Econstudentlog

A few diabetes papers of interest

i. Rates of Diabetic Ketoacidosis: International Comparison With 49,859 Pediatric Patients With Type 1 Diabetes From England, Wales, the U.S., Austria, and Germany.

“Rates of DKA in youth with type 1 diabetes vary widely nationally and internationally, from 15% to 70% at diagnosis (4) to 1% to 15% per established patient per year (911). However, data from systematic comparisons between countries are limited. To address this gap in the literature, we analyzed registry and audit data from three organizations: the Prospective Diabetes Follow-up Registry (DPV) in Germany and Austria, the National Paediatric Diabetes Audit (NPDA) in England and Wales, and the T1D Exchange (T1DX) in the U.S. These countries have similarly advanced, yet differing, health care systems in which data on DKA and associated factors are collected. Our goal was to identify indicators of risk for DKA admissions in pediatric patients with >1-year duration of disease with an aim to better understand where targeted preventive programs might lead to a reduction in the frequency of this complication of management of type 1 diabetes.”

RESULTS The frequency of DKA was 5.0% in DPV, 6.4% in NPDA, and 7.1% in T1DX […] Mean HbA1c was lowest in DPV (63 mmol/mol [7.9%]), intermediate in T1DX (69 mmol/mol [8.5%]), and highest in NPDA (75 mmol/mol [9.0%]). […] In multivariable analyses, higher odds of DKA were found in females (odds ratio [OR] 1.23, 99% CI 1.10–1.37), ethnic minorities (OR 1.27, 99% CI 1.11–1.44), and HbA1c ≥7.5% (≥58 mmol/mol) (OR 2.54, 99% CI 2.09–3.09 for HbA1c from 7.5 to <9% [58 to <75 mmol/mol] and OR 8.74, 99% CI 7.18–10.63 for HbA1c ≥9.0% [≥75 mmol/mol]).”

Poor metabolic control is obviously very important, but it’s important to remember that poor metabolic control is in itself an outcome that needs to be explained. I would note that the mean HbA1c values here, especially that 75 mmol/mol one, seem really high; this is not a very satisfactory level of glycemic control and corresponds to an average glucose level of 12 mmol/l. And that’s a population average, meaning that many individuals have values much higher than this. Actually the most surprising thing to me about these data is that the DKA event rates are not much higher than they are, considering the level of metabolic control achieved. Another slightly surprising finding is that teenagers (13-17 yrs) were not actually all that much more likely to have experienced DKA than small children (0-6 yrs); the OR is only ~1.5. Of course this can not be taken as an indication that DKA in teenagers do not make up a substantial proportion of the total amount of DKA events in pediatric samples, as the type 1 prevalence is much higher in teenagers than in small children (incidence peaks in adolescence).

“In 2004–2009 in the U.S., the mean hospital cost per pediatric DKA admission was $7,142 (range $4,125–11,916) (6), and insurance claims data from 2007 reported an excess of $5,837 in annual medical expenditures for youth with insulin-treated diabetes with DKA compared with those without DKA (7). In Germany, pediatric patients with diabetes with DKA had diabetes-related costs that were up to 3.6-fold higher compared with those without DKA (8).”

“DKA frequency was lower in pump users than in injection users (OR 0.84, 99% CI 0.76–0.93). Heterogeneity in the association with DKA between registries was seen for pump use and age category, and the overall rate should be interpreted accordingly. A lower rate of DKA in pump users was only found in T1DX, in contrast to no association of pump use with DKA in DPV or NPDA. […] In multivariable analyses […], age, type 1 diabetes duration, and pump use were not significantly associated with DKA in the fully adjusted model. […] pump use was associated with elevated odds of DKA in the <6-year-olds and in the 6- to <13-year-olds but with reduced odds of DKA in the 13- to <18-year-olds.”

Pump use should probably all else equal increase the risk of DKA, but all else is never equal and in these data pump users actually had a lower DKA event rate than did diabetics treated with injections. One should not conclude from this finding that pump use decreases the risk of DKA, selection bias and unobserved heterogeneities are problems which it is almost impossible to correct for in an adequate way – I find it highly unlikely that selection bias is only a potential problem in the US (see below). There are many different ways selection bias can be a relevant problem, financial- and insurance-related reasons (relevant particularly in the US and likely the one the authors are considering) is far from the only one; I could easily imagine selection dynamics playing a major role even in a setting where all new-diagnosed children were started on pump therapy as a matter of course. In such a setting you might have a situation where very poorly controlled individuals would have 10 DKA events in a short amount of time because they didn’t take the necessary amount of blood glucose tests/disregarded alarms/forgot or postponed filling up the pump when it’s near-empty/failed to switch the battery in time/etc. etc., and then what might happen would be that the diabetologist/endocrinologist would then proceed to recommend these patients doing very poorly on pump treatment to switch to injection therapy, and what you would end up with would be a compliant/motivated group of patients on pump therapy and a noncompliant/poorly motivated group on injection therapy. This would happen even if everybody started on pump therapy and so pump therapy exposure was completely unrelated to outcomes. Pump therapy requires more of the patient than does injection therapy, and if the patient is unwilling/unable to put in the work required that treatment option will fail. In my opinion the default here should be that these treatment groups are (‘significantly’) different, not that they are similar.

A few more quotes from the paper:

“The major finding of these analyses is high rates of pediatric DKA across the three registries, even though DKA events at the time of diagnosis were not included. In the prior 12 months, ∼1 in 20 (DPV), 1 in 16 (NPDA), and 1 in 14 (T1DX) pediatric patients with a duration of diabetes ≥1 year were diagnosed with DKA and required treatment in a health care facility. Female sex, ethnic minority status, and elevated HbA1c were consistent indicators of risk for DKA across all three registries. These indicators of increased risk for DKA are similar to previous reports (10,11,18,19), and our rates of DKA are within the range in the pediatric diabetes literature of 1–15% per established patient per year (10,11).

Compared with patients receiving injection therapy, insulin pump use was associated with a lower risk of DKA only in the U.S. in the T1DX, but no difference was seen in the DPV or NPDA. Country-specific factors on the associations of risk factors with DKA require further investigation. For pump use, selection bias may play a role in the U.S. The odds of DKA in pump users was not increased in any registry, which is a marked difference from some (10) but not all historic data (20).”

ii. Effect of Long-Acting Insulin Analogs on the Risk of Cancer: A Systematic Review of Observational Studies.

NPH insulin has been the mainstay treatment for type 1 diabetes and advanced type 2 diabetes since the 1950s. However, this insulin is associated with an increased risk of nocturnal hypoglycemia, and its relatively short half-life requires frequent administration (1,2). Consequently, structurally modified insulins, known as long-acting insulin analogs (glargine and detemir), were developed in the 1990s to circumvent these limitations. However, there are concerns that long-acting insulin analogs may be associated with an increased risk of cancer. Indeed, some laboratory studies showed long-acting insulin analogs were associated with cancer cell proliferation and protected against apoptosis via their higher binding affinity to IGF-I receptors (3,4).

In 2009, four observational studies associated the use of insulin glargine with an increased risk of cancer (58). These studies raised important concerns but were also criticized for important methodological shortcomings (913). Since then, several observational studies assessing the association between long-acting insulin analogs and cancer have been published but yielded inconsistent findings (1428). […] Several meta-analyses of observational studies have investigated the association between insulin glargine and cancer risk (3437). These meta-analyses assessed the quality of included studies, but the methodological issues particular to pharmacoepidemiologic research were not fully considered. In addition, given the presence of important heterogeneity in this literature, the appropriateness of pooling the results of these studies remains unclear. We therefore conducted a systematic review of observational studies examining the association between long-acting insulin analogs and cancer incidence, with a particular focus on methodological strengths and weaknesses of these studies.”

“[W]e assessed the quality of studies for key components, including time-related biases (immortal time, time-lag, and time-window), inclusion of prevalent users, inclusion of lag periods, and length of follow-up between insulin initiation and cancer incidence.

Immortal time bias is defined by a period of unexposed person-time that is misclassified as exposed person-time or excluded, resulting in the exposure of interest appearing more favorable (40,41). Time-lag bias occurs when treatments used later in the disease management process are compared with those used earlier for less advanced stages of the disease. Such comparisons can result in confounding by disease duration or severity of disease if duration and severity of disease are not adequately considered in the design or analysis of the study (29). This is particularly true for chronic disease with dynamic treatment processes such as type 2 diabetes. Currently, American and European clinical guidelines suggest using basal insulin (e.g., NPH, glargine, and detemir) as a last line of treatment if HbA1c targets are not achieved with other antidiabetic medications (42). Therefore, studies that compare long-acting insulin analogs to nonbasal insulin may introduce confounding by disease duration. Time-window bias occurs when the opportunity for exposure differs between case subjects and control subjects (29,43).

The importance of considering a lag period is necessary for latency considerations (i.e., a minimum time between treatment initiation and the development of cancer) and to minimize protopathic and detection bias. Protopathic bias, or reverse causation, is present when a medication (exposure) is prescribed for early symptoms related to the outcome of interest, which can lead to an overestimation of the association. Lagging the exposure by a predefined time window in cohort studies or excluding exposures in a predefined time window before the event in case-control studies is a means of minimizing this bias (44). Detection bias is present when the exposure leads to higher detection of the outcome of interest due to the increased frequency of clinic visits (e.g., newly diagnosed patients with type 2 diabetes or new users of another antidiabetic medication), which also results in an overestimation of risk (45). Thus, including a lag period, such as starting follow-up after 1 year of the initiation of a drug, simultaneously considers a latency period while also minimizing protopathic and detection bias.”

“We systematically searched MEDLINE and EMBASE from 2000 to 2014 to identify all observational studies evaluating the relationship between the long-acting insulin analogs and the risk of any and site-specific cancers (breast, colorectal, prostate). […] 16 cohort and 3 case-control studies were included in this systematic review (58,1428). All studies evaluated insulin glargine, with four studies also investigating insulin detemir (15,17,25,28). […] The study populations ranged from 1,340 to 275,164 patients […]. The mean or median durations of follow-up and age ranged from 0.9 to 7.0 years and from 52.3 to 77.4 years, respectively. […] Thirteen of 15 studies reported no association between insulin glargine and detemir and any cancer. Four of 13 studies reported an increased risk of breast cancer with insulin glargine. In the quality assessment, 7 studies included prevalent users, 11 did not consider a lag period, 6 had time-related biases, and 16 had short (<5 years) follow-up.”

“Of the 19 studies in this review, immortal time bias may have been introduced in one study based on the time-independent exposure and cohort entry definitions that were used in this cohort study […] Time-lag bias may have occurred in four studies […] A variation of time-lag bias was observed in a cohort study of new insulin users (28). For the exposure definition, highest duration since the start of insulin use was compared with the lowest. It is expected that the risk of cancer would increase with longer duration of insulin use; however, the opposite was reported (with RRs ranging from 0.50 to 0.90). The protective association observed could be due to competing risks (e.g., death from cardiovascular-related events) (47,48). Patients with diabetes have a higher risk of cardiovascular-related deaths compared with patients with no diabetes (49,50). Therefore, patients with diabetes who die of cardiovascular-related events do not have the opportunity to develop cancer, resulting in an underestimation of the risk of cancer. […] Time-window bias was observed in two studies (18,22). […] HbA1c and diabetes duration were not accounted for in 15 of the 19 studies, resulting in likely residual confounding (7,8,1418,2026,28). […] Seven studies included prevalent users of insulin (8,15,18,20,21,23,25), which is problematic because of the corresponding depletion of susceptible subjects in other insulin groups compared with long-acting insulin analogs. Protopathic or detection bias could have resulted in 11 of the 19 studies because a lag period was not incorporated in the study design (6,7,1416,1821,23,28).”

CONCLUSIONS The observational studies examining the risk of cancer associated with long-acting insulin analogs have important methodological shortcomings that limit the conclusions that can be drawn. Thus, uncertainty remains, particularly for breast cancer risk.”

iii. Impact of Socioeconomic Status on Cardiovascular Disease and Mortality in 24,947 Individuals With Type 1 Diabetes.

“Socioeconomic status (SES) is a powerful predictor of cardiovascular disease (CVD) and death. We examined the association in a large cohort of patients with type 1 diabetes. […] Clinical data from the Swedish National Diabetes Register were linked to national registers, whereby information on income, education, marital status, country of birth, comorbidities, and events was obtained. […] Type 1 diabetes was defined on the basis of epidemiologic data: treatment with insulin and a diagnosis at the age of 30 years or younger. This definition has been validated as accurate in 97% of the cases listed in the register (14).”

“We included 24,947 patients. Mean (SD) age and follow-up was 39.1 (13.9) and 6.0 (1.0) years. Death and fatal/nonfatal CVD occurred in 926 and 1378 individuals. Compared with being single, being married was associated with 50% lower risk of death, cardiovascular (CV) death, and diabetes-related death. Individuals in the two lowest quintiles had twice as great a risk of fatal/nonfatal CVD, coronary heart disease, and stroke and roughly three times as great a risk of death, diabetes-related death, and CV death as individuals in the highest income quintile. Compared with having ≤9 years of education, individuals with a college/university degree had 33% lower risk of fatal/nonfatal stroke.”

“Individuals with 10–12 years of education were comparable at baseline (considering distribution of age and sex) with those with a college/university degree […]. Individuals with a college/university degree had higher income, had 5 mmol/mol lower HbA1c, were more likely to be married/cohabiting, used insulin pump more frequently (17.5% vs. 14.5%), smoked less (5.8% vs. 13.1%), and had less albuminuria (10.8% vs. 14.2%). […] Women had substantially lower income and higher education, were more often married, used insulin pump more frequently, had less albuminuria, and smoked more frequently than men […] Individuals with high income were more likely to be married/cohabiting, had lower HbA1c, and had lower rates of smoking as well as albuminuria”.

CONCLUSIONS Low SES increases the risk of CVD and death by a factor of 2–3 in type 1 diabetes.”

“The effect of SES was striking despite rigorous adjustments for risk factors and confounders. Individuals in the two lowest income quintiles had two to three times higher risk of CV events and death than those in the highest income quintile. Compared with low educational level, having high education was associated with ∼30% lower risk of stroke. Compared with being single, individuals who were married/cohabiting had >50% lower risk of death, CV death, and diabetes-related death. Immigrants had 20–40% lower risk of fatal/nonfatal CVD, all-cause death, and diabetes-related death. Additionally, we show that males had 44%, 63%, and 29% higher risk of all-cause death, CV death, and diabetes-related death, respectively.

Despite rigorous adjustments for covariates and equitable access to health care at a negligible cost (20,21), SES and sex were robust predictors of CVD disease and mortality in type 1 diabetes; their effect was comparable with that of smoking, which represented an HR of 1.56 (95% CI 1.29–1.91) for all-cause death. […] Our study shows that men with type 1 diabetes are at greater risk of CV events and death compared with women. This should be viewed in the light of a recent meta-analysis of 26 studies, which showed higher excess risk in women compared with men. Overall, women had 40% greater excess risk of all-cause mortality, and twice the excess risk of fatal/nonfatal vascular events, compared with men (29). Thus, whereas the excess risk (i.e., the risk of patients with diabetes compared with the nondiabetic population) of vascular disease is higher in women with diabetes, we show that men with diabetes are still at substantially greater risk of all-cause death, CV death, and diabetes death compared with women with diabetes. Other studies are in line with our findings (10,11,13,3032).”

iv. Interventions That Restore Awareness of Hypoglycemia in Adults With Type 1 Diabetes: A Systematic Review and Meta-analysis.

“Hypoglycemia remains the major limiting factor toward achieving good glycemic control (1). Recurrent hypoglycemia reduces symptomatic and hormone responses to subsequent hypoglycemia (2), associated with impaired awareness of hypoglycemia (IAH). IAH occurs in up to one-third of adults with type 1 diabetes (T1D) (3,4), increasing their risk of severe hypoglycemia (SH) sixfold (3) and contributing to substantial morbidity, with implications for employment (5), driving (6), and mortality. Distribution of risk of SH is skewed: one study showed that 5% of subjects accounted for 54% of all SH episodes, with IAH one of the main risk factors (7). “Dead-in-bed,” related to nocturnal hypoglycemia, is a leading cause of death in people with T1D <40 years of age (8).”

“This systematic review assessed the clinical effectiveness of treatment strategies for restoring hypoglycemia awareness (HA) and reducing SH risk in those with IAH and performed a meta-analysis, where possible, for different approaches in restoring awareness in T1D adults. Interventions to restore HA were broadly divided into three categories: educational (inclusive of behavioral), technological, and pharmacotherapeutic. […] Forty-three studies (18 randomized controlled trials, 25 before-and-after studies) met the inclusion criteria, comprising 27 educational, 11 technological, and 5 pharmacological interventions. […] A meta-analysis for educational interventions on change in mean SH rates per person per year was performed. Combining before-and-after and RCT studies, six studies (n = 1,010 people) were included in the meta-analysis […] A random-effects meta-analysis revealed an effect size of a reduction in SH rates of 0.44 per patient per year with 95% CI 0.253–0.628. [here’s the forest plot, US] […] Most of the educational interventions were observational and mostly retrospective, with few RCTs. The overall risk of bias is considered medium to high and the study quality moderate. Most, if not all, of the RCTs did not use double blinding and lacked information on concealment. The strength of association of the effect of educational interventions is moderate. The ability of educational interventions to restore IAH and reduce SH is consistent and direct with educational interventions showing a largely positive outcome. There is substantial heterogeneity between studies, and the estimate is imprecise, as reflected by the large CIs. The strength of evidence is moderate to high.”

v. Trends of Diagnosis-Specific Work Disability After Newly Diagnosed Diabetes: A 4-Year Nationwide Prospective Cohort Study.

