 2 diabetes were, on average, older at the time of diagnosis and more likely to be female. They were more likely to have a higher BMIz, live in a rural area, have a low SES, and have albuminuria at diagnosis. […] one-half of the type 2 diabetes group was either a heterozygote (GS) or a homozygote (SS) for the HNF-1α polymorphism […] At the time of the last available follow-up in the DER-CA, the youth with diabetes were, on average, between 15 and 16 years of age. […] The median follow-up times in the repository were 4.4 (range 0–27.4) years for youth with type 2 diabetes, 6.7 ( 0–28.2) years for youth with type 1 diabetes, and 6.0 (0–29.9) years for nondiabetic control youth.”

“After controlling for low SES, sex, and BMIz, the risk associated with type 2 versus type 1 diabetes of any complication was an HR of 1.47 (1.02–2.12, P = 0.04). […] In the univariate analysis, youth with type 2 diabetes were at significantly higher risk of developing any vascular (HR 6.15 [4.26–8.87], P < 0.0001), microvascular (6.26 [4.32–9.10], P < 0.0001), or macrovascular (4.44 [1.71–11.52], P < 0.0001) disease compared with control youth without diabetes. In addition, the youth with type 2 diabetes had an increased risk of opthalmologic (19.49 [9.75–39.00], P < 0.0001), renal (16.13 [7.66–33.99], P < 0.0001), and neurologic (2.93 [1.79–4.80], P ≤ 0.001) disease. There were few cardiovascular, cerebrovascular, and peripheral vascular disease events in all groups (five or fewer events per group). Despite this, there was still a statistically significant higher risk of peripheral vascular disease in the type 2 diabetes group (6.25 [1.68–23.28], P = 0.006).”

“Differences in renal and neurologic complications between the two diabetes groups began to occur before 5 years postdiagnosis, whereas differences in ophthalmologic complications began 10 years postdiagnosis. […] Both cardiovascular and cerebrovascular complications were rare in both groups, but peripheral vascular complications began to occur 15 years after diagnosis in the type 2 diabetes group […] The presence of HNF-1α G319S polymorphism in youth with type 2 diabetes was found to be protective of complications. […] Overall, major complications were rare in the type 1 diabetes group, but they occurred in 1.1% of the type 2 diabetes cohort at 10 years, in 26.0% at 15 years, and in 47.9% at 20 years after diagnosis (P < 0.001) […] youth with type 2 diabetes have a higher risk of any complication than youth with type 1 diabetes and nondiabetic control youth. […] The time to both renal and neurologic complications was significantly shorter in youth with type 2 diabetes than in control youth, whereas differences were not significant with respect to opthalmologic and cardiovascular complications between cohorts. […] The current study is consistent with the literature, which has shown high rates of cardiovascular risk factors in youth with type 2 diabetes. However, despite the high prevalence of risk, this study reports low rates of clinical events. Because the median follow-up time was between 5 and 8 years, it is possible that a longer follow-up period would be required to correctly evaluate macrovascular outcomes in young adults. Also possible is that diagnoses of mild disease are not being made because of a low index of suspicion in 20- and 30-year-old patients.”

In conclusion, youth with type 2 diabetes have an increased risk of complications early in the course of their disease. Microvascular complications and cardiovascular risk factors are highly prevalent, whereas macrovascular complications are rare in young adulthood. HbA1c is an important modifiable risk factor; thus, optimizing glycemic control should remain an important goal of therapy.”

iv. HbA1c and Coronary Heart Disease Risk Among Diabetic Patients.

“We prospectively investigated the association of HbA1c at baseline and during follow-up with CHD risk among 17,510 African American and 12,592 white patients with type 2 diabetes. […] During a mean follow-up of 6.0 years, 7,258 incident CHD cases were identified. The multivariable-adjusted hazard ratios of CHD associated with different levels of HbA1c at baseline (<6.0 [reference group], 6.0–6.9, 7.0–7.9, 8.0–8.9, 9.0–9.9, 10.0–10.9, and ≥11.0%) were 1.00, 1.07 (95% CI 0.97–1.18), 1.16 (1.04–1.31), 1.15 (1.01–1.32), 1.26 (1.09–1.45), 1.27 (1.09–1.48), and 1.24 (1.10–1.40) (P trend = 0.002) for African Americans and 1.00, 1.04 (0.94–1.14), 1.15 (1.03–1.28), 1.29 (1.13–1.46), 1.41 (1.22–1.62), 1.34 (1.14–1.57), and 1.44 (1.26–1.65) (P trend <0.001) for white patients, respectively. The graded association of HbA1c during follow-up with CHD risk was observed among both African American and white diabetic patients (all P trend <0.001). Each one percentage increase of HbA1c was associated with a greater increase in CHD risk in white versus African American diabetic patients. When stratified by sex, age, smoking status, use of glucose-lowering agents, and income, this graded association of HbA1c with CHD was still present. […] The current study in a low-income population suggests a graded positive association between HbA1c at baseline and during follow-up with the risk of CHD among both African American and white diabetic patients with low socioeconomic status.”

A few more observations from the conclusions:

“Diabetic patients experience high mortality from cardiovascular causes (2). Observational studies have confirmed the continuous and positive association between glycemic control and the risk of cardiovascular disease among diabetic patients (4,5). But the findings from RCTs are sometimes uncertain. Three large RCTs (79) designed primarily to determine whether targeting different glucose levels can reduce the risk of cardiovascular events in patients with type 2 diabetes failed to confirm the benefit. Several reasons for the inconsistency of these studies can be considered. First, small sample sizes, short follow-up duration, and few CHD cases in some RCTs may limit the statistical power. Second, most epidemiological studies only assess a single baseline measurement of HbA1c with CHD risk, which may produce potential bias. The recent analysis of 10 years of posttrial follow-up of the UKPDS showed continued reductions for myocardial infarction and death from all causes despite an early loss of glycemic differences (10). The scientific evidence from RCTs was not sufficient to generate strong recommendations for clinical practice. Thus, consensus groups (AHA, ACC, and ADA) have provided a conservative endorsement (class IIb recommendation, level of evidence A) for the cardiovascular benefits of glycemic control (11). In the absence of conclusive evidence from RCTs, observational epidemiological studies might provide useful information to clarify the relationship between glycemia and CHD risk. In the current study with 30,102 participants with diabetes and 7,258 incident CHD cases during a mean follow-up of 6.0 years, we found a graded positive association by various HbA1c intervals of clinical relevance or by using HbA1c as a continuous variable at baseline and during follow-up with CHD risk among both African American and white diabetic patients. Each one percentage increase in baseline and follow-up HbA1c was associated with a 2 and 5% increased risk of CHD in African American and 6 and 11% in white diabetic patients. Each one percentage increase of HbA1c was associated with a greater increase in CHD risk in white versus African American diabetic patients.”

v. Blood Viscosity in Subjects With Normoglycemia and Prediabetes.

“Blood viscosity (BV) is the force that counteracts the free sliding of the blood layers within the circulation and depends on the internal cohesion between the molecules and the cells. Abnormally high BV can have several negative effects: the heart is overloaded to pump blood in the vascular bed, and the blood itself, more viscous, can damage the vessel wall. Furthermore, according to Poiseuille’s law (1), BV is inversely related to flow and might therefore reduce the delivery of insulin and glucose to peripheral tissues, leading to insulin resistance or diabetes (25).

It is generally accepted that BV is increased in diabetic patients (68). Although the reasons for this alteration are still under investigation, it is believed that the increase in osmolarity causes increased capillary permeability and, consequently, increased hematocrit and viscosity (9). It has also been suggested that the osmotic diuresis, consequence of hyperglycemia, could contribute to reduce plasma volume and increase hematocrit (10).

Cross-sectional studies have also supported a link between BV, hematocrit, and insulin resistance (1117). Recently, a large prospective study has demonstrated that BV and hematocrit are risk factors for type 2 diabetes. Subjects in the highest quartile of BV were >60% more likely to develop diabetes than their counterparts in the lowest quartile (18). This finding confirms previous observations obtained in smaller or selected populations, in which the association between hemoglobin or hematocrit and occurrence of type 2 diabetes was investigated (1922).

These observations suggest that the elevation in BV may be very early, well before the onset of diabetes, but definite data in subjects with normal glucose or prediabetes are missing. In the current study, we evaluated the relationship between BV and blood glucose in subjects with normal glucose or prediabetes in order to verify whether alterations in viscosity are appreciable in these subjects and at which blood glucose concentration they appear.”

“According to blood glucose levels, participants were divided into three groups: group A, blood glucose <90 mg/dL; group B, blood glucose between 90 and 99 mg/dL; and group C, blood glucose between 100 and 125 mg/dL. […] Hematocrit (P < 0.05) and BV (P between 0.01 and 0.001) were significantly higher in subjects with prediabetes and in those with blood glucose ranging from 90 to 99 mg/dL compared with subjects with blood glucose <90 mg/dL. […] The current study shows, for the first time, a direct relationship between BV and blood glucose in nondiabetic subjects. It also suggests that, even within glucose values ​​considered completely normal, individuals with higher blood glucose levels have increases in BV comparable with those observed in subjects with prediabetes. […] Overall, changes in viscosity in diabetic patients are accepted as common and as a result of the disease. However, the relationship between blood glucose, diabetes, and viscosity may be much more complex. […] the main finding of the study is that BV significantly increases already at high-normal blood glucose levels, independently of other common determinants of hemorheology. Intervention studies are needed to verify whether changes in BV can influence the development of type 2 diabetes.”

vi. Higher Relative Risk for Multiple Sclerosis in a Pediatric and Adolescent Diabetic Population: Analysis From DPV Database.

“Type 1 diabetes and multiple sclerosis (MS) are organ-specific inflammatory diseases, which result from an autoimmune attack against either pancreatic β-cells or the central nervous system; a combined appearance has been described repeatedly (13). For children and adolescents below the age of 21 years, the prevalence of type 1 diabetes in Germany and Austria is ∼19.4 cases per 100,000 population, and for MS it is 7–10 per 100,000 population (46). A Danish cohort study revealed a three times higher risk for the development of MS in patients with type 1 diabetes (7). Further, an Italian study conducted in Sardinia showed a five times higher risk for the development of type 1 diabetes in MS patients (8,9). An American study on female adults in whom diabetes developed before the age of 21 years yielded an up to 20 times higher risk for the development of MS (10).

These findings support the hypothesis of clustering between type 1 diabetes and MS. The pathogenesis behind this association is still unclear, but T-cell cross-reactivity was discussed as well as shared disease associations due to the HLA-DRB1-DQB1 gene loci […] The aim of this study was to evaluate the prevalence of MS in a diabetic population and to look for possible factors related to the co-occurrence of MS in children and adolescents with type 1 diabetes using a large multicenter survey from the Diabetes Patienten Verlaufsdokumentation (DPV) database.”

“We used a large database of pediatric and adolescent type 1 diabetic patients to analyze the RR of MS co-occurrence. The DPV database includes ∼98% of the pediatric diabetic population in Germany and Austria below the age of 21 years. In children and adolescents, the RR for MS in type 1 diabetes was estimated to be three to almost five times higher in comparison with the healthy population.”

November 2, 2017 Posted by | Cardiology, Diabetes, Epidemiology, Genetics, Immunology, Medicine, Nephrology, Statistics, Studies | Leave a comment

Molecules

This book is almost exclusively devoted to covering biochemistry topics. When the coverage is decent I find biochemistry reasonably interesting – for example I really liked Beer, Björk & Beardall’s photosynthesis book – and the coverage here was okay, but not more than that. I think that Ball was trying to cover a bit too much ground, or perhaps that there was really too much ground to cover for it to even make sense to try to write a book on this particular topic in a series like this. I learned a lot though.

As usual I’ve added some quotes from the coverage below, as well as some additional links to topics/concepts/people/etc. covered in the book.

