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.

Gestational diabetes mellitus (GDM) is a frequently occurring condition characterized by glucose intolerance that is first detected during pregnancy and has significant implications for both matern...

Estrogens, which bind to estrogen receptor alpha (ERα), are important for proper bone mineral density. When women go through menopause, estrogen levels decrease, and there is a decrease in bone quality, along with an increased risk for fractures. We previously identified an enhancer near FOXC1 as the most significantly enriched binding site for estrogen receptor alpha (ERα) in osteoblasts. FOXC1 is a transcription factor belonging to a large group of proteins known as forkhead box genes and is an important regulator of bone formation. Here, we demonstrate that 17β-estradiol (E2) increases the mRNA and protein levels of FOXC1 in primary mouse and human osteoblasts. GATA4 is a pioneer factor for ERα and it is also recruited to enhancers near Foxc1. Knockdown of Gata4 in mouse osteoblasts in vitro decreases Foxc1 expression as does knockout of Gata4 in vivo. Functionally, GATA4 and FOXC1 interact and regulate osteoblast proteins such as RUNX2, as demonstrated by ChIP-reChIP and luciferase assays. The most enriched motif in GATA4 binding sites from ChIP-seq is for FOXC1, supporting the notion that GATA4 and FOXC1 cooperate in regulating osteoblast differentiation. Together, these data demonstrate the interactions of the transcription factors ERα, GATA4, and FOXC1 to regulate each other’s expression and other osteoblast differentiation genes.

Aims/hypothesis: Type 1 diabetes is categorised into autoantibody positive and autoantibody negative. Most type 1 diabetes research has focused on European populations, leaving a gap in understanding in relation to other ethnic groups, including Thai populations. This lack of data is significant given Thailand’s poor prevention and therapeutic management strategies. We aimed to investigate the frequency and distribution of islet autoantibodies among Thai individuals with long-standing type 1 diabetes diagnosed before the age of 30 years. Methods: We conducted a nationwide population-based study involving 48 hospitals in Thailand from May 2020 to September 2023, enrolling 953 participants. Demographic and clinical characteristics of individuals with autoantibody-positive and -negative type 1 diabetes were analysed. The autoantibodies GAD65, IA-2 and ZnT8 were measured using ELISA. A random C-peptide level was detected by electrochemiluminescence immunoassay. Results: Thai individuals with autoantibody-negative type 1 diabetes comprised 34.2% of the population. Among all individuals, the frequency of GAD65, IA-2 and ZnT8 was 56%, 37% and 33%, respectively. Autoantibody-negative individuals with type 1 diabetes were older at diagnosis, had higher BMI and had higher random C-peptide levels compared with autoantibody-positive individuals with type 1 diabetes. Female individuals had a higher prevalence of type 1 diabetes than male individuals (58% vs 42%; p=1.531 × 10⁻⁵). The southern region of Thailand exhibited a distinct pattern of autoantibody frequency compared with other regions (p=0.0001561). Conclusions/interpretation: The frequency, distribution and characteristics of autoantibody-positive and -negative long-standing type 1 diabetes in Thailand showed uniqueness from other populations. This provides insight into the disease that may have implications for type 1 diabetes prediction, treatment and pathogenesis, especially in the Southeast Asian population. Graphical

Type 2 diabetes is a heterogeneous disease with strong genetic components. We showed earlier that newly diagnosed type 2 diabetes in Thai patients could be categorized into four clusters. This study aimed to determine the evidence of hereditary factors in these type 2 diabetes clusters. A total of 487 subjects who were diagnosed with type 2 diabetes in two years were enrolled in the Siriraj Diabetes Center, Siriraj Hospital Bangkok, Bangkok, Thailand. They were divided into four clusters as previously described. The associations between patients' characteristics, degree of family history of diabetes (FHD), and type 2 diabetes clusters were tested using multinomial logistic regression. Among four clusters of newly diagnosed type 2 diabetes, there were significant differences in characteristics at baseline, including age at diagnosis, BMI, waist circumference, blood sugar levels, vital signs, triglyceride, HDL, calculated LDL, creatinine and eGFR (all p < .05). A relatively young age at the time of diabetes diagnosis was associated with having second-degree relatives with diabetes (p < .05) in all clusters when using mild age-related diabetes (MARD) cluster with no FHD as a control. Patients in the severe insulin-deficient diabetes (SIDD) cluster had more first-degree relatives with diabetes (odds ratio = 1.85; p = .0354), while patients in the metabolic syndrome diabetes (MSD) cluster (odds ratio = 10.73; p < .001) and the mild obesity-related diabetes (MOD) group (odds ratio = 6.66; p = .002), had more second-degree relatives with diabetes. Genetic factors might have various roles in the pathogenesis of type 2 diabetes, at least in newly diagnosed Thai patients. Our findings supported that genetic heterogeneity contributed to clinical heterogeneity or four different clusters. Further studies are needed in a larger sample size of these patients is needed to identify genetic loci associated with each cluster.