“There is little evidence to show which specific diseases contribute to excess work disability among those with diabetes. […] In this study, we used a large nationwide register-based data set, which includes information on work disability for all working-age inhabitants of Sweden, in order to investigate trends of diagnosis-specific work disability (sickness absence and disability pension) among people with diabetes for 4 years directly after the recorded onset of diabetes. We compared work disability trends among people with diabetes with trends among those without diabetes. […] The register data of diabetes medication and in- and outpatient hospital visits were used to identify all recorded new diabetes cases among the population aged 25–59 years in Sweden in 2006 (n = 14,098). Data for a 4-year follow-up of ICD-10 physician-certified sickness absence and disability pension days (2007‒2010) were obtained […] Comparisons were made using a random sample of the population without recorded diabetes (n = 39,056).”

RESULTS The most common causes of work disability were mental and musculoskeletal disorders; diabetes as a reason for disability was rare. Most of the excess work disability among people with diabetes compared with those without diabetes was owing to mental disorders (mean difference adjusted for confounding factors 18.8‒19.8 compensated days/year), musculoskeletal diseases (12.1‒12.8 days/year), circulatory diseases (5.9‒6.5 days/year), diseases of the nervous system (1.8‒2.0 days/year), and injuries (1.0‒1.2 days/year).”

CONCLUSIONS The increased risk of work disability among those with diabetes is largely attributed to comorbid mental, musculoskeletal, and circulatory diseases. […] Diagnosis of diabetes as the cause of work disability was rare.”

August 19, 2017 Posted by | Cancer/oncology, Cardiology, Diabetes, Medicine, Statistics | Leave a comment

Infectious Disease Surveillance (III)

I have added some more observations from the book below.

“Zoonotic diseases are infections transmitted between animals and humans […]. A recent survey identified more than 1,400 species of human disease–causing agents, over half (58%) of which were zoonotic [2]. Moreover, nearly three-quarters (73%) of infectious diseases considered to be emerging or reemerging were zoonotic [2]. […] In many countries there is minimal surveillance for live animal imports or imported wildlife products. Minimal surveillance prevents the identification of wildlife trade–related health risks to the public, agricultural industry, and native wildlife [36] and has led to outbreaks of zoonotic diseases […] Southeast Asia [is] a hotspot for emerging zoonotic diseases because of rapid population growth, high population density, and high biodiversity […] influenza virus in particular is of zoonotic importance as multiple human infections have resulted from animal exposure [77–79].”

“[R]abies is an important cause of death in many countries, particularly in Africa and Asia [85]. Rabies is still underreported throughout the developing world, and 100-fold underreporting of human rabies is estimated for most of Africa [44]. Reasons for underreporting include lack of public health personnel, difficulties in identifying suspect animals, and limited laboratory capacity for rabies testing. […] Brucellosis […] is transmissible to humans primarily through consumption of unpasteurized milk or dairy products […] Brucella is classified as a category B bioterrorism agent [90] because of its potential for aerosolization [I should perhaps here mention that the book coverage does overlaps a bit with that of Fong & Alibek’s book – which I covered here – but that I decided against covering those topics in much detail here – US] […] The key to preventing brucellosis in humans is to control or eliminate infections in animals [91–93]; therefore, veterinarians are crucial to the identification, prevention, and control of brucellosis [89]. […] Since 1954 [there has been] an ongoing eradication program involving surveillance testing of cattle at slaughter, testing at livestock markets, and whole-herd testing on the farm [in the US] […] Except for endemic brucellosis in wildlife in the Greater Yellowstone Area, all 50 states and territories in the United States are free of bovine brucellosis [94].”

“Because of its high mortality rate in humans in the absence of early treatment, Y. pestis is viewed as one of the most pathogenic human bacteria [101]. In the United States, plague is most often found in the Southwest where it is transmitted by fleas and maintained in rodent populations [102]. Deer mice and voles typically serve as maintenance hosts [and] these animals are often resistant to plague [102]. In contrast, in amplifying host species such as prairie dogs, ground squirrels, chipmunks, and wood rats, plague spreads rapidly and results in high mortality [103]. […] Human infections with Y. pestis can result in bubonic, pneumonic, or septicemic plague, depending on the route of exposure. Bubonic plague is most common; however, pneumonic plague poses a more serious public health risk since it can be easily transmitted person-to-person through inhalation of aerosolized bacteria […] Septicemic plague is characterized by bloodstream infection with Y. pestis and can occur secondary to pneumonic or bubonic forms of infection or as a primary infection [6,60].
Plague outbreaks are often correlated with animal die-offs in the area [104], and rodent control near human residences is important to prevent disease [103]. […] household pets can be an important route of plague transmission and flea control in dogs and cats is an important prevention measure [105]. Plague surveillance involves monitoring three populations for infection: vectors (e.g., fleas), humans, and rodents [106]. In the past 20 years, the numbers of human cases of plague reported in the United States have varied from 1 to 17 cases per year [90]. […]
Since rodent species are the main reservoirs of the bacteria, these animals can be used for sentinel surveillance to provide an early warning of the public health risk to humans [106]. […] Rodent die-offs can often be an early indicator of a plague outbreak”.

“Zoonotic disease surveillance is crucial for protection of human and animal health. An integrated, sustainable system that collects data on incidence of disease in both animals and humans is necessary to ensure prompt detection of zoonotic disease outbreaks and a timely and focused response [34]. Currently, surveillance systems for animals and humans [operate] largely independently [34]. This results in an inability to rapidly detect zoonotic diseases, particularly novel emerging diseases, that are detected in the human population only after an outbreak occurs [109]. While most industrialized countries have robust disease surveillance systems, many developing countries currently lack the resources to conduct both ongoing and real-time surveillance [34,43].”

“Acute hepatitis of any cause has similar, usually indistinguishable, signs and symptoms. Acute illness is associated with fever, fatigue, nausea, abdominal pain, followed by signs of liver dysfunction, including jaundice, light to clay-colored stool, dark urine, and easy bruising. The jaundice, dark urine, and abnormal stool are because of the diminished capacity of the inflamed liver to handle the metabolism of bilirubin, which is a breakdown product of hemoglobin released as red blood cells are normally replaced. In severe hepatitis that is associated with fulminant liver disease, the liver’s capacity to produce clotting factors and to clear potential toxic metabolic products is severely impaired, with resultant bleeding and hepatic encephalopathy. […] An effective vaccine to prevent hepatitis A has been available for more than 15 years, and incidence rates of hepatitis A are dropping wherever it is used in routine childhood immunization programs. […] Currently, hepatitis A vaccine is part of the U.S. childhood immunization schedule recommended by the Advisory Committee on Immunization Practices (ACIP) [31].”

Chronic hepatitis — persistent and ongoing inflammation that can result from chronic infection — usually has minimal to no signs or symptoms […] Hepatitis B and C viruses cause acute hepatitis as well as chronic hepatitis. The acute component is often not recognized as an episode of acute hepatitis, and the chronic infection may have little or no symptoms for many years. With hepatitis B, clearance of infection is age related, as is presentation with symptoms. Over 90% of infants exposed to HBV develop chronic infection, while <1% have symptoms; 5–10% of adults develop chronic infection, but 50% or more have symptoms associated with acute infection. Among those who acquire hepatitis C, 15–45% clear the infection; the remainder have lifelong infection unless treated specifically for hepatitis C.”

“[D]ata are only received on individuals accessing care. Asymptomatic acute infection and poor or unavailable measurements for high risk populations […] have resulted in questionable estimates of the prevalence and incidence of hepatitis B and C. Further, a lack of understanding of the different types of viral hepatitis by many medical providers [18] has led to many undiagnosed individuals living with chronic infection, who are not captured in disease surveillance systems. […] Evaluation of acute HBV and HCV surveillance has demonstrated a lack of sensitivity for identifying acute infection in injection drug users; it is likely that most cases in this population go undetected, even if they receive medical care [36]. […] Best practices for conducting surveillance for chronic hepatitis B and C are not well established. […] The role of health departments in responding to infectious diseases is typically responding to acute disease. Response to chronic HBV infection is targeted to prevention of transmission to contacts of those infected, especially in high risk situations. Because of the high risk of vertical transmission and likely development of chronic disease in exposed newborns, identification and case management of HBV-infected pregnant women and their infants is a high priority. […] For a number of reasons, states do not conduct uniform surveillance for chronic hepatitis C. There is not agreement as to the utility of surveillance for chronic HCV infection, as it is a measurement of prevalent rather than incident cases.”

“Among all nationally notifiable diseases, three STDs (chlamydia, gonorrhea, and syphilis) are consistently in the top five most commonly reported diseases annually. These three STDs made up more than 86% of all reported diseases in the United States in 2010 [2]. […] The true burden of STDs is likely to be higher, as most infections are asymptomatic [4] and are never diagnosed or reported. A synthesis of a variety of data sources estimated that in 2008 there were over 100 million prevalent STDs and nearly 20 million incident STDs in the United States [5]. […] Nationally, 72% of all reported STDs are among persons aged 15–24 years [3], and it is estimated that 1 in 4 females aged 14–19 has an STD [7]. […] In 2011, the rates of chlamydia, gonorrhea, and primary and secondary syphilis among African-­Americans were, respectively, 7.5, 16.9, and 6.7 times the rates among whites [3]. Additionally, men who have sex with men (MSM) are disproportionately infected with STDs. […] several analyses have shown risk ratios above 100 for the associations between being an MSM and having syphilis or HIV [9,10]. […] Many STDs can be transmitted congenitally during pregnancy or birth. In 2008, over 400,000 neonatal deaths and stillbirths were associated with syphilis worldwide […] untreated chlamydia and gonorrhea can cause ophthalmia neonatorum in newborns, which can result in blindness [13]. The medical and societal costs for STDs are high. […] One estimate in 2008 put national costs at $15.6 billion [15].”

“A significant challenge in STD surveillance is that the term “STD” encompasses a variety of infections. Currently, there are over 35 pathogens that can be transmitted sexually, including bacteria […] protozoa […] and ectoparasites […]. Some infections can cause clinical syndromes shortly after exposure, whereas others result in no symptoms or have a long latency period. Some STDs can be easily diagnosed using self-collected swabs, while others require a sample of blood or a physical examination by a clinician. Consequently, no one particular surveillance strategy works for all STDs. […] The asymptomatic nature of most STDs limits inferences from case­-based surveillance, since in order to be counted in this system an infection must be diagnosed and reported. Additionally, many infections never result in disease. For example, an estimated 90% of human papillomavirus (HPV) infections resolve on their own without sequelae [24]. As such, simply counting infections may not be appropriate, and sequelae must also be monitored. […] Strategies for STD surveillance include case reporting; sentinel surveillance; opportunistic surveillance, including use of administrative data and positivity in screened populations; and population-­based studies […] the choice of strategy depends on the type of STD and the population of interest.”

“Determining which diseases and conditions should be included in mandatory case reporting requires balancing the benefits to the public health system (e.g., utility of the data) with the costs and burdens of case reporting. While many epidemiologists and public health practitioners follow the mantra “the more data, the better,” the costs (in both dollars and human resources) of developing and maintaining a robust case­-based reporting system can be large. Case­-based surveillance has been mandated for chlamydia, gonorrhea, syphilis, and chancroid nationally; but expansion of state­-initiated mandatory reporting for other STDs is controversial.”

August 18, 2017 Posted by | Books, Epidemiology, Immunology, Infectious disease, Medicine | Leave a comment

Type 1 Diabetes Is Associated With an Increased Risk of Fracture Across the Life Span

Type 1 Diabetes Is Associated With an Increased Risk of Fracture Across the Life Span: A Population-Based Cohort Study Using The Health Improvement Network (THIN).

I originally intended to include this paper in a standard diabetes post like this one, but the post gradually got more and more unwieldy as I added more stuff and so in the end I decided – like in this case – that it might be a better idea to just devote an entire post to the paper and then postpone my coverage of some of the other papers included in the post.

I’ve talked about this stuff before, but I’m almost certain the results of this paper were not included in Czernik and Fowlkes’ book as this paper was published at almost exactly the same time as was the book. It provides further support of some of the observations included in C&F’s publication. This is a very large and important study in the context of the relationship between type 1 diabetes and skeletal health. I have quoted extensively from the paper below, and also added some observations of my own along the way in order to provide a little bit of context where it might be needed:

“There is an emerging awareness that diabetes adversely affects skeletal health and that type 1 diabetes affects the skeleton more severely than type 2 diabetes (5). Studies in humans and animal models have identified a number of skeletal abnormalities associated with type 1 diabetes, including deficits in bone mineral density (BMD) (6,7) and bone structure (8), decreased markers of bone formation (9,10), and variable alterations in markers of bone resorption (10,11).

Previous studies and two large meta-analyses reported that type 1 diabetes is associated with an increased risk of fracture (1219). However, most of these studies were conducted in older adults and focused on hip fractures. Importantly, most affected individuals develop type 1 diabetes in childhood, before the attainment of peak bone mass, and therefore may be at increased risk of fracture throughout their life span. Moreover, because hip fractures are rare in children and young adults, studies limited to this outcome may underestimate the overall fracture burden in type 1 diabetes.

We used The Health Improvement Network (THIN) database to conduct a population-based cohort study to determine whether type 1 diabetes is associated with increased fracture incidence, to delineate age and sex effects on fracture risk, and to determine whether fracture site distribution is altered in participants with type 1 diabetes compared with participants without diabetes. […] 30,394 participants aged 0–89 years with type 1 diabetes were compared with 303,872 randomly selected age-, sex-, and practice-matched participants without diabetes. Cox regression analysis was used to determine hazard ratios (HRs) for incident fracture in participants with type 1 diabetes. […] A total of 334,266 participants, median age 34 years, were monitored for 1.9 million person-years. HR were lowest in males and females age <20 years, with HR 1.14 (95% CI 1.01–1.29) and 1.35 (95% CI 1.12–1.63), respectively. Risk was highest in men 60–69 years (HR 2.18 [95% CI 1.79–2.65]), and in women 40–49 years (HR 2.03 [95% CI 1.73–2.39]). Lower extremity fractures comprised a higher proportion of incident fractures in participants with versus those without type 1 diabetes (31.1% vs. 25.1% in males, 39.3% vs. 32% in females; P < 0.001). Secondary analyses for incident hip fractures identified the highest HR of 5.64 (95% CI 3.55–8.97) in men 60–69 years and the highest HR of 5.63 (95% CI 2.25–14.11) in women 30–39 years.”

“Conditions identified by diagnosis codes as covariates of interest were hypothyroidism, hyperthyroidism, adrenal insufficiency, celiac disease, inflammatory bowel disease, vitamin D deficiency, fracture before the start of the follow-up period, diabetic retinopathy, and diabetic neuropathy. All variables, with the exception of prior fracture, were treated as time-varying covariates. […] Multivariable Cox regression analysis was used to assess confounding by covariates of interest. Final models were stratified by age category (<20, 20–29, 30–39, 40–49, 50–59, 60–69, and ≥70 years) after age was found to be a significant predictor of fracture and to violate the assumption of proportionality of hazards […] Within each age stratum, models were again assessed for proportionality of hazards and further stratified where appropriate.”

A brief note on a few of those covariates. Some of them are obvious, other perhaps less so. Retinopathy is probably included mainly due to the associated vision issues, rather than some sort of direct pathophysiological linkage between the conditions; vision problems may increase the risk of falls, particularly in the elderly, and falls increase the fracture risk (they note this later on in the paper). Neuropathy could in my opinion affect risk in multiple ways, not only through an increased fall risk, but either way it certainly makes a lot of sense to include that variable if it’s available. Thyroid disorders can cause bone problems, and the incidence of thyroid disorders is elevated in type 1 – to the extent that e.g. the Oxford Handbook of Clinical Medicine recommends screening people with diabetes mellitus for abnormalities in thyroid function on the annual review. Both Addison‘s (adrenal insufficiency) and thyroid disorders in type 1 diabetics may be specific components of a more systemic autoimmune disease (relevant link here, see the last paragraph), by some termed autoimmune polyendocrine syndromes. When you treat people with Addison’s you give them glucocorticoids, and this treatment can have deleterious effects on bone density especially in the long run – they note in the paper that exposure to corticosteroids is a significant fracture predictor in their models, which is not surprising. In one of the chapters included in Horowitz & Samson‘s book (again, I hope to cover it in more detail later…) the authors note that the combination of coeliac disease and diabetes may lead to protein malabsorption (among other things), which can obviously affect bone health, and they also observe e.g. that common lab abnormalities found in patients with coeliac include “low levels of haemoglobin, albumin, calcium, potassium, magnesium and iron” and furthermore that “extra-intestinal symptoms [include] muscle cramps, bone pain due to osteoporotic fractures or osteomalacia” – coeliac is obviously relevant here, especially as the condition is much more common in type 1 diabetics than in non-diabetics (“The prevalence of coeliac disease in type 1 diabetic children varies from 1.0% to 3.5%, which is at least 15 times higher than the prevalence among children without diabetes” – also an observation from H&S’s book, chapter 5).

Moving on…

“During the study period, incident fractures occurred in 2,615 participants (8.6%) with type 1 diabetes compared with 18,624 participants (6.1%) without diabetes. […] The incidence in males was greatest in the 10- to 20-year age bracket, at 297.2 and 261.3 fractures per 10,000 person-years in participants with and without type 1 diabetes, respectively. The fracture incidence in women was greatest in the 80- to 90-year age bracket, at 549.1 and 333.9 fractures per 10,000 person-years in participants with and without type 1 diabetes, respectively.”

