Clinical characteristics and risk factors of dyslipidemia in patients with rheumatoid arthritis.

BACKGROUND

Dyslipidemia is highly prevalent in patients with rheumatoid arthritis (RA) and may be closely associated with systemic inflammation and disease activity. However, its clinical characteristics and predictive factors remain unclear.

METHODS

We retrospectively enrolled 312 RA patients and stratified them into dyslipidemia group (n = 168) and a normolipidemia group (n = 144) based on serum lipid profiles. Correlation analyses were performed to examine associations between lipid levels and inflammatory markers. Multivariate logistic regression was used to identify independent predictors of dyslipidemia. Receiver operating characteristic (ROC) curves were applied to evaluate the predictive performance of individual and combined models.

RESULTS

Patients with dyslipidemia had higher levels of total cholesterol (TC), triglycerides (TG), and low-density lipoprotein cholesterol (LDL-C), alongside reduced high-density lipoprotein cholesterol (HDLC). The dyslipidemia group also showed higher erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), and Disease Activity Score in 28 joints (DAS28). TC, TG, and LDL-C were positively correlated with inflammatory markers and DAS28, while HDL-C correlated negatively. Age, body mass index (BMI), female gender, smoking history, glucocorticoid use, and higher DAS28 were identified as independent risk factors for dyslipidemia. Elevated CRP and IL-6 further increased risk, whereas higher TNF-α levels were protective. Among predictive models, the combined Integrated Model achieved superior discriminative performance, outperforming any single clinical or inflammatory indicator.

CONCLUSION

The integrated predictive model combining clinical and inflammatory markers improves risk discrimination, providing a useful tool for early identification of dyslipidemia in RA patients.