{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T02:02:36Z","timestamp":1790301756387,"version":"4.1.0"},"reference-count":37,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T00:00:00Z","timestamp":1761696000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To investigate whether patients with ACPA-negative (ACPA\u2013) 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We performed high-throughput proteomic and metabolomic profiling on plasma from a well-characterized cohort comprising 40 patients with ACPA\u2013 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>ACPA\u2013 and ACPA+ RA exhibited distinct plasma proteomic and metabolomic biomolecular signatures. Complement proteins (CFB, CFHR5, and F9) and the anti-inflammatory cytokine IL1RN were exclusively elevated in ACPA\u2013 RA and remained distinct in a treatment-na\u00efve 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 RA subgroups. An integrative machine learning framework incorporating multi-omic features achieved high classification performance in cross-validation (AUC \u2265 0.90), outperforming models based on single-omic data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study suggests that ACPA status may not fully capture the biological heterogeneity between ACPA\u2013 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2025.1667662","type":"journal-article","created":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T06:30:10Z","timestamp":1761719410000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Integrative multi-omic profiling in blood reveals distinct immune and metabolic signatures between ACPA-negative and ACPA-positive rheumatoid arthritis"],"prefix":"10.3389","volume":"16","author":[{"given":"Benjamin","family":"Hur","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vinod 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