The synergistic pattern of metabolic-inflammatory changes jointly predicts cardiovascular risk in Chinese middle-aged and older adults: a prospective cohort study

The interplay between metabolic dysregulation and chronic inflammation is pivotal in the development of cardiovascular disease. However, the joint prognostic value of their dynamic trajectories remains unclear. Current risk assessment predominantly relies on single-timepoint, static measurements, which fail to capture their temporal evolution and synergistic interactions. This study aimed to investigate the synergistic pattern derived from the dynamic trajectories of metabolism and inflammation for predicting major adverse cardiovascular events(MACE). This prospective cohort study included 4,968 Chinese middle-aged and older adults free of baseline cardiovascular disease from the China Health and Retirement Longitudinal Study (CHARLS). The triglyceride-glucose-body mass index and high-sensitivity C-reactive protein were used to define metabolic and inflammatory status, respectively. Based on measurements at two time points, participants were classified as being at “high risk” if they exhibited sustained high levels or a transition from low to high in either metabolic or inflammatory status. By cross-classifying metabolic and inflammatory high-risk status, four mutually exclusive synergistic patterns were derived: low-low (reference), high-high, metabolism-dominant (high metabolic risk only), and inflammation-dominant (high inflammatory risk only). The primary outcome was incident MACE (a composite of cardiovascular death and self-reported physician-diagnosed heart attack, coronary heart disease, angina, congestive heart failure, other heart problems, or stroke). Cox proportional hazards models were used to calculate hazard ratios for each pattern compared with the low-low group. The incremental predictive value was assessed using the C-statistic, net reclassification improvement, and integrated discrimination improvement. Subgroup and sensitivity analyses were performed. Over a median follow-up of 5.25 years, 1,087 MACE events were documented. Compared with the low-low group, the metabolism-dominant group exhibited the highest risk of MACE (fully adjusted HR = 1.39, 95% CI: 1.15–1.68, p < 0.001), followed by the high-high group (HR = 1.31, 95% CI: 1.10–1.57, p = 0.002). The inflammation-dominant group was not significantly associated with MACE risk (HR = 1.15, 95% CI: 0.95–1.39, p = 0.146). In subgroup analyses, the association for the high-high pattern appeared stronger in participants aged < 60 years (HR = 1.54, 95% CI:1.16–2.05) compared with those aged ≥ 60 years (HR = 1.23, 95% CI: 0.91–1.66), although no significant interaction was detected (P for interaction = 0.204). Adding the synergistic patterns to a model containing traditional risk factors yielded a modest but significant improvement in risk prediction (ΔC-index = 0.006, P = 0.0465; NRI = 0.079, P = 0.031; IDI = 0.0024, P = 0.045). Sensitivity analyses using a stricter definition of high inflammatory status (≥ 75th percentile per wave) confirmed the robustness of the findings (HR for high-high = 1.33, 95% CI: 1.10–1.61, p = 0.003). This study demonstrates that the joint patterns of metabolic and inflammatory changes—defined using two repeated measurements—provide independent prognostic information for cardiovascular events beyond traditional risk factors. Among the four synergistic patterns identified, the metabolism-dominant group exhibited the highest MACE risk, suggesting that, within this cohort and under our measurement framework, metabolic dysregulation may be a more prominent predictor of cardiovascular risk than inflammation alone. The lack of significant association for the inflammation-dominant pattern alone may indicate that low-grade inflammation confers excess risk primarily when accompanied by adverse metabolic status; however, this interpretation should be made cautiously given potential assay-related heterogeneity and limited power for some contrasts.