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Causal Associations of Adiposity and Body Fat Distribution With Coronary Heart Disease, Stroke Subtypes, and Type 2 Diabetes Mellitus: A Mendelian Randomization Analysis
Open accessAbstract
Background: The implications of different adiposity measures on cardiovascular disease etiology remain unclear. In this article, we quantify and contrast causal associations of central adiposity (waist-to-hip ratio adjusted for body mass index [WHRadjBMI]) and general adiposity (body mass index [BMI]) with cardiometabolic disease. Methods: Ninety-seven independent single-nucleotide polymorphisms for BMI and 49 single-nucleotide polymorphisms for WHRadjBMI were used to conduct Mendelian randomization analyses in 14 prospective studies supplemented with coronary heart disease (CHD) data from CARDIoGRAMplusC4D (Coronary Artery Disease Genome-wide Replication and Meta-analysis [CARDIoGRAM] plus The Coronary Artery Disease [C4D] Genetics; combined total 66 842 cases), stroke from METASTROKE (12 389 ischemic stroke cases), type 2 diabetes mellitus from DIAGRAM (Diabetes Genetics Replication and Meta-analysis; 34 840 cases), and lipids from GLGC (Global Lipids Genetic Consortium; 213 500 participants) consortia. Primary outcomes were CHD, type 2 diabetes mellitus, and major stroke subtypes; secondary analyses included 18 cardiometabolic traits. Results: Each one standard deviation (SD) higher WHRadjBMI (1 SD≈0.08 U) associated with a 48% excess risk of CHD (odds ratio [OR] for CHD, 1.48; 95% confidence interval [CI], 1.28–1.71), similar to findings for BMI (1 SD≈4.6 kg/m 2 ; OR for CHD, 1.36; 95% CI, 1.22–1.52). Only WHRadjBMI increased risk of ischemic stroke (OR, 1.32; 95% CI, 1.03–1.70). For type 2 diabetes mellitus, both measures had large effects: OR, 1.82 (95% CI, 1.38–2.42) and OR, 1.98 (95% CI, 1.41–2.78) per 1 SD higher WHRadjBMI and BMI, respectively. Both WHRadjBMI and BMI were associated with higher left ventricular hypertrophy, glycemic traits, interleukin 6, and circulating lipids. WHRadjBMI was also associated with higher carotid intima-media thickness (39%; 95% CI, 9%–77% per 1 SD). Conclusions: Both general and central adiposity have causal effects on CHD and type 2 diabetes mellitus. Central adiposity may have a stronger effect on stroke risk. Future estimates of the burden of adiposity on health should include measures of central and general adiposity.
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“Obesity, characterized by excessive body fat, constitutes a major risk factor for the development of type 2 diabetes (T2D), dyslipidemia, cardiometabolic disease1, cancer2, and overall mortality3.”
From Transforming obesity: The advancement of multi-receptor drugs (2024) · cites this paper as [1]
Experimental — accuracy not yet measuredIn 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 [17]
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“HDL cholesterol 17,19 MRs do not provide evidence of causation, once unbalanced horizontal pleiotropy is taken into account”
From Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development (2021) · cites this paper as [17]
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Cites
Measuring inconsistency in meta-analyses
Higgins, J. P. T., Thompson, S. G., Deeks, J. J., & Altman, D. G. (2003) · BMJ · doi:10.1136/bmj.327.7414.557 · Cited by 63,139
Ng, M., Fleming, T., Robinson, M., Thomson, B., Graetz, N., Margono, C., Mullany, E. C., Biryukov, S., Abbafati, C., Abera, S. F., et al. (2014) · The Lancet · doi:10.1016/s0140-6736(14)60460-8 · Cited by 11,952
Bowden, J., Davey Smith, G., & Burgess, S. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyv080 · Cited by 9,816
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
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-citationWiller (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 · cited 1×
“We conducted 3 tests for the causal estimation of each adiposity measure on cardiometabolic outcomes: (1) inverse variance–weighted (IVW) method, (2) MR-Egger, and (3) weighted median.”
O'Donnell, M. J., Chin, S. L., Rangarajan, S., Xavier, D., Liu, L., Zhang, H., Rao-Melacini, P., Zhang, X., Pais, P., Agapay, S., et al. (2016) · The Lancet · doi:10.1016/s0140-6736(16)30506-2 · Cited by 2,296
Flagged first, then by citations
Research works printed but not linked: 1
- R: A language and environment for statistical computing [computer program]. Vienna, Austria; 2013.
