Association of Common Genetic Risk Variants With Gestational Diabetes Mellitus and Their Role in GDM Prediction

A risk prediction model of gestational diabetes mellitus based on traditional and genetic factors

BACKGROUND

Gestational diabetes mellitus (GDM) is a prevalent pregnancy complication during pregnancy. We aimed to evaluate a risk prediction model of GDM based on traditional and genetic factors.

METHODS

A total of 2744 eligible pregnant women were included. Face-to-face questionnaire surveys were conducted to gather general data. Serum test results were collected from the laboratory information system. Independent risk factors for GDM were identified using univariate and multivariate logistic regression analyses. A GDM risk prediction model was constructed and evaluated with the Hosmer-Lemeshow goodness-of-fit test, goodness-of-fit calibration plot, receiver operating characteristic curve and area under the curve.

RESULTS

Among traditional factors, age ≥30 years, family history, GDM history, impaired glucose tolerance history, systolic blood pressure ≥116.22 mmHg, diastolic blood pressure ≥74.52 mmHg, fasting plasma glucose ≥5.0 mmol/L, 1-hour postprandial blood glucose ≥8.8 mmol/L, 2-h postprandial blood glucose ≥7.9 mmol/L, total cholesterol ≥4.50 mmol/L, low-density lipoprotein ≥2.09 mmol/L and insulin ≥11.5 mIU/L were independent risk factors for GDM. Among genetic factors, 11 single nucleotide polymorphisms (SNPs) (rs2779116, rs5215, rs11605924, rs7072268, rs7172432, rs10811661, rs2191349, rs10830963, rs174550, rs13266634 and rs11071657) were identified as potential predictors of the risk of postpartum DM among women with GDM history, collectively accounting for 3.6% of the genetic risk.

CONCLUSIONS

Both genetic and traditional factors contribute to the risk of GDM in women, operating through diverse mechanisms. Strengthening the risk prediction of SNPs for postpartum DM among women with GDM history is crucial for maternal and child health protection.

Integrating polygenic risk scores in the prediction of gestational diabetes risk in China

Background

Polygenic risk scores (PRS) serve as valuable tools for connecting initial genetic discoveries with clinical applications in disease risk estimation. However, limited studies have explored the association between PRS and gestational diabetes mellitus (GDM), particularly in predicting GDM risk among Chinese populations.

Aim

To evaluate the relationship between PRS and GDM and explore the predictive capability of PRS for GDM risk in a Chinese population.

Methods

A prospective cohort study was conducted, which included 283 GDM and 2,258 non-GDM cases based on demographic information on pregnancies. GDM was diagnosed using the oral glucose tolerance test (OGTT) at 24-28 weeks. The strength of the association between PRS and GDM odds was assessed employing odds ratios (ORs) with 95% confidence intervals (CIs) derived from logistic regression. Receiver operating characteristic curves, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were employed to evaluate the improvement in prediction achieved by the new model.

Results

Women who developed GDM exhibited significantly higher PRS compared to control individuals (OR = 2.01, 95% CI = 1.33-3.07). The PRS value remained positively associated with fasting plasma glucose (FPG), 1-hour post-glucose load (1-h OGTT), and 2-hour post-glucose load (2-h OGTT) (all p < 0.05). The incorporation of PRS led to a statistically significant improvement in the area under the curve (0.71, 95% CI: 0.66-0.75, p = 0.024) and improved discrimination and classification (IDI: 0.007, 95% CI: 0.003-0.012, p < 0.001; NRI: 0.258, 95% CI: 0.135-0.382, p < 0.001).

Conclusions

This study highlights the increased odds of GDM associated with higher PRS values and modest improvements in predictive capability for GDM.

Functional genetic variants and susceptibility and prediction of gestational diabetes mellitus

The aetiological mechanism of gestational diabetes mellitus (GDM) has still not been fully understood. The aim of this study was to explore the associations between functional genetic variants screened from a genome-wide association study (GWAS) and GDM risk among 554 GDM patients and 641 healthy controls in China. Functional analysis of single nucleotide polymorphisms (SNPs) positively associated with GDM was further performed. Univariate regression and multivariate logistic regression analyses were used to screen clinical risk factors, and a predictive nomogram model was established. After adjusting for age and prepregnancy BMI, rs9283638 was significantly associated with GDM susceptibility (P < 0.05). Moreover, an obvious interaction between rs9283638 and clinical variables was detected (Pinteraction < 0.05). Functional analysis confirmed that rs9283638 can regulate not only target gene transcription factor binding, but it also regulates the mRNA levels of SAMD7 (P < 0.05). The nomogram model constructed with the factors of age, FPG, 1hPG, 2hPG, HbA1c, TG and rs9283638 revealed an area under the ROC curve of 0.920 (95% CI 0.902-0.939). Decision curve analysis (DCA) suggested that the model had greater net clinical benefit. Conclusively, genetic variants can alter women's susceptibility to GDM by affecting the transcription of target genes. The predictive nomogram model constructed based on genetic and clinical variables can effectively distinguish individuals with different GDM risk factors.

Associations between Diabetes Mellitus and Selected Cancers

Cancer is one of the major causes of mortality and is the second leading cause of death. Diabetes mellitus is a serious and growing problem worldwide, and its prevalence continues to grow; it is the 12th leading cause of death. An association between diabetes mellitus and cancer has been suggested for more than 100 years. Diabetes is a common disease diagnosed among patients with cancer, and evidence indicates that approximately 8-18% of patients with cancer have diabetes, with investigations suggesting an association between diabetes and some particular cancers, increasing the risk for developing cancers such as pancreatic, liver, colon, breast, stomach, and a few others. Breast and colorectal cancers have increased from 20% to 30% and there is a 97% increased risk of intrahepatic cholangiocarcinoma or endometrial cancer. On the other hand, a number of cancers and cancer therapies increase the risk of diabetes mellitus. Complications due to diabetes in patients with cancer may influence the choice of cancer therapy. Unfortunately, the mechanisms of the associations between diabetes mellitus and cancer are still unknown. The aim of this review is to summarize the association of diabetes mellitus with selected cancers and update the evidence on the underlying mechanisms of this association.

Comprehensive data on the relationship between KCNJ11 polymorphisms and gestational diabetes mellitus predisposition: a meta-analysis

Purpose

The genetic aspect of gestational diabetes mellitus (GDM) is influenced by multiple causal genetic variants, each with different effect sizes. The KCNJ11 gene is particularly noteworthy as a potential contributor to the risk of GDM due to its role in regulating glucose-induced insulin secretion. To evaluate the association between KCNJ11 polymorphisms and GDM, a comprehensive meta-analysis was conducted to review the existing literature and quantitatively assess the correlation.

Methods

A thorough search was performed on the PubMed, EMBASE, Scopus, and CNKI databases until December 25, 2023, using precise terms and keywords related to Gestational Diabetes, KCNJ11 gene, and polymorphism. Odds ratios and 95% confidence intervals were used to evaluate the relationships. The statistical analysis was conducted using Comprehensive Meta-Analysis software, and the Cochrane risk of bias assessment tool was used to determine bias presence.

Results

The meta-analysis comprised 9 studies with 3108 GDM cases and 5374 controls for the rs5219 polymorphism, and 3 studies with 1209 GDM cases and 1438 controls for the rs5210 polymorphism. The pooled data indicated a noteworthy link between the rs5219 polymorphism and GDM globally and among various ethnic groups, notably in Caucasian and Asian populations. However, no substantial association was observed between the rs5210 polymorphism and GDM.

Conclusions

Pooled data showed a correlation between the KCNJ11 rs5219 polymorphism and GDM susceptibility, but no association was found for the rs5210 polymorphism. Future research with larger sample sizes and more diverse populations is needed to improve result generalizability.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-024-01428-0.

The associations of IGF2, IGF2R and IGF2BP2 gene polymorphisms with gestational diabetes mellitus: A case-control study

OBJECTIVE

To investigate the associations of Insulin-like growth factor-II (IGF2) gene, Insulin-like growth factor-II receptor (IGF2R) gene and Insulin-like growth factor-II binding protein 2 (IGF2BP2) gene polymorphisms with the susceptibility to gestational diabetes mellitus (GDM) in Chinese population.

