Statin use and risk of developing diabetes: results from the Diabetes Prevention Program

Objective Several clinical trials of cardiovascular disease prevention with statins have reported increased risk of type 2 diabetes (T2DM) with statin therapy. However, participants in these studies were at relatively low risk for diabetes. Further, diabetes was often based on self-report and was not the primary outcome. It is unknown whether statins similarly modify diabetes risk in higher risk populations. Research design and methods During the Diabetes Prevention Program Outcomes Study (n=3234), the long-term follow-up to a randomized clinical trial of interventions to prevent T2DM, incident diabetes was assessed by annual 75 g oral glucose tolerance testing and semiannual fasting glucose. Lipid profile was measured annually, with statin treatment determined by a participant’s own physician outside of the protocol. Statin use was assessed at baseline and semiannual visits. Results At 10 years, the cumulative incidence of statin initiation prior to diabetes diagnosis was 33%–37% among the randomized treatment groups (p=0.36). Statin use was associated with greater diabetes risk irrespective of treatment group, with pooled HR (95% CI) for incident diabetes of 1.36 (1.17 to 1.58). This risk was not materially altered by adjustment for baseline diabetes risk factors and potential confounders related to indications for statin therapy. Conclusions In this population at high risk for diabetes, we observed significantly higher rates of diabetes with statin therapy in all three treatment groups. Confounding by indication for statin use does not appear to explain this relationship. The effect of statins to increase diabetes risk appears to extend to populations at high risk for diabetes. Trial registration number NCT00038727; Results

Lifestyle and metformin interventions have a durable effect to lower CRP and tPA levels in the diabetes prevention program except in those who develop diabetes.

OBJECTIVE: We evaluate whether lifestyle and metformin interventions used to prevent diabetes have durable effects on markers of inflammation and coagulation and whether the effects are influenced by the development of diabetes. RESEARCH DESIGN AND METHODS: The Diabetes Prevention Program was a controlled clinical trial of 3,234 subjects at high risk for diabetes who were randomized to lifestyle, metformin, or placebo interventions for 3.4 years. Diabetes was diagnosed semiannually by fasting glucose and annually by oral glucose tolerance testing. In addition to baseline testing, anthropometry was performed every 6 months; fasting insulin yearly; and hs-CRP, tissue plasminogen activator (tPA), and fibrinogen at 1 year and end of study (EOS). RESULTS: CRP and tPA levels were unchanged in the placebo group but fell in the lifestyle and metformin groups at 1 year and remained lower at EOS. These reductions were not seen in those who developed diabetes over the course of the study despite intervention. Fibrinogen was lower at 1 year in the lifestyle group. Differences in weight and weight change explained most of the influence of diabetes on the CRP response in the lifestyle group, but only partly in the placebo and metformin groups. Weight, insulin sensitivity, and hyperglycemia differences each accounted for the influence of diabetes on the tPA response. CONCLUSIONS: Lifestyle and metformin interventions have durable effects to lower hs-CRP and tPA. Incident diabetes prevented these improvements, and this was accounted for by differences in weight, insulin resistance, and glucose levels

Statin use and risk of developing diabetes: results from the Diabetes Prevention Program

Objective Several clinical trials of cardiovascular disease prevention with statins have reported increased risk of type 2 diabetes (T2DM) with statin therapy. However, participants in these studies were at relatively low risk for diabetes. Further, diabetes was often based on self-report and was not the primary outcome. It is unknown whether statins similarly modify diabetes risk in higher risk populations. Research design and methods During the Diabetes Prevention Program Outcomes Study (n=3234), the long-term follow-up to a randomized clinical trial of interventions to prevent T2DM, incident diabetes was assessed by annual 75 g oral glucose tolerance testing and semiannual fasting glucose. Lipid profile was measured annually, with statin treatment determined by a participant’s own physician outside of the protocol. Statin use was assessed at baseline and semiannual visits. Results At 10 years, the cumulative incidence of statin initiation prior to diabetes diagnosis was 33%–37% among the randomized treatment groups (p=0.36). Statin use was associated with greater diabetes risk irrespective of treatment group, with pooled HR (95% CI) for incident diabetes of 1.36 (1.17 to 1.58). This risk was not materially altered by adjustment for baseline diabetes risk factors and potential confounders related to indications for statin therapy. Conclusions In this population at high risk for diabetes, we observed significantly higher rates of diabetes with statin therapy in all three treatment groups. Confounding by indication for statin use does not appear to explain this relationship. The effect of statins to increase diabetes risk appears to extend to populations at high risk for diabetes. Trial registration number NCT00038727; Results

Metabolic syndrome components and their response to lifestyle and metformin interventions are associated with differences in diabetes risk in persons with impaired glucose tolerance

