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The overlapping clinical manifestations make differential diagnosis challenging, highlighting the need for novel biomarkers to facilitate early diagnosis, stratification, and personalized treatment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Five SARDs including idiopathic inflammatory myopathies (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009210), rheumatoid arthritis (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200984), systemic sclerosis (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009100), Sj\u00f6gren disease (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200999), and systemic lupus erythematosus (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200999), as well as healthy controls (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009400) and controls with acute infectious diseases (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009218) were selected for plasma protein profiling using Olink Explore 1536. Differential abundance analysis and machine learning were used to identify proteins with both known and novel association to SARDs.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The five SARDs share hundreds of proteins with consistently altered abundance compared to both healthy and infectious controls, reflecting common underlying molecular dysregulation. Despite the overlap, we identify multiple proteins with higher abundance specific to individual SARDs. Machine learning further enables accurate classification of the five SARDs, identifying a panel of 48 proteins with high discriminatory performance, several of which are also supported by differential abundance analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Altogether, this explorative cross-sectional study demonstrates the importance of a pan-disease approach, including also infectious and healthy controls, to identify robust and disease-informative protein panels for improved classification of SARDs. Protein levels from this study are available open access through the Human Protein Atlas, facilitating further plasma proteome research on autoimmune disease.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1038\/s43856-026-01779-0","type":"journal-article","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T17:34:24Z","timestamp":1783964064000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Pan-disease blood protein profiles of rheumatic autoimmune 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