METHODS

A total of 1703 pregnant women (835 GDM and 868 Non-GDM) were recruited in this case-control study. All participants underwent prenatal 75 g oral glucose tolerance test (OGTT) examinations during 24-28 gestational weeks at the Maternal and Child Health Hospital of Hubei Province from January 15, 2018 to March 31, 2019. Genotyping of candidate SNPs (IGF2 rs680, IGF2R rs416572, IGF2BP2 rs4402960, rs1470579, rs1374910, rs11705701, rs6777038, rs16860234, rs7651090) was performed on Sequenom MassARRAY platform. Logistic regression analysis was conducted to investigate the associations between candidate SNPs and risk of GDM. In addition, multifactor dimensionality reduction (MDR) method was applied to explore the effects of gene-gene interactions on GDM risk.

RESULTS

There were significant distribution differences between GDM group and non-GDM group in age, pre-pregnancy BMI, education level and family history of diabetes (P < 0.05). After adjusted for age, pre-pregnancy BMI, education level and family history of diabetes, there were no significant associations of the candidate SNPs polymorphisms and GDM risk (P > 0.05). Furthermore, there were no gene-gene interactions on the GDM risk among the candidate SNPs (P > 0.05). However, the fasting blood glucose (FBG) levels of rs6777038 CT carriers were significantly lower than TT carriers (4.69±0.69 vs. 5.03±1.57 mmol/L, P < 0.01), and the OGTT-2h levels of rs6777038 CC and CT genotype carriers were significantly lower than TT genotype carriers (8.10±1.91 and 8.08±1.87 vs. 8.99±2.90 mmol/L, P < 0.01).

CONCLUSIONS

IGF2 rs680, IGF2R rs416572, IGF2BP2 rs4402960, rs1470579, rs11705701, rs6777038, rs16860234, rs7651090 polymorphisms were not significantly associated with GDM risk in Wuhan, China. Further lager multicenter researches are needed to confirm these results.

Genetic and inflammatory factors underlying gestational diabetes mellitus: a review

Gestational diabetes mellitus (GDM) poses a significant global health concern, impacting both maternal and fetal well-being. Early detection and treatment are imperative to mitigate adverse outcomes during pregnancy. This review delves into the pivotal role of insulin function and the influence of genetic variants, including SLC30A8, CDKAL1, TCF7L2, IRS1, and GCK, in GDM development. These genetic variations affect beta-cell function and insulin activity in crucial tissues, such as muscle, disrupting glucose regulation during pregnancy. We propose a hypothesis that this variation may disrupt zinc transport, consequently impairing insulin production and secretion, thereby contributing to GDM onset. Furthermore, we discussed the involvement of inflammatory pathways, such as TNF-alpha and IL-6, in predisposing individuals to GDM. Genetic modulation of these pathways may exacerbate glucose metabolism dysregulation observed in GDM patients. We also discussed how GDM affects cardiovascular disease (CVD) through a direct correlation between pregnancy and cardiometabolic function, increasing atherosclerosis, decreased vascular function, dyslipidemia, and hypertension in women with GDM history. However, further research is imperative to unravel the intricate interplay between inflammatory pathways, genetics, and GDM. This understanding is pivotal for devising targeted gene therapies and pharmacological interventions to rectify genetic variations in SLC30A8, CDKAL1, TCF7L2, IRS1, GCK, and other pertinent genes. Ultimately, this review offers insights into the pathophysiological mechanisms of GDM, providing a foundation for developing strategies to mitigate its impact.

Association between insulin receptor substrate 1 gene polymorphism rs1801278 and gestational diabetes mellitus: an updated meta- analysis

OBJECTIVES

we performed this meta- analysis to investigate the impact of insulin receptor substrate 1 (IRS1) gene rs1801278 on susceptibility to gestational diabetes mellitus (GDM).

METHODS

The pooled odds ratio (OR) and 95% confidence interval (95% CI) were calculated, and p value is used to determine statistical significance. Sensitivity analysis was performed under three models (dominant, recessive and allele model), and the pooled ORs and 95%CI were calculated. Funnel plots and Begger's regression test were employed to test the publication bias.

RESULTS

The meta-analysis included 4777 participants (2116 cases and 2661 controls). The IRS1 rs1801278 (C/T) were not significant associated with GDM risk under the dominant and allele models, OR (95%CI) = 1.22 (0.88-1.70) and 1.24 (0.91-1.68), respectively (both p values were more than 0.05). But we also found the IRS1 rs1801278 (C/T) were significant associated with GDM risk under the recessive model, OR (95%CI) = 0.37 (0.16-0.86), p = 0.030. Our results showed that none of the studies affected the quality of the pooled OR. We also found no significant publication bias existed in this meta study for three genetic models, PTT + CT vs. CC = 0.445; PCC+CT vs. TT= 0.095; PC vs. T = 0.697.

CONCLUSION

this meta-analysis indicated that IRS1 rs1801278 (C/T) was associated with the GDM risk under the recessive model but was not associated with the GDM risk under dominant and allele models.

Correlation between TCF7L2 and CAPN10 gene polymorphisms and gestational diabetes mellitus in different geographical regions: a meta-analysis

BACKGROUND

The association between TCF7L2 and CAPN10 gene polymorphisms and gestational diabetes mellitus (GDM) has been explored in diverse populations across different geographical regions. Yet, most of these studies have been confined to a limited number of loci, resulting in inconsistent findings. In this study, we conducted a comprehensive review of published literature to identify studies examining the relationship between TCF7L2 and CAPN10 gene polymorphisms and the incidence of GDM in various populations. We specifically focused on five loci that were extensively reported in a large number of publications and performed a meta-analysis.

METHODS

We prioritized the selection of SNPs with well-documented correlations established in existing literature on GDM. We searched eight Chinese and English databases: Cochrane, Elton B. Stephens. Company (EBSCO), Embase, Scopus, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang, and China Science and Technology Journal Database and retrieved all relevant articles published between the inception of the database and July 2022. The Newcastle Ottawa Scale (NOS) was used to evaluate the selected articles, and the odds ratio (OR) was used as the combined effect size index to determine the association between genotypes, alleles, and GDM using different genetic models. Heterogeneity between the studies was quantified and the I2 value calculated. Due to large heterogeneities between different ethnic groups, subgroup analysis was used to explore the correlation between genetic polymorphisms and the incidence of GDM in the different populations. The stability of the results was assessed using sensitivity analysis. Begg's and Egger's tests were used to assess publication bias.

RESULTS

A total of 39 articles reporting data on 8,795 cases and 16,290 controls were included in the analysis. The frequency of the rs7901695 genotype was statistically significant between cases and controls in the European population (OR = 0.72, 95% CI: 0.65-0.86) and the American population (OR = 0.61, 95% CI: 0.48-0.77). The frequencies of rs12255372, rs7901695, rs290487, and rs2975760 alleles were also considerably different between the cases and controls in the populations analyzed.

CONCLUSIONS

rs7903146, rs12255372, rs7901695, rs290487, and rs2975760 were associated with the incidence of GDM in different populations.

Associations between a polygenic risk score and the risk of gestational diabetes mellitus in a Chinese population: a case-control study

Our objective was to construct a polygenic risk score (PRS) and assess its utility and effectiveness in predicting the risk of gestational diabetes mellitus (GDM) in a Chinese population. We performed a case-control study involving 638 patients with GDM and 1,062 healthy controls. Genotyping was conducted utilizing a genome-wide association study (GWAS), and a PRS was constructed. We identified 12 susceptibility loci that exhibited significant associations with the risk of GDM at a p-value threshold of ≤5.0 × 10-8, of which four loci were newly discovered. A higher PRS was associated with an increased risk of GDM (OR: 1.44; 95% CI: 1.03, 2.01 for the highest quartile compared to the lowest quartile). The PRS demonstrated a clear linear relationship with the fasting plasma glucose (FPG), 1-hour postprandial glucose (1hPG), and 2-hour postprandial glucose (2hPG) levels. The maximally adjusted β coefficients and their corresponding 95% CIs were 0.181 (0.041, 0.320) for FPG, 0.225 (0.103, 0.346) for 1hPG, and 0.172 (0.036, 0.307) for 2hPG. Among the genetic variants examined, TCF7L2 rs7903146 displayed the strongest association with GDM risk (logOR = 0.18, p = 2.37 × 10-19), followed by ADAMTSL1 rs10963767 (logOR = 0.14, p = 3.58 × 10-15). The areas under the curve (AUCs) was significantly increased from 0.703 (0.678, 0.728) in the traditional risk factor model to 0.765 (0.741, 0.788) by including PRS. These findings indicate that pregnant women with a higher PRS could potentially derive considerable advantages from the implementation of a feasible PRS-based GDM screening program aimed at delivering precision prevention strategies within Chinese populations.