“Most atoms on their own are highly reactive – they have a predisposition to join up with other atoms. Molecules are collectives of atoms, firmly welded together into assemblies that may contain anything up to many millions of them. […] By molecules, we generally mean assemblies of a discrete, countable number of atoms. […] Some pure elements adopt molecular forms; others do not. As a rough rule of thumb, metals are non-molecular […] whereas non-metals are molecular. […] molecules are the smallest units of meaning in chemistry. It is through molecules, not atoms, that one can tell stories in the sub-microscopic world. They are the words; atoms are just the letters. […] most words are distinct aggregates of several letters arranged in a particular order. We often find that longer words convey subtler and more finely nuanced meanings. And in molecules, as in words, the order in which the component parts are put together matters: ‘save’ and ‘vase’ do not mean the same thing.”

“There are something like 60,000 different varieties of protein molecule in human cells, each conducting a highly specialized task. It would generally be impossible to guess what this task is merely by looking at a protein. They are undistinguished in appearance, mostly globular in shape […] and composed primarily of carbon, hydrogen, nitrogen, oxygen, and a little sulphur. […] There are twenty varieties of amino acids in natural proteins. In the chain, one amino acid is linked to the next via a covalent bond called a peptide bond. Both molecules shed a few extraneous atoms to make this linkage, and the remainder – another link in the chain – is called a residue. The chain itself is termed a polypeptide. Any string of amino acid residues is a polypeptide. […] In a protein the order of amino acids along the chain – the sequence – is not arbitrary. It is selected […] to ensure that the chain will collapse and curl up in water into the precisely determined globular form of the protein, with all parts of the chain in the right place. This shape can be destroyed by warming the protein, a process called denaturation. But many proteins will fold up again spontaneously into the same globular structure when cooled. In other words, the chain has a kind of memory of its folded shape. The details of this folding process are still not fully understood – it is, in fact, one of the central unsolved puzzles of molecular biology. […] proteins are made not in the [cell] nucleus but in a different compartment called the endoplasmic reticulum […]. The gene is transcribed first into a molecule related to DNA, called RNA (ribonucleic acid). The RNA molecules travel from the nucleus to the endoplasmic reticulum, where they are translated to proteins. The proteins are then shipped off to where they are needed.”

[M]icrofibrils aggregate together in various ways. For example, they can gather in a staggered arrangement to form thick strands called banded fibrils. […] Banded fibrils constitute the connective tissues between cells – they are the cables that hold our flesh together. Bone consists of collagen banded fibrils sprinkled with tiny crystals of the mineral hydroxyapatite, which is basically calcium phosphate. Because of the high protein content of bone, it is flexible and resilient as well as hard. […] In contrast to the disorderly tangle of connective tissue, the eye’s cornea contains collagen fibrils packed side by side in an orderly manner. These fibrils are too small to scatter light, and so the material is virtually transparent. The basic design principle – one that recurs often in nature – is that, by tinkering with the chemical composition and, most importantly, the hierarchical arrangement of the same basic molecules, it is possible to extract several different kinds of material properties. […] cross-links determine the strength of the material: hair and fingernail are more highly cross-linked than skin. Curly or frizzy hair can be straightened by breaking some of [the] sulphur cross-links to make the hairs more pliable. […] Many of the body’s structural fabrics are proteins. Unlike enzymes, structural proteins do not have to conduct any delicate chemistry, but must simply be (for instance) tough, or flexible, or waterproof. In principle many other materials besides proteins would suffice; and indeed, plants use cellulose (a sugar-based polymer) to make their tissues.”

“In many ways, it is metabolism and not replication that provides the best working definition of life. Evolutionary biologists would say that we exist in order to reproduce – but we are not, even the most amorous of us, trying to reproduce all the time. Yet, if we stop metabolizing, even for a minute or two, we are done for. […] Whether waking or asleep, our bodies stay close to a healthy temperature of 37 °C. There is only one way of doing this: our cells are constantly pumping out heat, a by-product of metabolism. Heat is not really the point here – it is simply unavoidable, because all conversion of energy from one form to another squanders some of it this way. Our metabolic processes are primarily about making molecules. Cells cannot survive without constantly reinventing themselves: making new amino acids for proteins, new lipids for membranes, new nucleic acids so that they can divide.”

“In the body, combustion takes place in a tightly controlled, graded sequence of steps, and some chemical energy is drawn off and stored at each stage. […] A power station burns coal, oil, or gas […]. Burning is just a means to an end. The heat is used to turn water into steam; the pressure of the steam drives turbines; the turbines spin and send wire coils whirling in the arms of great magnets, which induces an electrical current in the wire. Energy is passed on, from chemical to heat to mechanical to electrical. And every plant has a barrage of regulatory and safety mechanisms. There are manual checks on pressure gauges and on the structural integrity of moving parts. Automatic sensors make the measurements. Failsafe devices avert catastrophic failure. Energy generation in the cell is every bit as complicated. […] The cell seems to have thought of everything, and has protein devices for fine-tuning it all.”

ATP is the key to the maintenance of cellular integrity and organization, and so the cell puts a great deal of effort into making as much of it as possible from each molecule of glucose that it burns. About 40 per cent of the energy released by the combustion of food is conserved in ATP molecules. ATP is rich in energy because it is like a coiled spring. It contains three phosphate groups, linked like so many train carriages. Each of these phosphate groups has a negative charge; this means that they repel one another. But because they are joined by chemical bonds, they cannot escape one another […]. Straining to get away, the phosphates pull an energetically powerful punch. […] The links between phosphates can be snipped in a reaction that involves water […] called hydrolysis (‘splitting with water’). Each time a bond is hydrolysed, energy is released. Setting free the outermost phosphate converts ATP to adenosine diphosphate (ADP); cleave the second phosphate and it becomes adenosine monophosphate (AMP). Both severances release comparable amounts of energy.”

“Burning sugar is a two-stage process, beginning with its transformation to a molecule called pyruvate in a process known as glycolysis […]. This involves a sequence of ten enzyme-catalysed steps. The first five of these split glucose in half […], powered by the consumption of ATP molecules: two of them are ‘decharged’ to ADP for every glucose molecule split. But the conversion of the fragments to pyruvate […] permits ATP to be recouped from ADP. Four ATP molecules are made this way, so that there is an overall gain of two ATP molecules per glucose molecule consumed. Thus glycolysis charges the cell’s batteries. Pyruvate then normally enters the second stage of the combustion process: the citric acid cycle, which requires oxygen. But if oxygen is scarce – that is, under anaerobic conditions – a contingency plan is enacted whereby pyruvate is instead converted to the molecule lactate. […] The first thing a mitochondrion does is convert pyruvate enzymatically to a molecule called acetyl coenzyme A (CoA). The breakdown of fatty acids and glycerides from fats also eventually generates acetyl CoA. The [citric acid] cycle is a sequence of eight enzyme-catalysed reactions that transform acetyl CoA first to citric acid and then to various other molecules, ending with […] oxaloacetate. This end is a new beginning, for oxaloacetate reacts with acetyl CoA to make citric acid. In some of the steps of the cycle, carbon dioxide is generated as a by-product. It dissolves in the bloodstream and is carried off to the lungs to be exhaled. Thus in effect the carbon in the original glucose molecules is syphoned off into the end product carbon dioxide, completing the combustion process. […] Also syphoned off from the cycle are electrons – crudely speaking, the citric acid cycle sends an electrical current to a different part of the mitochondrion. These electrons are used to convert oxygen molecules and positively charged hydrogen ions to water – an energy-releasing process. The energy is captured and used to make ATP in abundance.”

“While mammalian cells have fuel-burning factories in the form of mitochondria, the solar-power centres in the cells of plant leaves are compartments called chloroplasts […] chloroplast takes carbon dioxide and water, and from them constructs […] sugar. […] In the first part of photosynthesis, light is used to convert NADP to an electron carrier (NADPH) and to transform ADP to ATP. This is effectively a charging-up process that primes the chloroplast for glucose synthesis. In the second part, ATP and NADPH are used to turn carbon dioxide into sugar, in a cyclic sequence of steps called the Calvin–Benson cycle […] There are several similarities between the processes of aerobic metabolism and photosynthesis. Both consist of two distinct sub-processes with separate evolutionary origins: a linear sequence of reactions coupled to a cyclic sequence that regenerates the molecules they both need. The bridge between glycolysis and the citric acid cycle is the electron-ferrying NAD molecule; the two sub-processes of photosynthesis are bridged by the cycling of an almost identical molecule, NAD phosphate (NADP).”

“Despite the variety of messages that hormones convey, the mechanism by which the signal is passed from a receptor protein at the cell surface to the cell’s interior is the same in almost all cases. It involves a sequence of molecular interactions in which molecules transform one another down a relay chain. In cell biology this is called signal transduction. At the same time as relaying the message, these interactions amplify the signal so that the docking of a single hormone molecule to a receptor creates a big response inside the cell. […] The receptor proteins span the entire width of the membrane; the hormone-binding site protrudes on the outer surface, while the base of the receptor emerges from the inner surface […]. When the receptor binds its target hormone, a shape change is transmitted to the lower face of the protein, which enables it to act as an enzyme. […] The participants of all these processes [G protein, guanosine diphosphate and -triphosphate, adenylate cyclase… – figured it didn’t matter if I left out a few details – US…] are stuck to the cell wall. But cAMP floats freely in the cell’s cytoplasm, and is able to carry the signal into the cell interior. It is called a ‘second messenger’, since it is the agent that relays the signal of the ‘first messenger’ (the hormone) into the community of the cell. Cyclic AMP becomes attached to protein molecules called protein kinases, whereupon they in turn become activated as enzymes. Most protein kinases switch other enzymes on and off by attaching phosphate groups to them – a reaction called phosphorylation. […] The process might sound rather complicated, but it is really nothing more than a molecular relay. The signal is passed from the hormone to its receptor, then to the G protein, on to an enzyme and thence to the second messenger, and further on to a protein kinase, and so forth. The G-protein mechanism of signal transduction was discovered in the 1970s by Alfred Gilman and Martin Rodbell, for which they received the 1994 Nobel Prize for medicine. It represents one of the most widespread means of getting a message across a cell membrane. […] it is not just hormonal signalling that makes use of the G-protein mechanism. Our senses of vision and smell, which also involve the transmission of signals, employ the same switching process.”

“Although axon signals are electrical, they differ from those in the metal wires of electronic circuitry. The axon is basically a tubular cell membrane decorated along its length with channels that let sodium and potassium ions in and out. Some of these ion channels are permanently open; others are ‘gated’, opening or closing in response to electrical signals. And some are not really channels at all but pumps, which actively transport sodium ions out of the cell and potassium ions in. These sodium-potassium pumps can move ions […] powered by ATP. […] Drugs that relieve pain typically engage with inhibitory receptors. Morphine, the main active ingredient of opium, binds to so-called opioid receptors in the spinal cord, which inhibit the transmission of pain signals to the brain. There are also opioid receptors in the brain itself, which is why morphine and related opiate drugs have a mental as well as a somatic effect. These receptors in the brain are the binding sites of peptide molecules called endorphins, which the brain produces in response to pain. Some of these are themselves extremely powerful painkillers. […] Not all pain-relieving drugs (analgesics) work by blocking the pain signal. Some prevent the signal from ever being sent. Pain signals are initiated by peptides called prostaglandins, which are manufactured and released by distressed cells. Aspirin (acetylsalicylic acid) latches onto and inhibits one of the enzymes responsible for prostaglandin synthesis, cutting off the cry of pain at its source. Unfortunately, prostaglandins are also responsible for making the mucus that protects the stomach lining […], so one of the side effects of aspirin is the risk of ulcer formation.”

