Econstudentlog

A few diabetes papers of interest

i. Long-term Glycemic Variability and Risk of Adverse Outcomes: A Systematic Review and Meta-analysis.

“This systematic review and meta-analysis evaluates the association between HbA1c variability and micro- and macrovascular complications and mortality in type 1 and type 2 diabetes. […] Seven studies evaluated HbA1c variability among patients with type 1 diabetes and showed an association of HbA1c variability with renal disease (risk ratio 1.56 [95% CI 1.08–2.25], two studies), cardiovascular events (1.98 [1.39–2.82]), and retinopathy (2.11 [1.54–2.89]). Thirteen studies evaluated HbA1c variability among patients with type 2 diabetes. Higher HbA1c variability was associated with higher risk of renal disease (1.34 [1.15–1.57], two studies), macrovascular events (1.21 [1.06–1.38]), ulceration/gangrene (1.50 [1.06–2.12]), cardiovascular disease (1.27 [1.15–1.40]), and mortality (1.34 [1.18–1.53]). Most studies were retrospective with lack of adjustment for potential confounders, and inconsistency existed in the definition of HbA1c variability.

CONCLUSIONS HbA1c variability was positively associated with micro- and macrovascular complications and mortality independently of the HbA1c level and might play a future role in clinical risk assessment.”

Two observations related to the paper: One, although only a relatively small number of studies were included in the review, the number of patients included in some of those included studies was rather large – the 7 type 1 studies thus included 44,021 participants, and the 13 type 2 studies included in total 43,620 participants. Two, it’s noteworthy that some of the associations already look at least reasonably strong, despite interest in HbA1c variability being a relatively recent phenomenon. Confounding might be an issue, but then again it almost always might be, and to give an example, out of 11 studies analyzing the association between renal disease and HbA1c variability included in the review, ten of them support a link and the only one which does not was a small study on pediatric patients which was almost certainly underpowered to investigate such a link in the first place (the base rate of renal complications is, as mentioned before here on this blog quite recently (link 3), quite low in pediatric samples).

ii. Risk of Severe Hypoglycemia in Type 1 Diabetes Over 30 Years of Follow-up in the DCCT/EDIC Study.

(I should perhaps note here that I’m already quite familiar with the context of the DCCT/EDIC study/studies, and although readers may not be, and although background details are included in the paper, I decided not to cover such details here although they would make my coverage of the paper easier to understand. I instead decided to limit my coverage of the paper to a few observations which I myself found to be of interest.)

“During the DCCT, the rates of SH [Severe Hypoglycemia, US], including episodes with seizure or coma, were approximately threefold greater in the intensive treatment group than in the conventional treatment group […] During EDIC, the frequency of SH increased in the former conventional group and decreased in the former intensive group so that the difference in SH event rates between the two groups was no longer significant (36.6 vs. 40.8 episodes per 100 patient-years, respectively […] By the end of DCCT, with an average of 6.5 years of follow-up, 65% of the intensive group versus 35% of the conventional group experienced at least one episode of SH. In contrast, ∼50% of participants within each group reported an episode of SH during the 20 years of EDIC.”

“Of [the] participants reporting episodes of SH, during the DCCT, 54% of the intensive group and 30% of the conventional group experienced four or more episodes, whereas in EDIC, 37% of the intensive group and 33% of the conventional group experienced four or more events […]. Moreover, a subset of participants (14% [99 of 714]) experienced nearly one-half of all SH episodes (1,765 of 3,788) in DCCT, and a subset of 7% (52 of 709) in EDIC experienced almost one-third of all SH episodes (888 of 2,813) […] Fifty-one major accidents occurred during the 6.5 years of DCCT and 143 during the 20 years of EDIC […] The most frequent type of major accident was that involving a motor vehicle […] Hypoglycemia played a role as a possible, probable, or principal cause in 18 of 28 operator-caused motor vehicle accidents (MVAs) during DCCT […] and in 23 of 54 operator-caused MVAs during EDIC”.

“The T1D Exchange Clinic Registry recently reported that 8% of 4,831 adults with T1D living in the U.S. had a seizure or coma event during the 3 months before their most recent annual visit (11). During EDIC, we observed that 27% of the cohort experienced a coma or seizure event over the 20 years of 3-month reporting intervals (∼1.4% per year), a much lower annual risk than in the T1D Exchange Clinic Registry. In part, the open enrollment of patients into the T1D Exchange may be reflected without the exclusion of participants with a history of SH as in the DCCT and other clinical trials. The current data support the clinical perception that a small subset of individuals is more susceptible to SH (7% of patients with 11 or more SH episodes during EDIC, which represents 32% of all SH episodes in EDIC) […] a history of SH during DCCT and lower current HbA1c levels were the two major factors associated with an increased risk of SH during EDIC. Safety concerns were the reason why a history of frequent SH events was an exclusion criterion for enrollment in DCCT. […] Of note, we found that participants who entered the DCCT as adolescents were more likely to experience SH during EDIC.”

“In summary, although event rates in the DCCT/EDIC cohort seem to have fallen and stabilized over time, SH remains an ever-present threat for patients with T1D who use current technology, occurring at a rate of ∼36–41 episodes per 100 patient-years, even among those with longer diabetes duration. Having experienced one or more such prior events is the strongest predictor of a future SH episode.”

I didn’t actually like that summary. If a history of severe hypoglycemia was an exclusion criterion in the DCCT trial, which it was, then the event rate you’d get from this data set is highly likely to provide a biased estimator of the true event rate, as the Exchange Clinic Registry data illustrate. The true population event rate in unselected samples is higher.

Another note which may also be important to add is that many diabetics who do not have a ‘severe event’ during a specific time period might still experience a substantial number of hypoglycemic episodes; ‘severe events’ (which require the assistance of another individual) is a somewhat blunt instrument in particular for assessing quality-of-life aspects of hypoglycemia.

iii. The Presence and Consequence of Nonalbuminuric Chronic Kidney Disease in Patients With Type 1 Diabetes.

“This study investigated the prevalence of nonalbuminuric chronic kidney disease in type 1 diabetes to assess whether it increases the risk of cardiovascular and renal outcomes as well as all-cause mortality. […] This was an observational follow-up of 3,809 patients with type 1 diabetes from the Finnish Diabetic Nephropathy Study. […] mean age was 37.6 ± 11.8 years and duration of diabetes 21.2 ± 12.1 years. […] During 13 years of median follow-up, 378 developed end-stage renal disease, 415 suffered an incident cardiovascular event, and 406 died. […] At baseline, 78 (2.0%) had nonalbuminuric chronic kidney disease. […] Nonalbuminuric chronic kidney disease did not increase the risk of albuminuria (hazard ratio [HR] 2.0 [95% CI 0.9–4.4]) or end-stage renal disease (HR 6.4 [0.8–53.0]) but did increase the risk of cardiovascular events (HR 2.0 [1.4–3.5]) and all-cause mortality (HR 2.4 [1.4–3.9]). […] ESRD [End-Stage Renal Disease] developed during follow-up in 0.3% of patients with nonalbuminuric non-CKD [CKD: Chronic Kidney Disease], in 1.3% of patients with nonalbuminuric CKD, in 13.9% of patients with albuminuric non-CKD, and in 63.0% of patients with albuminuric CKD (P < 0.001).”

CONCLUSIONS Nonalbuminuric chronic kidney disease is not a frequent finding in patients with type 1 diabetes, but when present, it is associated with an increased risk of cardiovascular morbidity and all-cause mortality but not with renal outcomes.”

iv. Use of an α-Glucosidase Inhibitor and the Risk of Colorectal Cancer in Patients With Diabetes: A Nationwide, Population-Based Cohort Study.

This one relates closely to stuff covered in Horowitz & Samsom’s book about Gastrointestinal Function in Diabetes Mellitus which I just finished (and which I liked very much). Here’s a relevant quote from chapter 7 of that book (which is about ‘Hepato-biliary and Pancreatic Function’):

“Several studies have provided evidence that the risk of pancreatic cancer is increased in patients with type 1 and type 2 diabetes mellitus [136,137]. In fact, diabetes has been associated with an increased risk of several cancers, including those of the pancreas, liver, endometrium and kidney [136]. The pooled relative risk of pancreatic cancer for diabetics vs. non-diabetics in a meta-analysis was 2.1 (95% confidence interval 1.6–2.8). Patients presenting with diabetes mellitus within a period of 12 months of the diagnosis of pancreatic cancer were excluded because in these cases diabetes may be an early presenting sign of pancreatic cancer rather than a risk factor [137]”.

They don’t mention colon cancer there, but it’s obvious from the research which has been done – and which is covered extensively in that book – that diabetes has the potential to cause functional changes in a large number of components of the digestive system (and I hope to cover this kind of stuff in a lot more detail later on) so the fact that some of these changes may lead to neoplastic changes should hardly be surprising. However evaluating causal pathways is more complicated here than it might have been, because e.g. pancreatic diseases may also themselves cause secondary diabetes in some patients. Liver pathologies like hepatitis B and C also display positive associations with diabetes, although again causal pathways here are not completely clear; treatments used may be a contributing factor (interferon-treatment may induce diabetes), but there are also suggestions that diabetes should be considered one of the extrahepatic manifestations of hepatitis. This stuff is complicated.

The drug mentioned in the paper, acarbose, is incidentally a drug also discussed in some detail in the book. It belongs to a group of drugs called alpha glucosidase inhibitors, and it is ‘the first antidiabetic medication designed to act through an influence on intestinal functions.’ Anyway, some quotes from the paper:

“We conducted a nationwide, population-based study using a large cohort with diabetes in the Taiwan National Health Insurance Research Database. Patients with newly diagnosed diabetes (n = 1,343,484) were enrolled between 1998 and 2010. One control subject not using acarbose was randomly selected for each subject using acarbose after matching for age, sex, diabetes onset, and comorbidities. […] There were 1,332 incident cases of colorectal cancer in the cohort with diabetes during the follow-up period of 1,487,136 person-years. The overall incidence rate was 89.6 cases per 100,000 person-years. Patients treated with acarbose had a 27% reduction in the risk of colorectal cancer compared with control subjects. The adjusted HRs were 0.73 (95% CI 0.63–0.83), 0.69 (0.59–0.82), and 0.46 (0.37–0.58) for patients using >0 to <90, 90 to 364, and ≥365 cumulative defined daily doses of acarbose, respectively, compared with subjects who did not use acarbose (P for trend < 0.001).

CONCLUSIONS Acarbose use reduced the risk of incident colorectal cancer in patients with diabetes in a dose-dependent manner.”

It’s perhaps worth mentioning that the prevalence of type 1 is relatively low in East Asian populations and that most of the patients included were type 2 (this is also clearly indicated by this observation from the paper: “The median age at the time of the initial diabetes diagnosis was 54.1 years, and the median diabetes duration was 8.9 years.”). Another thing worth mentioning is that colon cancer is a very common type of cancer, and so even moderate risk reductions here at the individual level may translate into a substantial risk reduction at the population level. A third thing, noted in Horowitz & Samsom’s coverage, is that the side effects of acarbose are quite mild, so widespread use of the drug is not out of the question, at least poor tolerance is not likely to be an obstacle; the drug may cause e.g. excessive flatulence and something like 10% of patients may have to stop treatment because of gastrointestinal side effects, but although the side effects are annoying and may be unacceptable to some patients, they are not dangerous; it’s a safe drug which can be used even in patients with renal failure (a context where some of the other oral antidiabetic treatments available are contraindicated).

v. Diabetes, Lower-Extremity Amputation, and Death.

“Worldwide, every 30 s, a limb is lost to diabetes (1,2). Nearly 2 million people living in the U.S. are living with limb loss (1). According to the World Health Organization, lower-extremity amputations (LEAs) are 10 times more common in people with diabetes than in persons who do not have diabetes. In the U.S. Medicare population, the incidence of diabetic foot ulcers is ∼6 per 100 individuals with diabetes per year and the incidence of LEA is 4 per 1,000 persons with diabetes per year (3). LEA in those with diabetes generally carries yearly costs between $30,000 and $60,000 and lifetime costs of half a million dollars (4). In 2012, it was estimated that those with diabetes and lower-extremity wounds in the U.S. Medicare program accounted for $41 billion in cost, which is ∼1.6% of all Medicare health care spending (47). In 2012, in the U.K., it was estimated that the National Health Service spent between £639 and 662 million on foot ulcers and LEA, which was approximately £1 in every £150 spent by the National Health Service (8).”

“LEA does not represent a traditional medical complication of diabetes like myocardial infarction (MI), renal failure, or retinopathy in which organ failure is directly associated with diabetes (2). An LEA occurs because of a disease complication, usually a foot ulcer that is not healing (e.g., organ failure of the skin, failure of the biomechanics of the foot as a unit, nerve sensory loss, and/or impaired arterial vascular supply), but it also occurs at least in part as a consequence of a medical plan to amputate based on a decision between health care providers and patients (9,10). […] 30-day postoperative mortality can approach 10% […]. Previous reports have estimated that the 1-year post-LEA mortality rate in people with diabetes is between 10 and 50%, and the 5-year mortality rate post-LEA is between 30 and 80% (4,1315). More specifically, in the U.S. Medicare population mortality within a year after an incident LEA was 23.1% in 2006, 21.8% in 2007, and 20.6% in 2008 (4). In the U.K., up to 80% will die within 5 years of an LEA (8). In general, those with diabetes with an LEA are two to three times more likely to die at any given time point than those with diabetes who have not had an LEA (5). For perspective, the 5-year death rate after diagnosis of malignancy in the U.S. was 32% in 2010 (16).”

“Evidence on why individuals with diabetes and an LEA die is based on a few mainly small (e.g., <300 subjects) and often single center–based (13,1720) studies or <1 year duration of evaluation (11). In these studies, death is primarily associated with a previous history of cardiovascular disease and renal insufficiency, which are also major complications of diabetes; these complications are also associated with an increased risk of LEA. The goal of our study was to determine whether complications of diabetes well-known to be associated with death in those with diabetes such as cardiovascular disease and renal failure fully explain the higher rate of death in those who have undergone an LEA.”

“This is the largest and longest evaluation of the risk of death among those with diabetes and LEA […] Between 2003 and 2012, 416,434 individuals met the entrance criteria for the study. This cohort accrued an average of 9.0 years of follow-up and a total of 3.7 million diabetes person-years of follow-up. During this period of time, 6,566 (1.6%) patients had an LEA and 77,215 patients died (18.5%). […] The percentage of individuals who died within 30 days, 1 year, and by year 5 of their initial code for an LEA was 1.0%, 9.9%, and 27.2%, respectively. For those >65 years of age, the rates were 12.2% and 31.7%, respectively. For the full cohort of those with diabetes, the rate of death was 2.0% after 1 year of follow up and 7.3% after 5 years of follow up. In general, those with an LEA were more than three times more likely to die during a year of follow-up than an individual with diabetes who had not had an LEA. […] In any given year, >5% of those with diabetes and an LEA will die.”

“From 2003 to 2012, the HR [hazard rate, US] for death after an LEA was 3.02 (95% CI 2.90, 3.14). […] our a priori assumption was that the HR associating LEA with death would be fully diminished (i.e., it would become 1) when adjusted for the other risk factor variables. However, the fully adjusted LEA HR was diminished only ∼22% to 2.37 (95% CI 2.27, 2.48). With the exception of age >65 years, individual risk factors, in general, had minimal effect (<10%) on the HR of the association between LEA and death […] We conducted sensitivity analyses to determine the general statistical parameters of an unmeasured risk factor that could remove the association of LEA with death. We found that even if there existed a very strong risk factor with an HR of death of three, a prevalence of 10% in the general diabetes population, and a prevalence of 60% in those who had an LEA, LEA would still be associated with a statistically significant and clinically important risk of 1.30. These findings are describing a variable that would seem to be so common and so highly associated with death that it should already be clinically apparent. […] In summary, individuals with diabetes and an LEA are more likely to die at any given point in time than those who have diabetes but no LEA. While some of this variation can be explained by other known complications of diabetes, the amount that can be explained is small. Based on the results of this study, including a sensitivity analysis, it is highly unlikely that a “new” major risk factor for death exists. […] LEA is often performed because of an end-stage disease process like chronic nonhealing foot ulcer. By the time a patient has a foot ulcer and an LEA is offered, they are likely suffering from the end-stage consequence of diabetes. […] We would […] suggest that patients who have had an LEA require […] vigilant follow-up and evaluation to assure that their medical care is optimized. It is also important that GPs communicate to their patients about the risk of death to assure that patients have proper expectations about the severity of their disease.”

vi. Trends in Health Care Expenditure in U.S. Adults With Diabetes: 2002–2011.

Before quoting from the paper, I’ll remind people reading along here that ‘total medical expenditures’ != ‘total medical costs’. Lots of relevant medical costs are not included when you focus only on direct medical expenditures (sick days, early retirement, premature mortality and productivity losses associated therewith, etc., etc.). With that out of the way…

“This study examines trends in health care expenditures by expenditure category in U.S. adults with diabetes between 2002 and 2011. […] We analyzed 10 years of data representing a weighted population of 189,013,514 U.S. adults aged ≥18 years from the Medical Expenditure Panel Survey. […] Relative to individuals without diabetes ($5,058 [95% CI 4,949–5,166]), individuals with diabetes ($12,180 [11,775–12,586]) had more than double the unadjusted mean direct expenditures over the 10-year period. After adjustment for confounders, individuals with diabetes had $2,558 (2,266–2,849) significantly higher direct incremental expenditures compared with those without diabetes. For individuals with diabetes, inpatient expenditures rose initially from $4,014 in 2002/2003 to $4,183 in 2004/2005 and then decreased continuously to $3,443 in 2010/2011, while rising steadily for individuals without diabetes. The estimated unadjusted total direct expenditures for individuals with diabetes were $218.6 billion/year and adjusted total incremental expenditures were approximately $46 billion/year. […] in the U.S., direct medical costs associated with diabetes were $176 billion in 2012 (1,3). This is almost double to eight times the direct medical cost of other chronic diseases: $32 billion for COPD in 2010 (10), $93 billion for all cancers in 2008 (11), $21 billion for heart failure in 2012 (12), and $43 billion for hypertension in 2010 (13). In the U.S., total economic cost of diabetes rose by 41% from 2007 to 2012 (2). […] Our findings show that compared with individuals without diabetes, individuals with diabetes had significantly higher health expenditures from 2002 to 2011 and the bulk of the expenditures came from hospital inpatient and prescription expenditures.”

 

August 10, 2017 Posted by | Books, Cancer/oncology, Cardiology, Diabetes, Economics, Epidemiology, Gastroenterology, Medicine, Nephrology, Pharmacology | Leave a comment

A few diabetes papers of interest

i. Clinically Relevant Cognitive Impairment in Middle-Aged Adults With Childhood-Onset Type 1 Diabetes.

“Modest cognitive dysfunction is consistently reported in children and young adults with type 1 diabetes (T1D) (1). Mental efficiency, psychomotor speed, executive functioning, and intelligence quotient appear to be most affected (2); studies report effect sizes between 0.2 and 0.5 (small to modest) in children and adolescents (3) and between 0.4 and 0.8 (modest to large) in adults (2). Whether effect sizes continue to increase as those with T1D age, however, remains unknown.

A key issue not yet addressed is whether aging individuals with T1D have an increased risk of manifesting “clinically relevant cognitive impairment,” defined by comparing individual cognitive test scores to demographically appropriate normative means, as opposed to the more commonly investigated “cognitive dysfunction,” or between-group differences in cognitive test scores. Unlike the extensive literature examining cognitive impairment in type 2 diabetes, we know of only one prior study examining cognitive impairment in T1D (4). This early study reported a higher rate of clinically relevant cognitive impairment among children (10–18 years of age) diagnosed before compared with after age 6 years (24% vs. 6%, respectively) or a non-T1D cohort (6%).”

“This study tests the hypothesis that childhood-onset T1D is associated with an increased risk of developing clinically relevant cognitive impairment detectable by middle age. We compared cognitive test results between adults with and without T1D and used demographically appropriate published norms (1012) to determine whether participants met criteria for impairment for each test; aging and dementia studies have selected a score ≥1.5 SD worse than the norm on that test, corresponding to performance at or below the seventh percentile (13).”

“During 2010–2013, 97 adults diagnosed with T1D and aged <18 years (age and duration 49 ± 7 and 41 ± 6 years, respectively; 51% female) and 138 similarly aged adults without T1D (age 49 ± 7 years; 55% female) completed extensive neuropsychological testing. Biomedical data on participants with T1D were collected periodically since 1986–1988.  […] The prevalence of clinically relevant cognitive impairment was five times higher among participants with than without T1D (28% vs. 5%; P < 0.0001), independent of education, age, or blood pressure. Effect sizes were large (Cohen d 0.6–0.9; P < 0.0001) for psychomotor speed and visuoconstruction tasks and were modest (d 0.3–0.6; P < 0.05) for measures of executive function. Among participants with T1D, prevalent cognitive impairment was related to 14-year average A1c >7.5% (58 mmol/mol) (odds ratio [OR] 3.0; P = 0.009), proliferative retinopathy (OR 2.8; P = 0.01), and distal symmetric polyneuropathy (OR 2.6; P = 0.03) measured 5 years earlier; higher BMI (OR 1.1; P = 0.03); and ankle-brachial index ≥1.3 (OR 4.2; P = 0.01) measured 20 years earlier, independent of education.”

“Having T1D was the only factor significantly associated with the between-group difference in clinically relevant cognitive impairment in our sample. Traditional risk factors for age-related cognitive impairment, in particular older age and high blood pressure (24), were not related to the between-group difference we observed. […] Similar to previous studies of younger adults with T1D (14,26), we found no relationship between the number of severe hypoglycemic episodes and cognitive impairment. Rather, we found that chronic hyperglycemia, via its associated vascular and metabolic changes, may have triggered structural changes in the brain that disrupt normal cognitive function.”

Just to be absolutely clear about these results: The type 1 diabetics they recruited in this study were on average not yet fifty years old, yet more than one in four of them were cognitively impaired to a clinically relevant degree. This is a huge effect. As they note later in the paper:

“Unlike previous reports of mild/modest cognitive dysfunction in young adults with T1D (1,2), we detected clinically relevant cognitive impairment in 28% of our middle-aged participants with T1D. This prevalence rate in our T1D cohort is comparable to the prevalence of mild cognitive impairment typically reported among community-dwelling adults aged 85 years and older (29%) (20).”

The type 1 diabetics included in the study had had diabetes for roughly a decade more than I have. And the number of cognitively impaired individuals in that sample corresponds roughly to what you find when you test random 85+ year-olds. Having type 1 diabetes is not good for your brain.

ii. Comment on Nunley et al. Clinically Relevant Cognitive Impairment in Middle-Aged Adults With Childhood-Onset Type 1 Diabetes.

This one is a short comment to the above paper, below I’ve quoted ‘the meat’ of the comment:

“While the […] study provides us with important insights regarding cognitive impairment in adults with type 1 diabetes, we regret that depression has not been taken into account. A systematic review and meta-analysis published in 2014 identified significant objective cognitive impairment in adults and adolescents with depression regarding executive functioning, memory, and attention relative to control subjects (2). Moreover, depression is two times more common in adults with diabetes compared with those without this condition, regardless of type of diabetes (3). There is even evidence that the co-occurrence of diabetes and depression leads to additional health risks such as increased mortality and dementia (3,4); this might well apply to cognitive impairment as well. Furthermore, in people with diabetes, the presence of depression has been associated with the development of diabetes complications, such as retinopathy, and higher HbA1c values (3). These are exactly the diabetes-specific correlates that Nunley et al. (1) found.”

“We believe it is a missed opportunity that Nunley et al. (1) mainly focused on biological variables, such as hyperglycemia and microvascular disease, and did not take into account an emotional disorder widely represented among people with diabetes and closely linked to cognitive impairment. Even though severe or chronic cases of depression are likely to have been excluded in the group without type 1 diabetes based on exclusion criteria (1), data on the presence of depression (either measured through a diagnostic interview or by using a validated screening questionnaire) could have helped to interpret the present findings. […] Determining the role of depression in the relationship between cognitive impairment and type 1 diabetes is of significant importance. Treatment of depression might improve cognitive impairment both directly by alleviating cognitive depression symptoms and indirectly by improving treatment nonadherence and glycemic control, consequently lowering the risk of developing complications.”

iii. Prevalence of Diabetes and Diabetic Nephropathy in a Large U.S. Commercially Insured Pediatric Population, 2002–2013.

“[W]e identified 96,171 pediatric patients with diabetes and 3,161 pediatric patients with diabetic nephropathy during 2002–2013. We estimated prevalence of pediatric diabetes overall, by diabetes type, age, and sex, and prevalence of pediatric diabetic nephropathy overall, by age, sex, and diabetes type.”

“Although type 1 diabetes accounts for a majority of childhood and adolescent diabetes, type 2 diabetes is becoming more common with the increasing rate of childhood obesity and it is estimated that up to 45% of all new patients with diabetes in this age-group have type 2 diabetes (1,2). With the rising prevalence of diabetes in children, a rise in diabetes-related complications, such as nephropathy, is anticipated. Moreover, data suggest that the development of clinical macrovascular complications, neuropathy, and nephropathy may be especially rapid among patients with young-onset type 2 diabetes (age of onset <40 years) (36). However, the natural history of young patients with type 2 diabetes and resulting complications has not been well studied.”

I’m always interested in the identification mechanisms applied in papers like this one, and I’m a little confused about the high number of patients without prescriptions (almost one-third of patients); I sort of assume these patients do take (/are given) prescription drugs, but get them from sources not available to the researchers (parents get prescriptions for the antidiabetic drugs, and the researchers don’t have access to these data? Something like this..) but this is a bit unclear. The mechanism they employ in the paper is not perfect (no mechanism is), but it probably works:

“Patients who had one or more prescription(s) for insulin and no prescriptions for another antidiabetes medication were classified as having type 1 diabetes, while those who filled prescriptions for noninsulin antidiabetes medications were considered to have type 2 diabetes.”

When covering limitations of the paper, they observe incidentally in this context that:

“Klingensmith et al. (31) recently reported that in the initial month after diagnosis of type 2 diabetes around 30% of patients were treated with insulin only. Thus, we may have misclassified a small proportion of type 2 cases as type 1 diabetes or vice versa. Despite this, we found that 9% of patients had onset of type 2 diabetes at age <10 years, consistent with the findings of Klingensmith et al. (8%), but higher than reported by the SEARCH for Diabetes in Youth study (<3%) (31,32).”

Some more observations from the paper:

“There were 149,223 patients aged <18 years at first diagnosis of diabetes in the CCE database from 2002 through 2013. […] Type 1 diabetes accounted for a majority of the pediatric patients with diabetes (79%). Among these, 53% were male and 53% were aged 12 to <18 years at onset, while among patients with type 2 diabetes, 60% were female and 79% were aged 12 to <18 years at onset.”

“The overall annual prevalence of all diabetes increased from 1.86 to 2.82 per 1,000 during years 2002–2013; it increased on average by 9.5% per year from 2002 to 2006 and slowly increased by 0.6% after that […] The prevalence of type 1 diabetes increased from 1.48 to 2.32 per 1,000 during the study period (average increase of 8.5% per year from 2002 to 2006 and 1.4% after that; both P values <0.05). The prevalence of type 2 diabetes increased from 0.38 to 0.67 per 1,000 during 2002 through 2006 (average increase of 13.3% per year; P < 0.05) and then dropped from 0.56 to 0.49 per 1,000 during 2007 through 2013 (average decrease of 2.7% per year; P < 0.05). […] Prevalence of any diabetes increased by age, with the highest prevalence in patients aged 12 to <18 years (ranging from 3.47 to 5.71 per 1,000 from 2002 through 2013).” […] The annual prevalence of diabetes increased over the study period mainly because of increases in type 1 diabetes.”

“Dabelea et al. (8) reported, based on data from the SEARCH for Diabetes in Youth study, that the annual prevalence of type 1 diabetes increased from 1.48 to 1.93 per 1,000 and from 0.34 to 0.46 per 1,000 for type 2 diabetes from 2001 to 2009 in U.S. youth. In our study, the annual prevalence of type 1 diabetes was 1.48 per 1,000 in 2002 and 2.10 per 1,000 in 2009, which is close to their reported prevalence.”

“We identified 3,161 diabetic nephropathy cases. Among these, 1,509 cases (47.7%) were of specific diabetic nephropathy and 2,253 (71.3%) were classified as probable cases. […] The annual prevalence of diabetic nephropathy in pediatric patients with diabetes increased from 1.16 to 3.44% between 2002 and 2013; it increased by on average 25.7% per year from 2002 to 2005 and slowly increased by 4.6% after that (both P values <0.05).”

Do note that the relationship between nephropathy prevalence and diabetes prevalence is complicated and that you cannot just explain an increase in the prevalence of nephropathy over time easily by simply referring to an increased prevalence of diabetes during the same time period. This would in fact be a very wrong thing to do, in part but not only on account of the data structure employed in this study. One problem which is probably easy to understand is that if more children got diabetes but the same proportion of those new diabetics got nephropathy, the diabetes prevalence would go up but the diabetic nephropathy prevalence would remain fixed; when you calculate the diabetic nephropathy prevalence you implicitly condition on diabetes status. But this just scratches the surface of the issues you encounter when you try to link these variables, because the relationship between the two variables is complicated; there’s an age pattern to diabetes risk, with risk (incidence) increasing with age (up to a point, after which it falls – in most samples I’ve seen in the past peak incidence in pediatric populations is well below the age of 18). However diabetes prevalence increases monotonously with age as long as the age-specific death rate of diabetics is lower than the age-specific incidence, because diabetes is chronic, and then on top of that you have nephropathy-related variables, which display diabetes-related duration-dependence (meaning that although nephropathy risk is also increasing with age when you look at that variable in isolation, that age-risk relationship is confounded by diabetes duration – a type 1 diabetic at the age of 12 who’s had diabetes for 10 years has a higher risk of nephropathy than a 16-year old who developed diabetes the year before). When a newly diagnosed pediatric patient is included in the diabetes sample here this will actually decrease the nephropathy prevalence in the short run, but not in the long run, assuming no changes in diabetes treatment outcomes over time. This is because the probability that that individual has diabetes-related kidney problems as a newly diagnosed child is zero, so he or she will unquestionably only contribute to the denominator during the first years of illness (the situation in the middle-aged type 2 context is different; here you do sometimes have newly-diagnosed patients who have developed complications already). This is one reason why it would be quite wrong to say that increased diabetes prevalence in this sample is the reason why diabetic nephropathy is increasing as well. Unless the time period you look at is very long (e.g. you have a setting where you follow all individuals with a diagnosis until the age of 18), the impact of increasing prevalence of one condition may well be expected to have a negative impact on the estimated risk of associated conditions, if those associated conditions display duration-dependence (which all major diabetes complications do). A second factor supporting a default assumption of increasing incidence of diabetes leading to an expected decreasing rate of diabetes-related complications is of course the fact that treatment options have tended to increase over time, and especially if you take a long view (look back 30-40 years) the increase in treatment options and improved medical technology have lead to improved metabolic control and better outcomes.

That both variables grew over time might be taken to indicate that both more children got diabetes and that a larger proportion of this increased number of children with diabetes developed kidney problems, but this stuff is a lot more complicated than it might look and it’s in particular important to keep in mind that, say, the 2005 sample and the 2010 sample do not include the same individuals, although there’ll of course be some overlap; in age-stratified samples like this you always have some level of implicit continuous replacement, with newly diagnosed patients entering and replacing the 18-year olds who leave the sample. As long as prevalence is constant over time, associated outcome variables may be reasonably easy to interpret, but when you have dynamic samples as well as increasing prevalence over time it gets difficult to say much with any degree of certainty unless you crunch the numbers in a lot of detail (and it might be difficult even if you do that). A factor I didn’t mention above but which is of course also relevant is that you need to be careful about how to interpret prevalence rates when you look at complications with high mortality rates (and late-stage diabetic nephropathy is indeed a complication with high mortality); in such a situation improvements in treatment outcomes may have large effects on prevalence rates but no effect on incidence. Increased prevalence is not always bad news, sometimes it is good news indeed. Gleevec substantially increased the prevalence of CML.

In terms of the prevalence-outcomes (/complication risk) connection, there are also in my opinion reasons to assume that there may be multiple causal pathways between prevalence and outcomes. For example a very low prevalence of a condition in a given area may mean that fewer specialists are educated to take care of these patients than would be the case for an area with a higher prevalence, and this may translate into a more poorly developed care infrastructure. Greatly increasing prevalence may on the other hand lead to a lower level of care for all patients with the illness, not just the newly diagnosed ones, due to binding budget constraints and care rationing. And why might you have changes in prevalence; might they not sometimes rather be related to changes in diagnostic practices, rather than changes in the True* prevalence? If that’s the case, you might not be comparing apples to apples when you’re comparing the evolving complication rates. There are in my opinion many reasons to believe that the relationship between chronic conditions and the complication rates of these conditions is far from simple to model.

