Association of adiponectin gene alleles with type 2 diabetes mellitus in residents of Bashkortostan

Авзалетдинова, Д. Ш.; Sharipova, Liaisan F.; Кочетова, О. В.; Моругова, Т. В.; Мустафина, О. Е.

doi:10.14341/probl9426

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…The missense variant in the heterozygous form rs17366743 was identified in one carrier (male, age 38 years, BMI 27) with high levels of cholesterol, LDL, and glucose. According to the literature, the frequency of this variant was higher in patients with DM2 [40]. The 3 UTR variant of rs4068 was identified by us in two heterozygous patients with overweight and high levels of cholesterol, LDL, and blood glucose.…”

Section: Variants In Adipoqmentioning

confidence: 85%

Rare Variants of Obesity-Associated Genes in Young Adults with Abdominal Obesity

Bairqdar,

Shakhtshneider,

Ivanoshchuk

et al. 2023

JPM

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The increase in the prevalence of overweight, obesity and associated diseases is a serious problem. The aim of the study was to identify rare variants in obesity-associated genes in young adults with abdominal obesity in our population and to analyze information about these variants in other populations. Targeted high-throughput sequencing of obesity-associated genes was performed (203 young adults with an abdominal obesity phenotype). In our study, all of the 203 young adults with abdominal obesity had some rare variant in the genes associated with obesity. The widest range of rare and common variants was presented in ADIPOQ, FTO, GLP1R, GHRL, and INS genes. The use of targeted sequencing and clinical criteria makes it possible to identify carriers of rare clinically significant variants in a wide range of obesity-associated genes and to investigate their influence on phenotypic manifestations of abdominal obesity.

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Section: Variants In Adipoqmentioning

confidence: 85%

Rare Variants of Obesity-Associated Genes in Young Adults with Abdominal Obesity

Bairqdar,

Shakhtshneider,

Ivanoshchuk

et al. 2023

JPM

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“…Whole venous blood samples were obtained from each participant, stored at −4 °C, and used for total DNA extraction. DNA extraction and genotyping were performed using standard procedures as previously described [ 29 , 30 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ]. Genetic variants were selected for the analysis based on the results of the Phenome-Wide Association Studies (PheWASs).…”

Section: Methodsmentioning

confidence: 99%

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

Timasheva,

Balkhiyarova,

Avzaletdinova

et al. 2024

Nutrients

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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.

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“…Whole venous blood samples were obtained from each participant, stored at −4 °C, and used for total DNA extraction. DNA extraction and genotyping procedures were performed as previously described [ 68 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 ]. Genetic variants for the analysis were selected based on the previously detected associations with diabetes and related traits ( Supplementary Table S3 ), the results of the Phenome-Wide Association Studies (PheWAS), and the variants selected for the study included those associated with metabolic traits (cholesterol levels, fat mass, T2D) and related disorders including inflammatory diseases and complications caused by T2D ( Supplementary Table S4 ).…”

Section: Methodsmentioning

confidence: 99%

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

Timasheva

Balkhiyarova

Авзалетдинова

et al. 2023

IJMS

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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.

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Association of adiponectin gene alleles with type 2 diabetes mellitus in residents of Bashkortostan

Cited by 6 publications

References 10 publications

Rare Variants of Obesity-Associated Genes in Young Adults with Abdominal Obesity

Rare Variants of Obesity-Associated Genes in Young Adults with Abdominal Obesity

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

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

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