“Shape changes […] are common when a receptor binds its target. If binding alone is the objective, a big shape change is not terribly desirable, since the internal rearrangements of the receptor make heavy weather of the binding event and may make it harder to achieve. This is why many supramolecular hosts are designed so that they are ‘pre-organized’ to receive their guests, minimizing the shape change caused by binding.”

“The way that a protein chain folds up is determined by its amino-acid sequence […] so the ‘information’ for making a protein is uniquely specified by this sequence. DNA encodes this information using […] groups of three bases [to] represent each amino acid. This is the genetic code.* How a particular protein sequence determines the way its chain folds is not yet fully understood. […] Nevertheless, the principle of information flow in the cell is clear. DNA is a manual of information about proteins. We can think of each chromosome as a separate chapter, each gene as a word in that chapter (they are very long words!), and each sequential group of three bases in the gene as a character in the word. Proteins are translations of the words into another language, whose characters are amino acids. In general, only when the genetic language is translated can we understand what it means.”

“It is thought that only about 2–3 per cent of the entire human genome codes for proteins. […] Some people object to genetic engineering on the grounds that it is ethically wrong to tamper with the fundamental material of life – DNA – whether it is in bacteria, humans, tomatoes, or sheep. One can understand such objections, and it would be arrogant to dismiss them as unscientific. Nevertheless, they do sit uneasily with what we now know about the molecular basis of life. The idea that our genetic make-up is sacrosanct looks hard to sustain once we appreciate how contingent, not to say arbitrary, that make-up is. Our genomes are mostly parasite-riddled junk, full of the detritus of over three billion years of evolution.”

Links:

Roald Hoffmann.
Molecular solid.
Covalent bond.
Visible spectrum.
X-ray crystallography.
Electron microscope.
Valence (chemistry).
John Dalton.
Isomer.
Lysozyme.
Organic chemistry.
Synthetic dye industry/Alizarin.
Paul Ehrlich (staining).
Retrosynthetic analysis. [I would have added a link to ‘rational synthesis as well here if there’d been a good article on that topic, but I wasn’t able to find one. Anyway: “Organic chemists call [the] kind of procedure […] in which a starting molecule is converted systematically, bit by bit, to the desired product […] a rational synthesis.”]
Paclitaxel synthesis.
Protein.
Enzyme.
Tryptophan synthase.
Ubiquitin.
Amino acid.
Protein folding.
Peptide bond.
Hydrogen bond.
Nucleotide.
Chromosome.
Structural gene. Regulatory gene.
Operon.
Gregor Mendel.
Mitochondrial DNA.
RNA world.
Ribozyme.
Artificial gene synthesis.
Keratin.
Silk.
Vulcanization.
Aramid.
Microtubule.
Tubulin.
Carbon nanotube.
Amylase/pepsin/glycogen/insulin.
Cytochrome c oxidase.
ATP synthase.
Haemoglobin.
Thylakoid membrane.
Chlorophyll.
Liposome.
TNT.
Motor protein. Dynein. Kinesin.
Sarcomere.
Sliding filament theory of muscle action.
Photoisomerization.
Supramolecular chemistry.
Hormone. Endocrine system.
Neurotransmitter.
Ionophore.
DNA.
Mutation.
Intron. Exon.
Transposon.
Molecular electronics.

October 30, 2017 Posted by | Biology, Books, Botany, Chemistry, Genetics, Molecular biology, Neurology, Pharmacology | Leave a comment

A few diabetes papers of interest

i. The Pharmacogenetics of Type 2 Diabetes: A Systematic Review.

“We performed a systematic review to identify which genetic variants predict response to diabetes medications.

RESEARCH DESIGN AND METHODS We performed a search of electronic databases (PubMed, EMBASE, and Cochrane Database) and a manual search to identify original, longitudinal studies of the effect of diabetes medications on incident diabetes, HbA1c, fasting glucose, and postprandial glucose in prediabetes or type 2 diabetes by genetic variation.

RESULTS Of 7,279 citations, we included 34 articles (N = 10,407) evaluating metformin (n = 14), sulfonylureas (n = 4), repaglinide (n = 8), pioglitazone (n = 3), rosiglitazone (n = 4), and acarbose (n = 4). […] Significant medication–gene interactions for glycemic outcomes included 1) metformin and the SLC22A1, SLC22A2, SLC47A1, PRKAB2, PRKAA2, PRKAA1, and STK11 loci; 2) sulfonylureas and the CYP2C9 and TCF7L2 loci; 3) repaglinide and the KCNJ11, SLC30A8, NEUROD1/BETA2, UCP2, and PAX4 loci; 4) pioglitazone and the PPARG2 and PTPRD loci; 5) rosiglitazone and the KCNQ1 and RBP4 loci; and 5) acarbose and the PPARA, HNF4A, LIPC, and PPARGC1A loci. Data were insufficient for meta-analysis.

CONCLUSIONS We found evidence of pharmacogenetic interactions for metformin, sulfonylureas, repaglinide, thiazolidinediones, and acarbose consistent with their pharmacokinetics and pharmacodynamics.”

“In this systematic review, we identified 34 articles on the pharmacogenetics of diabetes medications, with several reporting statistically significant interactions between genetic variants and medications for glycemic outcomes. Most pharmacogenetic interactions were only evaluated in a single study, did not use a control group, and/or did not report enough information to judge internal validity. However, our results do suggest specific, biologically plausible, gene–medication interactions, and we recommend confirmation of the biologically plausible interactions as a priority, including those for drug transporters, metabolizers, and targets of action. […] Given the number of comparisons reported in the included studies and the lack of accounting for multiple comparisons in approximately 53% of studies, many of the reported findings may [however] be false positives.”

ii. Insights Offered by Economic Analyses.

“This issue of Diabetes Care includes three economic analyses. The first describes the incremental costs of diabetes over a lifetime and highlights how interventions to prevent diabetes may reduce lifetime costs (1). The second demonstrates that although an expensive, intensive lifestyle intervention for type 2 diabetes does not reduce adverse cardiovascular outcomes over 10 years, it significantly reduces the costs of non-intervention−related medical care (2). The third demonstrates that although the use of the International Association of the Diabetes and Pregnancy Study Groups (IADPSG) criteria for the screening and diagnosis of gestational diabetes mellitus (GDM) results in a threefold increase in the number of people labeled as having GDM, it reduces the risk of maternal and neonatal adverse health outcomes and reduces costs (3). The first report highlights the enormous potential value of intervening in adults at high risk for type 2 diabetes to prevent its development. The second illustrates the importance of measuring economic outcomes in addition to standard clinical outcomes to fully assess the value of new treatments. The third demonstrates the importance of rigorously weighing the costs of screening and treatment against the costs of health outcomes when evaluating new approaches to care.”

“The costs of diabetes monitoring and treatment accrue as of function of the duration of diabetes, so adults who are younger at diagnosis are more likely to survive to develop the late, expensive complications of diabetes, thus they incur higher lifetime costs attributable to diabetes. Zhuo et al. report that people with diabetes diagnosed at age 40 spend approximately $125,000 more for medical care over their lifetimes than people without diabetes. For people diagnosed with diabetes at age 50, the discounted lifetime excess medical spending is approximately $91,000; for those diagnosed at age 60, it is approximately $54,000; and for those diagnosed at age 65, it is approximately $36,000 (1).

These results are very consistent with results reported by the Diabetes Prevention Program (DPP) Research Group, which assessed the cost-effectiveness of diabetes prevention. […] In the simulated lifetime economic analysis [included in that study] the lifestyle intervention was more cost-effective in younger participants than in older participants (5). By delaying the onset of type 2 diabetes, the lifestyle intervention delayed or prevented the need for diabetes monitoring and treatment, surveillance of diabetic microvascular and neuropathic complications, and treatment of the late, expensive complications and comorbidities of diabetes, including end-stage renal disease and cardiovascular disease (5). Although this finding was controversial at the end of the randomized, controlled clinical trial, all but 1 of 12 economic analyses published by 10 research groups in nine countries have demonstrated that lifestyle intervention for the prevention of type 2 diabetes is very cost-effective, if not cost-saving, compared with a placebo intervention (6).

Empiric, within-trial economic analyses of the DPP have now demonstrated that the incremental costs of the lifestyle intervention are almost entirely offset by reductions in the costs of medical care outside the study, especially the cost of self-monitoring supplies, prescription medications, and outpatient and inpatient care (7). Over 10 years, the DPP intensive lifestyle intervention cost only ∼$13,000 per quality-adjusted life-year gained when the analysis used an intent-to-treat approach (7) and was even more cost-effective when the analysis assessed outcomes and costs among adherent participants (8).”

“The American Diabetes Association has reported that although institutional care (hospital, nursing home, and hospice care) still account for 52% of annual per capita health care expenditures for people with diabetes, outpatient medications and supplies now account for 30% of expenditures (9). Between 2007 and 2012, annual per capita expenditures for inpatient care increased by 2%, while expenditures for medications and supplies increased by 51% (9). As the costs of diabetes medications and supplies continue to increase, it will be even more important to consider cost savings arising from the less frequent use of medications when evaluating the benefits of nonpharmacologic interventions.”

iii. The Lifetime Cost of Diabetes and Its Implications for Diabetes Prevention. (This is the Zhuo et al. paper mentioned above.)

“We aggregated annual medical expenditures from the age of diabetes diagnosis to death to determine lifetime medical expenditure. Annual medical expenditures were estimated by sex, age at diagnosis, and diabetes duration using data from 2006–2009 Medical Expenditure Panel Surveys, which were linked to data from 2005–2008 National Health Interview Surveys. We combined survival data from published studies with the estimated annual expenditures to calculate lifetime spending. We then compared lifetime spending for people with diabetes with that for those without diabetes. Future spending was discounted at 3% annually. […] The discounted excess lifetime medical spending for people with diabetes was $124,600 ($211,400 if not discounted), $91,200 ($135,600), $53,800 ($70,200), and $35,900 ($43,900) when diagnosed with diabetes at ages 40, 50, 60, and 65 years, respectively. Younger age at diagnosis and female sex were associated with higher levels of lifetime excess medical spending attributed to diabetes.

CONCLUSIONS Having diabetes is associated with substantially higher lifetime medical expenditures despite being associated with reduced life expectancy. If prevention costs can be kept sufficiently low, diabetes prevention may lead to a reduction in long-term medical costs.”

The selection criteria employed in this paper are not perfect; they excluded all individuals below the age of 30 “because they likely had type 1 diabetes”, which although true is only ‘mostly true’. Some of those individuals had(/have) type 2, but if you’re evaluating prevention schemes it probably makes sense to error on the side of caution (better to miss some type 2 patients than to include some type 1s), assuming the timing of the intervention is not too important. This gets more complicated if prevention schemes are more likely to have large and persistent effects in young people – however I don’t think that’s the case, as a counterpoint drug adherence studies often seem to find that young people aren’t particularly motivated to adhere to their treatment schedules compared to their older counterparts (who might have more advanced disease and so are more likely to achieve symptomatic relief by adhering to treatments).

A few more observations from the paper:

“The prevalence of participants with diabetes in the study population was 7.4%, of whom 54% were diagnosed between the ages of 45 and 64 years. The mean age at diagnosis was 55 years, and the mean length of time since diagnosis was 9.4 years (39% of participants with diabetes had been diagnosed for ≤5 years, 32% for 6–15 years, and 27% for ≥16 years). […] The observed annual medical spending for people with diabetes was $13,966—more than twice that for people without diabetes.”

