Econstudentlog

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.”

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November 30, 2017 Posted by | Cardiology, Diabetes, Genetics, 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 | 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 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, but the rest of them clearly are) 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 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, 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

Utility of Research Autopsies for Understanding the Dynamics of Cancer

A few links:
Pancreatic cancer.
Jaccard index.
Limited heterogeneity of known driver gene mutations among the metastases of individual patients with pancreatic cancer.
Epitope.
Tissue-specific mutation accumulation in human adult stem cells during life.
Epigenomic reprogramming during pancreatic cancer progression links anabolic glucose metabolism to distant metastasis.

August 25, 2017 Posted by | Cancer/oncology, Genetics, Immunology, Lectures, Medicine, Statistics | Leave a comment

Quantifying tumor evolution through spatial computational modeling

Two general remarks: 1. She talks very fast, in my opinion unpleasantly fast – the lecture would have been at least slightly easier to follow if she’d slowed down a little. 2. A few of the lectures uploaded in this lecture series (from the IAS Mathematical Methods in Cancer Evolution and Heterogeneity Workshop) seem to have some sound issues; in this lecture there are multiple 1-2 seconds long ‘chunks’ where the sound drops out and some words are lost. This is really annoying, and a similar problem (which was likely ‘the same problem’) previously lead me to quit another lecture in the series; however in this case I decided to give it a shot anyway, and I actually think it’s not a big deal; the sound-losses are very short in duration, and usually no more than one or two words are lost so you can usually figure out what was said. During this lecture there was incidentally also some issues with the monitor roughly 27 minutes in, but this isn’t a big deal as no information was lost and unlike the people who originally attended the lecture you can just skip ahead approximately one minute (that was how long it took to solve that problem).

A few relevant links to stuff she talks about in the lecture:

A Big Bang model of human colorectal tumor growth.
Approximate Bayesian computation.
Site frequency spectrum.
Identification of neutral tumor evolution across cancer types.
Using tumour phylogenetics to identify the roots of metastasis in humans.

August 22, 2017 Posted by | Cancer/oncology, Evolutionary biology, Genetics, Lectures, Mathematics, Medicine, Statistics | Leave a comment

Depression and Heart Disease (II)

Below I have added some more observations from the book, which I gave four stars on goodreads.

“A meta-analysis of twin (and family) studies estimated the heritability of adult MDD around 40% [16] and this estimate is strikingly stable across different countries [17, 18]. If measurement error due to unreliability is taken into account by analysing MDD assessed on two occasions, heritability estimates increase to 66% [19]. Twin studies in children further show that there is already a large genetic contribution to depressive symptoms in youth, with heritability estimates varying between 50% and 80% [20–22]. […] Cardiovascular research in twin samples has suggested a clear-cut genetic contribution to hypertension (h2 = 61%) [30], fatal stroke (h2 = 32%) [31] and CAD (h2 = 57% in males and 38% in females) [32]. […] A very important, and perhaps underestimated, source of pleiotropy in the association of MDD and CAD are the major behavioural risk factors for CAD: smoking and physical inactivity. These factors are sometimes considered ‘environmental’, but twin studies have shown that such behaviours have a strong genetic component [33–35]. Heritability estimates for [many] established risk factors [for CAD – e.g. BMI, smoking, physical inactivity – US] are 50% or higher in most adult twin samples and these estimates remain remarkably similar across the adult life span [41–43].”

“The crucial question is whether the genetic factors underlying MDD also play a role in CAD and CAD risk factors. To test for an overlap in the genetic factors, a bivariate extension of the structural equation model for twin data can be used [57]. […] If the depressive symptoms in a twin predict the IL-6 level in his/her co-twin, this can only be explained by an underlying factor that affects both depression and IL-6 levels and is shared by members of a family. If the prediction is much stronger in MZ than in DZ twins, this signals that the underlying factor is their shared genetic make-up, rather than their shared (family) environment. […] It is important to note clearly here that genetic correlations do not prove the existence of pleiotropy, because genes that influence MDD may, through causal effects of MDD on CAD risk, also become ‘CAD genes’. The absence of a genetic correlation, however, can be used to falsify the existence of genetic pleiotropy. For instance, the hypothesis that genetic pleiotropy explains part of the association between depressive symptoms and IL-6 requires the genetic correlation between these traits to be significantly different from zero. [Furthermore,] the genetic correlation should have a positive value. A negative genetic correlation would signal that genes that increase the risk for depression decrease the risk for higher IL-6 levels, which would go against the genetic pleiotropy hypothesis. […] Su et al. [26] […] tested pleiotropy as a possible source of the association of depressive symptoms with Il-6 in 188 twin pairs of the Vietnam Era Twin (VET) Registry. The genetic correlation between depressive symptoms and IL-6 was found to be positive and significant (RA = 0.22, p = 0.046)”

“For the association between MDD and physical inactivity, the dominant hypothesis has not been that MDD causes a reduction in regular exercise, but instead that regular exercise may act as a protective factor against mood disorders. […] we used the twin method to perform a rigorous test of this popular hypothesis [on] 8558 twins and their family members using their longitudinal data across 2-, 4-, 7-, 9- and 11-year follow-up periods. In spite of sufficient statistical power, we found only the genetic correlation to be significant (ranging between *0.16 and *0.44 for different symptom scales and different time-lags). The environmental correlations were essentially zero. This means that the environmental factors that cause a person to take up exercise do not cause lower anxiety or depressive symptoms in that person, currently or at any future time point. In contrast, the genetic factors that cause a person to take up exercise also cause lower anxiety or depressive symptoms in that person, at the present and all future time points. This pattern of results falsifies the causal hypothesis and leaves genetic pleiotropy as the most likely source for the association between exercise and lower levels of anxiety and depressive symptoms in the population at large. […] Taken together, [the] studies support the idea that genetic pleiotropy may be a factor contributing to the increased risk for CAD in subjects suffering from MDD or reporting high counts of depressive symptoms. The absence of environmental correlations in the presence of significant genetic correlations for a number of the CAD risk factors (CFR, cholesterol, inflammation and regular exercise) suggests that pleiotropy is the sole reason for the association between MDD and these CAD risk factors, whereas for other CAD risk factors (e.g. smoking) and CAD incidence itself, pleiotropy may coexist with causal effects.”

“By far the most tested polymorphism in psychiatric genetics is a 43-base pair insertion or deletion in the promoter region of the serotonin transporter gene (5HTT, renamed SLC6A4). About 55% of Caucasians carry a long allele (L) with 16 repeat units. The short allele (S, with 14 repeat units) of this length polymorphism repeat (LPR) reduces transcriptional efficiency, resulting in decreased serotonin transporter expression and function [83]. Because serotonin plays a key role in one of the major theories of MDD [84], and because the most prescribed antidepressants act directly on this transporter, 5HTT is an obvious candidate gene for this disorder. […] The dearth of studies attempting to associate the 5HTTLPR to MDD or related personality traits tells a revealing story about the fate of most candidate genes in psychiatric genetics. Many conflicting findings have been reported, and the two largest studies failed to link the 5HTTLPR to depressive symptoms or clinical MDD [85, 86]. Even at the level of reviews and meta-analyses, conflicting conclusions have been drawn about the role of this polymorphism in the development of MDD [87, 88]. The initially promising explanation for discrepant findings – potential interactive effects of the 5HTTLPR and stressful life events [89] – did not survive meta-analysis [90].”

“Across the board, overlooking the wealth of candidate gene studies on MDD, one is inclined to conclude that this approach has failed to unambiguously identify genetic variants involved in MDD […]. Hope is now focused on the newer GWA [genome wide association] approach. […] At the time of writing, only two GWA studies had been published on MDD [81, 95]. […] In theory, the strategy to identify potential pleiotropic genes in the MDD–CAD relationship is extremely straightforward. We simply select the genes that occur in the lists of confirmed genes from the GWA studies for both traits. In practice, this is hard to do, because genetics in psychiatry is clearly lagging behind genetics in cardiology and diabetes medicine. […] What is shown by the reviewed twin studies is that some genetic variants may influence MDD and CAD risk factors. This can occur through one of three mechanisms: (a) the genetic variants that increase the risk for MDD become part of the heritability of CAD through a causal effect of MDD on CAD risk factors (causality); (b) the genetic variants that increase the risk for CAD become part of the heritability of MDD through a direct causal effect of CAD on MDD (reverse causality); (c) the genetic variants influence shared risk factors that independently increase the risk for MDD as well as CAD (pleiotropy). I suggest that to fully explain the MDD–CAD association we need to be willing to be open to the possibility that these three mechanisms co-exist. Even in the presence of true pleiotropic effects, MDD may influence CAD risk factors, and having CAD in turn may worsen the course of MDD.”

“Patients with depression are more likely to exhibit several unhealthy behaviours or avoid other health-promoting ones than those without depression. […] Patients with depression are more likely to have sleep disturbances [6]. […] sleep deprivation has been linked with obesity, diabetes and the metabolic syndrome [13]. […] Physical inactivity and depression display a complex, bidirectional relationship. Depression leads to physical inactivity and physical inactivity exacerbates depression [19]. […] smoking rates among those with depression are about twice that of the general population [29]. […] Poor attention to self-care is often a problem among those with major depressive disorder. In the most severe cases, those with depression may become inattentive to their personal hygiene. One aspect of this relationship that deserves special attention with respect to cardiovascular disease is the association of depression and periodontal disease. […] depression is associated with poor adherence to medical treatment regimens in many chronic illnesses, including heart disease. […] There is some evidence that among patients with an acute coronary syndrome, improvement in depression is associated with improvement in adherence. […] Individuals with depression are often socially withdrawn or isolated. It has been shown that patients with heart disease who are depressed have less social support [64], and that social isolation or poor social support is associated with increased mortality in heart disease patients [65–68]. […] [C]linicians who make recommendations to patients recovering from a heart attack should be aware that low levels of social support and social isolation are particularly common among depressed individuals and that high levels of social support appear to protect patients from some of the negative effects of depression [78].”

“Self-efficacy describes an individual’s self-confidence in his/her ability to accomplish a particular task or behaviour. Self-efficacy is an important construct to consider when one examines the psychological mechanisms linking depression and heart disease, since it influences an individual’s engagement in behaviour and lifestyle changes that may be critical to improving cardiovascular risk. Many studies on individuals with chronic illness show that depression is often associated with low self-efficacy [95–97]. […] Low self-efficacy is associated with poor adherence behaviour in patients with heart failure [101]. […] Much of the interest in self-efficacy comes from the fact that it is modifiable. Self-efficacy-enhancing interventions have been shown to improve cardiac patients’ self-efficacy and thereby improve cardiac health outcomes [102]. […] One problem with targeting self-efficacy in depressed heart disease patients is [however] that depressive symptoms reduce the effects of self-efficacy-enhancing interventions [105, 106].”

