Genetic Association of Waist-to-Hip Ratio With Cardiometabolic Traits, Type 2 Diabetes, and Coronary Heart Disease
Open accessAbstract
Importance: In observational studies, abdominal adiposity has been associated with type 2 diabetes and coronary heart disease (CHD). Whether these associations represent causal relationships remains uncertain. Objective: To test the association of a polygenic risk score for waist-to-hip ratio (WHR) adjusted for body mass index (BMI), a measure of abdominal adiposity, with type 2 diabetes and CHD through the potential intermediates of blood lipids, blood pressure, and glycemic phenotypes. Design, Setting, and Participants: A polygenic risk score for WHR adjusted for BMI, a measure of genetic predisposition to abdominal adiposity, was constructed with 48 single-nucleotide polymorphisms. The association of this score with cardiometabolic traits, type 2 diabetes, and CHD was tested in a mendelian randomization analysis that combined case-control and cross-sectional data sets. Estimates for cardiometabolic traits were based on a combined data set consisting of summary results from 4 genome-wide association studies conducted from 2007 to 2015, including up to 322 154 participants, as well as individual-level, cross-sectional data from the UK Biobank collected from 2007-2011, including 111 986 individuals. Estimates for type 2 diabetes and CHD were derived from summary statistics of 2 separate genome-wide association studies conducted from 2007 to 2015 and including 149 821 individuals and 184 305 individuals, respectively, combined with individual-level data from the UK Biobank. Exposures: Genetic predisposition to increased WHR adjusted for BMI. Main Outcomes and Measures: Type 2 diabetes and CHD. Results: Among 111 986 individuals in the UK Biobank, the mean age was 57 (SD, 8) years, 58 845 participants (52.5%) were women, and mean WHR was 0.875. Analysis of summary-level genome-wide association study results and individual-level UK Biobank data demonstrated that a 1-SD increase in WHR adjusted for BMI mediated by the polygenic risk score was associated with 27-mg/dL higher triglyceride levels, 4.1-mg/dL higher 2-hour glucose levels, and 2.1-mm Hg higher systolic blood pressure (each P < .001). A 1-SD genetic increase in WHR adjusted for BMI was also associated with a higher risk of type 2 diabetes (odds ratio, 1.77 [95% CI, 1.57-2.00]; absolute risk increase per 1000 participant-years, 6.0 [95% CI, CI, 4.4-7.8]; number of participants with type 2 diabetes outcome, 40 530) and CHD (odds ratio, 1.46 [95% CI, 1.32-1.62]; absolute risk increase per 1000 participant-years, 1.8 [95% CI, 1.3-2.4]; number of participants with CHD outcome, 66 440). Conclusions and Relevance: A genetic predisposition to higher waist-to-hip ratio adjusted for body mass index was associated with increased risk of type 2 diabetes and coronary heart disease. These results provide evidence supportive of a causal association between abdominal adiposity and these outcomes.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
MR is being widely applied to quantitative traits with the aim of discovering modifiable risk factors for disease that are causal and amenable to therapeutic modification.
“With increasing availability of large-scale phenotyping (and genotyping), MR is being widely applied to quantitative traits (e.g. markers or adiposity, such as body mass index 13-16 ; blood lipids such as HDL-cholesterol 17-19 ; inflammation-related proteins such as interleukin-6 receptor 20,21 12 ) with the aim of discovering modifiable risk factors for disease that are causal and amenable to the”
From Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development (2021) · cites this paper as [18]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Principal components analysis corrects for stratification in genome-wide association studies
Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., & Reich, D. (2006) · Nature Genetics · doi:10.1038/ng1847 · Cited by 10,248 · see citing works
Bowden, J., Davey Smith, G., Haycock, P. C., & Burgess, S. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21965 · Cited by 8,922 · see citing works
The metabolic phenotype in obesity: Fat mass, body fat distribution, and adipose tissue function
Goossens, G. H. (2017) · Obesity Facts · doi:10.1159/000471488 · Cited by 8,481 · see citing works
Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data
Burgess, S., Butterworth, A., & Thompson, S. G. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21758 · Cited by 6,050 · see citing works
Davey Smith, G., & Ebrahim, S. (2003) · International Journal of Epidemiology · doi:10.1093/ije/dyg070 · Cited by 5,882 · see citing works
Prospective Studies Collaboration (2009) · The Lancet · doi:10.1016/s0140-6736(09)60318-4 · Cited by 4,807 · see citing works
