{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["13(4)"],"submitter":["McLeish E"],"funding":["Brain Foundation"],"pubmed_abstract":["<h4>Objective</h4>Inclusion body myositis (IBM) is a progressive late-onset muscle disease characterised by preferential weakness of quadriceps femoris and finger flexors, with elusive causes involving immune, degenerative, genetic and age-related factors. Overlapping with normal muscle ageing makes diagnosis and prognosis problematic.<h4>Methods</h4>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-means clustering and the random forest one-versus-rest model classified patients into three immunophenotypic clusters. Functional outcome measures including mTUG, 2MWT, IBM-FRS, EAT-10, knee extension and grip strength were assessed across clusters."],"journal":["Clinical & translational immunology"],"pagination":["e1504"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10990804"],"repository":["biostudies-literature"],"pubmed_title":["Identification of distinct immune signatures in inclusion body myositis by peripheral blood immunophenotyping using machine learning models."],"pmcid":["PMC10990804"],"pubmed_authors":["Beer K","Cooper I","Mastaglia FL","Needham M","Sooda A","Coudert JD","Slater N","McLeish E"],"additional_accession":[]},"is_claimable":false,"name":"Identification of distinct immune signatures in inclusion body myositis by peripheral blood immunophenotyping using machine learning models.","description":"<h4>Objective</h4>Inclusion body myositis (IBM) is a progressive late-onset muscle disease characterised by preferential weakness of quadriceps femoris and finger flexors, with elusive causes involving immune, degenerative, genetic and age-related factors. Overlapping with normal muscle ageing makes diagnosis and prognosis problematic.<h4>Methods</h4>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-means clustering and the random forest one-versus-rest model classified patients into three immunophenotypic clusters. Functional outcome measures including mTUG, 2MWT, IBM-FRS, EAT-10, knee extension and grip strength were assessed across clusters.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024","modification":"2026-07-16T21:16:03.055Z","creation":"2025-04-07T12:45:01.79Z"},"accession":"S-EPMC10990804","cross_references":{"pubmed":["38585335"],"doi":["10.1002/cti2.1504"]}}