Triglyceride-glucose index and anti-Ro52 antibody for identification of cardiovascular disease in patients with primary Sjögren's disease.
Primary Sjögren's disease (pSjD) confers a markedly elevated risk for developing CVD, an important contributor to mortality in this population. This study was designed to identify factors associated with CVD in pSjD and to develop a classification model.
In this cross-sectional analysis of pSjD individuals (2013-2023), multivariable logistic regression was used to identify CVD-related factors. A classification model was constructed with variable selection via LASSO regression and the Boruta algorithm.
Among 734 patients with pSjD, 192 (26.2%) had CVD. Age (odds ratio [OR]: 1.06, P < 0.001), body mass index (BMI; OR: 1.14, P < 0.001), triglyceride-glucose (TyG) index (OR: 3.49, P < 0.001), and positive anti-Ro52 status (OR: 1.58, P = 0.022) were independent correlates of CVD in pSjD. Moreover, age, BMI, and TyG index showed a trend of gradually increasing CVD risk in patients with pSjD (P < 0.05). A five-variable classification model incorporating age, BMI, TyG index, anti-Ro52 antibody, and corrected QT interval was developed to identify CVD status. Good discrimination (area under the curve: 0.768), proper calibration, and clinical applicability were observed for this model. Its performance remained robust upon internal validation and testing in the test set (area under the curve: 0.753 and 0.821).
The TyG index and anti-Ro52 antibody serve as significant factors associated with prevalent CVD in patients with pSjD. A novel classification model that integrates these biomarkers with age, BMI, and corrected QT interval showed good performance and generalizability, and may provide a practical tool for identifying cardiovascular status in this population.
In this cross-sectional analysis of pSjD individuals (2013-2023), multivariable logistic regression was used to identify CVD-related factors. A classification model was constructed with variable selection via LASSO regression and the Boruta algorithm.
Among 734 patients with pSjD, 192 (26.2%) had CVD. Age (odds ratio [OR]: 1.06, P < 0.001), body mass index (BMI; OR: 1.14, P < 0.001), triglyceride-glucose (TyG) index (OR: 3.49, P < 0.001), and positive anti-Ro52 status (OR: 1.58, P = 0.022) were independent correlates of CVD in pSjD. Moreover, age, BMI, and TyG index showed a trend of gradually increasing CVD risk in patients with pSjD (P < 0.05). A five-variable classification model incorporating age, BMI, TyG index, anti-Ro52 antibody, and corrected QT interval was developed to identify CVD status. Good discrimination (area under the curve: 0.768), proper calibration, and clinical applicability were observed for this model. Its performance remained robust upon internal validation and testing in the test set (area under the curve: 0.753 and 0.821).
The TyG index and anti-Ro52 antibody serve as significant factors associated with prevalent CVD in patients with pSjD. A novel classification model that integrates these biomarkers with age, BMI, and corrected QT interval showed good performance and generalizability, and may provide a practical tool for identifying cardiovascular status in this population.
Authors
Zhou Zhou, Zhang Zhang, Lei Lei, Liu Liu, Chen Chen, Zhang Zhang, Tang Tang, Zhao Zhao, Wang Wang, Tao Tao, Luo Luo
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