Genetic studies of body mass index yield new insights for obesity biology
Study, T. L. C., Locke, A. E., The ADIPOGen Consortium, The AGEN-BMI Working Group, The CARDIOGRAMplusC4D Consortium, The CKDGen Consortium, The, G. L. G. C., The, I. C. B. P., The MAGIC Investigators, The MuTHER Consortium, et al. (2015) · Nature · doi:10.1038/nature14177 · Cited by 4,534 · see citing works
A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments
Stock, J. H., Wright, J. H., & Yogo, M. (2002) · Journal of Business and Economic Statistics · doi:10.1198/073500102288618658 · Cited by 4,151 · see citing works
Willer (2013) · doi:10.1038/ng.2797 · Cited by 2,983 · see citing works
Self-citationYusuf, S., Hawken, S., Ôunpuu, S., Bautista, L., Franzosi, M. G., Commerford, P., Lang, C. C., Rumboldt, Z., Onen, C. L., Lisheng, L., et al. (2005) · The Lancet · doi:10.1016/s0140-6736(05)67663-5 · Cited by 2,903 · see citing works
A comprehensive 1000 Genomes–based genome-wide association meta-analysis of coronary artery disease
the CARDIoGRAMplusC4D Consortium (2015) · Nature Genetics · doi:10.1038/ng.3396 · Cited by 2,447 · see citing works
Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk
The International Consortium for Blood Pressure Genome-Wide Association Studies (2011) · Nature · doi:10.1038/nature10405 · Cited by 2,033 · see citing works
Self-citationA Large-Scale, Consortium-Based Genomewide Association Study of Asthma
Moffatt, M. F., Gut, I. G., Démenais, F., Strachan, D. P., Bouzigon, E., Heath, S. C., von Mutius, E., Farrall, M., Lathrop, M. G., & Cookson, W. (2010) · New England Journal of Medicine · doi:10.1056/nejmoa0906312 · Cited by 2,003 · see citing works
the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Morris, A. P., Voight, B. F., Teslovich, T. M., Ferreira, T., Segrè, A. V., Steinthorsdottir, V., Strawbridge, R. J., Khan, H., Grallert, H., et al. (2012) · Nature Genetics · doi:10.1038/ng.2383 · Cited by 1,912 · see citing works
Triglycerides and Cardiovascular Disease: A Scientific Statement From the American Heart Association
Miller, M., Stone, N. J., Ballantyne, C., Bittner, V., Criqui, M. H., Ginsberg, H. N., Goldberg, A. C., Howard, W. J., Jacobson, M. S., Kris-Etherton, P. M., et al. (2011) · Circulation · doi:10.1161/cir.0b013e3182160726 · Cited by 1,871 · see citing works
Pierce, B. L., Ahsan, H., & VanderWeele, T. J. (2010) · International Journal of Epidemiology · doi:10.1093/ije/dyq151 · Cited by 1,837 · see citing works
New genetic loci link adipose and insulin biology to body fat distribution
The ADIPOGen Consortium, Shungin, D., The CARDIOGRAMplusC4D Consortium, The CKDGen Consortium, The GEFOS Consortium, The GENIE Consortium, The, G. L. G. C., The, I. C. B. P., The International Endogene Consortium, Study, T. L. C., et al. (2015) · Nature · doi:10.1038/nature14132 · Cited by 1,580 · cited 1× · see citing works
“O besity, typically definedon the basis of bodymass index (BMI), is a leading cause of type 2 diabetes and coronary heart disease (CHD) in the population.(1,2) However, foranygivenBMI,body fatdistributioncanvarysubstantially; some individuals store proportionally more fat aroundtheirvisceralorgans (abdominaladiposity)…”
BackgroundBody Fat Distribution and Risk of Cardiovascular Disease: An Update
Després, J.-P. (2012) · Circulation · doi:10.1161/circulationaha.111.067264 · Cited by 1,481 · see citing works
Triglycerides and cardiovascular disease
Nordestgaard, B. G., & Varbo, A. (2014) · The Lancet · doi:10.1016/s0140-6736(14)61177-6 · Cited by 1,419 · see citing works
Vazquez, G., Duval, S., Jacobs, D. R., & Silventoinen, K. (2007) · Epidemiologic Reviews · doi:10.1093/epirev/mxm008 · Cited by 976 · see citing works
Common variants associated with plasma triglycerides and risk for coronary artery disease
Do, R., Willer, C. J., Schmidt, E. M., Sengupta, S., Gao, C., Peloso, G. M., Gustafsson, S., Kanoni, S., Ganna, A., Chen, J., et al. (2013) · Nature Genetics · doi:10.1038/ng.2795 · Cited by 862 · see citing works
Scott, R. A., Lagou, V., Welch, R., Wheeler, E., Montasser, M. E., Luan, J., Mägi, R., Strawbridge, R. J., Rehnberg, E., Gustafsson, S., et al. (2012) · Nature Genetics · doi:10.1038/ng.2385 · Cited by 817 · see citing works
Sex Differences in Coronary Heart Disease
Barrett-Connor, E. L. (1997) · Circulation · doi:10.1161/01.cir.95.1.252 · Cited by 648 · see citing works
A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease
Nikpay, M., Goel, A., Won, H., Hall, L. M., Willenborg, C., Kanoni, S., Saleheen, D., Kyriakou, T., Nelson, C. P., Hopewell, J. C., et al. (2015) · EUR Research Repository (Erasmus University Rotterdam) · Cited by 581 · see citing works
