The DOI matches its Crossref record; 36 of the 38 references it cites were found in a registry or an index.
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.
Overview
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
Verification
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?
In the citing paper
“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.
Citations
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
Bowden, J., Davey Smith, G., Haycock, P. C., & Burgess, S. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21965 · Cited by 8,922
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
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
Davey Smith, G., & Ebrahim, S. (2003) · International Journal of Epidemiology · doi:10.1093/ije/dyg070 · Cited by 5,882
Prospective Studies Collaboration (2009) · The Lancet · doi:10.1016/s0140-6736(09)60318-4 · Cited by 4,807
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
Self-citationA 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
Willer (2013) · doi:10.1038/ng.2797 · Cited by 2,983
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
Flagged first, then by citations
2 of the 38 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 201–300 of 377
Obesity and Cardiometabolic Disease: Insights From Genetic Studies
Dash, S. (2025) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2025.01.034 · Cited by 10
Lee, Y., Kang, Y.-R., Chang, Y., Kim, J., & Sung, M. (2025) · Obesity Reviews · doi:10.1111/obr.70026 · Cited by 13
Song, Q., Huang, T., Song, J., Meng, X., Li, C., Wang, Y., & Wang, H. (2020) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2020.05.008 · Cited by 9
Schuermans, A., Nakao, T., Ruan, Y., Koyama, S., Yu, Z., Uddin, M. M., Haidermota, S., Hornsby, W., Lewandowski, A. J., Bick, A. G., et al. (2023) · Journal of the American Heart Association · doi:10.1161/jaha.123.030220 · Cited by 11
Self-citationKaur, Y., Wang, D. X., Liu, H., & Meyre, D. (2018) · Obesity Reviews · doi:10.1111/obr.12806 · Cited by 10
Zulyniak, M. A., Fuller, H., & Iles, M. M. (2020) · Lifestyle Genomics · doi:10.1159/000509663 · Cited by 10
Nonconserved Long Intergenic Noncoding RNAs Associate With Complex Cardiometabolic Disease Traits
Foulkes, A. S., Selvaggi, C. A., Cao, T., O'Reilly, M. E., Cynn, E., Ma, P., Lumish, H. S., Xue, C., & Reilly, M. P. (2020) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.120.315045 · Cited by 9
TrammelGraph: visual graph abstraction for comparison
Jin, Z., Chen, N., Shi, Y., Qian, W., Xu, M., & Cao, N. (2021) · Journal of Visualization · doi:10.1007/s12650-020-00706-2 · Cited by 9
Trichia, E., Malden, D. E., Jin, D., Wright, N., Taylor, H., Karpe, F., Sherliker, P., Murgia, F., Hopewell, J. C., Lacey, B., et al. (2023) · International Journal of Epidemiology · doi:10.1093/ije/dyad143 · Cited by 8
Educational Attainment and Ischemic Stroke: A Mendelian Randomization Study
Gao, L., Wang, K., Ni, Q., Fan, H., Zhao, L., Huang, L., Yang, M., & Li, H. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2021.794820 · Cited by 8
Li, H., Zhao, J., Liang, J., & Song, X. (2023) · PLoS ONE · doi:10.1371/journal.pone.0287027 · Cited by 10
Zhou, H., Qi, Y., Xu, Y., Qi, X., & Qi, H. (2024) · Scientific Reports · doi:10.1038/s41598-024-58182-9 · Cited by 8