“Taken together, [the] SADHART and ENRICHD [studies] suggest, but do not prove, that antidepressant drug therapy in general, and SSRI treatment in particular, improve cardiovascular outcomes in depressed post-acute coronary syndrome (ACS) patients. […] even large epidemiological studies of depression and antidepressant treatment are not usually informative, because they confound the effects of depression and antidepressant treatment. […] However, there is one Finnish cohort study in which all subjects […] were followed up through a nationwide computerised database [17]. The purpose of this study was not to examine the relationship between depression and cardiac mortality, but rather to look at the relationship between antidepressant use and suicide. […] unexpectedly, ‘antidepressant use, and especially SSRI use, was associated with a marked reduction in total mortality (=49%, p < 0.001), mostly attributable to a decrease in cardiovascular deaths’. The study involved 15 390 patients with a mean follow-up of 3.4 years […] One of the marked differences between the SSRIs and the earlier tricyclic antidepressants is that the SSRIs do not cause cardiac death in overdose as the tricyclics do [41]. There has been literature that suggested that tricyclics even at therapeutic doses could be cardiotoxic and more problematic than SSRIs [42, 43]. What has been surprising is that both in the clinical trial data from ENRICHD and the epidemiological data from Finland, tricyclic treatment has also been associated with a decreased risk of mortality. […] Given that SSRI treatment of depression in the post-ACS period is safe, effective in reducing depressed mood, able to improve health behaviours and may reduce subsequent cardiac morbidity and mortality, it would seem obvious that treating depression is strongly indicated. However, the vast majority of post-ACS patients will not see a psychiatrically trained professional and many cases are not identified [33].”

“That depression is associated with cardiovascular morbidity and mortality is no longer open to question. Similarly, there is no question that the risk of morbidity and mortality increases with increasing severity of depression. Questions remain about the mechanisms that underlie this association, whether all types of depression carry the same degree of risk and to what degree treating depression reduces that risk. There is no question that the benefits of treating depression associated with coronary artery disease far outweigh the risks.”

“Two competing trends are emerging in research on psychotherapy for depression in cardiac patients. First, the few rigorous RCTs that have been conducted so far have shown that even the most efficacious of the current generation of interventions produce relatively modest outcomes. […] Second, there is a growing recognition that, even if an intervention is highly efficacious, it may be difficult to translate into clinical practice if it requires intensive or extensive contacts with a highly trained, experienced, clinically sophisticated psychotherapist. It can even be difficult to implement such interventions in the setting of carefully controlled, randomised efficacy trials. Consequently, there are efforts to develop simpler, more efficient interventions that can be delivered by a wider variety of interventionists. […] Although much more work remains to be done in this area, enough is already known about psychotherapy for comorbid depression in heart disease to suggest that a higher priority should be placed on translation of this research into clinical practice. In many cases, cardiac patients do not receive any treatment for their depression.”

August 14, 2017 Posted by | Books, Cardiology, Diabetes, Genetics, Medicine, Pharmacology, Psychiatry, Psychology | Leave a comment

Depression and Heart Disease (I)

I’m currently reading this book. It’s a great book, with lots of interesting observations.

Below I’ve added some quotes from the book.

“Frasure-Smith et al. [1] demonstrated that patients diagnosed with depression post MI [myocardial infarction, US] were more than five times more likely to die from cardiac causes by 6 months than those without major depression. At 18 months, cardiac mortality had reached 20% in patients with major depression, compared with only 3% in non-depressed patients [5]. Recent work has confirmed and extended these findings. A meta-analysis of 22 studies of post-MI subjects found that post-MI depression was associated with a 2.0–2.5 increased risk of negative cardiovascular outcomes [6]. Another meta-analysis examining 20 studies of subjects with MI, coronary artery bypass graft (CABG), angioplasty or angiographically documented CAD found a twofold increased risk of death among depressed compared with non-depressed patients [7]. Though studies included in these meta-analyses had substantial methodological variability, the overall results were quite similar [8].”

“Blumenthal et al. [31] published the largest cohort study (N = 817) to date on depression in patients undergoing CABG and measured depression scores, using the CES-D, before and at 6 months after CABG. Of those patients, 26% had minor depression (CES-D score 16–26) and 12% had moderate to severe depression (CES-D score =27). Over a mean follow-up of 5.2 years, the risk of death, compared with those without depression, was 2.4 (HR adjusted; 95% CI 1.4, 4.0) in patients with moderate to severe depression and 2.2 (95% CI 1.2, 4.2) in those whose depression persisted from baseline to follow-up at 6 months. This is one of the few studies that found a dose response (in terms of severity and duration) between depression and death in CABG in particular and in CAD in general.”

“Of the patients with known CAD but no recent MI, 12–23% have major depressive disorder by DSM-III or DSM-IV criteria [20, 21]. Two studies have examined the prognostic association of depression in patients whose CAD was confirmed by angiography. […] In [Carney et al.], a diagnosis of major depression by DSM-III criteria was the best predictor of cardiac events (MI, bypass surgery or death) at 1 year, more potent than other clinical risk factors such as impaired left ventricular function, severity of coronary disease and smoking among the 52 patients. The relative risk of a cardiac event was 2.2 times higher in patients with major depression than those with no depression.[…] Barefoot et al. [23] provided a larger sample size and longer follow-up duration in their study of 1250 patients who had undergone their first angiogram. […] Compared with non-depressed patients, those who were moderately to severely depressed had 69% higher odds of cardiac death and 78% higher odds of all-cause mortality. The mildly depressed had a 38% higher risk of cardiac death and a 57% higher risk of all-cause mortality than non-depressed patients.”

“Ford et al. [43] prospectively followed all male medical students who entered the Johns Hopkins Medical School from 1948 to 1964. At entry, the participants completed questionnaires about their personal and family history, health status and health behaviour, and underwent a standard medical examination. The cohort was then followed after graduation by mailed, annual questionnaires. The incidence of depression in this study was based on the mailed surveys […] 1190 participants [were included in the] analysis. The cumulative incidence of clinical depression in this population at 40 years of follow-up was 12%, with no evidence of a temporal change in the incidence. […] In unadjusted analysis, clinical depression was associated with an almost twofold higher risk of subsequent CAD. This association remained after adjustment for time-dependent covariates […]. The relative risk ratio for CAD development with versus without clinical depression was 2.12 (95% CI 1.24, 3.63), as was their relative risk ratio for future MI (95% CI 1.11, 4.06), after adjustment for age, baseline serum cholesterol level, parental MI, physical activity, time-dependent smoking, hypertension and diabetes. The median time from the first episode of clinical depression to first CAD event was 15 years, with a range of 1–44 years.”

“In the Women’s Ischaemia Syndrome Evaluation (WISE) study, 505 women referred for coronary angiography were followed for a mean of 4.9 years and completed the BDI [46]. Significantly increased mortality and cardiovascular events were found among women with elevated BDI scores, even after adjustment for age, cholesterol, stenosis score on angiography, smoking, diabetes, education, hyper-tension and body mass index (RR 3.1; 95% CI 1.5, 6.3). […] Further compelling evidence comes from a meta-analysis of 28 studies comprising almost 80 000 subjects [47], which demonstrated that, despite heterogeneity and differences in study quality, depression was consistently associated with increased risk of cardiovascular diseases in general, including stroke.”

“The preponderance of evidence strongly suggests that depression is a risk factor for CAD [coronary artery disease, US] development. […] In summary, it is fair to conclude that depression plays a significant role in CAD development, independent of conventional risk factors, and its adverse impact endures over time. The impact of depression on the risk of MI is probably similar to that of smoking [52]. […] Results of longitudinal cohort studies suggest that depression occurs before the onset of clinically significant CAD […] Recent brain imaging studies have indicated that lesions resulting from cerebrovascular insufficiency may lead to clinical depression [54, 55]. Depression may be a clinical manifestation of atherosclerotic lesions in certain areas of the brain that cause circulatory deficits. The depression then exacerbates the onset of CAD. The exact aetiological mechanism of depression and CAD development remains to be clarified.”

“Rutledge et al. [65] conducted a meta-analysis in 2006 in order to better understand the prevalence of depression among patients with CHF and the magnitude of the relationship between depression and clinical outcomes in the CHF population. They found that clinically significant depression was present in 21.5% of CHF patients, varying by the use of questionnaires versus diagnostic interview (33.6% and 19.3%, respectively). The combined results suggested higher rates of death and secondary events (RR 2.1; 95% CI 1.7, 2.6), and trends toward increased health care use and higher rates of hospitalisation and emergency room visits among depressed patients.”

“In the past 15 years, evidence has been provided that physically healthy subjects who suffer from depression are at increased risk for cardiovascular morbidity and mortality [1, 2], and that the occurrence of depression in patients with either unstable angina [3] or myocardial infarction (MI) [4] increases the risk for subsequent cardiac death. Moreover, epidemiological studies have proved that cardiovascular disease is a risk factor for depression, since the prevalence of depression in individuals with a recent MI or with coronary artery disease (CAD) or congestive heart failure has been found to be significantly higher than in the general population [5, 6]. […] findings suggest a bidirectional association between depression and cardiovascular disease. The pathophysiological mechanisms underlying this association are, at present, largely unclear, but several candidate mechanisms have been proposed.”

“Autonomic nervous system dysregulation is one of the most plausible candidate mechanisms underlying the relationship between depression and ischaemic heart disease, since changes of autonomic tone have been detected in both depression and cardiovascular disease [7], and autonomic imbalance […] has been found to lower the threshold for ventricular tachycardia, ventricular fibrillation and sudden cardiac death in patients with CAD [8, 9]. […] Imbalance between prothrombotic and antithrombotic mechanisms and endothelial dysfunction have [also] been suggested to contribute to the increased risk of cardiac events in both medically well patients with depression and depressed patients with CAD. Depression has been consistently associated with enhanced platelet activation […] evidence has accumulated that selective serotonin reuptake inhibitors (SSRIs) reduce platelet hyperreactivity and hyperaggregation of depressed patients [39, 40] and reduce the release of the platelet/endothelial biomarkers ß-thromboglobulin, P-selectin and E-selectin in depressed patients with acute CAD [41]. This may explain the efficacy of SSRIs in reducing the risk of mortality in depressed patients with CAD [42–44].”

“[S]everal studies have shown that reduced endothelium-dependent flow-mediated vasodilatation […] occurs in depressed adults with or without CAD [48–50]. Atherosclerosis with subsequent plaque rupture and thrombosis is the main determinant of ischaemic cardiovascular events, and atherosclerosis itself is now recognised to be fundamentally an inflammatory disease [56]. Since activation of inflammatory processes is common to both depression and cardiovascular disease, it would be reasonable to argue that the link between depression and ischaemic heart disease might be mediated by inflammation. Evidence has been provided that major depression is associated with a significant increase in circulating levels of both pro-inflammatory cytokines, such as IL-6 and TNF-a, and inflammatory acute phase proteins, especially the C-reactive protein (CRP) [57, 58], and that antidepressant treatment is able to normalise CRP levels irrespective of whether or not patients are clinically improved [59]. […] Vaccarino et al. [79] assessed specifically whether inflammation is the mechanism linking depression to ischaemic cardiac events and found that, in women with suspected coronary ischaemia, depression was associated with increased circulating levels of CRP and IL-6 and was a strong predictor of ischaemic cardiac events”

“Major depression has been consistently associated with hyperactivity of the HPA axis, with a consequent overstimulation of the sympathetic nervous system, which in turn results in increased circulating catecholamine levels and enhanced serum cortisol concentrations [68–70]. This may cause an imbalance in sympathetic and parasympathetic activity, which results in elevated heart rate and blood pressure, reduced HRV [heart rate variability], disruption of ventricular electrophysiology with increased risk of ventricular arrhythmias as well as an increased risk of atherosclerotic plaque rupture and acute coronary thrombosis. […] In addition, glucocorticoids mobilise free fatty acids, causing endothelial inflammation and excessive clotting, and are associated with hypertension, hypercholesterolaemia and glucose dysregulation [88, 89], which are risk factors for CAD.”