All this said, kidney problems in children with diabetes is still rare, compared to the numbers you see when you look at adult samples with longer diabetes duration. It’s also worth distinguishing between microalbuminuria and overt nephropathy; children rarely proceed to develop diabetes-related kidney failure, although poor metabolic control may mean that they do develop this complication later, in early adulthood. As they note in the paper:

“It has been reported that overt diabetic nephropathy and kidney failure caused by either type 1 or type 2 diabetes are uncommon during childhood or adolescence (24). In this study, the annual prevalence of diabetic nephropathy for all cases ranged from 1.16 to 3.44% in pediatric patients with diabetes and was extremely low in the whole pediatric population (range 2.15 to 9.70 per 100,000), confirming that diabetic nephropathy is a very uncommon condition in youth aged <18 years. We observed that the prevalence of diabetic nephropathy increased in both specific and unspecific cases before 2006, with a leveling off of the specific nephropathy cases after 2005, while the unspecific cases continued to increase.”

iv. Adherence to Oral Glucose-Lowering Therapies and Associations With 1-Year HbA1c: A Retrospective Cohort Analysis in a Large Primary Care Database.

“Between a third and a half of medicines prescribed for type 2 diabetes (T2DM), a condition in which multiple medications are used to control cardiovascular risk factors and blood glucose (1,2), are not taken as prescribed (36). However, estimates vary widely depending on the population being studied and the way in which adherence to recommended treatment is defined.”

“A number of previous studies have used retrospective databases of electronic health records to examine factors that might predict adherence. A recent large cohort database examined overall adherence to oral therapy for T2DM, taking into account changes of therapy. It concluded that overall adherence was 69%, with individuals newly started on treatment being significantly less likely to adhere (19).”

“The impact of continuing to take glucose-lowering medicines intermittently, but not as recommended, is unknown. Medication possession (expressed as a ratio of actual possession to expected possession), derived from prescribing records, has been identified as a valid adherence measure for people with diabetes (7). Previous studies have been limited to small populations in managed-care systems in the U.S. and focused on metformin and sulfonylurea oral glucose-lowering treatments (8,9). Further studies need to be carried out in larger groups of people that are more representative of the general population.

The Clinical Practice Research Database (CPRD) is a long established repository of routine clinical data from more than 13 million patients registered with primary care services in England. […] The Genetics of Diabetes and Audit Research Tayside Study (GoDARTS) database is derived from integrated health records in Scotland with primary care, pharmacy, and hospital data on 9,400 patients with diabetes. […] We conducted a retrospective cohort study using [these databases] to examine the prevalence of nonadherence to treatment for type 2 diabetes and investigate its potential impact on HbA1c reduction stratified by type of glucose-lowering medication.”

“In CPRD and GoDARTS, 13% and 15% of patients, respectively, were nonadherent. Proportions of nonadherent patients varied by the oral glucose-lowering treatment prescribed (range 8.6% [thiazolidinedione] to 18.8% [metformin]). Nonadherent, compared with adherent, patients had a smaller HbA1c reduction (0.4% [4.4 mmol/mol] and 0.46% [5.0 mmol/mol] for CPRD and GoDARTs, respectively). Difference in HbA1c response for adherent compared with nonadherent patients varied by drug (range 0.38% [4.1 mmol/mol] to 0.75% [8.2 mmol/mol] lower in adherent group). Decreasing levels of adherence were consistently associated with a smaller reduction in HbA1c.”

“These findings show an association between adherence to oral glucose-lowering treatment, measured by the proportion of medication obtained on prescription over 1 year, and the corresponding decrement in HbA1c, in a population of patients newly starting treatment and continuing to collect prescriptions. The association is consistent across all commonly used oral glucose-lowering therapies, and the findings are consistent between the two data sets examined, CPRD and GoDARTS. Nonadherent patients, taking on average <80% of the intended medication, had about half the expected reduction in HbA1c. […] Reduced medication adherence for commonly used glucose-lowering therapies among patients persisting with treatment is associated with smaller HbA1c reductions compared with those taking treatment as recommended. Differences observed in HbA1c responses to glucose-lowering treatments may be explained in part by their intermittent use.”

“Low medication adherence is related to increased mortality (20). The mean difference in HbA1c between patients with MPR <80% and ≥80% is between 0.37% and 0.55% (4 mmol/mol and 6 mmol/mol), equivalent to up to a 10% reduction in death or an 18% reduction in diabetes complications (21).”

v. Health Care Transition in Young Adults With Type 1 Diabetes: Perspectives of Adult Endocrinologists in the U.S.

“Empiric data are limited on best practices in transition care, especially in the U.S. (10,1316). Prior research, largely from the patient perspective, has highlighted challenges in the transition process, including gaps in care (13,1719); suboptimal pediatric transition preparation (13,20); increased post-transition hospitalizations (21); and patient dissatisfaction with the transition experience (13,1719). […] Young adults with type 1 diabetes transitioning from pediatric to adult care are at risk for adverse outcomes. Our objective was to describe experiences, resources, and barriers reported by a national sample of adult endocrinologists receiving and caring for young adults with type 1 diabetes.”

“We received responses from 536 of 4,214 endocrinologists (response rate 13%); 418 surveys met the eligibility criteria. Respondents (57% male, 79% Caucasian) represented 47 states; 64% had been practicing >10 years and 42% worked at an academic center. Only 36% of respondents reported often/always reviewing pediatric records and 11% reported receiving summaries for transitioning young adults with type 1 diabetes, although >70% felt that these activities were important for patient care.”

“A number of studies document deficiencies in provider hand-offs across other chronic conditions and point to the broader relevance of our findings. For example, in two studies of inflammatory bowel disease, adult gastroenterologists reported inadequacies in young adult transition preparation (31) and infrequent receipt of medical histories from pediatric providers (32). In a study of adult specialists caring for young adults with a variety of chronic diseases (33), more than half reported that they had no contact with the pediatric specialists.

Importantly, more than half of the endocrinologists in our study reported a need for increased access to mental health referrals for young adult patients with type 1 diabetes, particularly in nonacademic settings. Report of barriers to care was highest for patient scenarios involving mental health issues, and endocrinologists without easy access to mental health referrals were significantly more likely to report barriers to diabetes management for young adults with psychiatric comorbidities such as depression, substance abuse, and eating disorders.”

“Prior research (34,35) has uncovered the lack of mental health resources in diabetes care. In the large cross-national Diabetes Attitudes, Wishes and Needs (DAWN) study (36) […] diabetes providers often reported not having the resources to manage mental health problems; half of specialist diabetes physicians felt unable to provide psychiatric support for patients and one-third did not have ready access to outside expertise in emotional or psychiatric matters. Our results, which resonate with the DAWN findings, are particularly concerning in light of the vulnerability of young adults with type 1 diabetes for adverse medical and mental health outcomes (4,34,37,38). […] In a recent report from the Mental Health Issues of Diabetes conference (35), which focused on type 1 diabetes, a major observation included the lack of trained mental health professionals, both in academic centers and the community, who are knowledgeable about the mental health issues germane to diabetes.”

August 3, 2017 Posted by | Diabetes, Epidemiology, Medicine, Nephrology, Neurology, Pharmacology, Psychiatry, Psychology, Statistics, Studies | Leave a comment

A few diabetes papers of interest

i. Cost-Effectiveness of Prevention and Treatment of the Diabetic Foot.

“A risk-based Markov model was developed to simulate the onset and progression of diabetic foot disease in patients with newly diagnosed type 2 diabetes managed with care according to guidelines for their lifetime. Mean survival time, quality of life, foot complications, and costs were the outcome measures assessed. Current care was the reference comparison. Data from Dutch studies on the epidemiology of diabetic foot disease, health care use, and costs, complemented with information from international studies, were used to feed the model.

RESULTS—Compared with current care, guideline-based care resulted in improved life expectancy, gain of quality-adjusted life-years (QALYs), and reduced incidence of foot complications. The lifetime costs of management of the diabetic foot following guideline-based care resulted in a cost per QALY gained of <$25,000, even for levels of preventive foot care as low as 10%. The cost-effectiveness varied sharply, depending on the level of foot ulcer reduction attained.

CONCLUSIONS—Management of the diabetic foot according to guideline-based care improves survival, reduces diabetic foot complications, and is cost-effective and even cost saving compared with standard care.”

I won’t go too deeply into the model setup and the results but some of the data they used to feed the model were actually somewhat interesting in their own right, and I have added some of these data below, along with some of the model results.

“It is estimated that 80% of LEAs [lower extremity amputations] are preceded by foot ulcers. Accordingly, it has been demonstrated that preventing the development of foot ulcers in patients with diabetes reduces the frequency of LEAs by 49–85% (6).”

“An annual ulcer incidence rate of 2.1% and an amputation incidence rate of 0.6% were among the reference country-specific parameters derived from this study and adopted in the model.”

“The health outcomes results of the cohort following standard care were comparable to figures reported for diabetic patients in the Netherlands. […] In the 10,000 patients followed until death, a total of 1,780 ulcer episodes occurred, corresponding to a cumulative ulcer incidence of 17.8% and an annual ulcer incidence of 2.2% (mean annual ulcer incidence for the Netherlands is 2.1%) (17). The number of amputations observed was 362 (250 major and 112 minor), corresponding to a cumulative incidence of 3.6% and an annual incidence of 0.4% (mean annual amputation incidence reported for the Netherlands is 0.6%) (17).”

“Cornerstones of guidelines-based care are intensive glycemic control (IGC) and optimal foot care (OFC). Although health benefits and economic efficiency of intensive blood glucose control (8) and foot care programs (914) have been individually reported, the health and economic outcomes and the cost-effectiveness of both interventions have not been determined. […] OFC according to guidelines includes professional protective foot care, education of patients and staff, regular inspection of the feet, identification of the high-risk patient, treatment of nonulcerative lesions, and a multidisciplinary approach to established foot ulcers. […] All cohorts of patients simulated for the different scenarios of guidelines care resulted in improved life expectancy, QALYs gained, and reduced incidence of foot ulcers and LEA compared with standard care. The largest effects on these outcomes were obtained when patients received IGC + OFC. When comparing the independent health effects of the two guidelines strategies, OFC resulted in a greater reduction in ulcer and amputation rates than IGC. Moreover, patients who received IGC + OFC showed approximately the same LEA incidence as patients who received OFC alone. The LEA decrease obtained was proportional to the level of foot ulcer reduction attained.”

“The mean total lifetime costs of a patient under either of the three guidelines care scenarios ranged from $4,088 to $4,386. For patients receiving IGC + OFC, these costs resulted in <$25,000 per QALY gained (relative to standard care). For patients receiving IGC alone, the ICER [here’s a relevant link – US] obtained was $32,057 per QALY gained, and for those receiving OFC alone, this ICER ranged from $12,169 to $220,100 per QALY gained, depending on the level of ulcer reduction attained. […] Increasing the effectiveness of preventive foot care in patients under OFC and IGC + OFC resulted in more QALYs gained, lower costs, and a more favorable ICER. The results of the simulations for the combined scenario (IGC + OFC) were rather insensitive to changes in utility weights and costing parameters. Similar results were obtained for parameter variations in the other two scenarios (IGC and OFC separately).”

“The results of this study suggest that IGC + OFC reduces foot ulcers and amputations and leads to an improvement in life expectancy. Greater health benefits are obtained with higher levels of foot ulcer prevention. Although care according to guidelines increases health costs, the cost per QALY gained is <$25,000, even for levels of preventive foot care as low as 10%. ICERs of this order are cost-effective according to the stratification of interventions for diabetes recently proposed (32). […] IGC falls into the category of a possibly cost-effective intervention in the management of the diabetic foot. Although it does not produce significant reduction in foot ulcers and LEA, its effectiveness resides in the slowing of neuropathy progression rates.

Extrapolating our results to a practical situation, if IGC + OFC was to be given to all diabetic patients in the Netherlands, with the aim of reducing LEA by 50% (St. Vincent’s declaration), the cost per QALY gained would be $12,165 and the cost for managing diabetic ulcers and amputations would decrease by 53 and 58%, respectively. From a policy perspective, this is clearly cost-effective and cost saving compared with current care.”

ii. Early Glycemic Control, Age at Onset, and Development of Microvascular Complications in Childhood-Onset Type 1 Diabetes.

“The aim of this work was to study the impact of glycemic control (HbA1c) early in disease and age at onset on the occurrence of incipient diabetic nephropathy (MA) and background retinopathy (RP) in childhood-onset type 1 diabetes.

RESEARCH DESIGN AND METHODS—All children, diagnosed at 0–14 years in a geographically defined area in northern Sweden between 1981 and 1992, were identified using the Swedish Childhood Diabetes Registry. From 1981, a nationwide childhood diabetes care program was implemented recommending intensified insulin treatment. HbA1c and urinary albumin excretion were analyzed, and fundus photography was performed regularly. Retrospective data on all 94 patients were retrieved from medical records and laboratory reports.

RESULTS—During the follow-up period, with a mean duration of 12 ± 4 years (range 5–19), 17 patients (18%) developed MA, 45 patients (48%) developed RP, and 52% had either or both complications. A Cox proportional hazard regression, modeling duration to occurrence of MA or RP, showed that glycemic control (reflected by mean HbA1c) during the follow-up was significantly associated with both MA and RP when adjusted for sex, birth weight, age at onset, and tobacco use as potential confounders. Mean HbA1c during the first 5 years of diabetes was a near-significant determinant for development of MA (hazard ratio 1.41, P = 0.083) and a significant determinant of RP (1.32, P = 0.036). The age at onset of diabetes significantly influenced the risk of developing RP (1.11, P = 0.021). Thus, in a Kaplan-Meier analysis, onset of diabetes before the age of 5 years, compared with the age-groups 5–11 and >11 years, showed a longer time to occurrence of RP (P = 0.015), but no clear tendency was seen for MA, perhaps due to lower statistical power.

CONCLUSIONS—Despite modern insulin treatment, >50% of patients with childhood-onset type 1 diabetes developed detectable diabetes complications after ∼12 years of diabetes. Inadequate glycemic control, also during the first 5 years of diabetes, seems to accelerate time to occurrence, whereas a young age at onset of diabetes seems to prolong the time to development of microvascular complications. […] The present study and other studies (15,54) indicate that children with an onset of diabetes before the age of 5 years may have a prolonged time to development of microvascular complications. Thus, the youngest age-groups, who are most sensitive to hypoglycemia with regard to risk of persistent brain damage, may have a relative protection during childhood or a longer time to development of complications.”

It’s important to note that although some people reading the study may think this is all ancient history (people diagnosed in the 80es?), to a lot of people it really isn’t. The study is of great personal interest to me, as I was diagnosed in ’87; if it had been a Danish study rather than a Swedish one I might well have been included in the analysis.

Another note to add in the context of the above coverage is that unlike what the authors of the paper seem to think/imply, hypoglycemia may not be the only relevant variable of interest in the context of the effect of childhood diabetes on brain development, where early diagnosis has been observed to tend to lead to less favourable outcomes – other variables which may be important include DKA episodes and perhaps also chronic hyperglycemia during early childhood. See this post for more stuff on these topics.

Some more stuff from the paper:

“The annual incidence of type 1 diabetes in northern Sweden in children 0–14 years of age is now ∼31/100,000. During the time period 1981–1992, there has been an increase in the annual incidence from 19 to 31/100,000 in northern Sweden. This is similar to the rest of Sweden […]. Seventeen (18%) of the 94 patients fulfilled the criteria for MA during the follow-up period. None of the patients developed overt nephropathy, elevated serum creatinine, or had signs of any other kidney disorder, e.g., hematuria, during the follow-up period. […] The mean time to diagnosis of MA was 9 ± 3 years (range 4–15) from diabetes onset. Forty-five (48%) of the 94 patients fulfilled the criteria for RP during the follow-up period. None of the patients developed proliferative retinopathy or were treated with photocoagulation. The mean time to diagnosis of RP was 11 ± 4 years (range 4–19) from onset of diabetes. Of the 45 patients with RP, 13 (29%) had concomitant MA, and thus 13 (76.5%) of the 17 patients with MA had concomitant RP. […] Altogether, among the 94 patients, 32 (34%) had isolated RP, 4 (4%) had isolated MA, and 13 (14%) had combined RP and MA. Thus, 49 (52%) patients had either one or both complications and, hence, 45 (48%) had neither of these complications.”

“When modeling MA as a function of glycemic level up to the onset of MA or during the entire follow-up period, adjusting for sex, birth weight, age at onset of diabetes, and tobacco use, only glycemic control had a significant effect. An increase in hazard ratio (HR) of 83% per one percentage unit increase in mean HbA1c was seen. […] The increase in HR of developing RP for each percentage unit rise in HbA1c during the entire follow-up period was 43% and in the early period 32%. […] Age at onset of diabetes was a weak but significant independent determinant for the development of RP in all regression models (P = 0.015, P = 0.018, and P = 0.010, respectively). […] Despite that this study was relatively small and had a retrospective design, we were able to show that the glycemic level already during the first 5 years may be an important predictor of later development of both MA and RP. This is in accordance with previous prospective follow-up studies (16,30).”

“Previously, male sex, smoking, and low birth weight have been shown to be risk factors for the development of nephropathy and retinopathy (6,4549). However, in this rather small retrospective study with a limited follow-up time, we could not confirm these associations”. This may just be because of lack of power, it’s a relatively small study. Again, this is/was of personal interest to me; two of those three risk factors apply to me, and neither of those risk factors are modifiable.

iii. Eighteen Years of Fair Glycemic Control Preserves Cardiac Autonomic Function in Type 1 Diabetes.

“Reduced cardiovascular autonomic function is associated with increased mortality in both type 1 and type 2 diabetes (14). Poor glycemic control plays an important role in the development and progression of diabetic cardiac autonomic dysfunction (57). […] Diabetic cardiovascular autonomic neuropathy (CAN) can be defined as impaired function of the peripheral autonomic nervous system. Exercise intolerance, resting tachycardia, and silent myocardial ischemia may be early signs of cardiac autonomic dysfunction (9).The most frequent finding in subclinical and symptomatic CAN is reduced heart rate variability (HRV) (10). […] No other studies have followed type 1 diabetic patients on intensive insulin treatment during ≥14-year periods and documented cardiac autonomic dysfunction. We evaluated the association between 18 years’ mean HbA1c and cardiac autonomic function in a group of type 1 diabetic patients with 30 years of disease duration.”

“A total of 39 patients with type 1 diabetes were followed during 18 years, and HbA1c was measured yearly. At 18 years follow-up heart rate variability (HRV) measurements were used to assess cardiac autonomic function. Standard cardiac autonomic tests during normal breathing, deep breathing, the Valsalva maneuver, and the tilt test were performed. Maximal heart rate increase during exercise electrocardiogram and minimal heart rate during sleep were also used to describe cardiac autonomic function.

RESULTS—We present the results for patients with mean HbA1c <8.4% (two lowest HbA1c tertiles) compared with those with HbA1c ≥8.4% (highest HbA1c tertile). All of the cardiac autonomic tests were significantly different in the high- and the low-HbA1c groups, and the most favorable scores for all tests were seen in the low-HbA1c group. In the low-HbA1c group, the HRV was 40% during deep breathing, and in the high-HbA1c group, the HRV was 19.9% (P = 0.005). Minimal heart rate at night was significantly lower in the low-HbA1c groups than in the high-HbA1c group (P = 0.039). With maximal exercise, the increase in heart rate was significantly higher in the low-HbA1c group compared with the high-HbA1c group (P = 0.001).

CONCLUSIONS—Mean HbA1c during 18 years was associated with cardiac autonomic function. Cardiac autonomic function was preserved with HbA1c <8.4%, whereas cardiac autonomic dysfunction was impaired in the group with HbA1c ≥8.4%. […] The study underlines the importance of good glycemic control and demonstrates that good long-term glycemic control is associated with preserved cardiac autonomic function, whereas a lack of good glycemic control is associated with cardiac autonomic dysfunction.”

These results are from Norway (Oslo), and again they seem relevant to me personally (‘from a statistical point of view’) – I’ve had diabetes for about as long as the people they included in the study.

iv. The Mental Health Comorbidities of Diabetes.

“Individuals living with type 1 or type 2 diabetes are at increased risk for depression, anxiety, and eating disorder diagnoses. Mental health comorbidities of diabetes compromise adherence to treatment and thus increase the risk for serious short- and long-term complications […] Young adults with type 1 diabetes are especially at risk for poor physical and mental health outcomes and premature mortality. […] we summarize the prevalence and consequences of mental health problems for patients with type 1 or type 2 diabetes and suggest strategies for identifying and treating patients with diabetes and mental health comorbidities.”

“Major advances in the past 2 decades have improved understanding of the biological basis for the relationship between depression and diabetes.2 A bidirectional relationship might exist between type 2 diabetes and depression: just as type 2 diabetes increases the risk for onset of major depression, a major depressive disorder signals increased risk for on set of type 2 diabetes.2 Moreover, diabetes distress is now recognized as an entity separate from major depressive disorder.2 Diabetes distress occurs because virtually all of diabetes care involves self-management behavior—requiring balance of a complex set of behavioral tasks by the person and family, 24 hours a day, without “vacation” days. […] Living with diabetes is associated with a broad range of diabetes-related distresses, such as feeling over-whelmed with the diabetes regimen; being concerned about the future and the possibility of serious complications; and feeling guilty when management is going poorly. This disease burden and emotional distress in individuals with type 1 or type 2 diabetes, even at levels of severity below the threshold for a psychiatric diagnosis of depression or anxiety, are associated with poor adherence to treatment, poor glycemic control, higher rates of diabetes complications, and impaired quality of life. […] Depression in the context of diabetes is […] associated with poor self-care with respect to diabetes treatment […] Depression among individuals with diabetes is also associated with increased health care use and expenditures, irrespective of age, sex, race/ethnicity, and health insurance status.3

“Women with type 1 diabetes have a 2-fold increased risk for developing an eating disorder and a 1.9-fold increased risk for developing subthreshold eating disorders than women without diabetes.6 Less is known about eating disorders in boys and men with diabetes. Disturbed eating behaviors in women with type 1 diabetes include binge eating and caloric purging through insulin restriction, with rates of these disturbed eating behaviors reported to occur in 31% to 40% of women with type 1 diabetes aged between 15 and 30 years.6 […] disordered eating behaviors persist and worsen over time. Women with type 1 diabetes and eating disorders have poorer glycemic control, with higher rates of hospitalizations and retinopathy, neuropathy, and premature death compared with similarly aged women with type 1 diabetes without eating disorders.6 […] few diabetes clinics provide mental health screening or integrate mental/behavioral health services in diabetes clinical care.4 It is neither practical nor affordable to use standardized psychiatric diagnostic interviews to diagnose mental health comorbidities in individuals with diabetes. Brief paper-and-pencil self-report measures such as the Beck Depression Inventory […] that screen for depressive symptoms are practical in diabetes clinical settings, but their use remains rare.”

The paper does not mention this, but it is important to note that there are multiple plausible biological pathways which might help to explain bidirectional linkage between depression and type 2 diabetes. Physiological ‘stress’ (think: inflammation) is likely to be an important factor, and so are the typical physiological responses to some of the pharmacological treatments used to treat depression (…as well as other mental health conditions); multiple drugs used in psychiatry, including tricyclic antidepressants, cause weight gain and have proven diabetogenic effects – I’ve covered these topics before here on the blog. I’ve incidentally also covered other topics touched briefly upon in the paper – here’s for example a more comprehensive post about screening for depression in the diabetes context, and here’s a post with some information about how one might go about screening for eating disorders; skin signs are important. I was a bit annoyed that the author of the above paper did not mention this, as observing whether or not Russell’s sign – which is a very reliable indicator of eating disorder – is present or not is easier/cheaper/faster than performing any kind of even semi-valid depression screen.

v. Diabetes, Depression, and Quality of Life. This last one covers topics related to the topics covered in the paper above.

“The study consisted of a representative population sample of individuals aged ≥15 years living in South Australia comprising 3,010 personal interviews conducted by trained health interviewers. The prevalence of depression in those suffering doctor-diagnosed diabetes and comparative effects of diabetic status and depression on quality-of-life dimensions were measured.

RESULTS—The prevalence of depression in the diabetic population was 24% compared with 17% in the nondiabetic population. Those with diabetes and depression experienced an impact with a large effect size on every dimension of the Short Form Health-Related Quality-of-Life Questionnaire (SF-36) as compared with those who suffered diabetes and who were not depressed. A supplementary analysis comparing both depressed diabetic and depressed nondiabetic groups showed there were statistically significant differences in the quality-of-life effects between the two depressed populations in the physical and mental component summaries of the SF-36.

CONCLUSIONS—Depression for those with diabetes is an important comorbidity that requires careful management because of its severe impact on quality of life.”

I felt slightly curious about the setup after having read this, because representative population samples of individuals should not in my opinion yield depression rates of either 17% nor 24%. Rates that high suggest to me that the depression criteria used in the paper are a bit ‘laxer’/more inclusive than what you see in some other contexts when reading this sort of literature – to give an example of what I mean, the depression screening post I link to above noted that clinical or major depression occurred in 11.4% of people with diabetes, compared to a non-diabetic prevalence of 5%. There’s a long way from 11% to 24% and from 5% to 17%. Another potential explanation for such a high depression rate could of course also be some sort of selection bias at the data acquisition stage, but that’s obviously not the case here. However 3000 interviews is a lot of interviews, so let’s read on…

“Several studies have assessed the impact of depression in diabetes in terms of the individual’s functional ability or quality of life (3,4,13). Brown et al. (13) examined preference-based time tradeoff utility values associated with diabetes and showed that those with diabetes were willing to trade a significant proportion of their remaining life in return for a diabetes-free health state.”

“Depression was assessed using the mood module of the Primary Care Evaluation of Mental Disorders questionnaire. This has been validated to provide estimates of mental disorder comparable with those found using structured and longer diagnostic interview schedules (16). The mental disorders examined in the questionnaire included major depressive disorder, dysthymia, minor depressive disorder, and bipolar disorder. [So yes, the depression criteria used in this study are definitely more inclusive than depression criteria including only people with MDD] […] The Short Form Health-Related Quality-of-Life Questionnaire (SF-36) was also included to assess the quality of life of the different population groups with and without diabetes. […] Five groups were examined: the overall population without diabetes and without depression; the overall diabetic population; the depression-only population; the diabetic population without depression; and the diabetic population with depression.”

“Of the population sample, 205 (6.8%) were classified as having major depression, 130 (4.3%) had minor depression, 105 (3.5%) had partial remission of major depression, 79 (2.6%) had dysthymia, and 5 (0.2%) had bipolar disorder (depressed phase). No depressive syndrome was detected in 2,486 (82.6%) respondents. The population point prevalence of doctor-diagnosed diabetes in this survey was 5.2% (95% CI 4.6–6.0). The prevalence of depression in the diabetic population was 23.6% (22.1–25.1) compared with 17.1% (15.8–18.4) in the nondiabetic population. This difference approached statistical significance (P = 0.06). […] There [was] a clear difference in the quality-of-life scores for the diabetic and depression group when compared with the diabetic group without depression […] Overall, the highest quality-of-life scores are experienced by those without diabetes and depression and the lowest by those with diabetes and depression. […] the standard scores of those with no diabetes have quality-of-life status comparable with the population mean or slightly better. At the other extreme those with diabetes and depression experience the most severe comparative impact on quality-of-life for every dimension. Between these two extremes, diabetes overall and the diabetes without depression groups have a moderate-to-severe impact on the physical functioning, role limitations (physical), and general health scales […] The results of the two-factor ANOVA showed that the interaction term was significant only for the PCS [Physical Component Score – US] scale, indicating a greater than additive effect of diabetes and depression on the physical health dimension.”

“[T]here was a significant interaction between diabetes and depression on the PCS but not on the MCS [Mental Component Score. Do note in this context that the no-interaction result is far from certain, because as they observe: “it may simply be sample size that has not allowed us to observe a greater than additive effect in the MCS scale. Although there was no significant interaction between diabetes and depression and the MCS scale, we did observe increases on the effect size for the mental health dimensions”]. One explanation for this finding might be that depression can influence physical outcomes, such as recovery from myocardial infarction, survival with malignancy, and propensity to infection. Various mechanisms have been proposed for this, including changes to the immune system (24). Other possibilities are that depression in diabetes may affect the capacity to maintain medication vigilance, maintain a good diet, and maintain other lifestyle factors, such as smoking and exercise, all of which are likely possible pathways for a greater than additive effect. Whatever the mechanism involved, these data indicate that the addition of depression to diabetes has a severe impact on quality of life, and this needs to be managed in clinical practice.”

May 25, 2017 Posted by | Cardiology, Diabetes, Medicine, Nephrology, Neurology, Papers, Personal, Pharmacology, Psychiatry, Psychology | Leave a comment

A few diabetes papers of interest

i. Association Between Blood Pressure and Adverse Renal Events in Type 1 Diabetes.

“The Joint National Committee and American Diabetes Association guidelines currently recommend a blood pressure (BP) target of <140/90 mmHg for all adults with diabetes, regardless of type (13). However, evidence used to support this recommendation is primarily based on data from trials of type 2 diabetes (46). The relationship between BP and adverse outcomes in type 1 and type 2 diabetes may differ, given that the type 1 diabetes population is typically much younger at disease onset, hypertension is less frequently present at diagnosis (3), and the basis for the pathophysiology and disease complications may differ between the two populations.

Prior prospective cohort studies (7,8) of patients with type 1 diabetes suggested that lower BP levels (<110–120/70–80 mmHg) at baseline entry were associated with a lower risk of adverse renal outcomes, including incident microalbuminuria. In one trial of antihypertensive treatment in type 1 diabetes (9), assignment to a lower mean arterial pressure (MAP) target of <92 mmHg (corresponding to ∼125/75 mmHg) led to a significant reduction in proteinuria compared with a MAP target of 100–107 mmHg (corresponding to ∼130–140/85–90 mmHg). Thus, it is possible that lower BP (<120/80 mmHg) reduces the risk of important renal outcomes, such as proteinuria, in patients with type 1 diabetes and may provide a synergistic benefit with intensive glycemic control on renal outcomes (1012). However, fewer studies have examined the association between BP levels over time and the risk of more advanced renal outcomes, such as stage III chronic kidney disease (CKD) or end-stage renal disease (ESRD)”.

“The primary objective of this study was to determine whether there is an association between lower BP levels and the risk of more advanced diabetic nephropathy, defined as macroalbuminuria or stage III CKD, within a background of different glycemic control strategies […] We included 1,441 participants with type 1 diabetes between the ages of 13 and 39 years who had previously been randomized to receive intensive versus conventional glycemic control in the Diabetes Control and Complications Trial (DCCT). The exposures of interest were time-updated systolic BP (SBP) and diastolic BP (DBP) categories. Outcomes included macroalbuminuria (>300 mg/24 h) or stage III chronic kidney disease (CKD) […] During a median follow-up time of 24 years, there were 84 cases of stage III CKD and 169 cases of macroalbuminuria. In adjusted models, SBP in the 2 (95% CI 1.05–1.21), and a 1.04 times higher risk of ESRD (95% CI 0.77–1.41) in adjusted Cox models. Every 10 mmHg increase in DBP was associated with a 1.17 times higher risk of microalbuminuria (95% CI 1.03–1.32), a 1.15 times higher risk of eGFR decline to 2 (95% CI 1.04–1.29), and a 0.80 times higher risk of ESRD (95% CI 0.47–1.38) in adjusted models. […] Because these data are observational, they cannot prove causation. It remains possible that subtle kidney disease may lead to early elevations in BP, and we cannot rule out the potential for reverse causation in our findings. However, we note similar trends in our data even when imposing a 7-year lag between BP and CKD ascertainment.”

CONCLUSIONS A lower BP (<120/70 mmHg) was associated with a substantially lower risk of adverse renal outcomes, regardless of the prior assigned glycemic control strategy. Interventional trials may be useful to help determine whether the currently recommended BP target of 140/90 mmHg may be too high for optimal renal protection in type 1 diabetes.”

It’s important to keep in mind when interpreting these results that endpoints like ESRD and stage III CKD are not the only relevant outcomes in this setting; even mild-stage kidney disease in diabetics significantly increase the risk of death from cardiovascular disease, and a substantial proportion of patients may die from cardiovascular disease before reaching a late-stage kidney disease endpoint (here’s a relevant link).

Identifying Causes for Excess Mortality in Patients With Diabetes: Closer but Not There Yet.

“A number of epidemiological studies have quantified the risk of death among patients with diabetes and assessed the causes of death (26), with highly varying results […] Overall, the studies to date have confirmed that diabetes is associated with an increased risk of all-cause mortality, but the magnitude of this excess risk is highly variable, with the relative risk ranging from 1.15 to 3.15. Nevertheless, all studies agree that mortality is mainly attributable to cardiovascular causes (26). On the other hand, studies of cancer-related death have generally been lacking despite the diabetes–cancer association and a number of plausible biological mechanisms identified to explain this link (8,9). In fact, studies assessing the specific causes of noncardiovascular death in diabetes have been sparse. […] In this issue of Diabetes Care, Baena-Díez et al. (10) report on an observational study of the association between diabetes and cause-specific death. This study involved 55,292 individuals from 12 Spanish population cohorts with no prior history of cardiovascular disease, aged 35 to 79 years, with a 10-year follow-up. […] This study found that individuals with diabetes compared with those without diabetes had a higher risk of cardiovascular death, cancer death, and noncardiovascular noncancer death with similar estimates obtained using the two statistical approaches. […] Baena-Díez et al. (10) showed that individuals with diabetes have an approximately threefold increased risk of cardiovascular mortality, which is much higher than what has been reported by recent studies (5,6). While this may be due to the lack of adjustment for important confounders in this study, there remains uncertainty regarding the magnitude of this increase.”

