Frontiers in immunology · 2026
Identifying the clinical signature of anti-centromere antibody-positive Sjögren's syndrome: a machine learning-based analysis of a multicenter cohort.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Zhu Wenlong, Fang Ting, Zhu Xinchao, Yang Zhe, Wang Jiayao, Wang Xinchang
Аннотация
OBJECTIVE: We sought to leverage machine learning algorithms to identify the complex clinical and serological signature of anti-centromere antibody (ACA) positivity in Sjögren's syndrome (SS) patients.
METHODS: This multicenter study analyzed clinical data from a cohort of 616 patients diagnosed SS, comprising 81 ACA-positive and 535 ACA-negative cases. To ensure robust model development, we randomly partitioned the dataset into training and validation subsets in a 7:3 ratio. We implemented and compared six machine learning models after identifying optimal predictors using the LASSO regression. We mainly evaluate the performance of the model through the AUC and a series of comprehensive indicators. To ensure clinical interpretability, we also employed the SHAP analysis method to quantify the influence of each feature on the model's outcome.
RESULTS: Among the evaluated models, GBDT exhibited superior predictive efficacy. The model achieved an AUC value of 0.812 in the training set and maintained a robust AUC of 0.811 (95% CI: 0.699-0.906) in the validation cohort. At the same time, the model has the highest sensitivity (0.750 in the validation test). The SHAP analysis revealed that the top predictors influencing the ACA-positive profile included a series of serological markers (anti-SSA/Ro52, anti-SSA/Ro60, anti-AMA-M2, anti-SSB, and IgM), demographic factors (age), and Raynaud's phenomenon (RP). Furthermore, SHAP interactions captured non-linear synergies, such as the predictive contribution of RP is significantly potentiated by advancing age, and the amplified predictive value of anti-AMA-M2 under lower IgM levels.
CONCLUSION: Our machine learning approach effectively structures and quantifies clinical and serological associations, capturing a complex predictive profile for the SS-ACA+ subgroup. These findings highlight the value of ML in identifying non-linear patterns within clinical variables, providing a robust quantitative framework for future prospective evaluations.
METHODS: This multicenter study analyzed clinical data from a cohort of 616 patients diagnosed SS, comprising 81 ACA-positive and 535 ACA-negative cases. To ensure robust model development, we randomly partitioned the dataset into training and validation subsets in a 7:3 ratio. We implemented and compared six machine learning models after identifying optimal predictors using the LASSO regression. We mainly evaluate the performance of the model through the AUC and a series of comprehensive indicators. To ensure clinical interpretability, we also employed the SHAP analysis method to quantify the influence of each feature on the model's outcome.
RESULTS: Among the evaluated models, GBDT exhibited superior predictive efficacy. The model achieved an AUC value of 0.812 in the training set and maintained a robust AUC of 0.811 (95% CI: 0.699-0.906) in the validation cohort. At the same time, the model has the highest sensitivity (0.750 in the validation test). The SHAP analysis revealed that the top predictors influencing the ACA-positive profile included a series of serological markers (anti-SSA/Ro52, anti-SSA/Ro60, anti-AMA-M2, anti-SSB, and IgM), demographic factors (age), and Raynaud's phenomenon (RP). Furthermore, SHAP interactions captured non-linear synergies, such as the predictive contribution of RP is significantly potentiated by advancing age, and the amplified predictive value of anti-AMA-M2 under lower IgM levels.
CONCLUSION: Our machine learning approach effectively structures and quantifies clinical and serological associations, capturing a complex predictive profile for the SS-ACA+ subgroup. These findings highlight the value of ML in identifying non-linear patterns within clinical variables, providing a robust quantitative framework for future prospective evaluations.