Integrative Multi-omic Profiling in Blood Reveals Distinct Immune and Metabolic Signatures between ACPA-negative and ACPA-positive Rheumatoid Arthritis

Benjamin Hur¹VINOD KUMAR GUPTA¹Minsik Oh²Hu Zeng¹Cynthia Crowson¹Kenneth Warrington¹Elena Myasoedova¹Vanessa Kronzer¹John Davis¹Jaeyun Sung^1*

• ¹Mayo Clinic Minnesota, Rochester, United States
• ²Myongji University, Seodaemun-gu, Republic of Korea

Objective: To investigate whether patients with ACPA-negative (ACPA–) and ACPA-positive (ACPA+) rheumatoid arthritis (RA) exhibit distinct immune and metabolic profiles in blood, using integrative proteomic and metabolomic analyses. By uncovering subgroup-specific molecular signatures, we aim to improve the biological understanding of RA heterogeneity and support the development of more precise diagnostic and stratification strategies. Methods: We performed high-throughput proteomic and metabolomic profiling on plasma from a well-characterized cohort comprising 40 patients with ACPA– RA, 40 patients with ACPA+ RA, and 40 healthy controls. To identify key immune and metabolic differences, we applied statistical comparisons, pathway enrichment analyses, and network inference methods. Additionally, an integrative network-based machine learning framework was used to distinguish RA subgroups from controls based on plasma molecular profiles. Results: ACPA– and ACPA+ RA exhibited distinct plasma proteomic and metabolomic biomolecular signatures. Complement proteins (CFB, CFHR5, and F9) and the anti-inflammatory cytokine IL1RN were specifically elevated in ACPA– RA and remained distinct in a treatment-naïve sub-cohort. Metabolomic analysis revealed subgroup-specific differences in lipid and pyrimidine metabolism, including contrasting patterns in bilirubin-derived metabolites. Correlation analyses identified differential associations between molecular features and clinical inflammatory markers across subgroups. An integrative machine learning framework incorporating multi-omic features achieved high classification performance (AUC ≥ 0.90), outperforming models based on single-omic data. Conclusion: This study suggests that ACPA status may not fully capture the biological heterogeneity between ACPA– and ACPA+ RA subgroups, indicating additional immune and metabolic distinctions that warrant further investigation. Our findings highlight the potential of multi-omic profiling to enhance RA diagnostics, refine disease stratification, and inform subgroup-specific disease management strategies.