“Most of the literature on [the] comorbidity [between major depressive disorder (MDD) and coronary artery disease (CAD), US] has tended to favour the hypothesis of a causal effect of MDD on CAD, but reversed causality has also been suggested to contribute. Patients with severe CAD at baseline, and consequently a worse prognosis, may simply be more prone to report mood disturbances than less severely ill patients. Furthermore, in pre-morbid populations, insipid atherosclerosis in cerebral vessels may cause depressive symptoms before the onset of actual cardiac or cerebrovascular events, a variant of reverse causality known as the ‘vascular depression’ hypothesis [2]. To resolve causality, comorbidity between MDD and CAD has been addressed in longitudinal designs. Most prospective studies reported that clinical depression or depressive symptoms at baseline predicted higher incidence of heart disease at follow-up [1], which seems to favour the hypothesis of causal effects of MDD. We need to remind ourselves, however […] [that] [p]rospective associations do not necessarily equate causation. Higher incidence of CAD in depressed individuals may reflect the operation of common underlying factors on MDD and CAD that become manifest in mental health at an earlier stage than in cardiac health. […] [T]he association between MDD and CAD may be due to underlying genetic factors that lead to increased symptoms of anxiety and depression, but may also independently influence the atherosclerotic process. This phenomenon, where low-level biological variation has effects on multiple complex traits at the organ and behavioural level, is called genetic ‘pleiotropy’. If present in a time-lagged form, that is if genetic effects on MDD risk precede effects of the same genetic variants on CAD risk, this phenomenon can cause longitudinal correlations that mimic a causal effect of MDD.”

 

August 12, 2017 Posted by | Books, Cardiology, Genetics, Medicine, Neurology, Pharmacology, Psychiatry, Psychology | Leave a comment

The Personality Puzzle (III)

I have added some more quotes and observations from the book below.

“Across many, many traits, the average correlation across MZ twins is about .60, and across DZ twins it is about .40, when adjusted for age and gender […] This means that, according to twin studies, the average heritability of many traits is about .40, which is interpreted to mean that 40 percent of phenotypic (behavioral) variance is accounted for by genetic variance. The heritabilities of the Big Five traits are a bit higher; according to one comprehensive summary they range from .42, for agreeableness, to .57, for openness (Bouchard, 2004). […] behavioral genetic analyses and the statistics they produce refer to groups or populations, not individuals. […] when research concludes that a personality trait is, say, 50 percent heritable, this does not mean that half of the extent to which an individual expresses that trait is determined genetically. Instead, it means that 50 percent of the degree to which the trait varies across the population can be attributed to genetic variation. […] Because heritability is the proportion of variation due to genetic influences, if there is no variation, then the heritability must approach zero. […] Heritability statistics are not the nature-nurture ratio; a biologically determined trait can have a zero heritability.”

The environment can […] affect heritability […]. For example, when every child receives adequate nutrition, variance in height is genetically controlled. […] But in an environment where some are well fed while others go hungry, variance in height will fall more under the control of the environment. Well-fed children will grow near the maximum of their genetic potential while poorly fed children will grow closer to their genetic minimum, and the height of the parents will not matter so much; the heritability coeffcient for height will be much closer to 0. […] A trait that is adaptive in one situation may be harmful in another […] the same environments that promote good outcomes for some people can promote bad outcomes for others, and vice versa […] More generally, the same circumstances might be experienced as stressful, enjoyable, or boring, depending on the genetic predispositions of the individuals involved; these variations in experience can lead to very different behaviors and, over time, to the development of different personality traits.”

Mihalyi Csikszentmihalyi [argued] that the best way a person can spend time is in autotelic activities, those that are enjoyable for their own sake. The subjective experience of an autotelic activity — the enjoyment itself — is what Csikszentmihalyi calls flow.
Flow is not the same thing as joy, happiness, or other, more familiar terms for subjective well-being. Rather, the experience of flow is characterized by tremendous concentration, total lack of distractibility, and thoughts concerning only the activity at hand. […] Losing track of time is one sign of experiencing flow. According to Csikszentmihalyi, flow arises when the challenges an activity presents are well matched with your skills. If an activity is too diffcult or too confusing, you will experience anxiety, worry, and frustration. If the activity is too easy, you will experience boredom and (again) anxiety. But when skills and challenges are balanced, you experience flow. […] Csikszentmihalyi thinks that the secret for enhancing your quality of life is to spend as much time in flow as possible. Achieving flow entails becoming good at something you find worthwhile and enjoyable. […] Even in the best of circumstances [however], flow seems to describe a rather solitary kind of happiness. […] The drawback with flow is that somebody experiencing it can be difficult to interact with”. [I really did not like most of the stuff included in the part of the book from which this quote is taken, but I did find Csikszentmihalyi’s flow concept quite interesting.]

“About 80 percent of the participants in psychological research come from countries that are Western, Educated, Industrialized, Rich, and Democratic — ”WEIRD” in other words — although only 12 percent of the world’s population live there (Henrich et al., 2010).”

“If an animal or a person performs a behavior, and the behavior is followed by a good result — a reinforcement — the behavior becomes more likely. If the behavior is followed by a punishment, it becomes less likely. […] the results of operant conditioning are not necessarily logical. It can increase the frequency of any behavior, regardless of its real connection with the consequences that follow.”

“A punishment is an aversive consequence that follows an act in order to stop it and prevent its repetition. […] Many people believe the only way to stop or prevent somebody from doing something is punishment. […] You can [however] use reward for this purpose too. All you have to do is find a response that is incompatible with the one you are trying to get rid of, and reward that incompatible response instead. Reward a child for reading instead of punishing him for watching television. […] punishment works well when it is done right. The only problem is, it is almost never done right. […] One way to see how punishment works, or fails to work, is to examine the rules for applying it correctly. The classic behaviorist analysis says that five principles are most important […] 1. Availability of Alternatives: An alternative response to the behavior that is being punished must be available. This alternative response must not be punished and should be rewarded. […] 2. Behavioral and Situational Specificity: Be clear about exactly what behavior you are punishing and the circumstances under which it will and will not be punished. […] 3. Timing and Consistency: To be effective, a punishment needs to be applied immediately after the behavior you wish to prevent, every time that behavior occurs. Otherwise, the person (or animal) being punished may not understand which behavior is forbidden. […] 4. Conditioning Secondary Punishing Stimuli: One can lessen the actual use of punishment by conditioning secondary stimuli to it [such as e.g.  verbal warnings] […] 5. Avoiding Mixed Messages: […] Sometimes, after punishing a child, the parent feels so guilty that she picks the child up for a cuddle. This is a mistake. The child might start to misbehave just to get the cuddle that follows the punishment. Punish if you must punish, but do not mix your message. A variant on this problem occurs when the child learns to play one parent against the other. For example, after the father punishes the child, the child goes to the mother for sympathy, or vice versa. This can produce the same counterproductive result.”

Punishment will backfire unless all of the guidelines [above] are followed. Usually, they are not. A punisher has to be extremely careful, for several reasons. […] The first and perhaps most important danger of punishment is that it creates emotion. […] powerful emotions are not conducive to clear thinking. […] Punishment [also] tends to vary with the punisher’s mood, which is one reason it is rarely applied consistently. […] Punishment [furthermore] [m]otivates [c]oncealment: The prospective punishee has good reasons to conceal behavior that might be punished. […] Rewards have the reverse effect. When workers anticipate rewards for good work instead of punishment for bad work, they are naturally motivated to bring to the boss’s attention everything they are doing, in case it merits reward.”

Gordon Allport observed years ago [that] [“]For some the world is a hostile place where men are evil and dangerous; for others it is a stage for fun and frolic. It may appear as a place to do one’s duty grimly; or a pasture for cultivating friendship and love.[“] […] people with different traits see the world differently. This perception affects how they react to the events in their lives which, in turn, affects what they do. […] People [also] differ in the emotions they experience, the emotions they want to experience, how strongly they experience emotions, how frequently their emotions change, and how well they understand and control their emotions.”

July 9, 2017 Posted by | Books, Genetics, Psychology | Leave a comment

A few SSC comments

I recently left a few comments in an open thread on SSC, and I figured it might make sense to crosspost some of the comments made there here on the blog. I haven’t posted all my contributions to the debate here, rather I’ve just quoted some specific comments and observations which might be of interest. I’ve also added some additional remarks and comments which relate to the topics discussed. Here’s the main link (scroll down to get to my comments).

“One thing worth keeping in mind when evaluating pre-modern medicine characterizations of diabetes and the natural history of diabetes is incidentally that especially to the extent that one is interested in type 1 survivorship bias is a major problem lurking in the background. Prognostic estimates of untreated type 1 based on historical accounts of how long people could live with the disease before insulin are not in my opinion likely to be all that reliable, because the type of patients that would be recognized as (type 1) diabetics back then would tend to mainly be people who had the milder forms, because they were the only ones who lived long enough to reach a ‘doctor’; and the longer they lived, and the milder the sub-type, the more likely they were to be studied/’diagnosed’. I was a 2-year old boy who got unwell on a Tueday and was hospitalized three days later. Avicenna would have been unlikely to have encountered me, I’d have died before he saw me. (Similar lines of reasoning might lead to an argument that the incidence of diseases like type 1 diabetes may also today be underdiagnosed in developing countries with poorly developed health care systems.)”

Douglas Knight mentioned during our exchange that medical men of the far past might have been more likely to attend to patients with acute illnesses than patients with chronic conditions, making them more likely to attend to such cases than would otherwise be the case, a point I didn’t discuss in any detail during the exchange. I did however think it important to note here that information exchange was significantly slower, and transportation costs were much higher, in the past than they are today. This should make such a bias less relevant, all else equal. Avicenna and his colleagues couldn’t take a taxi, or learn by phone that X is sick. He might have preferentially attended to the acute cases he learned about, but given high transportation costs and inefficient communication channels he might often never arrive in time, or at all. A particular problem here is that there are no good data on the unobserved cases, because the only cases we know about today are the ones people like him have told us about.