GCKR and ADIPOQ gene polymorphisms in women with gestational diabetes mellitus

AIMS

To investigate the associations of GCKR and ADIPOQ variants with the risk of gestational diabetes mellitus (GDM) in Chinese women.

METHODS

GCKR rs1260326, ADIPOQ rs266729, and rs1501299 were selected and genotyped in 519 GDM patients and 498 controls. Candidate SNPs were genotyped using multiplex polymerase chain reaction (PCR) combined with next-generation sequencing methods, and the association of these SNPs with GDM was analyzed.

RESULTS

We found that GCKR rs1260326 was significantly associated with an increased risk of GDM in the allele model, the codominant model (CC vs. TT), the dominant model, the recessive model, and the genotypic model distributions (p = 0.0029, p = 0.0022, p = 0.0402, p = 0.0038, and p = 0.0028, respectively). The rs1260326 polymorphism was shown to be associated with 1 h-OGTT level and gravidity in GDM patients (CC vs. TT: p = 0.0475 and p = 0.0220, respectively). Diastolic blood pressure (DBP) was significantly higher in the GDM patients with the rs266729 GG genotype compared to those with the CC or CG genotype (p = 0.0444 and p = 0.0339, respectively). The DBP of the GDM patients with the rs1501299 GT genotype was lower than that of those with the GG genotype (p = 0.0197). There was a weak linkage disequilibrium value between the GCKR and ADIPOQ SNPs.

CONCLUSIONS

The genes GCKR and ADIPOQ may be involved in the pathophysiology of GDM.

A Simplified Screening Model to Predict the Risk of Gestational Diabetes Mellitus in Pregnant Chinese Women

INTRODUCTION

This study aimed to develop a simplified screening model to identify pregnant Chinese women at risk of gestational diabetes mellitus (GDM) in the first trimester.

METHODS

This prospective study included 1289 pregnant women in their first trimester (6-12 weeks of gestation) with clinical parameters and laboratory data. Logistic regression was performed to extract coefficients and select predictors. The performance of the prediction model was assessed in terms of discrimination and calibration. Internal validation was performed through bootstrapping (1000 random samples).

RESULTS

The prevalence of GDM in our study cohort was 21.1%. Maternal age, prepregnancy body mass index (BMI), a family history of diabetes, fasting blood glucose levels, the alanine transaminase to aspartate aminotransferase ratio (ALT/AST), and the triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C) were selected for inclusion in the prediction model. The Hosmer-Lemeshow goodness-of-fit test showed good consistency between prediction and actual observation, and bootstrapping indicated good internal performance. The area under the receiver operating characteristic curve (ROC-AUC) of the multivariate logistic regression model and the simplified clinical screening model was 0.825 (95% confidence interval [CI] 0.797-0.853, P < 0.001) and 0.784 (95% CI 0.750-0.818, P < 0.001), respectively. The performance of our prediction model was superior to that of three other published models.

CONCLUSION

We developed a simplified clinical screening model for predicting the risk of GDM in pregnant Chinese women. The model provides a feasible and convenient protocol to identify women at high risk of GDM in early pregnancy. Further validations are needed to evaluate the performance of the model in other populations.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT03246295.

Prediction of gestational diabetes mellitus in Asian women using machine learning algorithms

This study developed a machine learning algorithm to predict gestational diabetes mellitus (GDM) using retrospective data from 34,387 pregnancies in multi-centers of South Korea. Variables were collected at baseline, E0 (until 10 weeks' gestation), E1 (11-13 weeks' gestation) and M1 (14-24 weeks' gestation). The data set was randomly divided into training and test sets (7:3 ratio) to compare the performances of light gradient boosting machine (LGBM) and extreme gradient boosting (XGBoost) algorithms, with a full set of variables (original). A prediction model with the whole cohort achieved area under the receiver operating characteristics curve (AUC) and area under the precision-recall curve (AUPR) values of 0.711 and 0.246 at baseline, 0.720 and 0.256 at E0, 0.721 and 0.262 at E1, and 0.804 and 0.442 at M1, respectively. Then comparison of three models with different variable sets were performed: [a] variables from clinical guidelines; [b] selected variables from Shapley additive explanations (SHAP) values; and [c] Boruta algorithms. Based on model [c] with the least variables and similar or better performance than the other models, simple questionnaires were developed. The combined use of maternal factors and laboratory data could effectively predict individual risk of GDM using a machine learning model.

CYP2E1 C-1054T and 96-bp I/D genetic variations and risk of gestational diabetes mellitus in chinese women: a case-control study

BACKGROUND

Cytochrome P450 2E1 (CYP2E1) plays a key role in the metabolism of xenobiotic and endogenous low-molecular-weight compounds. This study aimed to determine if the genetic variations of 96-bp insertion/deletion (I/D) and C-1054T (rs2031920) in CYP2E1 were associated with the risk of gestational diabetes mellitus (GDM).

METHODS

CYP2E1 polymorphisms were genotyped in a case-control study of 1,134 women with uncomplicated pregnancies and 723 women with GDM. The effects of genotype on the clinical, metabolic, and oxidative stress indices were assessed.

RESULTS

The CYP2E1 C-1054T variant was associated with an increased risk of GDM based on the genotype, recessive, dominant, and allele genetic models (P < 0.05). The TT + CT genotype remained a significant predictive factor for GDM risk after correcting for maternal age and pre-pregnancy body mass index (OR = 1.277, 95% CI: 1.042-1.563, P = 0.018). Moreover, fasting insulin concentrations and homeostatic model assessment of insulin resistance were significantly higher in GDM patients carrying the T allele than in those with the CC genotype (P < 0.05). Furthermore, the combined genotype II + ID/TT + CT of the 96-bp I/D and C-1054T polymorphisms further increased the risk of GDM when the combined genotype DD/CC was set as the reference category (OR = 1.676, 95% CI: 1.182-2.376, P = 0.004).

CONCLUSIONS

The T allele of the C-1054T polymorphism and its combination with the I allele of the 96-bp I/D variation in CYP2E1 are associated with an increased risk of GDM in the Chinese population. The - 1054T allele may be associated with more serious insulin resistance in patients.

An early prediction model for gestational diabetes mellitus based on metabolomic biomarkers

BACKGROUND

Gestational diabetes mellitus (GDM) represents the main metabolic alteration during pregnancy. The available methods for diagnosing GDM identify women when the disease is established, and pancreatic beta-cell insufficiency has occurred.The present study aimed to generate an early prediction model (under 18 weeks of gestation) to identify those women who will later be diagnosed with GDM.

METHODS

A cohort of 75 pregnant women was followed during gestation, of which 62 underwent normal term pregnancy and 13 were diagnosed with GDM. Targeted metabolomics was used to select serum biomarkers with predictive power to identify women who will later be diagnosed with GDM.

RESULTS

Candidate metabolites were selected to generate an early identification model employing a criterion used when performing Random Forest decision tree analysis. A model composed of two short-chain acylcarnitines was generated: isovalerylcarnitine (C5) and tiglylcarnitine (C5:1). An analysis by ROC curves was performed to determine the classification performance of the acylcarnitines identified in the study, obtaining an area under the curve (AUC) of 0.934 (0.873-0.995, 95% CI). The model correctly classified all cases with GDM, while it misclassified ten controls as in the GDM group. An analysis was also carried out to establish the concentrations of the acylcarnitines for the identification of the GDM group, obtaining concentrations of C5 in a range of 0.015-0.25 μmol/L and of C5:1 with a range of 0.015-0.19 μmol/L.

CONCLUSION

Early pregnancy maternal metabolites can be used to screen and identify pregnant women who will later develop GDM. Regardless of their gestational body mass index, lipid metabolism is impaired even in the early stages of pregnancy in women who develop GDM.

MIDO GDM: an innovative artificial intelligence-based prediction model for the development of gestational diabetes in Mexican women

Given the barriers to early detection of gestational diabetes mellitus (GDM), this study aimed to develop an artificial intelligence (AI)-based prediction model for GDM in pregnant Mexican women. Data were retrieved from 1709 pregnant women who participated in the multicenter prospective cohort study 'Cuido mi embarazo'. A machine-learning-driven method was used to select the best predictive variables for GDM risk: age, family history of type 2 diabetes, previous diagnosis of hypertension, pregestational body mass index, gestational week, parity, birth weight of last child, and random capillary glucose. An artificial neural network approach was then used to build the model, which achieved a high level of accuracy (70.3%) and sensitivity (83.3%) for identifying women at high risk of developing GDM. This AI-based model will be applied throughout Mexico to improve the timing and quality of GDM interventions. Given the ease of obtaining the model variables, this model is expected to be clinically strategic, allowing prioritization of preventative treatment and promising a paradigm shift in prevention and primary healthcare during pregnancy. This AI model uses variables that are easily collected to identify pregnant women at risk of developing GDM with a high level of accuracy and precision.

