Abstract
Background: Higher body-mass index (BMI) and waist-to-hip ratio (WHR) increase the risk of cardiovascular disease, but the extent to which this is mediated by blood pressure, diabetes, lipid traits and smoking is not fully understood.
Methods: Using consortia and UK Biobank genetic association summary data from 140,595 to 898,130 participants predominantly of European ancestry, Mendelian randomization mediation analysis was performed to investigate the degree to which systolic blood pressure (SBP), diabetes, lipid traits and smoking mediated an effect of BMI and WHR on risk of coronary artery disease (CAD), peripheral artery disease (PAD) and stroke.
Results: The odds ratio of CAD per 1-standard deviation increase in genetically predicted BMI was 1.49 (95% CI 1.39 to 1.60). This attenuated to 1.34 (95% CI 1.24 to 1.45) after adjusting for genetically predicted SBP (proportion mediated 27%, 95% CI 3% to 50%), to 1.27 (95% CI 1.17 to 1.37) after adjusting for genetically predicted diabetes (41% mediated, 95% CI 18% to 63%), to 1.47 (95% CI 1.36 to 1.59) after adjusting for genetically predicted lipids (3% mediated, 95% -23% to 29%), and to 1.46 (95% CI 1.34 to 1.58) after adjusting for genetically predicted smoking (6% mediated, 95% CI -20% to 32%). Adjusting for all the mediators together, the estimate attenuated to 1.14 (95% CI 1.04 to 1.26; 66% mediated, 95% CI 42% to 91%). A similar pattern was observed when considering genetically predicted WHR as the exposure, and PAD or stroke as the outcome.
Conclusions: Measures to reduce obesity will lower risk of cardiovascular disease primarily by impacting on downstream metabolic risk factors, particularly diabetes and hypertension. Reduction of obesity prevalence alongside control and management of its mediators is likely to be most effective for minimizing the burden of obesity.
Methods: Using consortia and UK Biobank genetic association summary data from 140,595 to 898,130 participants predominantly of European ancestry, Mendelian randomization mediation analysis was performed to investigate the degree to which systolic blood pressure (SBP), diabetes, lipid traits and smoking mediated an effect of BMI and WHR on risk of coronary artery disease (CAD), peripheral artery disease (PAD) and stroke.
Results: The odds ratio of CAD per 1-standard deviation increase in genetically predicted BMI was 1.49 (95% CI 1.39 to 1.60). This attenuated to 1.34 (95% CI 1.24 to 1.45) after adjusting for genetically predicted SBP (proportion mediated 27%, 95% CI 3% to 50%), to 1.27 (95% CI 1.17 to 1.37) after adjusting for genetically predicted diabetes (41% mediated, 95% CI 18% to 63%), to 1.47 (95% CI 1.36 to 1.59) after adjusting for genetically predicted lipids (3% mediated, 95% -23% to 29%), and to 1.46 (95% CI 1.34 to 1.58) after adjusting for genetically predicted smoking (6% mediated, 95% CI -20% to 32%). Adjusting for all the mediators together, the estimate attenuated to 1.14 (95% CI 1.04 to 1.26; 66% mediated, 95% CI 42% to 91%). A similar pattern was observed when considering genetically predicted WHR as the exposure, and PAD or stroke as the outcome.
Conclusions: Measures to reduce obesity will lower risk of cardiovascular disease primarily by impacting on downstream metabolic risk factors, particularly diabetes and hypertension. Reduction of obesity prevalence alongside control and management of its mediators is likely to be most effective for minimizing the burden of obesity.
