AUTHOR=Chang Ching-Kun , Li Yi-Chen , Chen Po-Ku , Chang Shih-Hsin , Chen Der-Yuan
TITLE=Elevated remnant cholesterol as a potential predictor for cardiovascular events in rheumatoid arthritis patients
JOURNAL=Frontiers in Cardiovascular Medicine
VOLUME=11
YEAR=2024
URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2024.1449219
DOI=10.3389/fcvm.2024.1449219
ISSN=2297-055X
ABSTRACT=ObjectiveThe risk of cardiovascular disease (CVD) in patients with rheumatoid arthritis (RA) remains inadequately defined. Consequently, this study aims to evaluate the predictive value of remnant cholesterol (RC) for assessing CVD risk in RA patients.
MethodsPlasma RC levels were measured in 114 RA patients and 41 healthy controls, calculated as total cholesterol minus HDL-C and LDL-C. These levels were further analyzed using 1H-NMR lipid/metabolomics. Meanwhile, the 28-joint Disease Activity Score (DAS28) assessed RA activity.
ResultsRC levels were significantly elevated in RA patients (19.0 mg/dl, p < 0.001) compared to healthy controls (14.5 mg/dl). Furthermore, RC levels were significantly elevated at 37.4 mg/dl in patients who experienced cardiovascular event (CVE) compared to 17.4 mg/dl in those without CVE (p < 0.001). To enhance the precision and reliability of RC measurements, RC concentrations were further validated using 1H-NMR spectroscopy. Additionally, a positive correlation was observed between RC levels and DAS28. Multivariate analysis identified RC as a significant predictor of CVE (odds ratio = 1.82, p = 0.013). ROC curve analysis revealed superior predictive capability of RC for CVE (AUC = 0.919, p < 0.001) compared to LDL-C (AUC = 0.669, p = 0.018), with a high sensitivity of 94.7% and a specificity of 82.1%.
ConclusionElevated RC levels demonstrate greater accuracy in predicting CVE occurrence in RA patients compared to traditional measures such as LDL-C. These findings suggest that elevated RC levels may serve as a novel predictor for occurrence of CVE in RA patients, facilitating early intervention strategies based on the risk stratification.