Saudi Community-Based Screening Study on Genetic Variants in β-Cell Dysfunction and Its Role in Women with Gestational Diabetes Mellitus

BACKGROUND

Diabetes (hyperglycemia) is defined as a multifactorial metabolic disorder in which insulin resistance and defects in pancreatic β-cell dysfunction are two major pathophysiologic abnormalities that underpin towards gestational diabetes mellitus (GDM). TCF7L2, KCNQ1, and KCNJ11 genes are connected to the mechanism of β-cell dysfunction. The purpose of this study was to investigate the genes associated with β-cell dysfunction and their genetic roles in the rs7903146, rs2237892, and rs5219 variants in Saudi women diagnosed with type 2 diabetes mellitus and GDM.

MATERIALS AND METHODS

In this case-control study, 100 women with GDM and 100 healthy volunteers (non-GDM) were recruited. Genotyping was performed using polymerase chain reaction (PCR), followed by restriction fragment length analysis. Validation was performed using Sanger sequencing. Statistical analyses were performed using multiple software packages.

RESULTS

Clinical studies showed a β-cell dysfunction positive association in women with GDM when compared to non-GDM women (p < 0.05). Both rs7903146 (CT vs. CC: OR-2.12 [95%CI: 1.13-3.96]; p = 0.01 & T vs. C: (OR-2.03 [95%CI: 1.32-3.11]; p = 0.001) and rs5219 SNPs (AG vs. AA: OR-3.37 [95%CI: 1.63-6.95]; p = 0.0006 & G vs. A: OR-3.03 [95%CI: 1.66-5.52]; p = 0.0001) showed a positive association with genotype and allele frequencies in women with GDM. ANOVA analysis confirmed that weight (p = 0.02), BMI (p = 0.01), and PPBG (p = 0.003) were associated with rs7903146 and BMI (p = 0.03) was associated with rs2237892 SNPs.

CONCLUSIONS

This study confirms that the SNPs rs7903146 (TCF7L2) and rs5219 (KCNJ11) are strongly associated with GDM in the Saudi population. Future studies should address the limitations of this study.

Mouse Models of Gestational Diabetes Mellitus and Its Subtypes: Recent Insights and Pitfalls

Gestational diabetes mellitus (GDM) is currently the most common complication of pregnancy and is defined as a glucose intolerance disorder with recognition during pregnancy. GDM is considered a uniform group of patients in conventional guidelines. In recent years, evidence of the disease's heterogeneity has led to a growing understanding of the value of dividing patients into different subpopulations. Furthermore, in view of the increasing incidence of hyperglycemia outside pregnancy, it is likely that many cases diagnosed as GDM are in fact patients with undiagnosed pre-pregnancy impaired glucose tolerance (IGT). Experimental models contribute significantly to the understanding of the pathogenesis of GDM and numerous animal models have been described in the literature. The aim of this review is to provide an overview of the existing mouse models of GDM, in particular those that have been obtained by genetic manipulation. However, these commonly used models have certain limitations in the study of the pathogenesis of GDM and cannot fully describe the heterogeneous spectrum of this polygenic disease. The polygenic New Zealand obese (NZO) mouse is introduced as a recently emerged model of a subpopulation of GDM. Although this strain lacks conventional GDM, it exhibits prediabetes and an IGT both preconceptionally and during gestation. In addition, it should be emphasized that the choice of an appropriate control strain is of great importance in metabolic studies. The commonly used control strain C57BL/6N, which exhibits IGT during gestation, is discussed in this review as a potential model of GDM.

Myeloperoxidase G-463A and CYBA C242T genetic variants in gestational diabetes mellitus

Oxidative stress plays an important role in the pathophysiology of gestational diabetes mellitus (GDM). We investigated the relationship between NADPH oxidase p22phox subunit (CYBA) C242T (rs4673) and myeloperoxidase (MPO) G-463A (rs2333227) genetic variants and GDM in 719 patients with GDM and 1205 control women. Clinical, metabolic, and oxidative stress parameters were analyzed. We found that frequencies of the A allele (15.6% vs 12.3%) and GA + AA genotype (28.5% vs 23.2%) of the MPO G-463A variation were significantly higher in patients with GDM than in the control women (OR = 1.318, 95% CI: 1.068-1.625, P = 0.010 for the dominant model; OR = 1.999, 95% CI: 1.040-3.843, P = 0.034 for the recessive model; OR = 1.320, 95% CI: 1.095-1.591, P = 0.004 for the allele model). Genotype GA + AA remained a significant predictor of GDM in a logistic regression model including age and BMI at delivery (OR = 1.282, 95% CI: 1.037‒1.583, P = 0.021). Furthermore, the ‒463A allele was associated with higher TG and the 242T allele was related to higher pre-pregnancy BMI and oxidative stress index in all subjects (P < 0.05). The 242T allele was also associated with higher homeostatic model assessment of insulin resistance but lower serum total antioxidant capacity in patients with GDM (P < 0.05). We conclude that the MPO G-463A, but not the CYBA C242T, genetic variation is associated with an increased risk of GDM in Chinese women. These two genetic polymorphisms may be linked to obesity, dyslipidemia, insulin resistance, and oxidative stress.

Multi-omics and machine learning for the prevention and management of female reproductive health

Females typically carry most of the burden of reproduction in mammals. In humans, this burden is exacerbated further, as the evolutionary advantage of a large and complex human brain came at a great cost of women's reproductive health. Pregnancy thus became a highly demanding phase in a woman's life cycle both physically and emotionally and therefore needs monitoring to assure an optimal outcome. Moreover, an increasing societal trend towards reproductive complications partly due to the increasing maternal age and global obesity pandemic demands closer monitoring of female reproductive health. This review first provides an overview of female reproductive biology and further explores utilization of large-scale data analysis and -omics techniques (genomics, transcriptomics, proteomics, and metabolomics) towards diagnosis, prognosis, and management of female reproductive disorders. In addition, we explore machine learning approaches for predictive models towards prevention and management. Furthermore, mobile apps and wearable devices provide a promise of continuous monitoring of health. These complementary technologies can be combined towards monitoring female (fertility-related) health and detection of any early complications to provide intervention solutions. In summary, technological advances (e.g., omics and wearables) have shown a promise towards diagnosis, prognosis, and management of female reproductive disorders. Systematic integration of these technologies is needed urgently in female reproductive healthcare to be further implemented in the national healthcare systems for societal benefit.

Association of CDKAL1 gene polymorphism (rs10946398) with gestational diabetes mellitus in Pakistani population

BACKGROUND

CDK5 regulatory subunit associated protein 1 like 1 (CDKAL1) encodes a tRNA modifying enzyme involved in the proper protein translation and regulation of insulin production encoded by the CDKL gene. Sequence variations in the CDKAL1 gene lead to the misreading of the Lys codon in proinsulin, resulting in decreased glucose-stimulated proinsulin production. Various polymorphic sequence variants of the CDKAL1 gene such as rs7754840, rs7756992, rs9465871, and rs10946398 are reported to be associated with type 2 diabetes mellitus and gestational diabetes mellitus (GDM) incidence. One of these single nucleotide polymorphisms i.e., rs10946398 has been reported to impact the risk of GDM and its outcomes in pregnant women of different ethnicities i.e., Egypt, Chinese, Korean, Indian, Arab, and Malaysian. Numerous findings have shown that rs10946398 overturns the regulation of CDKAL1 expression, resulting in decreased insulin production and elevated risk of GDM. However, there is no data regarding rs10946398 genotype association with GDM incidence in our population.

METHODOLOGY

In this study, 47 GDM patients and 40 age-matched controls were genotyped for rs10946398 CDKAL1 variant using Tetra primer Amplification Refractory Mutation System Polymerase Chain Reaction (Tetra ARMS-PCR).

RESULTS

Analysis of the results showed the significant association of the C allele of CDKAL1 SNP rs10946398 (χ² = 0.02 p = 0.001) with the risk of GDM development. Conclusively, the results support the role of SNP i.e., rs10946398 of CDKAL1 gene in GDM development in Pakistani female patients. However, future large-scale studies are needed to functionally authenticate the role of variant genotypes in the disease pathogenesis and progression.

