Anti-centromere antibody-positive Sjögren's syndrome profile predicted by serologic and clinical factors

ObjectiveWe 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.MethodsThis 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.ResultsAmong 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.ConclusionOur 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.