Cited by
Sorted by citations
Items 201–300 of 349
Chen, Y., Wang, Y., Xu, K., Zhou, J., Yu, L., Wang, N., Liu, T., & Fu, C. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph182111481 · Cited by 9
Inflammation proteomics datasets in the ALSPAC cohort
Goulding, N., Goudswaard, L. J., Hughes, D. A., Corbin, L. J., Groom, A., Ring, S. M., Timpson, N. J., Fraser, A., Northstone, K., & Suderman, M. J. (2022) · Wellcome Open Research · doi:10.12688/wellcomeopenres.18482.1 · Cited by 17
Lin, M., Zhou, S., & Gu, S. (2023) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2023.1269358 · Cited by 9
Kaur, Y., Wang, D. X., Liu, H., & Meyre, D. (2018) · Obesity Reviews · doi:10.1111/obr.12806 · Cited by 10
Analysis of the Associations between Obesity Indices and Left Ventricular Mass
Feng, P., Huang, Y., Wang, S., Yu, G., Li, G., Lei, H., Qin, S., & Huang, W. (2018) · Cardiology · doi:10.1159/000496177 · Cited by 8
Zhou, T., Liu, X., Liu, Y., & Li, X. (2019) · Medicine · doi:10.1097/md.0000000000015581 · Cited by 9
“However, there have also been publications reporting that the estimations for both types of obesity were comparable([59]) or that the estimated effect of general obesity overrode that of central obesity.([60]) There was an increasing trend of overweight rates among adults in China, from 22.”
BackgroundWang, F., Zhou, L., Chen, N., & Li, X. (2021) · Medicine · doi:10.1097/md.0000000000024664 · 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
“11,12 13–19 However, due to a lack of genetic information on large longitudinal cohorts, the majority of MR studies have used summary-level data for the primary analysis.”
BackgroundEducational 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
“Mendelian Randomization Analysis We selected three MR methods including inverse-varianceweighted meta-analysis (IVW), weighted median regression, and MR–Egger regression, as in recent studies (Dale et al., 2017; Larsson et al., 2017a; Tillmann et al., 2017; Liu et al., 2018; Liu et al., 2019).”
MethodZhao, Y., Han, M., Qie, R., Zhang, Y., Wu, Y., Fu, X., Zhang, D., Kuang, L., Qin, P., Hu, F., et al. (2024) · Diabetes Obesity and Metabolism · doi:10.1111/dom.15508 · Cited by 8
“4 Obesity, a recognized risk factor for type 2 diabetes mellitus (T2DM), 5,6 is commonly assessed by body mass index (BMI).”
BackgroundAdiposity and insulin resistance as mediators between age at menarche and type 2 diabetes mellitus
Liu, D., Zhao, Y., Liu, Y., Sun, X., Li, H., Yin, Z., Li, L., Wang, B., Ren, Y., Cheng, C., et al. (2020) · Menopause The Journal of The North American Menopause Society · doi:10.1097/gme.0000000000001504 · Cited by 8
“been confirmed as major risk factors of T2DM.(10,11) Hence, intensity activity of at least 20 min/d, (ii) 5 or more d/wk LIU ET AL”
BackgroundTürkmen, D., Bowden, J., Masoli, J. A. H., Delgado, J., Kuo, C., Melzer, D. R., & Pilling, L. C. (2024) · The Pharmacogenomics Journal · doi:10.1038/s41397-024-00333-2 · Cited by 8
“Adiposity is a common risk factor for many diseases, speci fi cally with cardiovascular diseases [31 – 33], with genetics supporting the well-established causal role of adiposity and cardio-metabolic risk [31].”
BackgroundLv, 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
Abrahim, M. (2022) · Cureus · doi:10.7759/cureus.27929 · Cited by 7
“Obesity, particularly, visceral obesity is an established risk factor for cardiovascular and metabolic disorders with a growing body of research demonstrating a potential causal association independent of total body weight [6].”
BackgroundMeulmeester, 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
Self-citationRamezankhani, A., Azizi, F., & Hadaegh, F. (2023) · Scientific Reports · doi:10.1038/s41598-023-50028-0 · Cited by 7
“General and central adiposity are well recognized as the major risk factors for CHD 35 .”
BackgroundRen, Z., Qian, Y., Hou, L., Luk, T. T., Qiu, Y., Xia, W., Zhu, Y., Song, P., & Rahimi, K. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.0437 · Cited by 6
Lu, J., Gong, S., Zhu, J., & Fang, Q. (2024) · Neurological Sciences · doi:10.1007/s10072-024-07415-w · Cited by 6
“Previous MR studies have found that central obesity, but not BMI, is a risk factor for ischemic stroke [30, 31].”