It’s important to note that the first percentages reported above (8.6% vs 6.1%) may be slightly misleading as the follow-up periods for the two groups were dissimilar; type 1s in the study were on average followed for a shorter amount of time than were the controls (4.7 years vs 3.89 years), meaning that raw incident fracture risk estimates like these cannot be translated directly into person-year estimates. The risk differential is thus at least slightly higher than these percentages would suggest. A good view of how the person-year risk difference evolves as a function of age/time are displayed in the paper’s figure 2.

“Hip fractures alone comprised 5.5% and 11.6% of all fractures in males and females with type 1 diabetes, compared with 4.1% and 8.6% in males and females without diabetes (P = 0.04 for males and P = 0.001 for females). Participants with type 1 diabetes with a lower extremity fracture were more likely to have retinopathy (30% vs. 22.5%, P < 0.001) and neuropathy (5.4% vs. 2.9%, P = 0.001) compared with those with fractures at other sites. The median average HbA1c did not differ between the two groups.”

I’ll reiterate this because it’s important: They care about lower-extremity fractures because some of those kinds of fractures, especially hip fractures, have a really poor prognosis. It’s not that it’s annoying and you’ll need a cast; I’ve seen estimates suggesting that roughly one-third of diabetics who sustain a hip fracture die within a year; a prognosis like that is much worse than many cancers. A few relevant observations from Czernik and Fowlkes:

“Together, [studies conducted during the last 15 years on type 1 diabetics] demonstrate an unequivocally increased fracture risk at the hip [compared to non-diabetic controls], with most demonstrating a six to ninefold increase in relative risk. […] type I DM patients have hip fractures at a younger age on average, with a mean of 43 for women and 41 for men in one study. Almost 7 % of people with type I DM can be expected to have sustained a hip fracture by age 65 [7] […] Patients with DM and hip fracture are at a higher risk of mortality than patients without DM, with 1-year rates as high as 32 % vs. 13 % of nondiabetic patients”.

Back to the paper:

“Incident hip fracture risk was increased in all age categories for female participants with type 1 diabetes, and in age categories >30 years in men. […] Type 1 diabetes remained significantly associated with fracture after adjustment for covariates in all previously significant sex and age strata, with the exception of women aged 40–49. […] Each 1% (11 mmol/mol) greater average HbA1c level was associated with a 5% greater risk of incident fracture in males and an 11% greater risk of fracture in females. Diabetic neuropathy was a significant risk factor for incident fracture in males (HR 1.33; 95% CI 1.03–1.72) and females (HR 1.52; 95% CI 1.19–1.92); however, diabetic retinopathy was significant only in males (HR 1.13; 95% CI 1.01–1.28). […] The presence of celiac disease was associated with an increased risk of fractures in females, with an HR of 1.8 (95% CI 1.18–2.76), but not in males. A higher BMI was protective against fracture. Smoking was a risk factor for fracture in males in the 13,763 participants with type 1 diabetes with smoking and BMI data available for analysis.”

The Hba1c-link was interesting to me because the relationships between glycemic control and fracture risk has in other contexts been somewhat unclear; one problem is that Hba1c levels in the lower ranges increase the risk of hypoglycemic episodes, and such episodes may increase the risk of fractures, so even if chronic hyperglycemia is bad for bone health if you don’t have access e.g. to event-level/-rate data on hypoglycemic episodes confounding may be an issue causing a (very plausible) chronic hyperglycemia-fracture risk link to perhaps be harder to detect than it otherwise might have been. It’s of note that these guys did not have access to data on hypoglycemic episodes. They observe later in the paper that: “If hypoglycemia was a major contributing factor, we might have expected a negative effect of HbA1c on fracture risk; our data indicated the opposite.” I don’t think you can throw out hypoglycemia as a contributing factor that easily.

Anyway, a few final observations from the paper:

CONCLUSIONS Type 1 diabetes was associated with increased risk of incident fracture that began in childhood and extended across the life span. Participants with type 1 diabetes sustained a disproportionately greater number of lower extremity fractures. These findings have important public health implications, given the increasing prevalence of type 1 diabetes and the morbidity and mortality associated with hip fractures.”

“To our knowledge, this is the first study to show that the increased fracture risk in type 1 diabetes begins in childhood. This finding has important implications for researchers planning future studies and for clinicians caring for patients in this population. Although peak bone mass is attained by the end of the third decade of life, peak bone accrual occurs in adolescence in conjunction with the pubertal growth spurt (31). This critical time for bone accrual may represent a period of increased skeletal vulnerability and also a window of opportunity for the implementation of therapies to improve bone formation (32). This is an especially important consideration in the population with type 1 diabetes, because the incidence of this disease peaks in early adolescence. Three-quarters of individuals will develop the condition before 18 years of age, and therefore before attainment of peak bone mass (33). The development and evaluation of therapies aimed at increasing bone formation and strength in adolescence may lead to a lifelong reduction in fracture risk.”

“The underlying mechanism for the increased fracture risk in patients with type 1 diabetes is not fully understood. Current evidence suggests that bone quantity and quality may both be abnormal in this condition. Clinical studies using dual-energy X-ray absorptiometry and peripheral quantitative computed tomography have identified mild to modest deficits in BMD and bone structure in both pediatric and adult participants with type 1 diabetes (6,8,34). Deficits in BMD are unlikely to be the only factor contributing to skeletal fragility in type 1 diabetes, however, as evidenced by a recent meta-analysis that found that the increased fracture risk seen in type 1 diabetes could not be explained by deficits in BMD alone (16). Recent cellular and animal models have shown that insulin signaling in osteoblasts and osteoblast progenitor cells promotes postnatal bone acquisition, suggesting that the insulin deficiency inherent in type 1 diabetes is a significant contributor to the pathogenesis of skeletal disease (35). Other proposed mechanisms contributing to skeletal fragility in type 1 diabetes include chronic hyperglycemia (36), impaired production of IGF-1 (37), and the accumulation of advanced glycation end products in bone (38). Our results showed that a higher average HbA1c was associated with an increased risk of fracture in participants with type 1 diabetes, supporting the hypothesis that chronic hyperglycemia and its sequelae contribute to skeletal fragility.”

“In summary, our study found that participants of all ages with type 1 diabetes were at increased risk of fracture. The adverse effect of type 1 diabetes on the skeleton is an underrecognized complication that is likely to grow into a significant public health burden given the increasing incidence and prevalence of this disease. […] Our novel finding that children with type 1 diabetes were already at increased risk of fracture suggests that therapeutic interventions aimed at children and adolescents may have an important effect on reducing lifelong fracture risk.”

August 15, 2017 Posted by | Diabetes, Epidemiology, Medicine, Studies | Leave a comment

Depression and Heart Disease (II)

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

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

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

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

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

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

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

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

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

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

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

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

Depression and Heart Disease (I)

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

Below I’ve added some quotes from the book.

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

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

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

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

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

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

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

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

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

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

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

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

 

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

Infectious Disease Surveillance (II)

Some more observation from the book below.

“There are three types of influenza viruses — A, B, and C — of which only types A and B cause widespread outbreaks in humans. Influenza A viruses are classified into subtypes based on antigenic differences between their two surface glycoproteins, hemagglutinin and neuraminidase. Seventeen hemagglutinin subtypes (H1–H17) and nine neuraminidase subtypes (N1–N9) have been identifed. […] The internationally accepted naming convention for influenza viruses contains the following elements: the type (e.g., A, B, C), geographical origin (e.g., Perth, Victoria), strain number (e.g., 361), year of isolation (e.g., 2011), for influenza A the hemagglutinin and neuraminidase antigen description (e.g., H1N1), and for nonhuman origin viruses the host of origin (e.g., swine) [4].”

“Only two antiviral drug classes are licensed for chemoprophylaxis and treatment of influenza—the adamantanes (amantadine and rimantadine) and the neuraminidase inhibitors (oseltamivir and zanamivir). […] Antiviral resistant strains arise through selection pressure in individual patients during treatment [which can lead to treatment failure]. […] they usually do not transmit further (because of impaired virus fitness) and have limited public health implications. On the other hand, primarily resistant viruses have emerged in the past decade and in some cases have completely replaced the susceptible strains. […] Surveillance of severe influenza illness is challenging because most cases remain undiagnosed. […] In addition, most of the influenza burden on the healthcare system is because of complications such as secondary bacterial infections and exacerbations of pre-existing chronic diseases, and often influenza is not suspected as an underlying cause. Even if suspected, the virus could have been already cleared from the respiratory secretions when the testing is performed, making diagnostic confirmation impossible. […] Only a small proportion of all deaths caused by influenza are classified as influenza-related on death certificates. […] mortality surveillance based only on death certificates is not useful for the rapid assessment of an influenza epidemic or pandemic severity. Detection of excess mortality in real time can be done by establishing specific monitoring systems that overcome these delays [such as sentinel surveillance systems, US].”

“Influenza vaccination programs are extremely complex and costly. More than half a billion doses of influenza vaccines are produced annually in two separate vaccine production cycles, one for the Northern Hemisphere and one for the Southern Hemisphere [54]. Because the influenza virus evolves constantly and vaccines are reformulated yearly, both vaccine effectiveness and safety need to be monitored routinely. Vaccination campaigns are also organized annually and require continuous public health efforts to maintain an acceptable level of vaccination coverage in the targeted population. […] huge efforts are made and resources spent to produce and distribute influenza vaccines annually. Despite these efforts, vaccination coverage among those at risk in many parts of the world remains low.”

“The Active Bacterial Core surveillance (ABCs) network and its predecessor have been examples of using surveillance as information for action for over 20 years. ABCs has been used to measure disease burden, to provide data for vaccine composition and recommended-use policies, and to monitor the impact of interventions. […] sites represent wide geographic diversity and approximately reflect the race and urban-to-rural mix of the U.S. population [37]. Currently, the population under surveillance is 19–42 million and varies by pathogen and project. […] ABCs has continuously evolved to address challenging questions posed by the six pathogens (H. influenzae; GAS [Group A Streptococcus], GBS [Group B Streptococcus], S.  pneumoniae, N. meningitidis, and MRSA) and other emerging infections. […] For the six core pathogens, the objectives are (1) to determine the incidence and epidemiologic characteristics of invasive disease in geographically diverse populations in the United States through active, laboratory, and population-based surveillance; (2) to determine molecular epidemiologic patterns and microbiologic characteristics of isolates collected as part of routine surveillance in order to track antimicrobial resistance; (3) to detect the emergence of new strains with new resistance patterns and/or virulence and contribute to development and evaluation of new vaccines; and (4) to provide an infrastructure for surveillance of other emerging pathogens and for conducting studies aimed at identifying risk factors for disease and evaluating prevention policies.”

“Food may become contaminated by over 250 bacterial, viral, and parasitic pathogens. Many of these agents cause diarrhea and vomiting, but there is no single clinical syndrome common to all foodborne diseases. Most of these agents can also be transmitted by nonfoodborne routes, including contact with animals or contaminated water. Therefore, for a given illness, it is often unclear whether the source of infection is foodborne or not. […] Surveillance systems for foodborne diseases provide extremely important information for prevention and control.”

“Since 1995, the Centers for Disease Control and Prevention (CDC) has routinely used an automated statistical outbreak detection algorithm that compares current reports of each Salmonella serotype with the preceding 5-year mean number of cases for the same geographic area and week of the year to look for unusual clusters of infection [5]. The sensitivity of Salmonella serotyping to detect outbreaks is greatest for rare serotypes, because a small increase is more noticeable against a rare background. The utility of serotyping has led to its widespread adoption in surveillance for food pathogens in many countries around the world [6]. […] Today, a new generation of subtyping methods […] is increasing the specificity of laboratory-based surveillance and its power to detect outbreaks […] Molecular subtyping allows comparison of the molecular “fingerprint” of bacterial strains. In the United States, the CDC coordinates a network called PulseNet that captures data from standardized molecular subtyping by PFGE [pulsed field gel electrophoresis]. By comparing new submissions and past data, public health officials can rapidly identify geographically dispersed clusters of disease that would otherwise not be apparent and evaluate them as possible foodborne-disease outbreaks [8]. The ability to identify geographically dispersed outbreaks has become increasingly important as more foods are mass-produced and widely distributed. […] Similar networks have been developed in Canada, Europe, the Asia Pacifc region, Latin America and the Caribbean region, the Middle Eastern region and, most recently, the African region”.

“Food consumption and practices have changed during the past 20 years in the United States, resulting in a shift from readily detectable, point-source outbreaks (e.g., attendance at a wedding dinner), to widespread outbreaks that occur over many communities with only a few illnesses in each community. One of the changes has been establishment of large food-producing facilities that disseminate products throughout the country. If a food product is contaminated with a low level of pathogen, contaminated food products are distributed across many states; and only a few illnesses may occur in each community. This type of outbreak is often difficult to detect. PulseNet has been critical for the detection of widely dispersed outbreaks in the United States [17]. […] The growth of the PulseNet database […] and the use of increasingly sophisticated epidemiological approaches have led to a dramatic increase in the number of multistate outbreaks detected and investigated.”

“Each year, approximately 35 million people are hospitalized in the United States, accounting for 170 million inpatient days [1,2]. There are no recent estimates of the numbers of healthcare-associated infections (HAI). However, two decades ago, HAI were estimated to affect more than 2 million hospital patients annually […] The mortality attributed to these HAI was estimated at about 100,000 deaths annually. […] Almost 85% of HAI in the United States are associated with bacterial pathogens, and 33% are thought to be preventable [4]. […] The primary purpose of surveillance [in the context of HAI] is to alert clinicians, epidemiologists, and laboratories of the need for targeted prevention activities required to reduce HAI rates. HAI surveillance data help to establish baseline rates that may be used to determine the potential need to change public health policy, to act and intervene in clinical settings, and to assess the effectiveness of microbiology methods, appropriateness of tests, and allocation of resources. […] As less than 10% of HAI in the United States occur as recognized epidemics [18], HAI surveillance should not be embarked on merely for the detection of outbreaks.”

“There are two types of rate comparisons — intrahospital and interhospital. The primary goals of intrahospital comparison are to identify areas within the hospital where HAI are more likely to occur and to measure the efficacy of interventional efforts. […] Without external comparisons, hospital infection control departments may [however] not know if the endemic rates in their respective facilities are relatively high or where to focus the limited fnancial and human resources of the infection control program. […] The CDC has been the central aggregating institution for active HAI surveillance in the United States since the 1960s.”

“Low sensitivity (i.e., missed infections) in a surveillance system is usually more common than low specificity (i.e., patients reported to have infections who did not actually have infections).”

“Among the numerous analyses of CDC hospital data carried out over the years, characteristics consistently found to be associated with higher HAI rates include affiliation with a medical school (i.e., teaching vs. nonteaching), size of the hospital and ICU categorized by the number of beds (large hospitals and larger ICUs generally had higher infection rates), type of control or ownership of the hospital (municipal, nonprofit, investor owned), and region of the country [43,44]. […] Various analyses of SENIC and NNIS/NHSN data have shown that differences in patient risk factors are largely responsible for interhospital differences in HAI rates. After controlling for patients’ risk factors, average lengths of stay, and measures of the completeness of diagnostic workups for infection (e.g., culturing rates), the differences in the average HAI rates of the various hospital groups virtually disappeared. […] For all of these reasons, an overall HAI rate, per se, gives little insight into whether the facility’s infection control efforts are effective.”

“Although a hospital’s surveillance system might aggregate accurate data and generate appropriate risk-adjusted HAI rates for both internal and external comparison, comparison may be misleading for several reasons. First, the rates may not adjust for patients’ unmeasured intrinsic risks for infection, which vary from hospital to hospital. […] Second, if surveillance techniques are not uniform among hospitals or are used inconsistently over time, variations will occur in sensitivity and specificity for HAI case finding. Third, the sample size […] must be sufficient. This issue is of concern for hospitals with fewer than 200 beds, which represent about 10% of hospital admissions in the United States. In most CDC analyses, rates from hospitals with very small denominators tend to be excluded [37,46,49]. […] Although many healthcare facilities around the country aggregate HAI surveillance data for baseline establishment and interhospital comparison, the comparison of HAI rates is complex, and the value of the aggregated data must be balanced against the burden of their collection. […] If a hospital does not devote sufficient resources to data collection, the data will be of limited value, because they will be replete with inaccuracies. No national database has successfully dealt with all the problems in collecting HAI data and each varies in its ability to address these problems. […] While comparative data can be useful as a tool for the prevention of HAI, in some instances no data might be better than bad data.”

August 10, 2017 Posted by | Books, Data, Epidemiology, Infectious disease, Medicine, Statistics | Leave a comment

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

Infectious Disease Surveillance (I)

Concepts and Methods in Infectious Disease Surveillance […] familiarizes the reader with basic surveillance concepts; the legal basis for surveillance in the United States and abroad; and the purposes, structures, and intended uses of surveillance at the local, state, national, and international level. […] A desire for a readily accessible, concise resource that detailed current methods and challenges in disease surveillance inspired the collaborations that resulted in this volume. […] The book covers major topics at an introductory-to-intermediate level and was designed to serve as a resource or class text for instructors. It can be used in graduate level courses in public health, human and veterinary medicine, as well as in undergraduate programs in public health–oriented disciplines. We hope that the book will be a useful primer for frontline public health practitioners, hospital epidemiologists, infection-control practitioners, laboratorians in public health settings, infectious disease researchers, and medical informatics specialists interested in a concise overview of infectious disease surveillance.”