Pattaro, C., Teumer, A., Gorski, M., Chu, A. Y., Li, M., Mijatovic, V., Garnaas, M., Tin, A., Sorice, R., Li, Y., et al. (2016) · Nature Communications · doi:10.1038/ncomms10023 · Cited by 514 · see citing works
Coding Variation in <i>ANGPTL4,</i> <i>LPL,</i> and <i>SVEP1</i> and the Risk of Coronary Disease
Myocardial Infarction Genetics and CARDIoGRAM Exome Consortia Investigators (2016) · New England Journal of Medicine · doi:10.1056/nejmoa1507652 · Cited by 493 · see citing works
Anti-obesity drugs: past, present and future
Rodgers, R. J., Tschöp, M. H., & Wilding, J. P. H. (2012) · Disease Models & Mechanisms · doi:10.1242/dmm.009621 · Cited by 431 · see citing works
Ashwell, M., Cole, T. J., & Dixon, A. K. (1985) · BMJ · doi:10.1136/bmj.290.6483.1692 · Cited by 402 · see citing works
South Asians and Cardiovascular Risk
Gupta, M., Singh, N., & Verma, S. (2006) · Circulation · doi:10.1161/circulationaha.105.583815 · Cited by 290 · see citing works
Nordestgaard, B. G., Palmer, T., Benn, M., Zacho, J., Tybjærg‐Hansen, A., Smith, G. D., & Timpson, N. J. (2012) · PLoS Medicine · doi:10.1371/journal.pmed.1001212 · Cited by 280 · see citing works
Instrumental Variable Analysis with a Nonlinear Exposure–Outcome Relationship
Burgess, S., Davies, N. M., & Thompson, S. G. (2014) · Epidemiology · doi:10.1097/ede.0000000000000161 · Cited by 208 · see citing works
Seidell, J. C., Björntorp, P. A., Sjöström, L.-G., Sannerstedt, R., Krotkiewski, M., & Kvist, H. (1989) · PubMed · Cited by 158 · see citing works
Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study
Voight, B. F., Peloso, G. M., Orho-Melander, M., Frikke-Schmidt, R., Barbalic, M., Jensen, M. K., Hindy, G., Hólm, H., Ding, E. L., Johnson, T., et al. (2012) · The Lancet · doi:10.1016/s0140-6736(12)60312-2 · Cited by 148 · see citing works
Genetic markers as instrumental variables
von Hinke, S., Smith, G. D., Lawlor, D. A., Propper, C., & Windmeijer, F. A. G. (2015) · Journal of Health Economics · doi:10.1016/j.jhealeco.2015.10.007 · Cited by 132 · see citing works
Separate associations of waist and hip circumference with lifestyle factors
Han, T. (1998) · International Journal of Epidemiology · doi:10.1093/ije/27.3.422 · Cited by 116 · see citing works
Ashwell, M., Cole, T. J., & Dixon, A. K. (1986) · Plastic & Reconstructive Surgery · doi:10.1097/00006534-198608000-00099 · Cited by 27 · see citing works
Flagged first, then by citations
2 of the 38 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Pulit, S. L., Harlow, C. E., Morris, A. P., Wood, A. R., Glastonbury, C. A., Tyrrell, J., Yengo, L., Ferreira, T. C., Marouli, E., Ji, Y., et al. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy327 · Cited by 1,169 · see citing works
Neeland, I. J., Poirier, P., & Després, J.-P. (2018) · Circulation · doi:10.1161/circulationaha.117.029617 · Cited by 881 · see citing works
Mendelian randomization for cardiovascular diseases: Principles and applications
Larsson, S. C., Butterworth, A. S., & Burgess, S. (2023) · European Heart Journal · doi:10.1093/eurheartj/ehad736 · Cited by 735 · see citing works
The science of obesity management: an endocrine society scientific statement
Bray, G. A., Heisel, W. E., Afshin, A., Jensen, M. D., Dietz, W. H., Long, M., Kushner, R. F., Daniels, S. R., Wadden, T. A., Tsai, A. G., et al. (2018) · Endocrine Reviews · doi:10.1210/er.2017-00253 · Cited by 812 · see citing works
Mendelian randomization in cardiometabolic disease: challenges in evaluating causality
Holmes, M. V., Ala-Korpela, M., & Smith, G. D. (2017) · Nature Reviews Cardiology · doi:10.1038/nrcardio.2017.78 · Cited by 679 · see citing works
Causes, consequences, and treatment of metabolically unhealthy fat distribution
Stefan, N. (2020) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(20)30110-8 · Cited by 548 · see citing works
Impaired adipogenesis and dysfunctional adipose tissue in human hypertrophic obesity
Hammarstedt, A., Gogg, S., Hedjazifar, S., Nerstedt, A., & Smith, U. (2018) · Physiological Reviews · doi:10.1152/physrev.00034.2017 · Cited by 462 · see citing works
Obesity as a multisystem disease: Trends in obesity rates and obesity‐related complications
Sarma, S., Sockalingam, S., & Dash, S. (2021) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14290 · Cited by 461 · see citing works
Psychiatric genetics and the structure of psychopathology
Smoller, J. W., Andreassen, O. A., Edenberg, H. J., Faraone, S. V., Glatt, S. J., & Kendler, K. S. (2019) · Molecular Psychiatry · doi:10.1038/s41380-017-0010-4 · Cited by 398 · see citing works
Dale, C. E., Fatemifar, G., Palmer, T. M., White, J., Prieto-Merino, D., Zabaneh, D., Engmann, J. E. L., Shah, T., Wong, A., Warren, H. R., et al. (2017) · Circulation · doi:10.1161/circulationaha.116.026560 · Cited by 345 · see citing works
Association Between Obesity and Cardiovascular Outcomes
