BMI as a Modifiable Risk Factor for Type 2 Diabetes: Refining and Understanding Causal Estimates Using Mendelian Randomization
Open accessAbstract
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/m2) 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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Additional plots might also aid in the visualisation of results, assessing assumption violation, and detecting potential influential or outlier points.
“Additional plots might also aid in the visualisation of results, assessing assumption violation, and detecting potential influential or outlier points.”
From Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomisation (STROBE-MR): explanation and elaboration. (2021) · cites this paper as [32]
Simulation studies have shown that selection bias can have a severe impact on Mendelian randomization estimates, but only when the selection effects are quite strong.
“Simulation studies have shown that selection bias can have a severe impact on Mendelian randomization estimates, but only when the selection effects are quite strong 72, 122 .”
From Guidelines for performing Mendelian randomization investigations (2020) · cites this paper as [72]
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- Authors
- Corbin, L. J., Richmond, R. C., Wade, K. H., Burgess, S., Bowden, J., Smith, G. D., & Timpson, N. J.
- Affiliations
- University of Bristol (GB)
- University of Cambridge (GB)
- Corresponding author
- Timpson, Nicholas J.
- ORCID
- Venue
- Diabetes
- Publisher
- American Diabetes Association
- Published
- 2016-07-08
- Volume
- 65
- Issue
- 10
- Pages
- 3002-3007
- ISSN
- 0012-1797 (ISSN-L)
- 1939-327X
- PMC
- PMC5279886
- OpenAlex
- W2475125285
- Cited by
- 184
- References
- 33
- Citations by year
- 2026: 18 · 2025: 15 · 2024: 19 · 2023: 14 · 2022: 15 · 2021: 21 · 2020: 25 · 2019: 15 · 2018: 20 · 2017: 17 · 2016: 5
- FWCI
- 15.35
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Diabetes (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Bristol Research (University of Bristol) (acceptedVersion, cc-by-nc)
- Apollo (University of Cambridge) (acceptedVersion)
- Apollo
- Funders
- Medical Research Council (MC_UU_12013/1, MC_UU_12013/2, MC_UU_12013/3, MR/N501906/1)
- Cancer Research UK (C18281/A19169)
- Wellcome Trust (100114)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Nutrition, Genetics, and Disease
- Keywords
- body mass index, type 2 diabetes, pleiotropy, genetic instruments, TCF7L2, MR-Egger, weighted median, two-sample Mendelian randomization, assessment validity
- MeSH terms
- Diabetes Mellitus, Type 2, Humans, Risk Factors, Body Mass Index, Odds Ratio, Genetic Predisposition to Disease, Mendelian Randomization Analysis, Transcription Factor 7-Like 2 Protein, Genetic Pleiotropy
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