Abstract
Gestational diabetes mellitus (GDM) is a common pregnancy complication that affects both maternal and neonatal health. Although the oral glucose tolerance test (OGTT) remains the standard diagnostic approach, it is generally performed at 24–28 weeks of gestation, which may limit opportunities for early risk assessment and preventive intervention. In Thailand, comprehensive genomic and metabolomic data on GDM remain limited. This study aimed to characterize clinical features, genetic factors, and metabolite profiles associated with GDM in Thai pregnant women and to generate evidence for future risk stratification and precision maternal care.
This study collected clinical data and blood-derived DNA samples from 586 pregnant women, including 265 women with GDM and 321 women with normal glucose tolerance (NGT). Women with GDM were older and had a higher body mass index than women with NGT. The GDM group had a median age of 31.0 (27.0, 34.0) years and a mean body mass index of 26.3 ± 4.8 kg/m², whereas the NGT group had a median age of 26.0 (22.0, 30.0) years and a mean body mass index of 23.2 ± 4.2 kg/m² (p < 0.001). Women with GDM also had higher proportions of pre-pregnancy overweight and obesity, as well as obesity at the time of diagnosis, than women with NGT. The GDM group had significantly higher fasting glucose, HbA1c, insulin, triglycerides, VLDL, and HOMA-IR-defined insulin resistance than the NGT group, whereas HDL-C was lower in the GDM group. These findings reflect key features of insulin resistance and metabolic dysregulation among pregnant women with GDM.
Clinical outcome analysis showed that women with GDM had a significantly higher cesarean delivery rate than women with NGT (61.3% vs. 38.9%, p < 0.001). They also had a higher frequency of preeclampsia (7.2% vs. 2.3%, p = 0.022). In contrast, birth size, APGAR scores, and neonatal complications did not differ significantly between the two groups. More than 97% of newborns had APGAR scores within the normal range, and 70% of neonates had no major complications. Most women with GDM achieved appropriate glycemic control; 80.4% were classified as having optimal postprandial glucose control. Treatment among women with GDM included diet control in 77.4%, insulin in 24.4%, and metformin in 1.8%.
A genome-wide association study (GWAS) was conducted using whole genome sequencing (WGS) data from 457 pregnant women, including 219 women with GDM and 238 women with NGT. The analysis identified several single nucleotide polymorphisms (SNPs) associated with GDM. The SNP rs116991923, located near CDH6, showed an age- and body mass index-adjusted odds ratio (OR) of 3.702 (95% confidence interval [CI]: 2.076–6.601, p = 7.55 × 10-6). The SNP rs10785079 in LINC02882 showed an adjusted OR of 1.731 (95% CI: 1.367–2.191, p = 5.14 × 10-6), while rs1774271 in HYDIN showed an adjusted OR of 0.534 (95% CI: 0.402–0.711, p = 1.68 × 10-5). Comparison of minor allele frequencies (MAFs) with gnomAD reference populations revealed population-specific allele frequency patterns, supporting the importance of studying GDM-associated genetic variation directly in Thai populations. However, these findings require further validation in larger independent cohorts.
Metabolomic analysis was performed using plasma samples from 271 participants, including 167 women with GDM and 104 women with NGT. Nuclear magnetic resonance (NMR) spectroscopy was combined with principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and machine learning. The analysis identified acetone, citrate, succinate, 3-hydroxybutyrate, and glucose as key metabolites that differed between the GDM and NGT groups. Functional enrichment analysis using MetaboAnalyst 6.0 indicated significant alteration of ketone body metabolism, with 3-hydroxybutyrate, succinic acid, and acetone as the main contributing metabolites. Biomarker analysis using a Linear Support Vector Machine (LSVM) ranked acetone, citrate, succinate, 3-hydroxybutyrate, and glucose as the top five discriminating metabolites. Model performance assessed by Monte Carlo Cross Validation (MCCV) yielded an area under the curve (AUC) of 0.749 (95% CI: 0.690–0.802).
This study demonstrates that Thai pregnant women with GDM differ from women with NGT in clinical characteristics, genetic profiles, and metabolomic signatures. The identified SNPs and metabolites provide a foundation for future development of GDM risk assessment tools in Thai populations. Integrating genomic and metabolomic data may support molecular screening, individualized pregnancy care, and further precision medicine research for maternal and neonatal health in Thailand.