“Regardless of diabetes status, the survival-adjusted annual medical spending decreased after age 60 years, primarily because of a decreasing probability of survival. Because the probability of survival decreased more rapidly in people with diabetes than in those without, corresponding spending declined as people died and no longer accrued medical costs. For example, among men diagnosed with diabetes at age 40 years, 34% were expected to survive to age 80 years; among men of the same age who never developed diabetes, 55% were expected to survive to age 80 years. The expected annual expenditure for a person diagnosed with diabetes at age 40 years declined from $8,500 per year at age 40 years to $3,400 at age 80 years, whereas the expenses for a comparable person without diabetes declined from $3,900 to $3,200 over that same interval. […] People diagnosed with diabetes at age 40 years lived with the disease for an average of 34 years after diagnosis. Those diagnosed when older lived fewer years and, therefore, lost fewer years of life. […] The annual excess medical spending attributed to diabetes […] was smaller among people who were diagnosed at older ages. For men diagnosed at age 40 years, annual medical spending was $3,700 higher than that of similar men without diabetes; spending was $2,900 higher for those diagnosed at age 50 years; $2,200 higher for those diagnosed at age 60 years; and $2,000 higher for those diagnosed at age 65 years. Among women diagnosed with diabetes, the excess annual medical spending was consistently higher than for men of the same age at diagnosis.”

“Regardless of age at diagnosis, people with diabetes spent considerably more on health care after age 65 years than their nondiabetic counterparts. Health care spending attributed to diabetes after age 65 years ranged from $23,900 to $40,900, depending on sex and age at diagnosis. […] Of the total excess lifetime medical spending among an average diabetic patient diagnosed at age 50 years, prescription medications and inpatient care accounted for 44% and 35% of costs, respectively. Outpatient care and other medical care accounted for 17% and 4% of costs, respectively.”

“Our findings differed from those of studies of the lifetime costs of other chronic conditions. For instance, smokers have a lower average lifetime medical cost than nonsmokers (29) because of their shorter life spans. Smokers have a life expectancy about 10 years less than those who do not smoke (30); life expectancy is 16 years less for those who develop smoking-induced cancers (31). As a result, smoking cessation leads to increased lifetime spending (32). Studies of the lifetime costs for an obese person relative to a person with normal body weight show mixed results: estimated excess lifetime medical costs for people with obesity range from $3,790 less to $39,000 more than costs for those who are nonobese (33,34). […] obesity, when considered alone, results in much lower annual excess medical costs than diabetes (–$940 to $1,150 for obesity vs. $2,000 to $4,700 for diabetes) when compared with costs for people who are nonobese (33,34).”

iv. Severe Hypoglycemia and Mortality After Cardiovascular Events for Type 1 Diabetic Patients in Sweden.

“This study examines factors associated with all-cause mortality after cardiovascular complications (myocardial infarction [MI] and stroke) in patients with type 1 diabetes. In particular, we aim to determine whether a previous history of severe hypoglycemia is associated with increased mortality after a cardiovascular event in type 1 diabetic patients.

Hypoglycemia is the most common and dangerous acute complication of type 1 diabetes and can be life threatening if not promptly treated (1). The average individual with type 1 diabetes experiences about two episodes of symptomatic hypoglycemia per week, with an annual prevalence of 30–40% for hypoglycemic episodes requiring assistance for recovery (2). We define severe hypoglycemia to be an episode of hypoglycemia that requires hospitalization in this study. […] Patients with type 1 diabetes are more susceptible to hypoglycemia than those with type 2 diabetes, and therefore it is potentially of greater relevance if severe hypoglycemia is associated with mortality (6).”

“This study uses a large linked data set comprising health records from the Swedish National Diabetes Register (NDR), which were linked to administrative records on hospitalization, prescriptions, and national death records. […] [The] study is based on data from four sources: 1) risk factor data from the Swedish NDR […], 2) hospital records of inpatient episodes from the National Inpatients Register (IPR) […], 3) death records […], and 4) prescription data records […]. A study comparing registered diagnoses in the IPR with information in medical records found positive predictive values of IPR diagnoses were 85–95% for most diagnoses (8). In terms of NDR coverage, a recent study found that 91% of those aged 18–34 years and with type 1 diabetes in the Prescribed Drug Register could be matched with those in the NDR for 2007–2009 (9).”

“The outcome of the study was all-cause mortality after a major cardiovascular complication (MI or stroke). Our sample for analysis included patients with type 1 diabetes who visited a clinic after 2002 and experienced a major cardiovascular complication after this clinic visit. […] We define type 1 diabetes as diabetes diagnosed under the age of 30 years, being reported as being treated with insulin only at some clinic visit, and when alive, having had at least one prescription for insulin filled per year between 2006 and 2010 […], and not having filled a prescription for metformin at any point between July 2005 and December 2010 (under the assumption that metformin users were more likely to be type 2 diabetes patients).”

“Explanatory variables included in both models were type of complication (MI or stroke), age at complication, duration of diabetes, sex, smoking status, HbA1c, BMI, systolic blood pressure, diastolic blood pressure, chronic kidney disease status based on estimated glomerular filtration rate, microalbuminuria and macroalbuminuria status, HDL, LDL, total–to–HDL cholesterol ratio, triglycerides, lipid medication status, clinic visits within the year prior to the CVD event, and prior hospitalization events: hypoglycemia, hyperglycemia, MI, stroke, heart failure, AF, amputation, PVD, ESRD, IHD/unstable angina, PCI, and CABG. The last known value for each clinical risk factor, prior to the cardiovascular complication, was used for analysis. […] Initially, all explanatory variables were included and excluded if the variable was not statistically significant at a 5% level (P < 0.05) via stepwise backward elimination.” [Aaaaaaargh! – US. These guys are doing a lot of things right, but this is not one of them. Just to mention this one more time: “Generally, hypothesis testing is a very poor basis for model selection […] There is no statistical theory that supports the notion that hypothesis testing with a fixed α level is a basis for model selection.” (Burnham & Anderson)]

“Patients who had prior hypoglycemic events had an estimated HR for mortality of 1.79 (95% CI 1.37–2.35) in the first 28 days after a CVD event and an estimated HR of 1.25 (95% CI 1.02–1.53) of mortality after 28 days post CVD event in the backward regression model. The univariate analysis showed a similar result compared with the backward regression model, with prior hypoglycemic events having an estimated HR for mortality of 1.79 (95% CI 1.38–2.32) and 1.35 (95% CI 1.11–1.65) in the logistic and Cox regressions, respectively. Even when all explanatory factors were included in the models […], the mortality increase associated with a prior severe hypoglycemic event was still significant, and the P values and SE are similar when compared with the backward stepwise regression. Similarly, when explanatory factors were included individually, the mortality increase associated with a prior severe hypoglycemic event was also still significant.” [Again, this sort of testing scheme is probably not a good approach to getting at a good explanatory model, but it’s what they did – US]

“The 5-year cumulative estimated mortality risk for those without complications after MI and stroke were 40.1% (95% CI 35.2–45.1) and 30.4% (95% CI 26.3–34.6), respectively. Patients with prior heart failure were at the highest estimated 5-year cumulative mortality risk, with those who suffered an MI and stroke having a 56.0% (95% CI 47.5–64.5) and 44.0% (95% CI 35.8–52.2) 5-year cumulative mortality risk, respectively. Patients who had a prior severe hypoglycemic event and suffered an MI had an estimated 5-year cumulative mortality risk at age 60 years of 52.4% (95% CI 45.3–59.5), and those who suffered a stroke had a 5-year cumulative mortality risk of 39.8% (95% CI 33.4–46.3). Patients at age 60 years who suffer a major CVD complication have over twofold risk of 5-year mortality compared with the general type 1 diabetic Swedish population, who had an estimated 5-year mortality risk of 13.8% (95% CI 12.0–16.1).”

“We found evidence that prior severe hypoglycemia is associated with reduced survival after a major CVD event but no evidence that prior severe hypoglycemia is associated with an increased risk of a subsequent CVD event.

Compared with the general type 1 diabetic Swedish population, a major CVD complication increased 5-year mortality risk at age 60 years by >25% and 15% in patients with an MI and stroke, respectively. Patients with a history of a hypoglycemic event had an even higher mortality after a major CVD event, with approximately an additional 10% being dead at the 5-year mark. This risk was comparable with that in those with late-stage kidney disease. This information is useful in determining the prognosis of patients after a major cardiovascular event and highlights the need to include this as a risk factor in simulation models (18) that are used to improve decision making (19).”

“This is the first study that has found some evidence of a dose-response relationship, where patients who experienced two or more severe hypoglycemic events had higher mortality after a cardiovascular event compared with those who experienced one severe hypoglycemic event. A lack of statistical power prevented us from investigating this further when we tried to stratify by number of prior severe hypoglycemic events in our regression models. There was no evidence of a dose-response relationship between repeated episodes of severe hypoglycemia and vascular outcomes or death in previous type 2 diabetes studies (5).”

v. Alterations in White Matter Structure in Young Children With Type 1 Diabetes.

“Careful regulation of insulin dosing, dietary intake, and activity levels are essential for optimal glycemic control in individuals with type 1 diabetes. However, even with optimal treatment many children with type 1 diabetes have blood glucose levels in the hyperglycemic range for more than half the day and in the hypoglycemic range for an hour or more each day (1). Brain cells may be especially sensitive to aberrant blood glucose levels, as glucose is the brain’s principal substrate for its energy needs.

Research in animal models has shown that white matter (WM) may be especially sensitive to dysglycemia-associated insult in diabetes (24). […] Early childhood is a period of rapid myelination and brain development (6) and of increased sensitivity to insults affecting the brain (6,7). Hence, study of the developing brain is particularly important in type 1 diabetes.”

“WM structure can be measured with diffusion tensor imaging (DTI), a method based on magnetic resonance imaging (MRI) that uses the movement of water molecules to characterize WM brain structure (8,9). Results are commonly reported in terms of mathematical scalars (representing vectors in vector space) such as fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD). FA reflects the degree of diffusion anisotropy of water (how diffusion varies along the three axes) within a voxel (three-dimensional pixel) and is determined by fiber diameter and density, myelination, and intravoxel fiber-tract coherence (increases in which would increase FA), as well as extracellular diffusion and interaxonal spacing (increases in which would decrease FA) (10). AD, a measure of water diffusivity along the main axis of diffusion within a voxel, is thought to reflect fiber coherence and structure of axonal membranes (increases in which would increase AD), as well as microtubules, neurofilaments, and axonal branching (increases in which would decrease AD) (11,12). RD, the mean of the diffusivities perpendicular to the vector with the largest eigenvalue, is thought to represent degree of myelination (13,14) (more myelin would decrease RD values) and axonal “leakiness” (which would increase RD). Often, however, a combination of these WM characteristics results in opposing contributions to the final observed FA/AD/RD value, and thus DTI scalars should not be interpreted globally as “good” or “bad” (15). Rather, these scalars can show between-group differences and relationships between WM structure and clinical variables and are suggestive of underlying histology. Definitive conclusions about histology of WM can only be derived from direct microscopic examination of biological tissue.”

“Children (ages 4 to <10 years) with type 1 diabetes (n = 127) and age-matched nondiabetic control subjects (n = 67) had diffusion weighted magnetic resonance imaging scans in this multisite neuroimaging study. Participants with type 1 diabetes were assessed for HbA1c history and lifetime adverse events, and glucose levels were monitored using a continuous glucose monitor (CGM) device and standardized measures of cognition.

RESULTS Between-group analysis showed that children with type 1 diabetes had significantly reduced axial diffusivity (AD) in widespread brain regions compared with control subjects. Within the type 1 diabetes group, earlier onset of diabetes was associated with increased radial diffusivity (RD) and longer duration was associated with reduced AD, reduced RD, and increased fractional anisotropy (FA). In addition, HbA1c values were significantly negatively associated with FA values and were positively associated with RD values in widespread brain regions. Significant associations of AD, RD, and FA were found for CGM measures of hyperglycemia and glucose variability but not for hypoglycemia. Finally, we observed a significant association between WM structure and cognitive ability in children with type 1 diabetes but not in control subjects. […] These results suggest vulnerability of the developing brain in young children to effects of type 1 diabetes associated with chronic hyperglycemia and glucose variability.”

