Background Cardiometabolic multimorbidity (CMM), encompassing conditions such as heart disease, diabetes, and stroke, is a leading cause of disability and mortality worldwide. Despite improvements in disease management, the absolute burden of CMM continues to increase due to population aging and shifting epidemiological trends. Traditional risk factors do not fully account for residual risk, necessitating the exploration of novel biomarkers for more accurate prediction and intervention in CMM.
Objective To investigate the association between the remnant cholesterol inflammatory index (RCII) and the risk of CMM in the middle-aged and older Chinese population, and to evaluate the value of RCII in predicting CMM risk compared to using remnant cholesterol (RC) or high-sensitivity C-reactive protein (hs-CRP) alone.
Methods This prospective cohort study was based on data from the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2018. A total of 9 194 participants aged ≥45 years without CMM at baseline were included. Exposures of interest were baseline RCII, RC, and hs-CRP, with participants divided into Q1 to Q4 groups according to their respective quartiles. Demographic, lifestyle, and clinical examination indicators were collected. The primary endpoint was new-onset CMM. Kaplan-Meier curves were used to assess the cumulative incidence of CMM across different RCII groups. Multivariable Cox regression models were employed to analyze the association between RCII and CMM risk, with sequential adjustment for sociodemographic characteristics, behavioral and physiological indicators, and chronic disease history. Subgroup analyses and sensitivity analyses were conducted to verify the robustness of the findings. The predictive performance of RCII for CMM was evaluated using receiver operating characteristic (ROC) curves, and its incremental predictive value over traditional risk factors was assessed using the net reclassification improvement (NRI) and integrated discrimination improvement (IDI).
Results Over a median follow-up of 7 years, 1 660 participants (18.1%) developed new-onset CMM. Kaplan-Meier curves showed significant graded associations of RCII, RC, and hs-CRP with the cumulative incidence of CMM (all Log-rank P<0.001). Multivariable Cox regression analysis indicated that for each standard deviation increase in RCII, the risk of CMM increased by 5% (HR=1.05, 95%CI=1.02-1.07); for RC, the risk increased by 8% (HR=1.08, 95%CI=1.04-1.13); and for hs-CRP, the risk increased by 6% (HR=1.06, 95%CI=1.02-1.10). Compared to the Q1 group, the risk of CMM in the Q2, Q3, and Q4 groups for RCII increased by 25% (HR=1.25, 95%CI=1.06-1.48), 31% (HR=1.31, 95%CI=1.11-1.54), and 73% (HR=1.73, 95%CI=1.48-2.02), respectively; for RC, the risk increased by 15% (HR=1.15, 95%CI=0.98-1.35), 30% (HR=1.30, 95%CI=1.11-1.52), and 44% (HR=1.44, 95%CI=1.24-1.68), respectively; for hs-CRP, the risk increased by 20% (HR=1.20, 95%CI=1.02-1.42), 29% (HR=1.29, 95%CI=1.10-1.51), and 53% (HR=1.53, 95%CI=1.31-1.79), respectively. Subgroup analyses revealed that the association between RCII and CMM was more pronounced in females, individuals aged ≥60 years, former smokers, urban residents, and overweight/obese individuals (BMI ≥24 kg/m2). Furthermore, sex, BMI, and residence significantly modified the association between RCII and CMM incidence(Pinteraction<0.05). The area under the ROC curve (AUC) for RCII in predicting CMM was 0.591, higher than that for RC (0.571) and hs-CRP (0.573) (both P<0.01). Adding RCII to traditional risk factors significantly improved risk reclassification (NRI=0.186, IDI=0.052, both P<0.05). Sensitivity analyses confirmed the robustness of the results.
Conclusion RCII, as a biomarker integrating lipid and inflammatory pathways, is an independent risk factor for new-onset CMM in the middle-aged and older Chinese population and holds significant value for the early identification of individuals at high risk for CMM.
