# A multicenter explainable machine learning analysis of autoimmune disease comorbidity in ankylosing spondylitis

## TL;DR

This study uses machine learning to identify autoimmune comorbidities in ankylosing spondylitis patients, helping improve early diagnosis and treatment.

## Contribution

The study introduces an explainable machine learning framework using routine clinical data to detect autoimmune comorbidities in ankylosing spondylitis.

## Key findings

- LightGBM machine learning model outperformed others in identifying AS with autoimmune comorbidities.
- Renal and metabolic markers were key indicators of autoimmune comorbidity in AS.
- AS with autoimmune comorbidities represents a distinct inflammatory-metabolic phenotype.

## Abstract

Ankylosing spondylitis (AS) frequently coexists with other autoimmune diseases, leading to increased clinical heterogeneity and diagnostic complexity. Early identification of autoimmune comorbidity in AS remains challenging in routine practice.

A multicenter, retrospective, cross-sectional study was conducted, where clinical and laboratory data were collected from three independent tertiary centers between 2012 and 2025. Patients were classified into three groups: AS alone, autoimmune diseases alone, and AS with autoimmune comorbidities. Routinely available variables, including demographic characteristics, systemic inflammatory indices, hematological parameters, and liver and renal function markers, were analyzed. Multiple machine learning algorithms were developed for two clinically relevant classification tasks: AS alone vs. AS with autoimmune comorbidities, and autoimmune diseases alone vs. AS with autoimmune comorbidities. Model performance was evaluated using AUC, calibration, decision curve analysis, and clinical impact curves. SHapley Additive exPlanations (SHAP) were applied to enhance interpretability.

Among all models, LightGBM consistently demonstrated superior and stable performance across discrimination, calibration, and clinical utility metrics. In distinguishing AS alone from AS with autoimmune comorbidities, key contributors included age, gender, renal function–related markers (eGFR, CysC, BUN, UA), and protein and hepatobiliary indices (ALB, DBIL). In comparisons between autoimmune diseases alone and AS with autoimmune comorbidities, SHAP highlighted metabolic- and synthesis-related features (GLOB, PREALB, CHE, ALP), acid–base balance (HCO3), and inflammatory activity (ESR). These patterns suggest that AS-associated autoimmune comorbidity represents a distinct systemic inflammatory–metabolic phenotype rather than a simple amplification of inflammation.

Using routinely available clinical data, an explainable machine learning framework enables accurate identification and characterization of autoimmune comorbidity in AS. This approach has practical potential for early risk stratification and clinical decision support in real-world settings.