Dynamic Co-Evolution of Obesity-Metabolic-Inflammatory for Cardiovascular Disease Risk Stratification in Middle-Aged and Older Adults: A Data-Driven Joint Trajectory Analysis.

BACKGROUND

Cardiovascular disease (CVD) risk assessment based on single-time-point measurements may inadequately capture the dynamic interplay of obesity, metabolic dysfunction, and chronic inflammation. Understanding their long-term co-evolution may improve CVD risk stratification.

METHODS

We conducted a prospective cohort study using data from the China Health and Retirement Longitudinal Study. Participants aged ≥ 45 years with repeated measurements of body mass index (BMI), triglyceride-glucose (TyG) index, and high-sensitivity C-reactive protein (hsCRP) between 2011 and 2015 were included. Joint trajectories of these obesity-metabolic-inflammatory markers were identified using the KmL3D clustering algorithm. Incident CVD (heart disease or stroke) was ascertained during follow-up from 2015 to 2020. Cox proportional hazards models were applied to evaluate associations between trajectory groups and CVD risk after adjustment for demographic, lifestyle, and clinical covariates.

RESULTS

Among 4483 participants (mean age 57.8 years; 52.6% women), four distinct joint trajectory patterns were identified: a sustained low-risk profile (46.8%), an obesity-predominant profile (26.7%), a metabolic-obesity-driven profile (17.1%), and an inflammation-dominant profile (9.4%). During a median follow-up of 5.0 years, 592 incident CVD events occurred. Compared with the low-risk profile, all adverse trajectory groups were associated with significantly higher CVD risk, with hazard ratios ranging from 1.35 to 1.46 after full adjustment. Associations were more pronounced for stroke, particularly among individuals with a metabolic-obesity-driven trajectory. Findings were robust across sensitivity analyses.

CONCLUSIONS

Distinct dynamic co-evolution patterns of obesity, metabolic dysfunction, and inflammation identify population subgroups with substantially different risks of incident CVD.