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Background <p>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.</p> Methods <p>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.</p> Results <p>Among 734 patients with pSjD, 192 (26.2%) had CVD. Age (odds ratio [OR]: 1.06, <i>P</i> &lt; 0.001), body mass index (BMI; OR: 1.14, <i>P</i> &lt; 0.001), triglyceride–glucose (TyG) index (OR: 3.49, <i>P</i> &lt; 0.001), and positive anti-Ro52 status (OR: 1.58, <i>P</i> = 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 (<i>P</i> &lt; 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).</p> Conclusion <p>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.</p> Graphical Abstract <p></p>

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Triglyceride–glucose index and anti-Ro52 antibody for identification of cardiovascular disease in patients with primary Sjögren’s disease

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月11日

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  • Xuan-yi Zhou,
  • Yan Zhang,
  • Chun-xin Lei,
  • Zi-han Liu,
  • Jia-qi Chen,
  • Xi-ya Zhang,
  • Bo-jie Tang,
  • Yun-Jie Zhao,
  • Zi-han Wang,
  • Qing-wen Tao,
  • Jing Luo

摘要

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.

Graphical Abstract