The effect of body mass index on fasting blood glucose after initiation of thiazide therapy in hypertensive patients

A causal data fusion method for the general exposure and outcome

With the advent of the big data era, the need to combine multiple individual data sets to draw causal effects arises naturally in many medical and biological applications. Especially each data set cannot measure enough confounders to infer the causal effect of an exposure on an outcome. In this article, we extend the method proposed by a previous study to causal data fusion of more than two data sets without external validation and to a more general (continuous or discrete) exposure and outcome. Theoretically, we obtain the condition for identifiability of exposure effects using multiple individual data sources for the continuous or discrete exposure and outcome. The simulation results show that our proposed causal data fusion method has unbiased causal effect estimate and higher precision than traditional regression, meta-analysis and statistical matching methods. We further apply our method to study the causal effect of BMI on glucose level in individuals with diabetes by combining two data sets. Our method is essential for causal data fusion and provides important insights into the ongoing discourse on the empirical analysis of merging multiple individual data sources.

A novel method for controlling unobserved confounding using double confounders

BACKGROUND

Controlling unobserved confounding still remains a great challenge in observational studies, and a series of strict assumptions of the existing methods usually may be violated in practice. Therefore, it is urgent to put forward a novel method.

METHODS

We are interested in the causal effect of an exposure on the outcome, which is always confounded by unobserved confounding. We show that, the causal effect of an exposure on a continuous or categorical outcome is nonparametrically identified through only two independent or correlated available confounders satisfying a non-linear condition on the exposure. Asymptotic theory and variance estimators are developed for each case. We also discuss an extension for more than two binary confounders.

RESULTS

The simulations show better estimation performance by our approach in contrast to the traditional regression approach adjusting for observed confounders. A real application is separately applied to assess the effects of Body Mass Index (BMI) on Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Fasting Blood Glucose (FBG), Triglyceride (TG), Total Cholesterol (TC), High Density Lipoprotein (HDL) and Low Density Lipoprotein (LDL) with individuals in Shandong Province, China. Our results suggest that SBP increased 1.60 (95% CI: 0.99-2.93) mmol/L with per 1- kg/m2 higher BMI and DBP increased 0.37 (95% CI: 0.03-0.76) mmol/L with per 1- kg/m2 higher BMI. Moreover, 1- kg/m2 increase in BMI was causally associated with a 1.61 (95% CI: 0.96-2.97) mmol/L increase in TC, a 1.66 (95% CI: 0.91-55.30) mmol/L increase in TG and a 2.01 (95% CI: 1.09-4.31) mmol/L increase in LDL. However, BMI was not causally associated with HDL with effect value - 0.20 (95% CI: - 1.71-1.44). And, the effect value of FBG per 1- kg/m2 higher BMI was 0.56 (95% CI: - 0.24-2.18).

CONCLUSIONS

We propose a novel method to control unobserved confounders through double binary confounders satisfying a non-linear condition on the exposure which is easy to access.

Robust analysis of secondary phenotypes in case-control genetic association studies

The case-control study is a common design for assessing the association between genetic exposures and a disease phenotype. Though association with a given (case-control) phenotype is always of primary interest, there is often considerable interest in assessing relationships between genetic exposures and other (secondary) phenotypes. However, the case-control sample represents a biased sample from the general population. As a result, if this sampling framework is not correctly taken into account, analyses estimating the effect of exposures on secondary phenotypes can be biased leading to incorrect inference. In this paper, we address this problem and propose a general approach for estimating and testing the population effect of a genetic variant on a secondary phenotype. Our approach is based on inverse probability weighted estimating equations, where the weights depend on genotype and the secondary phenotype. We show that, though slightly less efficient than a full likelihood-based analysis when the likelihood is correctly specified, it is substantially more robust to model misspecification, and can out-perform likelihood-based analysis, both in terms of validity and power, when the model is misspecified. We illustrate our approach with an application to a case-control study extracted from the Framingham Heart Study. Copyright © 2016 John Wiley & Sons, Ltd.

