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

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Abstract

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.

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Availability of data and material

Genetic instrumental variables and data sources are presented in Supplementary Tables.

Code availability

Code used to undertake mendelian randomization analyses can be found in the R packages “TwoSampleMR”, “MRPRESSO”, and “cause”.

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Acknowledgements

We thank the researchers from MRC-IEU who made the GWAS summary statistics publicly available for this study. We want to acknowledge the participants and investigators of the FinnGen study, MAGIC and GLGC. We acknowledge the help of English language editing from Dr. Wenjuan Qin from College of Foreign Languages and Literature, Fudan University.

Funding

The work was supported by Shanghai Municipal Science and Technology Major Project (Grant No. 2017SHZDZX01).

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Authors and Affiliations

  1. 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

    Hailuan Zeng & Xin Gao

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

    Chenhao Lin & Yan Zheng

  3. 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

    Sijia Wang

  4. Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China

    Sijia Wang

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  1. Hailuan Zeng
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  2. Chenhao Lin
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  3. Sijia Wang
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  4. Yan Zheng
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  5. Xin Gao
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Contributions

HZ conceived the study, performed the main analyses and drafted the original version of the manuscript. XG, YZ, and SW contributed to the study design, XG and SW provided the analysis platform, YZ and CL critically revised the manuscript. All authors were involved in the interpretation of results, helped refine the manuscript, and approved the final version.

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Correspondence to Sijia Wang, Yan Zheng or Xin Gao.

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The present research used publicly available summary data and did not contact with participants, where no extra ethical approval is required.

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Zeng, H., Lin, C., Wang, S. et al. Genetically predicted body composition in relation to cardiometabolic traits: a Mendelian randomization study. Eur J Epidemiol 36, 1157–1168 (2021). https://doi.org/10.1007/s10654-021-00779-9

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