Dual Layer Association of the C-Reactive Protein Triglyceride Glucose Index with Cardiovascular–Kidney–Metabolic Syndrome among Older Chinese Adults

Background: The cardiovascular–kidney–metabolic (CKM) syndrome reconceptualizes multimorbidity as a progressive, multisystem disorder. Yet, existing research focuses mainly on disease staging, neglecting the distinction between optimal health and any CKM risk burden. The C-reactive protein–triglyceride–glucose (CTI) index reflects both inflammation and insulin resistance; however, its significance in CKM has not been rigorously evaluated. Methods: We examined data from 10,316 persons aged 45 years and older in the 2015 wave of the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative cohort. We evaluated the association between CTI and (1) CKM presence (CKM vs. no CKM), and (2) stage-specific severity. Binary logistic, ordinal, multi-level binary logistic, and multinomial regression models were developed, controlling for an extensive array of covariates. A thorough series of sensitivity and robustness studies were conducted, encompassing E-value computation to evaluate the potential impact of unmeasured confounding, outlier-trimmed models, CTI tertile specification, and several propensity score methodologies (IPTW and 1:1 matching). Model diagnostics encompassed evaluations of multicollinearity, model fit (McFadden’s pseudo R²), and the proportional odds assumption using the Brant test. Robustness was additionally corroborated by convergence across several modeling approaches and studies stratified by geographic regions (East, Central, West China). Results: CTI had a positive and consistent association with CKM syndrome across all models. In fully adjusted binary logistic regression, each unit increase in CTI corresponded to significantly elevated odds of CKM (OR = 2.57; 95% CI: 2.02–3.27; p < 0.001). Tertile-based studies revealed a dose–response gradient, with the highest CTI tertile linked to a 15.02-fold increase in CKM chances relative to the lowest tertile. In ordinal and multi-level binary logistic models, CTI consistently shown a significant association with escalating CKM stage severity. Multinomial regression indicated no significant association with Stage 1 (isolated adiposity), but demonstrated robust relationships with Stage 2 (OR = 3.60; 95% CI: 2.91–4.44; p < 0.001), Stage 3 (OR = 4.07; 95% CI: 3.29–5.04; p < 0.001), and Stage 4 (OR = 4.19; 95% CI: 3.39–5.19; p < 0.001). Model diagnostics indicated the absence of multicollinearity and demonstrated a satisfactory model fit. The E-value analysis (E = 4.58) indicates that unmeasured variables must have an exceptionally strong correlation with both CTI and CKM to completely account for the observed association. The results remained strong after excluding CTI outliers, employing tertile-based categorization, and utilizing both inverse probability weighting and 1:1 propensity score matching. Regional stratification demonstrated consistent relationships in the eastern (OR = 2.76), central (OR = 2.97), and western (OR = 2.17) regions, with overlapping confidence ranges, so affirming geographic generalizability. The findings remained consistent across several modeling methodologies, risk classifications, and sensitivity analyses. Conclusion: This study provides the first nationally representative evidence of a dual-layer association between the C-reactive protein–triglyceride–glucose (CTI) index and cardiovascular–kidney–metabolic (CKM) syndrome—linking CTI both to the presence of any CKM risk and to stratified stage severity. Crucially, CTI was not associated with isolated adiposity (Stage 1), but demonstrated strong associations with advanced stages (Stages 2–4), highlighting its specificity for systemic metabolic-inflammatory dysfunction rather than general adiposity. These findings position CTI as a cost-effective, stage-sensitive biomarker for syndromic risk detection and stratification in aging populations.