“[A]ll studies of excess mortality associated with diabetes, including the current one, have produced highly variable results. The reasons may be methodological. For instance, it may be that because of the wide range of age in these studies, comparing the rates of death between the patients with diabetes and those without diabetes using a measure based on the ratio of the rates may be misleading because the ratio can vary by age [it almost certainly does vary by age, US]. Instead, a measure based on the difference in rates may be more appropriate (16). Another issue relates to the fact that the studies include patients with longstanding diabetes of variable duration, resulting in so-called prevalent cohorts that can result in muddled mortality estimates since these are necessarily based on a mix of patients at different stages of disease (17). Thus, a paradigm change may be in order for future observational studies of diabetes and mortality, in the way they are both designed and analyzed. With respect to cancer, such studies will also need to tease out the independent contribution of antidiabetes treatments on cancer incidence and mortality (1820). It is thus clear that the quantification of the excess mortality associated with diabetes per se will need more accurate tools.”

iii. Risk of Cause-Specific Death in Individuals With Diabetes: A Competing Risks Analysis. This is the paper some of the results of which were discussed above. I’ll just include the highlights here:

RESULTS We included 55,292 individuals (15.6% with diabetes and overall mortality of 9.1%). The adjusted hazard ratios showed that diabetes increased mortality risk: 1) cardiovascular death, CSH = 2.03 (95% CI 1.63–2.52) and PSH = 1.99 (1.60–2.49) in men; and CSH = 2.28 (1.75–2.97) and PSH = 2.23 (1.70–2.91) in women; 2) cancer death, CSH = 1.37 (1.13–1.67) and PSH = 1.35 (1.10–1.65) in men; and CSH = 1.68 (1.29–2.20) and PSH = 1.66 (1.25–2.19) in women; and 3) noncardiovascular noncancer death, CSH = 1.53 (1.23–1.91) and PSH = 1.50 (1.20–1.89) in men; and CSH = 1.89 (1.43–2.48) and PSH = 1.84 (1.39–2.45) in women. In all instances, the cumulative mortality function was significantly higher in individuals with diabetes.

CONCLUSIONS Diabetes is associated with premature death from cardiovascular disease, cancer, and noncardiovascular noncancer causes.”

“Summary

Diabetes is associated with premature death from cardiovascular diseases (coronary heart disease, stroke, and heart failure), several cancers (liver, colorectal, and lung), and other diseases (chronic obstructive pulmonary disease and liver and kidney disease). In addition, the cause-specific cumulative mortality for cardiovascular, cancer, and noncardiovascular noncancer causes was significantly higher in individuals with diabetes, compared with the general population. The dual analysis with CSH and PSH methods provides a comprehensive view of mortality dynamics in the population with diabetes. This approach identifies the individuals with diabetes as a vulnerable population for several causes of death aside from the traditionally reported cardiovascular death.”

iv. Disability-Free Life-Years Lost Among Adults Aged ≥50 Years With and Without Diabetes.

RESEARCH DESIGN AND METHODS Adults (n = 20,008) aged 50 years and older were followed from 1998 to 2012 in the Health and Retirement Study, a prospective biannual survey of a nationally representative sample of adults. Diabetes and disability status (defined by mobility loss, difficulty with instrumental activities of daily living [IADL], and/or difficulty with activities of daily living [ADL]) were self-reported. We estimated incidence of disability, remission to nondisability, and mortality. We developed a discrete-time Markov simulation model with a 1-year transition cycle to predict and compare lifetime disability-related outcomes between people with and without diabetes. Data represent the U.S. population in 1998.

RESULTS From age 50 years, adults with diabetes died 4.6 years earlier, developed disability 6–7 years earlier, and spent about 1–2 more years in a disabled state than adults without diabetes. With increasing baseline age, diabetes was associated with significant (P < 0.05) reductions in the number of total and disability-free life-years, but the absolute difference in years between those with and without diabetes was less than at younger baseline age. Men with diabetes spent about twice as many of their remaining years disabled (20–24% of remaining life across the three disability definitions) as men without diabetes (12–16% of remaining life across the three disability definitions). Similar associations between diabetes status and disability-free and disabled years were observed among women.

CONCLUSIONS Diabetes is associated with a substantial reduction in nondisabled years, to a greater extent than the reduction of longevity. […] Using a large, nationally representative cohort of Americans aged 50 years and older, we found that diabetes is associated with a substantial deterioration of nondisabled years and that this is a greater number of years than the loss of longevity associated with diabetes. On average, a middle-aged adult with diabetes has an onset of disability 6–7 years earlier than one without diabetes, spends 1–2 more years with disability, and loses 7 years of disability-free life to the condition. Although other nationally representative studies have reported large reductions in complications (9) and mortality among the population with diabetes in recent decades (1), these studies, akin to our results, suggest that diabetes continues to have a substantial impact on morbidity and quality of remaining years of life.”

v. Association Between Use of Lipid-Lowering Therapy and Cardiovascular Diseases and Death in Individuals With Type 1 Diabetes.

“People with type 1 diabetes have a documented shorter life expectancy than the general population without diabetes (1). Cardiovascular disease (CVD) is the main cause of the excess morbidity and mortality, and despite advances in management and therapy, individuals with type 1 diabetes have a markedly elevated risk of cardiovascular events and death compared with the general population (2).

Lipid-lowering treatment with hydroxymethylglutaryl-CoA reductase inhibitors (statins) prevents major cardiovascular events and death in a broad spectrum of patients (3,4). […] We hypothesized that primary prevention with lipid-lowering therapy (LLT) can reduce the incidence of cardiovascular morbidity and mortality in individuals with type 1 diabetes. The aim of the study was to examine this in a nationwide longitudinal cohort study of patients with no history of CVD. […] A total of 24,230 individuals included in 2006–2008 NDR with type 1 diabetes without a history of CVD were followed until 31 December 2012; 18,843 were untreated and 5,387 treated with LLT [Lipid-Lowering Therapy] (97% statins). The mean follow-up was 6.0 years. […] Hazard ratios (HRs) for treated versus untreated were as follows: cardiovascular death 0.60 (95% CI 0.50–0.72), all-cause death 0.56 (0.48–0.64), fatal/nonfatal stroke 0.56 (0.46–0.70), fatal/nonfatal acute myocardial infarction 0.78 (0.66–0.92), fatal/nonfatal coronary heart disease 0.85 (0.74–0.97), and fatal/nonfatal CVD 0.77 (0.69–0.87).

CONCLUSIONS This observational study shows that LLT is associated with 22–44% reduction in the risk of CVD and cardiovascular death among individuals with type 1 diabetes without history of CVD and underlines the importance of primary prevention with LLT to reduce cardiovascular risk in type 1 diabetes.”

vi. Prognostic Classification Factors Associated With Development of Multiple Autoantibodies, Dysglycemia, and Type 1 Diabetes—A Recursive Partitioning Analysis.

“In many prognostic factor studies, multivariate analyses using the Cox proportional hazards model are applied to identify independent prognostic factors. However, the coefficient estimates derived from the Cox proportional hazards model may be biased as a result of violating assumptions of independence. […] RPA [Recursive Partitioning Analysis] classification is a useful tool that could prioritize the prognostic factors and divide the subjects into distinctive groups. RPA has an advantage over the proportional hazards model in identifying prognostic factors because it does not require risk factor independence and, as a nonparametric technique, makes no requirement on the underlying distributions of the variables considered. Hence, it relies on fewer modeling assumptions. Also, because the method is designed to divide subjects into groups based on the length of survival, it defines groupings for risk classification, whereas Cox regression models do not. Moreover, there is no need to explicitly include covariate interactions because of the recursive splitting structure of tree model construction.”

“This is the first study that characterizes the risk factors associated with the transition from one preclinical stage to the next following a recommended staging classification system (9). The tree-structured prediction model reveals that the risk parameters are not the same across each transition. […] Based on the RPA classification, the subjects at younger age and with higher GAD65Ab [an important biomarker in the context of autoimmune forms of diabetes, US – here’s a relevant link] titer are at higher risk for progression to multiple positive autoantibodies from a single autoantibody (seroconversion). Approximately 70% of subjects with a single autoantibody were positive for GAD65Ab, much higher than for insulin autoantibody (24%) and IA-2A [here’s a relevant link – US] (5%). Our study results are consistent with those of others (2224) in that seroconversion is age related. Previous studies in infants and children at an early age have shown that progression from single to two or more autoantibodies occurs more commonly in children 25). The subjects ≤16 years of age had almost triple the 5-year risk compared with subjects >16 years of age at the same GAD65Ab titer level. Hence, not all individuals with a single islet autoantibody can be thought of as being at low risk for disease progression.”

“This is the first study that identifies the risk factors associated with the timing of transitions from one preclinical stage to the next in the development of T1D. Based on RPA risk parameters, we identify the characteristics of groups with similar 5-year risks for advancing to the next preclinical stage. It is clear that individuals with one or more autoantibodies or with dysglycemia are not homogeneous with regard to the risk of disease progression. Also, there are differences in risk factors at each stage that are associated with increased risk of progression. The potential benefit of identifying these groups allows for a more informed discussion of diabetes risk and the selective enrollment of individuals into clinical trials whose risk more appropriately matches the potential benefit of an experimental intervention. Since the risk levels in these groups are substantial, their definition makes possible the design of more efficient trials with target sample sizes that are feasible, opening up the field of prevention to additional at-risk cohorts. […] Our results support the evidence that autoantibody titers are strong predictors at each transition leading to T1D development. The risk of the development of multiple autoantibodies was significantly increased when the GAD65Ab titer level was elevated, and the risk of the development of dysglycemia was increased when the IA-2A titer level increased. These indicate that better risk prediction on the timing of transitions can be obtained by evaluating autoantibody titers. The results also suggest that an autoantibody titer should be carefully considered in planning prevention trials for T1D in addition to the number of positive autoantibodies and the type of autoantibody.”

May 17, 2017 Posted by | Diabetes, Immunology, Medicine, Nephrology, Statistics, Studies | Leave a comment

A few diabetes papers of interest

A couple of weeks ago I decided to cover some of the diabetes articles I’d looked at and bookmarked in the past, but there were a lot of articles and I did not get very far. This post will cover some more of these articles I had failed to cover here despite intending to do so at some point. Considering that I these days relatively regularly peruse e.g. the Diabetes Care archives I am thinking of making this sort of post a semi-regular feature of the blog.

i. Association Between Diabetes and Hippocampal Atrophy in Elderly Japanese: The Hisayama Study.

“A total of 1,238 community-dwelling Japanese subjects aged ≥65 years underwent brain MRI scans and a comprehensive health examination in 2012. Total brain volume (TBV), intracranial volume (ICV), and hippocampal volume (HV) were measured using MRI scans for each subject. We examined the associations between diabetes-related parameters and the ratios of TBV to ICV (an indicator of global brain atrophy), HV to ICV (an indicator of hippocampal atrophy), and HV to TBV (an indicator of hippocampal atrophy beyond global brain atrophy) after adjustment for other potential confounders.”

“The multivariable-adjusted mean values of the TBV-to-ICV, HV-to-ICV, and HV-to-TBV ratios were significantly lower in the subjects with diabetes compared with those without diabetes (77.6% vs. 78.2% for the TBV-to-ICV ratio, 0.513% vs. 0.529% for the HV-to-ICV ratio, and 0.660% vs. 0.676% for the HV-to-TBV ratio; all P < 0.01). These three ratios decreased significantly with elevated 2-h postload glucose (PG) levels […] Longer duration of diabetes was significantly associated with lower TBV-to-ICV, HV-to-ICV, and HV-to-TBV ratios. […] Our data suggest that a longer duration of diabetes and elevated 2-h PG levels, a marker of postprandial hyperglycemia, are risk factors for brain atrophy, particularly hippocampal atrophy.”

“Intriguingly, our findings showed that the subjects with diabetes had significantly lower mean HV-to-TBV ratio values, indicating […] that the hippocampus is predominantly affected by diabetes. In addition, in our subjects a longer duration and a midlife onset of diabetes were significantly associated with a lower HV, possibly suggesting that a long exposure of diabetes particularly worsens hippocampal atrophy.”

The reason why hippocampal atrophy is a variable of interest to these researchers is that hippocampal atrophy is a feature of Alzheimer’s Disease, and diabetics have an elevated risk of AD. This is incidentally far from the first study providing some evidence for the existence of potential causal linkage between impaired glucose homeostasis and AD (see e.g. also this paper, which I’ve previously covered here on the blog).

ii. A Population-Based Study of All-Cause Mortality and Cardiovascular Disease in Association With Prior History of Hypoglycemia Among Patients With Type 1 Diabetes.

“Although patients with T1DM may suffer more frequently from hypoglycemia than those with T2DM (8), very few studies have investigated whether hypoglycemia may also increase the risk of CVD (6,9,10) or death (1,6,7) in patients with T1DM; moreover, the results of these studies have been inconclusive (6,9,10) because of the dissimilarities in their methodological aspects, including their enrollment of populations with T1DM with different levels of glycemic control, application of different data collection methods, and adoption of different lengths of observational periods.”

“Only a few population-based studies have examined the potential cumulative effect of repeated severe hypoglycemia on all-cause mortality or CVD incidence in T1DM (9). The Action to Control Cardiovascular Risk in Diabetes (ACCORD) study of T2DM found a weakly inverse association between the annualized number of hypoglycemic episodes and the risk of death (11,12). By contrast, some studies find that repeated hypoglycemia may be an aggravating factor to atherosclerosis in T1DM (13,14). Studies on the compromised sympathetic-adrenal reaction in patients with repeated hypoglycemia have been inconclusive regarding whether such a reaction may further damage intravascular coagulation and thrombosis (15) or decrease the vulnerability of these patients to adverse health outcomes (12).

Apart from the lack of information on the potential dose–gradient effect associated with severe hypoglycemic events in T1DM from population-based studies, the risks of all-cause mortality/CVD incidence associated with severe hypoglycemia occurring at different periods before all-cause mortality/CVD incidence have never been examined. In this study, we used the population-based medical claims of a cohort of patients with T1DM to examine whether the risks of all-cause mortality/CVD incidence are associated with previous episodes of severe hypoglycemia in different periods and whether severe hypoglycemia may pose a dose–gradient effect on the risks of all-cause mortality/CVD incidence.”

“Two nested case-control studies with age- and sex-matched control subjects and using the time-density sampling method were performed separately within a cohort of 10,411 patients with T1DM in Taiwan. The study enrolled 564 nonsurvivors and 1,615 control subjects as well as 743 CVD case subjects and 1,439 control subjects between 1997 and 2011. History of severe hypoglycemia was identified during 1 year, 1–3 years, and 3–5 years before the occurrence of the study outcomes.”

“Prior severe hypoglycemic events within 1 year were associated with higher risks of all-cause mortality and CVD (adjusted OR 2.74 [95% CI 1.96–3.85] and 2.02 [1.35–3.01], respectively). Events occurring within 1–3 years and 3–5 years before death were also associated with adjusted ORs of 1.94 (95% CI 1.39–2.71) and 1.68 (1.15–2.44), respectively. Significant dose–gradient effects of severe hypoglycemia frequency on mortality and CVD were observed within 5 years. […] we found that a greater frequency of severe hypoglycemia occurring 1 year before death was significantly associated with a higher OR of all-cause mortality (1 vs. 0: 2.45 [95% CI 1.65–3.63]; ≥2 vs. 0: 3.49 [2.01–6.08], P < 0.001 for trend). Although the strength of the association was attenuated, a significant dose–gradient effect still existed for severe hypoglycemia occurring in 1–3 years (P < 0.001 for trend) and 3–5 years (P < 0.015 for trend) before death. […] Exposure to repeated severe hypoglycemic events can lead to higher risks of mortality and CVD.”

“Our findings are supported by two previous studies that investigated atherosclerosis risk in T1DM (13,14). The DCCT/EDIC project reported that the prevalence of coronary artery calcification, an established atherosclerosis marker, was linearly correlated with the incidence rate of hypoglycemia on the DCCT stage (14). Giménez et al. (13) also demonstrated that repeated episodes of hypoglycemia were an aggravating factor for preclinical atherosclerosis in T1DM. […] The mechanism of hypoglycemia that predisposes to all-cause mortality/CVD incidence remains unclear.”

iii. Global Estimates on the Number of People Blind or Visually Impaired by Diabetic Retinopathy: A Meta-analysis From 1990 to 2010.

“On the basis of previous large-scale population-based studies and meta-analyses, diabetic retinopathy (DR) has been recognized as one of the most common and important causes for visual impairment and blindness (1–19). These studies in general showed that DR was the leading cause of blindness globally among working-aged adults and therefore has a significant socioeconomic impact (20–22).”

“A previous meta-analysis (21) summarizing 35 studies with more than 20,000 patients with diabetes estimated a prevalence of any DR of 34.6%, of diabetic macular edema of 6.8%, and of vision-threating DR of 10.2% within the diabetes population. […] Yau et al. (21) estimated that ∼93 million people had some DR and 28 million people had sight-threatening stages of DR. However, this meta-analysis did not address the prevalence of visual impairment and blindness due to DR and thus the impact of DR on the general population. […] We therefore conducted the present meta-analysis of all available population-based studies performed worldwide within the last two decades as part of the Global Burden of Disease Study 2010 (GBD) to estimate the number of people affected by blindness and visual impairment.”

“DR [Diabetic Retinopathy] ranks as the fifth most common cause of global blindness and of global MSVI [moderate and severe vision impairment] (25). […] this analysis estimates that, in 2010, 1 out of every 39 blind people had blindness due to DR and 1 out of every 52 people had visual impairment due to DR. […] Globally in 2010, out of overall 32.4 million blind and 191 million visually impaired people, 0.8 million were blind and 3.7 million were visually impaired because of DR, with an alarming increase of 27% and 64%, respectively, spanning the two decades from 1990 to 2010. DR accounted for 2.6% of all blindness in 2010 and 1.9% of all MSVI worldwide, increasing from 2.1% and 1.3%, respectively, in 1990. […] The number of persons with visual impairment due to DR worldwide is rising and represents an increasing proportion of all blindness/MSVI causes. Age-standardized prevalence of DR-related blindness/MSVI was higher in sub-Saharan Africa and South Asia.”

“Our data suggest that the percentage of blindness and MSVI attributable to DR was lower in low-income regions with younger populations than in high-income regions with older populations. There are several reasons that may explain this observation. First, low-income societies may have a higher percentage of unoperated cataract or undercorrected refractive error–related blindness and MSVI (25), which is probably related to access to visual and ocular health services. Therefore, the proportional increase in blindness and MSVI attributable to DR may be rising because of the decreasing proportion attributable to cataract (25) as a result of the increasing availability of cataract surgery in many parts of the world (29) during the past decade. Improved visualization of the fundus afforded by cataract surgery should also improve the detection of DR. The increase in the percentage of global blindness caused by DR within the last two decades took place in all world regions except Western Europe and high-income North America where there was a slight decrease. This decrease may reflect the effect of intensified prevention and treatment of DR possibly in part due to the introduction of intravitreal injections of steroids and anti-VEGF (vascular endothelial growth factor) drugs (30,31).

Second, in regions with poor medical infrastructure, patients with diabetes may not live long enough to experience DR (32). This reduces the number of patients with diabetes, and, furthermore, it reduces the number of patients with DR-related vision loss. Studies in the literature have reported that the prevalence of severe DR decreased from 1990 to 2010 (21) while the prevalence of diabetes simultaneously increased (27), which implies a reduction in the prevalence of severe DR per person with diabetes. […] Third, […] younger populations may have a lower prevalence of diabetes (33). […] Therefore, considering further economic development in rural regions, improvements in medical infrastructure, the general global demographic transition to elderly populations, and the association between increasing economic development and obesity, we project the increase in the proportion of DR-related blindness and MSVI to continue to rise in the future.”

iv. Do Patient Characteristics Impact Decisions by Clinicians on Hemoglobin A1c Targets?

“In setting hemoglobin A1c (HbA1c) targets, physicians must consider individualized risks and benefits of tight glycemic control (1,2) by recognizing that the risk-benefit ratio may become unfavorable in certain patients, including the elderly and/or those with multiple comorbidities (3,4). Customization of treatment goals based on patient characteristics is poorly understood, partly due to insufficient data on physicians’ decisions in setting targets. We used the National Health and Nutrition Examination Survey (NHANES) to analyze patient-reported HbA1c targets set by physicians and to test whether targets are correlated with patient characteristics.”

“we did not find any evidence that U.S. physicians systematically consider important patient-specific information when selecting the intensity of glycemic control. […] the lack of variation with patient characteristics suggests overreliance on a general approach, without consideration of individual variation in the risks and benefits (or patient preference) of tight control.”

v. Cardiovascular Autonomic Neuropathy, Sexual Dysfunction, and Urinary Incontinence in Women With Type 1 Diabetes.

“This study evaluated associations among cardiovascular autonomic neuropathy (CAN), female sexual dysfunction (FSD), and urinary incontinence (UI) in women with type I diabetes mellitus (T1DM). […] We studied 580 women with T1DM in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study (DCCT/EDIC).”

“At EDIC year 17, FSD was observed in 41% of women and UI in 30%. […] We found that CAN was significantly more prevalent among women with FSD and/or UI, because 41% of women with FSD and 44% with UI had positive measures of CAN compared with 30% without FSD and 38% without UI at EDIC year 16/17. We also observed bivariate associations between FSD and several measures of CAN […] In long-standing T1DM, CAN may predict development of FSD and may be a useful surrogate for generalized diabetic autonomic neuropathy.”

“Although autonomic dysfunction has been considered an important factor in the etiology of many diabetic complications, including constipation, exercise intolerance, bladder dysfunction, erectile dysfunction, orthostatic hypotension, and impaired neurovascular function, our study is among the first to systematically demonstrate a link between CAN and FSD in a large cohort of well-characterized patients with T1DM (14).”

vi. Correlates of Medication Adherence in the TODAY Cohort of Youth With Type 2 Diabetes.

“A total of 699 youth 10–17 years old with recent-onset type 2 diabetes and ≥80% adherence to metformin therapy for ≥8 weeks during a run-in period were randomized to receive one of three treatments. Participants took two study pills twice daily. Adherence was calculated by pill count from blister packs returned at visits. High adherence was defined as taking ≥80% of medication; low adherence was defined as taking <80% of medication.”

“In this low socioeconomic cohort, high and low adherence did not differ by sex, age, family income, parental education, or treatment group. Adherence declined over time (72% high adherence at 2 months, 56% adherence at 48 months, P < 0.0001). A greater percentage of participants with low adherence had clinically significant depressive symptoms at baseline (18% vs. 12%, P = 0.0415). No adherence threshold predicted the loss of glycemic control. […] Most pediatric type 1 diabetes studies (5–7) consistently document a correlation between adherence and race, ethnicity, and socioeconomic status, and studies of adults with type 2 diabetes (8,9) have documented that depressed patients are less adherent to their diabetes regimen. There is a dearth of information in the literature regarding adherence to medication in pediatric patients with type 2 diabetes.”

“In the cohort, the presence of baseline clinically significant depressive symptoms was associated with subsequent lower adherence. […] The TODAY cohort demonstrated deterioration in study medication adherence over time, irrespective of treatment group assignment. […] Contrary to expectation, demographic factors (sex, race-ethnicity, household income, and parental educational level) did not predict medication adherence. The lack of correlation with these factors in the TODAY trial may be explained by the limited income and educational range of the families in the TODAY trial. Nearly half of the families in the TODAY trial had an annual income of <$25,000, and, for over half of the families, the highest level of parental education was a high school degree or lower. In addition, our run-in criteria selected for more adherent subjects. All subjects had to have >80% adherence to M therapy for ≥8 weeks before they could be randomized. This may have limited variability in medication adherence postrandomization. It is also possible that selecting for more adherent subjects in the run-in period also selected for subjects with a lower frequency of depressive symptoms.”

“In the TODAY trial, baseline clinically significant depressive symptoms were more prevalent in the lower-adherence group, suggesting that regular screening for depressive symptoms should be undertaken to identify youth who were at high risk for poor medication adherence. […] Studies in adults with type 2 diabetes (2328) consistently report that depressed patients are less adherent to their diabetes regimen and experience more physical complications of diabetes. Identifying youth who are at risk for poor medication adherence early in the course of disease would make it possible to provide support and, if needed, specific treatment. Although we were not able to determine whether the treatment of depressive symptoms changed adherence over time, our findings support the current guidelines for psychosocial screening in youth with diabetes (29,30).”

vii. Increased Risk of Incident Chronic Kidney Disease, Cardiovascular Disease, and Mortality in Patients With Diabetes With Comorbid Depression.

Another depression-related paper, telling another part of the story. If depressed diabetics are less compliant/adherent, which seems – as per the above study – to be the case both in the context of the adult and pediatric patient population, then you might also expect this reduced compliance/adherence to ‘translate’ into this group having poorer metabolic control, and thus be at higher risk of developing microvascular complications such as nephropathy. This seems to be what we observe, at least according to the findings of this study:

“It is not known if patients with diabetes with depression have an increased risk of chronic kidney disease (CKD). We examined the association between depression and incident CKD, mortality, and incident cardiovascular events in U.S. veterans with diabetes.”

“Among a nationally representative prospective cohort of >3 million U.S. veterans with baseline estimated glomerular filtration rate (eGFR) ≥60 mL/min/1.73 m2, we identified 933,211 patients with diabetes. Diabetes was ascertained by an ICD-9-CM code for diabetes, an HbA1c >6.4%, or receiving antidiabetes medication during the inclusion period. Depression was defined by an ICD-9-CM code for depression or by antidepressant use during the inclusion period. Incident CKD was defined as two eGFR levels 2 separated by ≥90 days and a >25% decline in baseline eGFR.”

“Depression was associated with 20% higher risk of incident CKD (adjusted hazard ratio [aHR] and 95% CI: 1.20 [1.19–1.21]). Similarly, depression was associated with increased all-cause mortality (aHR and 95% CI: 1.25 [1.24–1.26]). […] The presence of depression in patients with diabetes is associated with higher risk of developing CKD compared with nondepressed patients.”

It’s important to remember that the higher reported eGFRs in the depressed patient group may not be important/significant, and they should not be taken as an indication of relatively better kidney function in this patient population – especially in the type 2 context, the relationship between eGFR and kidney function is complicated. I refer to Bakris et al.‘s text on these topics for details (blog coverage here).

May 6, 2017 Posted by | Cardiology, Diabetes, Medicine, Nephrology, Neurology, Psychology, Studies | Leave a comment

A few diabetes papers of interest

1. Cognitive Dysfunction in Older Adults With Diabetes: What a Clinician Needs to Know. I’ve talked about these topics before here on the blog (see e.g. these posts on related topics), but this is a good summary article. I have added some observations from the paper below:

“Although cognitive dysfunction is associated with both type 1 and type 2 diabetes, there are several distinct differences observed in the domains of cognition affected in patients with these two types. Patients with type 1 diabetes are more likely to have diminished mental flexibility and slowing of mental speed, whereas learning and memory are largely not affected (8). Patients with type 2 diabetes show decline in executive function, memory, learning, attention, and psychomotor efficiency (9,10).”

“So far, it seems that the risk of cognitive dysfunction in type 2 diabetes may be influenced by glycemic control, hypoglycemia, inflammation, depression, and macro- and microvascular pathology (14). The cumulative impact of these conditions on the vascular etiology may further decrease the threshold at which cognition is affected by other neurological conditions in the aging brain. In patients with type 1 diabetes, it seems as though diabetes has a lesser impact on cognitive dysfunction than those patients with type 2 diabetes. […] Thus, the cognitive decline in patients with type 1 diabetes may be mild and may not interfere with their functionality until later years, when other aging-related factors become important. […] However, recent studies have shown a higher prevalence of cognitive dysfunction in older patients (>60 years of age) with type 1 diabetes (5).”

“Unlike other chronic diseases, diabetes self-care involves many behaviors that require various degrees of cognitive pliability and insight to perform proper self-care coordination and planning. Glucose monitoring, medications and/or insulin injections, pattern management, and diet and exercise timing require participation from different domains of cognitive function. In addition, the recognition, treatment, and prevention of hypoglycemia, which are critical for the older population, also depend in large part on having intact cognition.

The reason a clinician needs to recognize different domains of cognition affected in patients with diabetes is to understand which self-care behavior will be affected in that individual. […] For example, a patient with memory problems may forget to take insulin doses, forget to take medications/insulin on time, or forget to eat on time. […] Cognitively impaired patients using insulin are more likely to not know what to do in the event of low blood glucose or how to manage medication on sick days (34). Patients with diminished mental flexibility and processing speed may do well with a simple regimen but may fail if the regimen is too complex. In general, older patients with diabetes with cognitive dysfunction are less likely to be involved in diabetes self-care and glucose monitoring compared with age-matched control subjects (35). […] Other comorbidities associated with aging and diabetes also add to the burden of cognitive impairment and its impact on self-care abilities. For example, depression is associated with a greater decline in cognitive function in patients with type 2 diabetes (36). Depression also can independently negatively impact the motivation to practice self-care.”

“Recently, there is an increasing discomfort with the use of A1C as a sole parameter to define glycemic goals in the older population. Studies have shown that A1C values in the older population may not reflect the same estimated mean glucose as in the younger population. Possible reasons for this discrepancy are the commonly present comorbidities that impact red cell life span (e.g., anemia, uremia, renal dysfunction, blood transfusion, erythropoietin therapy) (45,46). In addition, A1C level does not reflect glucose excursions and variability. […] Thus, it is prudent to avoid A1C as the sole measure of glycemic goal in this population. […] In patients who need insulin therapy, simplification, also known as de-intensification of the regimen, is generally recommended in all frail patients, especially if they have cognitive dysfunction (37,49). However, the practice has not caught up with the recommendations as shown by large observational studies showing unnecessary intensive control in patients with diabetes and dementia (50–52).”

“With advances in the past few decades, we now see a larger number of patients with type 1 diabetes who are aging successfully and facing the new challenges that aging brings. […] Patients with type 1 diabetes are typically proactive in their disease management and highly disciplined. Cognitive dysfunction in these patients creates significant distress for the first time in their lives; they suddenly feel a “lack of control” over the disease they have managed for many decades. The addition of autonomic dysfunction, gastropathy, or neuropathy may result in wider glucose excursions. These patients are usually more afraid of hyperglycemia than hypoglycemia — both of which they have managed for many years. However, cognitive dysfunction in older adults with type 1 diabetes has been found to be associated with hypoglycemic unawareness and glucose variability (5), which in turn increases the risk of severe hypoglycemia (54). The need for goal changes to avoid hypoglycemia and accept some hyperglycemia can be very difficult for many of these patients.”

2. Trends in Drug Utilization, Glycemic Control, and Rates of Severe Hypoglycemia, 2006–2013.

“From 2006 to 2013, use increased for metformin (from 47.6 to 53.5%), dipeptidyl peptidase 4 inhibitors (0.5 to 14.9%), and insulin (17.1 to 23.0%) but declined for sulfonylureas (38.8 to 30.8%) and thiazolidinediones (28.5 to 5.6%; all P < 0.001). […] The overall rate of severe hypoglycemia remained the same (1.3 per 100 person-years; P = 0.72), declined modestly among the oldest patients (from 2.9 to 2.3; P < 0.001), and remained high among those with two or more comorbidities (3.2 to 3.5; P = 0.36). […] During the recent 8-year period, the use of glucose-lowering drugs has changed dramatically among patients with T2DM. […] The use of older classes of medications, such as sulfonylureas and thiazolidinediones, declined. During this time, glycemic control of T2DM did not improve in the overall population and remained poor among nearly a quarter of the youngest patients. Rates of severe hypoglycemia remained largely unchanged, with the oldest patients and those with multiple comorbidities at highest risk. These findings raise questions about the value of the observed shifts in drug utilization toward newer and costlier medications.”

“Our findings are consistent with a prior study of drug prescribing in U.S. ambulatory practice conducted from 1997 to 2012 (2). In that study, similar increases in DPP-4 inhibitor and insulin analog prescribing were observed; these changes were accompanied by a 61% increase in drug expenditures (2). Our study extends these findings to drug utilization and demonstrates that these increases occurred in all age and comorbidity subgroups. […] In contrast, metformin use increased only modestly between 2006 and 2013 and remained relatively low among older patients and those with two or more comorbidities. Although metformin is recommended as first-line therapy (26), it may be underutilized as the initial agent for the treatment of T2DM (27). Its use may be additionally limited by coexisting contraindications, such as chronic kidney disease (28).”

“The proportion of patients with a diagnosis of diabetes who did not fill any glucose-lowering medications declined slightly (25.7 to 24.1%; P < 0.001).”

That is, one in four people who had a diagnosis of type 2 diabetes were not taking any prescription drugs for their health condition. I wonder how many of those people have read wikipedia articles like this one

“When considering treatment complexity, the use of oral monotherapy increased slightly (from 24.3 to 26.4%) and the use of multiple (two or more) oral agents declined (from 33.0 to 26.5%), whereas the use of insulin alone and in combination with oral agents increased (from 6.0 to 8.5% and from 11.1 to 14.6%, respectively; all P values <0.001).”

“Between 1987 and 2011, per person medical spending attributable to diabetes doubled (4). More than half of the increase was due to prescription drug spending (4). Despite these spending increases and greater utilization of newly developed medications, we showed no concurrent improvements in overall glycemic control or the rates of severe hypoglycemia in our study. Although the use of newer and more expensive agents may have other important benefits (44), further studies are needed to define the value and cost-effectiveness of current treatment options.”

iii. Among Low-Income Respondents With Diabetes, High-Deductible Versus No-Deductible Insurance Sharply Reduces Medical Service Use.

