Lower BMI means lower diabetes risk, even among non-overweight people

December 10, 2019

Lower body mass index (BMI) is consistently associated with reduced type II diabetes risk, among people with varied family history, genetic risk factors and weight, according to a new study published this week in PLOS Medicine by Manuel Rivas of Stanford University, and colleagues.

Weight-loss interventions have shown demonstratable benefit for reducing the risk of type II diabetes in high-risk and pre-diabetic individuals but have not been well-studied in people at lower risk of diabetes. In the new study, researchers studied the association between BMI, diabetes family history and genetic risk factors affecting type II diabetes or BMI. They used data on 287,394 unrelated individuals of British ancestry recruited to participate in the UK Biobank from 2006 to 2010 when between the ages of 40 and 69.

Nearly 5% of the participants had a diagnosis of type II diabetes and diabetes prevalence was confirmed to be associated with higher BMI, a family history of type II disease and genetic risk factors. Moreover, a 1 kg/m2 BMI reduction was associated with a 1.37 fold reduction (95% CI 1.12-1.68) in type II diabetes among non-overweight individuals with a BMI of less than 25 and no family history of diabetes, similar to the effect of BMI reduction in obese individuals with a family history (1.21, 95% CI 1.13-1.29)

"These findings suggest that all individuals can substantially reduce their type II diabetes risk through weight loss," the authors say. However they also caution that the results must be taken with a grain of salt since they didn't study actual weight loss interventions. Although the new analysis "can determine that lower lifetime BMI is protective against diabetes, that does not necessarily imply weight loss later in life, after carrying excess weight for decades, would have the same result," they say.
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Research Article

Funding:

This research has been conducted using the UK Biobank Resource under Application Number 24983, "Generating effective therapeutic hypotheses from genomic and hospital linkage data" (http://www.ukbiobank.ac.uk/wp-content/uploads/2017/06/24983-Dr-Manuel-Rivas.pdf). M.I.M. is a Wellcome and NIHR senior investigator. This work was funded in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) (grant PGSD3-476082-2015 to M.W.), Stanford Bio-X Bowes fellowship (to M.W.), Stanford Graduate Fellowship (to N.S.-A.), National Defense Science & Engineering Grant (to N.S.-A.), NIH grants 1U24HG008956, R01HG010140 and 5U01HG009080 (to M.A.R.), 1DP2OD022870 and U01HG009431 (to A.K.) and 1R01DK106236, 1R01HL135313 and 1P30DK116074-01 (to E.I.), and Wellcome (090532, 098381, 203141), NIDDK (U01-DK105535) and NIHR (NF-SI-0617-10090) grants (to M.I.M.). The views expressed in this article are those of the authors and not necessarily those of the funders; funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing Interests:

I have read the journal's policy and the authors of this manuscript have the following competing interests: M.I.M has served on advisory panels for Pfizer, NovoNordisk, Zoe Global; received honoraria from Merck, Pfizer, NovoNordisk and Eli Lilly; has stock options in Zoe Global; and has received research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, NovoNordisk, Pfizer, Roche, Sanofi Aventis, Servier, Takeda. As of June 2019, M.I.M is an employee of Genentech and a holder of stock in Roche.

Citation:

Wainberg M, Mahajan A, Kundaje A, McCarthy MI, Ingelsson E, Sinnott-Armstrong N, et al. (2019) Homogeneity in the association of body mass index with type 2 diabetes across the UK Biobank: A Mendelian randomization study. PLoS Med 16(12): e1002982. https://doi.org/10.1371/journal.pmed.1002982

Image Credit: HansMartinPaul, Pixabay

Author Affiliations:

Department of Computer Science, Stanford University, Stanford, California, United States of America

Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom

Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, United Kingdom

Department of Genetics, Stanford University, Stanford, California, United States of America

NIHR Oxford Biomedical Research Centre, Churchill Hospital, Oxford, United Kingdom

Molecular Epidemiology, Department of Medical Sciences, Uppsala University, Uppsala, Sweden

Science for Life Laboratory, Uppsala University, Uppsala, Sweden

Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States of America

Stanford Cardiovascular Institute, Stanford University, Stanford, California, United States of America

Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America

In your coverage please use this URL to provide access to the freely available paper: http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002982

PLOS

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