AIMS: To determine the association of metabolic syndrome (MetS) and its components with diabetes risk in participants with impaired glucose tolerance (IGT), and whether intervention-related changes in MetS lead to differences in diabetes incidence. METHODS: We used the National Cholesterol Education Program/Adult Treatment Panel III (NCEP/ATP III) revised MetS definition at baseline and intervention-related changes of its components to predict incident diabetes using Cox models in 3234 Diabetes Prevention Program (DPP) participants with IGT over an average follow-up of 3.2 years. RESULTS: In an intention-to-treat analysis, the demographic-adjusted hazard ratios (95% confidence interval) for diabetes in those with MetS (vs. no MetS) at baseline were 1.7 (1.3-2.3), 1.7 (1.2-2.3) and 2.0 (1.3-3.0) for placebo, metformin and lifestyle groups, respectively. Higher levels of fasting plasma glucose and triglycerides at baseline were independently associated with increased risk of diabetes. Greater waist circumference (WC) was associated with higher risk in placebo and lifestyle groups, but not in the metformin group. In a multivariate model, favourable changes in WC (placebo and lifestyle) and high-density lipoprotein cholesterol (placebo and metformin) contributed to reduced diabetes risk. CONCLUSIONS: MetS and some of its components are associated with increased diabetes incidence in persons with IGT in a manner that differed according to DPP intervention. After hyperglycaemia, the most predictive factors for diabetes were baseline hypertriglyceridaemia and both baseline and lifestyle-associated changes in WC. Targeting these cardiometabolic risk factors may help to assess the benefits of interventions that reduce diabetes incidence

Metabolite Profiles of Incident Diabetes and Heterogeneity of Treatment Effect in the Diabetes Prevention Program

Novel biomarkers of type 2 diabetes (T2D) and response to preventative treatment in individuals with similar clinical risk may highlight metabolic pathways that are important in disease development. We profiled 331 metabolites in 2,015 baseline plasma samples from the Diabetes Prevention Program (DPP). Cox models were used to determine associations between metabolites and incident T2D, as well as whether associations differed by treatment group (i.e., lifestyle [ILS], metformin [MET], or placebo [PLA]), over an average of 3.2 years of follow-up. We found 69 metabolites associated with incident T2D regardless of treatment randomization. In particular, cytosine was novel and associated with the lowest risk. In an exploratory analysis, 35 baseline metabolite associations with incident T2D differed across the treatment groups. Stratification by baseline levels of several of these metabolites, including specific phospholipids and AMP, modified the effect that ILS or MET had on diabetes development. Our findings highlight novel markers of diabetes risk and preventative treatment effect in individuals who are clinically at high risk and motivate further studies to validate these interactions

Metabolomics Analytics Workflow for Epidemiological Research: Perspectives from the Consortium of Metabolomics Studies (COMETS)

The application of metabolomics technology to epidemiological studies is emerging as a new approach to elucidate disease etiology and for biomarker discovery. However, analysis of metabolomics data is complex and there is an urgent need for the standardization of analysis workflow and reporting of study findings. To inform the development of such guidelines, we conducted a survey of 47 cohort representatives from the Consortium of Metabolomics Studies (COMETS) to gain insights into the current strategies and procedures used for analyzing metabolomics data in epidemiological studies worldwide. The results indicated a variety of applied analytical strategies, from biospecimen and data pre-processing and quality control to statistical analysis and reporting of study findings. These strategies included methods commonly used within the metabolomics community and applied in epidemiological research, as well as novel approaches to pre-processing pipelines and data analysis. To help with these discrepancies, we propose use of open-source initiatives such as the online web-based tool COMETS Analytics, which includes helpful tools to guide analytical workflow and the standardized reporting of findings from metabolomics analyses within epidemiological studies. Ultimately, this will improve the quality of statistical analyses, research findings, and study reproducibility

An Imputation-Estimation Algorithm Using Time-Varying Auxiliary Covariates for a Longitudinal Model When Outcome is Missing by Design

In long term clinical trials, occurrence of missing data is an area of concern especially if the rate at which data are missing depends on the treatment group. Typically, some effort is spent on trying to identify the reasons the data are missing so that appropriate assumptions and analytic approaches can be properly applied. When data are missing by design, certain measurements are discontinued after meeting an endpoint, possibly due to ethical or financial constraints. Subjects who reach the absorbing barrier may stop data collection on some variables but may subsequent time-varying covariates available from continued follow-up. In this dissertation, we developed an Imputation-Estimation algorithm under an auxiliary missing at random assumption to assess whether the additional information from the time varying covariates can be used to improve estimation. Quality of estimates is evaluated in terms of bias, variance and coverage for the estimates of the parameters of interest. We contrast this method to other missing data approaches such as multiple imputation and available case analysis. We illustrate this method using data from the Diabetes Prevention Program (DPP). The DPP was a diabetes prevention study that showed reductions of 58\% and 31\% in diabetes risk using intensive lifestyle or metformin interventions compared to placebo. According to the DPP protocol, the oral glucose tolerance test is discontinued after diabetes diagnosis. Because of the significant reduction in diabetes incidence by the metformin and lifestyle interventions, the rates of missing IGR and CIR are different among the treatment groups. This differential discontinuation among treatment groups results in informative monotone missing assessments of 30 minute glucose and insulin values. These 30 minute values are used to calculate surrogate measures of insulin secretion such as Insulin Glucose Ratio (IGR = (30-min insulin - fasting insulin)/(30-min glucose - fasting glucose)). Fasting blood glucose is collected at all time points and is associated with 30-minute glucose. The imputation estimation algorithm is applied to estimate the mean 30 minute blood glucose utilizing auxiliary information from the fasting blood glucose. In this example, fasting glucose is also the source of the discontinuation since diabetes diagnosis is based on the fasting glucose and 2 hour values during the OGTT. Because of the strong dependence between the fasting and 30 minute glucose measured at the same visit, the resulting estimates from the IE algorithm using the complete vector were similar to multiple imputation. Because the Placebo group experienced higher rates of diabetes incidence, the difference between available case analysis and the regression based imputations were greater than in the lifestyle group