Some more comments:

“One thing I was considering adding to my remarks about survivorship bias is that it is not in my opinion unlikely that what you might term the nature of the disease has changed over the centuries; indeed it might still be changing today. Globally the incidence of type 1 has been increasing for decades and nobody seems to know why, though there’s consensus about an environmental trigger playing a major role. Maybe incidence is not the only thing that’s changed, maybe e.g. the time course of the ‘average case’ has also changed? Maybe due to secondary factors; better nutritional status now equals slower progression of beta cell failure than was the case in the past? Or perhaps the other way around: Less exposure to bacterial agents the immune system throughout evolutionary time has been used to having to deal with today means that the autoimmune process is accelerated today, compared to in the far past where standards of hygiene were different. Who knows? […] Maybe survivorship bias wasn’t that big of a deal, but I think one should be very cautious about which assumptions one might implicitly be making along the way when addressing questions of this sort of nature. Some relevant questions will definitely be unknowable due to lack of good data which we will never be able to obtain.”

I should perhaps interpose here that even if survivorship bias ‘wasn’t that big of a deal’, it’s still sort of a big problem in the analytical setting because it seems perfectly plausible to me to be making the assumption that it might even so have been a big deal. These kinds of problems magnify our error bars and reduce confidence in our conclusions, regardless of the extent to which they actually played a role. When you know the exact sign and magnitude of a given moderating effect you can try to correct for it, but this is very difficult to do when a large range of moderator effect sizes might be considered plausible. It might also here be worth mentioning explicitly that biases such as the survivorship bias mentioned can of course impact a lot of things besides just the prognostic estimates; for example if a lot of cases never come to the attention of the medical people because these people were unavailable (due to distance, cost, lack of information, etc.) to the people who were sick, incidence and prevalence will also implicitly be underestimated. And so on. Back to the comments:

“Once you had me thinking that it might have been harder [for people in the past] to distinguish [between type 1 and type 2 diabetes] than […] it is today, I started wondering about this, and the comments below relate to this topic. An idea that came to mind in relation to the type 1/type 2 distinction and the ability of people in the past to make this distinction: I’ve worked on various identification problems present in the diabetes context before, and I know that people even today make misdiagnoses and e.g. categorize type 1 diabetics as type 2. I asked a diabetes nurse working in the local endocrinology unit about this at one point, and she told me they had actually had a patient not long before then who had been admitted a short while after having been diagnosed with type 2. Turned out he was type 1, so the treatment failed. Misdiagnoses happen for multiple reasons, one is that obese people also sometimes develop type 1, and if it’s an acute onset setting the weight loss is not likely to be very significant. Patient history should in such a case provide the doctor with the necessary clues, but if the guy making the diagnosis is a stressed out GP who’s currently treating a lot of obese patients for type 2, mistakes happen. ‘Pre-scientific method’ this sort of individual would have been inconvenient to encounter, because a ‘counter-example’ like that supposedly demonstrating that the obese/thin(/young/old, acute/protracted…) distinction was ‘invalid’ might have held a lot more weight than it hopefully would today in the age of statistical analysis. A similar problem would be some of the end-stage individuals: A type 1 pre-insulin would be unlikely to live long enough to develop long term complications of the disease, but would instead die of DKA. The problem is that some untreated type 2 patients also die of DKA, though the degree of ketosis varies in type 2 patients. DKA in type 2 could e.g. be triggered by a superimposed cardiovascular event or an infection, increasing metabolic demands to an extent that can no longer be met by the organism, and so might well present just as acutely as it would in a classic acute-onset type 1 case. Assume the opposite bias you mention is playing a role; the ‘doctor’ in the past is more likely to see the patients in such a life-threatening setting than in the earlier stages. He observes a 55 year old fat guy dying in a very similar manner to the way a 12 year old girl died a few months back – very characteristic symptoms, breath smells fruity, Kussmaul respiration, polyuria and polydipsia…). What does he conclude? Are these different diseases?”

Making the doctor’s decision problem even harder is of course the fact that type 2 diabetes even today often goes undiagnosed until complications arise. Some type 2 patients get their diagnosis only after they had their first heart attack as a result of their illness. So the hypothetical obese middle-aged guy presenting with DKA might not have been known by anyone to be ‘a potentially different kind of diabetic’.

‘The Nybbler’ asked this question in the thread: “Wouldn’t reduced selection pressure be a major reason for increase of Type I diabetes? Used to be if you had it, chance of surviving to reproduce was close to nil.”

I’ll mention here that I’ve encountered this kind of theorizing before, but that I’ve never really addressed it – especially the second part – explicitly, though I’ve sometimes felt like doing that. I figured this post might be a decent place to at least scratch the surface. The idea that there are more type 1 diabetics now than there used to be because type 1 diabetics used to die of their disease and now they don’t (…and so now they are able to transmit their faulty genes to subsequent generations, leading to more diabetic individuals over time) sounds sort of reasonable if you don’t know very much about diabetes, but it sounds less reasonable to people who do. Genes matter, and changed selection pressures have probably played a role, but I find it hard to believe this particular mechanism is a major factor. I have included both my of my replies to ‘Nybbler’ below:

First comment:

“I’m not a geneticist and this is sort-of-kind-of near the boundary area of where I feel comfortable providing answers (given that others may be more qualified to evaluate questions like this than I am). However a few observations which might be relevant are the following:

i) Although I’ll later go on to say that vertical transmission is low, I first have to point out that some people who developed type 1 diabetes in the past did in fact have offspring, though there’s no doubt about the condition being fitness-reducing to a very large degree. The median age of diagnosis of type 1 is somewhere in the teenage years (…today. Was it the same way 1000 years ago, or has the age profile changed over time? This again relates to questions asked elsewhere in this discussion…), but people above the age of 30 get type 1 too.

ii) Although type 1 display some level of familia[l] clustering, most cases of type 1 are not the result of diabetics having had children who then proceed to inherit their parents’ disease. To the extent that reduced selection is a driver of increased incidence, the main cause would be broad selection effects pertaining to immune system functioning in general in the total population at risk (i.e. children in general, including many children with what might be termed suboptimal immune system functioning, being more likely to survive and later develop type 1 diabetes), not effects derived from vertical transmission of the disease (from parent to child). Roughly 90% of newly diagnosed type 1 diabetics in population studies have a negative family history of the disease, and on average only 2% of the children of type 1 diabetic mothers, and 5% of the children of type 1 diabetic fathers, go on to develop type 1 diabetes themselves.

iii) Historically vertical transmission has even in modern times been low. On top of the quite low transmission rates mentioned above, until well into the 80es or 90es many type 1 diabetic females were explicitly advised by their medical care providers not to have children, not because of the genetic risk of disease transmission but because pregnancy outcomes were likely to be poor; and many of those who disregarded the advice gave birth to offspring who were at a severe fitness disadvantage from the start. Poorly controlled diabetes during pregnancy leads to a very high risk of birth defects and/or miscarriage, and may pose health risks to the mother as well through e.g. an increased risk of preeclampsia (relevant link). It is only very recently that we’ve developed the knowledge and medical technology required to make pregnancy a reasonably safe option for female diabetics. You still had some diabetic females who gave birth before developing diabetes, like in the far past, and the situation was different for males, but either way I feel reasonably confident claiming that if you look for genetic causes of increasing incidence, vertical transmission should not be the main factor to consider.

iv) You need to be careful when evaluating questions like these to keep a distinction between questions relating to drivers of incidence and questions relating to drivers of prevalence at the back of your mind. These two sets of questions are not equivalent.

v) If people are interested to know more about the potential causes of increased incidence of type 1 diabetes, here’s a relevant review paper.”

I followed up with a second comment a while later, because I figured a few points of interest might not have been sufficiently well addressed in my first comment:

“@Nybbler:

A few additional remarks.

i) “Temporal trends in chronic disease incidence rates are almost certainly environmentally induced. If one observes a 50% increase in the incidence of a disorder over 20 yr, it is most likely the result of changes in the environment because the gene pool cannot change that rapidly. Type 1 diabetes is a very dynamic disease. […] results clearly demonstrate that the incidence of type 1 diabetes is rising, bringing with it a large public health problem. Moreover, these findings indicate that something in our environment is changing to trigger a disease response. […] With the exception of a possible role for viruses and infant nutrition, the specific environmental determinants that initiate or precipitate the onset of type 1 diabetes remain unclear.” (Type 1 Diabetes, Etiology and Treatment. Just to make it perfectly clear that although genes matter, environmental factors are the most likely causes of the rising levels of incidence we’ve seen in recent times.)

ii. Just as you need to always keep incidence and prevalence in mind when analyzing these things (for example low prevalence does not mean incidence is necessarily low, or was low in the past; low prevalence could also be a result of a combination of high incidence and high case mortality. I know from experience that even diabetes researchers tend to sometimes overlook stuff like this), you also need to keep the distinction between genotype and phenotype in mind. Given the increased importance of one or more environmental triggers in modern times, penetrance is likely to have changed over time. This means for example that ‘a diabetic genotype’ may have been less fitness reducing in the past than it is today, even if the associated ‘diabetic phenotype’ may on the other hand have been much more fitness reducing than it is now; people who developed diabetes died, but many of the people who might in the current environment be considered high-risk cases may not have been high risk in the far past, because the environmental trigger causing disease was absent, or rarely encountered. Assessing genetic risk for diabetes is complicated, and there’s no general formula for calculating this risk either in the type 1 or type 2 case; monogenic forms of diabetes do exist, but they account for a very small proportion of cases (1-5% of diabetes in young individuals) – most cases are polygenic and display variable levels of penetrance. Note incidentally that a story of environmental factors becoming more important over time is actually implicitly also, to the extent that diabetes is/has been fitness-reducing, a story of selection pressures against diabetic genotypes potentially increasing over time, rather than the opposite (which seems to be the default assumption when only taking into account stuff like the increased survival rates of type 1 diabetics over time). This stuff is complicated.”

I wasn’t completely happy with my second comment (I wrote it relatively fast and didn’t have time to go over it in detail after I’d written it), so I figured it might make sense to add a few details here. One key idea here is of course that you need to distinguish between people who are ‘vulnerable’ to developing type 1 diabetes, and people who actually do develop the disease. If fewer people who today would be considered ‘vulnerable’ developed the disease in the past than is the case now, selection against the ‘vulnerable’ genotype would – all else equal – have been lower throughout evolutionary time than it is today.

All else is not equal because of insulin treatment. But a second key point is that when you’re interested in fitness effects, mortality is not the only variable of interest; many diabetic women who were alive because of insulin during the 20th century but who were also being discouraged from having children may well have left no offspring. Males who committed suicide or died from kidney failure in their twenties likely also didn’t leave many offspring. Another point related to the mortality variable is that although diabetes mortality might in the past have been approximated reasonably well by a simple binary outcome variable/process (no diabetes = alive, diabetes = dead), type 1 diabetes has had large effects on mortality rates also throughout the chunk of history during which insulin has been a treatment option; mortality rates 3 or 4 times higher than those of non-diabetics are common in population studies, and such mortality rates add up over time even if base rates are low, especially in a fitness context, as they for most type 1 diabetics are at play throughout the entire fertile period of the life history. Type 2 diabetes is diagnosed mainly in middle-aged individuals, many of whom have already completed their reproductive cycle, but type 1 diabetes is very different in that respect. Of course there are multiple indirect effects at play as well here, e.g. those of mate choice; which is the more attractive potential partner, the individual with diabetes or the one without? What if the diabetic also happens to be blind?