“The profile of reduced overall AD in type 1 diabetes observed here suggests possible axonal damage associated with diabetes (30). Reduced AD was associated with duration of type 1 diabetes suggesting that longer exposure to diabetes worsens the insult to WM structure. However, measures of hyperglycemia and glucose variability were either not associated or were positively associated with AD values, suggesting that these measures did not contribute to the observed decreased AD in the type 1 diabetes group. A possible explanation for these observations is that several biological processes influence WM structure in type 1 diabetes. Some processes may be related to insulin insufficiency or C-peptide levels independent of glucose levels (31,32) and may affect WM coherence (and reduce AD values as observed in the between-group results). Other processes related to hyperglycemia and glucose variability may target myelin (resulting in reduced FA and increased RD) as well as reduced axonal branching (both would result in increased AD values). Alternatively, these seemingly conflicting AD observations may be due to a dominant effect of age, which could overshadow effects from dysglycemia.

Early age of onset is one of the most replicable risk factors for cognitive impairments in type 1 diabetes (33,34). It has been hypothesized that young children are especially vulnerable to brain insults resulting from episodes of chronic hyperglycemia, hypoglycemia, and acute hypoglycemic complications of type 1 diabetes (seizures and severe hypoglycemic episodes). In addition, fear of hypoglycemia often results in caregivers maintaining relatively higher blood glucose to avoid lows altogether (1), especially in very young children. However, our study suggests that this approach of aggressive hypoglycemia avoidance resulting in hyperglycemia may not be optimal and may be detrimental to WM structure in young children.

Neuronal damage (reflected in altered WM structure) may affect neuronal signal transfer and, thus, cognition (35). Cognitive domains commonly reported to be affected in children with type 1 diabetes include general intellectual ability, visuospatial abilities, attention, memory, processing speed, and executive function (3638). In our sample, even though the duration of illness was relatively short (2.9 years on average), there were modest but significant cognitive differences between children with type 1 diabetes and control subjects (24).”

“In summary, we present results from the largest study to date investigating WM structure in very young children with type 1 diabetes. We observed significant and widespread brain differences in the WM microstructure of children with type 1 diabetes compared with nondiabetic control subjects and significant associations between WM structure and measures of hyperglycemia, glucose variability, and cognitive ability in the type 1 diabetic population.”

vi. Ultrasound Findings After Surgical Decompression of the Tarsal Tunnel in Patients With Painful Diabetic Polyneuropathy: A Prospective Randomized Study.

“Polyneuropathy is a common complication in diabetes. The prevalence of neuropathy in patients with diabetes is ∼30%. During the course of the disease, up to 50% of the patients will eventually develop neuropathy (1). Its clinical features are characterized by numbness, tingling, or burning sensations and typically extend in a distinct stocking and glove pattern. Prevention plays a key role since poor glucose control is a major risk factor in the development of diabetic polyneuropathy (DPN) (1,2).

There is no clear definition for the onset of painful diabetic neuropathy. Different hypotheses have been formulated.

Hyperglycemia in diabetes can lead to osmotic swelling of the nerves, related to increased glucose conversion into sorbitol by the enzyme aldose reductase (2,3). High sorbitol concentrations might also directly cause axonal degeneration and demyelination (2). Furthermore, stiffening and thickening of ligamental structures and the plantar fascia make underlying structures more prone to biomechanical compression (46). A thicker and stiffer retinaculum might restrict movements and lead to alterations of the nerve in the tarsal tunnel.

Both swelling of the nerve and changes in the tarsal tunnel might lead to nerve damage through compression.

Furthermore, vascular changes may diminish endoneural blood flow and oxygen distribution. Decreased blood supply in the (compressed) nerve might lead to ischemic damage as well as impaired nerve regeneration.

Several studies suggest that surgical decompression of nerves at narrow anatomic sites, e.g., the tarsal tunnel, is beneficial and has a positive effect on pain, sensitivity, balance, long-term risk of ulcers and amputations, and quality of life (3,710). Since the effect of decompression of the tibial nerve in patients with DPN has not been proven with a randomized clinical trial, its contribution as treatment for patients with painful DPN is still controversial. […] In this study, we compare the mean CSA and any changes in shape of the tibial nerve before and after decompression of the tarsal tunnel using ultrasound in order to test the hypothesis that the tarsal tunnel leads to compression of the tibial nerve in patients with DPN.”

“This study, with a large sample size and standardized sonographic imaging procedure with a good reliability, is the first randomized controlled trial that evaluates the effect of decompression of the tibial nerve on the CSA. Although no effect on CSA after surgery was found, this study using ultrasound demonstrates a larger and swollen tibial nerve and thicker flexor retinaculum at the ankle in patients with DPN compared with healthy control subjects.”

I would have been interested to know if there were any observable changes in symptom relief measures post-surgery, even if such variables are less ‘objective’ than measures like CSA (less objective, but perhaps more relevant to the patient…), but the authors did not look at those kinds of variables.

vii. Nonalcoholic Fatty Liver Disease Is Independently Associated With an Increased Incidence of Chronic Kidney Disease in Patients With Type 1 Diabetes.

“Nonalcoholic fatty liver disease (NAFLD) has reached epidemic proportions worldwide (1). Up to 30% of adults in the U.S. and Europe have NAFLD, and the prevalence of this disease is much higher in people with diabetes (1,2). Indeed, the prevalence of NAFLD on ultrasonography ranges from ∼50 to 70% in patients with type 2 diabetes (35) and ∼40 to 50% in patients with type 1 diabetes (6,7). Notably, patients with diabetes and NAFLD are also more likely to develop more advanced forms of NAFLD that may result in end-stage liver disease (8). However, accumulating evidence indicates that NAFLD is associated not only with liver-related morbidity and mortality but also with an increased risk of developing cardiovascular disease (CVD) and other serious extrahepatic complications (810).”

“Increasing evidence indicates that NAFLD is strongly associated with an increased risk of CKD [chronic kidney disease, US] in people with and without diabetes (11). Indeed, we have previously shown that NAFLD is associated with an increased prevalence of CKD in patients with both type 1 and type 2 diabetes (1517), and that NAFLD independently predicts the development of incident CKD in patients with type 2 diabetes (18). However, many of the risk factors for CKD are different in patients with type 1 and type 2 diabetes, and to date, it is uncertain whether NAFLD is an independent risk factor for incident CKD in type 1 diabetes or whether measurement of NAFLD improves risk prediction for CKD, taking account of traditional risk factors for CKD.

Therefore, the aim of the current study was to investigate 1) whether NAFLD is associated with an increased incidence of CKD and 2) whether measurement of NAFLD improves risk prediction for CKD, adjusting for traditional risk factors, in type 1 diabetic patients.”

“Using a retrospective, longitudinal cohort study design, we have initially identified from our electronic database all Caucasian type 1 diabetic outpatients with preserved kidney function (i.e., estimated glomerular filtration rate [eGFR] ≥60 mL/min/1.73 m2) and with no macroalbuminuria (n = 563), who regularly attended our adult diabetes clinic between 1999 and 2001. Type 1 diabetes was diagnosed by the typical presentation of disease, the absolute dependence on insulin treatment for survival, the presence of undetectable fasting C-peptide concentrations, and the presence of anti–islet cell autoantibodies. […] Overall, 261 type 1 diabetic outpatients were included in the final analysis and were tested for the development of incident CKD during the follow-up period […] All participants were periodically seen (every 3–6 months) for routine medical examinations of glycemic control and chronic complications of diabetes. No participants were lost to follow-up. […] For this study, the development of incident CKD was defined as occurrence of eGFR <60 mL/min/1.73 m2 and/or macroalbuminuria (21). Both of these outcome measures were confirmed in all participants in a least two consecutive occasions (within 3–6 months after the first examination).”

“At baseline, the mean eGFRMDRD was 92 ± 23 mL/min/1.73 m2 (median 87.9 [IQR 74–104]), or eGFREPI was 98.6 ± 19 mL/min/1.73 m2 (median 99.7 [84–112]). Most patients (n = 234; 89.7%) had normal albuminuria, whereas 27 patients (10.3%) had microalbuminuria. NAFLD was present in 131 patients (50.2%). […] At baseline, patients who developed CKD at follow-up were older, more likely to be female and obese, and had a longer duration of diabetes than those who did not. These patients also had higher values of systolic blood pressure, A1C, triglycerides, serum GGT, and urinary ACR and lower values of eGFRMDRD and eGFREPI. Moreover, there was a higher percentage of patients with hypertension, metabolic syndrome, microalbuminuria, and some degree of diabetic retinopathy in patients who developed CKD at follow-up compared with those remaining free from CKD. The proportion using antihypertensive drugs (that always included the use of ACE inhibitors or angiotensin receptor blockers) was higher in those who progressed to CKD. Notably, […] this patient group also had a substantially higher frequency of NAFLD on ultrasonography.”

“During follow-up (mean duration 5.2 ± 1.7 years, range 2–10), 61 patients developed CKD using the MDRD study equation to estimate eGFR (i.e., ∼4.5% of participants progressed every year to eGFR <60 mL/min/1.73 m2 or macroalbuminuria). Of these, 28 developed an eGFRMDRD <60 mL/min/1.73 m2 with abnormal albuminuria (micro- or macroalbuminuria), 21 developed a reduced eGFRMDRD with normal albuminuria (but 9 of them had some degree of diabetic retinopathy at baseline), and 12 developed macroalbuminuria alone. None of them developed kidney failure requiring chronic dialysis. […] The annual eGFRMDRD decline for the whole cohort was 2.68 ± 3.5 mL/min/1.73 m2 per year. […] NAFLD patients had a greater annual decline in eGFRMDRD than those without NAFLD at baseline (3.28 ± 3.8 vs. 2.10 ± 3.0 mL/min/1.73 m2 per year, P < 0.005). Similarly, the frequency of a renal functional decline (arbitrarily defined as ≥25% loss of baseline eGFRMDRD) was greater among those with NAFLD than among those without the disease (26 vs. 11%, P = 0.005). […] Interestingly, BMI was not significantly associated with CKD.”

“Our novel findings indicate that NAFLD is strongly associated with an increased incidence of CKD during a mean follow-up of 5 years and that measurement of NAFLD improves risk prediction for CKD, independently of traditional risk factors (age, sex, diabetes duration, A1C, hypertension, baseline eGFR, and microalbuminuria [i.e., the last two factors being the strongest known risk factors for CKD]), in type 1 diabetic adults. Additionally, although NAFLD was strongly associated with obesity, obesity (or increased BMI) did not explain the association between NAFLD and CKD. […] The annual cumulative incidence rate of CKD in our cohort of patients (i.e., ∼4.5% per year) was essentially comparable to that previously described in other European populations with type 1 diabetes and similar baseline characteristics (∼2.5–9% of patients who progressed every year to CKD) (25,26). In line with previously published information (2528), we also found that hypertension, microalbuminuria, and lower eGFR at baseline were strong predictors of incident CKD in type 1 diabetic patients.”

“There is a pressing and unmet need to determine whether NAFLD is associated with a higher risk of CKD in people with type 1 diabetes. It has only recently been recognized that NAFLD represents an important burden of disease for type 2 diabetic patients (11,17,18), but the magnitude of the problem of NAFLD and its association with risk of CKD in type 1 diabetes is presently poorly recognized. Although there is clear evidence that NAFLD is closely associated with a higher prevalence of CKD both in those without diabetes (11) and in those with type 1 and type 2 diabetes (1517), only four prospective studies have examined the association between NAFLD and risk of incident CKD (18,2931), and only one of these studies was published in patients with type 2 diabetes (18). […] The underlying mechanisms responsible for the observed association between NAFLD and CKD are not well understood. […] The possible clinical implication for these findings is that type 1 diabetic patients with NAFLD may benefit from more intensive surveillance or early treatment interventions to decrease the risk for CKD. Currently, there is no approved treatment for NAFLD. However, NAFLD and CKD share numerous cardiometabolic risk factors, and treatment strategies for NAFLD and CKD should be similar and aimed primarily at modifying the associated cardiometabolic risk factors.”