I thought the book was sort of okay, but not really all that great. I assume part of the reason I didn’t like it as much as I might have is that someone like me don’t really need to know all the details about, say, the issues encountered in Florida while they were trying to implement electronic patient records, or whether or not the mandated reporting requirements for brucellosis in, say, Texas are different from those of, say, Florida – but the book has a lot of that kind of information. Useful knowledge if you work with this stuff, but if you don’t and you’re just curious about the topic ‘in a general way’ those kinds of details can subtract a bit from the experience. A lot of chapters cover similar topics and don’t seem all that well coordinated, in the sense that details which could easily have been left out of specific chapters without any significant information loss (because those details were covered elsewhere in the publication) are included anyway; we are probably told at least ten times what is the difference between active and passive surveillance. It probably means that the various chapters can be read more or less independently (you don’t need to read chapter 5 to understand the coverage in chapter 11), but if you’re reading the book from cover to cover the way I was that sort of approach is not ideal. However in terms of the coverage included in the individual chapters and the content in general, I feel reasonably confident that if you’re actually working in public health or related fields and so a lot of this stuff might be ‘work-relevant’ (especially if you’re from the US), it’s probably a very useful book to keep around/know about. I didn’t need to know how many ‘NBS-states’ there are, and whether or not South Carolina is such a state, but some people might.

As I’ve pointed out before, a two star goodreads rating on my part (which is the rating I gave this publication) is not an indication that I think a book is terrible, it’s an indication that the book is ‘okay’.

Below I’ve added some quotes and observations from the book. The book is an academic publication but it is not a ‘classic textbook’ with key items in bold etc.; I decided to use bold to highlight key concepts and observations below, to make the post easier to navigate later on (none of the bolded words below were in bold in the original text), but aside from that I have made no changes to the quotes included in this post. I would note that given that many of the chapters included in the book are not covered by copyright (many chapters include this observation: “Materials appearing in this chapter are prepared by individuals as part of their official duties as United States government employees and are not covered by the copyright of the book, and any views expressed herein do not necessarily represent the views of the United States government.”) I may decide to cover the book in a bit more detail than I otherwise would have.

“The methods used for infectious disease surveillance depend on the type of disease. Part of the rationale for this is that there are fundamental differences in etiology, mode of transmission, and control measures between different types of infections. […] Despite the fact that much of surveillance is practiced on a disease-specific basis, it is worth remembering that surveillance is a general tool used across all types of infectious and, noninfectious conditions, and, as such, all surveillance methods share certain core elements. We advocate the view that surveillance should not be regarded as a public health “specialty,” but rather that all public health practitioners should understand the general principles underlying surveillance.”

“Control of disease spread is achieved through public health actions. Public health actions resulting from information gained during the investigation usually go beyond what an individual physician can provide to his or her patients presenting in a clinical setting. Examples of public health actions include identifying the source of infection […] identifying persons who were in contact with the index case or any infected person who may need vaccines or antiinfectives to prevent them from developing the infection; closure of facilities implicated in disease spread; or isolation of sick individuals or, in rare circumstances, quarantining those exposed to an infected person. […] Monitoring surveillance data enables public health authorities to detect sudden changes in disease occurrence and distribution, identify changes in agents or host factors, and detect changes in healthcare practices […] The primary use of surveillance data at the local and state public health level is to identify cases or outbreaks in order to implement immediate disease control and prevention activities. […] Surveillance data are also used by states and CDC to monitor disease trends, demonstrate the need for public health interventions such as vaccines and vaccine policy, evaluate public health activities, and identify future research priorities. […] The final and most-important link in the surveillance chain is the application of […] data to disease prevention and control. A surveillance system includes a functional capacity for data collection, analysis, and dissemination linked to public health programs [6].

“The majority of reportable disease surveillance is conducted through passive surveillance methods. Passive surveillance means that public health agencies inform healthcare providers and other entities of their reporting requirements, but they do not usually conduct intensive efforts to solicit all cases; instead, the public health agency waits for the healthcare entities to submit case reports. Because passive surveillance is often incomplete, public health agencies may use hospital discharge data, laboratory testing records, mortality data, or other sources of information as checks on completeness of reporting and to identify additional cases. This is called active surveillance. Active surveillance usually includes intensive activities on the part of the public health agency to identify all cases of a specific reportable disease or group of diseases. […] Because it can be very labor intensive, active surveillance is usually conducted for a subset of reportable conditions, in a defined geographic locale and for a defined period of time.”

“Active surveillance may be conducted on a routine basis or in response to an outbreak […]. When an outbreak is suspected or identified, another type of surveillance known as enhanced passive surveillance may also be initiated. In enhanced passive surveillance methods, public health may improve communication with the healthcare community, schools, daycare centers, and other facilities and request that all suspected cases be reported to public health. […] Case-based surveillance is supplemented through laboratory-based surveillance activities. As opposed to case-based surveillance, the focus is on laboratory results themselves, independent of whether or not an individual’s result is associated with a “case” of illness meeting the surveillance case definition. Laboratory-based surveillance is conducted by state public health laboratories as well as the healthcare community (e.g., hospital, private medical office, and commercial laboratories). […] State and local public health entities participate in sentinel surveillance activities. With sentinel methods, surveillance is conducted in a sample of reporting entities, such as healthcare providers or hospitals, or in a specific population known to be an early indicator of disease activity (e.g., pediatric). However, because the goal of sentinel surveillance is not to identify every case, it is not necessarily representative of the underlying population of interest; and results should be interpreted accordingly.”

Syndromic surveillance identifies unexpected changes in prediagnostic information from a variety of sources to detect potential outbreaks [56]. Sources include work- or school-absenteeism records, pharmacy sales for over-the-counter pharmaceuticals, or emergency room admission data [51]. During the 2009 H1N1 pandemic, syndromic surveillance of emergency room visits for influenza-like illness correlated well with laboratory diagnosed cases of influenza [57]. […] According to a 2008 survey of U.S. health departments, 88% of respondents reported that they employ syndromic-based approaches as part of routine surveillance [21].

“Public health operated for many decades (and still does to some extent) using stand-alone, case-based information systems for collection of surveillance data that do not allow information sharing between systems and do not permit the ability to track the occurrences of different diseases in a specific person over time. One of the primary objectives of NEDSS [National Electronic Disease Surveillance System] is to promote person-based surveillance and integrated and interoperable surveillance systems. In an integrated person-based system, information is collected to create a public health record for a given person for different diseases over time. This enables public health to track public health conditions associated with a person over time, allowing analyses of public health events and comorbidities, as well as more robust public health interventions. An interoperable system can exchange information with other systems. For example, data are shared between surveillance systems or between other public health or clinical systems, such as an electronic health record or outbreak management system. Achieving the goal of establishing a public health record for an individual over time does not require one monolithic system that supports all needs; this can, instead, be achieved through integration and/or interoperability of systems.

“For over a decade, public health has focused on automation of reporting of laboratory results to public health from clinical laboratories and healthcare providers. Paper-based submission of laboratory results to public health for reportable conditions results in delays in receipt of information, incomplete ascertainment of possible cases, and missing information on individual reports. All of these aspects are improved through automation of the process [39–43].”

“During the pre-vaccine era, rotavirus infected nearly every unvaccinated child before their fifth birthday. In the absence of vaccine, multiple rotavirus infections may occur during infancy and childhood. Rotavirus causes severe diarrhea and vomiting (acute gastroenteritis [AGE]), which can lead to dehydration, electrolyte depletion, complications of viremia, shock, and death. Nearly one-half million children around the world die of rotavirus infections each year […] [In the US] this virus was responsible for 40–50% of hospitalizations because of acute gastroenteritis during the winter months in the era before vaccines were introduced. […] Because first infections have been shown to induce strong immunity against severe rotavirus reinfections [3] and because vaccination mimics such first infections without causing illness, vaccination was identified as the optimal strategy for decreasing the burden associated with severe and fatal rotavirus diarrhea. Any changes that may be later attributed to vaccination effects require knowledge of the pre-licensure (i.e., baseline) rates and trends in the target disease as a reference […] Efforts to obtain baseline data are necessary before a vaccine is licensed and introduced [13]. […] After the first year of widespread rotavirus vaccination coverage in 2008, very large and consistent decreases in rotavirus hospitalizations were noted around the country. Many of the decreases in childhood hospitalizations resulting from rotavirus were 90% or more, compared with the pre-licensure, baseline period.”

There is no single perfect data source for assessing any VPD [Vaccine-Preventable Disease, US]. Meaningful surveillance is achieved by the much broader approach of employing diverse datasets. The true impact of a vaccine or the accurate assessment of disease trends in a population is more likely the result of evaluating many datasets having different strengths and weaknesses. Only by understanding these strengths and weaknesses can a public health practitioner give the appropriate consideration to the findings derived from these data. […] In a Phase III clinical trial, the vaccine is typically administered to large numbers of people who have met certain inclusionary and exclusionary criteria and are then randomly selected to receive either the vaccine or a placebo. […] Phase III trials represent the “best case scenario” of vaccine protection […] Once the Phase III trials show adequate protection and safety, the vaccine may be licensed by the FDA […] When the vaccine is used in routine clinical practice, Phase IV trials (called post-licensure studies or post-marketing studies) are initiated. These are the evaluations conducted during the course of VPD surveillance that delineate additional performance information in settings where strict controls on who receives the vaccine are not present. […] Often, measuring vaccine performance in the broader population yields slightly lower protective results compared to Phase III clinical trials […] During these post-licensure Phase IV studies, it is not the vaccine’s efficacy but its effectiveness that is assessed. […] Administrative datasets may be created by research institutions, managed-care organizations, or national healthcare utilization repositories. They are not specifically created for VPD surveillance and may contain coded data […] on health events. They often do not provide laboratory confirmation of specific diseases, unlike passive and active VPD surveillance. […] administrative datasets offer huge sample sizes, which allow for powerful inferences within the confines of any data limitations.”

August 6, 2017 Posted by | Books, Epidemiology, Infectious disease, Medicine, 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 New Classification System for Diabetes: Rationale and Implications of the β-Cell–Centric Classification Schema

When I started writing this post I intended to write a standard diabetes post covering a variety of different papers, but while I was covering one of the papers I intended to include in the post I realized that I felt like I had to cover that paper in a lot of detail, and I figured I might as well make a separate post about it. Here’s a link to the paper: The Time Is Right for a New Classification System for Diabetes: Rationale and Implications of the β-Cell–Centric Classification Schema.

I have frequently discussed the problem of how best to think about and -categorize the various disorders of glucose homeostasis which are currently lumped together into the various discrete diabetes categories, both online and offline, see e.g. the last few paragraphs of this recent post. I have frequently noted in such contexts that simplistic and very large ‘boxes’ like ‘type 1’ and ‘type 2’ leave out a lot of details, and that some of the details that are lost by employing such a categorization scheme might well be treatment-relevant in some contexts. Individualized medicine is however expensive, so I still consider it an open question to which extent valuable information – which is to say, information that could potentially be used cost-effectively in the treatment context – is lost on account of the current diagnostic practices, but information is certainly lost and treatment options potentially neglected. Relatedly, what’s not cost-effective today may well be tomorrow.

As I decided to devote an entire post to this paper, it is of course a must-read if you’re interested in these topics. I have quoted extensively from the paper below:

“The current classification system presents challenges to the diagnosis and treatment of patients with diabetes mellitus (DM), in part due to its conflicting and confounding definitions of type 1 DM, type 2 DM, and latent autoimmune diabetes of adults (LADA). The current schema also lacks a foundation that readily incorporates advances in our understanding of the disease and its treatment. For appropriate and coherent therapy, we propose an alternate classification system. The β-cell–centric classification of DM is a new approach that obviates the inherent and unintended confusions of the current system. The β-cell–centric model presupposes that all DM originates from a final common denominator — the abnormal pancreatic β-cell. It recognizes that interactions between genetically predisposed β-cells with a number of factors, including insulin resistance (IR), susceptibility to environmental influences, and immune dysregulation/inflammation, lead to the range of hyperglycemic phenotypes within the spectrum of DM. Individually or in concert, and often self-perpetuating, these factors contribute to β-cell stress, dysfunction, or loss through at least 11 distinct pathways. Available, yet underutilized, treatments provide rational choices for personalized therapies that target the individual mediating pathways of hyperglycemia at work in any given patient, without the risk of drug-related hypoglycemia or weight gain or imposing further burden on the β-cells.”

“The essential function of a classification system is as a navigation tool that helps direct research, evaluate outcomes, establish guidelines for best practices for prevention and care, and educate on all of the above. Diabetes mellitus (DM) subtypes as currently categorized, however, do not fit into our contemporary understanding of the phenotypes of diabetes (16). The inherent challenges of the current system, together with the limited knowledge that existed at the time of the crafting of the current system, yielded definitions for type 1 DM, type 2 DM, and latent autoimmune diabetes in adults (LADA) that are not distinct and are ambiguous and imprecise.”

“Discovery of the role played by autoimmunity in the pathogenesis of type 1 DM created the assumption that type 1 DM and type 2 DM possess unique etiologies, disease courses, and, consequently, treatment approaches. There exists, however, overlap among even the most “typical” patient cases. Patients presenting with otherwise classic insulin resistance (IR)-associated type 2 DM may display hallmarks of type 1 DM. Similarly, obesity-related IR may be observed in patients presenting with “textbook” type 1 DM (7). The late presentation of type 1 DM provides a particular challenge for the current classification system, in which this subtype of DM is generally termed LADA. Leading diabetes organizations have not arrived at a common definition for LADA (5). There has been little consensus as to whether this phenotype constitutes a form of type 2 DM with early or fast destruction of β-cells, a late manifestation of type 1 DM (8), or a distinct entity with its own genetic footprint (5). Indeed, current parameters are inadequate to clearly distinguish any of the subforms of DM (Fig. 1).

https://i2.wp.com/care.diabetesjournals.org/content/diacare/39/2/179/F1.medium.gif

The use of IR to define type 2 DM similarly needs consideration. The fact that many obese patients with IR do not develop DM indicates that IR is insufficient to cause type 2 DM without predisposing factors that affect β-cell function (9).”

“The current classification schema imposes unintended constraints on individualized medicine. Patients diagnosed with LADA who retain endogenous insulin production may receive “default” insulin therapy as treatment of choice. This decision is guided largely by the categorization of LADA within type 1 DM, despite the capacity for endogenous insulin production. Treatment options that do not pose the risks of hypoglycemia or weight gain might be both useful and preferable for LADA but are typically not considered beyond use in type 2 DM (10). […] We believe that there is little rationale for limiting choice of therapy solely on the current definitions of type 1 DM, type 2 DM, and LADA. We propose that choice of therapy should be based on the particular mediating pathway(s) of hyperglycemia present in each individual patient […] the issue is not “what is LADA” or any clinical presentation of DM under the current system. The issue is the mechanisms and rate of destruction of β-cells at work in all DM. We present a model that provides a more logical approach to classifying DM: the β-cell–centric classification of DM. In this schema, the abnormal β-cell is recognized as the primary defect in DM. The β-cell–centric classification system recognizes the interplay of genetics, IR, environmental factors, and inflammation/immune system on the function and mass of β-cells […]. Importantly, this model is universal for the characterization of DM. The β-cell–centric concept can be applied to DM arising in genetically predisposed β-cells, as well as in strongly genetic IR syndromes, such as the Rabson-Mendenhall syndrome (28), which may exhaust nongenetically predisposed β-cells. Finally, the β-cell–centric classification of all DM supports best practices in the management of DM by identifying mediating pathways of hyperglycemia that are operative in each patient and directing treatment to those specific dysfunctions.”

“A key premise is that the mediating pathways of hyperglycemia are common across prediabetes, type 1 DM, type 2 DM, and other currently defined forms of DM. Accordingly, we believe that the current antidiabetes armamentarium has broader applicability across the spectrum of DM than is currently utilized.

The ideal treatment paradigm would be one that uses the least number of agents possible to target the greatest number of mediating pathways of hyperglycemia operative in the given patient. It is prudent to use agents that will help patients reach target A1C levels without introducing drug-related hypoglycemia or weight gain. Despite the capacity of insulin therapy to manage glucotoxicity, there is a concern for β-cell damage due to IR that has been exacerbated by exogenous insulin-induced hyperinsulinemia and weight gain (41).”

“We propose that the β-cell–centric model is a conceptual framework that could help optimize processes of care for DM. A1C, fasting blood glucose, and postprandial glucose testing remain the basis of DM diagnosis and monitoring. Precision medicine in the treatment of DM could be realized by additional diagnostic testing that could include C-peptide (1), islet cell antibodies or other markers of inflammation (1,65), measures of IR, improved assays for β-cell mass, and markers of environmental damage and by the development of markers for the various mediating pathways of hyperglycemia.

We uphold that there is, and will increasingly be, a place for genotyping in DM standard of care. Pharmacogenomics could help direct patient-level care (6669) and holds the potential to spur on research through the development of DM gene banks for analyzing genetic distinctions between type 1 DM, LADA, type 2 DM, and maturity-onset diabetes of the young. The cost for genotyping has become increasingly affordable.”