Chen, C., Zheng, X., Liao, S., Chen, S., Liang, M., Tang, K., Yin, M., Liu, H., & Ni, J. (2024) · BMC Public Health · doi:10.1186/s12889-024-18887-y · Cited by 8
Waist to hip ratio modifies the cardiovascular risk of lipoprotein (a): Insights from MESA
Ahmad, M. I., Chevli, P. A., Mirzai, S., Rikhi, R. R., Bhatia, H., Pagidipati, N. J., Blumenthal, R. S., Razavi, A. C., Ruddiman, K. R., Spitz, J. A., et al. (2025) · Progress in Cardiovascular Diseases · doi:10.1016/j.pcad.2025.03.001 · Cited by 8
Xu, J. Y., Hou, Y., Xu, M., Zhao, Z., Xuan, L., Wang, T., Li, M., Xu, Y., Lu, J., Bi, Y., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-41076-6 · Cited by 8
Gellert, K. S., Keil, A. P., Zeng, D., Lesko, C. R., Aubert, R. E., Avery, C. L., Lutsey, P. L., Siega‐Riz, A. M., Windham, B. G., & HEISS, G. M. (2020) · Journal of the American Heart Association · doi:10.1161/jaha.119.012214 · Cited by 8
Yu, Y., Hou, L., Shi, X., Sun, X., Liu, X., Yu, Y., Yuan, Z., Li, H., & Xue, F. (2022) · PLoS Genetics · doi:10.1371/journal.pgen.1010107 · Cited by 8
Wang, Z., Huang, X., Li, J., Liu, N., & Wei, Q. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.975327 · Cited by 9
Young, K. L., Fisher, V. A., Deng, X., Brody, J. A., Graff, M., Lim, E., Lin, B. M., Xu, H., Amin, N., An, P., et al. (2022) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2022.100163 · Cited by 8
Multi‐Omic Approaches to Identify Genetic Factors in Metabolic Syndrome
Clark, K. C., & Kwitek, A. E. (2021) · Comprehensive physiology · doi:10.1002/cphy.c210010 · Cited by 9
Adipose tissue-derived metabolite risk scores and risk for type 2 diabetes in South Asians
Gadgil, M. D., Cheng, J. Q., Herrington, D. M., Kandula, N. R., & Kanaya, A. M. (2024) · International Journal of Obesity · doi:10.1038/s41366-023-01457-4 · Cited by 7
Lv, W., Zhang, X., Fan, K., Xia, X., Zhang, Q., Li, H., Jiang, B.-Y., Zhang, W., & Deng, H. (2018) · European Journal of Human Genetics · doi:10.1038/s41431-018-0180-9 · Cited by 7
Young, E. P., & Stitziel, N. O. (2018) · Annual Review of Medicine · doi:10.1146/annurev-med-041717-085853 · Cited by 7
Truzzi, G., Naufel, M. F., Tufik, S. B., & Coelho, F. M. S. (2021) · Arquivos de Neuro-Psiquiatria · doi:10.1590/0004-282x-anp-2020-0352 · Cited by 7
Zhang, Y., Zhang, Y., Shi, Y., Dong, W., Mu, Y., Wang, J., Gao, Y., Hu, R., Xu, Y., Chen, Y., et al. (2021) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.732200 · Cited by 7
Meulmeester, F. L., van Dijk, K. W., Mooijaart, S. P., van Heemst, D., & Noordam, R. (2022) · GeroScience · doi:10.1007/s11357-022-00654-9 · Cited by 7
Adiyeva, M., Aukenov, N., NURZHANOVA, A., SLAMKHANOVA, N., & Kerimkulova, A. (2022) · Bratislavské lekárske listy/Bratislava medical journal · doi:10.4149/bll_2023_008 · Cited by 8
Beesley, L. J., Salvatore, M., Fritsche, L. G., Pandit, A., Rao, A., Brummett, C. M., Willer, C. J., Lisabeth, L. D., & Mukherjee, B. (2018) · Preprints.org · doi:10.20944/preprints201809.0388.v1 · Cited by 7
Xu, L., Qiu, J., Shen, P., Wang, Y., Wu, Y., Hu, J., Yang, Z., Zhu, Z., Lin, H., Shui, L., et al. (2025) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2025.103851 · Cited by 7
The liver clock modulates circadian rhythms in white adipose tissue
