Genetically predicted body composition in relation to cardiometabolic traits: a Mendelian randomization study.

Body Composition / genetics Body Mass Index Cardiovascular Diseases / epidemiology Diabetes Mellitus, Type 2 / epidemiology Genome-Wide Association Study Humans Mendelian Randomization Analysis Polymorphism, Single Nucleotide
Body composition Cardiovascular disease Glucose metabolism Lipids Mendelian randomization

Journal

European journal of epidemiology
ISSN: 1573-7284
Titre abrégé: Eur J Epidemiol
Pays: Netherlands
ID NLM: 8508062

Informations de publication

Date de publication:
Nov 2021
Historique:
received: 04 02 2021
accepted: 22 06 2021
pubmed: 2 7 2021
medline: 15 12 2021
entrez: 1 7 2021
Statut: ppublish

Résumé

Fat mass and fat-free mass are found to be associated with different health outcomes in observational studies, but the underlying causality remains unclear. We aimed to investigate the causal relationships between body composition and cardiometabolic traits using a two-sample Mendelian randomization (MR) approach. Independent genetic variants associated with body fat mass, fat-free mass, and fat percentage in UK Biobank population were used as genetic instrumental variables, and their causal effects on circulatory diseases, type 2 diabetes, glycemic traits, and lipid fractions were estimated from large-scale genome-wide association studies (GWAS) in European populations. Univariable, multivariable, and bidirectional MR analyses were performed. Genetically predicted high fat mass and fat percentage significantly increased risks of most cardiometabolic diseases, and high fat-free mass had protective effects on most cardiometabolic diseases after accounting for fat mass. Fat mass, fat-free mass, and fat percentage were all positively associated with higher risks of atrial fibrillation and flutter, varicose veins, and deep vein thrombosis and pulmonary embolism. High fat mass increased fasting glucose, homeostasis model assessment-insulin resistance (HOMA-IR), triglycerides, decreased high-density lipoprotein cholesterol, and high fat-free mass reduced HOMA-IR, triglycerides, and low-density lipoprotein cholesterol. Genetically predicted fat-free mass was bidirectionally negatively associated with 2-h glucose and total cholesterol. The findings may be helpful in risk stratification and tailoring management of body composition in patients with different cardiometabolic statuses.

Identifiants

DOI: 10.1007/s10654-021-00779-9 PMID: 34195880
pubmed: 34195880
doi: 10.1007/s10654-021-00779-9
pii: 10.1007/s10654-021-00779-9
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1157-1168

Subventions

Organisme : Shanghai Municipal Science and Technology Major Project24-Jun-2021
ID : 2017SHZDZX01

Informations de copyright

© 2021. Springer Nature B.V.

