Lipids in health and disease · 2026
Triglyceride-glucose index and anti-Ro52 antibody for identification of cardiovascular disease in patients with primary Sjögren's disease.
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Zhou Xuan-Yi, Zhang Yan, Lei Chun-Xin, Liu Zi-Han, Chen Jia-Qi, Zhang Xi-Ya, Tang Bo-Jie, Zhao Yun-Jie, Wang Zi-Han, Tao Qing-Wen, Luo Jing
Аннотация
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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).
CONCLUSION: 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.
METHODS: 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.
RESULTS: 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).
CONCLUSION: 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.