Riaz, H., Khan, M. S., Siddiqi, T. J., Usman, M., Shah, N. P., Goyal, A., Khan, S. S., Mookadam, F., Krasuski, R. A., & Ahmed, H. M. (2018) · JAMA Network Open · doi:10.1001/jamanetworkopen.2018.3788 · Cited by 359 · see citing works
Karlsson, T., Rask‐Andersen, M., Pan, G., Höglund, J., Wadelius, C., Ek, W. E., & Johansson, Å. (2019) · Nature Medicine · doi:10.1038/s41591-019-0563-7 · Cited by 318 · see citing works
Lassale, C. M., Tzoulaki, I., Moons, K. G. M., Sweeting, M. J., Boer, J. M. A., Johnson, L., Huerta, J. M., Agnoli, C., Freisling, H., Weiderpass, E., et al. (2017) · European Heart Journal · doi:10.1093/eurheartj/ehx448 · Cited by 298 · see citing works
Paternoster, L., Tilling, K., & Davey Smith, G. (2017) · PLOS Genetics · doi:10.1371/journal.pgen.1006944 · Cited by 271 · see citing works
Larsson, S. C., & Burgess, S. (2021) · BMC Medicine · doi:10.1186/s12916-021-02188-x · Cited by 279 · see citing works
Lotta, L. A., Wittemans, L. B. L., Zuber, V., Stewart, I. D., Sharp, S. J., Luan, J., Day, F. R., Li, C., Bowker, N., Cai, L., et al. (2018) · JAMA · doi:10.1001/jama.2018.19329 · Cited by 229 · see citing works
Zhu, Z., Lee, P. H., Chaffin, M. D., Chung, W., Loh, P., Lu, Q., Christiani, D. C., & Liang, L. (2018) · Nature Genetics · doi:10.1038/s41588-018-0121-0 · Cited by 235 · see citing works
Iliodromiti, S., Celis-Morales, C. A., Lyall, D. M., Anderson, J. J., Gray, S. R., Mackay, D., Nelson, S. M., Welsh, P. I., Pell, J. P., Gill, J. M. R., et al. (2018) · European Heart Journal · doi:10.1093/eurheartj/ehy057 · Cited by 210 · see citing works
Causal relationships between obesity and the leading causes of death in women and men
Censin, J. C., Peters, S. A. E., Bovijn, J., Ferreira, T. C., Pulit, S. L., Mägi, R., Mahajan, A., Holmes, M. V., & Lindgren, C. M. (2019) · PLoS Genetics · doi:10.1371/journal.pgen.1008405 · Cited by 204 · see citing works
Association of body mass index and waist-to-hip ratio with brain structure
Hamer, M., & Batty, G. D. (2019) · Neurology · doi:10.1212/wnl.0000000000006879 · Cited by 190 · see citing works
Holmes, M. V., Richardson, T. G., Ference, B. A., Davies, N. M., & Davey Smith, G. (2021) · Nature Reviews Cardiology · doi:10.1038/s41569-020-00493-1 · Cited by 184 · see citing works
Wang, Q., Holmes, M. V., Smith, G. D., & Ala‐Korpela, M. (2017) · Diabetes Care · doi:10.2337/dc17-1642 · Cited by 184 · see citing works
Blüher, M. (2025) · Diabetes Obesity and Metabolism · doi:10.1111/dom.16263 · Cited by 215 · see citing works
What lipodystrophies teach us about the metabolic syndrome
Mann, J. P., & Savage, D. B. (2019) · Journal of Clinical Investigation · doi:10.1172/jci129190 · Cited by 180 · see citing works
Surrogate Adiposity Markers and Mortality
Khan, I. I., Chong, M., Le, A., Mohammadi‐Shemirani, P., Morton, R. W., Brinza, C., Kiflen, M., Narula, S., Akhabir, L., Mao, S., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.34836 · Cited by 179 · see citing works
Wnt/β-Catenin Signaling and Obesity
Chen, N., & Wang, J. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.00792 · Cited by 169 · see citing works
Associations of Body Mass and Fat Indexes With Cardiometabolic Traits
Bell, J. A., Carslake, D., O’Keeffe, L. M., Frysz, M., Howe, L. D., Hamer, M., Wade, K. H., Timpson, N. J., & Smith, G. D. (2018) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2018.09.066 · Cited by 166 · see citing works
Inherited basis of visceral, abdominal subcutaneous and gluteofemoral fat depots
Agrawal, S., Wang, M., Klarqvist, M. D. R., Smith, K., Shin, J. H., Dashti, H., Diamant, N., Choi, S. H., Jurgens, S. J., Ellinor, P. T., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30931-2 · Cited by 151 · see citing works
Self-citationGenomic insights into the causes of type 2 diabetes
Langenberg, C., & Lotta, L. A. (2018) · The Lancet · doi:10.1016/s0140-6736(18)31132-2 · Cited by 142 · see citing works
Dissecting the genetic relationship between cardiovascular risk factors and Alzheimer’s disease
Broce, I. J., Tan, C. H., Fan, C. C., Jansen, I., Savage, J. E., Witoelar, A., Wen, N., Hess, C. P., Dillon, W. P., Glastonbury, C. M., et al. (2019) · Acta Neuropathologica · doi:10.1007/s00401-018-1928-6 · Cited by 140 · see citing works
Ren, J. T., Sun, M., Zhou, H., Ajoolabady, A., Zhou, Y., Tao, J., Sowers, J. R., & Zhang, Y. (2020) · Science Advances · doi:10.1126/sciadv.abc8561 · Cited by 137 · see citing works
Waist-To-Hip Ratio and Mortality in Heart Failure
Streng, K. W., Voors, A. A., Hillege, H. L., Anker, S. D., Cleland, J. G. F., Dickstein, K., Filippatos, G. S., Metra, M., Ng, L. L., Ponikowski, P. P., et al. (2018) · European Journal of Heart Failure · doi:10.1002/ejhf.1244 · Cited by 135 · see citing works
Genetic Association of Albuminuria with Cardiometabolic Disease and Blood Pressure