Disentangling the genetic overlap between ischemic stroke and obesity
Yang, R., Zhang, T. M., & Han, F. (2024) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-024-01555-x · Cited by 6
Carter, A. R., Ferreira, D. L. S., Taylor, A. E., Lawlor, D. A., Smith, G. D., Sattar, N. A., Chaturvedi, N., Hughes, A. D., & Howe, L. D. (2020) · Journal of the American Heart Association · doi:10.1161/jaha.120.016564 · Cited by 5
“For each of these exposure and outcome combinations the mediating effects of (1) individual metabolic traits, (2) PCs for the metabolic profile, (3) putative risk factors, and (4) putative risk factors plus PCs for the metabolic profile were estimated.”
InfluentialSelf-citationMeulmeester, F. L., Luo, J. M., Martens, L. G., Ashrafi, N., de Mutsert, R., Mook-Kanamori, D. O., Lamb, H. J., Rosendaal, F. R., van Dijk, K. W., Mills, K. A., et al. (2021) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2021.05.001 · Cited by 6
“One of the hypotheses is that the production of ROS is partially driven by pathological processes associated with excessive accumulation of adipose tissue [11 e 14].”
BackgroundSelf-citationThe relationship between soluble lymphocyte activation gene-3 and coronary artery disease
Xiong, X., Duan, Z., Zhou, H., Niu, L., Luo, Z., & Li, W. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.988582 · Cited by 6
A translational framework of genoproteomic studies for cardiovascular drug discovery
Yang, Z., Zhao, J. V., Qi, Y., Deng, X., Ji, Z., & Liu, J. (2024) · npj Cardiovascular Health · doi:10.1038/s44325-024-00015-9 · Cited by 6
“For example, body mass index (BMI), a polygenic biomarker, has been impli-catedintheriskoftype2diabetes(T2D)andcoronaryheartdisease(CHD), asdemonstratedinRCTsandMR 49,50 .”
Markofski, M. M., Jennings, K., Dolan, C., Davies, N. A., LaVoy, E. C. P., Ryan, E. J., & Carrillo, A. E. (2019) · American Journal of Lifestyle Medicine · doi:10.1177/1559827619866157 · Cited by 8
“However, the reduction in BMI does confer health benefits on its own.(1,55) The participants in the present study did not have high blood pressure, and this may be why there was not a change in blood pressure.”
BackgroundLifetime 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
Overweight and risk of type 2 diabetes: A prospective Chinese twin study
Xi, Y., Gao, W., Zheng, K., Lv, J., Yu, C., Wang, S., Huang, T., Sun, D., Liao, C., Pang, Y., et al. (2021) · Diabetes & Metabolism · doi:10.1016/j.diabet.2021.101278 · Cited by 6
Wang, Y., Zhang, W., Zhao, H., Wang, Y., Lu, C., Li, X., Wang, Y., Xiao, Y., Wang, Y., & Wang, B. (2017) · Gene · doi:10.1016/j.gene.2017.09.066 · Cited by 6
Han, D., Yu, F., & Zheng, L. (2024) · Epidemiology and Health · doi:10.4178/epih.e2024046 · Cited by 5
“Power calculations were conducted using the online tool mRnd, based on the outcome sample size, proportion of cases, R 2 sum, and a type I error rate of 0.05 [27,28].”
MethodInfluentialKrüger, A., van Dijk, K. W., van Heemst, D., & Noordam, R. (2025) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2025.119135 · Cited by 5
“The relationship between obesity and cardiometabolic disease has been confirmed in many Mendelian Randomization studies, providing evidence favouring a causal relationship between obesity and cardiometabolic diseases [1,9,10].”
Self-citationLee, M. A., Hatcher, C., McGuinness, L. A., McBride, N., Battram, T., Wan, W., Fang, S., Wade, K. H., Corbin, L. J., & Timpson, N. J. (2023) · Wellcome Open Research · doi:10.12688/wellcomeopenres.18657.2 · 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
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
Clinical impacts of cerebral microbleeds in patients with established coronary artery disease
Uemura, Y., Shibata, R., Imai, R., Ishikawa, S., Takemoto, K., Watarai, M., & Murohara, T. (2020) · Journal of Hypertension · doi:10.1097/hjh.0000000000002615 · Cited by 5
Wang, X., Zhao, D., Cheng, L., Gao, J., Li, J., & Geng, C. (2023) · Medicine · doi:10.1097/md.0000000000034638 · Cited by 7
“WMR can give a consistent estimator even when up to 50% invalid instrumental variables.”
MethodInfluentialPeri‐atrial epicardial adipose tissue—a marker of thromboembolism?
Agbaedeng, T. A., Wong, C. X. J., & Mahajan, R. (2019) · Journal of Cardiovascular Electrophysiology · doi:10.1111/jce.14168 · Cited by 5
“Similarly, measures of visceral adiposity appear to be more consistently associated with stroke than are indicators of generalized adiposity.(6) More recently, preliminary investigations are emerging regarding the possible relationship between visceral fat depots and AF‐related stroke.”
