Econstudentlog

A few diabetes papers of interest

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

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

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

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

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

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

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

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

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

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

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

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

“Summary

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Today’s Landscape of Pharmaceutical Research in Cancer

It’s been a while since I watched this lecture so I don’t remember the details very well, but I usually add notes in my bookmarks when I watch lectures so that I know what details to include in my comments here on the blog, and I have added the details from the bookmark notes below.

It is a short lecture, the lecture itself lasts only roughly 30 minutes; it doesn’t really start until roughly the 9 minutes and 30 seconds mark, and it finishes around the 44 min mark (the rest is Q&A – I skipped some of the introduction, but watched the Q&A). The lecture is not very technical, I think the content is perfectly understandable also to people without a medical background. One data point from the lecture which I thought worth including in these comments is this: According to Sigal, “roughly 30 per cent of the biopharmaceutical industry’s portfolio … is focused on research in oncology.”

May 17, 2017 Posted by | Cancer/oncology, Health Economics, Immunology, Lectures, Medicine, Pharmacology | Leave a comment