Session Type: Abstract Session
Session Time: 4:00PM-5:30PM
Background/Purpose: Mixed connective tissue disease (MCTD) is a heterogenous autoimmune disorder with overlapping clinical features of systemic lupus erythematosus (SLE), polymyositis/dermatomyositis, and systemic sclerosis (SSc). Despite its unique clinical characteristics, some patients may develop other rheumatic diseases during a follow-up period . To better understand the heterogeneity of MCTD and to stratify the patients, we developed machine learning models using immunophenotyping as a reference for SLE, idiopathic inflammatory myopathy (IIM), and SSc and identify differences in symptoms and transcriptome among the subgroups of MCTD.
Methods: We employed our large-scale database, Immune Cell Gene Expression Atlas from the University of Tokyo (ImmuNexUT) , to analyze the immunophenotyping (24 subsets) of patients with MCTD alongside SLE, IIM, and SSc. By utilizing the immunophenotyping data of SLE, IIM, and SSc, we developed machine learning models such as random forest, neural network, etc., to stratify MCTD patients. Following the stratification of the MCTD patients, we performed a transcriptome analysis, including a differentially expressed gene (DEG) analysis and pathway analysis. Finally, we compared the clinical features of the subgroup of MCTD patients (Figure 1).
Results: We enrolled a total of 215 patients with autoimmune diseases, consisting of 22 MCTD, 78 SLE, 63 IIM, and 52 SSc patients, and collected the data of immunophenotyping, bulk RNA-sequence on peripheral mononuclear blood cells, and clinical characteristics. We constructed machine learning models to classify patients with SLE, IIM, and SSc based on the immunophenotyping and applied these models to the MCTD patients. Of the 22 patients with MCTD, 16 were classified as SLE-phenotype, and 6 were classified as non-SLE-phenotype (4 IIM-phenotype and 2 SSc-phenotype). Among the MCTD patients, SLE-phenotype patients had significantly higher proportions of Th1, naïve B cells, and plasmablast (p=0.03, 0.01, and 0.01, respectively) (Figure 2). Regarding clinical features, the proportions of having the SLE symptoms such as lymphadenopathy, malar rash, serositis, and cytopenia were significantly higher in SLE-phenotype patients (87.5% vs 33.3%, p=0.025). The number of DEGs across the cell types between SLE patients and MCTD patients (SLE-phenotype and non-SLE-phenotype) was comparable.
Conclusion: Our study suggested the potential stratification of MCTD patients based on their immunophenotyping. This approach may help distinguish clinical phenotypes in MCTD.
 Reiseter S et al, Arthritis Res Ther 2017
 Ota M et al, Cell 2022
To cite this abstract in AMA style:Izuka S, Komai T, Itamiya T, Ota M, Yamada S, Nagafuchi Y, Shoda H, Matsuki K, Yamamoto K, Okamura T, Fujio K. Machine Learning-Based Stratification of Mixed Connective Tissue Disease Using Immunophenotyping Data from Patients with Related Autoimmune Diseases [abstract]. Arthritis Rheumatol. 2023; 75 (suppl 9). https://acrabstracts.org/abstract/machine-learning-based-stratification-of-mixed-connective-tissue-disease-using-immunophenotyping-data-from-patients-with-related-autoimmune-diseases/. Accessed .
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