BMI as a Modifiable Risk Factor for Type 2 Diabetes: Refining and Understanding Causal Estimates Using Mendelian Randomization

This study focused on resolving the relationship between BMI and type 2 diabetes. The availability of multiple variants associated with BMI offers a new chance to resolve the true causal effect of BMI on type 2 diabetes; however, the properties of these associations and their validity as genetic ins...

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Vydáno v:Diabetes (New York, N.Y.) Ročník 65; číslo 10; s. 3002 - 3007
Hlavní autoři: Corbin, Laura J, Richmond, Rebecca C, Wade, Kaitlin H, Burgess, Stephen, Bowden, Jack, Smith, George Davey, Timpson, Nicholas J
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 01.10.2016
Témata:
Body Mass Index
Diabetes Mellitus, Type 2 - epidemiology
Diabetes Mellitus, Type 2 - genetics
Genetic Pleiotropy - genetics
Genetic Predisposition to Disease - genetics
Humans
Mendelian Randomization Analysis
Odds Ratio
Risk Factors
Transcription Factor 7-Like 2 Protein - genetics
ISSN:1939-327X
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Shrnutí:This study focused on resolving the relationship between BMI and type 2 diabetes. The availability of multiple variants associated with BMI offers a new chance to resolve the true causal effect of BMI on type 2 diabetes; however, the properties of these associations and their validity as genetic instruments need to be considered alongside established and new methods for undertaking Mendelian randomization (MR). We explore the potential for pleiotropic genetic variants to generate bias, revise existing estimates, and illustrate value in new analysis methods. A two-sample MR approach with 96 genetic variants was used with three different analysis methods, two of which (MR-Egger and the weighted median) have been developed specifically to address problems of invalid instrumental variables. We estimate an odds ratio for type 2 diabetes per unit increase in BMI (kg/m(2)) of between 1.19 and 1.38, with the most stable estimate using all instruments and a weighted median approach (1.26 [95% CI 1.17, 1.34]). TCF7L2(rs7903146) was identified as a complex effect or pleiotropic instrument, and removal of this variant resulted in convergence of causal effect estimates from different causal analysis methods. This indicated the potential for pleiotropy to affect estimates and differences in performance of alternative analytical methods. In a real type 2 diabetes-focused example, this study demonstrates the potential impact of invalid instruments on causal effect estimates and the potential for new approaches to mitigate the bias caused.
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ISSN:1939-327X
DOI:10.2337/db16-0418