| Original language | English |
|---|---|
| Pages (from-to) | 1428-1438 |
| Number of pages | 11 |
| Journal | International Journal of Obesity |
| Volume | 45 |
| Issue number | 7 |
| Early online date | 17 May 2021 |
| DOIs | |
| Publication status | Published - 17 May 2021 |
Bibliographical note
Funding Information:Peter Wilson46, Rachel McArdle47, Louis Dellitalia48, Kristin Mattocks49, John Harley50, Jeffrey Whittle51, Frank Jacono52, Jean Beckham53, John Wells54, Salvador Gutierrez55, Gretchen Gibson56, Kimberly Hammer57, Laurence Kaminsky58, Gerardo Villareal34, Scott Kinlay59, Junzhe Xu27, Mark Hamner60, Roy Mathew61, Sujata Bhushan62, Pran Iruvanti63, Michael Godschalk64, Zuhair Ballas65, Douglas Ivins66, Stephen Mastorides67, Jonathan Moorman68, Saib Gappy69, Jon Klein70, Nora Ratcliffe71, Hermes Florez72, Olaoluwa Okusaga73, Maureen Murdoch74, Peruvemba Sriram75, Shing Shing Yeh76, Neeraj Tandon77, Darshana Jhala78, Samuel Aguayo79, David Cohen80, Satish Sharma81, Suthat Liangpunsakul82, Kris Ann Ours-ler83, Mary Whooley84, Sunil Ahuja85, Joseph Constans86, Paul Meyer87, Jennifer Greco88, Michael Rauchman89, Richard Servatius90, Melinda Gaddy91, Agnes Wallbom92, Timothy Morgan93, Todd Stapley94, Scott Sherman95, George Ross96, Philip Tsao97, Patrick Strollo98, Edward Boyko99, Laurence Meyer44, Samir Gupta44,100, Mostaqul Huq101, Joseph Fayad102, Adriana Hung103, Jack Lichy104, Robin Hurley105, Brooks Robey106, Robert Striker107 27VA Boston Healthcare System, Boston, MA, USA; 28US Department of Veterans Affairs, Washington, DC, USA; 29Durham VA Medical Center, Durham, NC, USA; 30Philadelphia VA Medical Center, Philadelphia, PA, USA; 31VA Palo Alto Health Care System, Palo Alto, CA, USA; 32Clinical Epidemiology Research Center (CERC), West Haven, West Haven VA Medical Center, West Haven, CT, USA; 33Cooperative Studies Program Clinical Research Pharmacy Coordinating Center, Albuquerque, New Mexico VA Health Care System, Albuquerque, NM, USA; 34Genomics Coordinating Center, Palo Alto, CA, USA; 35MVP Boston Coordinating Center, Boston, MA, USA; 36MVP Information Center, Canandaigua, Canandaigua VA Medical Center, Canandaigua, NY, USA; 37VA Central Biorepository, Boston, MA, USA; 38MVP Informatics, Boston, VA Boston Healthcare System, Boston, MA, USA; 39Science Operations, VA Boston Healthcare System, Boston, MA, USA; 40Genomics Core, VA Boston Healthcare System, Boston, MA, USA; 41Data and Computational Sciences, VA Boston Healthcare System, Boston, MA, USA; 42VA Informatics and Computing Infrastructure (VINCI), VA Salt Lake City Health Care System, Salt Lake City, UT, USA; 43Statistical Genetics, Durham VA Medical Center, Durham, NC, USA; 44Atlanta VA Medical Center, Decatur, GA, USA; 45West Haven VA Medical Center, West Haven, CT, USA; 46Atlanta VA Medical Center, Decatur, GA, USA; 47Bay Pines VA Healthcare System, Bay Pines, FL, USA; 48Birmingham VA Medical Center, Birmingham, AL, USA; 49Central Western Massachusetts Healthcare System, Leeds, MA, USA; 50Cincinnati VA Medical Center, Cincinnati, OH, USA; 51Clement J. Zablocki VA Medical Center, Milwaukee, WI, USA;52VA Northeast Ohio Healthcare System, Cleveland, OH, USA; 53Durham VA Medical Center, Durham, NC, USA; 54Edith Nourse Rogers Memorial Veterans Hospital, Bedford, MA, USA; 55Edward Hines, Jr. VA Medical Center, Hines, IL, USA; 56Veterans Health Care System of the Ozarks, Fayetteville, AR, USA; 57Fargo VA Health Care System, Fargo, ND, USA; 58VA Health Care Upstate New York, Albany, NY, USA; 59VA Western New York Healthcare System, Buffalo, NY, USA; 60Ralph H. Johnson VA Medical Center, Mental Health Research, Charleston, SC, USA; 61Columbia VA Health Care System, Columbia, SC, USA; 62VA North Texas Health Care System, Dallas, TX, USA; 63Hampton VA Medical Center, Hampton, VA, USA; 64Richmond VA Medical Center, Richmond, VA, USA; 65Iowa City VA Health Care System, Iowa City, IA, USA;66Eastern Oklahoma VA Health Care System, Muskogee, OK, USA; 67James A. Haley Veterans’ Hospital, Tampa, FL, USA; 68James H. Quillen VA Medical Center, Corner of Lamont and Veterans Way, Mountain Home, TN, USA; 69John D. Dingell VA Medical Center, Detroit, MI, USA; 70Louisville VA Medical Center, Louisville, KY, USA; 71Manchester VA Medical Center, Manchester, NH, USA; 72Miami VA Health Care System, Miami, FL, USA; 73Michael E. DeBakey VA Medical Center, Houston, TX, USA; 74Minneapolis VA Health Care System, Minneapolis, MN, USA; 75N. FL/S. GA Veterans Health System, Gainesville, FL, USA; 