 

October 25, 2017 Posted by | Cardiology, Diabetes, Epidemiology, Genetics, Health Economics, Medicine, Nephrology, Neurology, Pharmacology, Statistics, Studies | Leave a comment

Type 1 Diabetes Mellitus and Cardiovascular Disease

“Despite the known higher risk of cardiovascular disease (CVD) in individuals with type 1 diabetes mellitus (T1DM), the pathophysiology underlying the relationship between cardiovascular events, CVD risk factors, and T1DM is not well understood. […] The present review will focus on the importance of CVD in patients with T1DM. We will summarize recent observations of potential differences in the pathophysiology of T1DM compared with T2DM, particularly with regard to atherosclerosis. We will explore the implications of these concepts for treatment of CVD risk factors in patients with T1DM. […] The statement will identify gaps in knowledge about T1DM and CVD and will conclude with a summary of areas in which research is needed.”

The above quote is from this paper: Type 1 Diabetes Mellitus and Cardiovascular Disease: A Scientific Statement From the American Heart Association and American Diabetes Association.

I originally intended to cover this one in one of my regular diabetes posts, but I decided in the end that there was simply too much stuff to cover here for it to make sense not to devote an entire post to it. I have quoted extensively from the paper/statement below and I also decided to bold a few of the observations I found particularly important/noteworthy(/worth pointing out to people reading along?).

“T1DM has strong human leukocyte antigen associations to the DQA, DQB, and DRB alleles (2). One or more autoantibodies, including islet cell, insulin, glutamic acid decarboxylase 65 (GAD65), zinc transporter 8 (3), and tyrosine phosphatase IA-2β and IA-2β antibodies, can be detected in 85–90% of individuals on presentation. The rate of β-cell destruction varies, generally occurring more rapidly at younger ages. However, T1DM can also present in adults, some of whom can have enough residual β-cell function to avoid dependence on insulin until many years later. When autoantibodies are present, this is referred to as latent autoimmune diabetes of adulthood. Infrequently, T1DM can present without evidence of autoimmunity but with intermittent episodes of ketoacidosis; between episodes, the need for insulin treatment can come and go. This type of DM, called idiopathic diabetes (1) or T1DM type B, occurs more often in those of African and Asian ancestry (4). Because of the increasing prevalence of obesity in the United States, there are also obese individuals with T1DM, particularly children. Evidence of insulin resistance (such as acanthosis nigricans); fasting insulin, glucose, and C-peptide levels; and the presence of islet cell, insulin, glutamic acid decarboxylase, and phosphatase autoantibodies can help differentiate between T1DM and T2DM, although both insulin resistance and insulin insufficiency can be present in the same patient (5), and rarely, T2DM can present at an advanced stage with low C-peptide levels and minimal islet cell function.”

Overall, CVD events are more common and occur earlier in patients with T1DM than in nondiabetic populations; women with T1DM are more likely to have a CVD event than are healthy women. CVD prevalence rates in T1DM vary substantially based on duration of DM, age of cohort, and sex, as well as possibly by race/ethnicity (8,11,12). The Pittsburgh Epidemiology of Diabetes Complications (EDC) study demonstrated that the incidence of major coronary artery disease (CAD) events in young adults (aged 28–38 years) with T1DM was 0.98% per year and surpassed 3% per year after age 55 years, which makes it the leading cause of death in that population (13). By contrast, incident first CVD in the nondiabetic population ranges from 0.1% in 35- to 44-year-olds to 7.4% in adults aged 85–94 years (14). An increased risk of CVD has been reported in other studies, with the age-adjusted relative risk (RR) for CVD in T1DM being ≈10 times that of the general population (1517). One of the most robust analyses of CVD risk in this disease derives from the large UK General Practice Research Database (GPRD), comprising data from >7,400 patients with T1DM with a mean ± SD age of 33 ± 14.5 years and a mean DM duration of 15 ± 12 years (8). CVD events in the UK GPRD study occurred on average 10 to 15 years earlier than in matched nondiabetic control subjects.”

“When types of CVD are reported separately, CHD [coronary heart disease] predominates […] The published cumulative incidence of CHD ranges between 2.1% (18) and 19% (19), with most studies reporting cumulative incidences of ≈15% over ≈15 years of follow-up (2022). […] Although stroke is less common than CHD in T1DM, it is another important CVD end point. Reported incidence rates vary but are relatively low. […] the Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR) reported an incidence rate of 5.9% over 20 years (≈0.3%) (21); and the European Diabetes (EURODIAB) Study reported a 0.74% incidence of cerebrovascular disease per year (18). These incidence rates are for the most part higher than those reported in the general population […] PAD [peripheral artery disease] is another important vascular complication of T1DM […] The rate of nontraumatic amputation in T1DM is high, occurring at 0.4–7.2% per year (28). By 65 years of age, the cumulative probability of lower-extremity amputation in a Swedish administrative database was 11% for women with T1DM and 20.7% for men (10). In this Swedish population, the rate of lower-extremity amputation among those with T1DM was nearly 86-fold that of the general population.

“Abnormal vascular findings associated with atherosclerosis are also seen in patients with T1DM. Coronary artery calcification (CAC) burden, an accepted noninvasive assessment of atherosclerosis and a predictor of CVD events in the general population, is greater in people with T1DM than in nondiabetic healthy control subjects […] With regard to subclinical carotid disease, both carotid intima-media thickness (cIMT) and plaque are increased in children, adolescents, and adults with T1DM […] compared with age- and sex-matched healthy control subjects […] Endothelial function is altered even at a very early stage of T1DM […] Taken together, these data suggest that preclinical CVD can be seen more frequently and to a greater extent in patients with T1DM, even at an early age. Some data suggest that its presence may portend CVD events; however, how these subclinical markers function as end points is not clear.”

“Neuropathy in T1DM can lead to abnormalities in the response of the coronary vasculature to sympathetic stimulation, which may manifest clinically as resting tachycardia or bradycardia, exercise intolerance, orthostatic hypotension, loss of the nocturnal decline in BP, or silent myocardial ischemia on cardiac testing. These abnormalities can lead to delayed presentation of CVD. An early indicator of cardiac autonomic neuropathy is reduced heart rate variability […] Estimates of the prevalence of cardiac autonomic neuropathy in T1DM vary widely […] Cardiac neuropathy may affect as many as ≈40% of individuals with T1DM (45).”

CVD events occur much earlier in patients with T1DM than in the general population, often after 2 decades of T1DM, which in some patients may be by age 30 years. Thus, in the EDC study, CVD was the leading cause of death in T1DM patients after 20 years of disease duration, at rates of >3% per year (13). Rates of CVD this high fall into the National Cholesterol Education Program’s high-risk category and merit intensive CVD prevention efforts (48). […] CVD events are not generally expected to occur during childhood, even in the setting of T1DM; however, the atherosclerotic process begins during childhood. Children and adolescents with T1DM have subclinical CVD abnormalities even within the first decade of DM diagnosis according to a number of different methodologies”.

Rates of CVD are lower in premenopausal women than in men […much lower: “Cardiovascular disease develops 7 to 10 years later in women than in men” – US]. In T1DM, these differences are erased. In the United Kingdom, CVD affects men and women with T1DM equally at <40 years of age (23), although after age 40 years, men are affected more than women (51). Similar findings on CVD mortality rates were reported in a large Norwegian T1DM cohort study (52) and in the Allegheny County (PA) T1DM Registry (13), which reported the relative impact of CVD compared with the general population was much higher for women than for men (standardized mortality ratio [SMR] 13.2 versus 5.0 for total mortality and 24.7 versus 8.8 for CVD mortality, women versus men). […] Overall, T1DM appears to eliminate most of the female sex protection seen in the nondiabetic population.”

“The data on atherosclerosis in T1DM are limited. A small angiographic study compared 32 individuals with T1DM to 31 nondiabetic patients matched for age and symptoms (71). That study found atherosclerosis in the setting of T1DM was characterized by more severe (tighter) stenoses, more extensive involvement (multiple vessels), and more distal coronary findings than in patients without DM. A quantitative coronary angiographic study in T1DM suggested more severe, distal disease and an overall increased burden compared with nondiabetic patients (up to fourfold higher) (72).”

“In the general population, inflammation is a central pathological process of atherosclerosis (79). Limited pathology data suggest that inflammation is more prominent in patients with DM than in nondiabetic control subjects (70), and those with T1DM in particular are affected. […] Knowledge of the clinical role of inflammatory markers in T1DM and CVD prediction and management is in its infancy, but early data suggest a relationship with preclinical atherosclerosis. […] Studies showed C-reactive protein is elevated within the first year of diagnosis of T1DM (80), and interleukin-6 and fibrinogen levels are high in individuals with an average disease duration of 2 years (81), independent of adiposity and glycemia (82). Other inflammatory markers such as soluble interleukin-2 receptor (83) and CD40 ligand (84,85) are higher in patients with T1DM than in nondiabetic subjects. Inflammation is evident in youth, even soon after the diagnosis of T1DM. […] The mechanisms by which inflammation operates in T1DM are likely multiple but may include hyperglycemia and hypoglycemia, excess adiposity or altered body fat distribution, thrombosis, and adipokines. Several recent studies have demonstrated a relationship between acute hypoglycemia and indexes of systemic inflammation […] These studies suggest that acute hypoglycemia in T1DM produces complex vascular effects involved in the activation of proinflammatory, prothrombotic, and proatherogenic mechanisms. […] Fibrinogen, a prothrombotic acute phase reactant, is increased in T1DM and is associated with premature CVD (109), and it may be important in vessel thrombosis at later stages of CVD.”

“Genetic polymorphisms appear to influence the progression and prognosis of CVD in T1DM […] Like fibrinogen, haptoglobin is an acute phase protein that inhibits hemoglobin-induced oxidative tissue damage by binding to free hemoglobin (110). […] In humans, there are 2 classes of alleles at the haptoglobin locus, giving rise to 3 possible genotypes: haptoglobin 1-1, haptoglobin 2-1, and haptoglobin 2-2. […] In T1DM, there is an independent twofold increased incidence of CAD in haptoglobin 2-2 carriers compared with those with the haptoglobin 1-1 genotype (117); the 2-1 genotype is associated with an intermediate effect of increased CVD risk. More recently, an independent association was reported in T1DM between the haptoglobin 2-2 genotype and early progression to end-stage renal disease (ESRD) (118). In the CACTI study group, the presence of the haptoglobin 2-2 genotype also doubled the risk of CAC [coronary artery calcification] in patients free from CAC at baseline, after adjustment for traditional CVD risk factors (119). […] At present, genetic testing for polymorphisms in T1DM [however] has no clear clinical utility in CVD prediction or management.”

“Dysglycemia is often conceived of as a vasculopathic process. Preclinical atherosclerosis and epidemiological studies generally support this relationship. Clinical trial data from the DCCT supplied definitive findings strongly in favor of beneficial effects of better glycemic control on CVD outcomes. Glycemia is associated with preclinical atherosclerosis in studies that include tests of endothelial function, arterial stiffness, cIMT, autonomic neuropathy, and left ventricular (LV) function in T1DM […] LV mass and function improve with better glycemic control (126,135,136). Epidemiological evidence generally supports the relationship between hyperglycemia and clinical CHD events in T1DM. […] A large Swedish database review recently reported a reasonably strong association between HbA1c and CAD in T1DM (HR, 1.3 per 1% HbA1c increase) (141). […] findings support the recommendation that early optimal glycemic control in T1DM will have long-term benefits for CVD reduction.”