Vlassakev, I., Savva, C., Renzi, G., Ilamathi, H. S., Mamand, D. R., Smith, J. G., Greco, C., Litwin, C., Zhang, Q., Velez, L., et al. (2025) · Molecular Metabolism · doi:10.1016/j.molmet.2025.102249 · Cited by 6
Liu, C., Peng, J., Liu, Y., Peng, Y., & Ma, Q. (2024) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2024.118572 · Cited by 6
Zhou, Y., Hou, Y., Xiang, J., Dai, H., Li, M., Wang, T., Wang, S., Lin, H., Lu, J., Xu, Y., et al. (2021) · Nutrition & Metabolism · doi:10.1186/s12986-021-00629-1 · Cited by 6
Zhang, Y., Liu, H., Zhang, H., Han, Z., Wang, T., Wang, L., & Liu, G. (2023) · European Journal of Clinical Nutrition · doi:10.1038/s41430-023-01260-4 · Cited by 6
Lu, X., Xie, T., van Faassen, M. H. J. R., Kema, I. P., van Beek, A. P., Xu, X., Huo, X., Wolffenbuttel, B. H. R., van Vliet‐Ostaptchouk, J. V., Nolte, I. M., et al. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.169972 · Cited by 8
Lifetime Smoking and Asthma: A Mendelian Randomization Study
Shen, M., Liu, X. Z., Li, G., Li, Z., & Zhou, H. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00769 · Cited by 6
HRV-Guided Training for Elders after Stroke: A Protocol for a Cluster-Randomized Controlled Trial
Poyatos, M. C., Granero‐Gallegos, A., López-García, G. D., & López-Osca, R. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph191710868 · Cited by 8
Johnson, A. H., Phillips, S. R., & Rice, M. H. (2020) · Journal for Specialists in Pediatric Nursing · doi:10.1111/jspn.12288 · Cited by 6
Should We Use Genetic Scores in the Determination of Treatment Strategies to Control Dyslipidemias?
Lechner, K., Kessler, T., & Schunkert, H. (2020) · Current Cardiology Reports · doi:10.1007/s11886-020-01408-9 · Cited by 6
Bridging the variant-to-function gap in type 2 diabetes: advances and challenges
Maynard, A. G., Bhardwaj, R., Jones, T. R., & Claussnitzer, M. (2025) · Diabetologia · doi:10.1007/s00125-025-06600-6 · Cited by 7
Diagnosis, staging and management of obesity: a synthesis of contemporary guidelines
Tandar, C. E., Lin, J. C., & Stanford, F. C. (2026) · Nature Reviews Endocrinology · doi:10.1038/s41574-026-01279-0 · Cited by 5
Hashemy, H., Nguyen, A., Khafagy, R. T. M., Roshandel, D., Paterson, A. D., & Dash, S. (2024) · Diabetes Obesity and Metabolism · doi:10.1111/dom.15542 · Cited by 5
Jackisch, J., Noor, N., Raitakari, O. T., Lehtimäki, T. J., Kähönen, M. A. P., Cullati, S., Delpierre, C., Mika Kivimaki, B., & Carmeli, C. (2024) · BMJ Open · doi:10.1136/bmjopen-2023-078428 · Cited by 5
Ye, C., Kong, L., Wang, Y., Lin, H., Wang, S., Zhao, Z., Li, M., Xu, Y., Lu, J., Chen, Y., et al. (2022) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgac658 · Cited by 6
Luna-Ramirez, R. I., Kelly, A. C., Anderson, M. J., Bidwell, C. A., Goyal, R., & Limesand, S. W. (2023) · Endocrinology · doi:10.1210/endocr/bqad177 · Cited by 4
Andreoli, M. F., Gentreau, M., Rukh, G., Perelló, M., & Schiöth, H. B. (2024) · Diabetes Obesity and Metabolism · doi:10.1111/dom.15695 · Cited by 5
Apple or pear — waist-to-hip ratio and the risk of CHD and T2DM
Morris, A. W. J. (2017) · Nature Reviews Endocrinology · doi:10.1038/nrendo.2017.26 · Cited by 5
Prioritising Risk Factors for Type 2 Diabetes: Causal Inference through Genetic Approaches
Wittemans, L. B. L., Lotta, L. A., & Langenberg, C. (2018) · Current Diabetes Reports · doi:10.1007/s11892-018-1009-1 · Cited by 7
