4.8 Article

Quantitative bias analysis of the association of type 2 diabetes mellitus with 2,2′,4,4′,5,5′-hexachlorobiphenyl (PCB-153)

期刊

ENVIRONMENT INTERNATIONAL
卷 125, 期 -, 页码 291-299

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.envint.2018.12.036

关键词

Quantitative bias analysis; PBPK modeling; Persistent organic pollutants; Polychlorinated biphenyls; Type 2 diabetes mellitus; Confounding

资金

  1. American Chemistry Council (ACC) Long-Range Research Initiative

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An association between serum concentrations of persistent organic pollutants (POPs), such as 2,2',4,4',5,5'-hexachlorobiphenyl (PCB-153), and risk of type 2 diabetes mellitus (T2DM) has been reported. Conditional on body mass index (BMI) and waist circumference (WC), a higher serum PCB-153 concentration may be a marker of T2DM risk because it reflects other aspects of obesity that are related to T2DM risk and to PCB-153 clearance. To estimate the amount of residual confounding by other aspects of obesity, we performed a quantitative bias analysis on the results of a specific study. A physiologically-based pharmacokinetic (PBPK) model was developed to predict serum levels of PCB-153 for a simulated population. T2DM status was assigned to simulated subjects based on age, sex, BMI, WC, and visceral adipose tissue mass. The distributions of age, BMI, WC, and T2DM prevalence of the simulated population were tailored to closely match the target population. Analysis of the simulated data showed that a small part of the observed association appeared to be due to residual confounding. For example, the predicted odds ratio of T2DM that would have been obtained had the results been adjusted for visceral adipose tissue mass, for the >= 90th percentile of PCB-153 serum concentration, was 6.60 (95% CI 2.46-17.74), compared with an observed odds ratio of 7.13 (95% CI 2.65-19.13). Our results predict that the association between PCB-153 and risk of type 2 diabetes mellitus would not be substantially changed by additional adjustment for visceral adipose tissue mass in epidemiologic analyses. Confirmation of these predictions with longitudinal data would be reassuring.

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