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The association between Chinese visceral adiposity index and cardiometabolic multimorbidity among Chinese middle-aged and older adults: a national cohort study

腰围 医学 危险系数 体质指数 老年学 队列 置信区间 比例危险模型 糖尿病 队列研究 人口 接收机工作特性 人口学 内科学 内分泌学 环境卫生 社会学
作者
Xiaomei Ye,Guangru Zhang,Chenyu Han,Ping Wang,Jiaping Lu,Min Zhang
出处
期刊:Frontiers in Endocrinology [Frontiers Media]
卷期号:15: 1381949-1381949 被引量:12
标识
DOI:10.3389/fendo.2024.1381949
摘要

Objective This study aimed to explore the association between the Chinese visceral adiposity index (CVAI) and cardiometabolic multimorbidity in middle-aged and older Chinese adults. Methods The data used in this study were obtained from a national cohort, the China Health and Retirement Longitudinal Study (CHARLS, 2011-2018 wave). The CVAI was measured using previously validated biomarker estimation formulas, which included sex, age, body mass index, waist circumference, triglycerides, and high-density lipoprotein cholesterol. The presence of two or more of these cardiometabolic diseases (diabetes, heart disease, and stroke) is considered as cardiometabolic multimorbidity. We used Cox proportional hazard regression models to examine the association between CVAI and cardiometabolic multimorbidity, adjusting for a set of covariates. Hazard ratios (HRs) and 95% confidence intervals (CIs) were used to show the strength of the associations. We also conducted a subgroup analysis between age and sex, as well as two sensitivity analyses. Receiver operator characteristic curves (ROC) were used to test the predictive capabilities and cutoff value of the CVAI for cardiometabolic multimorbidity. Results A total of 9028 participants were included in the final analysis, with a mean age of 59.3 years (standard deviation: 9.3) and women accounting for 53.7% of the sample population. In the fully-adjusted model, compared with participants in the Q1 of CVAI, the Q3 (HR = 2.203, 95% CI = 1.039 – 3.774) and Q4 of CVAI (HR = 3.547, 95% CI = 2.100 – 5.992) were associated with an increased risk of cardiometabolic multimorbidity. There was no evidence of an interaction between the CVAI quartiles and sex or age in association with cardiometabolic multimorbidity ( P > 0.05). The results of both sensitivity analyses suggested that the association between CVAI and cardiometabolic multimorbidity was robust. In addition, the area under ROC and ideal cutoff value for CVAI prediction of cardiometabolic multimorbidity were 0.685 (95% CI = 0.649-0.722) and 121.388. Conclusion The CVAI is a valid biomarker with good predictive capability for cardiometabolic multimorbidity and can be used by primary healthcare organizations in the future for early warning, prevention, and intervention with regard to cardiometabolic multimorbidity.
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