Sex differences in the associations between relative fat mass and all-cause and cardiovascular mortality: A population-based prospective cohort study

前瞻性队列研究 危险系数 比例危险模型 医学 队列 体质指数 人口 队列研究 人体测量学 内科学 内分泌学 生理学 置信区间 环境卫生
作者
Jing Wang,Jingyuan Guan,Liyan Huang,Xinqing Li,Boping Huang,Jiayu Feng,Yuhui Zhang,Jian Zhang
出处
期刊:Nutrition Metabolism and Cardiovascular Diseases [Elsevier]
卷期号:34 (3): 738-754 被引量:3
标识
DOI:10.1016/j.numecd.2023.10.034
摘要

Background and aims The novel sex-specific anthropometric equation relative fat mass (RFM) is a new estimator of whole-body fat %. The study aimed to investigate the predictive role of RFM in cardiometabolic abnormalities, cardiovascular disease (CVD), all-cause and cardiovascular mortality, and explored potential sex differences. Methods and results The study analyzed data from 26,754 adults in NHANES 1999–2010, with a median follow-up of 13.8 years. The correlation between RFM and body composition as well as fat distribution assessed by dual-energy X-ray absorptiometry was investigated. Weighted multivariable generalized linear models, Cox proportional hazards models and restricted cubic spline were applied to investigate the predictive role of RFM in metabolic markers, cardiovascular risk factors, CVD, all-cause and cardiovascular mortality. RFM exhibited a robust correlation with both whole-body fat % and trunk fat %. Higher RFM exhibited a stronger association with impaired glucose homeostasis, serum lipids, the incidence of hypertension, and coronary heart disease in males, while a stronger association with C-reactive protein in females. A U-shaped association between RFM and all-cause mortality was observed only in males. The hazard ratio (HR) of all-cause and cardiovascular mortality in males increased rapidly when RFM exceeded 30. However, in females, the HR of all-cause and cardiovascular mortality fluctuated until RFM exceeded 45, after which it increased rapidly. Conclusion RFM was a sex-specific estimator for both general and central obesity, sex-specific differences in predicting cardiometabolic abnormalities and adverse events using RFM might be partially attributed to differences in body composition and fat distribution between sexes.
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