Aggregation of Genome-Wide Association Data from FinnGen and UK Biobank Replicates Multiple Risk Loci for Pregnancy Complications

Complications endangering mother or fetus affect around one in seven pregnant women. Investigation of the genetic susceptibility to such diseases is of high importance for better understanding of the disease biology as well as for prediction of individual risk. In this study, we collected and analyzed GWAS summary statistics from the FinnGen cohort and UK Biobank for 24 pregnancy complications. In FinnGen, we identified 11 loci associated with pregnancy hypertension, excessive vomiting, and gestational diabetes. When UK Biobank and FinnGen data were combined, we discovered six loci reaching genome-wide significance in the meta-analysis. These include rs35954793 in FGF5 (p=6.1×10-9), rs10882398 in PLCE1 (p=8.9×10-9), and rs167479 in RGL3 (p=5.2×10-9) for pregnancy hypertension, rs10830963 in MTNR1B (p=4.5×10-41) and rs36090025 in TCF7L2 (p=3.4×10-15) for gestational diabetes, and rs2963457 in the EBF1 locus (p=6.5×10-9) for preterm birth. In addition to the identified genome-wide associations, we also replicated 14 out of 40 previously reported GWAS markers for pregnancy complications, including four more preeclampsia-related variants. Finally, annotation of the GWAS results identified a causal relationship between gene expression in the cervix and gestational hypertension, as well as both known and previously uncharacterized genetic correlations between pregnancy complications and other traits. These results suggest new prospects for research into the etiology and pathogenesis of pregnancy complications, as well as early risk prediction for these disorders.

Genetic Risk Factors and Gene-Lifestyle Interactions in Gestational Diabetes

Paralleling the increasing trends of maternal obesity, gestational diabetes (GDM) has become a global health challenge with significant public health repercussions. In addition to short-term adverse outcomes, such as hypertensive pregnancy disorders and fetal macrosomia, in the long term, GDM results in excess cardiometabolic morbidity in both the mother and child. Recent data suggest that women with GDM are characterized by notable phenotypic and genotypic heterogeneity and that frequencies of adverse obstetric and perinatal outcomes are different between physiologic GDM subtypes. However, as of yet, GDM treatment protocols do not differentiate between these subtypes. Mapping the genetic architecture of GDM, as well as accurate phenotypic and genotypic definitions of GDM, could potentially help in the individualization of GDM treatment and assessment of long-term prognoses. In this narrative review, we outline recent studies exploring genetic risk factors of GDM and later type 2 diabetes (T2D) in women with prior GDM. Further, we discuss the current evidence on gene-lifestyle interactions in the development of these diseases. In addition, we point out specific research gaps that still need to be addressed to better understand the complex genetic and metabolic crosstalk within the mother-placenta-fetus triad that contributes to hyperglycemia in pregnancy.

Genetic variants for prediction of gestational diabetes mellitus and modulation of susceptibility by a nutritional intervention based on a Mediterranean diet

Hypothesis

Gestational diabetes mellitus (GDM) entails a complex underlying pathogenesis, with a specific genetic background and the effect of environmental factors. This study examines the link between a set of single nucleotide polymorphisms (SNPs) associated with diabetes and the development of GDM in pregnant women with different ethnicities, and evaluates its potential modulation with a clinical intervention based on a Mediterranean diet.

Methods

2418 women from our hospital-based cohort of pregnant women screened for GDM from January 2015 to November 2017 (the San Carlos Cohort, randomized controlled trial for the prevention of GDM ISRCTN84389045 and real-world study ISRCTN13389832) were assessed for evaluation. Diagnosis of GDM was made according to the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria. Genotyping was performed by IPLEX MassARRAY PCR using the Agena platform (Agena Bioscience, SanDiego, CA). 110 SNPs were selected for analysis based on selected literature references. Statistical analyses regarding patients' characteristics were performed in SPSS (Chicago, IL, USA) version 24.0. Genetic association tests were performed using PLINK v.1.9 and 2.0 software. Bioinformatics analysis, with mapping of SNPs was performed using STRING, version 11.5.

Results

Quality controls retrieved a total 98 SNPs and 1573 samples, 272 (17.3%) with GDM and 1301 (82.7%) without GDM. 1104 (70.2%) were Caucasian (CAU) and 469 (29.8%) Hispanic (HIS). 415 (26.4%) were from the control group (CG), 418 (26.6%) from the nutritional intervention group (IG) and 740 (47.0%) from the real-world group (RW). 40 SNPs (40.8%) presented some kind of significant association with GDM in at least one of the genetic tests considered. The nutritional intervention presented a significant association with GDM, regardless of the variant considered. In CAU, variants rs4402960, rs7651090, IGF2BP2; rs1387153, rs10830963, MTNR1B; rs17676067, GLP2R; rs1371614, DPYSL5; rs5215, KCNJ1; and rs2293941, PDX1 were significantly associated with an increased risk of GDM, whilst rs780094, GCKR; rs7607980, COBLL1; rs3746750, SLC17A9; rs6048205, FOXA2; rs7041847, rs7034200, rs10814916, GLIS3; rs3783347, WARS; and rs1805087, MTR, were significantly associated with a decreased risk of GDM, In HIS, variants significantly associated with increased risk of GDM were rs9368222, CDKAL1; rs2302593, GIPR; rs10885122, ADRA2A; rs1387153, MTNR1B; rs737288, BACE2; rs1371614, DPYSL5; and rs2293941, PDX1, whilst rs340874, PROX1; rs2943634, IRS1; rs7041847, GLIS3; rs780094, GCKR; rs563694, G6PC2; and rs11605924, CRY2 were significantly associated with decreased risk for GDM.

Conclusions

We identify a core set of SNPs in their association with diabetes and GDM in a large cohort of patients from two main ethnicities from a single center. Identification of these genetic variants, even in the setting of a nutritional intervention, deems useful to design preventive and therapeutic strategies.

Interactions of CDKAL1 rs7747752 polymorphism and serum levels of L-carnitine and choline are related to increased risk of gestational diabetes mellitus

BACKGROUND

Interactions between genetic, metabolic, and environmental factors lead to gestational diabetes mellitus (GDM). We aimed to examine interactive effects of cyclin-dependent kinase 5 regulatory subunit-associated protein1-like 1(CDKAL1) rs7747752 polymorphism with low serum levels of L-carnitine, choline, and betaine for GDM.

METHODS

A nested case-control study of 207 GDM women and their one-to-one, age-matched controls was organized from a prospective cohort of pregnant women in Tianjin, China. Conditional logistic regressions were used to test associations between CDKAL1 rs7747752 and serum levels of L-carnitine, choline, and betaine, and the risk of GDM. Additive interactions were performed to examine interactive effects of rs7747752 and low serum levels of L-carnitine, choline, and betaine on the risk of GDM.

RESULTS

The CDKAL1 rs7747752 G > C was associated with GDM in additive, dominant, and recessive model (P <0.05). The rs7747752 CC genotype enhanced the OR of L-carnitine ≤ vs. > 150 nmol/mL for GDM from 6.14 (2.61-14.4) to 19.6 (5.65-68.1) and the OR of choline ≤ vs. > 110 nmol/mL from 2.37 (1.07-5.28) to 12.1 (3.22-45.6), with significant additive interactions. Similarly, CG genotype also enhanced the OR of L-carnitine ≤ vs. > 150 nmol/mL for GDM from 4.70 (2.01-11.0) to 11.4 (3.98-32.9), with a significant additive interaction. However, the additive interaction between rs7747752 and betaine ≤ 200 nmol/mL on the risk of GDM was not significant.

CONCLUSIONS

The CC or CG genotype carriers in rs7747752 of CDKAL1 who have a low serum level of L-carnitine or choline are at a particular high risk of GDM. Randomized controlled trials are warranted to test the effect of supplement of L-carnitine or choline on the risk of GDM in the high-risk group.

Incidence and risk factors of gestational diabetes mellitus in Goba town, Southeast Ethiopia: a prospective cohort study

OBJECTIVE

Gestational diabetes mellitus (GDM) is becoming a public health concern in low/middle-income countries, and is known to cause severe morbidity and mortality for mothers and newborns. However, evidence reported for the incidence and risk factors of GDM is scant in Ethiopia. We aimed to assess the incidence of, and risk factors for, GDM in Goba town, Southeast Ethiopia.

DESIGN

Prospective cohort study.

SETTING

Goba town, Southeast Ethiopia.

PARTICIPANTS

Four hundred eighty pregnant women on antenatal care follow-up from 30 April to 30 September 2021.

PRIMARY AND SECONDARY OUTCOMES

Incidence and risk factors of GDM using fasting capillary blood glucose. Log-binomial model was used to identify the risk factors of GDM. Adjusted relative risk (aRR), along with 95% CIs, were calculated to estimate the strength of associations.

