Cardiovascular diabetology · 2026
Cumulative exposure and longitudinal exposure pattern of C-reactive protein-triglyceride-glucose index combined with Chinese visceral adiposity index (CTI-CVAI) and the risk of new-onset cardiovascular disease in middle-aged and older Chinese adults: a prospective cohort study based on the China Health and Retirement Longitudinal Survey (CHARLS).
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Chen Xiao, Wang Chaochao, Luo Yuan, Huang Qi, Gong Haibo, Chen Jing
Аннотация
BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death globally. The C-reactive protein-triglyceride-glucose index (CTI) integrates inflammation and insulin resistance. However, research on cumulative effects and longitudinal patterns of CTI combined with body shape indices for CVD risk assessment is limited. We examined association of cumulative CTI-CVAI (CumCTI-CVAI) exposure and longitudinal patterns with new-onset CVD, heart disease, and stroke in middle-aged and older Chinese adults.
METHODS: Data were from the CHARLS. A two-stage design (cross-sectional screening + longitudinal validation) was employed. Cross-sectional analysis (n = 9,475) identified CTI-CVAI as the optimal composite indicator. In longitudinal analysis (n = 3,803; median follow-up 56.6 months), cumulative CTI-CVAI was calculated using time-weighted averages (2011-2015). The landmark time was set at 2015 to avoid immortal time bias. Cox regression, RCS, K-means clustering, subgroup analyses, and sensitivity analyses (lag analysis, interval deletion analysis, competing risk model, etc.) were performed. NRI and IDI were calculated. A nomogram was constructed using LASSO regression, with AUC for discrimination and calibration/DCA for clinical utility.
RESULTS: Cross-sectional analysis identified CTI-CVAI as the optimal indicator (AUC = 0.617). In longitudinal analysis, each 1-SD increase in CumCTI-CVAI was associated with a 9% higher CVD risk (HR = 1.09, 95%CI:1.04-1.14, P < 0.001). The highest tertile had a 63% higher risk than the lowest (HR = 1.63, 95%CI:1.33-2.00). A nonlinear dose-response relationship was observed (P < 0.001; inflection point:6192.15). For secondary outcomes, each 1-SD increase in CumCTI-CVAI was associated with a 7% higher risk of heart disease (HR = 1.07, 95%CI:1.01-1.14) and a 16% higher risk of stroke (HR = 1.16, 95%CI:1.06-1.27). K-means clustering identified three exposure patterns: low-stable, moderate-stable, and high-stable. Compared to the low-stable group, the high-stable group had 68% higher CVD risk (HR = 1.68), 43% higher heart disease risk (HR = 1.43), and 133% higher stroke risk (HR = 2.33). Hierarchical NRI/IDI analysis showed that adding inflammation to TyG-CVAI significantly improved reclassification (IDI: from 0.018 to 0.090, P < 0.001). The nomogram (age, lung disease, CumCTI-CVAI) achieved AUCs of 0.618-0.638.
CONCLUSION: Elevated cumulative CTI-CVAI exposure and unfavorable longitudinal patterns are independently associated with increased CVD risk in middle-aged and older Chinese adults. CTI-CVAI provides incremental predictive value beyond obesity and insulin resistance alone, supporting its potential as an adjunctive screening tool in primary care.
METHODS: Data were from the CHARLS. A two-stage design (cross-sectional screening + longitudinal validation) was employed. Cross-sectional analysis (n = 9,475) identified CTI-CVAI as the optimal composite indicator. In longitudinal analysis (n = 3,803; median follow-up 56.6 months), cumulative CTI-CVAI was calculated using time-weighted averages (2011-2015). The landmark time was set at 2015 to avoid immortal time bias. Cox regression, RCS, K-means clustering, subgroup analyses, and sensitivity analyses (lag analysis, interval deletion analysis, competing risk model, etc.) were performed. NRI and IDI were calculated. A nomogram was constructed using LASSO regression, with AUC for discrimination and calibration/DCA for clinical utility.
RESULTS: Cross-sectional analysis identified CTI-CVAI as the optimal indicator (AUC = 0.617). In longitudinal analysis, each 1-SD increase in CumCTI-CVAI was associated with a 9% higher CVD risk (HR = 1.09, 95%CI:1.04-1.14, P < 0.001). The highest tertile had a 63% higher risk than the lowest (HR = 1.63, 95%CI:1.33-2.00). A nonlinear dose-response relationship was observed (P < 0.001; inflection point:6192.15). For secondary outcomes, each 1-SD increase in CumCTI-CVAI was associated with a 7% higher risk of heart disease (HR = 1.07, 95%CI:1.01-1.14) and a 16% higher risk of stroke (HR = 1.16, 95%CI:1.06-1.27). K-means clustering identified three exposure patterns: low-stable, moderate-stable, and high-stable. Compared to the low-stable group, the high-stable group had 68% higher CVD risk (HR = 1.68), 43% higher heart disease risk (HR = 1.43), and 133% higher stroke risk (HR = 2.33). Hierarchical NRI/IDI analysis showed that adding inflammation to TyG-CVAI significantly improved reclassification (IDI: from 0.018 to 0.090, P < 0.001). The nomogram (age, lung disease, CumCTI-CVAI) achieved AUCs of 0.618-0.638.
CONCLUSION: Elevated cumulative CTI-CVAI exposure and unfavorable longitudinal patterns are independently associated with increased CVD risk in middle-aged and older Chinese adults. CTI-CVAI provides incremental predictive value beyond obesity and insulin resistance alone, supporting its potential as an adjunctive screening tool in primary care.