Haas, M. E., Aragam, K. G., Emdin, C. A., Bick, A. G., Hemani, G., Smith, G. D., & Kathiresan, S. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.08.004 · Cited by 132 · see citing works
Self-citationLoos, R. J. F. (2018) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2018.02.009 · Cited by 127 · see citing works
Reilly, J. P., Wang, F., Jones, T. K., Palakshappa, J. A., Anderson, B. J., Shashaty, M. G. S., Dunn, T. G., Johansson, E. D., Riley, T. R., Lim, B., et al. (2018) · Intensive Care Medicine · doi:10.1007/s00134-018-5328-0 · Cited by 126 · see citing works
Hartwig, F. P., Tilling, K. M., Smith, G. D., Lawlor, D. A., & Borges, M. C. (2021) · International Journal of Epidemiology · doi:10.1093/ije/dyaa266 · Cited by 116 · see citing works
Genetics Insights in the Relationship Between Type 2 Diabetes and Coronary Heart Disease
Goodarzi, M. O., & Rotter, J. I. (2020) · Circulation Research · doi:10.1161/circresaha.119.316065 · Cited by 117 · see citing works
Phenotypic Consequences of a Genetic Predisposition to Enhanced Nitric Oxide Signaling
Emdin, C. A., Khera, A. V., Klarin, D. M., Natarajan, P., Zekavat, S. M., Nomura, A., Haas, M. E., Aragam, K. G., Ardissino, D., Wilson, J. G., et al. (2017) · Circulation · doi:10.1161/circulationaha.117.028021 · Cited by 118 · see citing works
Self-citationGeng, T., Smith, C. E., Li, C., & Huang, T. (2018) · Diabetes Care · doi:10.2337/dc17-2141 · Cited by 121 · see citing works
Sex Differences in Genomic Drivers of Adipose Distribution and Related Cardiometabolic Disorders
Lumish, H. S., O'Reilly, M. E., & Reilly, M. P. (2019) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.119.313154 · Cited by 118 · see citing works
Agrawal, S., Klarqvist, M., Diamant, N., Stanley, T., Ellinor, P., Mehta, N., Philippakis, A. A., Ng, K., Claussnitzer, M., Grinspoon, S., et al. (2023) · Nature Communications · doi:10.1038/s41467-022-35704-5 · Cited by 95 · see citing works
Self-citationPulit, S. L., Harlow, C. E., Morris, A. P., Wood, A. R., Glastonbury, C. A., Tyrrell, J., Yengo, L., Ferreira, T. C., Marouli, E., Ji, Y., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/304030 · Cited by 109 · see citing works
Justice, A. E., Karaderi, T., Highland, H. M., Young, K. L., Graff, M., Lu, Y., Turcot, V., Auer, P. L., Fine, R. S., Guo, X., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0334-2 · Cited by 98 · see citing works
Emdin, C. A., Khera, A. V., Chaffin, M. D., Klarin, D. M., Natarajan, P., Aragam, K. G., Haas, M. E., Bick, A. G., Zekavat, S. M., Nomura, A., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03911-8 · Cited by 89 · see citing works
Self-citationSpiller, W., Slichter, D., Bowden, J., & Davey Smith, G. (2018) · International Journal of Epidemiology · doi:10.1093/ije/dyy204 · Cited by 90 · see citing works
Multiomics technologies: role in disease biomarker discoveries and therapeutics
Dar, M. A., Arafah, A. M., Bhat, K. A., Khan, A., Khan, M. S., Ali, A., Ahmad, S. M., Rashid, S. M., & Rehman, M. U. (2022) · Briefings in Functional Genomics · doi:10.1093/bfgp/elac017 · Cited by 96 · see citing works
Links among Obesity, Type 2 Diabetes Mellitus, and Osteoporosis: Bone as a Target
Martiniaková, M., Bíro, R., Pénzes, N., Šarocká, A., Kováčová, V., Mondočková, V., & Omelka, R. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25094827 · Cited by 94 · see citing works
Periodontitis and metabolic diseases (diabetes and obesity): Tackling multimorbidity
Marruganti, C., Suvan, J. E., & D’Aiuto, F. (2023) · Periodontology 2000 · doi:10.1111/prd.12536 · Cited by 96 · see citing works
When Will Mendelian Randomization Become Relevant for Clinical Practice and Public Health?
Davey Smith, G., Paternoster, L., & Relton, C. (2017) · JAMA · doi:10.1001/jama.2016.21189 · Cited by 87 · see citing works
Wang, S., Wang, C., Turdi, S., Richmond, K. L., Zhang, Y., & Ren, J. T. (2018) · International Journal of Obesity · doi:10.1038/s41366-018-0030-4 · Cited by 85 · see citing works
The Genetic Epidemiology of Type 2 Diabetes: Opportunities for Health Translation
Meigs, J. B. (2019) · Current Diabetes Reports · doi:10.1007/s11892-019-1173-y · Cited by 82 · see citing works
Circulating miR-146a in healthy aging and type 2 diabetes: Age- and gender-specific trajectories
Mensa’, E., Giuliani, A., Matacchione, G., Gurău, F., Bonfigli, A. R., Romagnoli, F., Luca, M. D., Sabbatinelli, J., & Olivieri, F. (2019) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2019.03.001 · Cited by 82 · see citing works
Tanigawa, Y., Li, J., Justesen, J. M., Horn, H., Aguirre, M., DeBoever, C., Chang, C. C., Narasimhan, B., Lage, K., Hastie, T. J., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11953-9 · Cited by 82 · see citing works
Women's use of over-the-counter antifungal medications for gynecologic symptoms.