Références

Lee DH, Keum N, Hu FB, Orav EJ, Rimm EB, Willett WC, et al. Comparison of the association of predicted fat mass, body mass index, and other obesity indicators with type 2 diabetes risk: two large prospective studies in US men and women. Eur J Epidemiol. 2018;33(11):1113–23. https://doi.org/10.1007/s10654-018-0433-5 .
doi: 10.1007/s10654-018-0433-5 pubmed: 30117031
Son JW, Lee SS, Kim SR, Yoo SJ, Cha BY, Son HY, et al. Low muscle mass and risk of type 2 diabetes in middle-aged and older adults: findings from the KoGES. Diabetologia. 2017;60(5):865–72. https://doi.org/10.1007/s00125-016-4196-9 .
doi: 10.1007/s00125-016-4196-9 pubmed: 28102434
Byambasukh O, Eisenga MF, Gansevoort RT, Bakker SJ, Corpeleijn E. Body fat estimates from bioelectrical impedance equations in cardiovascular risk assessment: the PREVEND cohort study. Eur J Prev Cardiol. 2019;26(9):905–16. https://doi.org/10.1177/2047487319833283 .
doi: 10.1177/2047487319833283 pubmed: 30791699 pmcid: 6545622
Medina-Inojosa JR, Somers VK, Thomas RJ, Jean N, Jenkins SM, Gomez-Ibarra MA, et al. Association between adiposity and lean mass with long-term cardiovascular events in patients with coronary artery disease: no paradox. J Am Heart Assoc. 2018;7(10): e007505. https://doi.org/10.1161/jaha.117.007505 .
doi: 10.1161/jaha.117.007505 pubmed: 29739793 pmcid: 6015302
Xing Z, Tang L, Chen J, Pei J, Chen P, Fang Z, et al. Association of predicted lean body mass and fat mass with cardiovascular events in patients with type 2 diabetes mellitus. CMAJ. 2019;191(38):E1042–8. https://doi.org/10.1503/cmaj.190124 .
doi: 10.1503/cmaj.190124 pubmed: 31548190 pmcid: 6763326
Fenger-Grøn M, Overvad K, Tjønneland A, Frost L. Lean body mass is the predominant anthropometric risk factor for atrial fibrillation. J Am Coll Cardiol. 2017;69(20):2488–97. https://doi.org/10.1016/j.jacc.2017.03.558 .
doi: 10.1016/j.jacc.2017.03.558 pubmed: 28521886
Azarbal F, Stefanick ML, Assimes TL, Manson JE, Bea JW, Li W, et al. Lean body mass and risk of incident atrial fibrillation in post-menopausal women. Eur Heart J. 2016;37(20):1606–13. https://doi.org/10.1093/eurheartj/ehv423 .
doi: 10.1093/eurheartj/ehv423 pubmed: 26371115
Lawlor DA, Harbord RM, Sterne JA, Timpson N, Davey SG. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med. 2008;27(8):1133–63. https://doi.org/10.1002/sim.3034 .
doi: 10.1002/sim.3034 pubmed: 17886233
Lyall DM, Celis-Morales C, Ward J, Iliodromiti S, Anderson JJ, Gill JMR, et al. Association of body mass index with cardiometabolic disease in the uk biobank: a mendelian randomization study. JAMA Cardiol. 2017;2(8):882–9. https://doi.org/10.1001/jamacardio.2016.5804 .
doi: 10.1001/jamacardio.2016.5804 pubmed: 28678979 pmcid: 5710596
Richardson TG, Sanderson E, Elsworth B, Tilling K, Davey SG. Use of genetic variation to separate the effects of early and later life adiposity on disease risk: mendelian randomisation study. BMJ. 2020;369: m1203. https://doi.org/10.1136/bmj.m1203 .
doi: 10.1136/bmj.m1203 pubmed: 32376654 pmcid: 7201936
Wang N, Cheng J, Ning Z, Chen Y, Han B, Li Q, et al. Type 2 diabetes and adiposity induce different lipid profile disorders: a mendelian randomization analysis. J Clin Endocrinol Metab. 2018;103(5):2016–25. https://doi.org/10.1210/jc.2017-02789 .
doi: 10.1210/jc.2017-02789 pubmed: 29506267
Larsson SC, Bäck M, Rees JMB, Mason AM, Burgess S. Body mass index and body composition in relation to 14 cardiovascular conditions in UK Biobank: a Mendelian randomization study. Eur Heart J. 2020;41(2):221–6. https://doi.org/10.1093/eurheartj/ehz388 .
doi: 10.1093/eurheartj/ehz388 pubmed: 31195408
Tikkanen E, Gustafsson S, Knowles JW, Perez M, Burgess S, Ingelsson E. Body composition and atrial fibrillation: a Mendelian randomization study. Eur Heart J. 2019;40(16):1277–82. https://doi.org/10.1093/eurheartj/ehz003 .
doi: 10.1093/eurheartj/ehz003 pubmed: 30721963 pmcid: 6475522
Yeung CHC, Au Yeung SL, Fong SSM, Schooling CM. Lean mass, grip strength and risk of type 2 diabetes: a bi-directional Mendelian randomisation study. Diabetologia. 2019;62(5):789–99. https://doi.org/10.1007/s00125-019-4826-0 .
