ArticleRheumatology (Oxford, England)2025
Machine learning-driven immunophenotypic stratification of mixed connective tissue disease, corroborating the clinical heterogeneity.
Article in Rheumatology (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 5 citations in OpenAlex.
- Correlation of a combined serum biomarker panel with the composite physiologic index in connective tissue disease-associated interstitial lung disease.Frontiers in physiology · 2026Article
- Applications of artificial intelligence in systemic lupus erythematosus: integrating multi-omics data for precision medicine.Frontiers in immunology · 2026Review
- Transcriptome analysis unveils Th1 cell cycle signature as a distinctive feature of mixed connective tissue disease.Arthritis research & therapy · 2025Article
- Article
- Body weight in systemic lupus erythematosus is associated with disease activity and the adaptive immune system, independent of type I IFN.Frontiers in immunology · 2025Article
- The role of monocytes and macrophages in idiopathic inflammatory myopathies: insights into pathogenesis and potential targets.Frontiers in immunology · 2025Review
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Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
Abstract
objectiveThe objective of this study was to stratify patients with MCTD, based on their immunophenotype.
methodsWe analysed the immunophenotype and transcriptome of 24 immune cell subsets [from patients with MCTD, SLE, idiopathic inflammatory myopathy (IIM) and SSc] from our functional genome database, ImmuNexUT (https://www.immunexut.org/). MCTD patients were stratified by employing machine-learning models, including Random Forest, trained by immunophenotyping data from SLE, IIM and SSc patients. The transcriptomes were analysed with gene set variation analysis (GSVA), and the clinical features of the MCTD subgroups were compared.
resultsThis study included 215 patients, including 22 patients with MCTD. Machine-learning models, constructed to classify SLE, IIM and SSc patients, based on immunophenotyping, were applied to MCTD patients, resulting in 16 patients being classified as having an SLEimmunophenotype and 6 as having a non-SLE immunophenotype. Among the MCTD patients, patients with the SLE immunophenotype had higher proportions of Th1 cells {2.85% [interquartile range (IQR) 1.54-3.91] vs 1.33% (IQR 0.99-1.74) P = 0.027} and plasmablasts [6.35% (IQR 4.17-17.49) vs 2.00% (IQR 1.20-2.80) P = 0.010]. Notably, the number of SLE-related symptoms was higher in patients with the SLE immunophenotype [2.0 (IQR 1.0-2.0) vs 1.0 (IQR 1.0-1.0) P = 0.038]. Moreover, the GSVA scores of interferon-α and -γ responses were significantly higher in patients with the SLE immunophenotype in central memory CD8+ T cells, while hedgehog signalling was higher in patients with the non-SLE immunophenotype, in five-cell subsets.
conclusionThis study describes the stratification of MCTD patients, based on immunophenotyping, suggesting the presence of distinct immunological processes behind the clinical subtypes of MCTD.
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