“The ideal treatment regimens should not be potentially detrimental to the long-term integrity of the β-cells. Specifically, sulfonylureas and glinides should be ardently avoided. Any benefits associated with sulfonylureas and glinides (including low cost) are not enduring and are far outweighed by their attendant risks (and associated treatment costs) of hypoglycemia and weight gain, high rate of treatment failure and subsequent enhanced requirements for antihyperglycemic management, potential for β-cell exhaustion (42), increased risk of cardiovascular events (74), and potential for increased risk of mortality (75,76). Fortunately, there are a large number of classes now available that do not pose these risks.”

“Newer agents present alternatives to insulin therapy, including in patients with “advanced” type 2 DM with residual insulin production. Insulin therapy induces hypoglycemia, weight gain, and a range of adverse consequences of hyperinsulinemia with both short- and long-term outcomes (77–85). Newer antidiabetes classes may be used to delay insulin therapy in candidate patients with endogenous insulin production (19). […] When insulin therapy is needed, we suggest it be incorporated as add-on therapy rather than as substitution for noninsulin antidiabetes agents. Outcomes research is needed to fully evaluate various combination therapeutic approaches, as well as the potential of newer agents to address drivers of β-cell dysfunction and loss.

The principles of the β-cell–centric model provide a rationale for adjunctive therapy with noninsulin regimens in patients with type 1 DM (7,1216). Thiazolidinedione (TZD) therapy in patients with type 1 DM presenting with IR, for example, is appropriate and can be beneficial (17). Clinical trials in type 1 DM show that incretins (20) or SGLT-2 inhibitors (25,88) as adjunctive therapy to exogenous insulin appear to reduce plasma glucose variability.”

July 24, 2017 Posted by | Diabetes, Medicine, Papers | Leave a comment

Epilepsy Diagnosis & Treatment – 5 New Things Every Physician Should Know

Links to related stuff:
i. Sudden unexpected death in epilepsy (SUDEP).
ii. Status epilepticus.
iii. Epilepsy surgery.
iv. Temporal lobe epilepsy.
v. Lesional epilepsy surgery.
vi. Nonlesional neocortical epilepsy.
vii. A Randomized, Controlled Trial of Surgery for Temporal-Lobe Epilepsy.
viii. Stereoelectroencephalography.
ix. Accuracy of intracranial electrode placement for stereoencephalography: A systematic review and meta-analysis. (The results of the review is not discussed in the lecture, for obvious reasons – lecture is a few years old, this review is brand new – but seemed relevant to me.)
x. MRI-guided laser ablation in epilepsy treatment.
xi. Laser thermal therapy: real-time MRI-guided and computer-controlled procedures for metastatic brain tumors.
xii. Critical review of the responsive neurostimulator system for epilepsy (Again, not covered but relevant).
xiii. A Multicenter, Prospective Pilot Study of Gamma Knife Radiosurgery for Mesial Temporal Lobe Epilepsy: Seizure Response, Adverse Events, and Verbal Memory.
xiv. Gamma Knife radiosurgery for recurrent or residual seizures after anterior temporal lobectomy in mesial temporal lobe epilepsy patients with hippocampal sclerosis: long-term follow-up results of more than 4 years (Not covered but relevant).

July 19, 2017 Posted by | Lectures, Medicine, Neurology, Studies | Leave a comment

A few diabetes papers of interest

i. Long-Acting C-Peptide and Neuropathy in Type 1 Diabetes: A 12-Month Clinical Trial.

“Lack of C-peptide in type 1 diabetes may be an important contributing factor in the development of microvascular complications. Replacement of native C-peptide has been shown to exert a beneficial influence on peripheral nerve function in type 1 diabetes. The aim of this study was to evaluate the efficacy and safety of a long-acting C-peptide in subjects with type 1 diabetes and mild to moderate peripheral neuropathy. […] C-peptide, an integral component of the insulin biosynthesis, is the 31-amino acid peptide that makes up the connecting segment between the parts of the proinsulin molecule that become the A and B chains of insulin. It is split off from proinsulin and secreted together with insulin in equimolar amounts. Much new information on C-peptide physiology has appeared during the past 20 years […] Studies in animal models of diabetes and early clinical trials in patients with type 1 diabetes (T1DM) demonstrate that C-peptide in physiological replacement doses elicits beneficial effects on early stages of diabetes-induced functional and structural abnormalities of the peripheral nerves, the autonomic nervous system, and the kidneys (9). Even though much is still to be learned about C-peptide and its mechanism of action, the available evidence presents the picture of a bioactive peptide with therapeutic potential.”

“This was a multicenter, phase 2b, randomized, double-blind, placebo-controlled, parallel-group study. The study screened 756 subjects and enrolled 250 at 32 clinical sites in the U.S. (n = 23), Canada (n = 2), and Sweden (n = 7). […] A total of 250 patients with type 1 diabetes and peripheral neuropathy received long-acting (pegylated) C-peptide in weekly dosages […] for 52 weeks. […] Once-weekly subcutaneous administration of long-acting C-peptide for 52 weeks did not improve SNCV [sural nerve conduction velocity], other electrophysiological variables, or mTCNS [modified Toronto Clinical Neuropathy Score] but resulted in marked improvement of VPT [vibration perception threshold] compared with placebo. […] During the course of the 12-month study period, there were no significant changes in fasting blood glucose. Levels of HbA1c remained stable and varied within the treatment groups on average less than 0.1% (0.9 mmol/mol) between baseline and 52 weeks. […] There was a gradual lowering of VPT, indicating improvement in subjects receiving PEG–C-peptide […] after 52 weeks, subjects in the low-dose group had lowered their VPT by an average of 31% compared with baseline; the corresponding value for the high-dose group was 19%. […] The difference in VPT response between the dose groups did not attain statistical significance. In contrast to the SNCV results, VPT in the placebo group changed very little from baseline during the study […] The mTCNS, pain, and sexual function scores did not change significantly during the study nor did subgroup analysis involving the subjects most affected at baseline reveal significant differences between subjects treated with PEG–C-peptide or placebo subjects.”

“Evaluation of the safety population showed that PEG–C-peptide was well tolerated and that there was a low and similar incidence of treatment-related adverse events (11.3–16.4%) in all three treatment groups […] A striking finding in the current study is the observation of a progressive improvement in VPT during the 12-month treatment with PEG–C-peptide […], despite nonsignificant changes in SNCV. This finding may reflect differences in the mechanisms of conduction versus transduction of neural impulses. Changes in transduction reflect membrane receptor characteristics limited to the distal extreme of specific subtypes of sensory axons. In the case of vibration, the principal receptor is Pacinian corpuscles in the skin that are innervated by Aβ fibers. Transduction takes place uniquely at the distal extreme of the axon and is largely influenced by the integrity of this limited segment. Studies have documented that the initial effect of toxic neuropathy is a loss of the surface area of the pseudopod extensions of the distal axon within the Pacinian corpuscle and a consequent diminution of transduction (30). In contrast, changes in the speed of conduction are largely a function of factors that influence the elongated tract of the nerve, including the cross-sectional diameter of axons, the degree of myelination, and the integrity of ion clusters at the nodes of Ranvier (31). Thus, it is reasonable that some aspects of distal sensory function may be influenced by a treatment option that has little or no direct effect on nerve conduction velocity. The alternative is the unsupported belief that any intervention in the onset and progression of a sensory neuropathy must alter conduction velocity.

The marked VPT improvement observed in the current study, although associated with nonsignificant changes in SNCV, other electrophysiological variables, or mTCNS, can be interpreted as targeted improvement in a key aspect of sensory function (e.g., the conversion of mechanical energy to neural signals — transduction). […] Because progressive deficits in sensation are often considered the hallmark of diabetic polyneuropathy, the observed effects of C-peptide in the current study are an important finding.”

ii. Hyperbaric Oxygen Therapy Does Not Reduce Indications for Amputation in Patients With Diabetes With Nonhealing Ulcers of the Lower Limb: A Prospective, Double-Blind, Randomized Controlled Clinical Trial.

“Hyperbaric oxygen therapy (HBOT) is used for the treatment of chronic diabetic foot ulcers (DFUs). The controlled evidence for the efficacy of this treatment is limited. The goal of this study was to assess the efficacy of HBOT in reducing the need for major amputation and improving wound healing in patients with diabetes and chronic DFUs.”

“Patients with diabetes and foot lesions (Wagner grade 2–4) of at least 4 weeks’ duration participated in this study. In addition to comprehensive wound care, participants were randomly assigned to receive 30 daily sessions of 90 min of HBOT (breathing oxygen at 244 kPa) or sham (breathing air at 125 kPa). Patients, physicians, and researchers were blinded to group assignment. At 12 weeks postrandomization, the primary outcome was freedom from meeting the criteria for amputation as assessed by a vascular surgeon. Secondary outcomes were measures of wound healing. […] One hundred fifty-seven patients were assessed for eligibility, with 107 randomly assigned and 103 available for end point adjudication. Criteria for major amputation were met in 13 of 54 patients in the sham group and 11 of 49 in the HBOT group (odds ratio 0.91 [95% CI 0.37, 2.28], P = 0.846). Twelve (22%) patients in the sham group and 10 (20%) in the HBOT group were healed (0.90 [0.35, 2.31], P = 0.823).”

CONCLUSIONS HBOT does not offer an additional advantage to comprehensive wound care in reducing the indication for amputation or facilitating wound healing in patients with chronic DFUs.”

iii. Risk Factors Associated With Severe Hypoglycemia in Older Adults With Type 1 Diabetes.

“Older adults with type 1 diabetes (T1D) are a growing but underevaluated population (14). Of particular concern in this age group is severe hypoglycemia, which, in addition to producing altered mental status and sometimes seizures or loss of consciousness, can be associated with cardiac arrhythmias, falls leading to fractures, and in some cases, death (57). In Medicare beneficiaries with diabetes, hospitalizations related to hypoglycemia are now more frequent than those for hyperglycemia and are associated with high 1-year mortality (6). Emergency department visits due to hypoglycemia also are common (5). […] The T1D Exchange clinic registry reported a remarkably high frequency of severe hypoglycemia resulting in seizure or loss of consciousness in older adults with long-standing T1D (9). One or more such events during the prior year was reported by 1 in 5 of 211 participants ≥65 years of age with ≥40 years’ duration of diabetes (9).”

“Despite the high frequency of severe hypoglycemia in older adults with long-standing T1D, little information is available about the factors associated with its occurrence. We conducted a case-control study in adults ≥60 years of age with T1D of ≥20 years’ duration to assess potential contributory factors for the occurrence of severe hypoglycemia, including cognitive and functional measurements, social support, depression, hypoglycemia unawareness, various aspects of diabetes management, residual insulin secretion (as measured by C-peptide levels), frequency of biochemical hypoglycemia, and glycemic control and variability. […] A case-control study was conducted at 18 diabetes centers in the T1D Exchange Clinic Network. […] Case subjects (n = 101) had at least one severe hypoglycemic event in the prior 12 months. Control subjects (n = 100), frequency-matched to case subjects by age, had no severe hypoglycemia in the prior 3 years.”

RESULTS Glycated hemoglobin (mean 7.8% vs. 7.7%) and CGM-measured mean glucose (175 vs. 175 mg/dL) were similar between case and control subjects. More case than control subjects had hypoglycemia unawareness: only 11% of case subjects compared with 43% of control subjects reported always having symptoms associated with low blood glucose levels (P < 0.001). Case subjects had greater glucose variability than control subjects (P = 0.008) and experienced CGM glucose levels <60 mg/dL for ≥20 min on 46% of days compared with 33% of days in control subjects (P = 0.10). […] When defining high glucose variability as a coefficient of variation greater than the study cohort’s 75th percentile (0.481), 38% of case and 12% of control subjects had high glucose variability (P < 0.001).”

CONCLUSIONS In older adults with long-standing type 1 diabetes, greater hypoglycemia unawareness and glucose variability are associated with an increased risk of severe hypoglycemia.”

iv. Type 1 Diabetes and Polycystic Ovary Syndrome: Systematic Review and Meta-analysis.

“Even though PCOS is mainly an androgen excess disorder, insulin resistance and compensatory endogenous hyperinsulinemia, in close association with obesity and abdominal adiposity, are implicated in the pathogenesis of PCOS in many patients (3,4). In agreement, women with PCOS are at high risk for developing type 2 diabetes and gestational diabetes mellitus (3). […] Type 1 diabetes is a disease produced by an autoimmune injury to the endocrine pancreas that results in the abolition of endogenous insulin secretion. We hypothesized 15 years ago that PCOS could be associated with type 1 diabetes (8). The rationale was that women with type 1 diabetes needed supraphysiological doses of subcutaneous insulin to reach insulin concentrations at the portal level capable of suppressing hepatic glucose secretion, thus leading to exogenous systemic hyperinsulinism. Exogenous hyperinsulinism could then contribute to androgen excess in predisposed women, leading to PCOS as happens in insulin-resistance syndromes.

We subsequently published the first report of the association of PCOS with type 1 diabetes consisting of the finding of a threefold increase in the prevalence of this syndrome compared with that of women from the general population […]. Of note, even though this association was confirmed by all of the studies that addressed the issue thereafter (1016), with prevalences of PCOS as high as 40% in some series (10,16), this syndrome is seldom diagnosed and treated in women with type 1 diabetes.

With the aim of increasing awareness of the frequent association of PCOS with type 1 diabetes, we have conducted a systematic review and meta-analysis of the prevalence of PCOS and associated hyperandrogenic traits in adolescent and adult women with type 1 diabetes. […] Nine primary studies involving 475 adolescent or adult women with type 1 diabetes were included. The prevalences of PCOS and associated traits in women with type 1 diabetes were 24% (95% CI 15–34) for PCOS, 25% (95% CI 17–33) for hyperandrogenemia, 25% (95% CI 16–36) for hirsutism, 24% (95% CI 17–32) for menstrual dysfunction, and 33% (95% CI 24–44) for PCOM. These figures are considerably higher than those reported earlier in the general population without diabetes.”

CONCLUSIONS PCOS and its related traits are frequent findings in women with type 1 diabetes. PCOS may contribute to the subfertility of these women by a mechanism that does not directly depend on glycemic/metabolic control among other negative consequences for their health. Hence, screening for PCOS and androgen excess should be included in current guidelines for the management of type 1 diabetes in women.”

v. Impaired Awareness of Hypoglycemia in Adults With Type 1 Diabetes Is Not Associated With Autonomic Dysfunction or Peripheral Neuropathy.

“Impaired awareness of hypoglycemia (IAH), defined as a diminished ability to perceive the onset of hypoglycemia, is associated with an increased risk of severe hypoglycemia in people with insulin-treated diabetes (13). Elucidation of the pathogenesis of IAH may help to minimize the risk of severe hypoglycemia.

The glycemic thresholds for counterregulatory responses, generation of symptoms, and cognitive impairment are reset at lower levels of blood glucose in people who have developed IAH (4). This cerebral adaptation appears to be induced by recurrent exposure to hypoglycemia, and failure of cerebral autonomic mechanisms may be implicated in the pathogenesis (4). Awareness may be improved by avoidance of hypoglycemia (57), but this is very difficult to achieve and does not restore normal awareness of hypoglycemia (NAH) in all people with IAH. Because the prevalence of IAH in adults with type 1 diabetes increases with progressive disease duration (2,8,9), mechanisms that involve diabetic complications have been suggested to underlie the development of IAH.

Because activation of the autonomic nervous system is a fundamental physiological response to hypoglycemia and provokes many of the symptoms of hypoglycemia, autonomic neuropathy was considered to be a cause of IAH for many years (10). […] Studies of people with type 1 diabetes that have examined the glycemic thresholds for symptom generation in those with and without autonomic neuropathy (13,14,16) have [however] found no differences, and autonomic symptom generation was not delayed. […] The aim of the current study was […] to evaluate a putative association between IAH and the presence of autonomic neuropathy using composite Z (cZ) scores based on a battery of contemporary methods, including heart rate variability during paced breathing, the cardiovascular response to tilting and the Valsalva maneuver, and quantitative light reflex measurements by pupillometry.”

“Sixty-six adults with type 1 diabetes were studied, 33 with IAH and 33 with normal awareness of hypoglycemia (NAH), confirmed by formal testing. Participants were matched for age, sex, and diabetes duration. […] The [study showed] no difference in measures of autonomic function between adults with long-standing type 1 diabetes who had IAH, and carefully matched adults with type 1 diabetes with NAH. In addition, no differences between IAH and NAH participants were found with respect to the NCS [nerve conduction studies], thermal thresholds, and clinical pain or neuropathy scores. Neither autonomic dysfunction nor somatic neuropathy was associated with IAH. We consider that this study provides considerable value and novelty in view of the rigorous methodology that has been used. Potential confounding variables have been controlled for by the use of well-matched groups of participants, validated methods for classification of awareness, a large battery of neurophysiological tests, and a novel statistical approach to provide very high sensitivity for the detection of between-group differences.”

vi. Glucose Variability: Timing, Risk Analysis, and Relationship to Hypoglycemia in Diabetes.

“Glucose control, glucose variability (GV), and risk for hypoglycemia are intimately related, and it is now evident that GV is important in both the physiology and pathophysiology of diabetes. However, its quantitative assessment is complex because blood glucose (BG) fluctuations are characterized by both amplitude and timing. Additional numerical complications arise from the asymmetry of the BG scale. […] Our primary message is that diabetes control is all about optimization and balance between two key markers — frequency of hypoglycemia and HbA1c reflecting average BG and primarily driven by the extent of hyperglycemia. GV is a primary barrier to this optimization […] Thus, it is time to standardize GV measurement and thereby streamline the assessment of its two most important components — amplitude and timing.”