Ferris, D. G., Dekle, C. L., & Litaker, M. S. (1996) · PubMed · Cited by 82 · see citing works
Pagidipati, N. J., Zheng, Y., Green, J. B., McGuire, D. K., Mentz, R. J., Shah, S. H., Aschner, P. J., Delibaşı, T., Rodbard, H. W., Westerhout, C. M., et al. (2019) · American Heart Journal · doi:10.1016/j.ahj.2019.09.016 · Cited by 73 · see citing works
Obesity is associated with reduced cerebral blood flow – modified by physical activity
Knight, S., Laird, E., Williamson, W., O’Connor, J., Newman, L., Carey, D., Looze, C. D., Fagan, A., Chappell, M., Meaney, J., et al. (2021) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2021.04.008 · Cited by 58 · see citing works
Multi-omics profiling: the way toward precision medicine in metabolic diseases
Hu, C., & Jia, W. (2021) · Journal of Molecular Cell Biology · doi:10.1093/jmcb/mjab051 · Cited by 74 · see citing works
Keele, G. R., Prokop, J. W., He, H., Holl, K., Littrell, J. C., Deal, A. W., Francic, S., Cui, L., Gatti, D. M., Broman, K. W., et al. (2017) · Obesity · doi:10.1002/oby.22075 · Cited by 69 · see citing works
Dietary Fatty Acid Composition Modulates Obesity and Interacts with Obesity‐Related Genes
Hammad, S. S., & Jones, P. J. H. (2017) · Lipids · doi:10.1007/s11745-017-4291-9 · Cited by 65 · see citing works
Genes that make you fat, but keep you healthy
Loos, R. J. F., & Kilpeläinen, T. O. (2018) · Journal of Internal Medicine · doi:10.1111/joim.12827 · Cited by 60 · see citing works
Circulating vitamin E levels and Alzheimer's disease: a Mendelian randomization study
Liu, G., Zhao, Y., Jin, S., Hu, Y., Wang, T., Tian, R., Han, Z., Xu, D., & Jiang, Q. (2018) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2018.08.008 · Cited by 62 · see citing works
Porro, S., Genchi, V. A., Cignarelli, A., Natalicchio, A., Laviola, L., Giorgino, F., & Perrini, S. (2020) · Journal of Endocrinological Investigation · doi:10.1007/s40618-020-01446-8 · Cited by 63 · see citing works
Rare loss of function variants in the hepatokine gene INHBE protect from abdominal obesity
Deaton, A. M., Dubey, A., Ward, L. D., Dornbos, P., Flannick, J. A., Yee, E., Ticau, S., Noetzli, L. J., Parker, M. M., Hoffing, R. A., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31757-8 · Cited by 58 · see citing works
Zhu, K., Qian, F., Lu, Q., Li, R., Qiu, Z., Li, L., Li, R., Yu, H., Deng, Y., YANG, K. Y., et al. (2024) · Diabetes Care · doi:10.2337/dc23-1503 · Cited by 59 · see citing works
Sex differences in human adipose tissue gene expression and genetic regulation involve adipogenesisPreprint
Anderson, W. D., Soh, J. Y., Innis, S. E., Dimanche, A., Ma, L., Langefeld, C. D., Comeau, M. E., Das, S. K., Schadt, E. E., Björkegren, J., et al. (2020) · Genome Research · doi:10.1101/gr.264614.120 · Cited by 58 · see citing works
The Epigenomic Analysis of Human Obesity
Bell, C. G. (2017) · Obesity · doi:10.1002/oby.21909 · Cited by 57 · see citing works
Patrick, M. T., Stuart, P. E., Zhang, H., Zhao, Q., Yin, X., He, K., Zhou, S., Mehta, N. N., Voorhees, J. J., Boehnke, M., et al. (2020) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2020.11.025 · Cited by 56 · see citing works
Akbari, P., Sosina, O. A., Bovijn, J., Landheer, K., Nielsen, J. B., Kim, M., Aykul, S., De, T., Haas, M. E., Hindy, G., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32398-7 · Cited by 56 · see citing works
Lane, M. M., Travica, N., Gamage, E., Marshall, S., Trakman, G., Young, C., Teasdale, S. B., Dissanayaka, T. D., Dawson, S. L., Orr, R., et al. (2024) · Annual Review of Nutrition · doi:10.1146/annurev-nutr-062322-020650 · Cited by 55 · see citing works
A phenome-wide comparative analysis of genetic discordance between obesity and type 2 diabetes
Coral, D., Fernandez-Tajes, J. J., Tsereteli, N., Pomares‐Millan, H., Fitipaldi, H., Mutie, P. M., Atabaki‐Pasdar, N., Kalamajski, S., Poveda, A., Miller‐Fleming, T. W., et al. (2023) · Nature Metabolism · doi:10.1038/s42255-022-00731-5 · Cited by 53 · see citing works
Impact of Genes and Environment on Obesity and Cardiovascular Disease
Heianza, Y., & Qi, L. (2018) · Endocrinology · doi:10.1210/en.2018-00591 · Cited by 54 · see citing works
