Mendelian Randomization Analysis Identifies Inverse Causal Relationship between External Eating and Metabolic Phenotypes.

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.

Long-Term Outcomes of COVID-19 in Hospitalized Type 2 Diabetes Mellitus Patients.

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.

Integrating Common Risk Factors with Polygenic Scores Improves the Prediction of Type 2 Diabetes.

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, PFDR = 3.40 × 10-5), CCR5 rs333 (OR = 1.99, PFDR = 0.033), ADIPOQ rs17366743 (OR = 3.17, PFDR = 2.64 × 10-4), TCF7L2 rs114758349 (OR = 1.77, PFDR = 9.37 × 10-5), and CCL2 rs1024611 (OR = 1.38, PFDR = 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.

The association between eating behavior and polymorphisms in GRIN2B, GRIK3, GRIA1 and GRIN1 genes in people with type 2 diabetes mellitus.

Excess body weight is the main risk factor of type 2 diabetes. Recent studies have shown that psychological and behavioral factors affect weight. Additionally, emerging evidence indicates that polymorphisms of neurotransmitter genes can impact eating behavior. The aim of this study was to detect the associations between SNPs in glutamatergic system genes and type 2 diabetes in the ethnic group of Tatars origin living in the Republic of Bashkortostan (Russian Federation). In our case-control cross-sectional study, 501 patients with type 2 diabetes (170 men and 331 women, 60.9 ± 9.2 years old (mean ± SD), BMI 30.9 ± 3.9 kg/m2 (mean ± SD) of Tatar ethnicity, and a control group of 420 Tatars (170 men and 250 women, 56.3 ± 11.6 years old (mean ± SD), BMI 24.4 ± 4.3 kg/m2 (mean ± SD), were genotyped for five SNPs in four glutamatergic genes (GRIN2B, GRIK3, GRIA1, GRIN1). Three SNPs were associated with type 2 diabetes: rs7301328 in GRIN2B [odds ratio adjusted for age, sex and BMI (ORadj) = 0.77 (95% CI 0.63-0.93), padj = 0.0077], rs1805476 in GRIN2B [ORadj = 1.25 (95% CI 1.03-1.51), padj = 0.0240], and rs2195450 in GRIA1 [ORadj = 1.35 (95% CI 1.02-1.79), padj = 0.0340]. Regression analysis revealed that rs1805476 in GRIN2B was associated with LDL level, glomerular filtration rate, BMI (p = 0.020, p = 0.012 and p = 0.018, respectively). The SNP rs7301328 in GRIN2B was associated with triglyceride levels and HbA1c (p = 0.040, p = 0.023, respectively). These associations were not significant after Bonferroni correction. We found the association between rs534131 in GRIK3, rs2195450 in GRIA1, rs1805476 in GRIN2B and diabetic retinopathy (p = 0.005, p = 0.007, p = 0.040, respectively); rs7301328 in GRIN2B was associated with hypertension (p = 0.025) and cerebrovascular disease (p = 0.013). The association between rs534131 of GRIK3, rs2195450 of GRIA1 genes and diabetic retinopathy remained significant after Bonferroni correction. The SNPs rs6293 in GRIN1 was significantly associated with eating behavior in patients with type 2 diabetes (p = 0.01). Our results demonstrate that polymorphic variants of glutamatergic genes are associated with eating behavior and diabetic complications in Tatar ethnic group residing in the Republic of Bashkortostan. We detected novel associations of the polymorphic loci in GRIN1 (rs6293) gene with external eating behavior in type 2 diabetes patients, GRIK3 (rs534131) and GRIA1 (rs2195450) genes with diabetic retinopathy.

Multilocus associations of inflammatory genes with the risk of type 1 diabetes.

BACKGROUND: Genome-wide association studies have captured a large proportion of genetic variation related to type 1 diabetes mellitus (T1D). However, most of these studies are performed in populations of European ancestry and therefore the disease risk estimations can be inaccurate when extrapolated to other world populations. METHODS: We conducted a case-control study in 1866 individuals from the three major populations of the Republic of Bashkortostan (Russians, Tatars, and Bashkirs) in Russian Federation, using single-locus and multilocus approach to identify genetic predictors of T1D. RESULTS: We found that LTA rs909253 and TNF rs1800629 polymorphisms were associated with T1D in the group of Tatars. Meta-analysis of the association study results in the three ethnic groups has confirmed the association between the T1D risk and LTA rs909253 genetic variant. LTA rs909253 and TNF rs1800629 loci were also featured in combinations most significantly associated with T1D. CONCLUSION: Our findings suggest that LTA rs909253 and TNF rs1800629 polymorphisms are associated with the risk of T1D both independently and in combination with polymorphic markers in other inflammatory genes, and the analysis of multi-allelic combinations provides valuable insight in the study of polygenic traits.

Chemokine gene polymorphisms association with increased risk of type 2 diabetes mellitus in Tatar ethnic group, Russia.

Recent studies have shown that chemokines play an important role in the development of chronic inflammation in adipose tissue, obesity pathogenesis, glucose intolerance and type 2 diabetes. It has also been revealed that some SNPs in chemokine genes are associated with obesity, insulin resistance, type 2 diabetes and diabetes complications in different ethnic groups. The aim of this study was to determine the associations between SNPs in chemokine genes and type 2 diabetes in participants of Tatar ethnic group, living in Bashkortostan. Case-control and cross-sectional study were included in our study design. Five SNPs were genotyped in 440 type 2 diabetes (160 men and 280 women), 58.8 ± 9.2 years old (mean ± SD), BMI 29.3 ± 3.9 kg/m2 (mean ± SD) patients of Tatar ethnicity, and a control group of 500 Tatars (180 men and 320 women), 55.2 ± 11.6 years old (mean ± SD), BMI 25.9 ± 4.3 kg/m2 (mean ± SD). The SNPs rs6749704 in CCL20 [odds ratio (OR) = 2.77 (95% CI 1.81-4.25), р = 0.0001], rs2107538 in CCL5 [odds ratio (OR) = 1.80 (95% CI 1.46-2.22), p = 0.0001] were significantly associated with type 2 diabetes. Regression analysis revealed that rs1696941 in CCL11 was associated with the onset age and duration of type 2 diabetes as well as with HbA1c level (p = 0.034, p = 0.036 and p = 0.0054, respectively). The SNPs rs223828 in CCL17 and rs6749704 in CCL20 were correlated with obesity as estimated by BMI (p = 0.0004, p = 0.029, respectively). Rs223828 in CCL17 revealed the association with postprandial glucose level (p = 0.024) and HbA1c (p = 0.008). These data demonstrate that variants of chemokine genes are associated with type 2 diabetes and obesity of Tatar ethnic group inhabiting Bashkortostan Republic. Novel associations of the polymorphic loci in CCL20 (rs6749704) and CCL5 (rs2107538) genes with type 2 diabetes had been identified as a result of the conducted research.