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 |
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| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
01.10.2016
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| ISSN: | 1939-327X |
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| Abstract | 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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| AbstractList | 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. |
| Author | Bowden, Jack Richmond, Rebecca C Burgess, Stephen Timpson, Nicholas J Wade, Kaitlin H Smith, George Davey Corbin, Laura J |
| Author_xml | – sequence: 1 givenname: Laura J surname: Corbin fullname: Corbin, Laura J organization: MRC Integrative Epidemiology Unit, University of Bristol, Bristol, U.K – sequence: 2 givenname: Rebecca C surname: Richmond fullname: Richmond, Rebecca C organization: MRC Integrative Epidemiology Unit, University of Bristol, Bristol, U.K – sequence: 3 givenname: Kaitlin H surname: Wade fullname: Wade, Kaitlin H organization: MRC Integrative Epidemiology Unit, University of Bristol, Bristol, U.K – sequence: 4 givenname: Stephen surname: Burgess fullname: Burgess, Stephen organization: MRC Integrative Epidemiology Unit, University of Bristol, Bristol, U.K. Department of Public Health and Primary Care, University of Cambridge, Cambridge, U.K – sequence: 5 givenname: Jack surname: Bowden fullname: Bowden, Jack organization: MRC Integrative Epidemiology Unit, University of Bristol, Bristol, U.K. MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, U.K – sequence: 6 givenname: George Davey surname: Smith fullname: Smith, George Davey organization: MRC Integrative Epidemiology Unit, University of Bristol, Bristol, U.K – sequence: 7 givenname: Nicholas J surname: Timpson fullname: Timpson, Nicholas J email: n.j.timpson@bristol.ac.uk organization: MRC Integrative Epidemiology Unit, University of Bristol, Bristol, U.K. n.j.timpson@bristol.ac.uk |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/27402723$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | 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 |
| Title | BMI as a Modifiable Risk Factor for Type 2 Diabetes: Refining and Understanding Causal Estimates Using Mendelian Randomization |
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