We tested associations between 13 established genetic variants and type 2 diabetes (T2D) in 1371 study participants from the Volga-Ural region of the Eurasian continent, and evaluated the predictive ability of the model containing polygenic scores for the variants associated with T2D in our dataset, alone and in combination with other risk factors such as age and sex. Using logistic regression analysis, we found associations with T2D for the CCL20 rs6749704 (OR = 1.68, P_FDR = 3.40 × 10⁻⁵), CCR5 rs333 (OR = 1.99, P_FDR = 0.033), ADIPOQ rs17366743 (OR = 3.17, P_FDR = 2.64 × 10⁻⁴), TCF7L2 rs114758349 (OR = 1.77, P_FDR = 9.37 × 10⁻⁵), and CCL2 rs1024611 (OR = 1.38, P_FDR = 0.033) polymorphisms. We showed that the most informative prognostic model included weighted polygenic scores for these five loci, and non-genetic factors such as age and sex (AUC 85.8%, 95%CI 83.7–87.8%). Compared to the model containing only non-genetic parameters, adding the polygenic score for the five T2D-associated loci showed improved net reclassification (NRI = 37.62%, 1.39 × 10⁻⁶). Inclusion of all 13 tested SNPs to the model with age and sex did not improve the predictive ability compared to the model containing five T2D-associated variants (NRI = −17.86, p = 0.093). The five variants associated with T2D in people from the Volga-Ural region are linked to inflammation (CCR5, CCL2, CCL20) and glucose metabolism regulation (TCF7L, ADIPOQ2). Further studies in independent groups of T2D patients should validate the prognostic value of the model and elucidate the molecular mechanisms of the disease development.

With the onset of the coronavirus pandemic, it has become clear that patients with diabetes are at risk for more severe and fatal COVID-19. Type 2 diabetes mellitus (T2D) is a major risk factor for adverse COVID-19 outcomes. The goal of study was to assess the characteristics and outcomes of hospitalized patients with COVID-19 with or without T2D in the hospital and at 10-month follow-up (FU). Methods: A total of 2486 hospitalized patients in the first wave of COVID-19 were analyzed according to the absence/presence of T2D, with 2082 (84.1%) patients in the control COVID-19 group and 381 (15.5%) in the T2D group. Twenty-three patients had other types of diabetes and were therefore excluded from the study. In-hospital mortality and cardiovascular endpoints (myocardial infarction, stroke, cardiovascular deaths and hospitalizations and composite endpoints) at the 10-month follow-up were analyzed. To remove bias in patients’ characteristics disproportion, Propensity Score Matching (PSM) was used for hospital and follow-up endpoints. Results. Hospital mortality was considerably greater in T2D than in the control COVID-19 group (13.89% vs. 4.89%, p < 0.0001), and the difference remained after PSM (p < 0.0001). Higher glucose-level T2D patients had a higher mortality rate (p = 0.018). The most significant predictors of hospital death in T2D patients were a high CRP, glucose, neutrophils count, and Charlson Comorbidity Index. The follow-up of patients over 10 months showed a non-significant increase for all endpoints in the T2D group (p > 0.05), and significant increase in stroke (p < 0.042). After the PSM, the difference decreased in stroke (p = 0.090), but became significant in cardiovascular hospitalizations (p = 0.023). Conclusion. In T2D patients with COVID-19, an increase in hospital mortality, stroke and cardiovascular hospitalizations rates in the follow-up was observed.

Disordered eating contributes to weight gain, obesity, and type 2 diabetes (T2D), but the precise mechanisms underlying the development of different eating patterns and connecting them to specific metabolic phenotypes remain unclear. We aimed to identify genetic variants linked to eating behaviour and investigate its causal relationships with metabolic traits using Mendelian randomization (MR). We tested associations between 30 genetic variants and eating patterns in individuals with T2D from the Volga-Ural region and investigated causal relationships between variants associated with eating patterns and various metabolic and anthropometric traits using data from the Volga-Ural population and large international consortia. We detected associations between HTR1D and CDKAL1 and external eating; between HTR2A and emotional eating; between HTR2A, NPY2R, HTR1F, HTR3A, HTR2C, CXCR2, and T2D. Further analyses in a separate group revealed significant associations between metabolic syndrome (MetS) and the loci in CRP, ADCY3, GHRL, CDKAL1, BDNF, CHRM4, CHRM1, HTR3A, and AKT1 genes. MR results demonstrated an inverse causal relationship between external eating and glycated haemoglobin levels in the Volga-Ural sample. External eating influenced anthropometric traits such as body mass index, height, hip circumference, waist circumference, and weight in GWAS cohorts. Our findings suggest that eating patterns impact both anthropometric and metabolic traits.

Type 2 diabetes mellitus (T2DM) is one of the most acute problems of the modern world. The disease is characterized by high ratio of micro- and macrovascular complications. T2DM is a multifactorial and polygenic disease, structure of hereditary predisposition to which may be population-specific. Aim the analysis of allelic associations of adiponectin gene (ADIPOQ, rs17366743) with T2DM, its clinical and metabolic characteristics and complications in T2DM patients resident in the Republic of Bashkortostan. Material and methods. 3 PCR-based method of genotyping with polymorphic marker rs17366743 of ADIPOQ gene in 433 T2DM patients and 428 healthy controls, residents of Bashkortostan. Results. The ratio of genotype CT and allele С was higher in T2DM patients compared with controls (15.7% vs. 6.8%; p=0.0002 and 7.8% vs. 3.4%; p0.0001, respectively). Genotype ТТ and allele Т were less frequent in T2DM than in healthy subjects (84.3 and 93,2%; p=0.0002; 92.2 and 96.6%, p0.0001, respectively). The association with the development of diabetic retinopathy and cataract was shown (p=0,044, p=0,008, respectively). Conclusions. Allele C and genotype CT are risk markers of T2DM (OR=2.43 and 2.56 respectively).