“Using the 2011–2013 Medical Expenditure Panel Survey, bivariate and regression analyses were conducted to compare demographic characteristics, medical service use, diabetes care, and health status among privately insured adult respondents with diabetes, aged 18–64 years (N = 1,461) by lower (<200% of the federal poverty level) and higher (≥200% of the federal poverty level) income and deductible vs. no deductible (ND), low deductible ($1,000/$2,400) (LD), and high deductible (>$1,000/$2,400) (HD). The National Health Interview Survey 2012–2014 was used to analyze differences in medical debt and delayed/avoided needed care among adult respondents with diabetes (n = 4,058) by income. […] Compared with privately insured respondents with diabetes with ND, privately insured lower-income respondents with diabetes with an LD report significant decreases in service use for primary care, checkups, and specialty visits (27%, 39%, and 77% lower, respectively), and respondents with an HD decrease use by 42%, 65%, and 86%, respectively. Higher-income respondents with an LD report significant decreases in specialty (28%) and emergency department (37%) visits.”

“The reduction in ambulatory visits made by lower-income respondents with ND compared with lower-income respondents with an LD or HD is far greater than for higher-income patients. […] The substantial reduction in checkup (preventive) and specialty visits by those with a lower income who have an HDHP [high-deductible health plan, US] implies a very different pattern of service use compared with lower-income respondents who have ND and with higher-income respondents. Though preventive visits require no out-of-pocket costs, reduced preventive service use with HDHPs is well established and might be the result of patients being unaware of this benefit or their concern about findings that could lead to additional expenses (31). Such sharply reduced service use by low-income respondents with diabetes may not be desirable. Patients with diabetes benefit from assessment of diabetes control, encouragement and reinforcement of behavior change and medication use, and early detection and treatment of diabetes complications or concomitant disease.”

iv. Long-term Mortality and End-Stage Renal Disease in a Type 1 Diabetes Population Diagnosed at Age 15–29 Years in Norway.

OBJECTIVE To study long-term mortality, causes of death, and end-stage renal disease (ESRD) in people diagnosed with type 1 diabetes at age 15–29 years.

RESEARCH DESIGN AND METHODS This nationwide, population-based cohort with type 1 diabetes diagnosed during 1978–1982 (n = 719) was followed from diagnosis until death, emigration, or September 2013. Linkages to the Norwegian Cause of Death Registry and the Norwegian Renal Registry provided information on causes of death and whether ESRD was present.

RESULTS During 30 years’ follow-up, 4.6% of participants developed ESRD and 20.6% (n = 148; 106 men and 42 women) died. Cumulative mortality by years since diagnosis was 6.0% (95% CI 4.5–8.0) at 10 years, 12.2% (10.0–14.8) at 20 years, and 18.4% (15.8–21.5) at 30 years. The SMR [standardized mortality ratio] was 4.4 (95% CI 3.7–5.1). Mean time from diagnosis of diabetes to ESRD was 23.6 years (range 14.2–33.5). Death was caused by chronic complications (32.2%), acute complications (20.5%), violent death (19.9%), or any other cause (27.4%). Death was related to alcohol in 15% of cases. SMR for alcohol-related death was 6.8 (95% CI 4.5–10.3), for cardiovascular death was 7.3 (5.4–10.0), and for violent death was 3.6 (2.3–5.3).

CONCLUSIONS The cumulative incidence of ESRD was low in this cohort with type 1 diabetes followed for 30 years. Mortality was 4.4 times that of the general population, and more than 50% of all deaths were caused by acute or chronic complications. A relatively high proportion of deaths were related to alcohol.”

Some additional observations from the paper:

“Studies assessing causes of death in type 1 diabetes are most frequently conducted in individuals diagnosed during childhood (17) or without evaluating the effect of age at diagnosis (8,9). Reports on causes of death in cohorts of patients diagnosed during late adolescence or young adulthood, with long-term follow-up, are less frequent (10). […] Adherence to treatment during this age is poor and the risk of acute diabetic complications is high (1316). Mortality may differ between those with diabetes diagnosed during this period of life and those diagnosed during childhood.”

“Mortality was between four and five times higher than in the general population […]. The excess mortality was similar for men […] and women […]. SMR was higher in the lower age bands — 6.7 (95% CI 3.9–11.5) at 15–24 years and 7.3 (95% CI 5.2–10.1) at 25–34 years — compared with the higher age bands: 3.7 (95% CI 2.7–4.9) at 45–54 years and 3.9 (95% CI 2.6–5.8) at 55–65 years […]. The Cox regression model showed that the risk of death increased significantly by age at diagnosis (HR 1.1; 95% CI 1.1–1.2; P < 0.001) and was eight to nine times higher if ESRD was present (HR 8.7; 95% CI 4.8–15.5; P < 0.0001). […] the underlying cause of death was diabetes in 57 individuals (39.0%), circulatory in 22 (15.1%), cancer in 18 (12.3%), accidents or intoxications in 20 (13.7%), suicide in 8 (5.5%), and any other cause in 21 (14.4%) […] In addition, diabetes contributed to death in 29.5% (n = 43) and CVD contributed to death in 10.9% (n = 29) of the 146 cases. Diabetes was mentioned on the death certificate for 68.2% of the cohort but for only 30.0% of the violent deaths. […] In 60% (88/146) of the cases the review committee considered death to be related to diabetes, whereas in 40% (58/146) the cause was unrelated to diabetes or had an unknown relation to diabetes. According to the clinical committee, acute complications caused death in 20.5% (30/146) of the cases; 20 individuals died as a result of DKA and 10 from hypoglycemia. […] Chronic complications caused the largest proportion of deaths (47/146; 32.2%) and increased with increasing duration of diabetes […]. Among individuals dying as a result of chronic complications (n = 47), CVD caused death in 94% (n = 44) and renal failure in 6% (n = 3). ESRD contributed to death in 22.7% (10/44) of those dying from CVD. Cardiovascular death occurred at mortality rates seven times higher than those in the general population […]. ESRD caused or contributed to death in 13 of 14 cases, when present.”

“Violence (intoxications, accidents, and suicides) was the leading cause of death before 10 years’ duration of diabetes; thereafter it was only a minor cause […] Insulin was used in two of seven suicides. […] According to the available medical records and autopsy reports, about 20% (29/146) of the deceased misused alcohol. In 15% (22/146) alcohol-related ICD-10 codes were listed on the death certificate (18% [19/106] of men and 8% [3/40] of women). In 10 cases the cause of death was uncertain but considered to be related to alcohol or diabetes […] The SMR for alcohol-related death was high when considering the underlying cause of death (5.0; 95% CI 2.5–10.0), and even higher when considering all alcohol-related ICD-10 codes listed on the death certificate (6.8; 95% CI 4.5–10.3). The cause of death was associated with alcohol in 21.8% (19/87) of those who died with less than 20 years’ diabetes duration. Drug abuse was noted on the death certificate in only two cases.”

“During follow-up, 33 individuals (4.6%; 22 men and 11 women) developed ESRD as a result of diabetic nephropathy. Mean time from diagnosis of diabetes to ESRD was 23.6 years (range 14.2–33.5 years). Cumulative incidence of ESRD by years since diagnosis of diabetes was 1.4% (95% CI 0.7–2.7) at 20 years and 4.8% (95% CI 3.4–6.9) at 30 years.”

“This study highlights three important findings. First, among individuals who were diagnosed with type 1 diabetes in late adolescence and early adulthood and had good access to health care, and who were followed for 30 years, mortality was four to five times that of the general population. Second, 15% of all deaths were associated with alcohol, and the SMR for alcohol-related deaths was 6.8. Third, there was a relatively low cumulative incidence of ESRD (4.8%) 30 years after the diagnosis of diabetes.

We report mortality higher than those from a large, population-based study from Finland that found cumulative mortality around 6% at 20 years’ and 15% at 30 years’ duration of diabetes among a population with age at onset and year of diagnosis similar to those in our cohort (10). The corresponding numbers in our cohort were 12% and 18%, respectively; the discrepancy was particularly high at 20 years. The SMR in the Finnish cohort was lower than that in our cohort (2.6–3.0 vs. 3.7–5.1), and those authors reported the SMR to be lower in late-onset diabetes (at age 15–29 years) compared with early-onset diabetes (at age 23). The differences between the Norwegian and Finnish data are difficult to explain since both reports are from countries with good access to health care and a high incidence of type 1 diabetes.”

However the reason for the somewhat different SMRs in these two reasonably similar countries may actually be quite simple – the important variable may be alcohol:

“Finland and Norway are appropriate to compare because they share important population and welfare characteristics. There are, however, significant differences in drinking levels and alcohol-related mortality: the Finnish population consumes more alcohol and the Norwegian population consumes less. The mortality rates for deaths related to alcohol are about three to four times higher in Finland than in Norway (30). […] The markedly higher SMR in our cohort can probably be explained by the lower mortality rates for alcohol-related mortality in the general population. […] In conclusion, the high mortality reported in this cohort with an onset of diabetes in late adolescence and young adulthood draws attention to people diagnosed during a vulnerable period of life. Both acute and chronic complications cause substantial premature mortality […] Our study suggests that increased awareness of alcohol-related death should be encouraged in clinics providing health care to this group of patients.”

April 23, 2017 Posted by | Diabetes, Economics, Epidemiology, Medicine, Nephrology, Neurology, Papers, Pharmacology, Psychology | Leave a comment

Biodemography of aging (I)

“The goal of this monograph is to show how questions about the connections between and among aging, health, and longevity can be addressed using the wealth of available accumulated knowledge in the field, the large volumes of genetic and non-genetic data collected in longitudinal studies, and advanced biodemographic models and analytic methods. […] This monograph visualizes aging-related changes in physiological variables and survival probabilities, describes methods, and summarizes the results of analyses of longitudinal data on aging, health, and longevity in humans performed by the group of researchers in the Biodemography of Aging Research Unit (BARU) at Duke University during the past decade. […] the focus of this monograph is studying dynamic relationships between aging, health, and longevity characteristics […] our focus on biodemography/biomedical demography meant that we needed to have an interdisciplinary and multidisciplinary biodemographic perspective spanning the fields of actuarial science, biology, economics, epidemiology, genetics, health services research, mathematics, probability, and statistics, among others.”

The quotes above are from the book‘s preface. In case this aspect was not clear from the comments above, this is the kind of book where you’ll randomly encounter sentences like these:

The simplest model describing negative correlations between competing risks is the multivariate lognormal frailty model. We illustrate the properties of such model for the bivariate case.

“The time-to-event sub-model specifies the latent class-specific expressions for the hazard rates conditional on the vector of biomarkers Yt and the vector of observed covariates X …”

…which means that some parts of the book are really hard to blog; it simply takes more effort to deal with this stuff here than it’s worth. As a result of this my coverage of the book will not provide a remotely ‘balanced view’ of the topics covered in it; I’ll skip a lot of the technical stuff because I don’t think it makes much sense to cover specific models and algorithms included in the book in detail here. However I should probably also emphasize while on this topic that although the book is in general not an easy read, it’s hard to read because ‘this stuff is complicated’, not because the authors are not trying. The authors in fact make it clear already in the preface that some chapters are more easy to read than are others and that some chapters are actually deliberately written as ‘guideposts and way-stations‘, as they put it, in order to make it easier for the reader to find the stuff in which he or she is most interested (“the interested reader can focus directly on the chapters/sections of greatest interest without having to read the entire volume“) – they have definitely given readability aspects some thought, and I very much like the book so far; it’s full of great stuff and it’s very well written.

I have had occasion to question a few of the observations they’ve made, for example I was a bit skeptical about a few of the conclusions they drew in chapter 6 (‘Medical Cost Trajectories and Onset of Age-Associated Diseases’), but this was related to what some would certainly consider to be minor details. In the chapter they describe a model of medical cost trajectories where the post-diagnosis follow-up period is 20 months; this is in my view much too short a follow-up period to draw conclusions about medical cost trajectories in the context of type 2 diabetes, one of the diseases included in the model, which I know because I’m intimately familiar with the literature on that topic; you need to look 7-10 years ahead to get a proper sense of how this variable develops over time – and it really is highly relevant to include those later years, because if you do not you may miss out on a large proportion of the total cost given that a substantial proportion of the total cost of diabetes relate to complications which tend to take some years to develop. If your cost analysis is based on a follow-up period as short as that of that model you may also on a related note draw faulty conclusions about which medical procedures and -subsidies are sensible/cost effective in the setting of these patients, because highly adherent patients may be significantly more expensive in a short run analysis like this one (they show up to their medical appointments and take their medications…) but much cheaper in the long run (…because they take their medications they don’t go blind or develop kidney failure). But as I say, it’s a minor point – this was one condition out of 20 included in the analysis they present, and if they’d addressed all the things that pedants like me might take issue with, the book would be twice as long and it would likely no longer be readable. Relatedly, the model they discuss in that chapter is far from unsalvageable; it’s just that one of the components of interest –  ‘the difference between post- and pre-diagnosis cost levels associated with an acquired comorbidity’ – in the case of at least one disease is highly unlikely to be correct (given the authors’ interpretation of the variable), because there’s some stuff of relevance which the model does not include. I found the model quite interesting, despite the shortcomings, and the results were definitely surprising. (No, the above does not in my opinion count as an example of coverage of a ‘specific model […] in detail’. Or maybe it does, but I included no equations. On reflection I probably can’t promise much more than that, sometimes the details are interesting…)

Anyway, below I’ve added some quotes from the first few chapters of the book and a few remarks along the way.

“The genetics of aging, longevity, and mortality has become the subject of intensive analyses […]. However, most estimates of genetic effects on longevity in GWAS have not reached genome-wide statistical significance (after applying the Bonferroni correction for multiple testing) and many findings remain non-replicated. Possible reasons for slow progress in this field include the lack of a biologically-based conceptual framework that would drive development of statistical models and methods for genetic analyses of data [here I was reminded of Burnham & Anderson’s coverage, in particular their criticism of mindless ‘Let the computer find out’-strategies – the authors of that chapter seem to share their skepticism…], the presence of hidden genetic heterogeneity, the collective influence of many genetic factors (each with small effects), the effects of rare alleles, and epigenetic effects, as well as molecular biological mechanisms regulating cellular functions. […] Decades of studies of candidate genes show that they are not linked to aging-related traits in a straightforward fashion (Finch and Tanzi 1997; Martin 2007). Recent genome-wide association studies (GWAS) have supported this finding by showing that the traits in late life are likely controlled by a relatively large number of common genetic variants […]. Further, GWAS often show that the detected associations are of tiny size (Stranger et al. 2011).”

I think this ties in well with what I’ve previously read on these and related topics – see e.g. the second-last paragraph quoted in my coverage of Richard Alexander’s book, or some of the remarks included in Roberts et al. Anyway, moving on:

“It is well known from epidemiology that values of variables describing physiological states at a given age are associated with human morbidity and mortality risks. Much less well known are the facts that not only the values of these variables at a given age, but also characteristics of their dynamic behavior during the life course are also associated with health and survival outcomes. This chapter [chapter 8 in the book, US] shows that, for monotonically changing variables, the value at age 40 (intercept), the rate of change (slope), and the variability of a physiological variable, at ages 40–60, significantly influence both health-span and longevity after age 60. For non-monotonically changing variables, the age at maximum, the maximum value, the rate of decline after reaching the maximum (right slope), and the variability in the variable over the life course may influence health-span and longevity. This indicates that such characteristics can be important targets for preventive measures aiming to postpone onsets of complex diseases and increase longevity.”

The chapter from which the quotes in the next two paragraphs are taken was completely filled with data from the Framingham Heart Study, and it was hard for me to know what to include here and what to leave out – so you should probably just consider the stuff I’ve included below as samples of the sort of observations included in that part of the coverage.

“To mediate the influence of internal or external factors on lifespan, physiological variables have to show associations with risks of disease and death at different age intervals, or directly with lifespan. For many physiological variables, such associations have been established in epidemiological studies. These include body mass index (BMI), diastolic blood pressure (DBP), systolic blood pressure (SBP), pulse pressure (PP), blood glucose (BG), serum cholesterol (SCH), hematocrit (H), and ventricular rate (VR). […] the connection between BMI and mortality risk is generally J-shaped […] Although all age patterns of physiological indices are non-monotonic functions of age, blood glucose (BG) and pulse pressure (PP) can be well approximated by monotonically increasing functions for both genders. […] the average values of body mass index (BMI) increase with age (up to age 55 for males and 65 for females), and then decline for both sexes. These values do not change much between ages 50 and 70 for males and between ages 60 and 70 for females. […] Except for blood glucose, all average age trajectories of physiological indices differ between males and females. Statistical analysis confirms the significance of these differences. In particular, after age 35 the female BMI increases faster than that of males. […] [When comparing women with less than or equal to 11 years of education [‘LE’] to women with 12 or more years of education [HE]:] The average values of BG for both groups are about the same until age 45. Then the BG curve for the LE females becomes higher than that of the HE females until age 85 where the curves intersect. […] The average values of BMI in the LE group are substantially higher than those among the HE group over the entire age interval. […] The average values of BG for the HE and LE males are very similar […] However, the differences between groups are much smaller than for females.”

They also in the chapter compared individuals with short life-spans [‘SL’, died before the age of 75] and those with long life-spans [‘LL’, 100 longest-living individuals in the relevant sample] to see if the variables/trajectories looked different. They did, for example: “trajectories for the LL females are substantially different from those for the SL females in all eight indices. Specifically, the average values of BG are higher and increase faster in the SL females. The entire age trajectory of BMI for the LL females is shifted to the right […] The average values of DBP [diastolic blood pressure, US] among the SL females are higher […] A particularly notable observation is the shift of the entire age trajectory of BMI for the LL males and females to the right (towards an older age), as compared with the SL group, and achieving its maximum at a later age. Such a pattern is markedly different from that for healthy and unhealthy individuals. The latter is mostly characterized by the higher values of BMI for the unhealthy people, while it has similar ages at maximum for both the healthy and unhealthy groups. […] Physiological aging changes usually develop in the presence of other factors affecting physiological dynamics and morbidity/mortality risks. Among these other factors are year of birth, gender, education, income, occupation, smoking, and alcohol use. An important limitation of most longitudinal studies is the lack of information regarding external disturbances affecting individuals in their day-today life.”

I incidentally noted while I was reading that chapter that a relevant variable ‘lurking in the shadows’ in the context of the male and female BMI trajectories might be changing smoking habits over time; I have not looked at US data on this topic, but I do know that the smoking patterns of Danish males and females during the latter half of the last century were markedly different and changed really quite dramatically in just a few decades; a lot more males than females smoked in the 60es, whereas the proportions of male- and female smokers today are much more similar, because a lot of males have given up smoking (I refer Danish readers to this blog post which I wrote some years ago on these topics). The authors of the chapter incidentally do look a little at data on smokers and they observe that smokers’ BMI are lower than non-smokers (not surprising), and that the smokers’ BMI curve (displaying the relationship between BMI and age) grows at a slower rate than the BMI curve of non-smokers (that this was to be expected is perhaps less clear, at least to me – the authors don’t interpret these specific numbers, they just report them).

The next chapter is one of the chapters in the book dealing with the SEER data I also mentioned not long ago in the context of my coverage of Bueno et al. Some sample quotes from that chapter below:

“To better address the challenge of “healthy aging” and to reduce economic burdens of aging-related diseases, key factors driving the onset and progression of diseases in older adults must be identified and evaluated. An identification of disease-specific age patterns with sufficient precision requires large databases that include various age-specific population groups. Collections of such datasets are costly and require long periods of time. That is why few studies have investigated disease-specific age patterns among older U.S. adults and there is limited knowledge of factors impacting these patterns. […] Information collected in U.S. Medicare Files of Service Use (MFSU) for the entire Medicare-eligible population of older U.S. adults can serve as an example of observational administrative data that can be used for analysis of disease-specific age patterns. […] In this chapter, we focus on a series of epidemiologic and biodemographic characteristics that can be studied using MFSU.”

“Two datasets capable of generating national level estimates for older U.S. adults are the Surveillance, Epidemiology, and End Results (SEER) Registry data linked to MFSU (SEER-M) and the National Long Term Care Survey (NLTCS), also linked to MFSU (NLTCS-M). […] The SEER-M data are the primary dataset analyzed in this chapter. The expanded SEER registry covers approximately 26 % of the U.S. population. In total, the Medicare records for 2,154,598 individuals are available in SEER-M […] For the majority of persons, we have continuous records of Medicare services use from 1991 (or from the time the person reached age 65 after 1990) to his/her death. […] The NLTCS-M data contain two of the six waves of the NLTCS: namely, the cohorts of years 1994 and 1999. […] In total, 34,077 individuals were followed-up between 1994 and 1999. These individuals were given the detailed NLTCS interview […] which has information on risk factors. More than 200 variables were selected”

In short, these data sets are very large, and contain a lot of information. Here are some results/data:

“Among studied diseases, incidence rates of Alzheimer’s disease, stroke, and heart failure increased with age, while the rates of lung and breast cancers, angina pectoris, diabetes, asthma, emphysema, arthritis, and goiter became lower at advanced ages. [..] Several types of age-patterns of disease incidence could be described. The first was a monotonic increase until age 85–95, with a subsequent slowing down, leveling off, and decline at age 100. This pattern was observed for myocardial infarction, stroke, heart failure, ulcer, and Alzheimer’s disease. The second type had an earlier-age maximum and a more symmetric shape (i.e., an inverted U-shape) which was observed for lung and colon cancers, Parkinson’s disease, and renal failure. The majority of diseases (e.g., prostate cancer, asthma, and diabetes mellitus among them) demonstrated a third shape: a monotonic decline with age or a decline after a short period of increased rates. […] The occurrence of age-patterns with a maximum and, especially, with a monotonic decline contradicts the hypothesis that the risk of geriatric diseases correlates with an accumulation of adverse health events […]. Two processes could be operative in the generation of such shapes. First, they could be attributed to the effect of selection […] when frail individuals do not survive to advanced ages. This approach is popular in cancer modeling […] The second explanation could be related to the possibility of under-diagnosis of certain chronic diseases at advanced ages (due to both less pronounced disease symptoms and infrequent doctor’s office visits); however, that possibility cannot be assessed with the available data […this is because the data sets are based on Medicare claims – US]”

“The most detailed U.S. data on cancer incidence come from the SEER Registry […] about 60 % of malignancies are diagnosed in persons aged 65+ years old […] In the U.S., the estimated percent of cancer patients alive after being diagnosed with cancer (in 2008, by current age) was 13 % for those aged 65–69, 25 % for ages 70–79, and 22 % for ages 80+ years old (compared with 40 % of those aged younger than 65 years old) […] Diabetes affects about 21 % of the U.S. population aged 65+ years old (McDonald et al. 2009). However, while more is known about the prevalence of diabetes, the incidence of this disease among older adults is less studied. […] [In multiple previous studies] the incidence rates of diabetes decreased with age for both males and females. In the present study, we find similar patterns […] The prevalence of asthma among the U.S. population aged 65+ years old in the mid-2000s was as high as 7 % […] older patients are more likely to be underdiagnosed, untreated, and hospitalized due to asthma than individuals younger than age 65 […] asthma incidence rates have been shown to decrease with age […] This trend of declining asthma incidence with age is in agreement with our results.”

“The prevalence and incidence of Alzheimer’s disease increase exponentially with age, with the most notable rise occurring through the seventh and eight decades of life (Reitz et al. 2011). […] whereas dementia incidence continues to increase beyond age 85, the rate of increase slows down [which] suggests that dementia diagnosed at advanced ages might be related not to the aging process per se, but associated with age-related risk factors […] Approximately 1–2 % of the population aged 65+ and up to 3–5 % aged 85+ years old suffer from Parkinson’s disease […] There are few studies of Parkinsons disease incidence, especially in the oldest old, and its age patterns at advanced ages remain controversial”.

“One disadvantage of large administrative databases is that certain factors can produce systematic over/underestimation of the number of diagnosed diseases or of identification of the age at disease onset. One reason for such uncertainties is an incorrect date of disease onset. Other sources are latent disenrollment and the effects of study design. […] the date of onset of a certain chronic disease is a quantity which is not defined as precisely as mortality. This uncertainty makes difficult the construction of a unified definition of the date of onset appropriate for population studies.”

“[W]e investigated the phenomenon of multimorbidity in the U.S. elderly population by analyzing mutual dependence in disease risks, i.e., we calculated disease risks for individuals with specific pre-existing conditions […]. In total, 420 pairs of diseases were analyzed. […] For each pair, we calculated age patterns of unconditional incidence rates of the diseases, conditional rates of the second (later manifested) disease for individuals after onset of the first (earlier manifested) disease, and the hazard ratio of development of the subsequent disease in the presence (or not) of the first disease. […] three groups of interrelations were identified: (i) diseases whose risk became much higher when patients had a certain pre-existing (earlier diagnosed) disease; (ii) diseases whose risk became lower than in the general population when patients had certain pre-existing conditions […] and (iii) diseases for which “two-tail” effects were observed: i.e., when the effects are significant for both orders of disease precedence; both effects can be direct (either one of the diseases from a disease pair increases the risk of the other disease), inverse (either one of the diseases from a disease pair decreases the risk of the other disease), or controversial (one disease increases the risk of the other, but the other disease decreases the risk of the first disease from the disease pair). In general, the majority of disease pairs with increased risk of the later diagnosed disease in both orders of precedence were those in which both the pre-existing and later occurring diseases were cancers, and also when both diseases were of the same organ. […] Generally, the effect of dependence between risks of two diseases diminishes with advancing age. […] Identifying mutual relationships in age-associated disease risks is extremely important since they indicate that development of […] diseases may involve common biological mechanisms.”

“in population cohorts, trends in prevalence result from combinations of trends in incidence, population at risk, recovery, and patients’ survival rates. Trends in the rates for one disease also may depend on trends in concurrent diseases, e.g., increasing survival from CHD contributes to an increase in the cancer incidence rate if the individuals who survived were initially susceptible to both diseases.”

March 1, 2017 Posted by | Biology, Books, Cancer/oncology, Cardiology, Demographics, Diabetes, Epidemiology, Genetics, Medicine, Nephrology, Neurology | Leave a comment

Diabetic nephropathies

Bakris et al.‘s text on this topic is the first book I’ve read specifically devoted to the topic of DN. As I pointed out on goodreads, “this is a well-written and interesting work which despite the low page count cover quite a bit of ground. A well-sourced and to-the-point primer on these topics.” Below I have added a few observations from the book.

“Diabetic nephropathy (DN), also known as diabetic kidney disease (DKD), is one of the most important long-term complications of diabetes and the most common cause of endstage renal disease (ESRD) worldwide. DKD […] is defined as structural and functional renal damage manifested as clinically detected albuminuria in the presence of normal or abnormal glomerular filtration rate (GFR). […] Patients with DKD […] account for one-third of patients demanding renal transplantation. […] in the United States, Medicare expenditure on treating ESRD is approximately US $33 billion (as of 2010), which accounts for 8–9 % of the total annual health-care budget […] According to the United States Renal Data System […], the incidence of ESRD requiring RRT [in 2012] was 114,813 patients, with 44 % due to DKD [9]. A registry report from Japan revealed a nearly identical relative incidence, with 44.2 % of the patients with ESRD caused by diabetes”

Be careful not to confuse incidence and prevalence here; the proportion of diabetics diagnosed with ESDR in any given year is almost certainly higher than the proportion of people with ESDR who have diabetes, because diabetics with kidney failure die at a higher rate than do other people with kidney failure. This problem/fact tends to make some questions hard to answer; to give an example, how large a share of the total costs that diabetics contribute to the whole kidney disease component of medical costs seems to me to be far from an easy question to answer, because you in some sense are not really making an apples-to-apples comparison, and a lot might well depend on the chosen discount rate and how to address the excess mortality in the diabetes sample; and even ‘simply’ adding up medical outlays for the diabetes- and non-diabetes samples would require a lot of data (which may not be available) and work. You definitely cannot just combine the estimates provided above, and assume that the 44% incidence translates into 44% of people with ESDR having diabetes; it’s not clear in the text where the ‘one-third of patients’ number above comes from, but if that’s also US data then it should be obvious from the difference between these numbers that there’s a lot of excess mortality here in the diabetes sample (I have included specific data from the publication on these topics below). The book also talks about the fact that the type of dialysis used in a case of kidney failure will to some extent depend on the health status of the patient, and that diabetes is a significant variable in that context; this means that the available/tolerable treatment options for the kidney disease component may not be the same in the case of a diabetic and a case of a patient with, say, lupus nephritis, and it also means that the patient groups most likely are not ‘equally sick’, so basing cost estimates on cost averages might lead to misleading results if severity of disease and (true) treatment costs are related, as they usually are.

“A recent analysis revealed an estimated diabetes prevalence of 12–14 % among adults in the United States […] In the age group ≥65 years, this amounts to more than 20 %”.

It should be emphasized in the context of the above numbers that the prevalence of DKD is highly variable across countries/populations – the authors also include in the book the observation that: “Over a period of 20 years, 32 studies from 16 countries revealed a prevalence ranging from 11 to 83 % of patients with diabetes”. Some more prevalence data:

“DKD affects about 30 % of patients with type 1 diabetes and 25–40 % of the patients with type 2 diabetes. […] The global prevalence of micro- and macroalbuminuria is estimated at 39 % and 10 %, respectively […] (NHANES III) […] reported a prevalence of 35 % (microalbuminuria) and 6 % (macroalbuminuria) in patients with T2DM aged ≥40 years [24]. In another study, this was reported to be 43 % and 12 %, respectively, in a Japanese population [23]. According to the European Diabetes (EURODIAB) Prospective Complications Study Group, in patients with T1DM, the incidence of microalbuminuria was 12.6 % (over 7.3 years) [25]. This prevalence was further estimated at 33 % in an 18-year follow-up study in Denmark […] In the United Kingdom Prospective Diabetes Study (UKPDS), proteinuria [had] a peak incidence after around 15–20 years after diabetes diagnosis.”

I won’t cover the pathophysiology parts in too much detail here, but a few new things I learned does need to be mentioned:

“A natural history of DKD was first described in the 1970s by Danish physicians [32]. It was characterized by a long silent period without overt clinical signs and symptoms of nephropathy and progression through various stages, starting from hyperfiltration, microalbuminuria, macroalbuminuria, and overt renal failure to ESRD. Microalbuminuria (30–300 mg/day of albumin in urine) is a sign of early DKD, whereas macroalbuminuria (>300 mg/day) represents DKD progression. [I knew this stuff. The stuff that follows below was however something I did not know:]
However, this ‘classical’ natural evolution of urinary albumin excretion and change in GFR is not present in many patients with diabetes, especially those with type 2 diabetes [34]. These patients can have reduction or disappearance of proteinuria over time or can develop even overt renal disease in the absence of proteinuria [30, 35]. […] In the Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR) of patients with T2DM, 45.2 % of participants developed albuminuria, and 29 % developed renal impairment over a 15-year follow-up period [37]. Of those patients who developed renal impairment, 61 % did not have albuminuria beforehand, and 39 % never developed albuminuria during the study. Of the patients that developed albuminuria, only 24 % subsequently developed renal impairment during the study. A significant degree of discordance between development of albuminuria and renal impairment is apparent [37]. These data, thus, do not support the classical paradigm of albuminuria always preceding renal impairment in the progression of DKD. […] renal hyperfiltration and rapid GFR decline are considered stronger predictors of nephropathy progression in type 1 diabetes than presence of albuminuria [67]. The annual eGFR loss in patients with DKD is >3 mL/min/1.73 m2 or 3.3 % per year.”

As for the last part about renal hyperfiltration, they however also note later in the coverage in a different chapter that “recent long-term prospective surveys cast doubt on the validity of glomerular hyperfiltration being predictive of renal outcome in patients with type 1 diabetes”. Various factors mentioned in the coverage – some of which are very hard to avoid and some of which are actually diabetes-specific – contribute to measurement error, which may be part of the explanation for the sub-optimal performance of the prognostic markers employed.

An important observation I think I have mentioned before here on the blog is that diabetic nephropathy is not just bad because people who develop this complication may ultimately develop kidney failure, but is also bad because diabetics may die before they even do that; diabetics with even moderate stages of nephropathy have high mortality from cardiovascular disease, so if you only consider diabetics who actually develop kidney failure you may miss some of the significant adverse health effects of this complication; it might be argued that doing this would be a bit like analyzing the health outcomes of smokers while only tallying the cancer cases, and ignoring e.g. the smoking-associated excess deaths from cardiovascular disease. Some observations from the book on this topic:

“Comorbid DM and DKD are associated with high cardiovascular morbidity and mortality. The risk of cardiovascular disease is disproportionately higher in patients with DKD than patients with DM who do not have kidney disease [76]. The incident dialysis rate might even be higher after adjusting for patients dying from cardiovascular disease before reaching ESRD stage [19]. The United States Renal Data System (USRDS) data shows that elderly patients with a triad of DM, chronic kidney disease (CKD), and heart failure have a fivefold higher chance of death than progression to CKD and ESRD [36]. The 5-year survival rate for diabetic patients with ESRD is estimated at 20 % […] This is higher than the mortality rate for many solid cancers (including prostate, breast, or renal cell cancer). […] CVD accounts for more than half of deaths of patients undergoing dialysis […] the 5-year survival rate is much lower in diabetic versus nondiabetic patients undergoing hemodialysis […] Adler et al. tested whether HbA1c levels were associated with death in adults with diabetes starting HD or peritoneal dialysis [38]. Of 3157 patients observed for a median time of 2.7 years, 1688 died. [this example provided, I thought, a neat indication of what sort of data you end up with when you look at samples with a 20% 5-year survival rate] […] Despite modern therapies […] most patients continue to show progressive renal damage. This outcome suggests that the key pathogenic mechanisms involved in the induction and progression of DN remain, at least in part, active and unmodified by the presently available therapies.” (my emphasis)

The link between blood glucose (Hba1c) and risk of microvascular complications such as DN is strong and well-documented, but Hba1c does not explain everything:

“Only a subset of individuals living with diabetes […] develop DN, and studies have shown that this is not just due to poor blood glucose control [50–54]. DN appears to cluster in families […] Several consortia have investigated genetic risk factors […] Genetic risk factors for DN appear to differ between patients with type 1 and type 2 diabetes […] The pathogenesis of DN is complex and has not yet been completely elucidated […] [It] is multifactorial, including both genetic and environmental factors […]. Hyperglycemia affects patients carrying candidate genes associated with susceptibility to DN and results in metabolic and hemodynamic alterations. Hyperglycemia alters vasoactive regulators of glomerular arteriolar tone and causes glomerular hyperfiltration. Production of AGEs and oxidative stress interacts with various cytokines such as TGF-β and angiotensin II to cause kidney damage. Additionally, oxidative stress can cause endothelial dysfunction and systemic hypertension. Inflammatory pathways are also activated and interact with the other pathways to cause kidney damage.”