“Obesity is a known independent risk factor for CVD in nondiabetic populations, but the impact of obesity in T1DM has not been fully established. Traditionally, T1DM was a condition of lean individuals, yet the prevalence of overweight and obesity in T1DM has increased significantly […] This is related to epidemiological shifts in the population overall, tighter glucose control leading to less glucosuria, more frequent/greater caloric intake to fend off real and perceived hypoglycemia, and the specific effects of intensive DM therapy, which has been shown to increase the prevalence of obesity (152). Indeed, several clinical trials, including the DCCT, demonstrate that intensive insulin therapy can lead to excessive weight gain in a subset of patients with T1DM (152). […] No systematic evaluation has been conducted to assess whether improving insulin sensitization lowers rates of CVD. Ironically, the better glycemic control associated with insulin therapy may lead to weight gain, with a superimposed insulin resistance, which may be approached by giving higher doses of insulin. However, some evidence from the EDC study suggests that weight gain in the presence of improved glycemic control is associated with an improved CVD risk profile (162). […] Although T1DM is characteristically a disease of absolute insulin deficiency (154), insulin resistance appears to contribute to CHD risk in patients with T1DM. For example, having a family history of T2DM, which suggests a genetic predisposition for insulin resistance, has been associated with an increased CVD risk in patients with T1DM (155).”

“In general, the lipid levels of adults with well-controlled T1DM are similar to those of individuals without DM […] Worse glycemic control, higher weight (164), and more insulin resistance as measured by euglycemic clamp (165) are associated with a more atherogenic cholesterol distribution in men and women with T1DM […] Studies in pediatric and young adult populations suggest higher lipid values than in youth without T1DM, with glycemic control being a significant contributor (148). […] Most studies show that as is true for the general population, dyslipidemia is a risk factor for CVD in T1DM. Qualitative differences in lipid and lipoprotein fractions are being investigated to determine whether abnormal lipid function may contribute to this. The HDL-C fraction has been of particular interest because the metabolism of HDL-C in T1DM may be altered because of abnormal lipoprotein lipase and hepatic lipase activities related to exogenously administered insulin […] Additionally, as noted earlier, the less efficient handling of heme by the haptoglobin 2-2 genotype in patients with T1DM leaves these complexes less capable of being removed by macrophages, which allows them to associate with HDL, which renders it less functional (116). […] Conventionally, pharmacotherapy is used more aggressively for patients with T1DM and lipid disorders than for nondiabetic patients; however, recommendations for treatment are mostly extrapolated from interventional trials in adults with T2DM, in which rates of CVD events are equivalent to those in secondary prevention populations. Whether this is appropriate for T1DM is not clear […] Awareness of CVD risk and screening for hypercholesterolemia in T1DM have increased over time, yet recent data indicate that control is suboptimal, particularly in younger patients who have not yet developed long-term complications and might therefore benefit from prevention efforts (173). Adults with T1DM who have abnormal lipids and additional risk factors for CVD (e.g., hypertension, obesity, or smoking) who have not developed CVD should be treated with statins. Adults with CVD and T1DM should also be treated with statins, regardless of whether they have additional risk factors.”

“Diabetic kidney disease (DKD) is a complication of T1DM that is strongly linked to CVD. DKD can present as microalbuminuria or macroalbuminuria, impaired GFR, or both. These represent separate but complementary manifestations of DKD and are often, but not necessarily, sequential in their presentation. […] the risk of all-cause mortality increased with the severity of DKD, from microalbuminuria to macroalbuminuria to ESRD. […] Microalbuminuria is likely an indicator of diffuse vascular injury. […] Microalbuminuria is highly correlated with CVD (49,180182). In the Steno Diabetes Center (Gentofte, Denmark) cohort, T1DM patients with isolated microalbuminuria had a 4.2-fold increased risk of CVD (49,180). In the EDC study, microalbuminuria was associated with mortality risk, with an SMR of 6.4. In the FinnDiane study, mortality risk was also increased with microalbuminuria (SMR, 2.8). […] A recent review summarized these data. In patients with T1DM and microalbuminuria, there was an RR of all-cause mortality of 1.8 (95% CI, 1.5–2.1) that was unaffected by adjustment for confounders (183). Similar RRs were found for mortality from CVD (1.9; 95% CI, 1.3–2.9), CHD (2.1; 95% CI, 1.2–3.5), and aggregate CVD mortality (2.0; 95% CI, 1.5–2.6).”

“Macroalbuminuria represents more substantial kidney damage and is also associated with CVD. Mechanisms may be more closely related to functional consequences of kidney disease, such as higher LDL-C and lower HDL-C. Prospective data from Finland indicate the RR for CVD is ≈10 times greater in patients with macroalbuminuria than in those without macroalbuminuria (184). Historically, in the [Danish] Steno cohort, patients with T1DM and macroalbuminuria had a 37-fold increased risk of CVD mortality compared with the general population (49,180); however, a more recent report from EURODIAB suggests a much lower RR (8.7; 95% CI, 4.03–19.0) (185). […] In general, impaired GFR is a risk factor for CVD, independent of albuminuria […] ESRD [end-stage renal disease, US], the extreme form of impaired GFR, is associated with the greatest risk of CVD of all varieties of DKD. In the EDC study, ESRD was associated with an SMR for total mortality of 29.8, whereas in the FinnDiane study, it was 18.3. It is now clear that GFR loss and the development of eGFR <60 mL · min−1 · 1.73 m−2 can occur without previous manifestation of microalbuminuria or macroalbuminuria (177,178). In T1DM, the precise incidence, pathological basis, and prognosis of this phenotype remain incompletely described.”

“Prevention of DKD remains challenging. Although microalbuminuria and macroalbuminuria are attractive therapeutic targets for CVD prevention, there are no specific interventions directed at the kidney that prevent DKD. Inhibition of the renin-angiotensin-aldosterone system is an attractive option but has not been demonstrated to prevent DKD before it is clinically apparent. […] In contrast to prevention efforts, treatment of DKD with agents that inhibit the renin-angiotensin-aldosterone system is effective. […] angiotensin-converting enzyme (ACE) inhibitors reduce the progression of DKD and death in T1DM (200). Thus, once DKD develops, treatment is recommended to prevent progression and to reduce or minimize other CVD risk factors, which has a positive effect on CVD risk. All patients with T1DM and hypertension or albuminuria should be treated with an ACE inhibitor. If an ACE inhibitor is not tolerated, an angiotensin II receptor blocker (ARB) is likely to have similar efficacy, although this has not been studied specifically in patients with T1DM. Optimal dosing for ACE inhibitors or ARBs in the setting of DKD is not well defined; titration may be guided by BP, albuminuria, serum potassium, and creatinine. Combination therapy of ACE and ARB blockade cannot be specifically recommended at this time.”

“Hypertension is more common in patients with T1DM and is a powerful risk factor for CVD, regardless of whether an individual has DKD. In the CACTI [Coronary Artery Calcification in Type 1 Diabetes] study, hypertension was much more common in patients with T1DM than in age- and sex-matched control subjects (43% versus 15%, P < 0.001); in fact, only 42% of all T1DM patients met the Joint National Commission 7 goal (BP <130/80 mmHg) (201). Hypertension also affects youth with T1DM. The SEARCH trial of youth aged 3–17 years with T1DM (n = 3,691) found the prevalence of elevated BP was 5.9% […] Abnormalities in BP can stem from DKD or obesity. Hyperglycemia may also contribute to hypertension over the long term. In the DCCT/EDIC cohort, higher HbA1c was strongly associated with increased risk of hypertension, and intensive DM therapy reduced the long-term risk of hypertension by 24% (203). […] There are few published trials about the ideal pharmacotherapeutic agent(s) for hypertension in T1DM.”

“Smoking is a major risk factor for CVD, particularly PAD (213); however, there is little information on the prevalence or effects of smoking in T1DM. […] The added CVD risk of smoking may be particularly important in patients with DM, who are already vulnerable. In patients with T1DM, cigarette smoking [has been shown to increase] the risk of DM nephropathy, retinopathy, and neuropathy (214,215) […] Smoking increases CVD risk factors in T1DM via deterioration in glucose metabolism, lipids, and endothelial function (216). Unfortunately, smoking cessation can result in weight gain, which may deter smokers with DM from quitting (217). […] Smoking cessation should be strongly recommended to all patients with T1DM as part of an overall strategy to lower CVD, in particular PAD.”

“CVD risk factors are more common in children with T1DM than in the general pediatric population (218). Population-based studies estimate that 14–45% of children with T1DM have ≥2 CVD risk factors (219221). As with nondiabetic children, the prevalence of CVD risk factors increases with age (221). […] The American Academy of Pediatrics, the American Heart Association, and the ADA recognize patients with DM, and particularly T1DM, as being in a higher-risk group who should receive more aggressive risk factor screening and treatment than nondiabetic children […] The available data suggest many children and adolescents with T1DM do not receive the recommended treatment for their dyslipidemia and hypertension (220,222).”

“There are no CVD risk-prediction algorithms for patients with T1DM in widespread use. […] Use of the Framingham Heart Study and UK Prospective Diabetes Study (UKPDS) algorithms in the EDC study population did not provide good predictive results, which suggests that neither general or T2DM risk algorithms are sufficient for risk prediction in T1DM (235). On the basis of these findings, a model has been developed with the use of EDC cohort data (236) that incorporates measures outside the Framingham construct (white blood cell count, albuminuria, DM duration). Although this algorithm was validated in the EURODIAB Study cohort (237), it has not been widely adopted, and diagnostic and therapeutic decisions are often based on global CVD risk-estimation methods (i.e., Framingham risk score or T2DM-specific UKPDS risk engine [http://www.dtu.ox.ac.uk/riskengine/index.php]). Other options for CVD risk prediction in patients with T1DM include the ADA risk-assessment tool (http://main.diabetes.org/dorg/mha/main_en_US.html?loc=dorg-mha) and the Atherosclerosis Risk in Communities (ARIC) risk predictor (http://www.aricnews.net/riskcalc/html/RC1.html), but again, accuracy for T1DM is not clear.”

September 25, 2017 Posted by | Cardiology, Diabetes, Epidemiology, Genetics, Medicine, Nephrology, Neurology, Pharmacology, Studies | Leave a comment

The Biology of Moral Systems (III)

This will be my last post about the book. It’s an important work which deserves to be read by far more people than have already read it. I have added some quotes and observations from the last chapters of the book below.

“If egoism, as self-interest in the biologists’ sense, is the reason for the promotion of ethical behavior, then, paradoxically, it is expected that everyone will constantly promote the notion that egoism is not a suitable theory of action, and, a fortiori, that he himself is not an egoist. Most of all he must present this appearance to his closest associates because it is in his best interests to do so – except, perhaps, to his closest relatives, to whom his egoism may often be displayed in cooperative ventures from which some distant- or non-relative suffers. Indeed, it may be arguable that it will be in the egoist’s best interest not to know (consciously) or to admit to himself that he is an egoist because of the value to himself of being able to convince others he is not.”

“The function of [societal] punishments and rewards, I have suggested, is to manipulate the behavior of participating individuals, restricting individual efforts to serve their own interests at others’ expense so as to promote harmony and unity within the group. The function of harmony and unity […] is to allow the group to compete against hostile forces, especially other human groups. It is apparent that success of the group may serve the interests of all individuals in the group; but it is also apparent that group success can be achieved with different patterns of individual success differentials within the group. So […] it is in the interests of those who are differentially successful to promote both unity and the rules so that group success will occur without necessitating changes deleterious to them. Similarly, it may be in the interests of those individuals who are relatively unsuccessful to promote dissatisfaction with existing rules and the notion that group success would be more likely if the rules were altered to favor them. […] the rules of morality and law alike seem not to be designed explicitly to allow people to live in harmony within societies but to enable societies to be sufficiently united to deter their enemies. Within-society harmony is the means not the end. […] extreme within-group altruism seems to correlate with and be historically related to between-group strife.”