The VA Hypertension Primary Care Longitudinal Cohort: Electronic medical records in the post-genomic era

The Veterans Affairs Hypertension Primary Care Longitudinal Cohort (VAHC) was initiated in 2003 as a pilot study designed to link the VA electronic medical record system with individual genetic data. Between June 2003 and December 2004, 1,527 hypertensive participants were recruited. Protected health information (PHI) was extracted from the regional VA data warehouse. Differences between the clinic and mail recruits suggested that clinic recruitment resulted in an over-sampling of African Americans. A review of medical records in a random sample of study participants confirmed that the data warehouse accurately captured most selected diagnoses. Genomic DNA was acquired non-invasively from buccal cells in mouthwash; ~ 96.5 per cent of samples contained DNA suitable for genotyping, with an average DNA yield of 5.02 ± 0.12 micrograms, enough for several thousand genotypes. The coupling of detailed medical databases with genetic information has the potential to facilitate the genetic study of hypertension and other complex diseases.

The effect of body mass index on fasting blood glucose and development of diabetes mellitus after initiation of extended-release niacin

BACKGROUND

Niacin increases blood glucose, but whether the degree of increase is associated with increasing body mass index (BMI) is unknown. We evaluated the effect of extended-release niacin initiation on fasting plasma glucose (FPG) and the development of new-onset diabetes mellitus (DM) in relation to body mass index (kg/m(2)) in nondiabetic patients.

METHODS

This retrospective observational study used data from six facilities within a geographical region of the Department of Veterans Affairs (VA). Patients included were 18 years of age or older and on a stable extended-release niacin dose (minimum 100 days) of at least 250 mg/day between January, 2001, and April, 2007. Patients were excluded if they were new to the VA, on corticosteroids or insulin, if medication adherence was <80%, or if they met criteria for DM.

RESULTS

A total of 811 nondiabetic patients taking extended-release niacin initiation were studied. FPG after niacin initiation was stastically significantly correlated with increasing BMI (P < 0.001, R = 0.144 Pearson correlation coefficient). Factors independently associated with change in FPG using multiple linear regression were BMI (P = 0.043), baseline average glucose (P < 0.001), and baseline average triglycerides (P = 0.037). Of all patients started on niacin, 220 (27.1%) patients developed DM after niacin initiation. BMI, (P = 0.002) and baseline average glucose (P < 0.001) were independent predictors of the development of new-onset DM (logistic regression analysis).

CONCLUSIONS

We found an association between increasing BMI and increasing FPG and diagnosis of new-onset DM after initiation of extended-release niacin initiation. This suggests that extended-release niacin may increase FPG into the diabetic range, especially for obese patients.

Diabetes and obesity: therapeutic targeting and risk reduction - a complex interplay

Obesity is a major risk factor for the development of diabetes and predisposes individuals to hypertension and dyslipidaemia. Together these pathologies increase the risk for cardiovascular disease (CVD), the major cause of morbidity and mortality in type 2 diabetes mellitus (T2DM). Worsening trends in obesity and T2DM raise a serious conundrum, namely, how to control blood glucose, blood pressure, and lipids when many antidiabetic agents cause weight gain and thereby exacerbate other cardiovascular risk factors associated with T2DM. Further, evidence suggests that some established antihypertensive agents may worsen glucose intolerance. Many patients who are obese, hypertensive, and/or hyperlipidaemic fail to achieve blood pressure, lipid and glycaemic goals, and this failure may in part be explained by physician reluctance to utilize complex combination regimens for fear of off-target effects. Thus, a clear need exists for clinicians to understand the risks and benefits of different pharmacologic, and indeed non-pharmacologic, options in order to maximize treatment outcomes. While intensive lifestyle modification remains an elusive gold standard, newer diabetes targets, including the incretin axis, may offer greater cardiovascular risk reduction than other antidiabetes therapies, although definitive clinical trial data are needed. The glucagon-like peptide-1 (GLP-1) receptor agonists exenatide and liraglutide and the dipeptidyl peptidase-4 (DPP-4) inhibitors sitagliptin and vildagliptin effectively lower HbA1c; exenatide and liraglutide reduce weight and blood pressure and improve lipid profiles. Sitagliptin and vildagliptin are weight neutral but also appear to improve lipid profiles. Integration of incretin therapies into the therapeutic armamentarium is a promising approach to improving outcomes in T2DM, and perhaps even in reducing complications of T2DM, such as co-morbid hypertension and dyslipidaemia. Additional long-term studies, including CVD end-point studies, will be necessary to determine the appropriate places for incretin-based therapies in treatment algorithms.