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Objective: 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. Methods: 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. Results: 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+ T‐bet+ cells, CD4+ T cells skewed towards a Th1 phenotype and altered γδ T cell repertoire with a reduced proportion of Vγ9+Vδ2+ cells. IBM patients formed three clusters: (i) activated and inflammatory CD8+ and CD4+ T‐cell profile and the highest proportion of anti‐cN1A‐positive patients in cluster 1; (ii) limited inflammation in cluster 2; (iii) highly differentiated, pro‐inflammatory T‐cell 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. Conclusion: These findings unveil distinct immune profiles in IBM, shedding light on underlying pathological mechanisms for potential immunoregulatory therapeutic development. [ABSTRACT FROM AUTHOR]

Idiopathic inflammatory myopathies (IIMs) are a heterogeneous group of muscle disorders including adult and juvenile dermatomyositis, polymyositis, immune-mediated necrotising myopathy and sporadic inclusion body myositis, all of which present with variable symptoms and disease progression. The identification of effective biomarkers for IIMs has been challenging due to the heterogeneity between IIMs and within IIM subgroups, but recent advances in machine learning (ML) techniques have shown promises in identifying novel biomarkers. This paper reviews recent studies on potential biomarkers for IIM and evaluates their clinical utility. We also explore how data analytic tools and ML algorithms have been used to identify biomarkers, highlighting their potential to advance our understanding and diagnosis of IIM and improve patient outcomes. Overall, ML techniques have great potential to revolutionize biomarker discovery in IIMs and lead to more effective diagnosis and treatment. [ABSTRACT FROM AUTHOR]

This article discusses the potential of machine learning (ML) models to improve the phenotypic stratification of idiopathic inflammatory myopathies (IIMs), with a focus on anti-MDA5-positive dermatomyositis (MDA5-DM). MDA5-DM is a subgroup of dermatomyositis that is commonly associated with comorbidities, particularly rapidly progressing interstitial lung disease (RP-ILD). However, the variability in patient cohort sizes, clinical characteristics, and lack of standardized diagnostic criteria for MDA5-DM pose challenges for ML models. The article emphasizes the need for unbiased and consistent data across studies to achieve rigorous replication of ML models and highlights the importance of integrating scientific insights with the development of practical and easily applicable clinical variables. [Extracted from the article]