Risk prediction of cardiometabolic multimorbidity using the ZJU index in middle-aged and older Chinese population

Objective This study aimed to comprehensively examine the association between the ZJU index and the risk of developing cardiometabolic multimorbidity (CMM) among middle-aged and older Chinese adults. Methods Utilizing data from the Guizhou sub-cohort of the REACTION study, the ZJU index was derived by integrating key metrics, including body mass index (BMI), fasting plasma glucose (FPG), triglycerides (TG), and the alanine aminotransferase to aspartate aminotransferase ratio (ALT/AST). A total of 8,797 participants aged 40 and older were monitored over an average duration of 10.05 years, with 6,854 individuals (77.9%) included in the final analysis. Participants were categorized into quartiles based on their initial ZJU Index levels. To evaluate independent associations, multivariable logistic regression was employed; restricted cubic splines (RCS) were used to illustrate dose–response relationships, and recursive algorithms were applied to determine risk thresholds. The predictive accuracy of the ZJU index was compared with traditional single indicators using receiver operating characteristic (ROC) curves. Result Throughout the follow-up period, 381 new cases of CMM were recorded. After controlling for confounding variables, a linear positive relationship was observed between the ZJU index and CMM risk (P for non-linearity > 0.05). The highest quartile (Q4) exhibited a 2.29-fold increase in the risk of CMM (95% CI: 1.42–3.70) compared to the lowest quartile (Q1). The area under the ROC curve (AUC) for the ZJU index in predicting CMM was 0.651 (95% CI: 0.623–0.678), significantly outperforming the predictive capabilities of BMI, FPG, TG, and the ALT/AST ratio individually. Subgroup analyses revealed that this relationship remained consistent across various categories defined by sex, age, BMI, smoking status, and drinking status, with no significant interactions detected. Conclusion The ZJU index serves as an independent risk factor for CMM. Its straightforward calculation and excellent predictive ability make it highly suitable for early screening and risk assessment in community settings.