Associations of baseline and changes in the triglyceride glucose-weight adjusted waist index and cardiovascular disease risk: evidence from middle-aged and older individuals

医学 腰围 四分位数 体质指数 内科学 优势比 逻辑回归 置信区间 索引(排版) 基线(sea) 肥胖 人口学 社会学 海洋学 地质学 万维网 计算机科学
作者
Chenglin Duan,Meng Lyu,Jingjing Shi,Xintian Shou,Lu Zhao,Yuanhui Hu
出处
期刊:Cardiovascular Diabetology [Springer Nature]
卷期号:23 (1) 被引量:10
标识
DOI:10.1186/s12933-024-02511-9
摘要

Existing researches have predominantly focused on the implications of dynamic alterations in the triglyceride-glucose (TyG) index and traditional obesity measures for cardiovascular disease (CVD) risk. However, the application of the weight-adjusted waist index (WWI), which incorporates the dynamically changing body composition factors of weight and waist circumference, alongside the TyG index for predicting CVD risk, remains unexplored. This study explores the relationships between baseline TyG-WWI index and its dynamic changes with CVD risk. Subjects were drawn from the China Health and Retirement Longitudinal Study. Logistic regression analyses were conducted to determine the relationships between baseline and longitudinal changes in the TyG-WWI index and CVD risk, quantified through odds ratios (ORs) and 95% confidence intervals (CIs). The robustness of results was confirmed via subgroup analyses and E-values. Additionally, restricted cubic spline and quartile-based methods evaluated the relationships between baseline and cumulative TyG-WWI indices and CVD risk. Over two survey waves, 613 CVD events were recorded. Analysis using adjusted multivariable models demonstrated a significant relationship between the cumulative TyG-WWI index and increased CVD risk, with an adjusted OR (95% CI) of 1.005 (1.000, 1.009). Class 2 of the TyG-WWI index change showed greater risk of CVD compared to Class 1, with ORs of 1.270 (1.008, 1.605). However, no significant connection was observed between the baseline TyG-WWI index and CVD risk (OR = 1.007, 95% CI: 0.996, 1.019). These findings were corroborated through extensive sensitivity analyses.
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