A few other quotes from the comments:

“The majority of patients on insulin in the US are type 2 diabetics, and it is simply wrong that type 2 diabetics are not responsive to insulin treatment. They were likely found to be unresponsive in early trials because of errors of dosage, as they require higher levels of the drug to obtain the same effect as will young patients diagnosed with type 1 (the primary group on insulin in the 30es). However, insulin treatment is not the first-line option in the type 2 context because the condition can usually be treated with insulin-sensitizing agents for a while, until they fail (those drugs will on average fail in something like ~50% of subjects within five years of diagnosis, which is the reason – combined with the much (order(/s, depending on where you are) of magnitude) higher prevalence of type 2 – why a majority of patients on insulin have type 2), and these tend to a) be more acceptable to the patients (a pill vs an injection) and b) have fewer/less severe side effects on average. One reason which also played a major role in delaying the necessary use of insulin to treat type 2 diabetes which could not be adequately controlled via other means was incidentally the fact that insulin ca[u]ses weight gain, and the obesity-type 2 link was well known.”

“Type 1 is autoimmune, and most cases of type 2 are not, but some forms of type 2 seem to have an autoimmune component as well (“the overall autoantibody frequency in type 2 patients varies between 6% and 10%” – source) (these patients, who can be identified through genetic markers, will on average proceed to insulin dependence because of treatment failure in the context of insulin sensitizing-agents much sooner than is usually the case in patients with type 2). In general type 1 is caused by autoimmune beta cell destruction and type 2 mainly by insulin resistance, but combinations of the two are also possible […], and patients with type 1 can develop insulin resistance just as patients with type 2 can lose beta cells via multiple pathways. The major point here being that the sharp diagnostic distinction between type 1 and type 2 is a major simplification of what’s really going on, and it’s hiding a lot of heterogeneity in both samples. Some patients with type 1 will develop diabetes acutely or subacutely, within days or hours, whereas others will have elevated blood glucose levels for months before medical attention is received and a diagnosis is made (you can tell whether or not blood glucose has been elevated pre-diagnosis by looking at one of the key diagnostic variables, Hba1c, which is a measure of the average blood glucose over the entire lifetime of a red blood cell (~3-4 months) – in some newly diagnosed type 1s, this variable is elevated, in others it is not. Some type 1 patients will develop other autoimmune conditions later on, whereas others will not, and some will be more likely to develop complications than others who have the same level of glycemic control.

Type 1 and type 2 diabetes are quite different conditions, but in terms of many aspects of the diseases there are significant degrees of overlap (for example they develop many of the same complications, for similar (pathophysiological) reasons), yet they are both called diabetes. You don’t want to treat a type 2 diabetic with insulin if he can be treated with metformin, and treating a type 1 with metformin will not help – so different treatments are required.”

“In terms of whether it’s ideal to have one autistic diagnostic group or two (…or three, or…) [this question was a starting point for the debate from which I quote, but I decided not to go much into this topic here], I maintain that to a significant extent the answer to that question relates to what the diagnosis is supposed to accomplish. If it makes sense for researchers to be able to distinguish, which it probably does, but it is not necessary for support organizers/providers to know the subtype in order to provide aid, then you might end up with one ‘official’ category and two (or more) ‘research categories’. I would be fine with that (but again I don’t find this discussion interesting). Again a parallel might be made to diabetes research: Endocrinologists are well aware that there’s a huge amount of variation in both the type 1 and type 2 samples, to the extent that it’s sort of silly to even categorize these illnesses using the same name, but they do it anyway for reasons which are sort of obvious. If you’re type 1 diabetic and you have an HLA mutation which made you vulnerable to diabetes and you developed diabetes at the age of 5, well, we’ll start you on insulin, try to help you achieve good metabolic control, and screen you regularly for complications. If on the other hand you’re an adult guy who due to a very different genetic vulnerability developed type 1 diabetes at the age of 30 (and later on Graves’ disease at the age of 40, due to the same mutation), well, we’ll start you on insulin, try to help you achieve good metabolic control, and screen you regularly for complications. The only thing type 1 diabetics have in common is the fact that their beta cells die due to some autoimmune processes. But it could easily be conceived of instead as literally hundreds of different diseases. Currently the distinctions between the different disease-relevant pathophysiological processes don’t matter very much in the treatment context, but they might do that at some point in the future, and if that happens the differences will start to become more important. People might at that point start to talk about type 1a diabetes, which might be the sort you can delay or stop with gene therapy, and type 1b which you can’t delay or stop (…yet). Lumping ‘different’ groups together into one diagnostic category is bad if it makes you overlook variation which is important, and this may be a problem in the autism context today, but regardless of the sizes of the diagnostic groups you’ll usually still end up with lots of residual (‘unexplained’) variation.”

I can’t recall to which extent I’ve discussed this last topic – the extent to which type 1 diabetes is best modeled as one illness or many – but it’s an important topic to keep at the back of your mind when you’re reading the diabetes literature. I’m assuming that in some contexts the subgroup heterogeneities, e.g. in terms of treatment response, will be much more important than in other contexts, so you probably need specific subject matter knowledge to make any sort of informed decision about to which extent potential unobserved heterogeneities may be important in a specific setting, but even if you don’t have that ‘a healthy skepticism’, derived from keeping the potential for these factors to play a role in mind, is likely to be more useful than the alternative. In that context I think the (poor, but understandable) standard practice of lumping together type 1 and type 2 diabetics in studies may lead many people familiar with the differences between the two conditions to think along the lines that as long as you know the type, you’re good to go – ‘at least this study only looked at type 1 individuals, not like those crappy studies which do not distinguish between type 1 and type 2, so I can definitely trust these results to apply to the subgroup of type 1 diabetics in which I’m interested’ – and I think this tendency, to the extent that it exists, is unfortunate.

July 8, 2017 Posted by | autism, Diabetes, Epidemiology, Genetics, Medicine, Psychology | Leave a comment

Melanoma therapeutic strategies that select against resistance

A short lecture, but interesting:

If you’re not an oncologist, these two links in particular might be helpful to have a look at before you start out: BRAF (gene) & Myc. A very substantial proportion of the talk is devoted to math and stats methodology (which some people will find interesting and others …will not).

July 3, 2017 Posted by | Biology, Cancer/oncology, Genetics, Lectures, Mathematics, Medicine, Statistics | Leave a comment

The Biology of Moral Systems (II)

There are multiple really great books I have read ‘recently’ and which I have either not blogged at all, or not blogged in anywhere near the amount of detail they deserve; Alexander’s book is one of those books. I hope to get rid of some of the backlog soon. You can read my first post about the book here, and it might be a good idea to do so as I won’t allude to material covered in the first post here. In this post I have added some quotes from and comments related to the book’s second chapter, ‘A Biological View of Morality’.

“Moral systems are systems of indirect reciprocity. They exist because confluences of interest within groups are used to deal with conflicts of interest between groups. Indirect reciprocity develops because interactions are repeated, or flow among a society’s members, and because information about subsequent interactions can be gleaned from observing the reciprocal interactions of others.
To establish moral rules is to impose rewards and punishments (typically assistance and ostracism, respectively) to control social acts that, respectively, help or hurt others. To be regarded as moral, a rule typically must represent widespread opinion, reflecting the fact that it must apply with a certain degree of indiscrimininateness.”

“Moral philosophers have not treated the beneficence of humans as a part, somehow, of their selfishness; yet, as Trivers (1971) suggested, the biologist’s view of lifetimes leads directly to this argument. In other words, the normally expressed beneficence, or altruism, of parenthood and nepotism and the temporary altruism (or social investment) of reciprocity are expected to result in greater returns than their alternatives.
If biologists are correct, all that philosophers refer to as altruistic or utilitarian behavior by individuals will actually represent either the temporary altruism (phenotypic beneficence or social investment) of indirect somatic effort [‘Direct somatic effort refers to self-help that involves no other persons. Indirect somatic effort involves reciprocity, which may be direct or indirect. Returns from direct and indirect reciprocity may be immediate or delayed’ – Alexander spends some pages classifying human effort in terms of such ‘atoms of sociality’, which are useful devices for analytical purposes, but I decided not to cover that stuff in detail here – US] or direct and indirect nepotism. The exceptions are what might be called evolutionary mistakes or accidents that result in unreciprocated or “genetic” altruism, deleterious to both the phenotype and genotype of the altruist; such mistakes can occur in all of the above categories” [I should point out that Boyd and Richerson’s book Not by Genes Alone – another great book which I hope to blog soon – is worth having a look at if after reading Alexander’s book you think that he does not cover the topic of how and why such mistakes might happen in the amount of detail it deserves; they also cover related topics in some detail, from a different angle – US]

“It is my impression that many moral philosophers do not approach the problem of morality and ethics as if it arose as an effort to resolve conflicts of interests. Their involvement in conflicts of interest seems to come about obliquely through discussions of individuals’ views with respect to moral behavior, or their proximate feelings about morality – almost as if questions about conflicts of interest arise only because we operate under moral systems, rather than vice versa.”

“The problem, in developing a theory of moral systems that is consistent with evolutionary theory from biology, is in accounting for the altruism of moral behavior in genetically selfish terms. I believe this can be done by interpreting moral systems as systems of indirect reciprocity.
I regard indirect reciprocity as a consequence of direct reciprocity occurring in the presence of interested audiences – groups of individuals who continually evaluate the members of their society as possible future interactants from whom they would like to gain more than they lose […] Even in directly reciprocal interactions […] net losses to self […] may be the actual aim of one or even both individuals, if they are being scrutinized by others who are likely to engage either individual subsequently in reciprocity of greater significance than that occurring in the scrutinized acts. […] Systems of indirect reciprocity, and therefore moral systems, are social systems structured around the importance of status. The concept of status implies that an individual’s privileges, or its access to resources, are controlled in part by how others collectively think of him (hence, treat him) as a result of past interactions (including observations of interactions with others). […] The consequences of indirect reciprocity […] include the concomitant spread of altruism (as social investment genetically valuable to the altruist), rules, and efforts to cheat […]. I would not contend that we always carry out cost-benefit analyses on these issues deliberately or consciously. I do, however, contend that such analyses occur, sometimes consciously, sometimes not, and that we are evolved to be exceedingly accurate and quick at making them […] [A] conscience [is what] I have interpreted (Alexander, 1979a) as the “still small voice that tells us how far we can go in serving our own interests without incurring intolerable risks.””

