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Introduction Interleukin-1 is a central mediator of inflammation, with its activity governed by the opposing functions of the signaling-competent receptor IL-1R1 and the inhibitory decoy receptor IL-1R2. However, comprehensive profiling of IL-1 receptor co-expression across immune cell subsets in diseases with distinct pathogenesis remains limited. Methods We investigated the expression and co-expression patterns of IL-1R1 and IL-1R2 on twelve peripheral blood immune cell populations from patients with rheumatoid arthritis, patients with bronchial asthma, and healthy donors using multicolor flow cytometry. Receptor density was quantified as antibody-binding capacity per cell. Unsupervised clustering and logistic regression were applied to identify disease-associated candidate signatures and to develop exploratory diagnostic algorithms. Results Exploratory dimensionality reduction suggested separation of the three groups in this cohort, with each disease displaying a distinct IL-1 receptor remodeling pattern. Bronchial asthma was characterized by a global shift toward the double-negative phenotype across most populations, profound decoy receptor deficiency, and a striking predominance of the double-positive phenotype in naive T cells. Rheumatoid arthritis exhibited a less pronounced double-negative shift, selective loss of decoy receptor on monocytes, and mixed cytotoxic T cell profiles with preserved signaling-competent phenotypes. Logistic regression models incorporating three parameters for RA showed high discrimination in this case-control cohort, with apparent and cross-validated performance remaining robust for this disease. Conclusion These findings indicate that allergic and autoimmune inflammation are associated with distinct, cell-subset-specific IL-1 receptor signatures consistent with their divergent immunopathogenesis. The identified receptor profiles may serve as candidate biomarkers and provide hypotheses for mechanistic studies of interleukin-1 pathway dysregulation in chronic inflammatory diseases. The model requires external validation before any clinical application.
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Zhukova et al. (2026) studied this question.
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