Files and links (1)
CC BY V4.0, Open Access
Journal article
Identification of distinct immune signatures in inclusion body myositis by peripheral blood immunophenotyping using machine learning models
Clinical & Translational Immunology
2024
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.
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.
Details
- Title
- Identification of distinct immune signatures in inclusion body myositis by peripheral blood immunophenotyping using machine learning models
- Authors/Creators
- Emily McLeish - Murdoch UniversityAnu SoodaNataliya Slater - Murdoch UniversityKelly Beer - Perron Institute for Neurological and Translational ScienceIan D. Cooper - Perron Institute for Neurological and Translational ScienceFrank MastagliaMerrilee Needham - Murdoch University, Centre for Molecular Medicine and Innovative TherapeuticsJerome Coudert - Murdoch University, Centre for Molecular Medicine and Innovative Therapeutics
- Publication Details
- Clinical & Translational Immunology
- Publisher
- John Wiley & Sons Australia, Ltd on behalf of Australian and New Zealand Society for Immunology, Inc.
- Identifiers
- 991005652870207891
- Copyright
- © 2024 The Authors.
- Murdoch Affiliation
- Centre for Molecular Medicine and Innovative Therapeutics; Health Futures Institute
- Resource Type
- Journal article
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Source: InCites
Metrics
6 File views/ downloads
58 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- Citation topics
- 1 Clinical & Life Sciences
- 1.106 Rheumatology
- 1.106.1684 Dermatomyositis
- Web Of Science research areas
- Immunology
- ESI research areas
- Immunology