Wang, L., Qiao, Y., Zhang, H., Zhang, Y., Hua, J., Jin, S., & Liu, G. (2019) · Journal of Alzheimer s Disease · doi:10.3233/jad-190713 · Cited by 51 · see citing works
Winkler, T. W., Günther, F., Höllerer, S., Zimmermann, M. E., Loos, R. J. F., Kutalik, Z., & Heid, I. M. (2018) · Nature Communications · doi:10.1038/s41467-018-04124-9 · Cited by 51 · see citing works
Hu, Y., Zhang, Y., Zhang, H., Gao, S., Wang, L., Wang, T., Han, Z., Sun, B., & Liu, G. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01695-4 · Cited by 47 · see citing works
Lipid and metabolic syndrome traits in coronary artery disease: a Mendelian randomization study
Thomas, D. G., Wei, Y., & Tall, A. R. (2020) · Journal of Lipid Research · doi:10.1194/jlr.p120001000 · Cited by 52 · see citing works
Mendelian Randomization analysis of the causal effect of adiposity on hospital costs
Dixon, P., Hollingworth, W., Harrison, S., Davies, N. M., & Davey Smith, G. (2020) · Journal of Health Economics · doi:10.1016/j.jhealeco.2020.102300 · Cited by 48 · see citing works
Bell, J. A., Richardson, T. G., Wang, Q., Sanderson, E. C. M., Palmer, T., Walker, V. M., O’Keeffe, L. M., Timpson, N. J., Cichońska, A., Julkunen, H., et al. (2022) · The Lancet Regional Health - Europe · doi:10.1016/j.lanepe.2022.100457 · Cited by 47 · see citing works
Agrawal, S., Klarqvist, M. D. R., Emdin, C. A., Patel, A. P., Paranjpe, M. D., Ellinor, P. T., Philippakis, A., Ng, K., Batra, P., & Khera, A. V. (2021) · Patterns · doi:10.1016/j.patter.2021.100364 · Cited by 50 · see citing works
Self-citationParkinson’s disease and Alzheimer’s disease: a Mendelian randomization study
Han, Z., Tian, R., Ren, P., Zhou, W., Wang, P., Luo, M., Jin, S., & Jiang, Q. (2018) · BMC Medical Genetics · doi:10.1186/s12881-018-0721-7 · Cited by 46 · see citing works
Bi, H., Zhang, Y., Qin, P., Wang, C., Peng, X., Chen, H., Zhao, D., Xu, S., Wang, L., Zhao, P., et al. (2021) · Journal of the American Heart Association · doi:10.1161/jaha.121.022633 · Cited by 45 · see citing works
Activin E–ACVR1C cross talk controls energy storage via suppression of adipose lipolysis in mice
Adam, R. C., Pryce, D. S., Lee, J. S., Zhao, Y., Mintah, I. J., Min, S., Halasz, G., Mastaitis, J., Atwal, G. S., Aykul, S., et al. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2309967120 · Cited by 44 · see citing works
Impact of Serum Calcium Levels on Alzheimer’s Disease: A Mendelian Randomization Study
He, Y., Zhang, H., Wang, T., Han, Z., Ni, Q., Wang, K., Wang, L., Zhang, Y., Hu, Y., Jin, S., et al. (2020) · Journal of Alzheimer s Disease · doi:10.3233/jad-191249 · Cited by 45 · see citing works
Rentsch, C. T., Garfield, V., Mathur, R., Eastwood, S. V., Smeeth, L. L., Chaturvedi, N., & Bhaskaran, K. (2023) · The Lancet Regional Health - Europe · doi:10.1016/j.lanepe.2023.100693 · Cited by 44 · see citing works
Emdin, C. A., Khera, A. V., Aragam, K. G., Haas, M. E., Chaffin, M. D., Klarin, D. M., Natarajan, P., Bick, A. G., Zekavat, S. M., Nomura, A., et al. (2018) · Diabetes · doi:10.2337/db18-0857 · Cited by 42 · see citing works
Self-citationChen, G., Yan, H., Hao, Y., Shrestha, S., Wang, J., Li, Y., Wei, Y., Pan, J., & Zhang, Z. (2019) · BMC Pediatrics · doi:10.1186/s12887-019-1501-z · Cited by 43 · see citing works
Wainberg, M., Mahajan, A., Kundaje, A. B., McCarthy, M. I., Ingelsson, E., Sinnott-Armstrong, N., & Rivas, M. A. (2019) · PLoS Medicine · doi:10.1371/journal.pmed.1002982 · Cited by 42 · see citing works
Scicali, R., Rosenbaum, D. H., Pino, A. D., Giral, P., Cluzel, P., Redheuil, A. B., Piro, S., Rabuazzo, A. M., Purrello, F., Bruckert, É., et al. (2018) · Acta Diabetologica · doi:10.1007/s00592-018-1144-9 · Cited by 41 · see citing works
Xu, H., Jin, C., & Guan, Q.-B. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00603 · Cited by 43 · see citing works
Sbraccia, P., D’Adamo, M., & Guglielmi, V. (2021) · Eating and Weight Disorders - Studies on Anorexia Bulimia and Obesity · doi:10.1007/s40519-021-01109-4 · Cited by 42 · see citing works
