Association of polymorphisms in STRA6 gene with gestational diabetes mellitus in a Chinese Han population

Genetics of type 2 diabetes mellitus in Indian and Global Population: A Review

Background

Non-communicable diseases such as cardiovascular diseases, respiratory diseases and diabetes contribute to the majority of deaths in India. Public health programmes on non-communicable diseases (NCD) prevention primarily target the behavioural risk factors of the population. Hereditary is known as a risk factor for most NCDs, specifically, type 2 diabetes mellitus (T2DM), and hence, understanding of the genetic markers of T2DM may facilitate prevention, early case detection and management.

Main body

We reviewed the studies that explored marker-trait association with type 2 diabetes mellitus globally, with emphasis on India. Globally, single nucleotide polymorphisms (SNPs) rs7903146 of Transcription Factor 7-like 2 (TCF7L2) gene was common, though there were alleles that were unique to specific populations. Within India, the state-wise data were also taken to foresee the distribution of risk/susceptible alleles. The findings from India showcased the common and unique alleles for each region.

Conclusion

Exploring the known and unknown genetic determinants might assist in risk prediction before the onset of behavioural risk factors and deploy prevention measures. Most studies were conducted in non-representative groups with inherent limitations such as smaller sample size or looking into only specific marker-trait associations. Genome-wide association studies using data from extensive prospective studies are required in highly prevalent regions worldwide. Further research is required to understand the singular effect and the interaction of genes in predicting diabetes mellitus and other comorbidities.

Genetic Variations of Vitamin A-Absorption and Storage-Related Genes, and Their Potential Contribution to Vitamin A Deficiency Risks Among Different Ethnic Groups

Vitamin A, an essential fat-soluble micronutrient, plays a critical role in the body, by regulating vision, immune responses, and normal development, for instance. Vitamin A deficiency (VAD) is a major cause of xerophthalmia and increases the risk of death from infectious diseases. It is also emerging that prenatal exposure to VAD is associated with disease risks later in life. The overall prevalence of VAD has significantly declined over recent decades; however, the rate of VAD is still high in many low- and mid-income countries and even in high-income countries among specific ethnic/race groups. While VAD occurs when dietary intake is insufficient to meet demands, establishing a strong association between food insecurity and VAD, and vitamin A supplementation is the primary solution to treat VAD, genetic contributions have also been reported to effect serum vitamin A levels. In this review, we discuss genetic variations associated with vitamin A status and vitamin A bioactivity-associated genes, specifically those linked to uptake of the vitamin in the small intestine and its storage in the liver, as well as their potential contribution to vitamin A deficiency risks among different ethnic groups.

Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is the metabolic disorder that appears during pregnancy. The current investigation aimed to identify central differentially expressed genes (DEGs) in GDM. The transcription profiling by array data (E-MTAB-6418) was obtained from the ArrayExpress database. The DEGs between GDM samples and non-GDM samples were analyzed. Functional enrichment analysis were performed using ToppGene. Then we constructed the protein–protein interaction (PPI) network of DEGs by the Search Tool for the Retrieval of Interacting Genes database (STRING) and module analysis was performed. Subsequently, we constructed the miRNA–hub gene network and TF–hub gene regulatory network. The validation of hub genes was performed through receiver operating characteristic curve (ROC). Finally, the candidate small molecules as potential drugs to treat GDM were predicted by using molecular docking. Through transcription profiling by array data, a total of 869 DEGs were detected including 439 up-regulated and 430 down-regulated genes. Functional enrichment analysis showed these DEGs were mainly enriched in reproduction, cell adhesion, cell surface interactions at the vascular wall and extracellular matrix organization. Ten genes, HSP90AA1, EGFR, RPS13, RBX1, PAK1, FYN, ABL1, SMAD3, STAT3 and PRKCA were associated with GDM, according to ROC analysis. Finally, the most significant small molecules were predicted based on molecular docking. This investigation identified hub genes, signal pathways and therapeutic agents, which might help us, enhance our understanding of the mechanisms of GDM and find some novel therapeutic agents for GDM.

Retinol-Binding Protein 4 Activates STRA6, Provoking Pancreatic β-Cell Dysfunction in Type 2 Diabetes

Pancreatic β-cell dysfunction plays a decisive role in the progression of type 2 diabetes. Retinol-binding protein 4 (RBP4) is a prominent adipokine in type 2 diabetes, although its effect on β-cell function remains elusive, and the underlying mechanisms are unknown. Here, we found that elevated circulating RBP4 levels were inversely correlated with pancreatic β-cell function in db/db mice across different glycemic stages. RBP4 directly suppressed glucose-stimulated insulin secretion (GSIS) in primary isolated islets and INS-1E cells in a dose- and time-dependent manner. RBP4 transgenic (RBP4-Tg) overexpressing mice showed a dynamic decrease of GSIS, which appeared as early as 8 weeks old, preceding the impairment of insulin sensitivity and glucose tolerance. Islets isolated from RBP4-Tg mice showed a significant decrease of GSIS. Mechanistically, we demonstrated that the stimulated by retinoic acid 6 (STRA6), RBP4's only known specific membrane receptor, is expressed in β-cells and mediates the inhibitory effect of RBP4 on insulin synthesis through the Janus kinase 2/STAT1/ISL-1 pathway. Moreover, decreasing circulating RBP4 level could effectively restore β-cell dysfunction and ameliorate hyperglycemia in db/db mice. These observations revealed a role of RBP4 in pancreatic β-cell dysfunction, which provides new insight into the diabetogenic effect of RBP4.

Update on the genetic and epigenetic etiology of gestational diabetes mellitus: a review

Background

Many studies have been conducted on the genetic and epigenetic etiology of gestational diabetes mellitus (GDM) in the last two decades because of the disease’s increasing prevalence and role in global diabetes mellitus (DM) explosion. An update on the genetic and epigenetic etiology of GDM then becomes imperative to better understand and stem the rising incidence of the disease. This review, therefore, articulated GDM candidate genes and their pathophysiology for the awareness of stakeholders.

Main body (genetic and epigenetic etiology, GDM)

The search discovered 83 GDM candidate genes, of which TCF7L2, MTNR1B, CDKAL1, IRS1, and KCNQ1 are the most prevalent. Certain polymorphisms of these genes can modulate beta-cell dysfunction, adiposity, obesity, and insulin resistance through several mechanisms. Environmental triggers such as diets, pollutants, and microbes may also cause epigenetic changes in these genes, resulting in a loss of insulin-boosting and glucose metabolism functions. Early detection and adequate management may resolve the condition after delivery; otherwise, it will progress to maternal type 2 diabetes mellitus (T2DM) and fetal configuration to future obesity and DM. This shows that GDM is a strong risk factor for T2DM and, in rare cases, type 1 diabetes mellitus (T1DM) and maturity-onset diabetes of the young (MODY). This further shows that GDM significantly contributes to the rising incidence and burden of DM worldwide and its prevention may reverse the trend.

Conclusion

Mutations and epigenetic changes in certain genes are strong risk factors for GDM. For affected individuals with such etiologies, medical practitioners should formulate drugs and treatment procedures that target these genes and their pathophysiology.

Replication of previous genome-wide association studies of HKDC1, BACE2, SLC16A11 and TMEM163 SNPs in a gestational diabetes mellitus case-control sample from Han Chinese population

Background: Four novel glucose metabolism risk loci (HKDC1 rs4746822, BACE2 rs6517656, SLC16A11 rs13342232 and TMEM163 rs998451) were identified in recent genome-wide association studies (GWAS) of Afro-Caribbean, European, Hispanic, Thai, Mexican, Latin American and Indian populations. None of the abovementioned SNPs has been reported in a Han Chinese population. Aim: To replicate the relationships between HKDC1 rs4746822, BACE2 rs6517656, SLC16A11 rs13342232 and TMEM163 rs998451 with gestational diabetes mellitus (GDM) in a Han Chinese population. Methods: This was a case-control study which enrolled 334 pregnant women with GDM and 367 pregnant women with normal glucose tolerance. The linear regression and logistic regression were used to estimate the association between SNPs with the risk of GDM, HOMA-IR and fasting insulin levels. The fasting insulin concentration and HOMA-IR were log10 transformed before analysis. Results: No significant differences in the alleles and genotypes of SLC16A11 rs13342232, HKDC1 rs4746822 and BACE2 rs6517656 were observed between cases and controls. After adjusting the weekly BMI growth, pre-pregnancy BMI and maternal age, under the additive model, SLC16A11 rs13342232 was associated with log₁₀fasting serum insulin (Beta=0.046, p=0.016), log₁₀HOMA-IR level (Beta=0.061, p=0.003) and fasting plasma glucose level (Beta=0.164, p=0.011); HKDC1 rs4746822 was associated with OGTT 2-hr plasma glucose level (Beta=0.239, p=0.016); and BACE2 rs6517656 was associated with log₁₀fasting serum insulin (Beta=-0.053, p=0.044) and log₁₀HOMA-IR level (Beta=-0.060, p=0.048). After correction for multiple testing, the associations of SLC16A11 and HKDC1 with glucose metabolism remained statistically significant. The A allele of TMEM163 rs998451 was not detected in this population. Conclusion: HKDC1 rs4746822, BACE2 rs6517656 and SLC16A11 rs13342232 are associated with glucose metabolism in pregnant women of Han Chinese.