doi: 10.1007/s00125-019-4826-0 pubmed: 30798333
Shadrina AS, Sharapov SZ, Shashkova TI, Tsepilov YA. Varicose veins of lower extremities: Insights from the first large-scale genetic study. PLoS Genet. 2019;15(4): e1008110. https://doi.org/10.1371/journal.pgen.1008110 .
doi: 10.1371/journal.pgen.1008110 pubmed: 30998689 pmcid: 6490943
Elsworth B, Lyon M, Alexander T, Liu Y, Matthews P, Hallett J, et al. The MRC IEU OpenGWAS data infrastructure. bioRxiv. 2020:2020.08.10.244293. https://doi.org/10.1101/2020.08.10.244293 .
Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife. 2018;7: e34408. https://doi.org/10.7554/eLife.34408 .
doi: 10.7554/eLife.34408 pubmed: 29846171 pmcid: 5976434
Pierce BL, Ahsan H, Vanderweele TJ. Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. Int J Epidemiol. 2011;40(3):740–52. https://doi.org/10.1093/ije/dyq151 .
doi: 10.1093/ije/dyq151 pubmed: 20813862
Zhou W, Nielsen JB, Fritsche LG, Dey R, Gabrielsen ME, Wolford BN, et al. Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nat Genet. 2018;50(9):1335–41. https://doi.org/10.1038/s41588-018-0184-y .
doi: 10.1038/s41588-018-0184-y pubmed: 30104761 pmcid: 6119127
Morrison J, Knoblauch N, Marcus JH, Stephens M, He X. Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics. Nat Genet. 2020;52(7):740–7. https://doi.org/10.1038/s41588-020-0631-4 .
doi: 10.1038/s41588-020-0631-4 pubmed: 32451458 pmcid: 7343608
Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 2016;40(4):304–14. https://doi.org/10.1002/gepi.21965 .
doi: 10.1002/gepi.21965 pubmed: 27061298 pmcid: 4849733
Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44(2):512–25. https://doi.org/10.1093/ije/dyv080 .
doi: 10.1093/ije/dyv080 pubmed: 26050253 pmcid: 4469799
Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50(5):693–8. https://doi.org/10.1038/s41588-018-0099-7 .
doi: 10.1038/s41588-018-0099-7 pubmed: 29686387 pmcid: 6083837
Burgess S, Thompson SG. Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. Am J Epidemiol. 2015;181(4):251–60. https://doi.org/10.1093/aje/kwu283 .
doi: 10.1093/aje/kwu283 pubmed: 25632051 pmcid: 4325677
Larsson SC, Burgess S. Fat mass and fat-free mass in relation to cardiometabolic diseases: a two-sample Mendelian randomization study. J Intern Med. 2020;288(2):260–2. https://doi.org/10.1111/joim.13078 .
doi: 10.1111/joim.13078 pubmed: 32294276 pmcid: 7569509
Tyrovolas S, Panagiotakos D, Georgousopoulou E, Chrysohoou C, Tousoulis D, Haro JM, et al. Skeletal muscle mass in relation to 10 year cardiovascular disease incidence among middle aged and older adults: the ATTICA study. J Epidemiol Commun Health. 2020;74(1):26–31. https://doi.org/10.1136/jech-2019-212268 .
doi: 10.1136/jech-2019-212268
Zhang H, Lin S, Gao T, Zhong F, Cai J, Sun Y, et al. Association between sarcopenia and metabolic syndrome in middle-aged and older non-obese adults: a systematic review and meta-analysis. Nutrients. 2018;10(3):364. https://doi.org/10.3390/nu10030364 .
doi: 10.3390/nu10030364 pmcid: 5872782
Kim G, Lee SE, Jun JE, Lee YB, Ahn J, Bae JC, et al. Increase in relative skeletal muscle mass over time and its inverse association with metabolic syndrome development: a 7-year retrospective cohort study. Cardiovasc Diabetol. 2018;17(1):23. https://doi.org/10.1186/s12933-018-0659-2 .
doi: 10.1186/s12933-018-0659-2 pubmed: 29402279 pmcid: 5798183
Fenger-Grøn M, Vinter N, Frost L. Body mass and atrial fibrillation risk: status of the epidemiology concerning the influence of fat versus lean body mass. Trends Cardiovasc Med. 2020;30(4):205–11. https://doi.org/10.1016/j.tcm.2019.05.009 .
doi: 10.1016/j.tcm.2019.05.009 pubmed: 31178265
Fukaya E, Flores AM, Lindholm D, Gustafsson S, Zanetti D, Ingelsson E, et al. Clinical and genetic determinants of varicose veins. Circulation. 2018;138(25):2869–80. https://doi.org/10.1161/circulationaha.118.035584 .
doi: 10.1161/circulationaha.118.035584 pubmed: 30566020 pmcid: 6400474
