A Prediction Model of Gestational Diabetes Mellitus Based on OGTT in Early Pregnancy: A Prospective Cohort Study

Association and function analysis of genetic variants and the risk of gestational diabetes mellitus in a southern Chinese population

Background

Gestational diabetes mellitus (GDM) is a complex metabolic disease that has short-term and long-term adverse effects on mothers and infants. However, the specific pathogenic mechanism has not been elucidated.

Objective

The aim of this study was to confirm the associations between candidate genetic variants (rs4134819, rs720918, rs2034410, rs11109509, and rs12524768) and GDM risk and prediction in a southern Chinese population.

Methods

Candidate variants were genotyped in 538 GDM cases and 626 healthy controls. The odds ratio (OR) and its corresponding 95% confidence interval (CI) were calculated to assess the associations between genotypes and GDM risk. Then, the false-positive report probability (FPRP) analysis was adopted to confirm the significant associations, and bioinformatics tools were used to explore the potential biological function of studied variants. Finally, risk factors of genetic variants and clinical indicators identified by logistics regression were used to construct a nomogram model for GDM prediction.

Results

It was shown that the XAB2 gene rs4134819 was significantly associated with GDM susceptibility (CT vs. CC: adjusted OR = 1.38, 95% CI: 1.01-1.87, p = 0.044; CT/TT vs. CC: crude OR = 1.42, 95% CI: 1.08-1.86, p = 0.013). Functional analysis suggested that rs4134819 can alter the specific transcription factors (CPE bind and GATE-1) binding to the promoter of the XAB2 gene, regulating the transcription of XAB2. The nomogram established with factors such as age, FPG, HbA1c, 1hPG, 2hPG, TG, and rs4134819 showed a good discriminated and calibrated ability with an area under the curve (AUC) = 0.931 and a Hosmer-Lemeshow test p-value > 0.05.

Conclusion

The variant rs4134819 can significantly alter the susceptibility of the Chinese population to GDM possibly by regulating the transcription of functional genes. The nomogram prediction model constructed with genetic variants and clinical factors can help distinguish high-risk GDM individuals.

Advancement in predictive biomarkers for gestational diabetes mellitus diagnosis and related outcomes: a scoping review

OBJECTIVE

Gestational diabetes mellitus (GDM) is a metabolic disorder associated with adverse maternal and neonatal outcomes. While GDM is diagnosed by oral glucose tolerance testing between 24-28 weeks, earlier prediction of risk of developing GDM via circulating biomarkers has the potential to risk-stratify women and implement targeted risk reduction before adverse obstetric outcomes. This scoping review aims to collate biomarkers associated with GDM development, associated perinatal outcome and medication requirement in GDM.

DESIGN

The Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for scoping reviews was used to guide the study.

DATA SOURCES

This review searched for articles on PubMed, Embase, Scopus, Cochrane Central Register of Controlled Trials, the Cumulative Index to Nursing and Allied Health Literature and the Web of Science from January 2013 to February 2023.

ELIGIBILITY CRITERIA

The eligibility criteria included analytical observational studies published in English, focusing on pregnant women with maternal plasma or serum biomarkers collected between 6 and 24 weeks of gestation. Studies were excluded if they evaluated drug effects, non-GDM diabetes types or involved twin pregnancies, microbiota, genetic analyses or non-English publications.

DATA EXTRACTION AND SYNTHESIS

Two independent reviewers extracted data. One reviewer extracted data from papers included in the scoping review using Covidence. From the 8837 retrieved records, 137 studies were included.

RESULTS

A total of 278 biomarkers with significant changes in individuals with GDM compared with controls were identified. The univariate predictive biomarkers exhibited insufficient clinical sensitivity and specificity for predicting GDM, perinatal outcomes, and the necessity of medication. Multivariable models combining maternal risk factors with biomarkers provided more accurate detection but required validation for use in clinical settings.

CONCLUSION

This review recommends further research integrating novel omics technology for building accurate models for predicting GDM, perinatal outcome, and the necessity of medication while considering the optimal testing time.

A prediction model for gestational diabetes mellitus based on steroid hormonal changes in early and mid-down syndrome screening: A multicenter longitudinal study

BACKGROUND

Steroid hormones (SH) during pregnancy are associated with the development of gestational diabetes mellitus (GDM). Early and mid-Down syndrome screening is used to assess the risk of Down syndrome in the fetus. It is unclear whether changes in SH during this period can be used as an early predictor of GDM.

