Homogeneity in the association of body mass index with type 2 diabetes across the UK Biobank: A Mendelian randomization study

English version

Autoři: Michael Wainberg ^aff001; Anubha Mahajan ^aff002; Anshul Kundaje ^aff001; Mark I. McCarthy ^aff002; Erik Ingelsson ^aff006; Nasa Sinnott-Armstrong ^aff004; Manuel A. Rivas ^aff010
Působiště autorů: Department of Computer Science, Stanford University, Stanford, California, United States of America ^aff001; Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom ^aff002; Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, United Kingdom ^aff003; Department of Genetics, Stanford University, Stanford, California, United States of America ^aff004; NIHR Oxford Biomedical Research Centre, Churchill Hospital, Oxford, United Kingdom ^aff005; Molecular Epidemiology, Department of Medical Sciences, Uppsala University, Uppsala, Sweden ^aff006; Science for Life Laboratory, Uppsala University, Uppsala, Sweden ^aff007; Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States of America ^aff008; Stanford Cardiovascular Institute, Stanford University, Stanford, California, United States of America ^aff009; Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America ^aff010
Vyšlo v časopise: Homogeneity in the association of body mass index with type 2 diabetes across the UK Biobank: A Mendelian randomization study. PLoS Med 16(12): e32767. doi:10.1371/journal.pmed.1002982
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pmed.1002982

Souhrn

Background

Lifestyle interventions to reduce body mass index (BMI) are critical public health strategies for type 2 diabetes prevention. While weight loss interventions have shown demonstrable benefit for high-risk and prediabetic individuals, we aimed to determine whether the same benefits apply to those at lower risk.

Methods and findings

We performed a multi-stratum Mendelian randomization study of the effect size of BMI on diabetes odds in 287,394 unrelated individuals of self-reported white British ancestry in the UK Biobank, who were recruited from across the United Kingdom from 2006 to 2010 when they were between the ages of 40 and 69 years. Individuals were stratified on the following diabetes risk factors: BMI, diabetes family history, and genome-wide diabetes polygenic risk score. The main outcome measure was the odds ratio of diabetes per 1-kg/m² BMI reduction, in the full cohort and in each stratum. Diabetes prevalence increased sharply with BMI, family history of diabetes, and genetic risk. Conversely, predicted risk reduction from weight loss was strikingly similar across BMI and genetic risk categories. Weight loss was predicted to substantially reduce diabetes odds even among lower-risk individuals: for instance, a 1-kg/m² BMI reduction was associated with a 1.37-fold reduction (95% CI 1.12–1.68) in diabetes odds among non-overweight individuals (BMI < 25 kg/m²) without a family history of diabetes, similar to that in obese individuals (BMI ≥ 30 kg/m²) with a family history (1.21-fold reduction, 95% CI 1.13–1.29). A key limitation of this analysis is that the BMI-altering DNA sequence polymorphisms it studies represent cumulative predisposition over an individual’s entire lifetime, and may consequently incorrectly estimate the risk modification potential of weight loss interventions later in life.

Conclusions

In a population-scale cohort, lower BMI was consistently associated with reduced diabetes risk across BMI, family history, and genetic risk categories, suggesting all individuals can substantially reduce their diabetes risk through weight loss. Our results support the broad deployment of weight loss interventions to individuals at all levels of diabetes risk.

Klíčová slova:

Body Mass Index – Genome-wide association studies – Public and occupational health – Obesity – Weight loss – Medical risk factors

Zdroje

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