“An early clinical sign of DN is moderately increased urinary albumin excretion, referred to as microalbuminuria […] microalbuminuria has been shown to be closely associated with an increased risk of cardiovascular morbidity and mortality [and] is [thus] not only a biomarker for the early diagnosis of DN but also an important therapeutic target […] Moderately increased urinary albumin excretion that progresses to severely increased albuminuria is referred to as macroalbuminuria […] Severely increased albuminuria is defined as an ACR≥300 mg/g Cr; it leads to a decline in renal function, which is defined in terms of the GFR [8] and generally progresses to ESRD 6–8 years after the onset of overt proteinuria […] patients with type 1 diabetes are markedly younger than type 2 patients. The latter usually develop ESRD in their mid-fifties to mid-sixties. According to a small but carefully conducted study, both type 1 and type 2 patients take an average of 77–81 months from the stage of producing macroproteinuria with near-normal renal function to developing ESRD [17].”

“Patients with diabetes and kidney disease are at increased risk of hypoglycemia due to decreased clearance of some of the medications used to treat diabetes such as insulin, as well as impairment of renal gluconeogenesis from having a lower kidney mass. As the kidney is responsible for about 30–80 % of insulin removal, reduced kidney function is associated with a prolonged insulin half-life and a decrease in insulin requirements as estimated glomerular filtration rate (eGFR) decline […] Metformin [a first-line drug for treating type 2 diabetes, US] should be avoided in patients with an eGFR < 30 mL/min /1.73 m2. It is recommended that metformin is stopped in the presence of situations that are associated with hypoxia or an acute decline in kidney function such as sepsis/shock, hypotension, acute myocardial infarction, and use of radiographic contrast or other nephrotoxic agents […] The ideal medication regimen is based on the specific needs of the patient and physician experience and should be individualized, especially as renal function changes. […] Lower HbA1c levels are associated with higher risks of hypoglycemia so the HbA1c target should be individualized […] Whereas patients with mild renal insufficiency can receive most antihyperglycemic treatments without any concern, patients with CKD stage 3a and, in particular, with CKD stages 3b, 4, and 5 often require treatment adjustments according to the degree of renal insufficiency […] Higher HbA1c targets should be considered for those with shortened life expectancies, a known history of severe hypoglycemia or hypoglycemia unawareness, CKD, and children.”

“In cases where avoidance of development of DKD has failed, the second approach is slowing disease progression. The most important therapeutic issues at this stage are control of hypertension and hyperglycemia. […] Hypertension is present in up to 85 % of patients with DN/ DKD, depending on the duration and stage (e.g., higher in more progressive cases). […] In a recent meta-analysis, the efficacy and safety of blood pressure-lowering agents in adults with diabetes and kidney disease was analyzed […] In total, 157 studies comprising 43,256 participants, mostly with type 2 diabetes and CKD, were included in the network meta-analysis. No drug regimen was found to be more effective than placebo for reducing all-cause mortality. […] DKD is accompanied by abnormalities in lipid metabolism related to decline in kidney function. The association between higher low-density lipoprotein cholesterol (LDL-C) and risk of myocardial infarction is weaker for people with lower baseline eGFR, despite higher absolute risk of myocardial infarction [53]. Thus, increased LDL-C seems to be less useful as a marker of coronary risk among people with CKD than in the general population.”

“An analysis of the USRDS data revealed an RR of 0.27 (95 % CI, 0.24–0.30) 18 months after transplantation in patients with diabetes in comparison to patients on dialysis on a transplant waiting list [76]. The gain in projected years of life with transplantation amounted to 11 years in patients with DKD in comparison to patients without transplantation.”

October 27, 2016 Posted by | Books, Cardiology, Diabetes, Epidemiology, Medicine, Nephrology, Pharmacology | Leave a comment

Diabetes and the Metabolic Syndrome in Mental Health (I)

As I stated in my goodreads review, ‘If you’re a schizophrenic and/or you have a strong interest in e.g. the metabolic effects of various anti-psychotics, the book is a must-read’. If that’s not true, it’s a different matter. One reason why I didn’t give the book a higher rating is that many of the numbers in there are quite dated, which is a bit annoying because it means you might feel somewhat uncertain about how valid the estimates included still are at this point.

As pointed out in my coverage of the human drug metabolism text there are a lot of things that can influence the way that drugs are metabolized, and this text includes some details about a specific topic which may help to illustrate what I meant by stating in that post that people ‘self-experimenting’ may be taking on risks they may not be aware of. Now, diabetics who need insulin injections are taking a drug with a narrow therapeutic index, meaning that even small deviations from the optimal dose may have serious repercussions. A lot of things influence what is actually the optimal dose in a specific setting; food (“food is like a drug to a person with diabetes”, as pointed out in Matthew Neal’s endocrinology text, which is yet another text I, alas, have yet to cover here), sleep patterns, exercise (sometimes there may be an impact even days after you’ve exercised), stress, etc. all play a role, and even well-educated diabetics may not know all the details.

A lot of drugs also affect glucose metabolism and insulin sensitivity, one of the best known drug types of this nature probably being the corticosteroids because of their widespread use in a variety of disorders, including autoimmune disorders which tend to be more common in autoimmune forms of diabetes (mainly type 1). However many other types of drugs can also influence blood glucose, and on the topic of antidepressants and antipsychotics we actually know some stuff about these things and about how various medications influence glucose levels; it’s not a big coincidence that people have looked at this, they’ve done that because it has become clear that “[m]any medications, in particular psychotropics, including antidepressants, antipsychotics, and mood stabilizers, are associated with elevations in blood pressure, weight gain, dyslipidemias, and/or impaired glucose homeostasis.” (p. 49). Which may translate into an increased risk of type 2 diabetes, and impaired glucose control in diabetics. Incidentally the authors of this text observes in the text that: “Our research group was among the first in the field to identify a possible link between the development of obesity, diabetes, and other metabolic derangements (e.g., lipid abnormalities) and the use of newer, second-generation antipsychotic medications.” Did the people who took these drugs before this research was done/completed know that their medications might increase their risk of developing diabetes? No, because the people prescribing it didn’t know, nor did the people who developed the drugs. Some probably still don’t know, including some of the medical people prescribing these medications. But the knowledge is out there now, and the effect size is in the case of some drugs argued to be large enough to be clinically relevant. In the context of a ‘self-experimentation’-angle the example is also interesting because the negative effect in question here is significantly delayed; type 2 diabetes takes time to develop, and this is an undesirable outcome which you’re not going to spot the way you might link a headache the next day to a specific drug you just started out with (another example of a delayed adverse event is incidentally cancer). You’re not going to spot dyslipidemia unless you keep track of your lipid levels on your own or e.g. develop xanthomas as a consequence of it, leading you to consult a physician. It helps a lot if you have proper research protocols and large n studies with sufficient power when you want to discover things like this, and when you want to determine whether an association like this is ‘just an association’ or if the link is actually causal (and then clarifying what we actually mean by that, and whether the causal link is also clinically relevant and/or for whom it might be clinically relevant). Presumably many people taking all kinds of medical drugs these days are taking on risks which might in a similar manner be ‘hidden from view’ as was the risk of diabetes in people taking second-generation antipsychotics in the near-past; over time epidemiological studies may pick up on some of these risks, but many will probably remain hidden from view on account of the amount of complexity involved. Even if a drug ‘works’ as intended in the context of the target variable in question, you can get into a lot of trouble if you only focus on the target variable (“if a drug has no side effects, then it is unlikely to work“). People working in drug development know this.

The book has a lot of blog-worthy stuff so I decided to include some quotes in the coverage below. The quotes are from the first half of the book, and this part of the coverage actually doesn’t talk much about the effects of drugs; it mainly deals with epidemiology and cost estimates. I thus decided to save the ‘drug coverage’ to a later post. It should perhaps be noted that some of the things I’d hoped to learn from Ru-Band Lu et al.’s book (blog coverage here) was actually included in this one, which was nice.

“Those with mental illness are at higher risk and are more likely to suffer the severe consequences of comorbid medical illness. Adherence to treatment is often more difficult, and other factors such as psychoneuroendocrine interactions may complicate already problematic treatments. Additionally, psychiatric medications themselves often have severe side effects and can interact with other medications, rendering treatment of the mental illness more complicated. Diabetes is one example of a comorbid medical illness that is seen at a higher rate in people with mental illness.”

“Depression rates have been studied and are increased in type 1 and type 2 diabetes. In a meta-analysis, Barnard et al. reviewed 14 trials in which patients with type 1 diabetes were surveyed for rates of depression.16 […] subjects with type 1 diabetes had a 12.0% rate of depression compared with a rate of 3.4% in those without diabetes. In noncontrolled trials, they found an even higher rate of depression in patients with type 1 diabetes (13.4%). However, despite these overall findings, in trials that were considered of an adequate design, and with a substantially rigorous depression screening method (i.e., use of structured clinical interview rather than patient reported surveys), the rates were not statistically significantly increased (odds ratio [OR] 2.36, 95% confidence interval [CI] 0.69–5.4) but had such substantial variation that it was not sufficient to draw a conclusion regarding type 1 diabetes. […] When it comes to rates of depression, type 2 diabetes has been studied more extensively than type 1 diabetes. Anderson et al. compiled a large metaanalysis, looking at 42 studies involving more than 21,000 subjects to assess rates of depression among patients with type 1 versus type 2 diabetes mellitus.18 Regardless of how depression was measured, type 1 diabetes was associated with lower rates of depression than type 2 diabetes. […] Depression was significantly increased in both type 1 and type 2 diabetes, with increased ORs for subjects with type 1 (OR = 2.9, 95% CI 1.6 –5.5, […] p=0.0003) and type 2 disease (OR = 2.9, 95% CI 2.3–3.7, […] p = 0.0001) compared with controls. Overall, with multiple factors controlled for, the risk of depression in people with diabetes was approximately twofold. In another large meta-analysis, Ali et al. looked at more than 51,000 subjects in ten different studies to assess rates of depression in type 2 diabetes mellitus. […] the OR for comorbid depression among the diabetic patients studied was higher for men than for women, indicating that although women with diabetes have an overall increased prevalence of depression (23.8 vs. 12.8%, p = 0.0001), men with diabetes have an increased risk of developing depression (men: OR = 1.9, 95% CI = 1.7–2.1 vs. women: OR = 1.3, 95% CI = 1.2–1.4). […] Research has shown that youths 12–17 years of age with type 1 diabetes had double the risk of depression compared with a teenage population without diabetes.21 This amounted to nearly 15% of children meeting the criteria for depression.

As many as two-thirds of patients with diabetes and major depression have been ill with depression for more than 2 years.44 […] Depression has been linked to decreased adherence to self-care regimens (exercise, diet, and cessation of smoking) in patients with diabetes, as well as to the use of diabetes control medications […] Patients with diabetes and depression are twice as likely to have three or more cardiac risk factors such as smoking, obesity, sedentary lifestyle, or A1c > 8.0% compared with patients with diabetes alone.47 […] The costs for individuals with both major depression and diabetes are 4.5 times greater than for those with diabetes alone.53

“A 2004 cross-sectional and longitudinal study of data from the Health and Retirement Study demonstrated that the cumulative risk of incident disability over an 8-year period was 21.3% for individuals with diabetes versus 9.3% for those without diabetes. This study examined a cohort of adults ranging in age from 51 to 61 years from 1992 through 2000.”

Although people with diabetes comprise just slightly more than 4% of the U.S. population,3 19% of every dollar spent on health care (including hospitalizations, outpatient and physician visits, ambulance services, nursing home care, home health care, hospice, and medication/glucose control agents) is incurred by individuals with diabetes” (As I noted in the margin, these are old numbers, and prevalence in particular is definitely higher today than it was when that chapter was written, so diabetics’ proportion of the total cost is likely even higher today than it was when that chapter was written. As observed multiple times previously on this blog, most of these costs are unrelated to the costs of insulin treatment and oral anti-diabetics like metformin, and indirect costs make out a quite substantial proportion of the total costs).

In 1997, only 8% of the population with a medical claim of diabetes was treated for diabetes alone. Other conditions influenced health care spending, with 13.8% of the population with one other condition, 11.2% with two comorbidities, and 67% with three or more related conditions.6 Patients with diabetes who suffer from comorbid conditions related to diabetes have a greater impact on health services compared with those patients who do not have comorbid conditions. […] Overall, comorbid conditions and complications are responsible for 75% of total medical expenditures for diabetes.” (Again, these are old numbers)

“Heart disease and stroke are the largest contributors to mortality for individuals with diabetes; these two conditions are responsible for 65% of deaths. Death rates from heart disease in adults with diabetes are two to four times higher than in adults without diabetes. […] Adults with diabetes are more than twice as likely to have multiple diagnoses related to macrovascular disease compared to patients without diabetes […] Although the prevalence of cardiovascular disease increases with age for both diabetics and nondiabetics, adults with diabetes have a significantly higher rate of disease. […] The management of macrovascular disease, such as heart attacks and strokes, represents the largest factor driving medical service use and related costs, accounting for 52% of costs to treat diabetes over a lifetime. The average costs of treating macrovascular disease are $24,330 of a total of $47,240 per person (in year 2000 dollars) over the course of a lifetime.17 Moreover, macrovascular disease is an important determinant of cost at an earlier time than other complications, accounting for 85% of the cumulative costs during the first 5 years following diagnosis and 77% over the initial decade. [Be careful here: This is completely driven by type 2 diabetics; a 10-year old newly diagnosed type 1 diabetic does not develop heart disease in the first decade of disease – type 1s are also at high risk of cardiovascular disease, but the time profile here is completely different] […] Cardiovascular disease in the presence of diabetes affects not only cost but also the allocation of health care resources. Average annual individual costs attributed to the treatment of diabetes with cardiovascular disease were $10,172. Almost 51% of costs were for inpatient hospitalizations, 28% were for outpatient care, and 21% were for pharmaceuticals and related supplies. In comparison, the average annual costs for adults with diabetes and without cardiovascular disease were $4,402 for management and treatment of diabetes. Only 31.2% of costs were for inpatient hospitalizations, 40.3% were for outpatient care, and 28.6% were for pharmaceuticals.16

Of individuals with diabetes, 2% to 3% develop a foot ulcer during any given year. The lifetime incidence rate of lower extremity ulcers is 15% in the diabetic population.20 […] The rate of amputation in individuals with diabetes is ten times higher than in those without diabetes.5 Diabetic lower-extremity ulcers are responsible for 92,000 amputations each year,21 accounting for more than 60% of all nontraumatic amputations.5 The 10-year cumulative incidence of lower-extremity amputation is 7% in adults older than 30 years of age who are diagnosed with diabetes.22 […] Following amputation, the 5-year survival rate is 27%.23 […] The majority of annual costs associated with treating diabetic peripheral neuropathy are associated with treatment of ulcers […] Overall, inpatient hospitalization is a major driver of cost, accounting for 77% of expenditures associated with individual episodes of lower-extremity ulcers.24

By 2003, diabetes accounted for 37% of individuals being treated for renal disease in the United States. […] Diabetes is the leading cause of kidney failure, accounting for 44% of all newly diagnosed cases. […] The amount of direct medical costs for ESRD attributed to diabetes is substantial. The total adjusted costs in a 24-month period were 76% higher among ESRD patients with diabetes compared with those without diabetes. […] Nearly one half of the costs of ESRD are due to diabetes.27” [How much did these numbers change since the book was written? I’m not sure, but these estimates do provide some sort of a starting point, which is why I decided to include the numbers even though I assume some of them may have changed since the publication of the book]

Every percentage point decrease in A1c levels reduces the risk of microvascular complications such as retinopathy, neuropathy, and nephropathy by 40%.5 However, the trend is for A1c to drift upward at an average of 0.15% per year, increasing the risk of complications and costs.17 […] A1c levels also affect the cost of specific complications associated with diabetes. Increasing levels affect overall cost and escalate more dramatically when comorbidities are present. A1c along with cardiovascular disease, hypertension, and depression are significant independent predictors of health care
costs in adults with diabetes.”

August 10, 2016 Posted by | Books, Cardiology, Diabetes, Economics, Epidemiology, Medicine, Nephrology, Pharmacology, Psychiatry | Leave a comment

Human Drug Metabolism (I)

“It has been said that if a drug has no side effects, then it is unlikely to work. Drug therapy labours under the fundamental problem that usually every single cell in the body has to be treated just to exert a beneficial effect on a small group of cells, perhaps in one tissue. Although drug-targeting technology is improving rapidly, most of us who take an oral dose are still faced with the problem that the vast majority of our cells are being unnecessarily exposed to an agent that at best will have no effect, but at worst will exert many unwanted effects. Essentially, all drug treatment is really a compromise between positive and negative effects in the patient. […] This book is intended to provide a basic grounding in human drug metabolism, although it is useful if the reader has some knowledge of biochemistry, physiology and pharmacology from other sources. In addition, a qualitative understanding of chemistry can illuminate many facets of drug metabolism and toxicity. Although chemistry can be intimidating, I have tried to make the chemical aspects of drug metabolism as user-friendly as possible.”

I’m currently reading this book. To say that it is ‘useful if the reader has some knowledge’ of the topics mentioned is putting it mildly; I’d say it’s mandatory – my advice would be to stay far away from this book if you know nothing of pharmacology, biochem, and physiology. I know enough to follow most of the coverage, at least in terms of the big picture stuff, but some of the biochemistry details I frankly have been unable to follow; I think I could probably understand all of it if I were willing to look up all the words and concepts with which I’m unfamiliar, but I’m not willing to spend the time to do that. In this context it should also be mentioned that the book is very well written, in the sense that it is perfectly possible to read the book and follow the basic outline of what’s going on without necessarily understanding all details, so I don’t feel that the coverage in any way discourages me from reading the book the way I am – the significance of that hydrogen bond in the diagram will probably become apparent to you later, and even if it doesn’t you’ll probably manage.

In terms of general remarks about the book, a key point to be mentioned early on is also that the book is very dense and has a lot of interesting stuff. I find it hard at the moment to justify devoting time to blogging, but if that were not the case I’d probably feel tempted to cover this book in a lot of detail, with multiple posts delving into specific fascinating aspects of the coverage. Despite this being a book where I don’t really understand everything that’s going on all the time, I’m definitely at a five star rating at the moment, and I’ve read close to two-thirds of it at this point.

A few quotes:

“The process of drug development weeds out agents [or at least tries to weed out agents… – US] that have seriously negative actions and usually releases onto the market drugs that may have a profile of side effects, but these are relatively minor within a set concentration range where the drug’s pharmacological action is most effective. This range, or ‘therapeutic window’ is rather variable, but it will give some indication of the most ‘efficient’ drug concentration. This effectively means the most beneficial pharmacodynamic effects for the minimum side effects.”

If the dose is too low, you have a case of drug failure, where the drug doesn’t work. If the dose is too high, you experience toxicity. Both outcomes are problematic, but they manifest in different ways. Drug failure is usually a gradual process (days – “Therapeutic drug failure is usually a gradual process, where the time frame may be days before the problem is detected”), whereas toxicity may be of very rapid onset (hours).

“To some extent, every patient has a unique therapeutic window for each drug they take, as there is such huge variation in our pharmacodynamic drug sensitivities. This book is concerned with what systems influence how long a drug stays in our bodies. […] [The therapeutic index] has been defined as the ratio between the lethal or toxic dose and the effective dose that shows the normal range of pharmacological effect. In practice, a drug […] is listed as having a narrow TI if there is less than a twofold difference between the lethal and effective doses, or a twofold difference in the minimum toxic and minimum effective concentrations. Back in the 1960s, many drugs in common use had narrow TIs […] that could be toxic at relatively low levels. Over the last 30 years, the drug industry has aimed to replace this type of drug with agents with much higher TIs. […] However, there are many drugs […] which remain in use that have narrow or relatively narrow TIs”.

“metabolites are usually removed from the cell faster than the parent drug”

“The kidneys are mostly responsible for […] removal, known as elimination. The kidneys cannot filter large chemical entities like proteins, but they can remove the majority of smaller chemicals, depending on size, charge and water solubility. […] the kidney is a lipophilic (oil-loving) organ […] So the kidney is not efficient at eliminating lipophilic chemicals. One of the major roles of the liver is to use biotransforming enzymes to ensure that lipophilic agents are made water soluble enough to be cleared by the kidney. So the liver has an essential but indirect role in clearance, in that it must extract the drug from the circulation, biotransform (metabolize) it, then return the water-soluble product to the blood for the kidney to remove. The liver can also actively clear or physically remove its metabolic products from the circulation by excreting them in bile, where they travel through the gut to be eliminated in faeces.”

“Cell structures eventually settled around the format we see now, a largely aqueous cytoplasm bounded by a predominantly lipophilic protective membrane. Although the membrane does prevent entry and exit of many potential toxins, it is no barrier to other lipophilic molecules. If these molecules are highly lipophilic, they will passively diffuse into and become trapped in the membrane. If they are slightly less lipophilic, they will pass through it into the organism. So aside from ‘ housekeeping ’ enzyme systems, some enzymatic protection would have been needed against invading molecules from the immediate environment. […] the majority of living organisms including ourselves now possess some form of effective biotransformational enzyme capability which can detoxify and eliminate most hydrocarbons and related molecules. This capability has been effectively ‘stolen’ from bacteria over millions of years. The main biotransformational protection against aromatic hydrocarbons is a series of enzymes so named as they absorb UV light at 450 nm when reduced and bound to carbon monoxide. These specialized enzymes were termed cytochrome P450 monooxygenases or sometimes oxido-reductases. They are often referred to as ‘CYPs’ or ‘P450s’. […] All the CYPs accomplish their functions using the same basic mechanism, but each enzyme is adapted to dismantle particular groups of chemical structures. It is a testament to millions of years of ‘ research and development ’ in the evolution of CYPs, that perhaps 50,000 or more man-made chemical entities enter the environment for the first time every year and the vast majority can be oxidized by at least one form of CYP. […] To date, nearly 60 human CYPs have been identified […] It is likely that hundreds more CYP-mediated endogenous functions remain to be discovered. […] CYPs belong to a group of enzymes which all have similar core structures and modes of operation. […] Their importance to us is underlined by their key role in more than 75 per cent of all drug biotransformations.”

I would add a note here that a very large proportion of this book is, perhaps unsurprisingly in view of the above, about those CYPs; how they work, what exactly it is that they do, which different kinds there are and what roles they play in the metabolism of specific drugs and chemical compounds, variation in gene expression across individuals and across populations in the context of specific CYPs and how such variation may relate to differences in drug metabolism, etc.

“Drugs often parallel endogenous molecules in their oil solubility, although many are considerably more lipophilic than these molecules. Generally, drugs, and xenobiotic compounds, have to be fairly oil soluble or they would not be absorbed from the GI tract. Once absorbed these molecules could change both the structure and function of living systems and their oil solubility makes these molecules rather ‘elusive’, in the sense that they can enter and leave cells according to their concentration and are temporarily beyond the control of the living system. This problem is compounded by the difficulty encountered by living systems in the removal of lipophilic molecules. […] even after the kidney removes them from blood by filtering them, the lipophilicity of drugs, toxins and endogenous steroids means that as soon as they enter the collecting tubules, they can immediately return to the tissue of the tubules, as this is more oil-rich than the aqueous urine. So the majority of lipophilic molecules can be filtered dozens of times and only low levels are actually excreted. In addition, very high lipophilicity molecules like some insecticides and fire retardants might never leave adipose tissue at all […] This means that for lipophilic agents:
*the more lipophilic they are, the more these agents are trapped in membranes, affecting fluidity and causing disruption at high levels;
* if they are hormones, they can exert an irreversible effect on tissues that is outside normal physiological control;
*if they are toxic, they can potentially damage endogenous structures;
* if they are drugs, they are also free to cause any pharmacological effect for a considerable period of time.”

“A sculptor was once asked how he would go about sculpting an elephant from a block of stone. His response was ‘knock off all the bits that did not look like an elephant’. Similarly, drug-metabolizing CYPs have one main imperative, to make molecules more water-soluble. Every aspect of their structure and function, their position in the liver, their initial selection of substrate, binding, substrate orientation and catalytic cycling, is intended to accomplish this deceptively simple aim.”

“The use of therapeutic drugs is a constant battle to pharmacologically influence a system that is actively undermining the drugs’ effects by removing them as fast as possible. The processes of oxidative and conjugative metabolism, in concert with efflux pump systems, act to clear a variety of chemicals from the body into the urine or faeces, in the most rapid and efficient manner. The systems that manage these processes also sense and detect increases in certain lipophilic substances and this boosts the metabolic capability to respond to the increased load.”

“The aim of drug therapy is to provide a stable, predictable pharmacological effect that can be adjusted to the needs of the individual patient for as long is deemed clinically necessary. The physician may start drug therapy at a dosage that is decided on the basis of previous clinical experience and standard recommendations. At some point, the dosage might be increased if the desired effects were not forthcoming, or reduced if side effects are intolerable to the patient. This adjustment of dosage can be much easier in drugs that have a directly measurable response, such as a change in clotting time. However, in some drugs, this adjustment process can take longer to achieve than others, as the pharmacological effect, once attained, is gradually lost over a period of days. The dosage must be escalated to regain the original effect, sometimes several times, until the patient is stable on the dosage. In some cases, after some weeks of taking the drug, the initial pharmacological effect seen in the first few days now requires up to eight times the initial dosage to reproduce. It thus takes a significant period of time to create a stable pharmacological effect on a constant dose. In the same patients, if another drug is added to the regimen, it may not have any effect at all. In other patients, sudden withdrawal of perhaps only one drug in a regimen might lead to a gradual but serious intensification of the other drug’s side effects.”

“acceleration of drug metabolism as a response to the presence of certain drugs is known as ‘enzyme induction’ and drugs which cause it are often referred to as ‘inducers’ of drug metabolism. The process can be defined as: ‘An adaptive increase in the metabolizing capacity of a tissue’; this means that a drug or chemical is capable of inducing an increase in the transcription and translation of specific CYP isoforms, which are often (although not always) the most efficient metabolizers of that chemical. […] A new drug is generally regarded as an inducer if it produces a change in drug clearance which is equal to or greater than 40 per cent of an established potent inducer, usually taken as rifampicin. […] inducers are usually (but not always) lipophilic, contain aromatic groups and consequently, if they were not oxidized, they would be very persistent in living systems. CYP enzymes have evolved to oxidize this very type of agent; indeed, an elaborate and very effective system has also evolved to modulate the degree of CYP oxidation of these agents, so it is clear that living systems regard inducers as a particular threat among lipophilic agents in general. The process of induction is dynamic and closely controlled. The adaptive increase is constantly matched to the level of exposure to the drug, from very minor almost undetectable increases in CYP protein synthesis, all the way to a maximum enzyme synthesis that leads to the clearance of grammes of a chemical per day. Once exposure to the drug or toxin ceases, the adaptive increase in metabolizing capacity will subside gradually to the previous low level, usually within a time period of a few days. This varies according to the individual and the drug. […] it is clear there is almost limitless capacity for variation in terms of the basic pre-set responsiveness of the system as well as its susceptibility to different inducers and groups of inducers. Indeed, induction in different patients has been observed to differ by more than 20-fold.”

This one I added mostly because I didn’t know this and I thought it was worth including it here because it would make it easier for me to remember later (i.e., not because I figured other people might find this interesting):

CYP2E1 is very sensitive to diet, even becoming induced by high fat/low carbohydrate intakes. Surprisingly, starvation and diabetes also promote CYP2E1 functionality. Insulin levels fall during diet restriction, starvation and in diabetes and the formation of functional 2E1 is suppressed by insulin, so these conditions promote the increase of 2E1 metabolic capability. One of the consequences of diabetes and starvation is the major shift from glucose to fatty acid/tryglyceride oxidation, of which some of the by-products are small, hydrophilic and potentially toxic ‘ketone bodies’. These agents can cause a CNS intoxicating effect which is seen in diabetics who are very hypoglycaemic, they may appear ‘drunk’ and their breath will smell as if they had been drinking.”

A more general related point which may be of more interest to other people reading along here is that this is far from the only CYP which is sensitive to diet, and that diet-mediated effects may be very significant. I may go into this in more detail in a later post. Note that grapefruit is a major potentially problematic dietary component in many drug contexts:

“Although patients have been heroically consuming grapefruit juice for their health for decades, it took until the late 1980s before its effects on drug clearance were noted and several more years before it was realized that there could be a major problem with drug interactions […] The most noteworthy feature of the effect of grapefruit juice is its potency from a single ‘dose’ which coincides with a typical single breakfast intake of the juice, say around 200–300 ml. Studies with CYP3A substrates such as midazolam have shown that it can take up to three days before the effects wear off, which is consistent with the synthesis of new enzyme. […] there are a number of drugs that are subject to a very high gut wall component to their ‘first-pass’ metabolism […]; these include midazolam, terfenadine, lovastatin, simvastatin and astemizole. Their gut CYP clearance is so high that if the juice inhibits it, the concentration reaching the liver can increase six- or sevenfold. If the liver normally only extracts a relatively minor proportion of the parent agent, then plasma levels of such drugs increase dramatically towards toxicity […] the inhibitor effects of grapefruit juice in high first – pass drugs is particularly clinically relevant as it can occur after one exposure of the juice.”

It may sound funny, but there are two pages in this book about the effects of grapefruit juice, including a list of ‘Drugs that should not be taken with grapefruit juice’. Grapefruit is a well-known so-called mechanism-based inhibitor, and it may impact the metabolism of a lot of different drugs. It is far from the only known dietary component which may cause problems in a drug metabolism context – for example “cranberry juice has been known for some time as an inhibitor of warfarin metabolism”. On a general note the author remarks that: “There are hundreds of fruit preparations available that have been specifically marketed for their […] antioxidant capacities, such as purple grape, pomegranate, blueberry and acai juices. […] As they all contain large numbers of diverse phenolics and are pharmacologically active, they should be consumed with some caution during drug therapy.”

April 7, 2016 Posted by | Biology, Books, Medicine, Nephrology, Pharmacology | Leave a comment

Prioritization in medicine

This book is not exactly the first book I’ve read on these kinds of topics (see for example my previous coverage of related topics here, here, here, here, here, and here), but the book did have some new stuff and I decided in the end that it was worth blogging, despite the fact that I did not think the book was particularly great. The book is slightly different from previous books I’ve read on related topics because normative aspects are covered in much greater detail – as they put it in the preface:

“This volume addresses normative dimensions of methodological and theoretical approaches, international experiences concerning the normative framework and the process of priority setting as well as the legal basis behind priorities. It also examines specific criteria for prioritization and discusses economic evaluation. […] Prioritization is necessary and inevitable – not only for reasons of resource scarcity, which might become worse in the next few years. But especially in view of an optimization of the supply structures, prioritization is an essential issue that will contribute to the capability and stability of healthcare systems. Therefore, our volume may give useful impulses to face challenges of appropriate prioritization.”

I’m generally not particularly interested in normative questions, preferring instead to focus on the empirical side of things, but the book did have some data as well. In the post I’ll focus on topics I found interesting, and I have made no attempt here to make the coverage representative of the sort of topics actually covered in the book; this is (as usual) a somewhat biased account of the material covered.

The book observes early and often that there’s no way around prioritization in medicine; you can’t not prioritize, because “By giving priority to one group, you ration care to the second group.” Every time you spend a dollar on cancer treatment, well, that’s a dollar you can’t spend on heart disease. So the key question in this context is how best to prioritize, rather than whether you should do it. It is noted in the text that there is a wide consensus that approaching and handling health care allocation rules explicitly is preferable to implicit rationing, a point I believe was also made in Glied and Smith. A strong argument can be made that clear and well-defined decision-rules will lead to better outcomes than implicit allocation decisions made by doctors during their day-to-day workload. The risks of leaving allocation decisions to physicians involve overtaxing medical practitioners (they are implicitly required to repeatedly take decisions which may be emotionally very taxing), problematic and unfair distribution patters of care, and there’s also a risk that such practices may erode trust between patients and physicians.

