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Consensus clustering was performed on gene expression data from a subset of 45 synovial samples. Support vector machine learning was used to predict gene expression subtypes, using histologic data as the input. Corresponding clinical data were compared across subtypes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Consensus clustering of gene expression data revealed 3 distinct synovial subtypes, including a high inflammatory subtype characterized by extensive infiltration of leukocytes, a low inflammatory subtype characterized by enrichment in pathways including transforming growth factor \u03b2, glycoproteins, and neuronal genes, and a mixed subtype. Machine learning applied to histologic features, with gene expression subtypes serving as labels, generated an algorithm for the scoring of histologic features. Patients with the high inflammatory synovial subtype exhibited higher levels of markers of systemic inflammation and autoantibodies. C\u2010reactive protein (\n                      <jats:styled-content style=\"fixed-case\">CRP<\/jats:styled-content>\n                      ) levels were significantly correlated with the severity of pain in the high inflammatory subgroup but not in the others.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      Gene expression analysis of\n                      <jats:styled-content style=\"fixed-case\">RA<\/jats:styled-content>\n                      and\n                      <jats:styled-content style=\"fixed-case\">OA<\/jats:styled-content>\n                      synovial tissue revealed 3 distinct synovial subtypes. These labels were used to generate a histologic scoring algorithm in which the histologic scores were found to be associated with parameters of systemic inflammation, including the erythrocyte sedimentation rate,\n                      <jats:styled-content style=\"fixed-case\">CRP<\/jats:styled-content>\n                      level, and autoantibody levels. Comparison of gene expression patterns to clinical features revealed a potentially clinically important distinction: mechanisms of pain may differ in patients with different synovial subtypes.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/art.40428","type":"journal-article","created":{"date-parts":[[2018,2,22]],"date-time":"2018-02-22T00:02:44Z","timestamp":1519257764000},"page":"690-701","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":200,"title":["Identification of Three Rheumatoid Arthritis Disease Subtypes by Machine Learning Integration of Synovial Histologic Features and\n                    <scp>RNA<\/scp>\n                    Sequencing Data"],"prefix":"10.1002","volume":"70","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9859-7199","authenticated-orcid":false,"given":"Dana E.","family":"Orange","sequence":"first","affiliation":[{"name":"Hospital for Special Surgery The 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