Associations of body shapes with insulin resistance and cardiometabolic risk in middle-aged and elderly Chinese
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Abstract
Background: We aimed to define refined body shapes by using multiple anthropometric traits that represent fat distribution, and evaluate their associations with risk of insulin resistance (IR) and cardiometabolic disorders in a Chinese population. Methods: We performed a cross-sectional analysis in 6570 community-based participants aged ≥ 40 years. Four body circumferences (neck, waist, hip, and thigh) and their ratios were put simultaneously into an open-source Waikato Environment for Knowledge Analysis platform to select the worthiest indicators in determining IR. The ratio of the top 3 fat distribution indicators was used to define the refined body shapes. Results: We defined 8 distinct body shapes based on sex-specific combinations of waist-to-hip ratio (WHR), waist-to-thigh ratio (WTR), and waist-to-neck ratio (WNR), which differed in participants’ distribution and risk of IR and related cardiometabolic disorders. In women, as compared to the low WHR-low WTR-low WNR shape, all body shapes were significantly associated with IR and related cardiometabolic disorders; while in men, the low WHR-high WTR-high WNR shape and the higher WHR related shapes were significantly associated with IR and related cardiometabolic disorders. Stratified by WHR, the results were consistent in women; however, no significant associations were detected in men. Conclusions: We defined 8 distinct body shapes by taking WHR, WTR, and WNR, simultaneously into account, which differed in association with the risk of IR and related cardiometabolic disorders in women. This study suggests that body shapes defined by multiple anthropometric traits could provide a useful, convenient, and easily available method for identifying cardiometabolic risk.
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Cites
Trepanowski, J. F., Kroeger, C. M., Barnosky, A., Klempel, M. C., Bhutani, S., Hoddy, K. K., Gabel, K., Freels, S., Rigdon, J., Rood, J., et al. (2017) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2017.0936 · Cited by 645 · see citing works
Emdin, C. A., Khera, A. V., Natarajan, P., Klarin, D., Zekavat, S. M., Hsiao, A. J., & Kathiresan, S. (2017) · JAMA · doi:10.1001/jama.2016.21042 · Cited by 378 · see citing works
Teufel, F., Seiglie, J. A., Geldsetzer, P., Theilmann, M., Marcus, M. E., Ebert, C., Arboleda, W. A. L., Agoudavi, K., Andall-Brereton, G., Aryal, K. K., et al. (2021) · The Lancet · doi:10.1016/s0140-6736(21)00844-8 · Cited by 159 · see citing works
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- Authors
- Zhou, Y., Hou, Y., Xiang, J., Dai, H., Li, M., Wang, T., Wang, S., Lin, H., Lu, J., Xu, Y., Chen, Y., Wang, W., Bi, Y., Xu, M., & Zhao, Z.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Xu, Min
- Zhao, Zhiyun
- ORCID
- Venue
- Nutrition & Metabolism
- Published
- 2021-12
- Volume
- 18
- Issue
- 1
- Pages
- 103-103
- ISSN
- 1743-7075 (ISSN-L)
- PMC
- PMC8650554
- OpenAlex
- W4226094151
- Cited by
- 6
- References
- 3
- Citations by year
- 2025: 1 · 2023: 4 · 2022: 1
- FWCI
- 0.93
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nutrition & Metabolism (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (81941017, 81770842, 81970728, 81970706, 81870560)
- National Key R&D Program of China (2018YFC1311705, 2016YFC1304904, 2016YFC1305600)
- Clinical Research Plan of SHDC (SHDC2020CR1001A, SHDC2020CR3069B)
- Shanghai Municipal Education Commission–Gaofeng Clinical Medicine Grant Support (20161307, 20152508 Round 2)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Health Sciences › Medicine › Physiology › Body Composition Measurement Techniques
- Health Sciences › Medicine › Epidemiology › Adipokines, Inflammation, and Metabolic Diseases
- Keywords
- body shape, insulin resistance, cardiometabolic risk, waist-to-hip ratio, waist-to-thigh ratio, body fat distribution, anthropometric traits, middle-aged and older adults, sex differences, Chinese population
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