Lind, L., Strand, R., Michaëlsson, K., Kullberg, J., & Ahlström, H. K. (2019) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2019.06.017 · Cited by 5
Imahori, Y., Frost, C. D., Mathiesen, E. B., Ryabikov, A. N., Kudryavtsev, A. V., Malyutina, S. K., Kornev, M., Hughes, A. D., Hopstock, L. A., & Leon, D. A. (2020) · BMJ Open · doi:10.1136/bmjopen-2019-036583 · Cited by 5
Wang, Y., Hua, Y., Zhang, H., Liang, S., Cao, Z., Chen, L., Su, Z., & Zhang, W. (2023) · Journal of Clinical Hypertension · doi:10.1111/jch.14739 · Cited by 4
Qian, Y., Sun, B., Zhang, Y., Zhang, M.-B., Jiao, X., Lai, L.-Y., & Yang, W. (2022) · Annals of Palliative Medicine · doi:10.21037/apm-21-3072 · Cited by 5
Doost, H. D., Jounaghani, M. N., Riahi, R., Heidari-Beni, M., Hosseini, M., Sharifianjazi, F., & Kelishadi, R. (2025) · Journal of Obesity · doi:10.1155/jobe/8472196 · Cited by 5
Modeling the genomic architecture of adiposity and anthropometrics across the lifespan
Arehart, C. H., Lin, M. H., Gibson, R. A., Anderson, H. D., Aquilante, C. L., Arbogast, K. L., Arehart, C. H., Brooks, I. M., Brunetti, T. M., Brutus-Lestin, J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62730-w · Cited by 7
Savla, J. J., & Neeland, I. J. (2018) · Current Cardiovascular Risk Reports · doi:10.1007/s12170-018-0566-9 · Cited by 4
Li, S., Liu, B., Li, Q., Zhang, Y., Zhang, H., Gao, S., Wang, L., Wang, T., Han, Z., Liu, G., et al. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-220497 · Cited by 4
Izumi, T. (2023) · Endocrine Journal · doi:10.1507/endocrj.ej23-0112 · Cited by 4
Jia, H., Li, Z., Guo, F., Hua, Z., Zhou, X., Li, X., Li, R., Liu, Q., Liu, Y., & Dong, H. (2023) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2023.110872 · Cited by 4
Li, H., Zhao, Y., Jiang, Y., Zhang, Y., Zhang, P., Shen, L., & Chen, Z. (2024) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s450507 · Cited by 6
Systems genetics analysis of human body fat distribution genes identifies adipocyte processes
Reed, J. N., Jiansheng, H., Li, Y., Ma, L., Banka, D. S., Wabitsch, M., Wang, T., Ding, W. J., Björkegren, J., & Civelek, M. (2024) · Life Science Alliance · doi:10.26508/lsa.202402603 · Cited by 4
Unbiased causal inference with Mendelian randomization and covariate-adjusted GWAS data
Wang, P., Lin, Z., & Pan, W. (2025) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2025.100412 · Cited by 4
Association Between Body Mass Index and Myopia: Results from NHANES and Mendelian Randomization
Bai, W., Zhang, H., Ye, X., Xu, J., Guo, X., & He, J. (2025) · Ophthalmic Epidemiology · doi:10.1080/09286586.2025.2483684 · Cited by 4
Legault, M., Hartford, J., Arsenault, B. J., Yang, A. Y., & Pineau, J. (2025) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2025.04.010 · Cited by 7
The year in cardiology 2017: prevention
Nordestgaard, B. G., Cosentino, F., Landmesser, U., & Laufs, U. (2017) · European Heart Journal · doi:10.1093/eurheartj/ehx766 · Cited by 4
Meng, Q., Huang, L., Tao, K. X., Liu, Y., Jing, J., Wang, W., Qin, H., Feng, D., & Cai, Q. (2020) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2020.590903 · Cited by 4
da Cruz, S. P., da Cruz, S. P., Pereira, S., Saboya, C. J., Veiga, J. C. L., & Ramalho, A. (2022) · Nutrients · doi:10.3390/nu14030402 · Cited by 4