“Although reducing hyperglycemia and targeting HbA1c values of 7% or less result in decreased risk of micro- and macrovascular complications (14), the risk for hypoglycemia increases with tightening glycemic control (5,6). […] Thus, patients with diabetes face a lifelong optimization problem: reducing average glycemic levels and postprandial hyperglycemia while simultaneously avoiding hypoglycemia. A strategy for achieving such an optimization can only be successful if it reduces glucose variability (GV). This is because bringing average glycemia down is only possible if GV is constrained — otherwise blood glucose (BG) fluctuations would inevitably enter the range of hypoglycemia (9).”

“In health, glucose metabolism is tightly controlled by a hormonal network including the gut, liver, pancreas, and brain to ensure stable fasting BG levels and transient postprandial glucose fluctuations. In other words, BG fluctuations in type 1 diabetes result from the activity of a complex metabolic system perturbed by behavioral challenges. The frequency and extent of these challenges and the ability of the person’s system to absorb them determine the stability of glycemic control. The degree of system destabilization depends on each individual’s physiological parameters of glucose–insulin kinetics, including glucose appearance from food, insulin secretion, insulin sensitivity, and counterregulatory response.”

“There is strong evidence that feeding behavior is abnormal in both uncontrolled diabetes and hypoglycemia and that feeding signals within the brain and hormones affecting feeding, such as leptin and ghrelin, are implicated in diabetes (1214). Insulin secretion and action vary with the type and duration of diabetes. In type 1 diabetes, insulin secretion is virtually absent, which destroys the natural insulin–glucagon feedback loop and thereby diminishes the dampening effect of glucagon on hypoglycemia. In addition, insulin is typically administered subcutaneously, which adds delays to insulin action and thereby amplifies the amplitude of glucose fluctuations. […] impaired hypoglycemia counterregulation and increased GV in the hypoglycemic range are particularly relevant to type 1 diabetes: It has been shown that glucagon response is impaired (15), and epinephrine response is typically attenuated as well (16). Antecedent hypoglycemia shifts down BG thresholds for autonomic and cognitive responses, thereby further impairing both the hormonal defenses and the detection of hypoglycemia (17). Studies have established relationships between intensive therapy, hypoglycemia unawareness, and impaired counterregulation (16,1820) and concluded that recurrent hypoglycemia spirals into a “vicious cycle” known as hyperglycemia-associated autonomic failure (HAAF) (21). Our studies showed that increased GV and the extent and frequency of low BG are major contributors to hypoglycemia and that such changes are detectable by frequent BG measurement (2225).”

“The traditional statistical calculation of BG includes standard deviation (SD) (27), coefficient of variation (CV), or other metrics, such as the M-value introduced in 1965 (28), the mean amplitude of glucose excursions (MAGE) introduced in 1970 (29), the glycemic lability index (30), or the mean absolute glucose (MAG) change (31,32). […] the low BG index (LBGI), high BG index (HBGI), and average daily risk range (ADRR) […] are [all] based on a transformation of the BG measurement scale […], which aims to correct the substantial asymmetry of the BG measurement scale. Numerically, the hypoglycemic range (BG <70 mg/dL) is much narrower than that in the hyperglycemic range (BG >180 mg/dL) (34). As a result, whereas SD, CV, MAGE, and MAG are inherently biased toward hyperglycemia and have a relatively weak association with hypoglycemia, the LBGI and ADRR account well for the risk of hypoglycemic excursions. […] The analytical form of the scale transformation […] was based on accepted clinical assumptions, not on a particular data set, and was fixed 17 years ago, which made the approach extendable to any data set (34). On the basis of this transformation, we have developed our theory of risk analysis of BG data (35), defining a computational risk space that proved to be very suitable for quantifying the extent and frequency of glucose excursions. The utility of the risk analysis has been repeatedly confirmed (9,25,3638). We first introduced the LBGI and HBGI, which were specifically designed to be sensitive only to the low and high end of the BG scale, respectively, accounting for hypo- and hyperglycemia without overlap (24). Then in 2006, we introduced the ADRR, a measure of GV that is equally sensitive to hypo- and hyperglycemic excursions and is predictive of extreme BG fluctuations (38). Most recently, corrections were introduced that allowed the LBGI and HBGI to be computed from CGM data with results directly comparable to SMBG [self-monitoring of BG] (39).”

“[A]lthough GV has richer information content than just average glucose (HbA1c), its quantitative assessment is not straightforward because glucose fluctuations carry two components: amplitude and timing.

The standard assessment of GV is measuring amplitude. However, when measuring amplitude we should be mindful that deviations toward hypoglycemia are not equal to deviations toward hyperglycemia—a 20 mg/dL decline in BG levels from 70 to 50 mg/dL is clinically more important than a 20 mg/dL raise of BG from 160 to 180 mg/dL. We explained how to fix that with a well-established rescaling of the BG axis introduced more than 15 years ago (34). […] In addition, we should be mindful of the timing of BG fluctuations. There are a number of measures assessing GV amplitude from routine SMBG, but the timing of readings is frequently ignored even if the information is available (42). Yet, contrary to widespread belief, BG fluctuations are a process in time and the speed of transition from one BG state to another is of clinical importance. With the availability of CGM, the assessment of GV timing became not only possible but also required (32). Responding to this necessity, we should keep in mind that the assessment of temporal characteristics of GV benefits from mathematical computations that go beyond basic arithmetic. Thus, some assistance from the theory and practice of time series and dynamical systems analysis would be helpful. Fortunately, these fields are highly developed, theoretically and computationally, and have been used for decades in other areas of science […] The computational methods are standardized and available in a number of software products and should be used for the assessment of GV. […] There is no doubt that the timing of glucose fluctuations is clinically important, but there is a price to pay for its accurate assessment—a bit higher level of mathematical complexity. This, however, should not be a deterrent.”

vii. Predictors of Increased Carotid Intima-Media Thickness in Youth With Type 1 Diabetes: The SEARCH CVD Study.

“Adults with childhood-onset type 1 diabetes are at increased risk for premature cardiovascular disease (CVD) morbidity and mortality compared with the general population (1). The antecedents of CVD begin in childhood (2), and early or preclinical atherosclerosis can be detected as intima-media thickening in the artery wall (3). Carotid intima-media thickness (IMT) is an established marker of atherosclerosis because of its associations with CVD risk factors (4,5) and CVD outcomes, such as myocardial infarction and stroke in adults (6,7).

Prior work […] has shown that youth with type 1 diabetes have higher carotid IMT than control subjects (813). In cross-sectional studies, risk factors associated with higher carotid IMT include younger age at diabetes onset, male sex, adiposity, higher blood pressure (BP) and hemoglobin A1c (HbA1c), and lower vitamin C levels (8,9,11). Only one study has evaluated CVD risk factors longitudinally and the association with carotid IMT progression in youth with type 1 diabetes (14). In a German cohort of 70 youth with type 1 diabetes, Dalla Pozza et al. (14) demonstrated that CVD risk factors, including BMI z score (BMIz), systolic BP, and HbA1c, worsened over time. They also found that baseline HbA1c and baseline and follow-up systolic BP were significant predictors of change in carotid IMT over 4 years.”

“Before the current study, no published reports had assessed the impact of changes in CVD risk factors and carotid IMT in U.S. adolescents with type 1 diabetes. […] Participants in this study were enrolled in SEARCH CVD, an ancillary study to the SEARCH for Diabetes in Youth that was conducted in two of the five SEARCH centers (Colorado and Ohio). […] This report includes 298 youth who completed both baseline and follow-up SEARCH CVD visits […] At the initial visit, youth with type 1 diabetes were a mean age of 13.3 ± 2.9 years (range 7.6–21.3 years) and had an average disease duration of 3.6 ± 3.3 years. […] Follow-up data were obtained at a mean age of 19.2 ± 2.7 years, when the average duration of type 1 diabetes was 10.1 ± 3.9 years. […] In the current study, we show that older age (at baseline) and male sex were significantly associated with follow-up IMT. By using AUC measurements, we also show that a higher BMIz exposure over ∼5 years was significantly associated with IMT at follow-up. From baseline to follow-up, the mean BMI increased from within normal limits (21.1 ± 4.3 kg/m2) to overweight (25.1 ± 4.8 kg/m2), defined as a BMI ≥25 kg/m2 in adults (26,27). This large change in BMI may explain why BMIz was the only modifiable risk factor to be associated with follow-up IMT in the final models. Whether the observed increase in BMIz over time is part of the natural evolution of diabetes, aging in an obesogenic society, or a consequence of intensive insulin therapy is not known.”

“Data from the DCCT/EDIC cohorts have suggested nontraditional risk factors, including acute phase reactants, thrombolytic factors, cytokines/adipokines (34), oxidized LDL, and advanced glycation end products (30) may be important biomarkers of increased CVD risk in adults with type 1 diabetes. However, many of these nontraditional risk factors […] were not found to associate with IMT until 8–12 years after the DCCT ended, at the time when traditional CVD risk factors were also found to predict IMT. Collectively, these findings suggest that many traditional and nontraditional risk factors are not identified as relevant until later in the atherosclerotic process and highlight the critical need to better identify risk factors that may influence carotid IMT early in the course of type 1 diabetes because these may be important modifiable CVD risk factors of focus in the adolescent population. […] Although BMIz was the only identified risk factor to predict follow-up IMT at this age [in our study], it is possible that increases in dyslipidemia, BP, smoking, and HbA1c are related to carotid IMT but only after longer duration of exposure.”

July 13, 2017 Posted by | Cardiology, Diabetes, Medicine, Neurology, Studies | Leave a comment

A few SSC comments

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

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

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

Some more comments:

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

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

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

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

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

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

First comment:

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

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

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

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

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

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

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

“@Nybbler:

A few additional remarks.

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

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

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

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

A few other quotes from the comments:

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

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

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

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

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

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

Melanoma therapeutic strategies that select against resistance

A short lecture, but interesting:

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

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

A few diabetes papers of interest

i. An Inverse Relationship Between Age of Type 2 Diabetes Onset and Complication Risk and Mortality: The Impact of Youth-Onset Type 2 Diabetes.

“This study compared the prevalence of complications in 354 patients with T2DM diagnosed between 15 and 30 years of age (T2DM15–30) with that in a duration-matched cohort of 1,062 patients diagnosed between 40 and 50 years (T2DM40–50). It also examined standardized mortality ratios (SMRs) according to diabetes age of onset in 15,238 patients covering a wider age-of-onset range.”

“After matching for duration, despite their younger age, T2DM15–30 had more severe albuminuria (P = 0.004) and neuropathy scores (P = 0.003). T2DM15–30 were as commonly affected by metabolic syndrome factors as T2DM40–50 but less frequently treated for hypertension and dyslipidemia (P < 0.0001). An inverse relationship between age of diabetes onset and SMR was seen, which was the highest for T2DM15–30 (3.4 [95% CI 2.7–4.2]). SMR plots adjusting for duration show that for those with T2DM15–30, SMR is the highest at any chronological age, with a peak SMR of more than 6 in early midlife. In contrast, mortality for older-onset groups approximates that of the background population.”

“Young people with type 2 diabetes are likely to be obese, with a clustering of unfavorable cardiometabolic risk factors all present at a very early age (3,4). In adolescents with type 2 diabetes, a 10–30% prevalence of hypertension and an 18–54% prevalence of dyslipidemia have been found, much greater than would be expected in a population of comparable age (4).”

CONCLUSIONS The negative effect of diabetes on morbidity and mortality is greatest for those diagnosed at a young age compared with T2DM of usual onset.”

It’s important to keep base rates in mind when interpreting the reported SMRs, but either way this is interesting.

ii. Effects of Sleep Deprivation on Hypoglycemia-Induced Cognitive Impairment and Recovery in Adults With Type 1 Diabetes.

OBJECTIVE To ascertain whether hypoglycemia in association with sleep deprivation causes greater cognitive dysfunction than hypoglycemia alone and protracts cognitive recovery after normoglycemia is restored.”

CONCLUSIONS Hypoglycemia per se produced a significant decrement in cognitive function; coexisting sleep deprivation did not have an additive effect. However, after restoration of normoglycemia, preceding sleep deprivation was associated with persistence of hypoglycemic symptoms and greater and more prolonged cognitive dysfunction during the recovery period. […] In the current study of young adults with type 1 diabetes, the impairment of cognitive function that was associated with hypoglycemia was not exacerbated by sleep deprivation. […] One possible explanation is that hypoglycemia per se exerts a ceiling effect on the degree of cognitive dysfunction as is possible to demonstrate with conventional tests.”

iii. Intensive Diabetes Treatment and Cardiovascular Outcomes in Type 1 Diabetes: The DCCT/EDIC Study 30-Year Follow-up.

“The DCCT randomly assigned 1,441 patients with type 1 diabetes to intensive versus conventional therapy for a mean of 6.5 years, after which 93% were subsequently monitored during the observational Epidemiology of Diabetes Interventions and Complications (EDIC) study. Cardiovascular disease (nonfatal myocardial infarction and stroke, cardiovascular death, confirmed angina, congestive heart failure, and coronary artery revascularization) was adjudicated using standardized measures.”

“During 30 years of follow-up in DCCT and EDIC, 149 cardiovascular disease events occurred in 82 former intensive treatment group subjects versus 217 events in 102 former conventional treatment group subjects. Intensive therapy reduced the incidence of any cardiovascular disease by 30% (95% CI 7, 48; P = 0.016), and the incidence of major cardiovascular events (nonfatal myocardial infarction, stroke, or cardiovascular death) by 32% (95% CI −3, 56; P = 0.07). The lower HbA1c levels during the DCCT/EDIC statistically account for all of the observed treatment effect on cardiovascular disease risk.”

CONCLUSIONS Intensive diabetes therapy during the DCCT (6.5 years) has long-term beneficial effects on the incidence of cardiovascular disease in type 1 diabetes that persist for up to 30 years.”

I was of course immediately thinking that perhaps they had not considered if this might just be the result of the Hba1c differences achieved during the trial being maintained long-term (during follow-up), and so what they were doing was not as much measuring the effect of the ‘metabolic memory’ component as they were just measuring standard population outcome differences resulting from long-term Hba1c differences. But they (of course) had thought about that, and that’s not what’s going on here, which is what makes it particularly interesting:

“Mean HbA1c during the average 6.5 years of DCCT intensive therapy was ∼2% (20 mmol/mol) lower than that during conventional therapy (7.2 vs. 9.1% [55.6 vs. 75.9 mmol/mol], P < 0.001). Subsequently during EDIC, HbA1c differences between the treatment groups dissipated. At year 11 of EDIC follow-up and most recently at 19–20 years of EDIC follow-up, there was only a trivial difference between the original intensive and conventional treatment groups in the mean level of HbA1c

They do admittedly find a statistically significant difference between the Hba1cs of the two groups when you look at (weighted) Hba1cs long-term, but that difference is certainly nowhere near large enough to explain the clinical differences in outcomes you observe. Another argument in favour of the view that what’s driving these differences is metabolic memory is the observation that the difference in outcomes between the treatment and control groups are smaller now than they were ten years ago (my default would probably be to if anything expect the outcomes of the two groups to converge long-term if the samples were properly randomized to start with, but this is not the only plausible model and it sort of depends on how you model the risk function, as they also talk about in the paper):

“[T]he risk reduction of any CVD with intensive therapy through 2013 is now less than that reported previously through 2004 (30% [P = 0.016] vs. 47% [P = 0.005]), and likewise, the risk reduction per 10% lower mean HbA1c through 2013 was also somewhat lower than previously reported but still highly statistically significant (17% [P = 0.0001] vs. 20% [P = 0.001]).”

iv. Commonly Measured Clinical Variables Are Not Associated With Burden of Complications in Long-standing Type 1 Diabetes: Results From the Canadian Study of Longevity in Diabetes.

“The Canadian Study of Longevity in Diabetes actively recruited 325 individuals who had T1D for 50 or more years (5). Subjects completed a questionnaire, and recent laboratory tests and eye reports were provided by primary care physicians and eye specialists, respectively. […] The 325 participants were 65.5 ± 8.5 years old with diagnosis at age 10 years (interquartile range [IQR] 6.0, 16) and duration of 54.9 ± 6.4 years.”

“In univariable analyses, the following were significantly associated with a greater burden of complications: presence of hypertension, statin, aspirin and ACE inhibitor or ARB use, higher Problem Areas in Diabetes (PAID) and Geriatric Depression Scale (GDS) scores, and higher levels of triglycerides and HbA1c. The following were significantly associated with a lower burden of complications: current physical activity, higher quality of life, and higher HDL cholesterol.”

“In the multivariable analysis, a higher PAID score was associated with a greater burden of complications (risk ratio [RR] 1.15 [95% CI 1.06–1.25] for each 10-point-higher score). Aspirin and statin use were also associated with a greater burden of complications (RR 1.24 [95% CI 1.01–1.52] and RR 1.34 [95% CI 1.05–1.70], respectively) (Table 1), whereas HbA1c was not.”

“Our findings indicate that in individuals with long-standing T1D, burden of complications is largely not associated with historical characteristics or simple objective measurements, as associations with statistical significance likely reflect reverse causality. Notably, HbA1c was not associated with burden of complications […]. This further confirms that other unmeasured variables such as genetic, metabolic, or physiologic characteristics may best identify mechanisms and biomarkers of complications in long-standing T1D.”

v. Cardiovascular Risk Factor Targets and Cardiovascular Disease Event Risk in Diabetes: A Pooling Project of the Atherosclerosis Risk in Communities Study, Multi-Ethnic Study of Atherosclerosis, and Jackson Heart Study.

