Work

Identifying Systemic Lupus Erythematosus From Serum Proteomic Profiles Using Machine Learning and Genetic Risk Stratification

Year: 2026

Type: article

Abstract: OBJECTIVE: Proteome-wide risk models for lupus remain underexplored. We developed classification models to identify lupus from serum proteomic profiles. METHODS: Patients with lupus and individuals with other autoimmune diseases in the UK Biobank were included. Differential proteomic expressions were characterized and followed by hierarchical clustering analysis. Proteomic linear and machine learning models were developed for established disease classification and future lupus prediction and compared to polygenic risk scores. Two additional independent lupus cohorts from Sweden, as part of the Human Protein Atlas (HPA), and from China were used for replication. RESULTS: In the UK Biobank, 44,173 participants with proteomic data, including 2,063 individuals with at least one autoimmune disease at enrollment, were studied. This included 383 patients with lupus with a mean age of 43.6 ± SD 11.7 years at disease onset. Lupus showed the largest number of dysregulated proteins among autoimmune diseases and clustered with rheumatoid arthritis. Comparison with HPA showed that ~70% of lupus proteomic associations could be replicated with moderate correlation in effect sizes. The machine learning model outperformed the linear model in identifying pre-existing lupus and generalized well to future lupus prediction. Among patients with lupus on immunomodulatory medications, the model reached ~90% sensitivity at 95% specificity, which was replicated in an independent cohort. Model interpretation highlighted SCARB2, SOD2, CD302, Galectin-9, and GGT5 proteins with substantial effects on lupus identification. CONCLUSION: Proteomic machine learning models show excellent performance for identifying pre-existing lupus and generalize well for predicting lupus before clinical diagnosis. Model interpretation identified novel candidate biomarkers for lupus.

Source: Arthritis & Rheumatology

DOI: 10.1002/art.70156

Authors: Mehmet Hocaoǧlu, Jishnu Das, Amr H. Sawalha

Institution: University of Pittsburgh

Language: English

Topic: Systemic Lupus Erythematosus Research

Subfield: Rheumatology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $5,190