Exploring genetic heterogeneity in type 2 diabetes subtypes

Background/Objectives: Type 2 diabetes (T2D) is a clinically and genetically heterogeneous disease. In this study, we aimed to stratify patients with T2D from the Volga-Ural region of Eurasia into distinct subgroups based on clinical characteristics and to investigate the genetic underpinnings of these clusters. Methods: A total of 254 Tatar individuals with T2D and 361 ethnically matched controls were recruited. Clinical clustering was performed using k-means and hierarchical algorithms on five variables: age at diagnosis, body mass index (BMI), glycated hemoglobin (HbA1c), insulin resistance (HOMA-IR), and β-cell function (HOMA-B). Genetic association analysis was conducted using logistic regression under an additive model, adjusted for age and sex, and corrected for multiple comparisons using the Benjamini–Hochberg method. Results: Four distinct T2D subtypes were identified—mild age-related diabetes (MARD, n = 25), mild obesity-related diabetes (MOD, n = 72), severe insulin-resistant diabetes (SIRD, n = 66), and severe insulin-deficient diabetes (SIDD, n = 52)—each with unique clinical and comorbidity profiles. SIDD patients exhibited the highest burden of microvascular complications and lowest estimated glomerular filtration rate. Nine genetic variants showed significant associations with T2D and/or specific subtypes, including loci in genes related to neurotransmission (e.g., HTR1B, CHRM5), appetite regulation (NPY2R), insulin signaling (TCF7L2, PTEN), and other metabolic pathways. Some variants demonstrated subtype-specific associations, underscoring the genetic heterogeneity of T2D. Conclusions: Our findings support the utility of clinical clustering in uncovering biologically and clinically meaningful T2D subtypes and reveal genetic variants that may contribute to this heterogeneity. These insights may inform future precision medicine approaches for T2D diagnosis and management.

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