Establishment and evaluation of a risk prediction model for coronary heart disease in primary Sjögren’s syndrome based on peripheral blood IL-6 and Treg percentages

Abstract

ObjectiveThis study aims to establish and evaluate a risk prediction model for coronary heart disease (CHD) in patients with primary Sjögren’s syndrome (pSS) based on peripheral blood levels of interleukin-6 (IL-6) and the percentage of regulatory T cells (Treg%). This model is intended to facilitate the timely identification of high-risk patients and the implementation of preventive measures.MethodsClinical data were collected from 120 pSS patients who visited the Second Hospital of Shanxi Medical University between November 2021 and September 2023. Patients were classified into pSS and pSS-CHD groups according to CHD diagnostic criteria. Peripheral blood lymphocyte subsets and cytokine levels were assessed using flow cytometry. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors, and a nomogram was constructed based on these factors. The model’s discriminatory ability, calibration, and clinical utility were evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis.ResultsThe univariate and multivariate logistic regression analyses identified several independent risk factors for CHD in pSS patients: erythrocyte sedimentation rate (ESR) (OR=1.10, P=0.019), triglycerides (TG) (OR=3.67, P=0.041), IL-6 (OR=1.29, P=0.048), and Treg% (OR=0.25, P=0.004). A nomogram incorporating these factors demonstrated an area under the curve (AUC) of 0.96, indicating excellent predictive performance, and showed good calibration (P=0.599), suggesting significant clinical applicability. Furthermore, Treg% exhibited a negative correlation with cholesterol (CHOL) and low-density lipoprotein cholesterol (LDL-C) levels, while IL-6 showed a positive correlation with CHOL and LDL-C levels. TG was positively correlated with C-reactive protein (CRP).ConclusionThis study successfully developed a risk prediction model based on peripheral blood IL-6 and Treg% levels, providing critical evidence for the early identification and personalized prevention of CHD in pSS patients, with potential clinical implications.