METHODS

This study was a multicenter, longitudinal cohort study. GDM is diagnosed by an oral glucose tolerance test (OGTT) between 24 and 28 weeks of gestation. We measured SH levels at early and mid-Down syndrome screening, respectively. Based on the SH changes, logistic regression analysis was used to construct a prediction model for GDM. Finally, evaluated the model's predictive performance by creating a receiver operating characteristic curve (ROC) and performing external validation.

RESULTS

This study enrolled 193 pregnant women (discovery cohort, n = 157; validation cohort, n = 36). SH changes occur dynamically after pregnancy. At early Down syndrome screening, only cortisol (F) (p < 0.05, 95 % CI 4780.95-46083.68) was elevated in GDM. At mid-Down syndrome screening, free testosterone (FT) (p < 0.01, 95 % CI 0.10-0.55) and estradiol (E2) (p < 0.05, 95 % CI 203.55-1784.78) were also significantly elevated. There were significant differences in the rates of change in E2 (Fold change (FC) = 1.3425, p = 0.0072), albumin (ALB) (FC=1.5759, p = 0.0117), and dihydrotestosterone (DHT) (FC=-2.1234, p = 0.0165) between GDM and no-GDM. Stepwise logistic regression analysis resulted in the best predictive model, including six variables (Δweight, ΔF, Δcortisone (E), ΔE2, Δprogesterone (P), ΔDHT). The area under the curve for this model was 0.791, and for the external validation cohort, it was 0.799.

CONCLUSIONS

A GDM prediction model can be constructed using SH measures during early and mid-Down syndrome screening.

Investigation of serum Metrnl levels in pregnant women with gestational diabetes mellitus: a prospective non-interventional cohort study

OBJECTIVE

The objective of this study was to investigate serum Metrnl levels in pregnant women with gestational diabetes mellitus and compare them with pregnant women without gestational diabetes mellitus.

METHODS

The gestational diabetes mellitus group consisted of 87 pregnant women diagnosed with gestational diabetes mellitus, and the control group consisted of 93 healthy pregnant women without gestational diabetes mellitus. Serum Metrnl levels were determined by the enzyme-linked immunosorbent assay method.

RESULTS

The two groups were similar in terms of demographic features. The median serum Metrnl level was found to be 1.16 ng/mL in the gestational diabetes mellitus group, while it was determined as 2.2 ng/mL in the control group (p=0.001). The two groups were divided into two subgroups based on participants' body mass index, normal weight and overweight. The lowest median Metrnl level was detected in the normal weight gestational diabetes mellitus group, followed by the overweight gestational diabetes mellitus group, normal weight control group, and overweight control group (1.1, 1.2, 2, and 2.4 ng/mL, respectively). Receiver operating curve analysis was performed to determine the value of the serum Metrnl level in terms of predicting gestational diabetes mellitus. The area under the curve analysis of serum Metrnl for gestational diabetes mellitus estimation was 0.768 (p=0.000, 95%CI 0.698-0.839). The optimal cutoff value for serum Metrnl level was determined as 1.53 ng/mL with 69% sensitivity and 70% specificity.

CONCLUSION

Serum Metrnl levels in pregnant women with gestational diabetes mellitus were found to be significantly lower than in pregnant women without gestational diabetes mellitus. The mechanisms underlying the decrease in serum Metrnl levels in gestational diabetes mellitus remain unclear for now, and future studies will reveal the role of Metrnl in the pathophysiology of gestational diabetes mellitus.

Longitudinal observation of tRNA-derived fragments profiles in gestational diabetes mellitus and its diagnostic value

BACKGROUND

Gestational Diabetes Mellitus (GDM) poses significant risks to maternal and fetal health. Current diagnostic methods based on glucose tolerance tests have limitations for early detection. tRNA-derived small RNAs (tsRNAs) have emerged as potential molecular regulators in various diseases, including metabolic disorders. However, the diagnostic value of tsRNAs in plasma for early GDM or postpartum remains unclear.

METHODS

This longitudinal study profiled the expression of tsRNAs across different gestational stages and postpartum in women with GDM (n = 40) and healthy control gestational women (HCs, n = 40). High-throughput small RNA sequencing identified candidate tsRNAs, which were then validated and correlated with clinical biochemical markers such as fasting blood glucose (FBG), HOMA-IR, and GHbA1c.