BackgroundFat-brain axis indicated by mutual impacts between body fat and brain phenotypes
Baranova, A. V., Fu, L., Zhao, Q., Liu, D., Cao, H., Chandhoke, V., & Zhang, F. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-07453-8 · Cited by 4
“Current literature indicates that abdominal obesity contributes to stroke in a direct, causal fashion [64], with an emphasis on BFP and fat distribution being related to cerebral vascular damage [65] caused by the vessels’ exposure to free fatty acids and soluble inflammatory factors [66].”
Unraveling the Link Between Obesity and Keratoconus Risk Based on Genetic Evidence
Huang, Y., Zhuang, J., Liu, C. X., Liu, S., Ren, H., Zhang, Q., Yan, L., Qiu, J. F., Yu, N., Yu, K., et al. (2025) · Translational Vision Science & Technology · doi:10.1167/tvst.14.5.20 · Cited by 4
“26 Our findings proposed that KCN is not correlated with body fat distribution.”
Savla, J. J., & Neeland, I. J. (2018) · Current Cardiovascular Risk Reports · doi:10.1007/s12170-018-0566-9 · Cited by 4
Vasconcelos, C. E. G. D. C., Cabral, M., Ramos, E., & Mendes, R. (2021) · Journal of Nutritional Science · doi:10.1017/jns.2021.50 · Cited by 4
Cui, B., Li, W., Wang, G., Li, P., Zhu, L., & Zhu, S. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.1068917 · Cited by 5
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
“…to hip circumference (WHR), an approximation of human fat distribution, and WHR independent of overall obesity (WHR adjBMI ), are both similar or better predictors for cardiovascular disease risk and Type II Diabetes risk than BMI (Schleinitz et al, 2014a; Dale et al, 2017; Emdin et al, 2017).”
BackgroundDiastolic and systolic blood pressure and gout: a Mendelian randomization study
Li, Y., Xie, Y., Li, J., Chang, Z., Zhang, J., Zhou, Z., Ren, R., & Chen, Y. (2024) · Frontiers in Endocrinology · doi:10.3389/fendo.2024.1367621 · Cited by 4
Accuracy of self-reported height, weight, and waist circumference in poor, rural areas of China
Cao, H., Zhang, Z., Zhang, J., Wang, Q., Yu, L., Xie, S., Zhang, L., Fu, H., Li, K., Shen, T., et al. (2024) · PeerJ · doi:10.7717/peerj.18525 · Cited by 4
Oral GLP-1 Receptor Agonists for Weight Loss
Krinsky, D., Marcucci, A., Mullally, J. A., & Frishman, W. H. (2024) · Cardiology in Review · doi:10.1097/crd.0000000000000833 · Cited by 4
Bagheri, M., Nouri, M., Kohanmoo, A., Homayounfar, R., & Akhlaghi, M. (2025) · BMC Nutrition · doi:10.1186/s40795-024-00931-0 · Cited by 5
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
“In the supplemental information section S1B.1.1, we derived the analytical expression of b AG i X through an asymptotic least-squares approach 14 : (Equation 10) We can rewrite b MG i Y as: 11) Case 3: When only the GWAS of outcome Y is adjusted for H We derived the covariate-adjusted SNP effect b AG i Y : (Equation 1…”
InfluentialChen, S., Ding, D., Cui, Q., Zhao, X., Feng, A., Xia, Y., Xu, Q., & Li, J. (2025) · European Journal of Preventive Cardiology · doi:10.1093/eurjpc/zwaf059 · Cited by 4
Interactive effects of abdominal obesity and insulin resistance on cardiometabolic risk
Lin, M., Chen, X., Wu, M., Xiao, J., Li, S., Tang, H., Tan, X., & Chen, Y. (2025) · Revista Española de Cardiología (English Edition) · doi:10.1016/j.rec.2025.03.006 · Cited by 4
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
Combating diabetes in China: a long-term perspective is needed
Yu, X. Q., & Baade, P. D. (2018) · The Lancet Public Health · doi:10.1016/s2468-2667(18)30048-3 · Cited by 4
Lee, M. A., Hatcher, C., McGuinness, L. A., McBride, N., Battram, T., Wan, W., Fang, S., Wade, K. H., Corbin, L. J., & Timpson, N. J. (2022) · Wellcome Open Research · doi:10.12688/wellcomeopenres.18657.1 · Cited by 4
“Only the study of the association between BMI and haemorrhagic stroke by Dale et al. , (2017) 29 was ranked as high quality.”
