Abstract
OBJECTIVE: To investigate the association between the triglyceride-glucose index combined with waist circumference (TyG×WC) and cardiovascular disease (CVD) risk in older adults across multiple populations. METHODS: This study utilized data from three population sources: NHANES (2011-2018), a Chinese community cohort, and a tertiary hospital, enrolling a total of 3,443 eligible older adults. The TyG index was calculated as ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2], and then multiplied by waist circumference (WC). The resulting TyG×WC values were standardized using z-score normalization and subsequently categorized into quartiles. Cardiovascular disease (CVD) status was used as the outcome variable. Multivariable logistic regression models were constructed to evaluate the association between TyG×WC and CVD risk. Trend tests and subgroup analyses by sex and region were also performed. Model performance was assessed using receiver operating characteristic (ROC) curves, the DeLong test, net reclassification improvement (NRI), integrated discrimination improvement (IDI), Brier score, and 10-fold cross-validation. Clinical utility was evaluated through decision curve analysis (DCA), while E-value analysis was used to estimate the potential impact of unmeasured confounding. The trend effect across the three populations was synthesized using random-effects meta-analysis to assess heterogeneity. RESULTS: A total of 3,443 participants were included: 1,684 from NHANES (48.91%), 1,263 hospitalized patients from a tertiary hospital (36.68%), and 496 from a community cohort (14.41%). Significant differences were observed across regions in age, TG, TC, LDL, HDL, FPG, ACR, HbA1c, BMI, WC, uric acid, TyG, gender, and CVD prevalence. Multivariable logistic regression indicated a significant positive association between the TyG×WC index and CVD risk. After adjusting for confounders, participants in Q3 and Q4 had significantly higher CVD risk (OR = 1.94 and 2.47, respectively; both P<0.001), with a significant linear trend (P for trend = 2.44×10(-19)). Subgroup analyses showed a stronger predictive effect in females (Q4 vs Q1: OR = 2.34, 95% CI: 1.75-3.14) and in the NHANES population (Q4 vs Q1: OR = 4.64, 95% CI: 3.19-6.85). Heterogeneity analysis revealed no significant differences across regions (I²=30.3%, P = 0.238). Regarding model performance, the extended model including TyG×WC showed an improvement in AUC (from 0.692 to 0.701, DeLong P = 0.038), along with significant improvements in NRI (0.222, P<0.001), IDI (0.0215, P<0.001), and favorable DCA results. The E-value analysis indicated robust results against unmeasured confounding (point estimate E-value = 4.27; lower bound E-value = 3.29). CONCLUSION: The TyG×WC composite indicator is an independent predictor of CVD risk, with more pronounced effects observed in women and the general population. The association between TyG×WC and CVD risk demonstrates a stable and progressive trend across quartiles and is consistent across different populations. The inclusion of TyG×WC enhances predictive accuracy (AUC, NRI, IDI) and clinical utility (DCA), suggesting strong generalizability and practical application. This indicator may serve as a valuable tool for screening high-risk individuals and guiding CVD prevention strategies.