RESULTS

The cumulative incidence rate of GDM in this study was 15.7% (95% CI: 12.3% to 19.2%). Being unemployed (aRR=2.73; 95% CI: 1.36 to 5.47), having a family history of diabetes mellitus (DM) (3.01; 2.09 to 4.35), low physical activity (2.43; 1.11 to 5.32), inadequate dietary diversity (1.48; 1.29 to 1.92), anaemia (2.51; 1.32 to 3.54) and antenatal depression (4.95; 3.35 to 7.31) were significantly associated with GDM.

CONCLUSION

The cumulative incidence of GDM was relatively high among the study participants. Having antenatal depression symptoms, low physical activity, inadequate dietary diversity, being unemployed, anaemia and a family history of DM were significant risk factors for GDM.

Different types of diabetes mellitus and risk of thyroid cancer: A meta-analysis of cohort studies

Objective

Sex-specific thyroid cancer risk exists in patients diagnosed with diabetes mellitus (DM). However, thyroid cancer risk in different types of DM is still unclear. This meta-analysis aims to identify the real correlation between different types of DM and thyroid cancer risk in both sexes.

Methods

Studies were identified by an electronic search of PubMed, EMBASE, and Cochrane Library on 16 January 2022. A random-effects model was used to estimate the relative risks (RRs). The Cochran's Q and I2 statistics were computed to detect heterogeneity between studies.

Results

In comparison with non-DM counterparts, patients with DM had a 1.32-fold higher risk of thyroid cancer (95% CI, 1.22-1.44) with 1.26-fold (95% CI, 1.12-1.41) in men and 1.36-fold (95% CI, 1.22-1.52) in women, respectively. Subgroup analysis by the type of DM showed that the RR of thyroid cancer in patients with type 2 diabetes was 1.34 (95% CI, 1.17-1.53) in the study population with 1.32 (95% CI, 1.12-1.54) in men and 1.37 (95% CI, 1.12-1.68) in women, respectively; the RR of thyroid cancer was 1.30 (95% CI, 1.17-1.43) in patients with gestational diabetes; the risk of thyroid cancer in patients with type 1 diabetes was 1.51-fold in women but not in men. Although there were some heterogeneities, it did not affect the above results of this study.

Conclusion

This study indicates that, compared with non-DM individuals, patients with any type of DM have an elevated thyroid cancer risk. This positive correlation between type 2 diabetes and thyroid cancer risk exists in both men and women.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/, CRD42022304028.

Common single-nucleotide polymorphisms combined with a genetic risk score provide new insights regarding the etiology of gestational diabetes mellitus

AIMS

Few studies have constructed a genetic risk score (GRS) to predict the risk of gestaional diabetes mellitus (GDM). We tested the hypothesis that single-nucleotide polymorphisms (SNPs) confirmed for diabetes and obesity and the GRS are associated with GDM.

METHODS

We conducted a case-control study comprising 971 GDM cases and 1682 controls from the University of Hong Kong Shenzhen Hospital. A total of 1448 SNPs reported with type 2 diabetes (T2D), type 1 diabetes (T1D), and obesity were selected and the GRS based on SNPs associated with GDM was created.

RESULTS

We confirmed that rs10830963 (OR = 1.41,95% CI = 1.25, 1.59) in MTNR1B and rs2206734 (OR = 1.38, 95% CI = 1.22, 1.55) in CDKAL1 were strongly associated with the risk of GDM. Compared with participants with GRS based on T2D SNPs in the low tertile, the ORs of GDM across increasing GRS tertiles were 1.63 (95% CI 1.29, 2.06) and 2.72 (95% CI 2.18, 3.38) in the middle and high tertile, respectively. The positive associations between the GRS and the risk of GDM were also observed in GRS based on obesity/waist-to-hip ratio (WHR)/body mass index (BMI) SNPs. The resulting GRS for each allele increase was significantly associated with higher glycemic indices and lower HOMA-B values for GRS based on T2D SNPs, but not for GRS based on T1D SNPs and GRS based on obesity/WHR/BMI SNPs.

CONCLUSION

These findings indicate that GDM may share a common genetic background with T2D and obesity and that SNPs associated with insulin secretion defects have a vital role in the development of GDM.

Fasting serum fructose is associated with risk of gestational diabetes mellitus

Objective

To investigate the association of fasting serum fructose concentrations and the incidence of GDM.

Research design and methods

Five hundred twenty six pregnant women who attended the obstetric clinic of Xinhua Hospital, Chongming Branch were recruited prospectively from September 2019 to November 2020. Fasting serum fructose concentrations were measured by a validated liquid chromatography–tandem mass spectrometry method. GDM was diagnosed according to the criteria of the IADPSG. Independent sample t-test was used to compare the differences between groups. Multiple stepwise regression analysis was used to estimate the associations of serum fructose and other variables. Multivariate logistic regression models were adopted to evaluate the odds ratios (ORs) for GDM.

Results

Of the 526 pregnant women, 110 were diagnosed with GDM. Fasting fructose concentrations were increased significantly in GDM patients compared to those without GDM (1.30 ug/ml vs 1.16 ug/ml, p<0.001). Fasting fructose concentration was independently associated with GDM after adjusting the potential confounders, 1 ug/ml increase in fasting serum fructose level was associated with an 81.1% increased risk of GDM (1.811, [1.155-2.840]). Taking fructose <1.036 ug/ml as the reference, the OR for GDM was significantly higher in fructose ≥1.036 ug/ml group (OR, 1.669; 95% CI, 1.031–2.701) after all the potential confounders were adjusted.

Conclusions

Increased fasting serum fructose levels were independently associated with the incidence of GDM.

Association of glucokinase gene and glucokinase regulatory protein gene polymorphisms with gestational diabetes mellitus: A case-control study

OBJECTIVES

The aim of this study was to investigate the association of glucokinase (GCK) gene, glucokinase regulatory protein (GCKR) gene polymorphisms with the susceptibility to GDM in Chinese population.

RESEARCH DESIGN AND METHODS

This case-control study included 835 GDM patients and 870 non-diabetic pregnant women who had their prenatal examinations at 24-28 gestational weeks at the Maternal and Child Health Hospital of Hubei Province from January 15, 2018 to March 31, 2019. The nurses were trained to collect clinical information and blood samples. The candidate single nucleotide polymorphism (SNPs, GCK rs1799884, rs4607517, rs10278336, rs2268574, rs730497 and GCKR rs780094, rs1260326) were genotyped on Sequenom Massarray platform. Statistical analysis including independent sample t test, chi-square test, logistic regression and one-way ANOVA were performed to evaluate the differences in allele and genotype distributions and their correlations with the odds of GDM.

RESULTS

There were statistically significant differences in age, pre-gestational BMI, education level and family history of diabetes between case and control group (P < 0.05). After adjusting for these confounders, GCK rs1799884 was still significantly associated with GDM (P < 0.05), but there were no significant associations between rs4607517, rs10278336 and rs2268574, rs780094 and rs1260326 polymorphisms and GDM odds (P > 0.05). In addition, the pregnant women with rs4607517 TT genotype had the significantly higher fasting blood glucose level than CC genotype (P < 0.05).

CONCLUSION

GCK rs1799884 mutation is associated with higher GDM odds in Chinese population. Further larger studies are needed to explore the association between GCK and GCKR polymorphisms and GDM susceptibility.

Hyperglycemia First Detected in Pregnancy in South Africa: Facts, Gaps, and Opportunities

This review contextualizes hyperglycemia in pregnancy from a South-African perspective. It aims to create awareness of the importance of hyperglycemia in pregnancy in low-middle-income countries. We address unanswered questions to guide future research on sub-Saharan African women with hyperglycemia first detected in pregnancy (HFDP). South African women of childbearing age have the highest prevalence of obesity in sub-Saharan Africa. They are predisposed to Type 2 diabetes (T2DM), the leading cause of death in South African women. T2DM remains undiagnosed in many African countries, with two-thirds of people living with diabetes unaware. With the South African health policy's increased focus on improving antenatal care, women often gain access to screening for non-communicable diseases for the first time in pregnancy. While screening practices and diagnostic criteria for gestational diabetes mellitus (GDM) differ amongst geographical areas in South Africa (SA), hyperglycemia of varying degrees is often first detected in pregnancy. This is often erroneously ascribed to GDM, irrespective of the degree of hyperglycemia and not overt diabetes. T2DM and GDM convey a graded increased risk for the mother and fetus during and after pregnancy, with cardiometabolic risk accumulating across the lifespan. Resource limitations and high patient burden have hampered the opportunity to implement accessible preventative care in young women at increased risk of developing T2DM in the broader public health system in SA. All women with HFDP, including those with true GDM, should be followed and undergo glucose assessment postpartum. In SA, studies conducted early postpartum have noted persistent hyperglycemia in a third of women after GDM. Interpregnancy care is advantageous and may attain a favourable metabolic legacy in these young women, but the yield of return following delivery is suboptimal. We review the current best evidence regarding HFDP and contextualize the applicability in SA and other African or low-middle-income countries. The review identifies gaps and shares pragmatic solutions regarding clinical factors that may improve awareness, identification, diagnosis, and management of women with HFDP.