Aberra, Y. T., Ma, L., Björkegren, J., & Civelek, M. (2023) · eLife · doi:10.7554/elife.79834 · Cited by 4
Duan, D., Li, H., Xu, J., Wong, L. P., Xu, G., Kong, F., Li, S., Gong, Q., Zhang, X., Zhao, J., et al. (2019) · Journal of Diabetes Research · doi:10.1155/2019/2591709 · 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
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
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
“Causal associations between higher adiposity and adverse effects on glycaemia, blood pressure, and lipids have been confirmed by MR studies [11, 24], while randomized trials of weight loss have demonstrated that these adverse effects can be reversed [25–27].”
InfluentialDisentangling the relationship between glucose, insulin and brain health: A UK Biobank study
Mason, A. C., Fatih, N., Sofat, R., Rentsch, C. T., Smeeth, L. L., Bhaskaran, K., Chaturvedi, N., & Garfield, V. (2025) · Diabetes Obesity and Metabolism · doi:10.1111/dom.70353 · Cited by 9
Self-citationHerman, 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
“…with several cardiometabolic traits, including a nominally signi fi cant association with lower waist-to-hip ratio following adjustment for BMI — a phenotype causally linked to cardiometabolic health 47,48 — and a borderline signi fi cant association with reduced HbA1C levels (Supplementary Fig.”
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
Age, body composition analysis, and gender differences of morbidly obese Omani subjects
Al-Maskari, J., Al-Hadhrami, B., Waly, M. I., Al-Subhi, L., & Ali, A. (2024) · International journal of Nutrition Pharmacology Neurological Diseases · doi:10.4103/ijnpnd.ijnpnd_12_24 · Cited by 3
Kelley, G. A., Kelley, K. S., & Stauffer, B. L. (2019) · Journal of Investigative Medicine · doi:10.1136/jim-2019-001069 · Cited by 3
Blauw, L. L., Noordam, R., van der Laan, S. W., Trompet, S., Kooijman, S., van Heemst, D., Jukema, J. W., van Setten, J., de Borst, G. J., Tybjærg‐Hansen, A., et al. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10050932 · Cited by 3
“However, this finding is rather counterintuitive given the earlier described causal relation between higher BMI and increased risk of stroke [15].”
ResultSelf-citationNon-random sampling leads to biased estimates of transcriptome association
Foulkes, A. S., Balasubramanian, R., Qian, J., & Reilly, M. P. (2020) · Scientific Reports · doi:10.1038/s41598-020-62575-x · Cited by 3
Body mass index and mortality in UK Biobank: revised estimates using Mendelian randomizationPreprint
Wade, K. H., Carslake, D., Sattar, N. A., Smith, G. D., & Timpson, N. J. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/281436 · Cited by 3
Self-citationLifelong genetically lowered sclerostin and risk of cardiovascular diseasePreprint
Bovijn, J., Krebs, K., Chen, C., Boxall, R., Censin, J. C., Ferreira, T. D. G. M., Pulit, S. L., Glastonbury, C. A., Laber, S., Millwood, I. Y., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/531004 · Cited by 3
Self-citationSobczyk, M. K., Zheng, J., Smith, G. D., & Gaunt, T. R. (2022) · medRxiv · doi:10.1101/2022.04.11.22273633 · Cited by 3
Self-citationSun, D., Sun, Q., Ding, Y., Yu, C., Sun, D., Pang, Y., Pei, P., Yang, L., Millwood, I. Y., Walters, R. G., et al. (2025) · Genome Medicine · doi:10.1186/s13073-025-01487-9 · Cited by 2
“There is evidence that the association of central obesity with CVD is independent of BMI [26, 27].”
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
“This phenomenon agrees with the fact that ectopic fat distribution or visceral fat deposition shows stronger correlations to CVDs [29,30].”
Huang, H., Bu, H., Liao, L., Xiang, Y., Chen, Q., Fu, G., Cheng, W., Kou, Y., Liu, Z.-Z., Zheng, K., et al. (2025) · Gut Microbes · doi:10.1080/19490976.2025.2587400 · Cited by 2
“1,2 Recent breakthroughs in the use of glucagon-like peptide−1 (GLP−1) receptor agonists and their integration into multiple-hormone receptor agonists have revolutionized antiobesity pharmaco-therapy.”
Quality Control Measures and Validation in Gene Association Studies: Lessons for Acute Illness
Cohen, M., Lamparello, A. J., Schimunek, L., El-Dehaibi, F., Namas, R. A., Xu, Y., Kaynar, A. M., Billiar, T. R., & Yoram Vodovotz, B. (2019) · Shock · doi:10.1097/shk.0000000000001409 · Cited by 2
“SHOCK MARCH 2020 QUALITY CONTROL OF GENETIC STUDIES IN ACUTE ILLNESS 261 Studies in the setting of ischemic events after cerebral endovascular therapy Susceptibility loci have been implicated with the risk of stroke and clopidogrel efficacy, central adiposity, and folic acid intervention (34, 57, 75, 76).”