Make Big Data Alive: Interactive Data Visualization in Metabolomics Research

Metabolomics research has rapidly evolved in recent years. In this data-intensive field, effective and simple data visualization tools empower researchers to present the big data in a meaningful way that people can quickly understand and use. Compared with traditional static graphics and tables, interactive visualization takes the concept a step further by allowing self-service faceting, probing and drill down._x000D_ We developed several interactive data visualization applications for metabolomics research using Shiny by RStudio coupled with R packages ggvis and plotly. The applications present information including quality control and regression analysis of more than 3000 metabolites in thousands of different models. Results are conveyed both in data tables and statistical graphs. Data tables contain complete information and are downloadable. In statistical graphs, users are allowed to view pointwise values using mouse-over controls, to drill down for detail through zooming, to compare and contrast the models and to display subsets of results by filtering on p-values, treatment groups, model adjustments, metabolites classes or even selecting an individual metabolite. The application can be published on websites to allow public or secure (authenticated) access and share with others. The above features of these Shiny applications enable a self-service, meaningful and flexible way to review and communicate data

Non-traditional biomarkers and incident diabetes in the Diabetes Prevention Program: comparative effects of lifestyle and metformin interventions.

We compared the associations of circulating biomarkers of inflammation, endothelial and adipocyte dysfunction and coagulation with incident diabetes in the placebo, lifestyle and metformin intervention arms of the Diabetes Prevention Program, a randomised clinical trial, to determine whether reported associations in general populations are reproduced in individuals with impaired glucose tolerance, and whether these associations are independent of traditional diabetes risk factors. We further investigated whether biomarker-incident diabetes associations are influenced by interventions that alter pathophysiology, biomarker concentrations and rates of incident diabetes. METHODS: The Diabetes Prevention Program randomised 3234 individuals with impaired glucose tolerance into placebo, metformin (850 mg twice daily) and intensive lifestyle groups and showed that metformin and lifestyle reduced incident diabetes by 31% and 58%, respectively compared with placebo over an average follow-up period of 3.2 years. For this study, we measured adiponectin, leptin, tissue plasminogen activator (as a surrogate for plasminogen activator inhibitor 1), high-sensitivity C-reactive protein, IL-6, monocyte chemotactic protein 1, fibrinogen, E-selectin and intercellular adhesion molecule 1 at baseline and at 1 year by specific immunoassays. Traditional diabetes risk factors were defined as family history, HDL-cholesterol, triacylglycerol, BMI, fasting and 2 h glucose, HbA1c, systolic blood pressure, inverse of fasting insulin and insulinogenic index. Cox proportional hazard models were used to assess the effects of each biomarker on the development of diabetes assessed semi-annually and the effects of covariates on these. RESULTS: E-selectin, (HR 1.19 [95% CI 1.06, 1.34]), adiponectin (0.84 [0.71, 0.99]) and tissue plasminogen activator (1.13 [1.03, 1.24]) were associated with incident diabetes in the placebo group, independent of diabetes risk factors. Only the association between adiponectin and diabetes was maintained in the lifestyle (0.69 [0.52, 0.92]) and metformin groups (0.79 [0.66, 0.94]). E-selectin was not related to diabetes development in either lifestyle or metformin groups. A novel association appeared for change in IL-6 in the metformin group (1.09 [1.021, 1.173]) and for baseline leptin in the lifestyle groups (1.31 [1.06, 1.63]). CONCLUSIONS/INTERPRETATION: These findings clarify associations between an extensive group of biomarkers and incident diabetes in a multi-ethnic cohort with impaired glucose tolerance, the effects of diabetes risk factors on these, and demonstrate differential modification of associations by interventions. They strengthen evidence linking adiponectin to diabetes development, and argue against a central role for endothelial dysfunction. The findings have implications for the pathophysiology of diabetes development and its prevention