A point related to the fact that any prioritization decision made within the medical sector, regardless of whether the decision is made implicitly or explicitly, will necessarily affect all patient populations by virtue of the fact that resources used for one purpose cannot be used for another purpose, is that the health care sector is not the only sector in the economy; when you spend money on medicine that’s also money you can’t be spending on housing or education: “The competition between health-related resources and other goods is generally left to a political process. The fact that a societal budget for meeting health needs is the result of such a political process means that in all societies, some method of resolving disagreements about priorities is needed.” Different countries have different approaches to how to resolve these disagreements (and in large countries in particular, lower-level regional differences may also be important in terms of realized care provision allocation decisions), and the book covers systems applied in multiple different countries, including England, Germany, Norway, Sweden, and the US state of Oregon.

Some observations and comments:

“A well-known unfairness objection against conventional cost-effectiveness analysis is the severity of diseases objection – the objection that the approach is blind as to whether the QALYs go to severely or to slightly ill patients. Another is the objection of disability discrimination – the objection that the approach is not blind between treating a life-threatening disease when it befalls a disabled patient and treating the same disease when it befalls a non-disabled patient. An ad hoc amendment for fairness problems like these is equity weighting. Equity weights are multiplication factors that are introduced in order to make some patient group’s QALYs count more than others.”

“There were an estimated 3 million people with diabetes in England in 2009; estimates suggest that the number of people with diabetes could rise to 4.6 million by 2030. There has also been a rapid rise in gastrointestinal diseases, particularly chronic liver disease where the under-65 mortality rate has increased 5-fold since 1970. Liver disease is strongly linked to the harmful use of alcohol and rising levels of obesity. […] the poorest members of the community are at most risk of neglecting their health. This group is more likely to eat, drink and smoke to excess and fail to take sufficient exercise.22 Accordingly, life expectancy in this community is shorter and the years spent of suffering from disability are much longer. […] Generic policies are effective in the sense that aggregate levels of health status improve and overall levels of morbidity and mortality fall. However, they are ineffective in reducing health inequalities; indeed, they may make them worse. The reason is that better-off groups respond more readily to public health campaigns. […] If policy-makers [on the other hand] disinvest from the majority to narrow the inequality gap with a minority resistant to change, this could reduce aggregate levels of health status in the community as a whole. [Health behaviours also incidentally tend to be quite resistant to change in general, and we really don’t know all that much about which sort of interventions work and/or how well they work – see also Thirlaway & Upton’s coverage] […] two out of three adults [in the UK] are overweight or obese; and inequalities in health remain widespread, with people in the poorest areas living on average 7 years fewer than those in the richest areas, and spending up to 17 more years living with poor health. […] the proportion of the total health budget invested in preventive medicine and health promotion […] is small. The UK spends about 3.6 % of its entire healthcare budget on public health projects of this nature (which is more than many other EU member states).”

Let’s talk a little bit about rationing. Rationing by delay (waiting lists) is a well-known method of limiting care, but it’s far from the only way to implicitly ration care in a manner which may be hidden from view; another way to limit care provision is to ration by dilution. This may happen when patients are seen on time (do recall that waiting lists are very common in the medical sector, for very natural reasons which I’ve discussed here on the blog before), but the quality of care that is provided to patients receiving care goes down. Rationing by dilution may sometimes be a result of attempts to limit rationing by delay; if you measure hospitals on whether or not they treat people within a given amount of time, the time dimension becomes very important in the treatment context and it may thus end up dominating other decision variables which should ideally take precedence over this variable in the specific clinical context. The book mentions as an example the Bristol Eye Hospital, where it is thought that 25 patients may have lost their sights because even though they were urgent cases which should have been high priority, they were not treated in time because there was a great institutional focus on not allowing waiting times of any patients on the waiting lists to cross the allowed maximum waiting time, meaning that much less urgent cases were treated instead of the urgent cases in order to make the numbers look good. A(n excessive?) focus on waiting lists may thus limit focus on patient needs, and similar problems pop up when other goals aside from patient needs are emphasized in an institutional context; hospital reorganisations undertaken in order to improve financial efficiency may also result in lower standards of care, and in the book multiple examples of this having happened in a British context are discussed. The chapter in question does not discuss this aspect, but it seems to me likely that rationing by dilution, or at least something quite similar to this, may also happen in the context of a rapid increase in capacity as a result of an attempt to address long waiting lists; if you for example decide to temporarily take on a lot of new and inexperienced nurses to lower the waiting list, these new nurses may not provide the same level of care as do the experienced nurses already present. A similar dynamic may probably be observed in a setting where the number of nurses does not change, but each patient is allocated less time with any given nurse than was previously the case.

“Public preferences have been shown not to align with QALY maximization (or health benefit maximization) across a variety of contexts […] and considerations affecting these preferences often extend well beyond strict utilitarian concerns […] age has been shown to be among the most frequently cited variables affecting the public’s prioritization decisions […] Most people are willing to use age as a criterion at least in some circumstances and at least in some ways. This is shown by empirical studies of public views on priority setting […] most studies suggest that a majority accepts that age can have some role in priority setting. […] Oliver [(2009)] found […] a wide range of context-dependent ‘decision rules’ emerged across the decision tasks that appeared to be dependent on the scenario presented. Respondents referenced reasons including maximizing QALYs,11 maximizing life-years or post-treatment quality of life,12 providing equal access to health care, maximizing health based on perceptions of adaptation, maximizing societal productivity (including familial roles, i.e. ‘productivity ageism’), minimizing suffering, minimizing costs, and distributing available resources equitably. As an illustration of its variability, he noted that 46 of the 50 respondents were inconsistent in their reasoning across the questions. Oliver commented that underlying values influence the respondents’ decisions, but if these values are context dependent, it becomes a challenge – if not impossible – to identify a preferred, overarching rule by which to distribute resources. […] Given the empirical observations that respondents do not seem to rely upon a consistent decision rule that is independent of the prioritization context, some have suggested that deliberative judgments be used to incorporate equity considerations […]. This means that decision makers may call upon a host of different ‘rules’ to set priorities depending on the context. When the patients are of similar ages, prioritization by severity may offer a morally justifiable solution, for example. In contrast, as the age discrepancy becomes greater between the two patients, there may be a point at which ‘the priority view’ (i.e. those who in the most dire conditions take precedence) no longer holds […] There is some evidence that indicates that public preferences do not support giving priority in instances where the intervention has a poor prognosis […] If older patients have poorer health outcomes as a result of certain interventions, [this] finding might imply that in these instances, they should receive lower priority or not be eligible for certain care. […] A substantial body of evidence indicates that the utilitarian approach of QALY maximization fails to adequately capture public preferences for a greater degree of equity into health-care distribution; however, how to go about incorporating these concerns remains unresolved.”

“roughly 35 % of the […] [UK] health expenditures were spent on the 13 % of our population over the age of 65. A similar statistic holds true for the European Union as well […] the elderly, on average, have many more health needs than the non-elderly. In the United States, 23 % of the elderly have five or more chronic health problems, some life-threatening, some quality-of-life diminishing (Thorpe et al. 2010). Despite this statistic, the majority of the elderly in any given year is quite healthy and makes minimal use of the health care system. Health needs tend to be concentrated. The sickest 5 % of the Medicare population consume 39 % of total Medicare expenditures, and the sickest 10 % consume 58 % of Medicare expenditures (Schoenman 2012). […] we are […] faced with the problem of where to draw the line with regard to a very large range of health deficiencies associated with advanced age. It used to be the case in the 1970s that neither dialysis nor kidney transplantation were offered as an option to patients in end-stage kidney failure who were beyond age 65 because it was believed they were not medically suitable. That is, both procedures were judged to be too burdensome for individuals who already had diminished health status. But some centers started dialyzing older patients with good results, and consequently, the fastest growing segment of the dialysis population today (2015) is over age 75. This phenomenon has now been generalized across many areas of surgery and medicine. […] What [many new] procedures have in common is that they are very expensive: $70,000 for coronary bypass surgery (though usually much more costly due to complication rates among the hyper-elderly); $200,000 for the LVAD [Left Ventricular Assist Device]; $100,000+ per month for prolonged mechanical ventilation. […] The average older recipient of an LVAD will gain one to two extra years of life […] there are now (2015) about 5.5 million Americans in various stages of heart failure and 550,000 new cases annually. Versions of the LVAD are still being improved, but the potential is that 200,000 of these devices could be implanted annually in the United States. That would add at least $40 billion per year to the cost of the Medicare program.”

“In the USA, around 40 % of premature mortality is attributed to behavioral patterns,2 and it is estimate[d] that around $1.3 trillion annually — around a third of the total health budget — is spent on preventable diseases.3 […] among the ten leading risk factors contributing to the burden of disease in high-income countries, seven can be directly attributed to unhealthy lifestyles. […] Private health insurance takes such factors into account when calculating premiums for health insurances (Olsen 2009). In contrast, publicly funded health-care systems are mainly based on the so-called solidarity principle, which generally excludes risk-based premiums. However, in some countries, several incentive schemes such as “fat taxes” […], bonuses, or reductions of premiums […] have recently been implemented in order to incorporate aspects of personal responsibility in public health-care systems. […] [An important point in this context is that] there are fundamental questions about whether […] better health leads to lower cost. Among other things, cost reductions are highly dependent on the period of time that one considers. What services are covered by a health system, and how its financing is managed, also matters. Regarding the relative lifetime cost of smokers, obese, and healthy people (never smokers, normal body mass index [BMI]) in the Netherlands, it has been suggested that the latter, and not the former two groups, are most costly — chiefly due to longer life and higher cost of care at the end of life.44 Other research suggests that incentivizing disease management programs rather than broader prevention programs is far more effective.45 Cost savings can therefore not be taken for granted but require consideration of the condition being incentivized, the organizational specifics of the health system, and, in particular, the time horizon over which possible savings are assessed. […] Policies seeking to promote personal responsibility for health can be structured in a very wide variety of ways, with a range of different consequences. In the best case, the stars are aligned and programs empower people’s health literacy and agency, reduce overall healthcare spending, alleviate resource allocation dilemmas, and lead to healthier and more productive workforces. But the devil is often in the detail: A focus on controlling or reducing cost can also lead to an inequitable distribution of benefits from incentive programs and penalize people for health risk factors that are beyond their control.”

January 21, 2016 Posted by | Books, Cardiology, Economics, Epidemiology, Medicine, Nephrology | Leave a comment

Oxford Handbook of Clinical Medicine (III)

Here are my first two posts about the book, which I have now finished. I gave the book three stars on goodreads, but I’m close to a four star rating and I may change my opinion later – overall it’s a pretty good book. I’ve read about many of the topics covered before but there was also quite a bit of new stuff along the way; as a whole the book spans very widely, but despite this the level of coverage of individual topics is not bad – I actually think the structure of the book makes it more useful as a reference tool than is McPhee et al. (…in terms of reference books which one might find the need to refer to in order to make sense of medical tests and test results, I should of course add that no book can beat Newman & Kohn). I have tried to take this into account along the way in terms of the way I’ve been reading the book, in the sense that I’ve tried to make frequent references in the margin to other relevant works going into more detail about specific topics whenever this seemed like it might be useful, and I think if one does something along those lines systematically a book like this one can become a really powerful tool – you get the short version with the most important information (…or at least what the authors considered to be the most important information) here almost regardless of what topic you’re interested in – I should note in this context that the book has only very limited coverage of mental health topics, so this is one area where you definitely need to go elsewhere for semi-detailed coverage – and if you need more detail than what’s provided in the coverage you’ll also know from your notes where to go next.

In my last post I talked a bit about which topics were covered in the various chapters in the book – I figured it might make sense here to list the remaining chapter titles in this post. After the (long) surgery chapter, the rest of the chapters deal with epidemiology (I thought this was a poor chapter and the authors definitely did not consider this topic to be particularly important; they spent only 12 pages on it), clinical chemistry (lab results, plasma proteins, topics like ‘what is hypo- and hypernatremia’, …), eponymous syndromes (a random collection of diseases, many of which are quite rare), radiology (MRI vs X-ray? When to use, or not use, contrast material? Etc.), ‘reference intervals etc.‘ (the ‘etc.’ part covered drug therapeutic ranges for some commonly used drugs, as well as some important drug interactions – note to self: The effects of antidiabetic drugs are increased by alcohol, beta-blockers, bezafibrate, and MAOIs, and are decreased by contraceptive steroids, corticosteroids, diazoxide, diuretics, and possibly also lithium), practical procedures (I was considering skipping this chapter because I’m never going to be asked to e.g. insert a chest drain and knowing how to do it seems to be of limited benefit to me, but I figured I might as well read it anyway; there were some details about what can go wrong in the context of specific procedures and what should be done when this happens, and this seemed like valuable information. Also, did you know that “There is no evidence that lying flat post procedure prevents headache” in the context of lumbar punctures? I didn’t, and a lot of doctors probably also don’t. You can actually go even further than that: “Despite years of anecdotal advice to the contrary, none of the following has ever been shown to be a risk factor [for post-LP headache]: position during or after the procedure; hydration status before, during, or after; amount of CSF removed; immediate activity or rest post-LP.”), and emergencies.

In this post I won’t cover specific chapters of the book in any detail, rather I’ll talk about a few specific topics and observations I could be bothered to write some stuff about here. Let’s start with some uplifting news about the topic of liver tumours: Most of these (~90%) are secondary (i.e. metastatic) tumours with an excellent prognosis (“Often ↑3yr survival to 59% from 13%; but ~50% have recurrence by 3yrs.[3] Liver transplant gives a 5yr survival rate of 70%.” It should be noted in a disease impact context that this type of cancer is far more common in areas of the world with poorly developed health care systems like Africa and China.

Alcoholism is another one of the causes of liver tumors. In the book they include the observation that the lifetime prevalence of alcoholism is around 10% for men and 4% for women, but such numbers are of course close to being completely meaningless almost regardless of where they’re coming from. Alcoholism is dangerous; in cases with established cirrhosis roughly half (52%) of people who do not stop drinking will be dead within 5 years, whereas this is also the case for 23% of the people who do stop drinking. Excessive alcohol consumption can cause alcoholic hepatitis; “[m]ild episodes hardly affect mortality” but in severe cases half will be dead in a month, and in general 40% of people admitted to the hospital for alcoholic hepatitis will be dead within one year of admission. Alcohol can cause portal hypertension (80% of cases are caused by cirrhosis in the UK), which may lead to the development of abnormal blood vessels e.g. in the oesophagus which will have a tendency to cause bleeding, which can be fatal.  Roughly 30% of cirrhotics with varices bleed, and rebleeding is common: “After a 1st variceal bleed, 60% rebleed within 1yr” and “40% of rebleeders die of complications.” Alcoholism can kill you in a variety of different ways (acute poisonings and accidents should probably also be included here as well), and many don’t survive long enough to develop cancer.

As mentioned in the first post about the book acute kidney injury is common in a hospital setting. In the following I’ve added a few more observations about renal disease. “Renal pain is usually a dull ache, constant and in the loin.” But renal disease don’t always cause pain, and in general: “There is often a poor correlation between symptoms and severity of renal disease. Progression [in chronic disease] may be so insidious that patients attribute symptoms to age or a minor illnesses. […] Serious renal failure may cause no symptoms at all.” The authors note that odd chronic symptoms like fatigue should not be dismissed without considering a renal function test first. The book has a nice brief overview of the pathophysiology of diabetic nephropathy – this part is slightly technical, but I decided to include it here anyway before moving on to a different topic:
“Early on, glomerular and tubular hypertrophy occur, increasing GFR [glomerular filtration rate, an indicator variable used to assess kidney function] transiently, but ongoing damage from advanced glycosylation end-products (AGE—caused by non-enzymatic glycosylation of proteins from chronic hyperglycaemia) triggers more destructive disease. These AGE trigger an inflammatory response leading to deposition of type IV collagen and mesangial expansion, eventually leading to arterial hyalinization, thickening of the mesangium and glomerular basement membrane and nodular glomerulosclerosis (Kimmelstiel–Wilson lesions). Progression generally occurs in four stages:
1 GFR elevated: early in disease renal blood flow increases, increasing the GFR and leading to microalbuminuria. […]
2 Glomerular hyperfiltration: in the next 5–10yrs mesangial expansion gradually occurs and hyperfiltration at the glomerulus is seen without microalbuminuria.
3 Microalbuminuria: as soon as this is detected it indicates progression of disease, GFR may be raised or normal. This lasts another 5–10yrs.
4 Nephropathy: GFR begins to decline and proteinuria increases.
Patients with type 2 DM may present at the later stages having had undetected hyperglycaemia for many years before diagnosis.”

Vitamin B12 deficiency is quite common, the authors note that it occurs in up to 15% of older people. Severe B12 deficiency is not the sort of thing which will lead to you feeling ‘a bit under the weather’ – it can lead to permanent brain damage and damage to the spinal cord. “Vitamin B12 is found in meat, fish, and dairy products, but not in plants.” It’s important to note that “foods of non-animal origin contain no B12 unless fortified or contain bacteria.” The wiki article incidentally includes even higher prevalence estimates (“It is estimated to occur in about 6% of those under the age of 60 and 20% of those over the age of 60. Rates may be as high as 80% in parts of Africa and Asia.”) than the one included in the book – this vitamin deficiency is common, and if severe it can have devastating consequences.

On bleeding disorders: “After injury, 3 processes halt bleeding: vasoconstriction, gap-plugging by platelets, and the coagulation cascade […]. Disorders of haemostasis fall into these 3 groups. The pattern of bleeding is important — vascular and platelet disorders lead to prolonged bleeding from cuts, bleeding into the skin (eg easy bruising and purpura), and bleeding from mucous membranes (eg epistaxis [nose bleeds], bleeding from gums, menorrhagia). Coagulation disorders cause delayed bleeding into joints and muscle.” An important observation in the context of major bleeds is incidentally this: “Blood should only be given if strictly necessary and there is no alternative. Outcomes are often worse after a transfusion.” The book has some good chapters about the leukaemias, but they’re relatively rare diseases and some of them are depressing (e.g. acute myeloid leukaemia: according to the book coverage death occurs in ~2 months if untreated, and roughly four out of five treated patients are dead within 3 years) so I won’t talk a lot about them. One thing I found somewhat interesting about the blood disorders covered in the book is actually how rare they are, all things considered: “every day each of us makes 175 billion red cells, 70 billion granulocytes, and 175 billion platelets”. There are lots of opportunities for things to go wrong here…

Some ways to prevent traveller’s diarrhea: “If in doubt, boil all water. Chlorination is OK, but doesn’t kill amoebic cysts (get tablets from pharmacies). Filter water before purifying. Distinguish between simple gravity filters and water purifiers (which also attempt to sterilize chemically). […] avoid surface water and intermittent tap supplies. In Africa assume that all unbottled water is unsafe. With bottled water, ensure the rim is clean & dry. Avoid ice. […] Avoid salads and peel your own fruit. If you cannot wash your hands, discard the part of the food that you are holding […] Hot, well-cooked food is best (>70°C for 2min is no guarantee; many pathogens survive boiling for 5min, but few last 15min)”

An important observation related to this book’s coverage about how to control hospital acquired infection: “Cleaning hospitals: Routine cleaning is necessary to ensure that the hospital is visibly clean and free from dust and soiling. 90% of microorganisms are present within ‘visible dirt’, and the purpose of routine cleaning is to eliminate this dirt. Neither soap nor detergents have antimicrobial activity, and the cleaning process depends essentially on mechanical action.”

Falciparum malaria causes one million deaths/year, according to the book, and mortality is close to 100% in untreated severe malaria – treatment reduces this number to 15-20%. Malaria in returning travellers is not particularly common, but there are a couple thousand cases in the UK each year. Malaria prophylaxis does not give full protection, and “[t]here is no good protection for parts of SE Asia.” Multidrug resistance is common.

November 8, 2015 Posted by | alcohol, Books, Cancer/oncology, Epidemiology, Infectious disease, Medicine, Nephrology | Leave a comment

Oxford Handbook of Clinical Medicine (I)

“We wrote this book not because we know so much, but because we know we remember so little…the problem is not simply the quantity of information, but the diversity of places from which it is dispensed. Trailing eagerly behind the surgeon, the student is admonished never to forget alcohol withdrawal as a cause of post-operative confusion. The scrap of paper on which this is written spends a month in the pocket before being lost for ever in the laundry. At different times, and in inconvenient places, a number of other causes may be presented to the student. Not only are these causes and aphorisms never brought together, but when, as a surgical house officer, the former student faces a confused patient, none is to hand.”

‘But now you don’t need to look for those scraps of paper anymore because we’ve collected all that information right here, in this book,’ the authors would argue. Or at least some of the important information is included here (despite this being a 900+ page textbook, many books on subtopics covered in the book are much longer than that; for example the Holmes et al. textbook dealing only with sexually transmitted diseases is more than twice as long as this one. Of course a book with that kind of page count will only ever be a ‘handbook’ to someone with acromegaly…).

Anyway, I’m currently reading this book and I figured I should probably talk about a few of the observations made in the book here, to make them easier to remember later on. The book is intended to be used as a reference work for doctors so in a way trying to remember stuff written in it is a strange thing to do – the point of the book is after all that you don’t need to remember all that stuff – but I would prefer to remember some of the things written in this book and this’ll be easier to do if I write about them here on the blog, instead of just ‘keeping them hidden in the book’, so to speak.

I’m assuming nobody reading along here are planning on reading this book so I wasn’t sure how much sense it would make to add impressions about the way it’s written etc. here, but I decided to note down a few things on these topics anyway. I have noted along the way that the authors sometimes include comments about a condition which they only cover later in the same chapter, and this has bothered me a few times; on the other hand I’m well aware that when you’re trying to write a book where it’s supposed to be easy to look things up quickly you need to make some key decisions here and there which will be likely to impact the reading experience of people who read the book from cover to cover the way I am negatively. Most chapters are structured a bit the same way the ‘[Topic X] At a glance…’ textbooks I’ve read in the past were (Medical Statistics at a Glance, Nutrition at a Glance, The Endocrine System at a Glance); the chapters vary in length (for example there are roughly 70 pages about cardiovascular medicine, 40 pages about endocrinology, 50 pages about gastroenterology, and 30 pages about renal medicine) but they generally seem to be structured in much the same way; the chapters are segmented – many chapter segments are two-page segments, which were also predominant in the At a glance texts – and each segment deals with a specific topic in some detail, with details about many aspects of the disease/condition in question, such as information about e.g. incidence/prevalence, risk factors, some notes on pathophysiology, presentation/symptoms/signs, diagnostics (tests to perform, key symptoms to keep in mind, etc.), treatment options (drugs/surgery/etc.?, dosage, indications/contraindications, side effects, drug interactions, etc.), potential complications, and prognostic information. Not all chapters are structured in the ‘two-page-segments’ way even though this seems to be the baseline structure in many contexts; it’s clear that they’ve given some thought as to how best to present the information included in the coverage. I recall from the At a glance texts that I occasionally thought that the structure felt unnatural, and that they seemed to have committed to a suboptimal coverage format in the specific context – I have not thought along such lines while reading this book, which to me is a sign that they’ve handled these things well. Deviation from the default format occurs e.g. in the chapter on cardiovascular medicine, which has quite a few successive pages on which various types of ECG abnormalities are illustrated (I looked at that stuff and I like to think that I understand this stuff better than I used to now, but I must admit that this was one of the sections of this book into which I did not put a lot of effort, as it in some sense felt like ‘irrelevant knowledge’ – so don’t expect me to be able to tell a right bundle branch block from an acute anterior myocardial infarction on an EEG without having access to this book…). It’s perhaps important to point out that despite the condensed structure of the book the coverage is reasonably detailed; this is not a book with two pages about ‘heart disease’, it’s a book with two pages about rheumatic fever, two pages about right heart valve disease, two pages about infective endocarditis, two pages about broad complex tachycardia, etc. And many of the pages include a lot of information. I have read textbooks dealing with many of the topics they cover and this is also not my first general ‘clinical medicine’ text (that was McPhee et al.), but I’m learning new stuff from the book even about topics with which I’m familiar, which is really nice. It’s a pretty good book so far, even if it’s not perfect; I’m probably at a four star rating at the moment.

In the parts to follow I’ll talk about some of the observations included in the book which I figured might be worth repeating here.

The first observation: They note in the book that 80% of people above the age of 85 years (in Britain) live at home and that 70% of those people can manage stairs; they argue in the same context that any deterioration in an elderly patient should be considered to be from treatable disease until proven otherwise (i.e., the default should not be to say that ‘that’s probably just ageing’).

“Unintentional weight loss should always ring alarm bells”.

A diabetic is probably well-advised to be aware of some of the signs of peripheral arterial disease. These include loss of hair, pallor, shiny skin, cyanosis (bluish discoloration of the skin), dry skin, scaling, deformed toenails, and lowered skin temperature.

“Normally 400-1300mL of gas is expelled PR in 8-20 discrete (or indiscrete) episodes per day. […] most patients with ‘flatulence’ have no GI disease. Air swallowing (aerophagy) is the main cause of flatus; here N2 is the chief gas. If flatus is mostly methane, N2 and CO2, then fermentation by bowel bacteria is the cause,[42] and reducing carbohydrate intake (eg less lactose and wheat) may help.[43]”

If there are red blood cells in the urine, this is due to cancer or glomerulonephritis (let’s not go into details here – we’ll just call this one ‘kidney disease’ for now) until proven otherwise. Painless visual haematuria (blood in the urine) usually equals bladder cancer – it’s definitely a symptom one should have a talk with a doctor about. The book does not mention this, but it’s important to keep in mind however that red/brownish urine is not always due to blood in the urine; it can also be caused by drugs and vegetable dyes (link). I was very surprised about this one in the context of ways to prevent UTIs: “There is no evidence that post-coital voiding, or pre-voiding, or advice on wiping patterns in females is of benefit.[6]” Drinking more water and drinking cranberry or lingo berry juice daily works/lowers risk.

Kidney function is often impaired in people who are hospitalized, with acute kidney injury (-AKI) occurring in up to 18% of hospital patients. It’s an important risk factor for mortality. Mortality can be very high in people with AKI, for example people admitted with burns who develop AKI have an 80% mortality rate, and with trauma/surgery it’s 60%. Up to 30 % of cases are preventable, and preventable causes include medications (continuing medications as usual e.g. after surgery can be catastrophic, and some of the drugs that can cause kidney problems are drugs people take regularly for chronic conditions such as high blood pressure or diabetes (metformin in particular)) and contrast material used in CT scans and procedures. Kidney function is incidentally often also (chronically) impaired in old people, most of which have no symptoms; “many elderly people fall into CKD [chronic kidney disease] stage 3 but have little or no progression over many years.” Symptoms of chronic kidney disease will usually not present until stage four is reached, but if onset of kidney failure is slow even people in the later stages may remain asymptomatic. The authors question whether it makes sense to label the old people in stage 3 with an illness; I’m not sure I completely agree (lowered kidney function increases cardiovascular risk, and some of those people may want to address this, if possible), but I’d certainly agree with the position that there’s a risk of overdiagnosis here.

A few more observations about kidneys. The chief cause of death from renal failure is cardiovascular disease, and in the first two stages of chronic kidney disease, the risk of dying from cardiovascular disease is higher than the risk of ever reaching stage 5, end-stage-renal-failure. Blood pressure control is very important in kidney disease as the authors argue that even a small drop in blood pressure may save significant kidney function. The causal link between BP and kidney disease goes both ways: “Hypertension often causes renal problems […] and most renal diseases can cause hypertension”. Once people require renal replacement therapy (RRT) such as haemodialysis mortality is high: Annual mortality is ~20%, mainly due to cardiovascular disease. The authors talk a little bit about diabetes and kidney disease in the book and among other things include the following observations:

“Diabetes is best viewed as a vascular disease with the kidney as one of its chief targets for end-organ damage. The single most important intervention in the long-term care of DM is the control of BP, to protect the heart, the brain, and the kidney. Renal damage may be preventable with good BP and glycaemic control.
In type 1 DM nephropathy is rare in the first 5yrs, after 10yrs annual incidence rises to a peak at 15yrs, then falls again. Those who have not developed nephropathy at 35yrs are unlikely to do so. In type 2 DM around 10% have nephropathy at diagnosis and up to half will go on to develop it over the next 20yrs. 20% of people with type 2 DM will develop ESRF.”

I was surprised by the observation above that “Those who have not developed nephropathy at 35yrs are unlikely to do so”, and I’m not sure I’d agree with the authors about that. The incidence of diabetes-related nephropathy peaks after a diabetes duration of 10-20 years and declines thereafter, but it doesn’t go to zero: “The risk for the development of diabetic nephropathy is low in a normoalbuminuric patient with diabetes’ duration of greater than 30 years. Patients who have no proteinuria after 20-25 years have a risk of developing overt renal disease of only approximately 1% per year.” (link). I’d note that a risk of 1% per year translates to a roughly 25% risk of developing overt renal disease over a 30 year time-frame, and that diabetics with the disease might not agree that a risk of that magnitude means that they are ‘unlikely’ to develop nephropathy, even if the annual risk is not high. Even if the annual risk were only half of that, 0,5%, the cumulative risk over a 30 year period would still be 14%, or roughly one in seven – are people with risks of that magnitude really ‘unlikely’ to develop nephropathy? This is certainly arguable. Many type 1 diabetics are diagnosed in childhood (peak incidence is in the early teenage years) and they can expect to live significantly longer than 20-25 years with the disease – if you disregard the ‘tail risk’ here, you seem in my opinion to be likely to neglect a substantial proportion of the total risk. This is incidentally not the only part of the book where I take issue with their coverage of topics related to diabetes, elsewhere in the book they note that:

“People who improve and maintain their fitness live longer […] Avoiding obesity helps too, but weight loss per se is only useful in reducing cardiovascular risk and the risk of developing diabetes when combined with regular exercise.”

Whereas in the case of nephropathy you can sort of argue about the language being imprecise and/or words meaning different things to different people, here things are a bit more clear because this is just plain WRONG. See e.g. Rana et al. (“Obesity and physical inactivity independently contribute to the development of type 2 diabetes; however, the magnitude of risk contributed by obesity is much greater than that imparted by lack of physical activity”). This is in my opinion the sort of error you should not find in a medical textbook.

Moving on to other parts of the coverage, let’s talk about angina. There are two types of angina – stable and unstable angina. Stable angina is induced by effort and relieved by rest. Unstable angina is angina of increasing severity or frequency, and it occurs at rest or minimal exertion. Unstable angina requires hospital admission and urgent treatment as it dramatically increases the risk of myocardial infarction. Some more stuff on related topics from the book:

“ACS [acute coronary syndrome] includes unstable angina and evolving MI [myocardial infarction], which share a common underlying pathology—plaque rupture, thrombosis, and inflammation”. Symptoms are: “Acute central chest pain, lasting >20min, often associated with nausea, sweatiness, dyspnoea [shortness of breath], palpitations [awareness of your heart beat]. May present without chest pain (‘silent’ infarct), eg in the elderly or diabetics. In such patients, presentations may include: syncope [fainting], pulmonary oedema, epigastric pain and vomiting, […] acute confusional state, stroke, and diabetic hyperglycaemic states.”

The two key questions to ask in the context of ACS are whether troponin (a cardiac enzyme) levels are elevated and whether there is ST-segment elevation. If there’s no ST-segment elevation and symptoms settle without a rise in troponin levels -> no myocardial damage (that’s the best case scenario – the alternatives are not as great..). In ACS, many deaths occur very soon after symptoms present; 50 % of deaths occur within two hours of symptom onset. “Up to 7% die before discharge.” Some MI complications have very high associated mortalities, for example a ventricular septal defect following an MI implies a 50% mortality rate during the first week alone.

Heart failure is a state in which the cardiac output is inadequate for the requirements of the body. It’s actually not that uncommon; the prevalence is 1-3% of the general population, increasing to roughly 10% “among elderly patients”. 25-50% die within 5 years of diagnosis, and if admission is needed the five year mortality rises to 75%.

Hypertension is a major risk factor for stroke and MI and according to the authors causes ~50% of all vascular deaths. Aside from malignant hypertension, which is relatively rare, hypertension is usually asymptomatic; the authors note specifically that “Headache is no more common than in the general population.” Isolated systolic hypertension, the most common form of hypertension, affects more than half of all people above the age of 60. “It is not benign: doubles risk of MI, triples risk of CVA [cerebrovascular accident, i.e. stroke].” The authors argue that: “Almost any adult over 50 would benefit from [antihypertensives], whatever their starting BP.” I think that’s downplaying the potential side effects of treatment, but it’s obvious that many people might benefit from treatment. Steps you can take to lower your BP without using medications according to the authors include: Reducing alcohol and salt intake, increasing exercise, reducing weight if obese, stop smoking, low-fat diet. They talk quite a bit about the different medications used to treat hypertension – I won’t cover that stuff in much detail, but I thought it was worth including the observation that ACE-inhibitors may be the 1st choice option in diabetics (especially if there’s renal involvement). On a related note, beta-blockers and thiazides may both increase the risk of new-onset diabetes.