Callcott, E. T., Santhakumar, A. B., Luo, J., & BLANCHARD, C. L. (2018) · Journal of Applied Biomedicine · doi:10.1016/j.jab.2018.03.001 · Cited by 40 · see citing works
Adipocyte Heterogeneity Underlying Adipose Tissue Functions
Yang, Z., Solivan-Rivera, J., & Corvera, S. (2021) · Endocrinology · doi:10.1210/endocr/bqab138 · Cited by 40 · see citing works
Identification of TBX15 as an adipose master trans regulator of abdominal obesity genes
Pan, D. Z., Miao, Z., Comenho, C., Rajkumar, S., Koka, A., Lee, S. H., Alvarez, M., Kamińska, D., Ko, A., Sinsheimer, J. S., et al. (2021) · Genome Medicine · doi:10.1186/s13073-021-00939-2 · Cited by 39 · see citing works
Body Composition and COPD: A New Perspective
Wang, X., Liang, Q., Li, Z., & Li, F. (2023) · International Journal of COPD · doi:10.2147/copd.s394907 · Cited by 42 · see citing works
Wang, T., Zhang, R., Ma, X., Wang, S., He, Z., Huang, Y., Xu, B., Li, Y., Zhang, H., Jiang, F., et al. (2018) · Obesity · doi:10.1002/oby.22167 · Cited by 39 · see citing works
Natarajan, P., Pampana, A., Graham, S. E., Ruotsalainen, S., Perry, J. A., de Vries, P. S., Broome, J., Pirruccello, J. P., Honigberg, M. C., Aragam, K. G., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22339-1 · Cited by 36 · see citing works
Self-citationCardiometabolic Outcomes Among Adults With Abdominal Obesity and Normal Body Mass Index
Ahmed, K. Y., Aychiluhm, S. B., Thapa, S., Tegegne, T. K., Ketema, D. B., Kassa, Z. Y., Kibret, G. D., Duko, B., Shifti, D. M., Bore, M. G., et al. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.37942 · Cited by 38 · see citing works
Viitasalo, A., Schnurr, T. M., Pitkänen, N., Hollensted, M., Nielsen, T. R. H., Pahkala, K., Atalay, M., Lind, M. V., Heikkinen, S., Frithioff‐Bøjsøe, C., et al. (2019) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/nqz187 · Cited by 38 · see citing works
Elhakeem, A., Taylor, A. E., Inskip, H. M., Huang, J. Y., Mansell, T., Rodrigues, C., Asta, F., Blaauwendraad, S. M., Håberg, S. E., Halliday, J. L., et al. (2022) · European Heart Journal · doi:10.1093/eurheartj/ehac726 · Cited by 37 · see citing works
Multivariable MR Can Mitigate Bias in Two‐Sample MR Using Covariable‐Adjusted Summary Associations
Gilbody, J., Borges, M. C., Smith, G. D., & Sanderson, E. C. M. (2025) · Genetic Epidemiology · doi:10.1002/gepi.22606 · Cited by 37 · see citing works
Lind, L., Strand, R., Michaëlsson, K., Ahlström, H. K., & Kullberg, J. (2019) · Radiology · doi:10.1148/radiol.2019191035 · Cited by 36 · see citing works
- Authors
- Emdin, C. A., Khera, A. V., Natarajan, P., Klarin, D., Zekavat, S. M., Hsiao, A. J., & Kathiresan, S.
- Affiliations
- Broad Institute (US)
- Massachusetts Institute of Technology (US)
- Corresponding author
- Kathiresan, Sekar
- ORCID
- Venue
- JAMA
- Publisher
- American Medical Association (AMA)
- Published
- 2017-02-14
- Volume
- 317
- Issue
- 6
- Pages
- 626
- ISSN
- 0002-9955 (ISSN-L)
- 0098-7484
- PMC
- PMC5571980
- OpenAlex
- W2589093959
- Cited by
- 378
- References
- 38
- Citations by year
- 2026: 14 · 2025: 38 · 2024: 30 · 2023: 38 · 2022: 54 · 2021: 45 · 2020: 37 · 2019: 37 · 2018: 63 · 2017: 21
- FWCI
- 28.61
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- JAMA (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (5t32hl007734-30, KL2TR001100)
- National Heart, Lung, and Blood Institute (R01HL127564, T-32HL007734)
- National Center for Advancing Translational Sciences
- 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
- waist-to-hip ratio, abdominal fat, type 2 diabetes, coronary artery disease, polygenic risk score, Mendelian randomization, cardiometabolic traits, triglycerides, blood pressure, glycemic traits, causal inference, single nucleotide polymorphisms, body mass index adjustment, genetic susceptibility, UK Biobank
- MeSH terms
- Blood Glucose, Blood Pressure, Coronary Disease, Cross-Sectional Studies, Diabetes Mellitus, Type 2, Female, Humans, Male, Middle Aged, Triglycerides, Body Mass Index, Odds Ratio, Case-Control Studies, Polymorphism, Single Nucleotide, Waist-Hip Ratio, Obesity, Abdominal, Mendelian Randomization Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