BackgroundSideburns Obesity Syndrome: Is Sideburn Fat the Window to the Heart?
Abrahim, M. (2021) · Cureus · doi:10.7759/cureus.12706 · Cited by 2
“Visceral obesity is strongly associated with cardiovascular disease risk, with a growing body of research demonstrating a potential causal association [1].”
BackgroundAbrahim, M. (2021) · Cureus · doi:10.7759/cureus.15744 · Cited by 2
“Concurrently, visceral obesity is an established risk factor for cardiometabolic diseases, with a growing body of research demonstrating a potential causal association [5].”
BackgroundNoordam, R., Brochard, T. A., Drewes, Y. M., Gussekloo, J., Mooijaart, S. P., van Dijk, K. W., Trompet, S., Jukema, J. W., & van Heemst, D. (2023) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2023.05.011 · Cited by 2
Self-citationNeutrophil count and reduced risk of venous thromboembolism: a Mendelian randomization study
Zhao, J., Zhang, W., Wei, H., Liu, S., Cui, S., & Chen, S. (2024) · Hematology · doi:10.1080/16078454.2024.2428481 · Cited by 2
“Mendelian randomization (MR) is an emerging method utilizing genetic variations to investigate the causal relationship between biomarkers and disease risk [11,12].”
Clinical and preclinical obesity: cardiovascular implications of a new diagnostic paradigm
Nam, G. E. (2025) · Cardiovascular Prevention and Pharmacotherapy · doi:10.36011/cpp.2025.7.e6 · Cited by 2
“Additionally, a Mendelian randomization study provided causal evidence linking body fat distribution to cardiovascular disease independent of BMI [8].”
Metabolic Health and Carotid Intima-Media Thickness: Association of Different Definitions in Women
Magri, C. J., Xuereb, S., Xuereb, R. A., & Fava, S. (2023) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2023.08.003 · Cited by 2
An exposome-wide assessment of 6600 SomaScan proteins with non-genetic factors in Chinese adultsPreprint
Chan, K. H., Clarke, J., Kakkoura, M., Iona, A., Wang, B., Clarke, C., Wright, N., Yao, P., Mazidi, M., Im, P. K., et al. (2024) · medRxiv · doi:10.1101/2024.10.24.24316041 · Cited by 2
Lu, G., Wang, T., Yang, F., Sun, X., Yang, R., Luo, J., Tong, X., Gu, Y., Wang, J., Tong, Z., et al. (2024) · International Journal of Surgery · doi:10.1097/js9.0000000000001766 · Cited by 2
Meulmeester, F. L., van Dijk, K. W., van Heemst, D., & Noordam, R. (2024) · Diabetes Obesity and Metabolism · doi:10.1111/dom.15966 · Cited by 1
Self-citationLin, M., Zhou, Y., Han, S., Wu, M., Xiao, J., Li, Y., Tang, H., Tan, X., & Chen, Y. (2025) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2025.104101 · Cited by 1
Schoonakker, M. P., van den Burg, E. L., van Peet, P. G., le Cessie, S., Penny, E., Numans, M. E., Pijl, H., & Lamb, H. J. (2025) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2025.104111 · Cited by 1
Lin, M., Chen, X., Wu, M., Xiao, J., Li, S., Tan, X., Tan, X., & Chen, Y.-F. (2025) · Revista Española de Cardiología · doi:10.1016/j.recesp.2025.03.006 · Cited by 1
Lin, Y., Li, J., Liu, T., Chiou, J., Lu, H., Ariyoshi, K., Uchiyama, S., Terao, C. C., Yang, J., Chou, C.-H., et al. (2025) · Obesity · doi:10.1002/oby.24335 · Cited by 1
“…risk score (PRS)-based phenome-wide association studies (PheWAS) and Mendelian randomization (MR) have used these genetic risk factors to explore the relationship between obesity and related diseases [8, 9], consistently highlighting strong genetic evidence linking obesity to these conditions.”
Kim, T. Y., Jeon, D., Kim, J.-H., & Park, Y. S. (2026) · Annals of Surgical Treatment and Research · doi:10.4174/astr.2026.110.1.47 · Cited by 2
“%) is noteworthy, as central adiposity is associated with cardiometabolic risk factors [17,18] and its reduction contributes to the metabolic benefits following MBS [19].”