“Controlling cardiovascular disease (CVD) risk factors in diabetes mellitus (DM) reduces the number of CVD events, but the effects of multifactorial risk factor control are not well quantified. We examined whether being at targets for blood pressure (BP), LDL cholesterol (LDL-C), and glycated hemoglobin (HbA1c) together are associated with lower risks for CVD events in U.S. adults with DM. […] We studied 2,018 adults, 28–86 years of age with DM but without known CVD, from the Atherosclerosis Risk in Communities (ARIC) study, Multi-Ethnic Study of Atherosclerosis (MESA), and Jackson Heart Study (JHS). Cox regression examined coronary heart disease (CHD) and CVD events over a mean 11-year follow-up in those individuals at BP, LDL-C, and HbA1c target levels, and by the number of controlled risk factors.”

“Of 2,018 DM subjects (43% male, 55% African American), 41.8%, 32.1%, and 41.9% were at target levels for BP, LDL-C, and HbA1c, respectively; 41.1%, 26.5%, and 7.2% were at target levels for any one, two, or all three factors, respectively. Being at BP, LDL-C, or HbA1c target levels related to 17%, 33%, and 37% lower CVD risks and 17%, 41%, and 36% lower CHD risks, respectively (P < 0.05 to P < 0.0001, except for BP in CHD risk); those subjects with one, two, or all three risk factors at target levels (vs. none) had incrementally lower adjusted risks of CVD events of 36%, 52%, and 62%, respectively, and incrementally lower adjusted risks of CHD events of 41%, 56%, and 60%, respectively (P < 0.001 to P < 0.0001). Propensity score adjustment showed similar findings.”

“In our pooled analysis of subjects with DM in three large-scale U.S. prospective studies, the more factors among HbA1c, BP, and LDL-C that were at goal levels, the lower are the observed CHD and CVD risks (∼60% lower when all three factors were at goal levels compared with none). However, fewer than one-tenth of our subjects were at goal levels for all three factors. These findings underscore the value of achieving target or lower levels of these modifiable risk factors, especially in combination, among persons with DM for the future prevention of CHD and CVD events.”

In some studies you see very low proportions of patients reaching target variables because the targets are stupid (to be perfectly frank about it). The HbA1c target applied in this study was a level <53.0 mmol/mol (7%), which is definitely not crazy if the majority of the individuals included were type 2, which they almost certainly were. You can argue about the BP goal, but it’s obvious here that the authors are perfectly aware of the contentiousness of this variable.

It’s incidentally noteworthy – and the authors do take note of it, of course – that one of the primary results of this study (~60% lower risk when all risk factors reach the target goal), which includes a large proportion of African Americans in the study sample, is almost identical to the results of the Danish Steno-2 clinical trial, which included only Danish white patients (and the results of which I have discussed here on the blog before). In the Steno study, the result was “a 57% reduction in CVD death and a 59% reduction in CVD events.”

vi. Illness Identity in Adolescents and Emerging Adults With Type 1 Diabetes: Introducing the Illness Identity Questionnaire.

“The current study examined the utility of a new self-report questionnaire, the Illness Identity Questionnaire (IIQ), which assesses the concept of illness identity, or the degree to which type 1 diabetes is integrated into one’s identity. Four illness identity dimensions (engulfment, rejection, acceptance, and enrichment) were validated in adolescents and emerging adults with type 1 diabetes. Associations with psychological and diabetes-specific functioning were assessed.”

“A sample of 575 adolescents and emerging adults (14–25 years of age) with type 1 diabetes completed questionnaires on illness identity, psychological functioning, diabetes-related problems, and treatment adherence. Physicians were contacted to collect HbA1c values from patients’ medical records. Confirmatory factor analysis (CFA) was conducted to validate the IIQ. Path analysis with structural equation modeling was used to examine associations between illness identity and psychological and diabetes-specific functioning.”

“The first two identity dimensions, engulfment and rejection, capture a lack of illness integration, or the degree to which having diabetes is not well integrated as part of one’s sense of self. Engulfment refers to the degree to which diabetes dominates a person’s identity. Individuals completely define themselves in terms of their diabetes, which invades all domains of life (9). Rejection refers to the degree to which diabetes is rejected as part of one’s identity and is viewed as a threat or as unacceptable to the self. […] Acceptance refers to the degree to which individuals accept diabetes as a part of their identity, besides other social roles and identity assets. […] Enrichment refers to the degree to which having diabetes results in positive life changes, benefits one’s identity, and enables one to grow as a person (12). […] These changes can manifest themselves in different ways, including an increased appreciation for life, a change of life priorities, and a more positive view of the self (14).”

“Previous quantitative research assessing similar constructs has suggested that the degree to which individuals integrate their illness into their identity may affect psychological and diabetes-specific functioning in patients. Diabetes intruding upon all domains of life (similar to engulfment) [has been] related to more depressive symptoms and more diabetes-related problems […] In contrast, acceptance has been related to fewer depressive symptoms and diabetes-related problems and to better glycemic control (6,15). Similarly, benefit finding has been related to fewer depressive symptoms and better treatment adherence (16). […] The current study introduces the IIQ in individuals with type 1 diabetes as a way to assess all four illness identity dimensions.”

“The Cronbach α was 0.90 for engulfment, 0.84 for rejection, 0.85 for acceptance, and 0.90 for enrichment. […] CFA indicated that the IIQ has a clear factor structure, meaningfully differentiating four illness identity dimensions. Rejection was related to worse treatment adherence and higher HbA1c values. Engulfment was related to less adaptive psychological functioning and more diabetes-related problems. Acceptance was related to more adaptive psychological functioning, fewer diabetes-related problems, and better treatment adherence. Enrichment was related to more adaptive psychological functioning. […] the concept of illness identity may help to clarify why certain adolescents and emerging adults with diabetes show difficulties in daily functioning, whereas others succeed in managing developmental and diabetes-specific challenges.”

June 30, 2017 Posted by | Cardiology, Diabetes, Medicine, Psychology, Studies | Leave a comment

Neurology Grand Rounds – Typical and Atypical Diabetic Neuropathy

The lecture is not particularly easy to follow if you’re not a neurologist, and/but I assume even neurologists might have difficulties with Liewluck’s (? the second guy’s…) contribution because that guy’s English pronunciation is not great. But if you’re the sort of person who watches neurology lectures online it’s well worth watching.

Said noted in his book on these topics that: “In general pharmacological treatments will not cause anywhere near complete pain relief: “For patients receiving pharmacological treatment, the average pain reduction is about 20-30%, and only 20-35% of patients will achieve at least a 50% pain reduction with available drugs. […] often only partial pain relief from neuropathic pain can be expected, and […] sensory deficits are unlikely to respond to treatment.” Treatment of neuropathic pain is often a trial-and-error process.”

These guys make an even stronger point than Said did: Diabetics who develop painful neuropathies do not get rid of the pain even with treatment – the pain can be managed, but it’s permanent in (…almost? …a few young type 1 diabetics, maybe? But the 60-year old neurologist had never encountered one of those, so odds are against you being one of the lucky ones…) every single case. This of course has some consequences for how patients should be managed – for example you want to devote some time and effort to managing expectations, so people don’t get/have unrealistic ideas about what the treatments which are available may actually accomplish. Another aspect related to this is which sort of treatment options to consider in such a setting, as also noted in the lecture – tolerance development is for example an easily foreseeable problem with opiate treatment which is likely to cause problems down the line if not addressed (but as I pointed out a few years ago, my impression is that: “‘it may not work particularly well in the long run, and there are a lot of side-effects’ is a better argument against [chronic opioid treatment] than the potential for addiction”).

June 23, 2017 Posted by | Diabetes, Lectures, Medicine, Neurology, Pharmacology | Leave a comment

A few papers

i. To Conform or to Maintain Self-Consistency? Hikikomori Risk in Japan and the Deviation From Seeking Harmony.

A couple of data points and observations from the paper:

“There is an increasing number of youth in Japan who are dropping out of society and isolating themselves in their bedrooms from years to decades at a time. According to Japan’s Ministry of Health, Labor and Welfare’s first official 2003 guidelines on this culture-bound syndrome, hikikomori (social isolation syndrome) has the following specific diagnostic criteria: (1) no motivation to participate in school or work; (2) no signs of schizophrenia or any other known psychopathologies; and (3) persistence of social withdrawal for at least six months.”

“One obvious dilemma in studying hikikomori is that most of those suffering from hikikomori, by definition, do not seek treatment. More importantly, social isolation itself is not even a symptom of any of the DSM diagnosis often assigned to an individual afflicted with hikikomori […] The motivation for isolating oneself among a hikikomori is simply to avoid possible social interactions with others who might know or judge them (Zielenziger, 2006).”

“Saito’s (2010) and Sakai and colleagues’ (2011) data suggest that 10% to 15% of the hikikomori population suffer from an autism spectrum disorder. […] in the first epidemiological study conducted on hikikomori that was as close to a nation-wide random sample as possible, Koyama and colleagues (2010) conducted a face-to-face household survey, including a structured diagnostic interview, by randomly picking households and interviewing 4,134 individuals. They confirmed a hikikomori lifetime prevalence rate of 1.2% in their nationwide sample. Among these hikikomori individuals, the researchers found that only half suffered from a DSM-IV diagnosis. However, and more importantly, there was no particular diagnosis that was systematically associated with hikikomori. […] the researchers concluded that any DSM diagnosis was an epiphenomenon to hikikomori at best and that hikikomori is rather a “psychopathology characterized by impaired motivation” p. 72).”

ii. Does the ‘hikikomori’ syndrome of social withdrawal exist outside Japan?: A preliminary international investigation.

Purpose

To explore whether the ‘hikikomori’ syndrome (social withdrawal) described in Japan exists in other countries, and if so, how patients with the syndrome are diagnosed and treated.

Methods

Two hikikomori case vignettes were sent to psychiatrists in Australia, Bangladesh, India, Iran, Japan, Korea, Taiwan, Thailand and the USA. Participants rated the syndrome’s prevalence in their country, etiology, diagnosis, suicide risk, and treatment.

Results

Out of 247 responses to the questionnaire (123 from Japan and 124 from other countries), 239 were enrolled in the analysis. Respondents’ felt the hikikomori syndrome is seen in all countries examined and especially in urban areas. Biopsychosocial, cultural, and environmental factors were all listed as probable causes of hikikomori, and differences among countries were not significant. Japanese psychiatrists suggested treatment in outpatient wards and some did not think that psychiatric treatment is necessary. Psychiatrists in other countries opted for more active treatment such as hospitalization.

Conclusions

Patients with the hikikomori syndrome are perceived as occurring across a variety of cultures by psychiatrists in multiple countries. Our results provide a rational basis for study of the existence and epidemiology of hikikomori in clinical or community populations in international settings.”

“Our results extend rather than clarify the debate over diagnosis of hikikomori. In our survey, a variety of diagnoses, such as psychosis, depression anxiety and personality disorders, were proffered. Opinions as to whether hikikomori cases can be diagnosed using ICD-10/DSV-IV criteria differed depending on the participants’ countries and the cases’ age of onset. […] a recent epidemiological survey in Japan reported approximately a fifty-fifty split between hikikomori who had experienced a psychiatric disorder and had not [14]. These data and other studies that have not been able to diagnose all cases of hikikomori may suggest the existence of ‘primary hikikomori’ that is not an expression of any other psychiatric disorder [28,8,9,5,29]. In order to clarify differences between ‘primary hikikomori’ (social withdrawal not associated with any underlying psychiatric disorder) and ‘secondary hikikomori’ (social withdrawal caused by an established psychiatric disorder), further epidemiological and psychopathological studies are needed. […] Even if all hikikomori cases prove to be within some kind of psychiatric disorders, it is valuable to continue to focus on the hikikomori phenomenon because of its associated morbidity, similar to how suicidality is examined in various fields of psychiatry [30]. Reducing the burden of hikikomori symptoms, regardless of what psychiatric disorders patients may have, may provide a worthwhile improvement in their quality of life, and this suggests another direction of future hikikomori research.”

“Our case vignette survey indicates that the hikikomori syndrome, previously thought to exist only in Japan, is perceived by psychiatrists to exist in many other countries. It is particularly perceived as occurring in urban areas and might be associated with rapid global sociocultural changes. There is no consensus among psychiatrists within or across countries about the causes, diagnosis and therapeutic interventions for hikikomori yet.”

iii. Hikikomori: clinical and psychopathological issues (review). A poor paper, but it did have a little bit of data of interest:

“The prevalence of hikikomori is difficult to assess […]. In Japan, more than one million cases have been estimated by experts, but there is no population-based study to confirm these data (9). […] In 2008, Kiyota et al. summarized 3 population-based studies involving 12 cities and 3951 subjects, highlighting that a percentage comprised between 0.9% and 3.8% of the sample had an hikikomori history in anamnesis (11). The typical hikikomori patient is male (4:1 male-to-female ratio) […] females constitute a minor fraction of the reported cases, and usually their period of social isolation is limited.”

iv. Interpreting results of ethanol analysis in postmortem specimens: A review of the literature.

A few observations from the paper:

“A person’s blood-alcohol concentration (BAC) and state of inebriation at the time of death is not always easy to establish owing to various postmortem artifacts. The possibility of alcohol being produced in the body after death, e.g. via microbial contamination and fermentation is a recurring issue in routine casework. If ethanol remains unabsorbed in the stomach at the time of death, this raises the possibility of continued local diffusion into surrounding tissues and central blood after death. Skull trauma often renders a person unconscious for several hours before death, during which time the BAC continues to decrease owing to metabolism in the liver. Under these circumstances blood from an intracerebral or subdural clot is a useful specimen for determination of ethanol. Bodies recovered from water are particular problematic to deal with owing to possible dilution of body fluids, decomposition, and enhanced risk of microbial synthesis of ethanol. […] Alcoholics often die at home with zero or low BAC and nothing more remarkable at autopsy than a fatty liver. Increasing evidence suggests that such deaths might be caused by a pronounced ketoacidosis.”

“The concentrations of ethanol measured in blood drawn from different sampling sites tend to vary much more than expected from inherent variations in the analytical methods used [49]. Studies have shown that concentrations of ethanol and other drugs determined in heart blood are generally higher than in blood from a peripheral vein although in any individual case there are likely to be considerable variations [50–53].”

“The BAC necessary to cause death is often an open question and much depends on the person’s age, drinking experience and degree of tolerance development [78]. The speed of drinking plays a role in alcohol toxicity as does the kind of beverage consumed […] Drunkenness and hypothermia represent a dangerous combination and deaths tend to occur at a lower BAC when people are exposed to cold, such as, when an alcoholic sleeps outdoors in the winter months [78]. Drinking large amounts of alcohol to produce stupor and unconsciousness combined with positional asphyxia or inhalation of vomit are common causes of death in intoxicated individuals who die of suffocation [81–83]. The toxicity of ethanol is often considerably enhanced by the concomitant use of other drugs with their site of action in the brain, especially opiates, propoxyphene, antidepressants and some sedative hypnotics [84]. […] It seems reasonable to assume that the BAC at autopsy will almost always be lower than the maximum BAC reached during a drinking binge, owing to metabolism of ethanol taking place up until the moment of death [85–87]. During the time after discontinuation of drinking until death, the BAC might decrease appreciably depending on the speed of alcohol elimination from blood, which in heavy drinkers could exceed 20 or 30 mg/100 mL per h (0.02 or 0.03 g% per h) [88].”

“When the supply of oxygen to the body ends, the integrity of cell membranes and tissue compartments gradually disintegrate through the action of various digestive enzymes. This reflects the process of autolysis (self digestion) resulting in a softening and liquefaction of the tissue (freezing the body prevents autolysis). During this process, bacteria from the bowel invade the surrounding tissue and vascular system and the rate of infiltration depends on many factors including the ambient temperature, position of the body and whether death was caused by bacterial infection. Glucose concentrations increase in blood after death and this sugar is probably the simplest substrate for microbial synthesis of ethanol [20,68]. […] Extensive trauma to a body […] increases the potential for spread of bacteria and heightens the risk of ethanol production after death [217]. Blood-ethanol concentrations as high as 190 mg/100 mL have been reported in postmortem blood after particularly traumatic events such as explosions and when no evidence existed to support ingestion of ethanol before the disaster [218].”

v. Interventions based on the Theory of Mind cognitive model for autism spectrum disorder (ASD) (Cochrane review).

“The ‘Theory of Mind’ (ToM) model suggests that people with autism spectrum disorder (ASD) have a profound difficulty understanding the minds of other people – their emotions, feelings, beliefs, and thoughts. As an explanation for some of the characteristic social and communication behaviours of people with ASD, this model has had a significant influence on research and practice. It implies that successful interventions to teach ToM could, in turn, have far-reaching effects on behaviours and outcome.”

“Twenty-two randomised trials were included in the review (N = 695). Studies were highly variable in their country of origin, sample size, participant age, intervention delivery type, and outcome measures. Risk of bias was variable across categories. There were very few studies for which there was adequate blinding of participants and personnel, and some were also judged at high risk of bias in blinding of outcome assessors. There was also evidence of some bias in sequence generation and allocation concealment.”

