Аннотация
Abstract Introduction Adult-onset diabetes is increasingly recognized as a heterogeneous condition with diverse metabolic and immunoinflammatory phenotypes, but their integrated characterization remains limited. Purpose This study aimed to identify distinct phenotypes in adults with newly diagnosed diabetes using data-driven clustering. Methods This cross-sectional study included 240 adults with newly diagnosed diabetes. Clinical, metabolic, and immunological parameters were assessed, including BMI, HbA1c, basal C-peptide, HOMA2-IR, HOMA2-%B, islet autoantibodies (GADA, IA-2A, ZnT8A, ICA), and cytokines (IL-1β, IL-1Ra, IL-6). Standardized variables were analyzed using hierarchical clustering with Ward’s method. Results Four phenotypic clusters were identified. Cluster 1 (n=64, 26.7%) included older individuals with preserved metabolic function but reduced IL-1Ra and an increased IL-1β/IL-1Ra ratio, suggesting impaired anti-inflammatory regulation. Cluster 2 (n=71, 29.6%) showed younger age, high HbA1c, low C-peptide, and reduced HOMA2-%B, indicating predominant β-cell dysfunction. Cluster 3 (n=51, 20.8%) demonstrated the highest autoimmune activity, elevated IL-6, and moderate metabolic impairment, consistent with an immunoinflammatory phenotype. Cluster 4 (n=54, 22.5%) was characterized by higher BMI, increased C-peptide, and elevated HOMA2-%B, reflecting insulin resistance with compensatory β-cell hyperfunction. Conclusion Adult-onset diabetes shows marked metabolic and immunoinflammatory heterogeneity. These clusters support phenotype-based classification for personalized management and targeted therapy.