{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T14:19:11Z","timestamp":1765808351120,"version":"3.41.2"},"reference-count":48,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2024,4,3]],"date-time":"2024-04-03T00:00:00Z","timestamp":1712102400000},"content-version":"vor","delay-in-days":93,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000942","name":"Brain Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000942","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clin &amp; Trans Imm"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>Inclusion body myositis (IBM) is a progressive late\u2010onset muscle disease characterised by preferential weakness of quadriceps femoris and finger flexors, with elusive causes involving immune, degenerative, genetic and age\u2010related factors. Overlapping with normal muscle ageing makes diagnosis and prognosis problematic.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We characterised peripheral blood leucocytes in 81 IBM patients and 45 healthy controls using flow cytometry. Using a random forest classifier, we identified immune changes in IBM compared to HC. K\u2010means clustering and the random forest one\u2010versus\u2010rest model classified patients into three immunophenotypic clusters. Functional outcome measures including mTUG, 2MWT, IBM\u2010FRS, EAT\u201010, knee extension and grip strength were assessed across clusters.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The random forest model achieved a 94% AUC ROC with 82.76% specificity and 100% sensitivity. Significant differences were found in IBM patients, including increased CD8<jats:sup>+<\/jats:sup> T\u2010bet<jats:sup>+<\/jats:sup> cells, CD4<jats:sup>+<\/jats:sup> T\u00a0cells skewed towards a Th1 phenotype and altered \u03b3\u03b4 T\u00a0cell repertoire with a reduced proportion of V\u03b39<jats:sup>+<\/jats:sup>V\u03b42<jats:sup>+<\/jats:sup> cells. IBM patients formed three clusters: (i) activated and inflammatory CD8<jats:sup>+<\/jats:sup> and CD4<jats:sup>+<\/jats:sup> T\u2010cell profile and the highest proportion of anti\u2010cN1A\u2010positive patients in cluster 1; (ii) limited inflammation in cluster 2; (iii) highly differentiated, pro\u2010inflammatory T\u2010cell profile in cluster 3. Additionally, no significant differences in patients' age and gender were detected between immunophenotype clusters; however, worsening trends were detected with several functional outcomes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>These findings unveil distinct immune profiles in IBM, shedding light\u00a0on\u00a0underlying pathological mechanisms for potential immunoregulatory therapeutic development.<\/jats:p><\/jats:sec>","DOI":"10.1002\/cti2.1504","type":"journal-article","created":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T01:39:55Z","timestamp":1712194795000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Identification of distinct immune signatures in inclusion body myositis by peripheral blood immunophenotyping using machine learning 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