“Studies were grouped into four main categories according to intervention target/primary outcome measure. These were: emotion recognition studies, joint attention and social communication studies, imitation studies, and studies teaching ToM itself. […] There was very low quality evidence of a positive effect on measures of communication based on individual results from three studies. There was low quality evidence from 11 studies reporting mixed results of interventions on measures of social interaction, very low quality evidence from four studies reporting mixed results on measures of general communication, and very low quality evidence from four studies reporting mixed results on measures of ToM ability. […] While there is some evidence that ToM, or a precursor skill, can be taught to people with ASD, there is little evidence of maintenance of that skill, generalisation to other settings, or developmental effects on related skills. Furthermore, inconsistency in findings and measurement means that evidence has been graded of ‘very low’ or ‘low’ quality and we cannot be confident that suggestions of positive effects will be sustained as high-quality evidence accumulates. Further longitudinal designs and larger samples are needed to help elucidate both the efficacy of ToM-linked interventions and the explanatory value of the ToM model itself.”

vi. Risk of Psychiatric and Neurodevelopmental Disorders Among Siblings of Probands With Autism Spectrum Disorders.

“The Finnish Prenatal Study of Autism and Autism Spectrum Disorders used a population-based cohort that included children born from January 1, 1987, to December 31, 2005, who received a diagnosis of ASD by December 31, 2007. Each case was individually matched to 4 control participants by sex and date and place of birth. […] Among the 3578 cases with ASD (2841 boys [79.4%]) and 11 775 controls (9345 boys [79.4%]), 1319 cases (36.9%) and 2052 controls (17.4%) had at least 1 sibling diagnosed with any psychiatric or neurodevelopmental disorder (adjusted RR, 2.5; 95% CI, 2.3-2.6).”

Conclusions and Relevance Psychiatric and neurodevelopmental disorders cluster among siblings of probands with ASD. For etiologic research, these findings provide further evidence that several psychiatric and neurodevelopmental disorders have common risk factors.”

vii. Treatment for epilepsy in pregnancy: neurodevelopmental outcomes in the child (Cochrane review).

“Accumulating evidence suggests an association between prenatal exposure to antiepileptic drugs (AEDs) and increased risk of both physical anomalies and neurodevelopmental impairment. Neurodevelopmental impairment is characterised by either a specific deficit or a constellation of deficits across cognitive, motor and social skills and can be transient or continuous into adulthood. It is of paramount importance that these potential risks are identified, minimised and communicated clearly to women with epilepsy.”

“Twenty-two prospective cohort studies were included and six registry based studies. Study quality varied. […] the IQ of children exposed to VPA [sodium valproate] (n = 112) was significantly lower than for those exposed to CBZ [carbamazepine] (n = 191) (MD [mean difference] 8.69, 95% CI 5.51 to 11.87, P < 0.00001). […] IQ was significantly lower for children exposed to VPA (n = 74) versus LTG [lamotrigine] (n = 84) (MD -10.80, 95% CI -14.42 to -7.17, P < 0.00001). DQ [developmental quotient] was higher in children exposed to PHT (n = 80) versus VPA (n = 108) (MD 7.04, 95% CI 0.44 to 13.65, P = 0.04). Similarly IQ was higher in children exposed to PHT (n = 45) versus VPA (n = 61) (MD 9.25, 95% CI 4.78 to 13.72, P < 0.0001). A dose effect for VPA was reported in six studies, with higher doses (800 to 1000 mg daily or above) associated with a poorer cognitive outcome in the child. We identified no convincing evidence of a dose effect for CBZ, PHT or LTG. Studies not included in the meta-analysis were reported narratively, the majority of which supported the findings of the meta-analyses.”

“The most important finding is the reduction in IQ in the VPA exposed group, which are sufficient to affect education and occupational outcomes in later life. However, for some women VPA is the most effective drug at controlling seizures. Informed treatment decisions require detailed counselling about these risks at treatment initiation and at pre-conceptual counselling. We have insufficient data about newer AEDs, some of which are commonly prescribed, and further research is required. Most women with epilepsy should continue their medication during pregnancy as uncontrolled seizures also carries a maternal risk.”

Do take note of the effect sizes reported here. To take an example, the difference between being treated with valproate and lamotrigine might equal 10 IQ points in the child – these are huge effects.

June 11, 2017 Posted by | Medicine, Neurology, Pharmacology, Psychiatry, Psychology, Studies | Leave a comment

Harnessing phenotypic heterogeneity to design better therapies

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

A few papers mentioned/discussed in the lecture:

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

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

A few papers

i. Quality of life of adolescents with autism spectrum disorders: comparison to adolescents with diabetes.

“The goals of our study were to clarify the consequences of autistic disorder without mental retardation on […] adolescents’ daily lives, and to consider them in comparison with the impact of a chronic somatic disease (diabetes) […] Scores for adolescents with ASD were significantly lower than those of the control and the diabetic adolescents, especially for friendships, leisure time, and affective and sexual relationships. On the other hand, better scores were obtained for the relationships with parents and teachers and for self-image. […] For subjects with autistic spectrum disorders and without mental retardation, impairment of quality of life is significant in adolescence and young adulthood. Such adolescents are dissatisfied with their relationships, although they often have real motivation to succeed with them.”

As someone who has both conditions, that paper was quite interesting. A follow-up question of some personal interest to me would of course be this: How do the scores/outcomes of these two groups compare to the scores of the people who have both conditions simultaneously? This question is likely almost impossible to answer in any confident manner, certainly if the conditions are not strongly dependent (unlikely), considering the power issues; global prevalence of autism is around 0.6% (link), and although type 1 prevalence is highly variable across countries, the variation just means that in some countries almost nobody gets it whereas in other countries it’s just rare; prevalence varies from 0.5 per 100.000 to 60 per 100.000 children aged 0-15 years. Assuming independence, if you look at combinations of the sort of conditions which affect one in a hundred people with those affecting one in a thousand, you’ll need on average in the order of 100.000 people to pick up just one individual with both of the conditions of interest. It’s bothersome to even try to find people like that, and good luck doing any sort of sensible statistics on that kind of sample. Of course type 1 diabetes prevalence increases with age in a way that autism does not because people continue to be diagnosed with it into late adulthood, whereas most autistics are diagnosed as children, so this makes the rarity of the condition less of a problem in adult samples, but if you’re looking at outcomes it’s arguable whether it makes sense to not differentiate between someone diagnosed with type 1 diabetes as a 35 year old and someone diagnosed as a 5 year old (are these really comparable diseases, and which outcomes are you interested in?). At least that is the case for developed societies where people with type 1 diabetes have high life expectancies; in less developed societies there may be stronger linkage between incidence and prevalence because of high mortality in the patient group (because people who get type 1 diabetes in such countries may not live very long because of inadequate medical care, which means there’s a smaller disconnect between how many new people get the disease during each time period and how many people in total have the disease than is the case for places where the mortality rates are lower). You always need to be careful about distinguishing between incidence and prevalence when dealing with conditions like T1DM with potential high mortality rates in settings where people have limited access to medical care because differential cross-country mortality patterns may be important.

ii. Exercise for depression (Cochrane review).

Background

Depression is a common and important cause of morbidity and mortality worldwide. Depression is commonly treated with antidepressants and/or psychological therapy, but some people may prefer alternative approaches such as exercise. There are a number of theoretical reasons why exercise may improve depression. This is an update of an earlier review first published in 2009.

Objectives

To determine the effectiveness of exercise in the treatment of depression in adults compared with no treatment or a comparator intervention. […]

Selection criteria 

Randomised controlled trials in which exercise (defined according to American College of Sports Medicine criteria) was compared to standard treatment, no treatment or a placebo treatment, pharmacological treatment, psychological treatment or other active treatment in adults (aged 18 and over) with depression, as defined by trial authors. We included cluster trials and those that randomised individuals. We excluded trials of postnatal depression.

Thirty-nine trials (2326 participants) fulfilled our inclusion criteria, of which 37 provided data for meta-analyses. There were multiple sources of bias in many of the trials; randomisation was adequately concealed in 14 studies, 15 used intention-to-treat analyses and 12 used blinded outcome assessors.For the 35 trials (1356 participants) comparing exercise with no treatment or a control intervention, the pooled SMD for the primary outcome of depression at the end of treatment was -0.62 (95% confidence interval (CI) -0.81 to -0.42), indicating a moderate clinical effect. There was moderate heterogeneity (I² = 63%).

When we included only the six trials (464 participants) with adequate allocation concealment, intention-to-treat analysis and blinded outcome assessment, the pooled SMD for this outcome was not statistically significant (-0.18, 95% CI -0.47 to 0.11). Pooled data from the eight trials (377 participants) providing long-term follow-up data on mood found a small effect in favour of exercise (SMD -0.33, 95% CI -0.63 to -0.03). […]

Authors’ conclusions

Exercise is moderately more effective than a control intervention for reducing symptoms of depression, but analysis of methodologically robust trials only shows a smaller effect in favour of exercise. When compared to psychological or pharmacological therapies, exercise appears to be no more effective, though this conclusion is based on a few small trials.”

iii. Risk factors for suicide in individuals with depression: A systematic review.

“The search strategy identified 3374 papers for potential inclusion. Of these, 155 were retrieved for a detailed evaluation. Thirty-two articles fulfilled the detailed eligibility criteria. […] Nineteen studies (28 publications) were included. Factors significantly associated with suicide were: male gender (OR = 1.76, 95% CI = 1.08–2.86), family history of psychiatric disorder (OR = 1.41, 95% CI= 1.00–1.97), previous attempted suicide (OR = 4.84, 95% CI = 3.26–7.20), more severe depression (OR = 2.20, 95% CI = 1.05–4.60), hopelessness (OR = 2.20, 95% CI = 1.49–3.23) and comorbid disorders, including anxiety (OR = 1.59, 95% CI = 1.03–2.45) and misuse of alcohol and drugs (OR = 2.17, 95% CI = 1.77–2.66).
Limitations: There were fewer studies than suspected. Interdependence between risk factors could not be examined.”

iv. Cognitive behaviour therapy for social anxiety in autism spectrum disorder: a systematic review.

“Individuals who have autism spectrum disorders (ASD) commonly experience anxiety about social interaction and social situations. Cognitive behaviour therapy (CBT) is a recommended treatment for social anxiety (SA) in the non-ASD population. Therapy typically comprises cognitive interventions, imagery-based work and for some individuals, behavioural interventions. Whether these are useful for the ASD population is unclear. Therefore, we undertook a systematic review to summarise research about CBT for SA in ASD.”

I mostly include this review here to highlight how reviews aren’t everything – I like them, but you can’t do reviews when a field hasn’t been studied. This is definitely the case here. The review was sort of funny, but also depressing. So much work for so little insight. Here’s the gist of it:

“Using a priori criteria, we searched for English-language peer-reviewed empirical studies in five databases. The search yielded 1364 results. Titles, abstracts and relevant publications were independently screened by two reviewers. Findings: Four single case studies met the review inclusion criteria; data were synthesised narratively. Participants (three adults and one child) were diagnosed with ASD and social anxiety disorder.”

You search the scientific literature systematically, you find more than a thousand results, and you carefully evaluate which ones of them should be included in this kind of study …and what you end up with is 4 individual case studies…

(I won’t go into the results of the study as they’re pretty much worthless.)

v. Immigrant Labor Market Integration across Admission Classes.

“We examine patterns of labor market integration across immigrant groups. The study draws on Norwegian longitudinal administrative data covering labor earnings and social insurance claims over a 25‐year period and presents a comprehensive picture of immigrant‐native employment and social insurance differentials by admission class and by years since entry.”

Some quotes from the paper:

“A recent study using 2011 administrative data from Sweden finds an average employment gap to natives of 30 percentage points for humanitarian migrants (refugees) and 26 percentage point for family immigrants (Luik et al., 2016).”

“A considerable fraction of the immigrants leaves the country after just a few years. […] this is particularly the case for immigrants from the old EU and for students and work-related immigrants from developing countries. For these groups, fewer than 50 percent remain in the country 5 years after entry. For refugees and family migrants, the picture is very different, and around 80 percent appear to have settled permanently in the country. Immigrants from the new EU have a settlement pattern somewhere in between, with approximately 70 percent settled on a permanent basis. An implication of such differential outmigration patterns is that the long-term labor market performance of refugees and family immigrants is of particular economic and fiscal importance. […] the varying rates of immigrant inflows and outflows by admission class, along with other demographic trends, have changed the composition of the adult (25‐66) population between 1990 and 2015. In this population segment, the overall immigrant share increased from 4.9 percent in 1990 to 18.7 percent in 2015 — an increase by a factor of 3.8 over 25 years. […] Following the 2004 EU enlargement, the fraction of immigrants in Norway has increased by a steady rate of approximately one percentage point per year.”

“The trends in population and employment shares varies considerably across admission classes, with employment shares of refugees and family immigrants lagging their growth in population shares. […] In 2014, refugees and family immigrants accounted for 12.8 percent of social insurance claims, compared to 5.7 percent of employment (and 7.7 percent of the adult population). In contrast, the two EU groups made up 9.3 percent of employment (and 8.8 percent of the adult population) but only 3.6 percent of social insurance claimants. Although these patterns do illuminate the immediate (short‐term) fiscal impacts of immigration at each particular point in time, they are heavily influenced by each year’s immigrant composition – in terms of age, years since migration, and admission classes – and therefore provide little information about long‐term consequences and impacts of fiscal sustainability. To assess the latter, we need to focus on longer‐term integration in the Norwegian labor market.”

Which they then proceed to do in the paper. From the results of those analyses:

“For immigrant men, the sample average share in employment (i.e., whose main source of income is work) ranges from 58 percent for refugees to 89 percent for EU immigrants, with family migrants somewhere between (around 80 percent). The average shares with social insurance as the main source of income ranges from only four percent for EU immigrants to as much as 38 percent for refugees. The corresponding shares for native men are 87 percent in employment and 12 percent with social insurance as their main income source. For women, the average shares in employment vary from 46 percent for refugees to 85 percent for new EU immigrants, whereas the average shares in social insurance vary from five percent for new EU immigrants to 42 percent for refugees. The corresponding rates for native women are 80 percent in employment and 17 percent with social insurance as their main source of income.”

“The profiles estimated for refugees are particularly striking. For men, we find that the native‐immigrant employment gap reaches its minimum value at 20 percentage points after five to six years of residence. The gap then starts to increase quite sharply again, and reaches 30 percentage points after 15 years. This development is mirrored by a corresponding increase in social insurance dependency. For female refugees, the employment differential reaches its minimum of 30 percentage points after 5‐9 years of residence. The subsequent decline is less dramatic than what we observe for men, but the differential stands at 35 percentage points 15 years after admission. […] The employment difference between refugees from Bosnia and Somalia is fully 22.2 percentage points for men and 37.7 points for women. […] For immigrants from the old EU, the employment differential is slightly in favor of immigrants regardless of years since migration, and the social insurance differentials remain consistently negative. In other words, employment of old EU immigrants is almost indistinguishable from that of natives, and they are less likely to claim social insurance benefits.”

vi. Glucose Peaks and the Risk of Dementia and 20-Year Cognitive Decline.

“Hemoglobin A1c (HbA1c), a measure of average blood glucose level, is associated with the risk of dementia and cognitive impairment. However, the role of glycemic variability or glucose excursions in this association is unclear. We examined the association of glucose peaks in midlife, as determined by the measurement of 1,5-anhydroglucitol (1,5-AG) level, with the risk of dementia and 20-year cognitive decline.”

“Nearly 13,000 participants from the Atherosclerosis Risk in Communities (ARIC) study were examined. […] Over a median time of 21 years, dementia developed in 1,105 participants. Among persons with diabetes, each 5 μg/mL decrease in 1,5-AG increased the estimated risk of dementia by 16% (hazard ratio 1.16, P = 0.032). For cognitive decline among participants with diabetes and HbA1c <7% (53 mmol/mol), those with glucose peaks had a 0.19 greater z score decline over 20 years (P = 0.162) compared with those without peaks. Among participants with diabetes and HbA1c ≥7% (53 mmol/mol), those with glucose peaks had a 0.38 greater z score decline compared with persons without glucose peaks (P < 0.001). We found no significant associations in persons without diabetes.

CONCLUSIONS Among participants with diabetes, glucose peaks are a risk factor for cognitive decline and dementia. Targeting glucose peaks, in addition to average glycemia, may be an important avenue for prevention.”

vii. Gaze direction detection in autism spectrum disorder.

“Detecting where our partners direct their gaze is an important aspect of social interaction. An atypical gaze processing has been reported in autism. However, it remains controversial whether children and adults with autism spectrum disorder interpret indirect gaze direction with typical accuracy. This study investigated whether the detection of gaze direction toward an object is less accurate in autism spectrum disorder. Individuals with autism spectrum disorder (n = 33) and intelligence quotients–matched and age-matched controls (n = 38) were asked to watch a series of synthetic faces looking at objects, and decide which of two objects was looked at. The angle formed by the two possible targets and the face varied following an adaptive procedure, in order to determine individual thresholds. We found that gaze direction detection was less accurate in autism spectrum disorder than in control participants. Our results suggest that the precision of gaze following may be one of the altered processes underlying social interaction difficulties in autism spectrum disorder.”

“Where people look at informs us about what they know, want, or attend to. Atypical or altered detection of gaze direction might thus lead to impoverished acquisition of social information and social interaction. Alternatively, it has been suggested that abnormal monitoring of inner states […], or the lack of social motivation […], would explain the reduced tendency to follow conspecific gaze in individuals with ASD. Either way, a lower tendency to look at the eyes and to follow the gaze would provide fewer opportunities to practice GDD [gaze direction detection – US] ability. Thus, impaired GDD might either play a causal role in atypical social interaction, or conversely be a consequence of it. Exploring GDD earlier in development might help disentangle this issue.”

June 1, 2017 Posted by | Diabetes, Economics, Epidemiology, Medicine, Neurology, Psychiatry, Psychology, 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