October 22, 2015 Posted by | Books, Cardiology, Diabetes, Medicine, Nephrology, Pharmacology | Leave a comment

A few lectures

This one was mostly review for me, but there was also some new stuff and it was a ‘sort of okay’ lecture even if I was highly skeptical about a few points covered. I was debating whether to even post the lecture on account of those points of contention, but I figured that by adding a few remarks below I could justify doing it. So below a few skeptical comments relating to content covered in the lecture:

a) 28-29 minutes in he mentions that the cutoff for hypertension in diabetics is a systolic pressure above 130. Here opinions definitely differ, and opinions about treatment cutoffs differ; in the annual report from the Danish Diabetes Database they follow up on whether hospitals and other medical decision-making units are following guidelines (I’ve talked about the data on the blog, e.g. here), and the BP goal of involved decision-making units evaluated is currently whether diabetics with systolic BP above 140 receive antihypertensive treatment. This recent Cochrane review concluded that: “At the present time, evidence from randomized trials does not support blood pressure targets lower than the standard targets in people with elevated blood pressure and diabetes” and noted that: “The effect of SBP targets on mortality was compatible with both a reduction and increase in risk […] Trying to achieve the ‘lower’ SBP target was associated with a significant increase in the number of other serious adverse events”.

b) Whether retinopathy screenings should be conducted yearly or biennially is also contested, and opinions differ – this is not mentioned in the lecture, but I sort of figure maybe it should have been. There’s some evidence that annual screening is better (see e.g. this recent review), but the evidence base is not great and clinical outcomes do not seem to differ much in general; as noted in the review, “Observational and economic modelling studies in low-risk patients show little difference in clinical outcomes between screening intervals of 1 year or 2 years”. To stratify based on risk seems desirable from a cost-effectiveness standpoint, but how to stratify optimally seems to not be completely clear at the present point in time.

c) The Somogyi phenomenon is highly contested, and I was very surprised about his coverage of this topic – ‘he’s a doctor lecturing on this topic, he should know better’. As the wiki notes: “Although this theory is well known among clinicians and individuals with diabetes, there is little scientific evidence to support it.” I’m highly skeptical, and I seriously question the advice of lowering insulin in the context of morning hyperglycemia. As observed in Cryer’s text: “there is now considerable evidence against the Somogyi hypothesis (Guillod et al. 2007); morning hyperglycemia is the result of insulin lack, not post-hypoglycemic insulin resistance (Havlin and Cryer 1987; Tordjman et al. 1987; Hirsch et al. 1990). There is a dawn phenomenon—a growth hormone–mediated increase in the nighttime to morning plasma glucose concentration (Campbell et al. 1985)—but its magnitude is small (Periello et al. 1991).”

I decided not to embed this lecture in the post mainly because the resolution is unsatisfactorily low so that a substantial proportion of the visual content is frankly unintelligible; I figured this would bother others more than it did me and that a semi-satisfactory compromise solution in terms of coverage would be to link to the lecture, but not embed it here. You can hear what the lecturer is saying, which was enough for me, but you can’t make out stuff like effect differences, p-values, or many of the details in the graphic illustrations included. Despite the title of the lecture on youtube, the lecture actually mainly consists of a brief overview of pharmacological treatment options for diabetes.

If you want to skip the introduction, the first talk/lecture starts around 5 minutes and 30 seconds into the video. Note that despite the long running time of this video the lectures themselves only take about 50 minutes in total; the rest of it is post-lecture Q&A and discussion.

October 3, 2015 Posted by | Diabetes, Lectures, Mathematics, Medicine, Nephrology, Pharmacology | Leave a comment

Stuff

Sorry for the infrequent updates. I realized blogging Wodehouse books takes more time than I’d imagined, so posting this sort of stuff is probably a better idea.

i. Dunkirk evacuation (wikipedia ‘good article’). Fascinating article, as are a few of the related ones which I’ve also been reading (e.g. Operation Ariel).

“On the first day of the evacuation, only 7,669 men were evacuated, but by the end of the eighth day, a total of 338,226 soldiers had been rescued by a hastily assembled fleet of over 800 boats. Many of the troops were able to embark from the harbour’s protective mole onto 39 British destroyers and other large ships, while others had to wade out from the beaches, waiting for hours in the shoulder-deep water. Some were ferried from the beaches to the larger ships by the famous little ships of Dunkirk, a flotilla of hundreds of merchant marine boats, fishing boats, pleasure craft, and lifeboats called into service for the emergency. The BEF lost 68,000 soldiers during the French campaign and had to abandon nearly all of their tanks, vehicles, and other equipment.”

One way to make sense of the scale of the operations here is to compare them with the naval activities on D-day four years later. The British evacuated more people from France during three consecutive days in 1940 (30th and 31st of May, and 1st of June) than the Allies (Americans and British combined) landed on D-day four years later, and the British evacuated roughly as many people on the 31st of May (68,014) as they landed by sea on D-day (75,215). Here’s a part of the story I did not know:

“Three British divisions and a host of logistic and labour troops were cut off to the south of the Somme by the German “race to the sea”. At the end of May, a further two divisions began moving to France with the hope of establishing a Second BEF. The majority of the 51st (Highland) Division was forced to surrender on 12 June, but almost 192,000 Allied personnel, 144,000 of them British, were evacuated through various French ports from 15–25 June under the codename Operation Ariel.[104] […] More than 100,000 evacuated French troops were quickly and efficiently shuttled to camps in various parts of southwestern England, where they were temporarily lodged before being repatriated.[106] British ships ferried French troops to Brest, Cherbourg, and other ports in Normandy and Brittany, although only about half of the repatriated troops were deployed against the Germans before the surrender of France. For many French soldiers, the Dunkirk evacuation represented only a few weeks’ delay before being killed or captured by the German army after their return to France.[107]

ii. A pretty awesome display by the current world chess champion:

If you feel the same way I do about Maurice Ashley, you’ll probably want to skip the first few minutes of this video. Don’t miss the games, though – this is great stuff. Do keep in mind when watching this video that the clock is a really important part of this event; other players in the past have played a lot more people at the same time while blindfolded than Carlsen does here – “Although not a full-time chess professional [Najdorf] was one of the world’s leading chess players in the 1950s and 1960s and he excelled in playing blindfold chess: he broke the world record twice, by playing blindfold 40 games in Rosario, 1943,[8] and 45 in São Paulo, 1947, becoming the world blindfold chess champion” (link) – but a game clock changes things a lot. A few comments and discussion here.
In very slightly related news, I recently got in my first win against a grandmaster in a bullet game on the ICC.

iii. Gastric-brooding frog.

Rheobatrachus_silus

“The genus was unique because it contained the only two known frog species that incubated the prejuvenile stages of their offspring in the stomach of the mother.[3] […] What makes these frogs unique among all frog species is their form of parental care. Following external fertilization by the male, the female would take the eggs or embryos into her mouth and swallow them.[19] […] Eggs found in females measured up to 5.1 mm in diameter and had large yolk supplies. These large supplies are common among species that live entirely off yolk during their development. Most female frogs had around 40 ripe eggs, almost double that of the number of juveniles ever found in the stomach (21–26). This means one of two things, that the female fails to swallow all the eggs or the first few eggs to be swallowed are digested. […] During the period that the offspring were present in the stomach the frog would not eat. […] The birth process was widely spaced and may have occurred over a period of as long as a week. However, if disturbed the female may regurgitate all the young frogs in a single act of propulsive vomiting.”

Fascinating creatures.. Unfortunately they’re no longer around (they’re classified as extinct).

iv. I’m sort of conflicted about what to think about this:

“Epidemiological studies show that patients with type-2-diabetes (T2DM) and individuals with a diabetes-independent elevation in blood glucose have an increased risk for developing dementia, specifically dementia due to Alzheimer’s disease (AD). These observations suggest that abnormal glucose metabolism likely plays a role in some aspects of AD pathogenesis, leading us to investigate the link between aberrant glucose metabolism, T2DM, and AD in murine models. […] Recent epidemiological studies demonstrate that individuals with type-2 diabetes (T2DM) are 2–4 times more likely to develop AD (35), individuals with elevated blood glucose levels are at an increased risk to develop dementia (5), and those with elevated blood glucose levels have a more rapid conversion from mild cognitive impairment (MCI) to AD (6), suggesting that disrupted glucose homeostasis could play a […] causal role in AD pathogenesis. Although several prominent features of T2DM, including increased insulin resistance and decreased insulin production, are at the forefront of AD research (710), questions regarding the effects of elevated blood glucose independent of insulin resistance on AD pathology remain largely unexplored. In order to investigate the potential role of glucose metabolism in AD, we combined glucose clamps and in vivo microdialysis as a method to measure changes in brain metabolites in awake, freely moving mice during a hyperglycemic challenge. Our findings suggest that acute hyperglycemia raises interstitial fluid (ISF) Aβ levels by altering neuronal activity, which increases Aβ production. […] Since extracellular Aβ, and subsequently tau, aggregate in a concentration-dependent manner during the preclinical period of AD while individuals are cognitively normal (27), our findings suggest that repeated episodes of transient hyperglycemia, such as those found in T2DM, could both initiate and accelerate plaque accumulation. Thus, the correlation between hyperglycemia and increased ISF Aβ provides one potential explanation for the increased risk of AD and dementia in T2DM patients or individuals with elevated blood glucose levels. In addition, our work suggests that KATP channels within the hippocampus act as metabolic sensors and couple alterations in glucose concentrations with changes in electrical activity and extracellular Aβ levels. Not only does this offer one mechanistic explanation for the epidemiological link between T2DM and AD, but it also provides a potential therapeutic target for AD. Given that FDA-approved drugs already exist for the modulation of KATP channels and previous work demonstrates the benefits of sulfonylureas for treating animal models of AD (26), the identification of these channels as a link between hyperglycemia and AD pathology creates an avenue for translational research in AD.”

Why am I conflicted? Well, on the one hand it’s nice to know that they’re making progress in terms of figuring out why people get Alzheimer’s and potential therapeutic targets are being identified. On the other hand this – “our findings suggest that repeated episodes of transient hyperglycemia […] could both initiate and accelerate plaque accumulation” – is bad news if you’re a type 1 diabetic (I’d much rather have them identify risk factors to which I’m not exposed).

v. I recently noticed that Khan Academy has put up some videos about diabetes. From the few ones I’ve had a look at they don’t seem to contain much stuff I don’t already know so I’m not sure I’ll explore this playlist in any more detail, but I figured I might as well share a few of the videos here; the first one is about the pathophysiology of type 1 diabetes and the second one’s about diabetic nephropathy (kidney disease):

vi. On Being the Right Size, by J. B. S. Haldane. A neat little text. A few quotes:

“To the mouse and any smaller animal [gravity] presents practically no dangers. You can drop a mouse down a thousand-yard mine shaft; and, on arriving at the bottom, it gets a slight shock and walks away, provided that the ground is fairly soft. A rat is killed, a man is broken, a horse splashes. For the resistance presented to movement by the air is proportional to the surface of the moving object. Divide an animal’s length, breadth, and height each by ten; its weight is reduced to a thousandth, but its surface only to a hundredth. So the resistance to falling in the case of the small animal is relatively ten times greater than the driving force.

An insect, therefore, is not afraid of gravity; it can fall without danger, and can cling to the ceiling with remarkably little trouble. It can go in for elegant and fantastic forms of support like that of the daddy-longlegs. But there is a force which is as formidable to an insect as gravitation to a mammal. This is surface tension. A man coming out of a bath carries with him a film of water of about one-fiftieth of an inch in thickness. This weighs roughly a pound. A wet mouse has to carry about its own weight of water. A wet fly has to lift many times its own weight and, as everyone knows, a fly once wetted by water or any other liquid is in a very serious position indeed. An insect going for a drink is in as great danger as a man leaning out over a precipice in search of food. If it once falls into the grip of the surface tension of the water—that is to say, gets wet—it is likely to remain so until it drowns. A few insects, such as water-beetles, contrive to be unwettable; the majority keep well away from their drink by means of a long proboscis. […]

It is an elementary principle of aeronautics that the minimum speed needed to keep an aeroplane of a given shape in the air varies as the square root of its length. If its linear dimensions are increased four times, it must fly twice as fast. Now the power needed for the minimum speed increases more rapidly than the weight of the machine. So the larger aeroplane, which weighs sixty-four times as much as the smaller, needs one hundred and twenty-eight times its horsepower to keep up. Applying the same principle to the birds, we find that the limit to their size is soon reached. An angel whose muscles developed no more power weight for weight than those of an eagle or a pigeon would require a breast projecting for about four feet to house the muscles engaged in working its wings, while to economize in weight, its legs would have to be reduced to mere stilts. Actually a large bird such as an eagle or kite does not keep in the air mainly by moving its wings. It is generally to be seen soaring, that is to say balanced on a rising column of air. And even soaring becomes more and more difficult with increasing size. Were this not the case eagles might be as large as tigers and as formidable to man as hostile aeroplanes.

But it is time that we pass to some of the advantages of size. One of the most obvious is that it enables one to keep warm. All warmblooded animals at rest lose the same amount of heat from a unit area of skin, for which purpose they need a food-supply proportional to their surface and not to their weight. Five thousand mice weigh as much as a man. Their combined surface and food or oxygen consumption are about seventeen times a man’s. In fact a mouse eats about one quarter its own weight of food every day, which is mainly used in keeping it warm. For the same reason small animals cannot live in cold countries. In the arctic regions there are no reptiles or amphibians, and no small mammals. The smallest mammal in Spitzbergen is the fox. The small birds fly away in winter, while the insects die, though their eggs can survive six months or more of frost. The most successful mammals are bears, seals, and walruses.” [I think he’s a bit too categorical in his statements here and this topic is more contested today than it probably was when he wrote his text – see wikipedia’s coverage of Bergmann’s rule].

May 26, 2015 Posted by | Biology, Chess, Diabetes, Epidemiology, History, Khan Academy, Lectures, Medicine, Nephrology, Neurology, Wikipedia, Zoology | Leave a comment

An Introduction to Medical Diagnosis (II)

Here’s my first post about the book. In this post I’ll cover two more of the individual systems chapters – the first of the chapters I’ll talk about is the one about the renal system (kidneys). Some key symptoms which may suggest renal pathology are disorders of micturition (urination), disorders of urine volume, changes in urine composition, loin pain, oedema, and hypertension. Disorders of micturition can relate to frequency, poor urinary stream (typically caused by outflow obstructions) and dysuria (pain on micturition). There are 19 different causes of frequency mentioned in a table in the chapter, so there are a lot of possible causes. Volume changes may be termed polyuria (increase in volume), oliguria (decrease-), or anuria (total loss of urine output – this is bad); it’s important to note that frequency does not necessarily imply polyuria. Blood in the urine is called haematuria, a symptom which will often cause people to seek medical attention – for good reason: “Any patient above the age of 40 years with haematuria (visible or invisible) requires urgent evaluation by a urologist to look for malignant disease of the urinary tract.” It should however be noted that red/brown urine doesn’t necessarily indicate haematuria – other common causes are drugs and vegetable dyes – and relatedly it should be mentioned that blood in the urine may not be visible (haematuria is sometimes caught as an incidental finding by dip-stick analysis of the urine). When blood is present in the urine at the start of micturition only it usually indicates urethral bleeding, whereas bleeding towards the end of micturition is indicative of bladder/prostate bleeding. In the context of kidney disease pain patterns are inconsistent, but when there’s pain it’s usually due to renal tract inflammation or obstruction (e.g. due to a kidney stone). Cancer need not cause pain: “The cardinal feature of transitional cell carcinoma of the urinary tract is painless haematuria”, which may or may not be visible to the naked eye. In bladder cancer ‘local’ symptoms such as frequency and nocturia present before systemic symptoms such as weight loss, and the latter symptoms usually present late. Risk factors include smoking, occupational exposure to hydrocarbons, ionizing radiation (e.g. previous cancer treatment), prolonged immunosuppression, and bladder stones.

There are a number of inherited renal diseases, as well as a huge number of medical conditions associated with renal disease (18 of them are listed in the chapter). Aside from specific medical conditions a large number of drugs may also impact kidney function and the risk of developing renal disease. Pregnancy is a risk factor. Dietary factors may be important in some cases; for example excessive salt intake may lead to hypertension, as may alcohol, and hypertension is bad for the kidneys. Another example would be inadequate fluid intake or high intake of animal protein, both of which may promote lithiasis (stone formation). Tobacco is a significant risk factor for the development and progression of kidney disease. Of the many causes of kidney failure, diabetic renal disease is the most common cause of end-stage renal disease (-ESRD) in the Western world, according to the book accounting for 20-50% of new patients with end-stage renal disease (presumably the estimate is so broad-banded due to major cross-country differences). Another important cause is autosomal dominant polycystic kidney disease (ADPKD), which make up 10 per cent of patients with ESRD. Women have urinary tract infections (-UTI) much more often than men, and 50-60 per cent of women have at least one UTI during their lives. In males the risk has been estimated to be 5/10.000/year. It’s noted in the next chapter of the book that “Urinary tract infections (UTIs) are common in women, but uncommon in men under 50 years old”, but that “[o]lder men may get UTIs secondary to bladder outflow obstruction from prostatic hypertrophy”. I won’t talk much about that chapter, about the genitourinary system, as I’ve talked quite a bit about these sorts of things before when covering Holmes et al., e.g. here and here, but one other important quote is probably worth including here as well: “Seventy-five per cent of people infected with HSV [herpes simplex virus] are not aware that they have genital herpes either because their symptoms are very mild/absent or because the symptoms have been assumed to be due to something else (most commonly thrush).”

The other main chapter I’ll cover here is the chapter about the nervous system. I liked the way the author starts out the chapter – here’s a quote from the beginning of the chapter: “Inexperienced clinicians often order sophisticated (and expensive!) investigations hoping that the diagnosis may be revealed, but sadly this rarely happens. Many investigations are relatively sensitive but not necessarily disease specific”. Later on he also notes that it is his opinion that: “Electroencephalograms (EEGs) are grossly overordered. They should not be used as a diagnostic tool in epilepsy as they are relatively non-specific and non-sensitive.” I liked this stuff in part because I’m the sort of person who cares about cost-effectiveness, but also because Eysenck and Keane’s Cognitive Psychology text, part of which I read last December, contained some reasonably detailed coverage of various imaging methods used in these contexts and what you can and cannot use them for; and I think it’s highly likely that the author of the chapter is right. I may go into much more details about this kind of stuff later if I decide to cover E&K’s book, but I won’t talk about it here. One related observation worth including here is however that in the context of a seizure, something as ‘low-tech’ as an available eye-witness is often crucial (was there jerking? pallor? gaze aversion?) to make a diagnosis and distinguish between an epileptic seizure and a cardiovascular syncope (the most common diagnostic dilemma here).

Headaches are common. It’s useful to know that whereas an acute headache may be a sign of sinister pathology, chronic headaches rarely are. Acute headaches may be almost instantaneous (hyperacute), or they may develop over hours to days. Instantaneous headache may be (but of course needn’t be) due to life-threatening conditions such as subarachnoid haemorrhage, venous sinus thrombosis, cerebral haemorrhage, and phaeochromocytoma, all of which may present that way. The combination of neck stiffness and photophobia (together with headache) is called meningism and this is something that requires urgent investigation, as it may be due to meningitis or encephalitis. Muscle weakness is a common neurological symptom, and here it’s important to note that hyperacute limb weakness is usually caused by a stroke, and is most commonly unilateral (i.e. affecting e.g. only one arm or leg, rather than both), whereas bilateral weakness is a marker of spinal cord disease. Sensory symptoms may be either ‘positive’ (e.g. tingling, dysaesthesia) or negative (numbness); stroke usually causes negative symptoms, whereas various genetic or acquired disorders may also present with ‘positive’ symptoms as well. “Neuropathic pain (cf diabetes) is often lower limb predominant and described as burning, stinging or throbbing.” Relatedly: “Diabetes is the commonest cause of neuropathy in the UK; distal predominantly sensory neuropathy, diabetic amyotrophy (pain and wasting in femoral distribution), nerve entrapments (carpal tunnel syndrome), cranial neuropathy and autonomic neuropathy are relatively common complications.” As for the aforementioned strokes, they’re sometimes (in 15 per cent of cases, according to the book) preceded by a TIA (a transient ischaemic attack), a sort of ‘mini-stroke’ which causes a reversible neurological deficit lasting less than 24 hours (‘in practice much shorter duration’). A recent TIA puts you in high immediate risk of stroke, which is probably useful to know – for more details, see this link.

The nervous system deals with a lot of stuff, so a lot of things can go wrong. Autonomic nervous system disorders may cause symptoms/problems such as: sphincter disturbances (e.g. incontinence), change in sweating patterns, photophobia (when the pupil is affected), night blindness, orthostatic hypotension, dry mouth, dry eyes, erectile/ejaculatory failure, and vomiting. Specific nerves doing specific things can cause specific symptoms when they stop working the way they’re supposed to, and these sorts of symptoms are very far from limited to ‘people being unable to move their arms or legs’; neurological problems can also cause you to e.g. go blind or deaf. The distinction between monolateral (‘vascular’) and bilateral (‘neurological’) symptoms and how this distinction relate to the underlying medical cause seems to apply not only to major limbs, but also to other areas of the body – for example if you’re experiencing vision loss in both eyes it’s more often a neurological problem, whereas problems caused by retinal pathology tend to cause unilateral symptoms. On a related note, in elderly people monocular loss of vision can be a harbinger of stroke. They mention in this chapter that neurological dysphagia (difficulty swallowing) may affect liquids first, whereas a mechanical obstruction (e.g. due to a tumour) will preferentially affect solids (I mentioned this in my last post about the book). “The duration of anterograde amnesia is an extremely useful indicator of the severity of head injury.” In the last part of the chapter the author talks about various specific conditions causing neurological problems, such as Parkinson’s disease, Motor Neuron Disease, Multiple Sclerosis, Myasthenia gravis, and Guillain–Barré syndrome – I won’t cover these in any detail as the book only covers them very briefly (you can google them if you’re curious).

February 6, 2015 Posted by | Books, Epidemiology, Medicine, Nephrology, Neurology | Leave a comment

Diabetes: The Biography

“When I retired from clinical practice in 1998, my intention was (and still is) to write a definitive, exhaustively referenced, history of diabetes, which would be of interest primarily to doctors. However, I jumped at the suggestion of the editors of this series at Oxford University Press that I should write a biography of diabetes that would be about a tenth of the length of a full history with a minimum of references, for a wide general readership.”

This book is the result. As I pointed out on goodreads, this book is really great. The book is not particularly technical compared to other books about diabetes which I’ve read in the past, however this semi-critical review does make the point that the coverage is occasionally implicitly ‘asking too much’ even from diabetic readers (“There were parts of all this that lost my interest or that I lacked the background to appreciate”). Whereas the reviewer was apparently to some extent getting lost in the details, so was I – but in a completely different way; I was simply amazed at the amount of small details and interesting observations included in the book that I did not know, and I loved every single chapter of the book. The author of the other review incidentally also states that: “I don’t recommend that anyone read this who is not already familiar with diabetes, either by having it or knowing someone with it.” I’d note that I’m not sure I agree with this recommendation, to the extent that it’s even ‘relevant’ – these days such people who don’t even know anyone with diabetes might well be a bit hard to find, on account of the fact that diabetes has become a quite common illness. Presumably a significant proportion of the people who assume they don’t know anyone with the disease might well do so anyway, because a very large number of people have type 2 diabetes without knowing it. I think a reader would get more out of the book if he or she has diabetes or knows someone with diabetes, but a lot of people who do not would also benefit from knowing the stuff in this book. Not only in a ‘and now you know how bad type 2 is and why you should get checked out if you think you’re at risk’-sense (there’s incidentally also a lot of stuff about type 1), but also in the ‘the history of diabetes is really quite fascinating’-sense. I do think it is.

Have a look at this image. The book included a similar picture (not exactly the same one, but it’s of the same patient and the ‘before’ picture is obviously taken at the same time this one was), which is of Billy Leroy, a type 1 diabetic, before and after he started insulin. He was one of the first patients treated with insulin (the first human treated with insulin was Leonard Thompson, in 1922). Billy Leroy’s weight in the first picture, where he was 3 years old, was 6.8 kg (the 5 % (underweight) CDC body weight cutoff at the age of 3 is 12 kg) – during the three months after he started on insulin, his weight doubled. The author argues in the beginning of the book that: “When people are asked to rank diseases in order of seriousness, diabetes is usually at the mild end of the spectrum.” This may or may not be true, but the picture to which I link above certainly adds a detail which is important to keep in mind but easy to forget when evaluating ‘the severity’ of the disease today – type 1 diabetes in particular is not much fun if you don’t have access to insulin, and until the early 1920s people with this disease simply died, most of them quite fast. (They all still do – like all other humans – but they live a lot longer before they die…)

The author knows his stuff and the book has a lot of content, making it hard to know what to pick out and mention in particular in a review like this – however below I have added a few quotes from the book and some observations made along the way. The content covering the late nineteenth century and the first couple of decades of the twentieth century, before it was discovered that insulin could save the lives of a lot of sick children, would in my opinion on its own be a strong reason for reading the book; but the chapters covering the periods that came after are wonderful as well. When insulin was discovered a religiously inclined mind might well be tempted to think of the effects on young type 1 diabetic children as almost miraculous; but gradually doctors treating diabetics came to realize (the patients never knew, because they were not told – it is pointed out in the book that the fact that it might make a lot of sense to give patients with a disease like diabetes some discretion in terms of how to treat their illness is a in a historical context very new idea; active patient involvement in medical decision-making is one of the cornerstones of current treatment regimes, for good reason, and I found it really surprising and frustrating to learn how this disease was treated in the past) that things might be more complicated than they had initially been assumed to be. Type 2 diabetics had suffered from late stage complications like blindness and kidney failure for centuries, but such complications had never before been observed in type type 1 diabetics before insulin, because diabetes presenting in children were pre-insulin universally fatal. It turned out that many of the children who were initially ‘saved’ by insulin in the early 1920s ended up suffering from severe complications just a couple of decades later, and many of them died early from these complications:

“After the Second World War it became clear that [diabetic] kidney disease could also affect the young, and there were increasingly frequent reports of diabetics who had been saved by insulin as children only to succumb to kidney failure in their 20s and 30s. Fifty of Joslin’s child patients who had started insulin before 1929 were followed up in 1949, when a third had died at an average age of 25, after having had diabetes for an average of 17.6 years. One half had died of kidney failure and the other half of tuberculosis and other infections. […] In the experience of the Joslin group, only 2 per cent of deaths of young diabetic patients before 1937 were due to kidney disease, but, of those who died between 1944 and 1950, more than half had advanced kidney disease. Results in Europe were equally bad. In 1955 all of eighty-seven Swiss children had signs of kidney disease after sixteen years of diabetes, and after twenty-one years all had died. Most young people with diabetic kidney disease also had severe retinopathy and many became blind—by the mid 1950s diabetes was the commonest cause of new blindness in people under the age of 50. […] Such devastating cases were being increasingly reported in the medical literature in the late 1940s and early 1950s, but they were not publicized in the lay press, presumably to avoid spreading despair and despondency and puncturing the myth that insulin had solved the problem of diabetes […] The British Diabetic Association (founded in 1935) produced a quarterly Diabetic Journal for its lay members, but no issue from 1940 to 1960 mentions complications”.

The book makes it clear that patients were for many years basically to some extent kept in the dark about the severity of their condition, but in all fairness for a long time the doctors treating them frankly didn’t know enough to give them good information on a lot of topics anyway. The book has some really interesting observations included about how medical men of the times thought about various aspects of the illness and treatment, and how many of the things we know today, some of which ‘seem obvious’, really were not to people at the time. Many attempts have been made over time to explain why people got diabetes, and especially type 1 was really quite hard to pin down – type 2 was somewhat easier because the lifestyle component was hard to miss; however it was natural to explain the disease in terms of the symptoms it caused, and some of those symptoms in type 2 diabetics were complications which are best considered secondary to the ‘true’ disease process. For example because many type 2 diabetics suffered from disorders of the nervous system, neuropathy, the nervous system was for a while assumed to be implicated in causing diabetes – but although disorders of the nervous system can and often do present in long-standing diabetes, they are not why type 2 diabetics get sick. Kidney problems were thought to be “part and parcel of diabetes in the 19th century.” Oskar Minkowsky made it clear in 1889 that removal of the pancreas caused severe (‘type 1-like’) diabetes in dogs – but despite this discovery it still took a long time for people to figure out how it worked. This wasn’t because people at the time were stupid. One problem faced at the time was that the pancreas actually looked quite normal in people who died from diabetes – the islet cells which are implicated in the disease weigh around 1-1.5 grams altogether, and make up only a very small proportion of the pancreas (1% or so). Many doctors found it hard to imagine that the islets cells could be reponsible for controlling carbohydrate metabolism (and other aspects of metabolism as well – “It is important to realize that diabetes is not just a glucose disease. There are also abnormalities of fat metabolism”). The pancreas wasn’t the only organ that looked normal – despite the excessive urination the kidneys did as well, and so did other organs, to the naked eye. All major features of diabetic retinopathy (diabetic eye disease) had been described by the year 1890 with the aid of the ophthalmoscope, so people knew the eyes of people with long-standing diabetes looked different; how to interpret these findings was however not clear at the time – some argued the eye damage found in diabetics was not different from eye damage caused by hypertension, and treatment options were non-existent anyway.

Many of the treatment options discussed among medical men before insulin were diets, and although dietary considerations are important in the treatment context today, it’s probably fair to say that not all of the supposed dietary remedies of the past were equally sensible: “One diet that had a short vogue in the 1850s was sugar feeding, brainchild of the well-known but eccentric French physician Pierre Piorry (1794–1879). He thought that diabetics lost weight and felt so weak because of the amount of sugar they lost in the urine and that replacing it should restore their strength”. (Aargh!). For the curious (or desperate) man, though, there were alternatives to diets: “A US government publication in 1894 listed no less than forty-two anti-diabetic remedies including bromides, uranium nitrate, and arsenic.” Relatedly, “in England until 1925, any drug could be advertised and marketed as a cure for any disease, even if it was completely ineffective”. Whether or not diets ‘worked’ depended in part on what those proposed diets included (see above..), whether people followed them, and whether people who presented were thin or fat. In the book Tattersall mentions that already from the middle of the nineteenth century many physicians thought that there were two different types of diabetes (there are more than two, but…). The thin young people presenting with symptoms were by many for decades considered hopeless cases (that they were hopeless cases was even noted in medical textbooks at the time), because they had this annoying habit of dying no matter what you did.

It should be noted that the book indirectly provides some insights into the general state of medical research and medical treatment options over time; for an example of the former it is mentioned that the first clinical trial (with really poor randomization/selection mechanisms, it seems from the description in the book) dealing with diabetes was undertaken in the 1960es: “the FDA demanded randomized controlled trials for the first time in 1962, and [the University Group Diabetes Program (UGDP)] was the first in diabetes. Before 1962 the evidence in support of therapeutic efficacy put to the FDA was often just ‘testimonials’ from physicians who casually tested experimental drugs on their patients and were paid for doing so.” See also this link. An example of the latter would be the observation made in the book that: “until the 1970s treatment for a heart attack was bed rest for five or six weeks, while nature took its course.” Diabetics were not the only sick people who had a tough time in the past.

One interesting question related to what people didn’t know in the beginning after the introduction of insulin was how the treatment might work long-term. The author notes that newspapers in the early years made people believe that insulin would be a cure; it was thought that insulin might nurse the islet cells back to health, so that they’d start producing insulin on their own again – which was actually not a completely stupid idea, as e.g. kidneys had the ability to recover after acute glomerulonephritis. The fact that diabetics often started on high doses which could then be lowered a month or two later even lent support to this idea; however it was discovered quite fast that regeneration was not taking place. Remarkably, insulin was explored as a treatment option for other diseases in the 1920s, and was actually used to stimulate appetite in tuberculosis patients and ‘in the insane refusing food’, an idea which came about because one of its most obvious effects was weight gain. This effect was also part of the reason why insulin was for a long time not considered an attractive option for type 2 diabetics, who instead were treated only with diet unless this treatment failed to reduce blood sugar levels sufficiently (these were the only two treatment options until the 1950s); most of them were already overweight and insulin caused weight gain, and besides insulin didn’t work nearly as well in them as it did in young and lean people with type 1 because of insulin resistance, which lead to the requirement of high doses of the drug.

Throughout much of the history of diabetes, diabetics did not measure their blood glucose regularly – what they did instead was measuring their urine, figuring out if it contained glucose or not (glucose in the urine indicates that the blood glucose is quite high). This meant that the only metric they had available to them to monitor their disease on a day to day basis was one which was unable to measure low blood glucose, and which could only (badly) distinguish between much too high blood glucose values and not-much-too-high values. Any type of treatment regime like the one I’m currently on would be completely impossible without regular blood tests on a daily basis, and I was very surprised about how late the idea of self-monitoring of blood glucose appeared; like the measurement of Hba1c, this innovation did not appear until the late 1970s. Few years after that, the first insulin pen revolutionized treatment regimes and made treatment regimes using multiple rejections each day much more common than they had been in the past, facilitating much better metabolic control.

