{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T12:32:56Z","timestamp":1793104376389,"version":"4.5.0"},"reference-count":49,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T00:00:00Z","timestamp":1777852800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T00:00:00Z","timestamp":1777852800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000060","name":"National Institute of Allergy and Infectious Diseases","doi-asserted-by":"publisher","award":["R01 AI097134"],"award-info":[{"award-number":["R01 AI097134"]}],"id":[{"id":"10.13039\/100000060","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["acrjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Arthritis &amp; Rheumatology"],"published-print":{"date-parts":[[2026,11]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Proteome\u2010wide risk models for lupus remain underexplored. We developed classification models to identify lupus from serum proteomic profiles.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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\u2009\u00b1 SD\u200911.7 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\u2009~70% of lupus proteomic associations could be replicated with moderate correlation in effect sizes. The machine learning model outperformed the linear model in identifying pre\u2010existing lupus and generalized well to future lupus prediction. Among patients with lupus on immunomodulatory medications, the model reached\u2009~90% sensitivity at 95% specificity, which was replicated in an independent cohort. Model interpretation highlighted SCARB2, SOD2, CD302, Galectin\u20109, and GGT5 proteins with substantial effects on lupus identification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Proteomic machine learning models show excellent performance for identifying pre\u2010existing lupus and generalize well for predicting lupus before clinical diagnosis. Model interpretation identified novel candidate biomarkers for lupus.<\/jats:p>\n                    <jats:p>\n                      <jats:boxed-text content-type=\"graphic\" position=\"anchor\">\n                        <jats:graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" mimetype=\"image\/png\" position=\"anchor\" specific-use=\"enlarged-web-image\" xlink:href=\"graphic\/art70156-gra-0001-m.png\">\n                          <jats:alt-text>image<\/jats:alt-text>\n                        <\/jats:graphic>\n                      <\/jats:boxed-text>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/art.70156","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T08:41:05Z","timestamp":1774514465000},"page":"2280-2291","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Identifying Systemic Lupus Erythematosus From Serum Proteomic Profiles Using Machine Learning and Genetic Risk Stratification"],"prefix":"10.1002","volume":"78","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1974-8740","authenticated-orcid":false,"given":"Mehmet","family":"Hocao\u01e7lu","sequence":"first","affiliation":[{"name":"Division of Rheumatology and Clinical Immunology, Department of Medicine University of Pittsburgh  Pittsburgh Pennsylvania"},{"name":"Lupus Center of Excellence University of Pittsburgh School of Medicine  Pittsburgh Pennsylvania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5747-064X","authenticated-orcid":false,"given":"Jishnu","family":"Das","sequence":"additional","affiliation":[{"name":"Center for Systems Immunology, Departments of Immunology and Computational &amp; Systems Biology University of Pittsburgh  Pittsburgh Pennsylvania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3884-962X","authenticated-orcid":false,"given":"Amr H.","family":"Sawalha","sequence":"additional","affiliation":[{"name":"Division of Rheumatology and Clinical Immunology, Department of Medicine University of Pittsburgh  Pittsburgh Pennsylvania"},{"name":"Lupus Center of Excellence University of Pittsburgh School of Medicine  Pittsburgh Pennsylvania"},{"name":"Division of Rheumatology, Department of Pediatrics University of Pittsburgh  Pittsburgh Pennsylvania"},{"name":"Department of Immunology University of Pittsburgh  Pittsburgh 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