Development of CuO@a-MWCNTs buckypaper for non-enzymatic glucose biosensing
Park, H. J., Kim, M. H., Park, J. H., Shin, D. M., Yang, J. H., Noh, J. P., & Huh, S. (2026) · Journal of Mechanical Science and Technology · doi:10.1007/s12206-026-0146-8 · Cited by 1
Ascandar, N. E., Elgendi, K., Tarbay, A., Al-Qutbi, T., & Omar, S. A. (2026) · The American Journal of Surgery · doi:10.1016/j.amjsurg.2026.117011 · Cited by 1
Chan, K. H., Clarke, J., Kakkoura, M., Iona, A., Wang, B., Clarke, C., Wright, N., Yao, P., Mazidi, M., Im, P. K., et al. (2026) · Scientific Reports · doi:10.1038/s41598-026-41444-z · Cited by 1
“Future studies should also consider assessing the associations of SomaScan proteins with central adiposity (e.g. waist circumference, waist-to-hip ratio), as it is known to be more strongly associated with risk of cardio-metabolic disease 49,50 and with Olink proteins 16 , compared with BMI.”
Song, J., Wu, Y., Juan, J., Cao, Y., Wu, T., & Hu, Y. (2019) · Journal of Thrombosis and Thrombolysis · doi:10.1007/s11239-019-01855-4 · Cited by 1
Nutrition in conditions of martial law: anti-stress diet
PRYTULSKA, N., & Fedorova, D. (2022) · International Scientific-Practical Journal COMMODITIES AND MARKETS · doi:10.31617/2.2022(42)08 · Cited by 1
Schell, R. C., Dow, W. H., Fernald, L. C. H., Bradshaw, P. T., & Rehkopf, D. H. (2023) · SSM - Population Health · doi:10.1016/j.ssmph.2023.101351 · Cited by 1
“The benefit of using WHRadjBMI is that it has a more direct correlation with adiposity and measures central adiposity, which serves as an independent predictor of CVD risk beyond total body adiposity (Prentice & Jebb, 2001) (Dale et al., 2017).”
BackgroundHariri, M., Shamshirgaran, S. M., Aminisani, N., Abasi, H., & Gholami, A. (2023) · Irish Journal of Medical Science (1971 -) · doi:10.1007/s11845-023-03449-9 · Cited by 3
Higher Prevalence of Cancer in Patients with Diabetic Foot Syndrome
Goretti, C., Prete, A., Brocchi, A., Iacopi, E., Pieruzzi, L., & Piaggesi, A. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13051448 · Cited by 1
“DM could represent an independent risk factor for higher cancer-related mortality, particularly evident for colon, pancreas and breast cancers in women and for liver and intestine cancers in men [13–16].”
BackgroundMetabolomic Alteration in Adipose Monocyte Chemotactic Protein-1 Deficient Mice Fed a High-Fat Diet
Yan, L., RUST, B. M., Sundaram, S., & Nielsen, F. H. (2024) · Nutrition and Metabolic Insights · doi:10.1177/11786388241280859 · Cited by 1
The genetic underpinnings of obesity
Kentistou, K. A., Wilson, J. F., Joshi, P. K., & Morton, N. M. (2019) · Current Opinion in Physiology · doi:10.1016/j.cophys.2019.10.008 · Cited by 2
Mušo, M., Dumbell, R., Pulit, S. L., Sinnott-Armstrong, N., Laber, S., Zolkiewski, L., Bentley, L., Claussnitzer, M., & Cox, R. (2020) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2020.194640 · Cited by 1
Lotta, L. A., Dong, L., Li, C., Patel, S., Stewart, I. D., Lim, K., Day, F. R., Wheeler, E., Glastonbury, C. A., de Streek, M. V., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/372128 · Cited by 2
“6×10 158412701 (1) 1 >99% 2 rs2444770 chr2:158503739:C:T (14.”
BackgroundMethodResultInfluentialSun, Q., Hu, Y., Yu, C., Yu, C., Pei, P., Yang, L., Chen, Y., Du, H., Sun, D., Pang, Y., et al. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4136282 · Cited by 1
Is stroke the cause of vascular dementia? A two-sample Mendelian Randomization analysisPreprint
Zhang, K., Chen, L., Zhang, P., Sun, X., Qu, Y., Yi, Y., & Guo, Z. (2022) · Research Square · doi:10.21203/rs.3.rs-2015772/v1 · Cited by 1
Habitual sleep quantity and quality and the risk of obesity: What's the chicken, and what's the egg?