“There are often few or no legitimate or rational expectations of reciprocity or “fairness” between social groups (especially warring or competing groups such as tribes or nations). Perhaps partly as a consequence, lying, deceit, or otherwise nasty or even heinous acts committed against enemies may sometimes not be regarded as immoral by others withing the group of those who commit them. They may even be regarded as highly moral if they seem dramatically to serve the interests of the group whose members commit them.”

“Two major assumptions, made universally or most of the time by philosophers, […] are responsible for the confusion that prevents philosophers from making sense out of morality […]. These assumptions are the following: 1. That proximate and ultimate mechanisms or causes have the same kind of significance and can be considered together as if they were members of the same class of causes; this is a failure to understand that proximate causes are evolved because of ultimate causes, and therefore may be expected to serve them, while the reverse is not true. Thus, pleasure is a proximate mechanism that in the usual environments of history is expected to impel us toward behavior that will contribute to our reproductive success. Contrarily, acts leading to reproductive success are not proximate mechanisms that evolved because they served the ultimate function of bringing us pleasure. 2. That morality inevitably involves some self-sacrifice. This assumption involves at least three elements: a. Failure to consider altruism as benefits to the actor. […] b. Failure to comprehend all avenues of indirect reciprocity within groups. c. Failure to take into account both within-group and between-group benefits.”

“If morality means true sacrifice of one’s own interests, and those of his family, then it seems to me that we could not have evolved to be moral. If morality requires ethical consistency, whereby one does not do socially what he would not advocate and assist all others also to do, then, again, it seems to me that we could not have evolved to be moral. […] humans are not really moral at all, in the sense of “true sacrifice” given above, but […] the concept of morality is useful to them. […] If it is so, then we might imagine that, in the sense and to the extent that they are anthropomorphized, the concepts of saints and angels, as well as that of God, were also created because of their usefulness to us. […] I think there have been far fewer […] truly self-sacrificing individuals than might be supposed, and most cases that might be brought forward are likely instead to be illustrations of the complexity and indirectness of reciprocity, especially the social value of appearing more altruistic than one is. […] I think that […] the concept of God must be viewed as originally generated and maintained for the purpose – now seen by many as immoral – of furthering the interests of one group of humans at the expense of one or more other groups. […] Gods are inventions originally developed to extend the notion that some have greater rights than others to design and enforce rules, and that some are more destined to be leaders, others to be followers. This notion, in turn, arose out of prior asymmetries in both power and judgment […] It works when (because) leaders are (have been) valuable, especially in the context of intergroup competition.”

“We try to move moral issues in the direction of involving no conflict of interest, always, I suggest, by seeking universal agreement with our own point of view.”

“Moral and legal systems are commonly distinguished by those, like moral philosophers, who study them formally. I believe, however, that the distinction between them is usually poorly drawn, and based on a failure to realize that moral as well as legal behavior occurs as a result of probably and possible punishments and reward. […] we often internalize the rules of law as well as the rules of morality – and perhaps by the same process […] It would seem that the rules of law are simply a specialized, derived aspect of what in earlier societies would have been a part of moral rules. On the other hand, law covers only a fraction of the situations in which morality is involved […] Law […] seems to be little more than ethics written down.”

“Anyone who reads the literature on dispute settlement within different societies […] will quickly understand that genetic relatedness counts: it allows for one-way flows of benefits and alliances. Long-term association also counts; it allows for reliability and also correlates with genetic relatedness. […] The larger the social group, the more fluid its membership; and the more attenuated the social interactions of its membership, the more they are forced to rely on formal law”.

“[I]ndividuals have separate interests. They join forces (live in groups; become social) when they share certain interests that can be better realized for all by close proximity or some forms of cooperation. Typically, however, the overlaps of interests rarely are completely congruent with those of either other individuals or the rest of the group. This means that, even during those times when individual interests within a group are most broadly overlapping, we may expect individuals to temper their cooperation with efforts to realize their own interests, and we may also expect them to have evolved to be adept at using others, or at thwarting the interests of others, to serve themselves (and their relatives). […] When the interests of all are most nearly congruent, it is essentially always due to a threat shared equally. Such threats almost always have to be external (or else they are less likely to affect everyone equally […] External threats to societies are typically other societies. Maintenance of such threats can yield situations in which everyone benefits from rigid, hierarchical, quasi-military, despotic government. Liberties afforded leaders – even elaborate perquisites of dictators – may be tolerated because such threats are ever-present […] Extrinsic threats, and the governments they produce, can yield inflexibilities of political structures that can persist across even lengthy intervals during which the threats are absent. Some societies have been able to structure their defenses against external threats as separate units (armies) within society, and to keep them separate. These rigidly hierarchical, totalitarian, and dictatorial subunits rise and fall in size and influence according to the importance of the external threat. […] Discussion of liberty and equality in democracies closely parallels discussions of morality and moral systems. In either case, adding a perspective from evolutionary biology seems to me to have potential for clarification.”

“It is indeed common, if not universal, to regard moral behavior as a kind of altruism that necessarily yields the altruist less than he gives, and to see egoism as either the opposite of morality or the source of immorality; but […] this view is usually based on an incomplete understanding of nepotism, reciprocity, and the significance of within-group unity for between-group competition. […] My view of moral systems in the real world, however, is that they are systems in which costs and benefits of specific actions are manipulated so as to produce reasonably harmonious associations in which everyone nevertheless pursues his own (in evolutionary terms) self-interest. I do not expect that moral and ethical arguments can ever be finally resolved. Compromises and contracts, then, are (at least currently) the only real solutions to actual conflicts of interest. This is why moral and ethical decisions must arise out of decisions of the collective of affected individuals; there is no single source of right and wrong.

I would also argue against the notion that rationality can be easily employed to produce a world of humans that self-sacrifice in favor of other humans, not to say nonhuman animals, plants, and inanimate objects. Declarations of such intentions may themselves often be the acts of self-interested persons developing, consciously or not, a socially self-benefiting view of themselves as extreme altruists. In this connection it is not irrelevant that the more dissimilar a species or object is to one’s self the less likely it is to provide a competitive threat by seeking the same resources. Accordingly, we should not be surprised to find humans who are highly benevolent toward other species or inanimate objects (some of which may serve them uncomplainingly), yet relatively hostile and noncooperative with fellow humans. As Darwin (1871) noted with respect to dogs, we have selected our domestic animals to return our altruism with interest.”

“It is not easy to discover precisely what historical differences have shaped current male-female differences. If, however, humans are in a general way similar to other highly parental organisms that live in social groups […] then we can hypothesize as follows: for men much of sexual activity has had as a main (ultimate) significance the initiating of pregnancies. It would follow that when a man avoids copulation it is likely to be because (1) there is no likelihood of pregnancy or (2) the costs entailed (venereal disease, danger from competition with other males, lowered status if the event becomes public, or an undesirable commitment) are too great in comparison with the probability that pregnancy will be induced. The man himself may be judging costs against the benefits of immediate sensory pleasures, such as orgasms (i.e., rather than thinking about pregnancy he may say that he was simply uninterested), but I am assuming that selection has tuned such expectations in terms of their probability of leading to actual reproduction […]. For women, I hypothesize, sexual activity per se has been more concerned with the securing of resources (again, I am speaking of ultimate and not necessarily conscious concerns) […]. Ordinarily, when women avoid or resist copulation, I speculate further, the disinterest, aversion, or inhibition may be traceable eventually to one (or more) of three causes: (1) there is no promise of commitment (of resources), (2) there is a likelihood of undesirable commitment (e.g., to a man with inadequate resources), or (3) there is a risk of loss of interest by a man with greater resources, than the one involved […] A man behaving so as to avoid pregnancies, and who derives from an evolutionary background of avoiding pregnancies, should be expected to favor copulation with women who are for age or other reasons incapable of pregnancy. A man derived from an evolutionary process in which securing of pregnancies typically was favored, may be expected to be most interested sexually in women most likely to become pregnant and near the height of the reproductive probability curve […] This means that men should usually be expected to anticipate the greatest sexual pleasure with young, healthy, intelligent women who show promise of providing superior parental care. […] In sexual competition, the alternatives of a man without resources are to present himself as a resource (i.e., as a mimic of one with resources or as one able and likely to secure resources because of his personal attributes […]), to obtain sex by force (rape), or to secure resources through a woman (e.g., allow himself to be kept by a relatively undesired woman, perhaps as a vehicle to secure liaisons with other women). […] in nonhuman species of higher animals, control of the essential resources of parenthood by females correlates with lack of parental behavior by males, promiscuous polygyny, and absence of long-term pair bonds. There is some evidence of parallel trends within human societies (cf. Flinn, 1981).” [It’s of some note that quite a few good books have been written on these topics since Alexander first published his book, so there are many places to look for detailed coverage of topics like these if you’re curious to know more – I can recommend both Kappeler & van Schaik (a must-read book on sexual selection, in my opinion) & Bobby Low. I didn’t think too highly of Miller or Meston & Buss, but those are a few other books on these topics which I’ve read – US].

“The reason that evolutionary knowledge has no moral content is [that] morality is a matter of whose interests one should, by conscious and willful behavior, serve, and how much; evolutionary knowledge contains no messages on this issue. The most it can do is provide information about the reasons for current conditions and predict some consequences of alternative courses of action. […] If some biologists and nonbiologists make unfounded assertions into conclusions, or develop pernicious and fallible arguments, then those assertions and arguments should be exposed for what they are. The reason for doing this, however, is not […should not be..? – US] to prevent or discourage any and all analyses of human activities, but to enable us to get on with a proper sort of analysis. Those who malign without being specific; who attack people rather than ideas; who gratuitously translate hypotheses into conclusions and then refer to them as “explanations,” “stories,” or “just-so-stories”; who parade the worst examples of argument and investigation with the apparent purpose of making all efforts at human self-analysis seem silly and trivial, I see as dangerously close to being ideologues at least as worrisome as those they malign. I cannot avoid the impression that their purpose is not to enlighten, but to play upon the uneasiness of those for whom the approach of evolutionary biology is alien and disquieting, perhaps for political rather than scientific purposes. It is more than a little ironic that the argument of politics rather than science is their own chief accusation with respect to scientists seeking to analyze human behavior in evolutionary terms (e.g. Gould and Levontin, 1979 […]).”

“[C]urrent selective theory indicates that natural selection has never operated to prevent species extinction. Instead it operates by saving the genetic materials of those individuals or families that outreproduce others. Whether species become extinct or not (and most have) is an incidental or accidental effect of natural selection. An inference from this is that the members of no species are equipped, as a direct result of their evolutionary history, with traits designed explicitly to prevent extinction when that possibility looms. […] Humans are no exception: unless their comprehension of the likelihood of extinction is so clear and real that they perceive the threat to themselves as individuals, and to their loved ones, they cannot be expected to take the collective action that will be necessary to reduce the risk of extinction.”

“In examining ourselves […] we are forced to use the attributes we wish to analyze to carry out the analysis, while resisting certain aspects of the analysis. At the very same time, we pretend that we are not resisting at all but are instead giving perfectly legitimate objections; and we use our realization that others will resist the analysis, for reasons as arcane as our own, to enlist their support in our resistance. And they very likely will give it. […] If arguments such as those made here have any validity it follows that a problem faced by everyone, in respect to morality, is that of discovering how to subvert or reduce some aspects of individual selfishness that evidently derive from our history of genetic individuality.”

“Essentially everyone thinks of himself as well-meaning, but from my viewpoint a society of well-meaning people who understand themselves and their history very well is a better milieu than a society of well-meaning people who do not.”

September 22, 2017 Posted by | Anthropology, Biology, Books, Evolutionary biology, Genetics, Philosophy, Psychology, Religion | Leave a comment