Zöller B, Ji J, Sundquist J, Sundquist K. Body height and incident risk of venous thromboembolism: a cosibling design. Circ Cardiovasc Genet. 2017;10(5): e001651. https://doi.org/10.1161/circgenetics.116.001651 .
doi: 10.1161/circgenetics.116.001651 pubmed: 28874396
Roetker NS, Armasu SM, Pankow JS, Lutsey PL, Tang W, Rosenberg MA, et al. Taller height as a risk factor for venous thromboembolism: a Mendelian randomization meta-analysis. J Thromb Haemost. 2017;15(7):1334–43. https://doi.org/10.1111/jth.13719 .
doi: 10.1111/jth.13719 pubmed: 28445597 pmcid: 5504700
MacLeod SF, Terada T, Chahal BS, Boulé NG. Exercise lowers postprandial glucose but not fasting glucose in type 2 diabetes: a meta-analysis of studies using continuous glucose monitoring. Diabetes Metab Res Rev. 2013;29(8):593–603. https://doi.org/10.1002/dmrr.2461 .
doi: 10.1002/dmrr.2461 pubmed: 24038928
Kalyani RR, Corriere M, Ferrucci L. Age-related and disease-related muscle loss: the effect of diabetes, obesity, and other diseases. Lancet Diabetes Endocrinol. 2014;2(10):819–29. https://doi.org/10.1016/s2213-8587(14)70034-8 .
doi: 10.1016/s2213-8587(14)70034-8 pubmed: 24731660 pmcid: 4156923
Xu L, Borges MC, Hemani G, Lawlor DA. The role of glycaemic and lipid risk factors in mediating the effect of BMI on coronary heart disease: a two-step, two-sample Mendelian randomisation study. Diabetologia. 2017;60(11):2210–20. https://doi.org/10.1007/s00125-017-4396-y .
doi: 10.1007/s00125-017-4396-y pubmed: 28889241 pmcid: 6342872
Holmes MV, Lange LA, Palmer T, Lanktree MB, North KE, Almoguera B, et al. Causal effects of body mass index on cardiometabolic traits and events: a mendelian randomization analysis. Am J Hum Genet. 2014;94(2):198–208. https://doi.org/10.1016/j.ajhg.2013.12.014 .
doi: 10.1016/j.ajhg.2013.12.014 pubmed: 24462370 pmcid: 3928659
Martins C, Strømmen M, Stavne OA, Nossum R, Mårvik R, Kulseng B. Bariatric surgery versus lifestyle interventions for morbid obesity–changes in body weight, risk factors and comorbidities at 1 year. Obes Surg. 2011;21(7):841–9. https://doi.org/10.1007/s11695-010-0131-1 .
doi: 10.1007/s11695-010-0131-1 pubmed: 20379796
Robinson JG. Statins and diabetes risk: how real is it and what are the mechanisms? Curr Opin Lipidol. 2015;26(3):228–35. https://doi.org/10.1097/mol.0000000000000172 .
doi: 10.1097/mol.0000000000000172 pubmed: 25887679
Lagou V, Mägi R, Hottenga JJ, Grallert H, Perry JRB, Bouatia-Naji N, et al. Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability. Nat Commun. 2021;12(1):24. https://doi.org/10.1038/s41467-020-19366-9 .
doi: 10.1038/s41467-020-19366-9 pubmed: 33402679 pmcid: 7785747
Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, et al. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45(11):1274–83. https://doi.org/10.1038/ng.2797 .
doi: 10.1038/ng.2797 pubmed: 24097068 pmcid: 3838666
Dupuis J, Langenberg C, Prokopenko I, Saxena R, Soranzo N, Jackson AU, et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet. 2010;42(2):105–16. https://doi.org/10.1038/ng.520 .
doi: 10.1038/ng.520 pubmed: 3018764 pmcid: 3018764
Saxena R, Hivert MF, Langenberg C, Tanaka T, Pankow JS, Vollenweider P, et al. Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge. Nat Genet. 2010;42(2):142–8. https://doi.org/10.1038/ng.521 .
doi: 10.1038/ng.521 pubmed: 20081857 pmcid: 2922003
Fry A, Littlejohns TJ, Sudlow C, Doherty N, Adamska L, Sprosen T, et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am J Epidemiol. 2017;186(9):1026–34. https://doi.org/10.1093/aje/kwx246 .
doi: 10.1093/aje/kwx246 pubmed: 28641372 pmcid: 5860371
Taylor AE, Jones HJ, Sallis H, Euesden J, Stergiakouli E, Davies NM, et al. Exploring the association of genetic factors with participation in the avon longitudinal study of parents and children. Int J Epidemiol. 2018;47(4):1207–16. https://doi.org/10.1093/ije/dyy060 .
doi: 10.1093/ije/dyy060 pubmed: 29800128 pmcid: 6124613
Koellinger PD, de Vlaming R. Mendelian randomization: the challenge of unobserved environmental confounds. Int J Epidemiol. 2019;48(3):665–71. https://doi.org/10.1093/ije/dyz138 .
doi: 10.1093/ije/dyz138 pubmed: 31263889 pmcid: 6659461