76Northport VA Medical Center, Northport, NY, USA; 77Overton Brooks VA Medical Center, Shreveport, LA, USA; 78Philadelphia VA Medical Center, Philadelphia, PA, USA; 79Phoenix VA Health Care System, Phoenix, AZ, USA; 80Portland VA Medical Center, Portland, OR, USA; 81Providence VA Medical Center, Providence, RI, USA; 82Richard Roudebush VA Medical Center, Indianapolis, IN, USA; 83Salem VA Medical Center, Salem, VA, USA; 84San Francisco VA Health Care System, San Francisco, CA, USA; 85South Texas Veterans Health Care System, San Antonio, TX, USA; 86Southeast Louisiana Veterans Health Care System, New Orleans, LA, USA; 87Southern Arizona VA Health Care System, Tucson, AZ, USA; 88Sioux Falls VA Health Care System, Sioux Falls, SD, USA; 89St. Louis VA Health Care System, St. Louis, MO, USA; 90Syracuse VA Medical Center, Syracuse, NY, USA; 91VA Eastern Kansas Health Care System, Leavenworth, KS, USA; 92VA Greater Los Angeles Health Care System, Los Angeles, CA, USA; 93VA Long Beach Healthcare System, Long Beach, CA, USA; 94VA Maine Healthcare System, 1 VA Center, Augusta, ME, USA; 95VA New York Harbor Healthcare System, New York, NY, USA; 96VA Pacific Islands Health Care System, Honolulu, HI, USA; 97VA Palo Alto Health Care System, Palo Alto, CA, USA; 98VA Pittsburgh Health Care System, University Drive, Pittsburgh, PA, USA; 99VA Puget Sound Health Care System, Seattle, WA, USA; 100VA San Diego Healthcare System, San Diego, CA, USA; 101VA Sierra Nevada Health Care System, Reno, NV, USA; 102VA Southern Nevada Healthcare System, North Las Vegas, NV, USA; 103VA Tennessee Valley Healthcare System, South Nashville, TN, USA; 104Washington DC VA Medical Center, Washington, DC, USA; 105W.G. (Bill) Hefner VA Medical Center, Salisbury, NC, USA; 106White River Junction VA Medical Center, Hartford, VT, USA; 107William S. Middleton Memorial Veterans Hospital, Madison, WI, USA Funding This work was supported by funding from the US Department of Veterans Affairs Office of Research and Development, Million Veteran Programme Grant MVP003 (I01-BX003362). This publication does not represent the views of the Department of Veterans Affairs of the US Government. The MEGASTROKE project received funding from sources specified at http://www.megastroke.org/acknow ledgments.html. Details of all MEGASTROKE authors are available at http://www.megastroke.org/authors.html. This work was supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at the University Hospitals Bristol National Health Service (NHS) Foundation Trust and the University of Bristol. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. DG and JP-S are funded by the Wellcome 4i Clinical Ph.D. Programme at Imperial College London (203928/Z/16/Z). DG is supported by the British Heart Foundation Centre of Research Excellence at Imperial College London (RE/18/4/34215) and by a National Institute for Health Research Clinical Lectureship at St. George’s, University of London (CL-2020-16-001). ARC and ES are funded by and work in a unit that receives core funding from the Medical Research Council (MRC) and the University of Bristol (MC_UU00011/1). VK is funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant (721567). REW is a member of the MRC Integrative Epidemiology Unit at the University of Bristol funded by the MRC (MC_UU_00011/7). SMD was supported by the Department of Veterans Affairs Office of Research and Development (IK2-CX001780). SB is supported by Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (204623/Z/16/ Z). PE acknowledges support from the MRC (MR/S019669/1), the NIHR Imperial Biomedical Research Centre, Imperial College London
Funding Information:
Conflict of interest DG is employed part-time by Novo Nordisk. JP-S reports personal fees from Novo Nordisk related to consultancy outside of the submitted work. SMD has received grants from the U.S. Department of Veterans Affairs, Calico Labs, and Renalytix AI plc outside the submitted work. All other authors have no conflicts of interest to declare.
Funding Information:
(RDF03), the UK Dementia Research Institute (DRI) at Imperial College London funded by UK DRI Ltd (funded by MRC, Alzheimer’s Society, Alzheimer’s Research UK), and Health Data Research (HDR) UK London funded by HDR UK Ltd (funded by a consortium led by the MRC 1004231). The funding sources for this work were not involved in study design, data analysis, interpretation of results or writing of the manuscript.
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