Association between pre-conceptional carbohydrate quality index and the incidence of gestational diabetes: the SUN cohort study

The aim of the study was to investigate the association between pre-gestational carbohydrate quality index (CQI) and the incidence of gestational diabetes mellitus (GDM). Data from the 'Seguimiento Universidad de Navarra' (SUN) cohort were used, which includes 3827 women who notified at least one pregnancy between December 1999 and December 2019. We used a validated semi-quantitative 136-item FFQ to evaluate dietary exposures at baseline and at 10-year follow-up. The CQI was defined by four criteria: glycaemic index, whole-grain/total-grain carbohydrate, dietary fibre intake and solid/total carbohydrate ratio. We fitted generalised estimating equations with repeated measurements of the CQI to assess its relationship with incident GDM. A total of 6869 pregnancies and 202 new cases of incident GDM were identified. The inverse association between the global quality of carbohydrate and the development of GDM was not statistically significant: OR the highest v. the lowest CQI category: 0·67, 95 % CI (0·40, 1·10), Pfor trend = 0·10. Participants at the highest CQI category and with daily carbohydrate amounts ≥50 % of total energy intake had the lowest incidence of GDM (OR = 0·29 (95 % CI (0·09, 0·89)) compared with those with the lowest quality (lowest CQI) and quantity (≤40 %). Further studies are needed to overcome the limitations of our study. Those studies should jointly consider the quality and the quantity of dietary carbohydrates, as the quality might be of importance, especially in women with a higher intake of carbohydrates.

Relationship between gestational body mass index change and the risk of gestational diabetes mellitus: a community-based retrospective study of 41,845 pregnant women

Background

Gestational diabetes mellitus (GDM) is associated with adverse health consequences for women and their offspring. It is associated with maternal body mass index (BMI) and may be associated with gestational weight gain (GWG). But due to the heterogeneity of diagnosis and treatment and the potential effect of GDM treatment on GWG, the association between the two has not been thoroughly clarified. Compared to body weight, BMI has the advantage that it considers height during the whole course of pregnancy. Understanding BMI changes during pregnancy may provide new evidence for the prevention of GDM.

Methods

This study investigated the BMI change of pregnant women based on a retrospective study covering all communities in Tianjin, China. According to the results of GDM screening at 24–28 weeks of gestation, pregnancies were divided into the GDM group and the non-GDM group. We compared gestational BMI change and GWG in the two groups from early pregnancy to GDM screening. GWG was evaluated according to the IOM guidelines. Logistic regression was applied to determine the significance of variables with GDM.

Results

A total of 41,845 pregnant women were included in the final analysis (GDM group, n = 4257 vs. non-GDM group, n = 37,588). BMI gain has no significant differences between the GDM and non-GDM groups at any early pregnancy BMI categories (each of 2 kg/m²), as well as weight gain (P > 0.05). Early pregnancy BMI was a risk factor for GDM (OR 1.131, 95% CI 1.122–1.139). And BMI gain was associated with a decreased risk of GDM in unadjusted univariate analysis (OR 0.895, 95% CI 0.869–0.922). After adjusting on early pregnancy BMI and other confounding factors, the effect of BMI gain was no longer significant (AOR 1.029, 95% CI 0.999–1.061), as well as weight gain (AOR 1.006, 95% CI 0.995–1.018) and GWG categories (insufficient: AOR 1.016, 95% CI 0.911–1.133; excessive: AOR 1.044, 95% CI 0.957–1.138).

Conclusions

BMI in early pregnancy was a risk factor for GDM, while BMI gain before GDM screening was not associated with the risk of GDM. Therefore, the optimal BMI in early pregnancy is the key to preventing GDM.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12884-022-04672-5.

Development and evaluation of an online questionnaire to identify women at high and low risk of developing gestational diabetes mellitus

BACKGROUND

Established risk factors for Gestational Diabetes Mellitus (GDM) include age, ethnicity, family history of diabetes and previous GDM. Additional significant influences have recently been demonstrated in the literature. The oral glucose tolerance test (OGTT) used for GDM diagnosis has sub-optimal sensitivity and specificity, thus often results in GDM misdiagnoses. Comprehensive screening of risk factors may allow more targeted monitoring and more accurate diagnoses, preventing the devastating consequences of untreated or misdiagnosed GDM. We aimed to develop a comprehensive online questionnaire of GDM risk factors and triangulate it with the OGTT and continuous glucose monitoring (CGM) parameters to better evaluate GDM risk and diagnosis.

METHODS

Pregnant women participating in two studies on the use of CGM for GDM were invited to complete the online questionnaire. A risk score, based on published literature, was calculated for each participant response and compared with the OGTT result. A total risk score (TRS) was then calculated as a normalised sum of all risk factors. Triangulation of OGTT, TRS and CGM score of variability (CGMSV) was analysed to expand evaluation of OGTT results.

RESULTS

Fifty one women completed the questionnaire; 29 were identified as 'high-risk' for GDM. High-risk ethnic background (p < 0.01), advanced age, a family diabetic history (p < 0.05) were associated with a positive OGTT result. The triangulation analysis (n = 45) revealed six (13%) probable misdiagnoses (both TRS and CGMSV discordant with OGTT), consisting of one probable false positive and five probable false negative by OGTT results.

CONCLUSIONS

This study identified pregnant women at high risk of developing GDM based on an extended evaluation of risk factors. Triangulation of TRS, OGTT and CGMSV suggested potential misdiagnoses of the OGTT. Future studies to explore the correlation between TRS, CGMSV and pregnancy outcomes as well as additional GDM pregnancy biomarkers and outcomes to efficiently evaluate OGTT results are needed.

miRNAs as Predictive Factors in Early Diagnosis of Gestational Diabetes Mellitus

Introduction

Circulating miRNAs are important mediators in epigenetic changes. These non-coding molecules regulate post-transcriptional gene expression by binding to mRNA. As a result, they influence the development of many diseases, such as gestational diabetes mellitus (GDM). Therefore, this study investigates the changes in the miRNA profile in GDM patients before hyperglycemia appears.

Materials and Methods

The study group consisted of 24 patients with GDM, and the control group was 24 normoglycemic pregnant women who were matched for body mass index (BMI), age, and gestational age. GDM was diagnosed with an oral glucose tolerance test between the 24th and 26th weeks of pregnancy. The study had a prospective design, and serum for analysis was obtained in the first trimester of pregnancy. Circulating miRNAs were measured using the NanoString quantitative assay platform. Validation with real time-polymerase chain reaction (RT-PCR) was performed on the same group of patients. Mann-Whitney U-test and Spearman correlation were done to assess the significance of the results.

Results

Among the 800 miRNAs, 221 miRNAs were not detected, and 439 were close to background noise. The remaining miRNAs were carefully investigated for their average counts, fold changes, p-values, and false discovery rate (FDR) scores. We selected four miRNAs for further validation: miR-16-5p, miR-142-3p, miR-144-3p, and miR-320e, which showed the most prominent changes between the studied groups. The validation showed up-regulation of miR-16-5p (p<0.0001), miR-142-3p (p=0.001), and miR-144-3p (p=0.003).

Conclusion

We present changes in miRNA profile in the serum of GDM women, which may indicate significance in the pathophysiology of GDM. These findings emphasize the role of miRNAs as a predictive factor that could potentially be useful in early diagnosis.

Interaction between rs10830962 polymorphism in MTNR1B and lifestyle intervention on maternal and neonatal outcomes: secondary analyses of the DALI lifestyle randomized controlled trial

BACKGROUND

Interactions between polymorphisms of the melatonin receptor 1B (MTNR1B) gene and lifestyle intervention for gestational diabetes have been described. Whether these are specific for physical activity or the healthy eating intervention is unknown.