Auteurs

Hailuan Zeng (H)

Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan Institute for Metabolic Diseases, and Human Phenome Institute, Fudan University, NO.180 Fenglin Road, Shanghai, 200032, China.

Chenhao Lin (C)

State Key Laboratory of Genetic Engineering, Human Phenome Institute, and School of Life Sciences, Fudan University, Shanghai, China.

Sijia Wang (S)

CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China. wangsijia@picb.ac.cn.
Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China. wangsijia@picb.ac.cn.

Yan Zheng (Y)

State Key Laboratory of Genetic Engineering, Human Phenome Institute, and School of Life Sciences, Fudan University, Shanghai, China. yan_zheng@fudan.edu.cn.

Xin Gao (X)

Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan Institute for Metabolic Diseases, and Human Phenome Institute, Fudan University, NO.180 Fenglin Road, Shanghai, 200032, China. happy20061208@126.com.

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female

Evaluation of Low-Value Services Across Major Medicare Advantage Insurers and Traditional Medicare.

Ciara Duggan, Adam L Beckman, Ishani Ganguli et al.
1.00
Humans United States Aged Cross-Sectional Studies Medicare Part C

Effectiveness of Virtual Yoga for Chronic Low Back Pain: A Randomized Clinical Trial.

Hallie Tankha, Devyn Gaskins, Amanda Shallcross et al.
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH

questionsmedicales.fr › Phénomènes physiologiques › Constitution physique › Composition corporelle › Genetically predicted body composition in...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Mesures épidémiologiques › Biométrie › Anthropométrie › Indice de masse corporelle › Genetically predicted body composition in...
questionsmedicales.fr › Maladies cardiovasculaires › Genetically predicted body composition in...
questionsmedicales.fr › Maladies endocriniennes › Diabète › Diabète de type 2 › Genetically predicted body composition in...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Épidémiologie moléculaire › Étude d'association pangénomique › Genetically predicted body composition in...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Genetically predicted body composition in...
questionsmedicales.fr › Techniques d'investigation › Analyse de randomisation mendélienne › Genetically predicted body composition in...
questionsmedicales.fr › Phénomènes génétiques › Variation génétique › Polymorphisme génétique › Polymorphisme de nucléotide simple › Genetically predicted body composition in...