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Objective: We aimed to explore the associations between common genetic risk variants with gestational diabetes mellitus (GDM) risk in Russian women and to assess their utility in the identification of GDM cases.
Methods: We conducted a case-control study including 1,142 pregnant women (688 GDM cases and 454 controls) enrolled at Almazov National Medical Research Centre. The International Association of Diabetes and Pregnancy Study Groups criteria were used to diagnose GDM. A total of 11 single- nucleotide polymorphisms (SNPs), including those in (rs10762264), (rs1799884), (rs10830963 and rs1387153), (rs7903146 and rs12255372), (rs5219), (rs4402960), (rs1801278), (rs9939609), and (rs7754840) were genotyped using Taqman assays. A logistic regression model was used to calculate odds ratios (ORs) and their confidence intervals (CIs). A simple-count genetic risk score (GRS) was calculated using 6 SNPs. The area under the receiver operating characteristic curve (c-statistic) was calculated for the logistic regression model predicting the risk of GDM using clinical covariates, SNPs that had shown a significant association with GDM in our study, GRS, and their combinations.
Results: Two variants in (rs1387153 and rs10830963) demonstrated a significant association with an increased risk of GDM. The association remained significant after adjustment for age, pre-gestational BMI, arterial hypertension, GDM in history, impaired glucose tolerance, polycystic ovary syndrome, family history of diabetes, and parity (P = 0.001 and P < 0.001, respectively). After being conditioned by each other, the effect of rs1387153 on GDM predisposition weakened while the effect of rs10830963 remained significant (P = 0.004). The risk of GDM was predicted by clinical variables (c-statistic 0.712, 95 % CI: 0.675 - 0.749), and the accuracy of prediction was modestly improved by adding GRS to the model (0.719, 95 % CI 0.682 - 0.755), and more by adding only rs10830963 (0.729, 95 % CI 0.693 - 0.764).
Conclusion: Among 11 SNPs associated with T2D and/or GDM in other populations, we confirmed significant association with GDM for two variants in in Russian women. However, these variants showed limited value in the identification of GDM cases.
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http://dx.doi.org/10.3389/fendo.2021.628582 | DOI Listing |
Arch Gynecol Obstet
September 2025
Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Objective: To investigate adverse pregnancy and delivery outcomes in women with GDMA1 during pregnancies conceived through fertility treatments.
Methods: This population-based retrospective cohort study examined adverse pregnancy and delivery outcomes in pregnancies affected by GDMA1 following fertility treatments compared to those conceived naturally. Women with GDMA1 who conceived via fertility treatments were classified as cases, while those who conceived naturally were designated as controls.
Medicine (Baltimore)
September 2025
Department of Obstetrics, Nantong University Affiliated Maternal and Child Health Hospital, Nantong, Jiangsu, China.
This study aimed to evaluate the association between a dietary education approach grounded in the transtheoretical model and cognitive load theory and glycemic control and pregnancy-related outcomes in patients diagnosed with gestational diabetes mellitus (GDM). A retrospective analysis was performed using clinical data from 126 pregnant women with GDM who received care at our hospital between September 2021 and September 2023. Participants were grouped based on the type of nursing intervention received: a control group that underwent standard care and an observation group that received an additional cognitive load-informed dietary education program based on transtheoretical model.
View Article and Find Full Text PDFJ Ultrasound Med
September 2025
Department of Clinical Analysis, Federal University of Santa Catarina (UFSC), Florianópolis, Brazil.
Objectives: To evaluate the performance of artificial intelligence (AI)-based models in predicting elevated neonatal insulin levels through fetal hepatic echotexture analysis.
Methods: This diagnostic accuracy study analyzed ultrasound images of fetal livers from pregnancies between 37 and 42 weeks, including cases with and without gestational diabetes mellitus (GDM). Images were stored in Digital Imaging and Communications in Medicine (DICOM) format, annotated by experts, and converted to segmented masks after quality checks.
Front Public Health
September 2025
Emergency Department, Qingdao Central Hospital, University of Health and Rehabilitation Sciences, Qingdao, Shandong, China.
Background: Gestational diabetes mellitus (GDM) prevalence is rising in China, necessitating an understanding of knowledge, attitudes, and practices (KAP) among affected women to inform interventions.
Methods: This cross-sectional study (June 2020-June 2024) surveyed 3,426 Chinese women with GDM, aged 20-60 years, from urban and rural prenatal clinics across Qingdao city, China. A validated 25-item KAP questionnaire used a three-option response format (yes, no, maybe).
BJOG
September 2025
Department of Obstetrics and Gynaecology, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia.
Objectives: To examine the combined influence of food environment, built environment, socio-economic status and individual factors (maternal age, parity, smoking status and need for an interpreter) on maternal overweight, gestational diabetes mellitus (GDM) and large-for-gestational age (LGA) births in Australia.
Design: Retrospective cohort study.
Setting: Melbourne, Australia.