Zhang, W., Zhang, B., Zhang, H., Zhang, Y., Sun, J., Gao, L., & Yang, T. (2022) · BioMed Research International · doi:10.1155/2022/3046554 · Cited by 4
RetractedAberra, Y. T., Ma, L., Björkegren, J., & Civelek, M. (2023) · eLife · doi:10.7554/elife.79834 · Cited by 4
Trunk fat and leg fat in relation to free triiodothyronine in euthyroid postmenopausal women
Nie, X., Xu, Y., Ma, X., Shen, Y., Wang, Y., & Bao, Y. (2019) · Endocrine Connections · doi:10.1530/ec-19-0394 · Cited by 4
Song, J., Jiang, X., Juan, J., Cao, Y., Chibnik, L. B., Albert Hofman, D., Wu, T., & Hu, Y. (2018) · Journal of Diabetes · doi:10.1111/1753-0407.12882 · Cited by 4
Genetically increased serum calcium levels reduce Alzheimer’s disease riskPreprint
Jiang, Q., Hu, Y., Jin, S., & Liu, G. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/255059 · Cited by 4
Nizamani, S., Knight-Agarwal, C. R., Li, L., Mekanna, A. N., & McFarlane, R. A. (2025) · Nutrients · doi:10.3390/nu17122037 · Cited by 3
Gnatiuc, L., Kuri-Morales, P. A., Trichia, E., Staplin, N., Torres, J., Alegre-Díaz, J., Baca, P., Garcilazo‐Ávila, A., González-Carballo, C., Ramírez-Reyes, R., et al. (2025) · International Journal of Epidemiology · doi:10.1093/ije/dyaf110 · Cited by 3
Tutkuvienė, J., Gervickaitė, S., Sveikataite, M., Stulgyte, G., Rugelyte, J., Šimkūnaitė-Rizgelienė, R., & Ramašauskaitė, D. (2025) · American Journal of Human Biology · doi:10.1002/ajhb.70169 · Cited by 3
Boettger, P., Sedighi, J., Juenemann, M. B., Braun, T., Karrasch, T., Buerke, M., & Omar, O. A. (2025) · Cerebrovascular Diseases Extra · doi:10.1159/000549790 · Cited by 3
Yang, Y., Cheng, X., Chen, S., Xie, J.-Z., Chen, G., Li, L., Feng, Y., Chen, Y., Liu, G., Yin, Y., et al. (2026) · International Journal of Surgery · doi:10.1097/js9.0000000000004566 · Cited by 3
Herman, J. L., Dornbos, P., Landheer, K., Geraghty, B. J., Egerman, M. A., Phan, D., Germino, M., Mastaitis, J. W., Walls, J. R., Lotta, L. A., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-70422-2 · Cited by 4
Metabolic polygenic risk scores for prediction of obesity, type 2 diabetes, and related morbidities
Kim, M. S., Chen, Q., Sui, Y., Yang, X., Wang, S., Weng, L.-C. C., Cho, S. M. J., Koyama, S., Zhu, X., Yu, K., et al. (2026) · Cell Metabolism · doi:10.1016/j.cmet.2026.02.009 · Cited by 4
Self-citationWang, X., Zhou, Y., Huang, Z., Yu, X., Chen, Z., Cai, Z., Lan, Y., Li, W., Cai, Z., Fang, W., et al. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.882984 · Cited by 3
Deal, M., Kar, A., Lee, S. H., Alvarez, M., Rajkumar, S., Arasu, U. T., Kamińska, D., Männistö, V., Heinonen, S., van der Kolk, B. W., et al. (2024) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2024.10.001 · Cited by 3
Jia, X., Wang, A., Yang, L., Cheng, Y., Wang, Y., Ba, J., Dou, J., Mu, Y., Zhao, D., & Lyu, Z. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.839195 · Cited by 3
Hao, X., Li, W., Shi, R., & Wang, Q. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.1090867 · Cited by 3
The genetics of adipose tissue metabolism
Dumbell, R., & Cox, R. (2024) · Royal Society Open Science · doi:10.1098/rsos.231478 · Cited by 3
Metabolic health and cardiovascular disease across BMI categories: NHANES findings