Noordam, R., & van Heemst, D. (2023) · Sleep Medicine · doi:10.1016/j.sleep.2023.07.028 · Cited by 1
Self-citation
Metadata
- Authors
- 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., McLachlan, S., Trompet, S., Moldovan, M., Morris, R. W., Sofat, R., Kumari, M., Hyppönen, E., Jefferis, B. J., Gaunt, T. R., Ben-Shlomo, Y., Zhou, A., Gentry-Maharaj, A., Ryan, A., Mutsert, R. D., Noordam, R., Caulfield, M. J., Jukema, J. W., Worrall, B. B., Munroe, P. B., Menon, U., Power, C., Kuh, D., Lawlor, D. A., Humphries, S. E., Mook-Kanamori, D. O., Sattar, N., Kivimaki, M., Price, J. F., Davey Smith, G., Dudbridge, F., Hingorani, A. D., Holmes, M. V., & Casas, J. P.
- Affiliations
- Farr Institute (GB)
- From Farr Institute of Health Informatics Research, UCL Institute of Health Informatics, University College London, United Kingdom (C.E.D., G.F., D.P.-M., A.D.H., J.P.C.); Department of Mathematics and Statistics, Lancaster University, United Kingdom (T.M.P.); UCLGenetics Institute, University College London, United Kingdom (J.W.); Applied Statistical Methods in Medical Research Group, Universidad Catolica de San Antonio de Murcia, Spain (D.P.-M.); Social Genetic & Developmental Psychiatry,...
- ORCID
- Dale, Caroline E.
- Fatemifar, Ghazaleh
- Palmer, Tom M.
- White, Jon
- Zabaneh, Delilah
- Engmann, Jorgen E.L.
- Shah, Tina
- Wong, Andrew
- Warren, Helen R.
- McLachlan, Stela
- Trompet, Stella
- Moldovan, Max
- Morris, Richard W.
- Sofat, Reecha
- Hyppönen, Elina
- Jefferis, Barbara J.
- Gaunt, Tom R.
- Zhou, Ang
- Ryan, Andy
- Mutsert, Renée de
- Noordam, Raymond
- Caulfield, Mark J.
- Jukema, J. Wouter
- Worrall, Bradford B.
- Munroe, Patricia B.
- Menon, Usha
- Power, Chris
- Kuh, Diana
- Lawlor, Debbie A.
- Humphries, Steve E.
- Sattar, Naveed
- Dudbridge, Frank
- Hingorani, Aroon D.
- Holmes, Michael V.
- Casas, Juan P.
- Venue
- Circulation
- Published
- 2017-06-13
- Volume
- 135
- Issue
- 24
- Pages
- 2373-2388
- ISSN
- 0009-7322 (ISSN-L)
- 1524-4539
- PMC
- PMC5515354
- OpenAlex
- W2626288726
- Cited by
- 345
- References
- 45
- Citations by year
- 2026: 14 · 2025: 25 · 2024: 34 · 2023: 42 · 2022: 48 · 2021: 41 · 2020: 39 · 2019: 45 · 2018: 46 · 2017: 11
- FWCI
- 25.53
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Circulation (publishedVersion)
- PubMed (publishedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (publishedVersion, other-oa)
- Bristol Research (University of Bristol) (publishedVersion, other-oa)
- Lancaster EPrints (Lancaster University) (acceptedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion)
- Open Access at Essex (University of Essex) (acceptedVersion)
- LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
- Leicester Research Archive (University of Leicester) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Figshare (submittedVersion)
- Research Portal (King's College London) (submittedVersion)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion)
- Explore Bristol Research (submittedVersion, other-oa)
- Funders
- Pfizer (RG/10/12/28456, K013351, ET2DS)
- National Institute for Health and Care Research (NF-SI-0515-10091, MC_UU_12013/1, NF-SI-0512-10113)
- British Heart Foundation (RG/08/008/25291, K013351, RG/10/12/28456, RG008/08, PG/13/66/30442, SP/13/6/30554)
- Imperial College London
- University College London
- European Commission (602757)
- Universiteit Leiden
- ZonMw (91614023)
- NordForsk (K013351)
- Leids Universitair Medisch Centrum
- Medical Research Council (MR/K013351/1, MR/K006584/1, RG/10/12/28456, K013351, MC_UU_12013, MR/N01104X/1, MC_UU_12013/1‐9, MCUU12013/1, MR/N01104X/2, ET2DS)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Disease and Adiposity
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Keywords
- adiposity, abdominal obesity, waist-to-hip ratio, body mass index, coronary artery disease, ischemic stroke, type 2 diabetes, cardiometabolic disease, left ventricular hypertrophy, carotid intima-media thickness, circulating lipids, interleukin-6, glycemic traits
- MeSH terms
- Coronary Disease, Diabetes Mellitus, Type 2, Humans, Longitudinal Studies, Prospective Studies, Stroke, Polymorphism, Single Nucleotide, Adiposity, Body Fat Distribution, Mendelian Randomization Analysis, Observational Studies as Topic
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