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Identification of distinct immune signatures in inclusion body myositis by peripheral blood immunophenotyping using machine learning models.
McLeish, Emily; Sooda, Anuradha; Slater, Nataliya; Beer, Kelly; Cooper, Ian; Mastaglia, Frank L; Needham, Merrilee; Coudert, Jerome D.
Afiliación
  • McLeish E; Centre for Molecular Medicine and Innovative Therapeutics Murdoch University Murdoch WA Australia.
  • Sooda A; Centre for Molecular Medicine and Innovative Therapeutics Murdoch University Murdoch WA Australia.
  • Slater N; Centre for Molecular Medicine and Innovative Therapeutics Murdoch University Murdoch WA Australia.
  • Beer K; Centre for Molecular Medicine and Innovative Therapeutics Murdoch University Murdoch WA Australia.
  • Cooper I; Perron Institute for Neurological and Translational Science Nedlands WA Australia.
  • Mastaglia FL; Centre for Molecular Medicine and Innovative Therapeutics Murdoch University Murdoch WA Australia.
  • Needham M; Perron Institute for Neurological and Translational Science Nedlands WA Australia.
  • Coudert JD; Perron Institute for Neurological and Translational Science Nedlands WA Australia.
Clin Transl Immunology ; 13(4): e1504, 2024.
Article en En | MEDLINE | ID: mdl-38585335
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Clin Transl Immunology Año: 2024 Tipo del documento: Article Pais de publicación: Australia
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Clin Transl Immunology Año: 2024 Tipo del documento: Article Pais de publicación: Australia