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

ABSTRACT:

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.

SUBMITTER: McLeish E 

PROVIDER: S-EPMC10990804 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature