Several type 2 diabetes (T2D) susceptibility loci identified via genome-wide association studies were found to be replicated among various populations. However, the influence of these loci on T2D in Thai population is unknown. The aim of this study was to investigate the influence of eight single nucleotide polymorphisms (SNPs) reported in GWA studies on T2D and related quantitative traits in Thai population. Eight SNPs in or near the KCNQ1, CDKN2A/2B, SLC30A8, HHEX, CDKAL1, TCF7L2, MTNR1B, and UBE2E2 genes were genotyped. A case-control association study comprising 500 Thai patients with T2D and 500 ethnically-matched control subjects was conducted. Associations between SNPs and T2D were examined by logistic regression analysis. The impact of these SNPs on quantitative traits was examined by linear regression among case and control subjects. Five SNPs in KCNQ1 (rs2237892), CDK2A/2B (rs108116610, SLC30A8 (rs13266634), TCF7L2 (rs7903146) and MTNR1B (rs1387153) were found to be marginally associated with risk of developing T2D, with odds ratios ranging from 1.43 to 2.02 (p = 0.047 to 3.0 × 10–4) with adjustments for age, sex, and body mass index. Interestingly, SNP rs13266634 of SLC30A8 gene reached statistical significance after correcting for multiple testing (p = 0.0003) (p < 0.006 after Bonferroni correction). However, no significant association was detected between HHEX (rs1111875), CDKAL1 (rs7756992), or UBE2E2 (rs7612463) and T2D. We also observed association between rs10811661 and both waist circumference and waist-hip ratio (p = 0.007 and p = 0.023, respectively). In addition, rs13266634 in SLC30A8 was associated with glycated hemoglobin (p = 0.018), and rs7903146 in TCF7L2 was associated with high-density lipoprotein cholesterol level (p = 0.023). Of the eight genes included in our analysis, significant association was observed between KCNQ1, CDKN2A/2B, SLC30A8, TCF7L2, and MTNR1B loci and T2D in our Thai study population. Of these, CDKN2A/2B, SLC30A8, and TCF7L2 genes were also significantly associated with anthropometric, glycemic and lipid characteristics. Larger cohort studies and meta-analyses are needed to further confirm the effect of these variants in Thai population.

Introduction: Type 2 diabetes mellitus (T2D) is highly heterogeneous in disease progression and risk of complications. This study aimed to categorize Thai T2D into subgroups using variables that are commonly available based on routine clinical parameters to predict disease progression and treatment outcomes. Research design and methods: This was a cohort study. Data-driven cluster analysis was performed using a Python program in patients with newly diagnosed T2D (n=721) of the Siriraj Diabetes Registry using five variables (age, body mass index (BMI), glycated hemoglobin (HbA_1c), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C)). Disease progression and risk of diabetic complications among clusters were compared using the Χ² and Kruskal-Wallis test. Cox regression and the Kaplan-Meier curve were used to compare the time to diabetic complications and the time to insulin initiation. Results: The mean age was 53.4±11.3 years, 58.9% were women. The median follow-up time was 21.1 months (9.2–35.2). Four clusters were identified: cluster 1 (18.6%): high HbA_1c, low BMI (insulin-deficiency diabetes); cluster 2 (11.8%): high TG, low HDL-C, average age and BMI (metabolic syndrome group); cluster 3 (23.3%): high BMI, low HbA_1c, young age (obesity-related diabetes); cluster 4 (46.3%): older age and low HbA_1c at diagnosis (age-related diabetes). Patients in cluster 1 had the highest prevalence of insulin treatment. Patients in cluster 2 had the highest risk of diabetic kidney disease and diabetic retinopathy. Patients in cluster 4 had the lowest prevalence of diabetic retinopathy, nephropathy, and insulin use. Conclusions: We were able to categorize Thai patients with newly diagnosed T2D into four clusters using five routine clinical parameters. This clustering method can help predict disease progression and risk of diabetic complications similar to previous studies using parameters including insulin resistance and insulin sensitivity markers.

Few studies have identified the metabolic consequences of the post-acute phase of nonsevere COVID-19. This prospective study examined metabolic outcomes and associated factors in nonsevere, RT-PCR-confirmed COVID-19. The participants’ metabolic parameters, the prevalence of long-term multiple metabolic abnormalities (≥ 2 components), and factors influencing the prevalence were assessed at 1, 3, and 6 months post-onset. Six hundred individuals (mean age 45.5 ± 14.5 years, 61.7% female, 38% high-risk individuals) with nonsevere COVID-19 attended at least one follow-up visit. The prevalence of worsening metabolic abnormalities was 26.0% for BMI, 43.2% for glucose, 40.5% for LDL-c, 19.1% for liver, and 14.8% for C-reactive protein. Except for lipids, metabolic-component abnormalities were more prevalent in high-risk hosts than in healthy individuals. The prevalence of multiple metabolic abnormalities at the 6-month follow-up was 41.3% and significantly higher in high-risk than healthy hosts (49.2% vs 36.5%; P = 0.007). Factors independently associated with a lower risk of these abnormalities were being female, having dyslipidemia, and receiving at least 3 doses of the COVID-19 vaccine. These findings suggest that multiple metabolic abnormalities are the long-term consequences of COVID-19. For both high-risk and healthy individuals with nonsevere COVID-19, healthcare providers should monitor metabolic profiles, encourage healthy behaviors, and ensure complete vaccination.

Gestational diabetes mellitus (GDM) is a significant pregnancy complication linked to perinatal complications and an elevated risk of future metabolic disorders for both mothers and their children. GDM is diagnosed when women without prior diabetes develop chronic hyperglycemia due to β-cell dysfunction during gestation. Global research focuses on the association between GDM and single nucleotide polymorphisms (SNPs) and aims to enhance our understanding of GDM’s pathogenesis, predict its risk, and guide patient management. This review offers a summary of various SNPs linked to a heightened risk of GDM and explores their biological mechanisms within the tissues implicated in the development of the condition.

Background: Gestational diabetes mellitus (GDM) is the most common association with hyperglycemia and glucose intolerance during pregnancy. The adipokines play an important to control insulin secretion and glucose. This study aimed to investigate the association between maternal circulating adipokine levels and ADIPOQ gene polymorphism among pregnant women subjects with GDM and normal glucose tolerance (NGT). Methods: Participants including 229 normal pregnant women and 197 GDM pregnant women were enrolled from 2015 to 2018 at Siriraj hospital. Serum adipokine levels including adiponectin, adipsin/factor D, NGAL/Lipocalin-2, total PAI-1, and resistin were measured by immunoassay. ADIPOQ variations were investigated including − 11377C/G (rs266729), +45T/G (rs2241766), and +276G/T (rs1501299). Results: Serum adiponectin concentration was also significantly decreased among the GDM who had aged less than 35 years old whereas adipsin levels were significantly lower among the GDM who had aged more than 35 years old. Also, adiponectin and total PAI-1 levels were significantly lower among the GDM who had a BMI of less than 30 kg/m². The G allele frequency of ADIPOQ +45T/G was significantly different between GDM and controls (p = 0.03). ADIPOQ +45T/G was associated with an increased risk of GDM (odds ratio [OR]: 1.554; 95% confidence interval [CI]: 1.010– 2.390; p=0.045). The − 11377C/G was affected by the level of adiponectin (p = 0.04). The C allele of − 11377C/G SNP declined serum adiponectin levels and may be a risk factor for GDM. Conclusion: This study revealed that genetics play important roles in circulating adipokines among pregnant women. ADIPOQ polymorphisms had significant associations with adiponectin levels in GDM patients.