Identification of distinct immune signatures in inclusion body myositis by peripheral blood immunophenotyping using machine learning models.

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.