OBJECTIVES

The aim was to assess the interaction between MTNR1B rs10830962 and rs10830963 polymorphisms and lifestyle interventions during pregnancy.

METHODS

Women with a BMI (in kg/m2) of ≥29 (n = 436) received counseling on healthy eating (HE), physical activity (PA), or both. The control group received usual care. This secondary analysis had a factorial design with comparison of HE compared with no HE and PA compared with no PA. Maternal outcomes at 24-28 wk were gestational weight gain (GWG), maternal fasting glucose, insulin, insulin resistance (HOMA-IR), disposition index, and development of GDM. Neonatal outcomes were cord blood leptin and C-peptide and estimated neonatal fat percentage. The interaction between receiving either the HE or PA intervention and genotypes of both rs10830962 and rs10830963 was assessed using multilevel regression analysis.

RESULTS

GDM risk was increased in women homozygous for the G allele of rs10830962 (OR: 2.60; 95% CI: 1.34, 5.06) or rs10830963 (OR: 2.83; 95% CI: 1.24, 6.47). Significant interactions between rs10830962 and interventions were found: in women homozygous for the G allele but not in the other genotypes, the PA intervention reduced maternal fasting insulin (β: -0.16; 95% CI: -0.33, 0.02; P = 0.08) and HOMA-IR (β: -0.17; 95% CI: -0.35, 0.01; P = 0.06), and reduced cord blood leptin (β: -0.84; 95% CI: -1.42, -0.25; P = 0.01) and C-peptide (β: -0.62; 95% CI: -1.07, -0.17; P = 0.01). In heterozygous women, the HE intervention had no effect, whereas in women homozygous for the C allele, HE intervention reduced GWG (β: -1.6 kg; 95% CI: -2.4, -0.8 kg). No interactions were found.

CONCLUSIONS

In women homozygous for the risk allele of MTNR1B rs10830962, GDM risk was increased and PA intervention might be more beneficial than HE intervention for reducing maternal insulin resistance, cord blood C-peptide, and cord blood leptin.

A 646C > G (rs41423247) polymorphism of the glucocorticoid receptor as a risk factor for hyperglycaemia diagnosed in pregnancy-data from an observational study

AIM

Hyperglycaemia diagnosed in pregnancy (HiP) is a serious and frequent complication of pregnancy, increasing the risk for adverse maternal and neonatal outcomes. Investigate whether allelic variations of the glucocorticoid receptor are related to an increased risk of HiP.

METHOD

The following polymorphisms of the glucocorticoid receptor (GR) were investigated in the cohort study of N = 197 pregnant women with HiP and N = 133 normoglycemic pregnant controls: 646C > G (rs41423247), N363S (rs6195), ER23/22EK (rs6190, rs6189).

RESULTS

A GG variant of the rs41423247 polymorphism was associated with a significantly higher risk for HiP: OR 1.94 (1.18; 3.18), p = 0.009. The relationship remained significant after controlling for maternal age and prepregnancy BMI: OR 3.09 (1.25; 7.64), p = 0.014.

CONCLUSIONS

The allelic GG variant of the 646C > G (rs41423247) polymorphism is associated with an increased risk for hyperglycaemia in pregnancy.

Genetic risk score to improve prediction and treatment in gestational diabetes mellitus

Diabetes mellitus is a chronic disease caused by the interaction of genetics and the environment that can lead to chronic damage to many organ systems. Genome-wide association studies have identified accumulating single-nucleotide polymorphisms related to type 2 diabetes mellitus and gestational diabetes mellitus. Genetic risk score (GRS) has been utilized to evaluate the incidence risk to improve prediction and optimize treatments. This article reviews the research progress in the use of the GRS in diabetes mellitus in recent years and discusses future prospects.

Genetic risk factors associated with gestational diabetes in a multi-ethnic population

AIMS

Genome-wide association studies have shown an increased risk of type-2-diabetes (T2DM) in patients who carry single nucleotide polymorphisms in several genes. We investigated whether the same gene loci confer a risk for gestational diabetes mellitus (GDM) in women from Hawaii, and in particular, Pacific Islander and Filipino populations.

METHODS

Blood was collected from 291 women with GDM and 734 matched non-diabetic controls (Pacific Islanders: 71 GDM, 197 non-diabetic controls; Filipinos: 162 GDM, 395 controls; Japanese: 58 GDM, 142 controls). Maternal DNA was used to genotype and show allele frequencies of 25 different SNPs mapped to 18 different loci.

RESULTS

After adjusting for age, BMI, parity and gravidity by multivariable logistic regression, several SNPs showed significant associations with GDM and were ethnicity specific. In particular, SNPs rs1113132 (EXT2), rs1111875 (HHEX), rs2237892 (KCNQ1), rs2237895 (KCNQ1), rs10830963 (MTNR1B) and rs13266634 (SLC30A8) showed significant associations with GDM in Filipinos. For Japanese, SNPs rs4402960 (IGFBP2) and rs2237892 (KCNQ1) were significantly associated with GDM. For Pacific Islanders, SNPs rs10830963 (MTNR1B) and rs13266634 (SLC30A8) showed significant associations with GDM. Individually, none of the SNPs showed a consistent association with GDM across all three investigated ethnicities.

CONCLUSION

Several SNPs associated with T2DM are found to confer increased risk for GDM in a multiethnic cohort in Hawaii.

ADCY5, CAPN10 and JAZF1 Gene Polymorphisms and Placental Expression in Women with Gestational Diabetes

Gestational diabetes mellitus (GDM) is carbohydrate intolerance that occurs during pregnancy. This disease may lead to various maternal and neonatal complications; therefore, early diagnosis is very important. Because of the similarity in pathogenesis of type 2 diabetes and GDM, the genetic variants associated with type 2 diabetes are commonly investigated in GDM. The aim of the present study was to examine the associations between the polymorphisms in the ADCY5 (rs11708067, rs2877716), CAPN10 (rs2975760, rs3792267), and JAZF1 (rs864745) genes and GDM as well as to determine the expression of these genes in the placenta. This study included 272 pregnant women with GDM and 348 pregnant women with normal glucose tolerance. The diagnosis of GDM was based on a 75 g oral glucose tolerance test (OGTT) at 24–28 weeks gestation, according to International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria. There were no statistically significant differences in the distribution of the ADCY5 gene (rs11708067, rs2877716) and CAPN10 gene (rs2975760, rs3792267) polymorphisms between pregnant women with normal carbohydrate tolerance and pregnant women with GDM. We have shown a lower frequency of JAZF1 gene rs864745 C allele carriers among women with GDM CC + CT vs. TT (OR = 0.60, 95% CI = 0.41–0.87, p = 0.006), and C vs. T (OR = 0.75, 95% CI = 0.60–0.95, p = 0.014). In addition, ADCY5 and JAZF1 gene expression was statistically significantly increased in the placentas of women with GDM compared with that of healthy women. The expression of the CAPN10 gene did not differ significantly between women with and without GDM. Our results indicate increased expression of JAZF1 and ADCY5 genes in the placentas of women with GDM as well as a protective effect of the C allele of the JAZF1 rs864745 gene polymorphism on the development of GDM in pregnant women.

Putrescine as a Novel Biomarker of Maternal Serum in First Trimester for the Prediction of Gestational Diabetes Mellitus: A Nested Case-Control Study

AIMS

Early identification of gestational diabetes mellitus (GDM) aims to reduce the risk of adverse maternal and perinatal outcomes. Currently, no acknowledged biomarker has proven clinically useful for the accurate prediction of GDM. In this study, we tested whether serum putrescine level changed in the first trimester and could improve the prediction of GDM.

METHODS

This study is a nested case-control study conducted in Beijing Obstetrics and Gynecology Hospital. We examined serum putrescine at 8-12 weeks pregnancy in 47 women with GDM and 47 age- and body mass index (BMI)-matched normoglycaemic women. Anthropometric, clinical and laboratory variables were obtained during the same period. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to assess the discrimination and calibration of the prediction models.

RESULTS

Serum putrescine in the first trimester was significantly higher in women who later developed GDM. When using putrescine alone to predict the risk of GDM, the AUC of the nomogram was 0.904 (sensitivity of 100% and specificity of 83%, 95% CI=0.832-0.976, P<0.001). When combined with traditional risk factors (prepregnant BMI and fasting blood glucose), the AUC was 0.951 (sensitivity of 89.4% and specificity of 91.5%, 95% CI=0.906-0.995, P<0.001).

CONCLUSION

This study revealed that GDM women had an elevated level of serum putrescine in the first trimester. Circulating putrescine may serve as a valuable predictive biomarker for GDM.