The book has a lot of stuff about specific complications and the history of treatment advances – both the ones that worked and some of the ones that didn’t. If you’re a diabetic today, you tend to take a lot of stuff for granted – and reading a book like this will really make you appreciate how many ideas had to be explored, how many false starts there were, how much work by so many different people actually went into giving you the options you have today, keeping you alive, and perhaps even relatively well. One example of the type of treatment options which were considered in the past but turned out not to work was curative pancreas transplants, which were explored in the 60es and 70es: “Pancreas transplantation offered a potential cure of type 1 diabetes. The first was done in 1966 […] Worldwide in the next eleven years, fifty-seven transplants were done, but only two worked for more than a year”. Recent attempts to stop people at risk of developing type 1 diabetes from becoming sick are also discussed in the last part of the book, and in this context he makes a point I was familiar with: “[Repeated] failures [in this area] are particularly frustrating, because, in the best animal model of type 1 diabetes, the NOD mouse, over 100 different interventions can prevent diabetes.” This is one of the reasons why I tend to be skeptical about results from animal studies. Although he spends many pages on complications – which in a book like this makes a lot of sense given how common these complications were (and to some extent still are), and how important a role they have played in the lives of people suffering from diabetes throughout the ages – I have talked about many of these things before, just as I have talked about the results of various large-scale trials like the DCCT trial and the UKPDS (see e.g. this and this), so I will not discuss such topics in detail here. I do however want to briefly remind people of what kind of a disease badly managed type 2 diabetes (the by far most common of the two) is, especially if it is true as the author argues in the introduction that many people perceive of it as a relatively mild disease – so I’ll end the post with a few quotes from the book:

“I took over the diabetic clinic in Nottingham in 1975 and three years later met Lilian, an overweight 60-year-old woman who was on tablets for diabetes. She had had sugar in her urine during her last pregnancy in 1957 but was well until 1963, when genital itching (pruritus vulvae) led to a diagnosis of diabetes. She attended the clinic for two years but was then sent back to her GP with a letter that read: ‘I am discharging this lady with mild maturity onset diabetes back to your care.’ She continued to collect her tablets but had no other supervision. When I met her after she had had diabetes for eighteen years she was blind, had had a heart attack, and had had one leg amputated below the knee. The reason for the referral to me was an ulcer on her remaining foot, which would not heal. […] Someone whose course is not dissimilar to that of Lilian is Sue Townsend (b. 1946), author of the Adrian Mole books. She developed diabetes at the age of 38 and after only fifteen years was blind from retinopathy and wheelchair bound because of a Charcot foot, a condition in which the ankle disintegrates as a result of nerve damage. Neuropathy has also destroyed the nerve endings in her fingers, so that, like most other blind diabetics, she cannot read Braille. She blames her complications on the fact that she cavalierly disregarded the disease and kept her blood sugars high to avoid the inconvenience of hypoglycaemic (low-blood-sugar) attacks.”

January 25, 2015 Posted by | Books, Diabetes, Medicine, Nephrology | Leave a comment

A brief note on diabetes cures

I friend pointed me to a Danish article talking about this. I pointed out a few problems and reasons to be skeptical to my friend, and I figured I might as well share a few thoughts on these matters here as well. I do not have access to my library at the present point in time, so this post will be less well sourced than most posts I’ve written on related topics in the past.

i. I’ve had diabetes for over 25 years. A cure for type 1 diabetes has been just around the corner for decades. This is not a great argument for assuming that a cure will not be developed in a few years’ time, but you do at some point become a bit skeptical.

ii. The type of ‘mouse diabetes’ people use when they’re doing research on animal models such as e.g. NOD mice, from which many such ‘breakthroughs’ are derived, is different from ‘human diabetes’. As pointed out in the reddit thread, “Doug’s group alone has cured diabetes in mice nearly a dozen times”. This may or may not be true, but I’m pretty sure that at the present point in time my probability of being cured of diabetes would be significantly higher if I happened to be one of those lab mice.

iii. A major related point often overlooked in contexts like these is that type 1 diabetes is not one disease – it is a group of different disorders all sharing the feature that the disease process involved leads to destruction of the pancreatic beta-cells. At least this is not a bad way to think about it. This potentially important neglected heterogeneity is worth mentioning when we’re talking about cures. To talk about ‘type 1 diabetes’ as if it’s just one disease is a gross simplification, as multiple different, if similar, disease processes are at work in different patients; some people with ‘the disease’ get sick in days or weeks, in others it takes years to get to the point where symptoms develop. Multiple different gene complexes are involved. Prognosis – both regarding the risk of diabetes-related organ damage and the risk of developing ‘other’ autoimmune conditions (‘other’ because it may be the same disease process causing the ‘other’ diseases as well), such as Hashimoto’s thyroiditis – depends to some extent on the mutations involved. This stuff relates also to the question of what we mean by the word ‘cure’ – more on this below. You might argue that although diabetics are different from each other and vary in a lot of ways, the same thing could be said about the sufferers of all kinds of other diseases, such as, say, prostate cancer. So maybe heterogeneity within this particular patient population is not that important. But the point remains that we don’t treat all prostate cancer patients the same way, and that some are much easier to cure than others.

iv. The distinction between types (type 1, type 2) makes it easy to overlook the fact that there are significant within-group heterogeneities, as mentioned above. But the complexity of the processes involved are perhaps even better illustrated by pointing out that even between-group distinctions can also sometimes be quite complicated. The distinction between type 1 and type 2 diabetes is a case in point; usually people say only type 1 is auto-immune, but it was made clear in Sperling et al.’s textbook that that’s not really true; in a minority of type 2 diabetics autoimmune processes are also clearly involved – and this is actually highly relevant as these subgroups of patients have a much worse prognosis than the type 2 diabetics without autoantibody markers, as they’ll on average progress to insulin-dependent disease (uncontrollable by e.g. insulin-sensitizers) much faster than people without an auto-immune disease process. In my experience most people who talk about diabetes online, also well-informed people e.g. in reddit/askscience threads, are not (even?) aware of this. I mention it because it’s one obvious example of how within-group hidden heterogeneities can have huge relevance for which treatment modalities are desirable or useful. You’d expect type 2’s with auto-immune processes involved would need a different sort of ‘cure’ than ‘ordinary type 2’s’. For a little more on different ‘varieties’ of diabetes, see also this and this.

There are as already mentioned also big differences in outcomes between subgroups within the type 1 group; some people with type 1 diabetes will end up with three or four ‘different'(?) auto-immune diseases, whereas others will get lucky and ‘only’ ever get type 1 diabetes. Not only that, we also know that glycemic control differences between those groups do not account for all the variation in between-group differences in outcomes in terms of diabetes-related complications; type 1 diabetics hit by ‘other’ auto-immune processes (e.g. Graves’ disease) tend to be more likely to develop complications to their diabetes than the rest, regardless of glycemic control. Would successful beta-cell transplants, assuming these at some point become feasible, and achieved euglycemia in that patient population still prevent thyroid failure later on? Would the people more severely affected, e.g. people with multiple autoimmune conditions, still develop some of the diabetes-related complications, such as cardiovascular complications, even if they had functional beta cells and were to achieve euglycemia, because those problems may be caused by disease aspects like accelerated atherosclerosis to some extent perhaps unrelated to glycemic control? These are things we really don’t know. It’s very important in that context to note that most diabetics, both type 1 and type 2, die from cardiovascular disease, and that the link between glycemic control and cardiovascular outcomes is much weaker than the one between glycemic control and microvascular complications (e.g., eye disease, kidney disease). There may be reasons why we do not yet have a good picture of just how important euglycemia really is, e.g. because glucose variability and not just average glucose levels may be important in terms of outcomes (I recall seeing this emphasized recently in a paper, but I’m not going to look for a source) – and Hba1c only account for the latter. So maybe it does all come back to glycemic control, it’s just that we don’t have the full picture yet. Maybe. But to the extent that e.g. cardiovascular outcomes – or other complications in diabetics – are unrelated to glycemic control, beta-cell transplants may not improve cardiovascular outcomes at all. One potential cure might be one where diabetics get beta-cell transplants, achieve euglycemia and are able to drop the insulin injections – yet they still die too soon from heart disease because other aspects of the disease process has not been addressed by the ‘cure’. I don’t think at the current point in time that we really know enough about these diseases to really judge if a hypothetical diabetic with functional transplanted beta-cells may not still to some extent be ‘sick’.

v. If your cure requires active suppression of the immune system, not much will really be gained. A to some people perhaps surprising fact is that we already know how to do ‘curative’ pancreas transplants in diabetics, and these are sometimes done in diabetic patients with kidney failure (“In most cases, pancreas transplantation is performed on individuals with type 1 diabetes with end-stage renal disease, brittle diabetes [poor glycemic control, US] and hypoglycaemia unawareness. The majority of pancreas transplantation (>90%) are simultaneous pancreas-kidney transplantation.” – link) – these people would usually be dead without a kidney transplant and as they already have to suffer through all the negative transplant-related effects of immune suppression and so on, the idea is that you might as well switch both defective organs now you’re at it, if they’re both available. But immune suppression sucks and these patients do not have great prognoses so this is not a good way to deal with diabetes in a ‘healthy diabetic’; if rejection problems are not addressed in a much better manner than the ones currently available in whole-organ-transplant cases, the attractiveness of any such type of intervention/’cure’ goes down a lot. In the study they tried to engineer their way around this issue, but whether they’ve been successful in any meaningful way is subject to discussion – I share ‘SirT6”s skepticism at the original reddit link. I’d have to see something like this working in humans for some years before I get too optimistic.

vi. One final aspect is perhaps noting. Even a Complete and Ideal Cure involving beta-cell transplants in a setting where it turns out that everything that goes wrong with all diabetics is really blood-glucose related, is not going to repair the damage that’s already been done. Such aspects will of course matter much more to some people than to others.

October 12, 2014 Posted by | Diabetes, Nephrology | Leave a comment

100 Cases in Clinical Pathology

This book is another publication from the 100 Cases … series which I’ve talked about before – I refer to these posts for some general comments about what this series is like and some talk about the other books in the series which I’ve read. The book is much like the others, though of course the specific topics covered are different in the various publications. I liked this book and gave it 3 stars on goodreads. The book has three sections: a section dealing with ‘chemical pathology, immunology and genetics’; a section dealing with ‘histopathology’; and a section dealing with ‘haematology’. As usual I knew a lot more about some of the topics covered than I did about some of the others. Some cases were quite easy, others were not. Some of the stuff covered in Greenstein & Wood’s endocrinology text came in handy along the way and enabled me for example to easily identify a case of Cushing’s syndrome and a case of Graves’ disease. I don’t think I’ll spoil anything by noting that two of the cases in this book involved these disorders, but if you plan on reading it later on you may want to skip the coverage below, as I have included some general comments from the answer sections of the book in this post.

As someone who’s not working in the medical field and who will almost certainly never need to know how to interpret a water deprivation test (also covered in detail in Greenstein and Wood, incidentally), there are some parts of books like this one which are not particularly ‘relevant’ to me; however I’d argue that far from all the stuff included in a book like this one is ‘stuff you don’t need to know’, as there are also for example a lot of neat observations included about how specific symptoms (and symptom complexes) are linked to specific disorders, some related ideas about which other medical conditions might cause similar health problems, and which risk factors are potentially important to have in mind in specific contexts. If you’ve had occasional fevers, night sweats and experienced weight loss over the last few months, you should probably have seen a doctor a while ago – knowledge included in books like this one may make the reader perhaps a bit less likely to overlook an important and potentially treatable health problem, and/or increase awareness of potential modifiable risk factors in specific contexts. A problem is however that the book will be hard to read if you have not read any medical textbooks before, and in that case I would probably advise you against reading it as it’s almost certainly not worth the effort.

I have added a few observations from the book below.

“After a bone marrow transplant (and any associated chemotherapy), the main risks are infection (from low white cell counts and the use of immunosuppressants, such as cyclosporin), bleeding (from low platelet counts) and graft versus host disease (GVHD). […] An erythematous rash that develops on the palms or soles of the feet of a patient 10–30 days after a bone marrow transplant is characteristic of GVHD. […] GVHD is a potentially life-threatening problem that can occur in up to 80% of successful allogeneic bone marrow transplants. […] Clinically, GVHD manifests like an autoimmune disease with a macular-papular rash, jaundice and hepatosplenomegaly and ultimately organ fibrosis. It classically involves the skin, gastrointestinal tract and the liver. […] Depending on severity, treatment of acute GVHD may involve topical and intravenous steroid therapy, immunosuppression (e.g. cyclosporine), or biologic therapies targeting TNF-α […], a key inflammatory cytokine. […] Prognosis is related to response to treatment. The mortality of patients who completely respond can still be around 20%, and the mortality in those who do not respond is as high as 75%.”

“The leading indication for a liver transplant is alcoholic cirrhosis in adults and biliary atresia in children. […] The overall one-year survival of a liver transplant is over 90%, with 10-year survival of around 70%. […] Transplant rejection can be classified by time course, which relates to the underlying immune mechanism: • Hyperacute organ rejection occurs within minutes of the graft perfusion in the operating theatre. […] The treatment for hyperacute rejection is immediate removal of the graft. • Acute organ rejection take place a number of weeks after the transplant […] The treatment for acute rejection includes high dose steroids. • Chronic organ rejection can take place months to years after the transplant. […] As it is irreversible, treatment for chronic rejection is difficult, and may include re-transplantation.”

“Chronic kidney disease (CKD) is characterized by a reduction in GFR over a period of 3 or more months (normal GFR is >90–120 mL/min). It arises from a progressive impairment of renal function with a decrease in the number of functioning nephrons; generally, patients remain asymptomatic until GFR reduces to below 15 mL/min (stage V CKD). Common causes of CKD are (1) diabetes mellitus, (2) hypertension, (3) glomerulonephritis, (4) renovascular disease, (5) chronic obstruction or interstitial nephritis, and (6) hereditary or cystic renal disease”

“The definition of an aneurysm is an abnormal permanent focal dilatation of all the layers of a blood vessel. An AAA [abdominal aortic aneurysm] is defined when the aortic diameter, as measured below the level of the renal arteries, is one and a half times normal. Women have smaller aortas, but for convenience, more than 3 cm qualifies as aneurysmal. The main risk factors for aneurysm formation are male gender, smoking, hypertension, Caucasian/European descent and atherosclerosis. Although atherosclerosis is a risk factor and both diseases share common predisposing factors, there are also differences. Atherosclerosis is primarily a disease of the intima, the innermost layer of the vessel wall, whereas in aneurysms there is degeneration of the media, the middle layer. […] The annual risk of rupture equals and begins to outstrip the risk of dying from surgery when the aneurysm exceeds 5.5 cm. This is the size above which surgical repair is recommended, comorbidities permitting. […] Catastrophic rupture, as in this case, presents with hypovolaemic shock and carries a dismal prognosis.” [The patient in the case history died soon after having arrived at the hospital]

“Stroke refers to an acquired focal neurological deficit caused by an acute vascular event. The neurological deficit persists beyond 24 hours, in contrast to a transient ischaemic attack (TIA) where symptoms resolve within 24 hours, although the distinction is now blurred with the advent of thrombolysis. […] Strokes are broadly categorized into ischaemic and haemorrhagic types, the majority being ischaemic. The pathophysiology in a haemorrhagic stroke is rupture of a blood vessel causing extravasation of blood into the brain substance with tissue damage and disruption of neuronal connections. The resulting haematoma also compresses surrounding normal tissue. In most ischaemic strokes, there is thromboembolic occlusion of vessels due to underlying atherosclerosis of the aortic arch and carotid arteries. In 15–20% of cases, there is atherosclerotic disease of smaller intrinsic blood vessels within the brain[…]. A further 15–20% are due to emboli from the heart. […] The territory and the extent of the infarct influences the prognosis; [for example] expressive dysphasia and right hemiparesis are attributable to infarcts in Broca’s area and the motor cortex, both frontal lobe territories supplied by the left middle cerebral artery.”

“The stereotypical profile of a gallstone patient is summed up by the 4Fs: female, fat, fertile and forty. However, while gallstones are twice as common in females, increasing age is a more important risk factor. Above the age of 60, 10–20% of the Western population have gallstones. […] Most people with cholelithiasis are asymptomatic, but there is a 1–4% annual risk of developing symptoms or complications. […] Complications depend on the size of the stones. Smaller stones may escape into the common bile duct, but may lodge at the narrowing of the hepatopancreatic sphincter (sphincter of Oddi), obstructing the common bile duct and pancreatic duct, leading to obstructive jaundice and pancreatitis respectively. […] In most series, alcohol and gallstones each account for 30–35% of cases [of acute pancreatitis]. […] Once symptomatic, the definitive treatment of gallstone disease is generally surgical via a cholecystectomy.”

“Breast cancer affects 1 in 8 women (lifetime risk) in the UK. […] Between 10 and 40% of women who are found to have a mass by mammography will have breast cancer. […] The presence of lymphovascular invasion indicates the likelihood of spread of tumour cells beyond the breast, thereby conferring a poorer outlook. Without lymph node involvement, the 10-year disease-free survival is close to 70–80% but falls progressively with the number of involved nodes.”

“Melanoma is a cancer of melanocytes, the pigmented cells in the skin, and is caused by injury to lightly pigmented skin by excessive exposure to ultraviolet (UV) radiation […] The change in colour of a pre-existing pigmented lesion with itching and bleeding and irregular margins on examination are indicators of transformation to melanoma. Melanomas progress through a radial growth phase to a vertical growth phase. In the radial growth phase, the lesion expands horizontally within the epidermis and superficial dermis often for a long period of time. Progression to the vertical phase is characterized by downward growth of the lesion into the deeper dermis and with absence of maturation of cells at the advancing front. During this phase, the lesion acquires the potential to metastasize through lymphovascular channels. The probability of this happening increases with increasing depth of invasion (Breslow thickness) by the melanoma cells. […] The ABCDE mnemonic aids in the diagnosis of melanoma: Asymmetry – melanomas are likely to be irregular or asymmetrical. Border – melanomas are more likely to have an irregular border with jagged edges. Colour – melanomas tend to be variegated in colour […]. Diameter – melanomas are usually more than 7 mm in diameter. Evolution – look for changes in the size, shape or colour of a mole.”

“CLL [chronic lymphocytic leukaemia] is the most common leukaemia in the Western world. Typically, it is picked up via an incidental lymphocytosis in an asymptomatic individual. […] The disease is staged according to the Binet classification. Typically, patients with Binet stage A disease require no immediate treatment. Symptomatic stage B and all stage C patients receive chemotherapy. […] cure is rare and the aim is to achieve periods of remission and symptom control. […] The median survival in CLL is between four and six years, though some patients survive a decade or more. […] There is […] a tendency of CLL to transform into a more aggressive leukaemia, typically a prolymphocytic transformation (in 15–30% of patients) or, less commonly (<10% of cases), transformation into a diffuse large B-cell lymphoma (a so-called Richter transformation). Appearance of transformative disease is an ominous sign, with few patients surviving for more than a year with such disease.”

“Pain, swelling, warmth, tenderness and immobility are the five cardinal signs of acute inflammation.”

“Osteomyelitis is an infection of bone that is characterized by progressive inflammatory destruction with the formation of sequestra (dead pieces of bone within living bone), which if not treated leads to new bone formation occurring on top of the dead and infected bone. It can affect any bone, although it occurs most commonly in long bones. […] Bone phagocytes engulf the bacteria and release osteolytic enzymes and toxic oxygen free radicals, which lyse the surrounding bone. Pus raises intraosseus pressure and impairs blood flow, resulting in thrombosis of the blood vessels. Ischaemia results in bone necrosis and devitalized segments of bone (known as sequestra). These sequestra are important in the pathogenesis of non-resolving infection, acting as an ongoing focus of infection if not removed. Osteomyelitis is one of the most difficult infections to treat. Treatment may require surgery in addition to antibiotics, especially in chronic osteomyelitis where sequestra are present. […] Poorly controlled diabetics are at increased risk of infections, and having an infection leads to poor control of diabetes via altered physiology occurring during infection. Diabetics are prone to developing foot ulcers, which in turn are prone to becoming infected, which then act as a source of bacteria for infecting the contiguous bones of the feet. This process is exacerbated in patients with peripheral neuropathy, poor diabetic control and peripheral vascular disease, as these all increase the risk of development of skin breakdown and subsequent osteomyelitis.” [The patient was of course a diabetic…]

“Recent onset fever and back pain suggest an upper UTI [urinary tract infection]. UTIs are classified by anatomy into lower and upper UTIs. Lower UTIs refer to infections at or below the level of the bladder, and include cystitis, urethritis, prostatitis, and epididymitis (the latter three being more often sexually transmitted). Upper UTIs refer to infection above the bladder, and include the ureters and kidneys. Infection of the urinary tract above the bladder is known as pyelonephritis [which] may be life threatening or lead to permanent kidney damage if not promptly treated. UTIs are also classified as complicated or uncomplicated. UTIs in men, the elderly, pregnant women, those who have an indwelling catheter, and anatomic or functional abnormality of the urinary tract are considered to be complicated. A complicated UTI will often receive longer courses of broader spectrum antibiotics. Importantly, the clinical history alone of dysuria and frequency (without vaginal discharge) is associated with more than 90% probability of a UTI in healthy women. […] In women, a UTI develops when urinary pathogens from the bowel or vagina colonize the urethral mucosa, and ascend via the urethra into the bladder. During an uncomplicated symptomatic UTI in women, it is rare for infection to ascend via the ureter into the kidney to cause pyelonephritis. […] Up to 40% of uncomplicated lower UTIs in women will resolve spontaneously without antimicrobial therapy. The use of antibiotics in this cohort is controversial when taking into account the side effects of antibiotics and their effect on normal flora. If prescribed, antibiotics for uncomplicated lower UTIs should be narrow-spectrum […] Most healthcare-associated UTIs are associated with the use of urinary catheters. Each day the catheter remains in situ, the risk of UTI rises by around 5%. Thus inserting catheters only when absolutely needed, and ensuring they are removed as soon as possible, can prevent these.”

September 24, 2014 Posted by | alcohol, Books, Cancer/oncology, Cardiology, Diabetes, Immunology, Medicine, Microbiology, Nephrology, Neurology | Leave a comment

Managing Cardiovascular Complications in Diabetes (1)

I finished the book today. I wrote a brief review of the book on goodreads and gave it three stars. Many things covered in this book I’ve read about in detail elsewhere, e.g. in Sperling et al., Edwards et al., or, say, Eckel et al, but there was some new stuff in here as well. I really liked the first chapter, about ‘The Vascular Endothelium in Diabetes’; it covered some stuff which I’d never really gotten to the bottom of before (but due to the technical nature of that chapter I decided against covering it here). There are still a lot of details which I will not claim to fully understand, but I understand some of the main principles/mechanisms much better than I did. The book was occasionally difficult for me to read because it required knowledge about areas about which I didn’t know a great deal (e.g. haematology), and you should certainly not read this book if you don’t read more or less fluent medical textbook (“The focus of this book is to assist the physician or surgeon in preventing and managing CVD and CVD risk in diabetic patients”). As I pointed out in my goodreads review, the book was difficult for me to read for another reason as well. Authors of academic books should not use acronyms which they do not explain to the reader. Authors of such books should not explain unexplained acronyms five pages after they have used them for the first time. If they do, people might get angry at them.

I’m sure some people don’t care about such things, but this is the sort of stuff that can really piss me off, and it’s part of the reason why this book got three stars. Combining behaviour like that with some formatting errors and a few sentences which don’t make any sense because nobody seems to have proofread the damn thing, and you can end up with an academic publication which looks amateurish, even if it’s most certainly nothing of the sort. In terms of the formatting errors I will note that this is not the first Wiley-Blackwell publication like this I’ve seen – as I point out in my review of that book, the Edwards et al publication to which I link above had similar problems. It’s much rarer, I think, to see stuff like that in Springer publications.

I have added some observations from the book below. I plan to write another post about the book later on as I don’t think it’s fair to only give this book one post, considering how much stuff is in there. When I started out writing this post I was thinking that I’d make the quotes easier to read by adding relevant links where they might help. I realized quite fast that adding enough links to actually make a huge difference would most certainly not be worth it, though I have added a link here and there anyway in order to make the post more readable. I have also added a few bold sections below – I don’t like writing long posts and then have people not reading them because they’re long, so if you don’t particularly care about the topic covered below you might want to read the bolded parts in order to at least learn something from the post. There’s a lot more stuff about type 2 diabetes than about type 1 in this book, so when reading ‘diabetes’ below you should probably just think ‘type 2’.

I remember recently reading an article somewhere stating that there are many errors in medicine-related wikipedia articles and how that’s a problem, and I actually encountered an example of this while reading the book, though I can’t now remember which article it was. You should take it for granted that wiki articles to which I link in posts like these may have errors and inaccuracies (they may actually contain statements which are contradicted by the material covered in the book…), and I usually only link to them in posts like these to ‘translate’ the terms used without having to add a lot of additional text to the post in question. I’ll often not have read the articles to which I link when I link to as many as I do in this post, and a link to an article does not mean that I think all the stuff included in the article is correct. Okay, on to the book coverage:

“There is no doubt that diabetes is a significant contributor to the global burden of chronic non-communicable disease which accounts for over 36 million (63%) of deaths worldwide. Importantly, 80% of these deaths occur in low and middle income countries. [here’s a link to the source, the data above is from page 16. Note that “17.3 million (30%) [of all 57 million deaths worldwide] were due to CVDs.”] […] In an important contribution from the Global Burden of Metabolic Risk Factor of Chronic Disease Collaborating Group [4] national, regional and global trends in fasting plasma glucose and diabetes prevalence since 1980 were studied in a systematic analysis of health examination surveys involving over two and a half million participants and 370 country-years observations. They estimated that the number of people with diabetes increased from 153 (95% uncertainty interval 127–182) million in 1980 to 347 (314382) million in 2008 [4]. [I included the quote partly because those numbers are interesting, partly because this quote from the introduction contains a good example of the kind of sloppiness I mention in the goodreads review; that last parenthesis was surely meant to say 314-382. But it doesn’t. And those kinds of small errors are all over the place.] […] In addition to increased risk of CVD patients with diabetes and established vascular disease have a poorer outcome than those without diabetes [7, 8]. Peripheral arterial disease is increased 2-4 fold in the diabetic population and lower limb amputations are at least 10 fold more common such that half of non-traumatic amputations are performed in diabetic patients [3, 7, 8].”

a mean duration of diabetes of about a decade appears to confer an equivalent risk of CVD to a prior history of MI. In addition, recent work has shown that a history of DM results in six years of life years lost, mostly from CVD [3]. […] 20% of all vascular events occur in patients without any traditional risk factors, necessitating the need for more precise clinical tools that aid clinicians in identifying those at highest risk [4]. To help achieve this goal, there is growing interest in the development and exploitation of new biomarkers. […] A biomarker was defined by a National Institutes of Health (NIH) working group as “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention” [5]. […] A biomarker should meet several criteria to be deemed clinically useful. This is structured around three fundamental questions [6]: 1 Is the biomarker measurable? 2 Does the biomarker add new information? 3 Will the biomarker help the clinician to manage patients? Additional criteria include cost-effectiveness, safety, and replication of the biomarker in clinical scenarios. […] [Reclassification] is a relatively new concept, but potentially the most clinically relevant [of four criteria covered] as it assesses the ability of a test to reclassify individuals correctly into a different risk category; for example, an intermediate-risk subject into a high-risk subject, or a low-risk subject into an intermediate-risk subject […] The ability of the new test to achieve reclassification can be statistically examined by net reclassification improvement (NRI) or integrated discrimination improvement (IDI). The NRI method, which is determined by the proportion of individuals whose risk is correctly escalated or de-escalated, is more useful in primary prevention, where well-accepted categories of risk exist. The IDI estimates the change in predicted probability of an outcome between those with and without the outcome after the biomarker is added to the prediction model. The larger the value of the NRI or the IDI, the better the biomarker.”

Quite a few biomarkers are covered in the chapter, but I’d rather not talk too much about that stuff. There are various types of circulating biomarkers, imaging biomarkers and genetic biomarkers. A few have been included in national guidelines and the only class which does not seem to be useful in this context is the genetic one [“The AHA has given genomic testing in risk assessment in asymptomatic adults a Class III recommendation (no benefit)”]. Naturally reasons besides those related to assessing cardiovascular risk exist for doing genetic testing on diabetics, but if such tests are not useful in that respect then of course that limits their potential somewhat. Incidentally many biomarkers they talk about seem to measure similar things, meaning that adding them together don’t add a lot of information:

“It is logical to assume that if one biomarker measure gives a small incremental gain in risk prediction, multiple biomarkers would result in a larger one. However, trials of multiple biomarkers have disappointingly only shown at best a moderate improvement in usefulness when compared to standard risk factors [72].”

The biomarkers are assumed to hold most promise in the context of primary prevention, but “there is scant data on cost-effectiveness or differential benefit from specific treatments”. Okay, on to other stuff:

“Diabetic kidney disease […] is a clinical diagnosis and is defined by the presence of albuminuria, often with associated abnormal kidney function (an increase in creatinine or a decrease in creatinine clearance or estimated glomerular filtration rate [eGFR]) […] Diabetic nephropathy is a histological diagnosis, characterized by typical histopathological features including mesangial expansion, glomerular basement membrane thickening, and glomerulosclerosis with Kimmelstiel–Wilson lesions. Diabetic kidney disease is most commonly caused by diabetic nephropathy, but other kidney pathologies may be present […] Diabetic kidney disease is a chronic complication of diabetes and affects approximately one third of all diabetic patients [1, 2]. It is the most common cause of kidney failure requiring renal replacement therapy in Western countries [3] and can occur in both type 1 and type 2 diabetes with equivalent risks [4]. The natural history and prognosis of diabetic kidney disease differ somewhat based on the type of diabetes and whether microalbuminuria is present […] In people with type 1 diabetes who have microalbuminuria, if left untreated, approximately 80% will develop macroalbuminuria (also called overt nephropathy) within 6–14 years [6, 7]. Subsequently, half of these will develop end-stage kidney disease (ESKD) over 10 years if there is still a lack of specific intervention. In contrast, approximately 20–40% of people with type 2 diabetes and microalbuminuria develop macroalbuminuria without intervention, and ESKD has been reported to develop in 20% of patients with overt nephropathy within 20 years [8]. Some of these differences may relate to the older age and greater burden of comorbidity experienced by people with type 2 diabetes for a given duration of diabetes, meaning that more of them will die of cardiovascular and other complications before developing kidney disease.”

“Diabetic kidney disease has a heterogeneous presentation. Early stages are often asymptomatic and only detected by abnormal laboratory tests (albuminuria and changes in GFR). Albuminuria is one of the earliest detectable features of diabetic kidney disease […] As diabetes manifests as a systemic disease, patients with type 1 DM almost always have other signs of diabetic microvascular complications, such as retinopathy and neuropathy. Diabetic retinopathy usually precedes the onset of overt nephropathy, while the relationship between diabetic kidney disease and retinopathy is less predictable in type 2 diabetes. […] For people with type 1 diabetes, approximately 20–30% will have microalbuminuria after a mean duration of diabetes of 15 years [37, 38]. Similarly, 25% of individuals with type 2 diabetes have microalbuminuria after 10 years […] Proteinuria and abnormal kidney function are independent risk factors for renal outcomes in diabetes [28]. […] As with treatment strategies for end-stage kidney disease secondary to other causes, dialysis and renal transplantation are both options for treatment for ESKD caused by diabetes. Lower survival rates have been observed for people with ESKD caused by diabetic kidney disease, with five years’ survival of 30%, according to USRDS data.”

Cardiovascular disease (CVD) including coronary heart disease (CHD) is the major cause of mortality in patients with diabetes […] no more than 25% of the excess CHD risk in diabetes can be accounted for by established risk factors […] Hyperglycemia as a risk factor for CVD has been established for many years. Mortality from CVD accounts for more than 60% of deaths in patients with type 2 diabetes mellitus and clearly accounts for this ultimate complication of diabetes [3, 8]. The association between differing degrees of hyperglycemia and CVD risk has been an area of debate. The United Kingdom Prospective Diabetes Study (UKPDS) demonstrated that the incidence of myocardial infarction rose by 14% per 1% rise in HBA1c [9]. This is in line with other studies showing that glucose is a continuous risk factor in people with both type 1 and type 2 diabetes. […] There is also evidence that glucose fluctuations (the highs and lows) are associated with increased oxidative stress […] Increased oxidative stress results from an imbalance between oxidant production and antioxidant defenses […] Diabetes mellitus, obesity, micro- and macrovascular complications have been consistently associated with increased oxidative stress [37, 38, 39] and several studies have demonstrated that hyperglycemia per se is associated with increased oxidative stress [39, 40]. […] Hypoglycemia is also associated with increased cardiovascular mortality [58, 59], although the mechanisms behind this remain unclear. […] As well as being associated with increased oxidative stress [62], hypoglycemia also has pro-inflammatory effects on the vasculature. […] These changes contribute to a hypercoagulable state associated with increased platelet aggregation and plasma concentrations of coagulation factors […] Acute hypoglycemia has also been associated with long QT syndrome, which is associated with an increased risk of sudden cardiac death [65].”

“The majority of people with type 2 DM [diabetes mellitus] are hypertensives […] There is no question about the need to treat hypertension in either the primary prevention or secondary prevention settings for cerebrovascular disease, irrespective of the presence of diabetes. A systematic review of the effects of different BP-lowering drug regimens in people with hypertension, diabetes, or vascular disease found that the relative risks of stroke and other major vascular outcomes were proportional to the BP reduction achieved [62]. […] there is a general consensus that ACE inhibitors or ARB are the first-line drugs of choice in both diabetes and metabolic syndrome. In primary prevention, the only question is the level of BP above which treatment is indicated. […] The recommended threshold for treatment in primary prevention is currently under discussion in both diabetics and nondiabetics. […] there is increasing uncertainty about the use of absolute thresholds of BP to determine the need for treatment […] Although “lower should be better,” the results of recent clinical trials examining the benefits of normalizing risk-factor levels have been counter-intuitive and, sometimes, disconcerting, and have called into question this belief […] Many hypertensive patients in clinical practice receive more than one antihypertensive drug, and the use of combination therapy is widely recommended in hypertension guidelines. Combinations may be especially important for patients with diabetes, for whom recommended BP targets are challenging.”

June 26, 2014 Posted by | Books, Cardiology, Diabetes, Epidemiology, Medicine, Nephrology | Leave a comment