“The long-term existence of complex patterns of indirect reciprocity […] seems to favor the evolution of keen abilities to (1) make one’s self seem more beneficent than is the case; and (2) influence others to be beneficent in such fashions as to be deleterious to themselves and beneficial to the moralizer, e.g. to lead others to (a) invest too much, (b) invest wrongly in the moralizer or his relatives and friends, or (c) invest indiscriminately on a larger scale than would otherwise be the case. According to this view, individuals are expected to parade the idea of much beneficence, and even of indiscriminate altruism as beneficial, so as to encourage people in general to engage in increasing amounts of social investment whether or not it is beneficial to their interests. […] They may also be expected to depress the fitness of competitors by identifying them, deceptively or not, as reciprocity cheaters (in other words, to moralize and gossip); to internalize rules or evolve the ability to acquire a conscience, interpreted […] as the ability to use or own judgment to serve our own interests; and to self-deceive and display false sincerity as defenses against detection of cheating and attributions of deliberateness in cheating […] Everyone will with to appear more beneficent than he is. There are two reasons: (1) this appearance, if credible, is more likely to lead to direct social rewards than its alternatives; (2) it is also more likely to encourage others to be more beneficent.”

“Consciousness and related aspects of the human psyche (self-awareness, self-reflection, foresight, planning, purpose, conscience, free will, etc.) are here hypothesized to represent a system for competing with other humans for status, resources, and eventually reproductive success. More specifically, the collection of these attributes is viewed as a means of seeing ourselves and our life situations as others see us and our life situations – most particularly in ways that will cause (the most and the most important of) them to continue to interact with us in fashions that will benefit us and seem to benefit them.
Consciousness, then, is a game of life in which the participants are trying to comprehend what is in one another’s minds before, and more effectively than, it can be done in reverse.”

“Provided with a means of relegating our deceptions to the subconsciousness […] false sincerity becomes easier and detection more difficult. There are reasons for believing that one does not need to know his own personal interests consciously in order to serve them as much as he needs to know the interests of others to thwart them. […] I have suggested that consciousness is a way of making our social behavior so unpredictable as to allow us to outmaneuver others; and that we press into subconsciousness (as opposed to forgetting) those things that remain useful to us but would be detrimental to us if others knew about them, and on which we are continually tested and would have to lie deliberately if they remained in our conscious mind […] Conscious concealment of interests, or disavowal, is deliberate deception, considered more reprehensible than anything not conscious. Indeed, if one does not know consciously what his interests are, he cannot, in some sense, be accused of deception even though he may be using an evolved ability of self-deception to deceive others. So it is not always – maybe not usually – in our evolutionary or surrogate-evolutionary interests to make them conscious […] If people can be fooled […] then there will be continual selection for becoming better at fooling others […]. This may include causing them to think that it will be best for them to help you when it is not. This ploy works because of the thin line everybody must continually tread with respect to not showing selfishness. If some people are self-destructively beneficent (i.e., make altruistic mistakes), and if people often cannot tell if one is such a mistake-maker, it might be profitable even to try to convince others that one is such a mistake-maker so as to be accepted as a cooperator or so that the other will be beneficent in expectation of large returns (through “mistakes”) later. […] Reciprocity may work this way because it is grounded evolutionarily in nepotism, appropriate dispensing of nepotism (as well as reciprocity) depends upon learning, and the wrong things can be learned. [Boyd and Richerson talk about this particular aspect, the learning part, in much more detail in their books – US] Self-deception, then may not be a pathological or detrimental trait, at least in most people most of the time. Rather, it may have evolved as a way to deceive others.”

“The only time that utilitarianism (promoting the greatest good to the greatest number) is predicted by evolutionary theory is when the interests of the group (the “greatest number”) and the individual coincide, and in such cases utilitarianism is not really altruistic in either the biologists’ or the philosophers’ sense of the term. […] If Kohlberg means to imply that a significant proportion of the populace of the world either implicitly or explicitly favors a system in which everyone (including himself) behaves so as to bring the greatest good to the greatest number, then I simply believe that he is wrong. If he supposes that only a relatively few – particularly moral philosophers and some others like them – have achieved this “stage,” then I also doubt the hypothesis. I accept that many people are aware of this concept of utility, that a small minority may advocate it, and that an even smaller minority may actually believe that they behave according to it. I speculate, however, that with a few inadvertent or accidental exceptions, no one actually follows this precept. I see the concept as having its main utility as a goal towards which one may exhort others to aspire, and towards which one may behave as if (or talk as if) aspiring, which actually practicing complex forms of self-interest.”

“Generally speaking, the bigger the group, the more complex the social organization, and the greater the group’s unity of purpose the more limited is individual entrepreneurship.”

“The function or raison d’etre [sic] of moral systems is evidently to provide the unity required to enable the group to compete successfully with other human groups. […] the argument that human evolution has been guided to some large extent by intergroup competition and aggression […] is central to the theory of morality presented here”.

June 29, 2017 Posted by | Anthropology, Biology, Books, Evolutionary biology, Genetics, Philosophy | Leave a comment

Harnessing phenotypic heterogeneity to design better therapies

Unlike many of the IAS lectures I’ve recently blogged this one is a new lecture – it was uploaded earlier this week. I have to say that I was very surprised – and disappointed – that the treatment strategy discussed in the lecture had not already been analyzed in a lot of detail and been implemented in clinical practice for some time. Why would you not expect the composition of cancer cell subtypes in the tumour microenvironment to change when you start treatment – in any setting where a subgroup of cancer cells has a different level of responsiveness to treatment than ‘the average’, that would to me seem to be the expected outcome. And concepts such as drug holidays and dose adjustments as treatment responses to evolving drug resistance/treatment failure seem like such obvious approaches to try out here (…the immunologists dealing with HIV infection have been studying such things for decades). I guess ‘better late than never’.

A few papers mentioned/discussed in the lecture:

Impact of Metabolic Heterogeneity on Tumor Growth, Invasion, and Treatment Outcomes.
Adaptive vs continuous cancer therapy: Exploiting space and trade-offs in drug scheduling.
Exploiting evolutionary principles to prolong tumor control in preclinical models of breast cancer.

June 11, 2017 Posted by | Cancer/oncology, Genetics, Immunology, Lectures, Mathematics, Medicine, Studies | Leave a comment

Standing on the Shoulders of Mice: Aging T-cells

Most of the lecture is not about mice, but rather about stuff like this and this (both papers are covered in the lecture). I’ve read about related topics before (see e.g this), but if you haven’t some parts of the lecture will probably be too technical for you to follow.

May 3, 2017 Posted by | Cancer/oncology, Cardiology, Genetics, Immunology, Lectures, Medicine, Papers | Leave a comment

Biodemography of aging (IV)

My working assumption as I was reading part two of the book was that I would not be covering that part of the book in much detail here because it would simply be too much work to make such posts legible to the readership of this blog. However I then later, while writing this post, had the thought that given that almost nobody reads along here anyway (I’m not complaining, mind you – this is how I like it these days), the main beneficiary of my blog posts will always be myself, which lead to the related observation/notion that I should not be limiting my coverage of interesting stuff here simply because some hypothetical and probably nonexistent readership out there might not be able to follow the coverage. So when I started out writing this post I was working under the assumption that it would be my last post about the book, but I now feel sure that if I find the time I’ll add at least one more post about the book’s statistics coverage. On a related note I am explicitly making the observation here that this post was written for my benefit, not yours. You can read it if you like, or not, but it was not really written for you.

I have added bold a few places to emphasize key concepts and observations from the quoted paragraphs and in order to make the post easier for me to navigate later (all the italics below are on the other hand those of the authors of the book).

Biodemography is a multidisciplinary branch of science that unites under its umbrella various analytic approaches aimed at integrating biological knowledge and methods and traditional demographic analyses to shed more light on variability in mortality and health across populations and between individuals. Biodemography of aging is a special subfield of biodemography that focuses on understanding the impact of processes related to aging on health and longevity.”

“Mortality rates as a function of age are a cornerstone of many demographic analyses. The longitudinal age trajectories of biomarkers add a new dimension to the traditional demographic analyses: the mortality rate becomes a function of not only age but also of these biomarkers (with additional dependence on a set of sociodemographic variables). Such analyses should incorporate dynamic characteristics of trajectories of biomarkers to evaluate their impact on mortality or other outcomes of interest. Traditional analyses using baseline values of biomarkers (e.g., Cox proportional hazards or logistic regression models) do not take into account these dynamics. One approach to the evaluation of the impact of biomarkers on mortality rates is to use the Cox proportional hazards model with time-dependent covariates; this approach is used extensively in various applications and is available in all popular statistical packages. In such a model, the biomarker is considered a time-dependent covariate of the hazard rate and the corresponding regression parameter is estimated along with standard errors to make statistical inference on the direction and the significance of the effect of the biomarker on the outcome of interest (e.g., mortality). However, the choice of the analytic approach should not be governed exclusively by its simplicity or convenience of application. It is essential to consider whether the method gives meaningful and interpretable results relevant to the research agenda. In the particular case of biodemographic analyses, the Cox proportional hazards model with time-dependent covariates is not the best choice.

“Longitudinal studies of aging present special methodological challenges due to inherent characteristics of the data that need to be addressed in order to avoid biased inference. The challenges are related to the fact that the populations under study (aging individuals) experience substantial dropout rates related to death or poor health and often have co-morbid conditions related to the disease of interest. The standard assumption made in longitudinal analyses (although usually not explicitly mentioned in publications) is that dropout (e.g., death) is not associated with the outcome of interest. While this can be safely assumed in many general longitudinal studies (where, e.g., the main causes of dropout might be the administrative end of the study or moving out of the study area, which are presumably not related to the studied outcomes), the very nature of the longitudinal outcomes (e.g., measurements of some physiological biomarkers) analyzed in a longitudinal study of aging assumes that they are (at least hypothetically) related to the process of aging. Because the process of aging leads to the development of diseases and, eventually, death, in longitudinal studies of aging an assumption of non-association of the reason for dropout and the outcome of interest is, at best, risky, and usually is wrong. As an illustration, we found that the average trajectories of different physiological indices of individuals dying at earlier ages markedly deviate from those of long-lived individuals, both in the entire Framingham original cohort […] and also among carriers of specific alleles […] In such a situation, panel compositional changes due to attrition affect the averaging procedure and modify the averages in the total sample. Furthermore, biomarkers are subject to measurement error and random biological variability. They are usually collected intermittently at examination times which may be sparse and typically biomarkers are not observed at event times. It is well known in the statistical literature that ignoring measurement errors and biological variation in such variables and using their observed “raw” values as time-dependent covariates in a Cox regression model may lead to biased estimates and incorrect inferences […] Standard methods of survival analysis such as the Cox proportional hazards model (Cox 1972) with time-dependent covariates should be avoided in analyses of biomarkers measured with errors because they can lead to biased estimates.