RESULTS

tRF-1:32-Val-AAC-1-M6, tRF-1:31-Glu-CTC-1-M2, and tRF-1:30-Gly-CCC-1-M4 were consistently upregulated in the GDM group compared to HCs during the second trimester (p < 0.05). Only tRF-1:31-Glu-CTC-1-M2 was highly expressed during the first trimester, and tRF-1:30-Gly-CCC-1-M4 increased during postpartum. tRF-1:31-Glu-CTC-1-M2 showed a significant correlation with FBG levels in the first trimester (R = 0.317, p = 0.047). The expression of tRF-1:30-Gly-CCC-1-M4 was significantly correlated with HOMA-IR (r = 0.65, p < 0.001) and GHBA1c (r = 0.33, p = 0.037) during postpartum. A joint diagnostic model incorporating tsRNAs expression and clinical markers demonstrated enhanced predictive power for GDM (ROC AUC = 0.768).

CONCLUSION

Our results revealed distinct expression patterns of specific tsRNAs in GDM, showcasing their correlation with key metabolic parameters. This underscores their promising role as biomarkers for early prediction and diagnosis of GDM. The integration of tRFs into a composite biomarker panel holds the potential to improve clinical outcomes by enabling personalized risk assessment and targeted interventions.

The role of machine learning algorithms in detection of gestational diabetes; a narrative review of current evidence

Gestational Diabetes Mellitus (GDM) poses significant health risks to mothers and infants. Early prediction and effective management are crucial to improving outcomes. Machine learning techniques have emerged as powerful tools for GDM prediction. This review compiles and analyses the available studies to highlight key findings and trends in the application of machine learning for GDM prediction. A comprehensive search of relevant studies published between 2000 and September 2023 was conducted. Fourteen studies were selected based on their focus on machine learning for GDM prediction. These studies were subjected to rigorous analysis to identify common themes and trends. The review revealed several key themes. Models capable of predicting GDM risk during the early stages of pregnancy were identified from the studies reviewed. Several studies underscored the necessity of tailoring predictive models to specific populations and demographic groups. These findings highlighted the limitations of uniform guidelines for diverse populations. Moreover, studies emphasised the value of integrating clinical data into GDM prediction models. This integration improved the treatment and care delivery for individuals diagnosed with GDM. While different machine learning models showed promise, selecting and weighing variables remains complex. The reviewed studies offer valuable insights into the complexities and potential solutions in GDM prediction using machine learning. The pursuit of accurate, early prediction models, the consideration of diverse populations, clinical data, and emerging data sources underscore the commitment of researchers to improve healthcare outcomes for pregnant individuals at risk of GDM.

Development and validation of a clinical model to predict preconception risk of gestational diabetes mellitus in nulliparous women: A retrospective cohort study

OBJECTIVE

To develop and validate a model to predict the preconception risk of gestational diabetes mellitus (GDM) in nulliparous women.

METHODS

This was a retrospective cohort study. A total of 1565 women in early pregnancy who underwent preconception health examinations in the Women and Children's Hospital of Chongqing Medical University between January 2020 and June 2021 were invited to participate in a questionnaire survey. Logistic regression analysis was performed to determine the preconception risk factors for GDM. These factors were used to construct a model to predict GDM risk in nulliparous women. Then, the model was used to assess the preconception risk of GDM in 1060 nulliparous women.

RESULTS

Independent preconception risk factors for GDM included the following: age 35 years or greater, diastolic blood pressure 80 mm Hg or greater, fasting plasma glucose 5.1 mmol/L or greater, body mass index (BMI, calculated as weight in kilograms divided by the square of height in meters) 24 or greater, weight gain 10 kg or greater in the year before pregnancy, age of menarche 15 years or greater, three or more previous pregnancies, daily staple food intake 300 g or greater, fondness for sweets, and family history of diabetes. BMI less than 18.5, daily physical activity duration 1 h or greater, and high-intensity physical activity were protective factors. These factors were used to construct a model to predict GDM risk in nulliparous women, and the incidence of GDM significantly increased as the risk score increased. The area under the curve of the prediction model was 0.82 (95% confidence interval 0.80-0.85).

CONCLUSION

The preconception GDM risk prediction model demonstrated good predictive efficacy and can be used to identify populations at high risk of GDM before pregnancy, which provides the possibility for preconception intervention.