What your genes can (and can't) tell you about BMI and diabetes

• Authors: Carmen D Ng, Emory University; Jordan Weiss, University of Pennsylvania
• Journal Title: BIODEMOGRAPHY AND SOCIAL BIOLOGY
• Volume: Volume 66, Number 1
• Publisher: ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD | 2020-01-02, Pages 40-49
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1080/19485565.2020.1806032
• Subjects: Health Sciences, Public Health
• Keywords: Social Sciences; Demography; Social Sciences, Biomedical; Sociology; Biomedical Social Sciences; BODY-MASS INDEX; OBESITY; WEIGHT; HEALTH; CONSEQUENCES; OVERWEIGHT; HEIGHT

Abstract

Body mass index (BMI) is commonly used as a proxy for adiposity in epidemiological and public health studies. However, BMI may suffer from issues of misreporting and, because it fluctuates over the life course, its association with morbidities such as diabetes is difficult to measure. We examined the associations between actual BMI, genetic propensity for high BMI, and diabetes to better understand whether a BMI polygenic score (PGS) explained more variation in diabetes than self-reported BMI. We used a sample of non-Hispanic white adults from the longitudinal Health and Retirement Study (1992–2016). Structural equation models were used to determine how much variation in BMI could be explained by a BMI PGS. Then, we used logistic regression models (n = 12,086) to study prevalent diabetes at baseline and Cox regression models (n = 11,129) to examine incident diabetes with up to 24 years of follow-up. We observed that while both actual BMI and the BMI PGS were significantly associated with diabetes, actual BMI had a stronger association than its genetic counterpart and resulted in better model performance. Moreover, actual BMI explained more variation in baseline and incident diabetes than its genetic counterpart which may suggest that actual BMI captures more than just adiposity as intended.