“Statistical methods aimed at analyses of time-to-event data jointly with longitudinal measurements have become known in the mainstream biostatistical literature as “joint models for longitudinal and time-to-event data” (“survival” or “failure time” are often used interchangeably with “time-to-event”) or simply “joint models.” This is an active and fruitful area of biostatistics with an explosive growth in recent years. […] The standard joint model consists of two parts, the first representing the dynamics of longitudinal data (which is referred to as the “longitudinal sub-model”) and the second one modeling survival or, generally, time-to-event data (which is referred to as the “survival sub-model”). […] Numerous extensions of this basic model have appeared in the joint modeling literature in recent decades, providing great flexibility in applications to a wide range of practical problems. […] The standard parameterization of the joint model (11.2) assumes that the risk of the event at age t depends on the current “true” value of the longitudinal biomarker at this age. While this is a reasonable assumption in general, it may be argued that additional dynamic characteristics of the longitudinal trajectory can also play a role in the risk of death or onset of a disease. For example, if two individuals at the same age have exactly the same level of some biomarker at this age, but the trajectory for the first individual increases faster with age than that of the second one, then the first individual can have worse survival chances for subsequent years. […] Therefore, extensions of the basic parameterization of joint models allowing for dependence of the risk of an event on such dynamic characteristics of the longitudinal trajectory can provide additional opportunities for comprehensive analyses of relationships between the risks and longitudinal trajectories. Several authors have considered such extended models. […] joint models are computationally intensive and are sometimes prone to convergence problems [however such] models provide more efficient estimates of the effect of a covariate […] on the time-to-event outcome in the case in which there is […] an effect of the covariate on the longitudinal trajectory of a biomarker. This means that analyses of longitudinal and time-to-event data in joint models may require smaller sample sizes to achieve comparable statistical power with analyses based on time-to-event data alone (Chen et al. 2011).”

“To be useful as a tool for biodemographers and gerontologists who seek biological explanations for observed processes, models of longitudinal data should be based on realistic assumptions and reflect relevant knowledge accumulated in the field. An example is the shape of the risk functions. Epidemiological studies show that the conditional hazards of health and survival events considered as functions of risk factors often have U- or J-shapes […], so a model of aging-related changes should incorporate this information. In addition, risk variables, and, what is very important, their effects on the risks of corresponding health and survival events, experience aging-related changes and these can differ among individuals. […] An important class of models for joint analyses of longitudinal and time-to-event data incorporating a stochastic process for description of longitudinal measurements uses an epidemiologically-justified assumption of a quadratic hazard (i.e., U-shaped in general and J-shaped for variables that can take values only on one side of the U-curve) considered as a function of physiological variables. Quadratic hazard models have been developed and intensively applied in studies of human longitudinal data”.

“Various approaches to statistical model building and data analysis that incorporate unobserved heterogeneity are ubiquitous in different scientific disciplines. Unobserved heterogeneity in models of health and survival outcomes can arise because there may be relevant risk factors affecting an outcome of interest that are either unknown or not measured in the data. Frailty models introduce the concept of unobserved heterogeneity in survival analysis for time-to-event data. […] Individual age trajectories of biomarkers can differ due to various observed as well as unobserved (and unknown) factors and such individual differences propagate to differences in risks of related time-to-event outcomes such as the onset of a disease or death. […] The joint analysis of longitudinal and time-to-event data is the realm of a special area of biostatistics named “joint models for longitudinal and time-to-event data” or simply “joint models” […] Approaches that incorporate heterogeneity in populations through random variables with continuous distributions (as in the standard joint models and their extensions […]) assume that the risks of events and longitudinal trajectories follow similar patterns for all individuals in a population (e.g., that biomarkers change linearly with age for all individuals). Although such homogeneity in patterns can be justifiable for some applications, generally this is a rather strict assumption […] A population under study may consist of subpopulations with distinct patterns of longitudinal trajectories of biomarkers that can also have different effects on the time-to-event outcome in each subpopulation. When such subpopulations can be defined on the base of observed covariate(s), one can perform stratified analyses applying different models for each subpopulation. However, observed covariates may not capture the entire heterogeneity in the population in which case it may be useful to conceive of the population as consisting of latent subpopulations defined by unobserved characteristics. Special methodological approaches are necessary to accommodate such hidden heterogeneity. Within the joint modeling framework, a special class of models, joint latent class models, was developed to account for such heterogeneity […] The joint latent class model has three components. First, it is assumed that a population consists of a fixed number of (latent) subpopulations. The latent class indicator represents the latent class membership and the probability of belonging to the latent class is specified by a multinomial logistic regression function of observed covariates. It is assumed that individuals from different latent classes have different patterns of longitudinal trajectories of biomarkers and different risks of event. The key assumption of the model is conditional independence of the biomarker and the time-to-events given the latent classes. Then the class-specific models for the longitudinal and time-to-event outcomes constitute the second and third component of the model thus completing its specification. […] the latent class stochastic process model […] provides a useful tool for dealing with unobserved heterogeneity in joint analyses of longitudinal and time-to-event outcomes and taking into account hidden components of aging in their joint influence on health and longevity. This approach is also helpful for sensitivity analyses in applications of the original stochastic process model. We recommend starting the analyses with the original stochastic process model and estimating the model ignoring possible hidden heterogeneity in the population. Then the latent class stochastic process model can be applied to test hypotheses about the presence of hidden heterogeneity in the data in order to appropriately adjust the conclusions if a latent structure is revealed.”

The longitudinal genetic-demographic model (or the genetic-demographic model for longitudinal data) […] combines three sources of information in the likelihood function: (1) follow-up data on survival (or, generally, on some time-to-event) for genotyped individuals; (2) (cross-sectional) information on ages at biospecimen collection for genotyped individuals; and (3) follow-up data on survival for non-genotyped individuals. […] Such joint analyses of genotyped and non-genotyped individuals can result in substantial improvements in statistical power and accuracy of estimates compared to analyses of the genotyped subsample alone if the proportion of non-genotyped participants is large. Situations in which genetic information cannot be collected for all participants of longitudinal studies are not uncommon. They can arise for several reasons: (1) the longitudinal study may have started some time before genotyping was added to the study design so that some initially participating individuals dropped out of the study (i.e., died or were lost to follow-up) by the time of genetic data collection; (2) budget constraints prohibit obtaining genetic information for the entire sample; (3) some participants refuse to provide samples for genetic analyses. Nevertheless, even when genotyped individuals constitute a majority of the sample or the entire sample, application of such an approach is still beneficial […] The genetic stochastic process model […] adds a new dimension to genetic biodemographic analyses, combining information on longitudinal measurements of biomarkers available for participants of a longitudinal study with follow-up data and genetic information. Such joint analyses of different sources of information collected in both genotyped and non-genotyped individuals allow for more efficient use of the research potential of longitudinal data which otherwise remains underused when only genotyped individuals or only subsets of available information (e.g., only follow-up data on genotyped individuals) are involved in analyses. Similar to the longitudinal genetic-demographic model […], the benefits of combining data on genotyped and non-genotyped individuals in the genetic SPM come from the presence of common parameters describing characteristics of the model for genotyped and non-genotyped subsamples of the data. This takes into account the knowledge that the non-genotyped subsample is a mixture of carriers and non-carriers of the same alleles or genotypes represented in the genotyped subsample and applies the ideas of heterogeneity analyses […] When the non-genotyped subsample is substantially larger than the genotyped subsample, these joint analyses can lead to a noticeable increase in the power of statistical estimates of genetic parameters compared to estimates based only on information from the genotyped subsample. This approach is applicable not only to genetic data but to any discrete time-independent variable that is observed only for a subsample of individuals in a longitudinal study.

“Despite an existing tradition of interpreting differences in the shapes or parameters of the mortality rates (survival functions) resulting from the effects of exposure to different conditions or other interventions in terms of characteristics of individual aging, this practice has to be used with care. This is because such characteristics are difficult to interpret in terms of properties of external and internal processes affecting the chances of death. An important question then is: What kind of mortality model has to be developed to obtain parameters that are biologically interpretable? The purpose of this chapter is to describe an approach to mortality modeling that represents mortality rates in terms of parameters of physiological changes and declining health status accompanying the process of aging in humans. […] A traditional (demographic) description of changes in individual health/survival status is performed using a continuous-time random Markov process with a finite number of states, and age-dependent transition intensity functions (transitions rates). Transitions to the absorbing state are associated with death, and the corresponding transition intensity is a mortality rate. Although such a description characterizes connections between health and mortality, it does not allow for studying factors and mechanisms involved in the aging-related health decline. Numerous epidemiological studies provide compelling evidence that health transition rates are influenced by a number of factors. Some of them are fixed at the time of birth […]. Others experience stochastic changes over the life course […] The presence of such randomly changing influential factors violates the Markov assumption, and makes the description of aging-related changes in health status more complicated. […] The age dynamics of influential factors (e.g., physiological variables) in connection with mortality risks has been described using a stochastic process model of human mortality and aging […]. Recent extensions of this model have been used in analyses of longitudinal data on aging, health, and longevity, collected in the Framingham Heart Study […] This model and its extensions are described in terms of a Markov stochastic process satisfying a diffusion-type stochastic differential equation. The stochastic process is stopped at random times associated with individuals’ deaths. […] When an individual’s health status is taken into account, the coefficients of the stochastic differential equations become dependent on values of the jumping process. This dependence violates the Markov assumption and renders the conditional Gaussian property invalid. So the description of this (continuously changing) component of aging-related changes in the body also becomes more complicated. Since studying age trajectories of physiological states in connection with changes in health status and mortality would provide more realistic scenarios for analyses of available longitudinal data, it would be a good idea to find an appropriate mathematical description of the joint evolution of these interdependent processes in aging organisms. For this purpose, we propose a comprehensive model of human aging, health, and mortality in which the Markov assumption is fulfilled by a two-component stochastic process consisting of jumping and continuously changing processes. The jumping component is used to describe relatively fast changes in health status occurring at random times, and the continuous component describes relatively slow stochastic age-related changes of individual physiological states. […] The use of stochastic differential equations for random continuously changing covariates has been studied intensively in the analysis of longitudinal data […] Such a description is convenient since it captures the feedback mechanism typical of biological systems reflecting regular aging-related changes and takes into account the presence of random noise affecting individual trajectories. It also captures the dynamic connections between aging-related changes in health and physiological states, which are important in many applications.”

April 23, 2017 Posted by | Biology, Books, Demographics, Genetics, Mathematics, Statistics | Leave a comment

Biodemography of aging (III)

Latent class representation of the Grade of Membership model.
Singular value decomposition.
Affine space.
Lebesgue measure.
General linear position.

The links above are links to topics I looked up while reading the second half of the book. The first link is quite relevant to the book’s coverage as a comprehensive longitudinal Grade of Membership (-GoM) model is covered in chapter 17. Relatedly, chapter 18 covers linear latent structure (-LLS) models, and as observed in the book LLS is a generalization of GoM. As should be obvious from the nature of the links some of the stuff included in the second half of the text is highly technical, and I’ll readily admit I was not fully able to understand all the details included in the coverage of chapters 17 and 18 in particular. On account of the technical nature of the coverage in Part 2 I’m not sure I’ll cover the second half of the book in much detail, though I probably shall devote at least one more post to some of those topics, as they were quite interesting even if some of the details were difficult to follow.