Tu, D., Li, P., & Li, K. (2025) · Journal of Health Population and Nutrition · doi:10.1186/s41043-025-01003-0 · Cited by 3
Genetics of Central Obesity and Body Fat
Heianza, Y., & Qi, L. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816093-0.00014-8 · Cited by 4
Wang, Z., Lai, W., & Zhong, S. (2021) · PubMed Central · doi:10.12122/j.issn.1673-4254.2021.02.16 · Cited by 3
Confounding is not the only bias influencing associations of adiposity with cardiovascular disease
Staplin, N. (2018) · European Heart Journal · doi:10.1093/eurheartj/ehy133 · Cited by 3
Dixon, P., Hollingworth, W., Harrison, S., Davies, N. M., & Smith, G. D. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/589820 · Cited by 3
Obesity, Ectopic Fat and Type 2 Diabetes
Verhaegen, A. A., & Gaal, L. F. V. (2022) · Wiley eBooks · doi:10.1002/9781119695257.ch10 · Cited by 3
Sukhatme, M. G., Kar, A., Arasu, U. T., Lee, S. H., Alvarez, M., Garske, K. M., Gelev, K. Z., Rajkumar, S., Das, S. S., Kamińska, D., et al. (2024) · medRxiv · doi:10.1101/2024.11.22.24317804 · Cited by 5
Zhang, Y., David, N. L., Pesaresi, T., Andrews, R. E., Kumar, G. V. N., Chen, H., Qiao, W., Yang, J., Patel, K., Amorim, T., et al. (2024) · JCI Insight · doi:10.1172/jci.insight.184140 · Cited by 3
Type 2 diabetes and cause-specific mortality in Mexico City: a Mendelian randomisation analysis
Bragg, F., Kuri-Morales, P. A., Trichia, E., Torres, J., Baca, P., Garcilazo‐Ávila, A., González-Carballo, C., Ramírez-Reyes, R., Rivas, F., Aguilar-Ramirez, D. J., et al. (2025) · The Lancet Regional Health - Americas · doi:10.1016/j.lana.2025.101082 · Cited by 2
Investigating the Sexual Dimorphism of Waist-to-Hip Ratio and Its Associations with Complex Traits
Li, H., Hui, S., Cai, X., He, R., Yu, M., Li, Y., Yu, R., & Huang, P. (2025) · Genes · doi:10.3390/genes16060711 · Cited by 2
Feng, Z., Li, J., Zhang, H., Liu, S., Wang, Y., Zhou, M., Ding, Z., & Xiao, L. (2025) · Scientific Reports · doi:10.1038/s41598-025-16817-5 · Cited by 3
Zhou, Y., Xu, K., Hu, H., Ba, Q., Shen, N., & Lu, Y. (2025) · Discover Oncology · doi:10.1007/s12672-025-03924-y · Cited by 2
Xu, C., Khurana, R., Gao, X., Nie, X., Lehertshuber, C., Li, L., Romer, A., Brizzi, L. F., von Scheidt, M., Yadav, A., et al. (2025) · International Journal of Cardiology Cardiovascular Risk and Prevention · doi:10.1016/j.ijcrp.2025.200539 · Cited by 2
She, Y., & Liu, Z. (2022) · Cellular and Molecular Biology · doi:10.14715/cmb/2022.68.3.25 · Cited by 2
da Silva, G. H. C., Marques, D. C. D. S., Santos, I. C., de Oliveira, F. M., Marques, M. G. D. S., Júnior, R. B. D. S., Pendić, L., & Branco, B. H. M. (2022) · Retos · doi:10.47197/retos.v46.93066 · Cited by 2
Zhou, Z., Wang, L.-F., Wang, J., Liu, N., Liu, Y., & Sun, L. (2023) · Investigación Clínica · doi:10.54817/ic.v64n3a04 · Cited by 2
Multi‐Omic Approaches to Identify Genetic Factors in Metabolic Syndrome
Clark, K. C., & Kwitek, A. E. (2021) · Comprehensive physiology · doi:10.1002/j.2040-4603.2022.tb00197.x · Cited by 2
Oh, M., Jacobs, D. R., Gabriel, K. P., Bao, W., Pierce, G. L., Carr, L. J., Terry, J. G., Ding, J., Carr, J. J., & Whitaker, K. M. (2022) · Journal of Physical Activity and Health · doi:10.1123/jpah.2021-0726 · Cited by 2
Metadata
- 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