I have almost finished the book at this point, and I have already decided to both give the book five stars and include it on my list of favorite books on goodreads; it’s really well written, and it provides consistently highly detailed coverage of very high quality. As I also noted in the first post about the book the authors have given readability aspects some thought, and I am sure most readers would learn quite a bit from this text even if they were to skip some of the more technical chapters. The main body of Part 2 of the book, the subtitle of which is ‘Statistical Modeling of Aging, Health, and Longevity’, is however probably in general not worth the effort of reading unless you have a solid background in statistics.

This post includes some observations and quotes from the last chapters of the book’s Part 1.

“The proportion of older adults in the U.S. population is growing. This raises important questions about the increasing prevalence of aging-related diseases, multimorbidity issues, and disability among the elderly population. […] In 2009, 46.3 million people were covered by Medicare: 38.7 million of them were aged 65 years and older, and 7.6 million were disabled […]. By 2031, when the baby-boomer generation will be completely enrolled, Medicare is expected to reach 77 million individuals […]. Because the Medicare program covers 95 % of the nation’s aged population […], the prediction of future Medicare costs based on these data can be an important source of health care planning.”

“Three essential components (which could be also referred as sub-models) need to be developed to construct a modern model of forecasting of population health and associated medical costs: (i) a model of medical cost projections conditional on each health state in the model, (ii) health state projections, and (iii) a description of the distribution of initial health states of a cohort to be projected […] In making medical cost projections, two major effects should be taken into account: the dynamics of the medical costs during the time periods comprising the date of onset of chronic diseases and the increase of medical costs during the last years of life. In this chapter, we investigate and model the first of these two effects. […] the approach developed in this chapter generalizes the approach known as “life tables with covariates” […], resulting in a new family of forecasting models with covariates such as comorbidity indexes or medical costs. In sum, this chapter develops a model of the relationships between individual cost trajectories following the onset of aging-related chronic diseases. […] The underlying methodological idea is to aggregate the health state information into a single (or several) covariate(s) that can be determinative in predicting the risk of a health event (e.g., disease incidence) and whose dynamics could be represented by the model assumptions. An advantage of such an approach is its substantial reduction of the degrees of freedom compared with existing forecasting models  (e.g., the FEM model, Goldman and RAND Corporation 2004). […] We found that the time patterns of medical cost trajectories were similar for all diseases considered and can be described in terms of four components having the meanings of (i) the pre-diagnosis cost associated with initial comorbidity represented by medical expenditures, (ii) the cost peak associated with the onset of each disease, (iii) the decline/reduction in medical expenditures after the disease onset, and (iv) the difference between post- and pre-diagnosis cost levels associated with an acquired comorbidity. The description of the trajectories was formalized by a model which explicitly involves four parameters reflecting these four components.”

As I noted earlier in my coverage of the book, I don’t think the model above fully captures all relevant cost contributions of the diseases included, as the follow-up period was too short to capture all relevant costs to be included in the part iv model component. This is definitely a problem in the context of diabetes. But then again nothing in theory stops people from combining the model above with other models which are better at dealing with the excess costs associated with long-term complications of chronic diseases, and the model results were intriguing even if the model likely underperforms in a few specific disease contexts.

Moving on…

“Models of medical cost projections usually are based on regression models estimated with the majority of independent predictors describing demographic status of the individual, patient’s health state, and level of functional limitations, as well as their interactions […]. If the health states needs to be described by a number of simultaneously manifested diseases, then detailed stratification over the categorized variables or use of multivariate regression models allows for a better description of the health states. However, it can result in an abundance of model parameters to be estimated. One way to overcome these difficulties is to use an approach in which the model components are demographically-based aggregated characteristics that mimic the effects of specific states. The model developed in this chapter is an example of such an approach: the use of a comorbidity index rather than of a set of correlated categorical regressor variables to represent the health state allows for an essential reduction in the degrees of freedom of the problem.”

“Unlike mortality, the onset time of chronic disease is difficult to define with high precision due to the large variety of disease-specific criteria for onset/incident case identification […] there is always some arbitrariness in defining the date of chronic disease onset, and a unified definition of date of onset is necessary for population studies with a long-term follow-up.”

“Individual age trajectories of physiological indices are the product of a complicated interplay among genetic and non-genetic (environmental, behavioral, stochastic) factors that influence the human body during the course of aging. Accordingly, they may differ substantially among individuals in a cohort. Despite this fact, the average age trajectories for the same index follow remarkable regularities. […] some indices tend to change monotonically with age: the level of blood glucose (BG) increases almost monotonically; pulse pressure (PP) increases from age 40 until age 85, then levels off and shows a tendency to decline only at later ages. The age trajectories of other indices are non-monotonic: they tend to increase first and then decline. Body mass index (BMI) increases up to about age 70 and then declines, diastolic blood pressure (DBP) increases until age 55–60 and then declines, systolic blood pressure (SBP) increases until age 75 and then declines, serum cholesterol (SCH) increases until age 50 in males and age 70 in females and then declines, ventricular rate (VR) increases until age 55 in males and age 45 in females and then declines. With small variations, these general patterns are similar in males and females. The shapes of the age-trajectories of the physiological variables also appear to be similar for different genotypes. […] The effects of these physiological indices on mortality risk were studied in Yashin et al. (2006), who found that the effects are gender and age specific. They also found that the dynamic properties of the individual age trajectories of physiological indices may differ dramatically from one individual to the next.”

“An increase in the mortality rate with age is traditionally associated with the process of aging. This influence is mediated by aging-associated changes in thousands of biological and physiological variables, some of which have been measured in aging studies. The fact that the age trajectories of some of these variables differ among individuals with short and long life spans and healthy life spans indicates that dynamic properties of the indices affect life history traits. Our analyses of the FHS data clearly demonstrate that the values of physiological indices at age 40 are significant contributors both to life span and healthy life span […] suggesting that normalizing these variables around age 40 is important for preventing age-associated morbidity and mortality later in life. […] results [also] suggest that keeping physiological indices stable over the years of life could be as important as their normalizing around age 40.”

“The results […] indicate that, in the quest of identifying longevity genes, it may be important to look for candidate genes with pleiotropic effects on more than one dynamic characteristic of the age-trajectory of a physiological variable, such as genes that may influence both the initial value of a trait (intercept) and the rates of its changes over age (slopes). […] Our results indicate that the dynamic characteristics of age-related changes in physiological variables are important predictors of morbidity and mortality risks in aging individuals. […] We showed that the initial value (intercept), the rate of changes (slope), and the variability of a physiological index, in the age interval 40–60 years, significantly influenced both mortality risk and onset of unhealthy life at ages 60+ in our analyses of the Framingham Heart Study data. That is, these dynamic characteristics may serve as good predictors of late life morbidity and mortality risks. The results also suggest that physiological changes taking place in the organism in middle life may affect longevity through promoting or preventing diseases of old age. For non-monotonically changing indices, we found that having a later age at the peak value of the index […], a lower peak value […], a slower rate of decline in the index at older ages […], and less variability in the index over time, can be beneficial for longevity. Also, the dynamic characteristics of the physiological indices were, overall, associated with mortality risk more significantly than with onset of unhealthy life.”

“Decades of studies of candidate genes show that they are not linked to aging-related traits in a straightforward manner […]. Recent genome-wide association studies (GWAS) have reached fundamentally the same conclusion by showing that the traits in late life likely are controlled by a relatively large number of common genetic variants […]. Further, GWAS often show that the detected associations are of tiny effect […] the weak effect of genes on traits in late life can be not only because they confer small risks having small penetrance but because they confer large risks but in a complex fashion […] In this chapter, we consider several examples of complex modes of gene actions, including genetic tradeoffs, antagonistic genetic effects on the same traits at different ages, and variable genetic effects on lifespan. The analyses focus on the APOE common polymorphism. […] The analyses reported in this chapter suggest that the e4 allele can be protective against cancer with a more pronounced role in men. This protective effect is more characteristic of cancers at older ages and it holds in both the parental and offspring generations of the FHS participants. Unlike cancer, the effect of the e4 allele on risks of CVD is more pronounced in women. […] [The] results […] explicitly show that the same allele can change its role on risks of CVD in an antagonistic fashion from detrimental in women with onsets at younger ages to protective in women with onsets at older ages. […] e4 allele carriers have worse survival compared to non-e4 carriers in each cohort. […] Sex stratification shows sexual dimorphism in the effect of the e4 allele on survival […] with the e4 female carriers, particularly, being more exposed to worse survival. […] The results of these analyses provide two important insights into the role of genes in lifespan. First, they provide evidence on the key role of aging-related processes in genetic susceptibility to lifespan. For example, taking into account the specifics of aging-related processes gains 18 % in estimates of the RRs and five orders of magnitude in significance in the same sample of women […] without additional investments in increasing sample sizes and new genotyping. The second is that a detailed study of the role of aging-related processes in estimates of the effects of genes on lifespan (and healthspan) helps in detecting more homogeneous [high risk] sub-samples”.

“The aging of populations in developed countries requires effective strategies to extend healthspan. A promising solution could be to yield insights into the genetic predispositions for endophenotypes, diseases, well-being, and survival. It was thought that genome-wide association studies (GWAS) would be a major breakthrough in this endeavor. Various genetic association studies including GWAS assume that there should be a deterministic (unconditional) genetic component in such complex phenotypes. However, the idea of unconditional contributions of genes to these phenotypes faces serious difficulties which stem from the lack of direct evolutionary selection against or in favor of such phenotypes. In fact, evolutionary constraints imply that genes should be linked to age-related phenotypes in a complex manner through different mechanisms specific for given periods of life. Accordingly, the linkage between genes and these traits should be strongly modulated by age-related processes in a changing environment, i.e., by the individuals’ life course. The inherent sensitivity of genetic mechanisms of complex health traits to the life course will be a key concern as long as genetic discoveries continue to be aimed at improving human health.”

“Despite the common understanding that age is a risk factor of not just one but a large portion of human diseases in late life, each specific disease is typically considered as a stand-alone trait. Independence of diseases was a plausible hypothesis in the era of infectious diseases caused by different strains of microbes. Unlike those diseases, the exact etiology and precursors of diseases in late life are still elusive. It is clear, however, that the origin of these diseases differs from that of infectious diseases and that age-related diseases reflect a complicated interplay among ontogenetic changes, senescence processes, and damages from exposures to environmental hazards. Studies of the determinants of diseases in late life provide insights into a number of risk factors, apart from age, that are common for the development of many health pathologies. The presence of such common risk factors makes chronic diseases and hence risks of their occurrence interdependent. This means that the results of many calculations using the assumption of disease independence should be used with care. Chapter 4 argued that disregarding potential dependence among diseases may seriously bias estimates of potential gains in life expectancy attributable to the control or elimination of a specific disease and that the results of the process of coping with a specific disease will depend on the disease elimination strategy, which may affect mortality risks from other diseases.”

April 17, 2017 Posted by | Biology, Books, Cancer/oncology, Demographics, Economics, Epidemiology, Genetics, Health Economics, Medicine, Statistics | Leave a comment