A risk-prediction model using parameters of maternal body composition to identify gestational diabetes mellitus in early pregnancy

Visceral Adipose Tissue Depth as a Novel Predictor for Gestational Diabetes Mellitus: A Comprehensive Meta-Analysis and Systematic Review

Background and Objectives: The escalating prevalence of gestational diabetes mellitus (GDM) and the limitations associated with utilizing body mass index (BMI) as a predictive measure underscore the imperative need for identifying an optimal early pregnancy predictor. Such a predictor not only mitigates the risk of GDM but also allows for timely implementation of interventions. Materials and Methods: This meta-analysis aimed to explore the association between visceral adipose tissue (VAT) depth and the risk of GDM. A thorough search of PubMed, Embase, and Web of Science databases was conducted up to 30 September 2023. The analysis employed a random-effects model to assess the relationship between VAT depth and the likelihood of GDM. Results: The inclusion criteria encompassed seven studies involving 1315 women, including 225 diagnosed with GDM. Significantly lower VAT depth was observed in the non-GDM group in comparison to the GDM group (Standardized Mean Difference [SMD]: 0.84; 95% Confidence Interval [CI]: 0.52-1.15; p < 0.001). Substantial statistical heterogeneity was noted among studies (I2 = 72.88%, p = 0.001). Through meticulous sensitivity and subgroup analyses, the source of heterogeneity was identified and thoroughly discussed. Subgroup analyses suggest that different GDM diagnostic criteria and VAT definitions all indicate higher VAT depth in GDM patients during early pregnancy. Conclusions: Our findings propose that, during the first trimester, GDM patients exhibit higher VAT depth compared to non-GDM women, highlighting VAT depth as a potential predictive factor for GDM in early pregnancy. This study contributes valuable evidence to the growing body of knowledge surrounding novel predictors for GDM, emphasizing the importance of early intervention strategies.

Enhancing gestational diabetes mellitus risk assessment and treatment through GDMPredictor: a machine learning approach

BACKGROUND

Gestational diabetes mellitus (GDM) is a serious health concern that affects pregnant women worldwide and can lead to adverse pregnancy outcomes. Early detection of high-risk individuals and the implementation of appropriate treatment can enhance these outcomes.

METHODS

We conducted a study on a cohort of 3467 pregnant women during their pregnancy, with a total of 5649 clinical and biochemical records collected. We utilized this dataset as our training dataset to develop a web server called GDMPredictor. The GDMPredictor utilizes advanced machine learning techniques to predict the risk of GDM in pregnant women. We also personalize treatment recommendations based on essential biochemical indicators, such as A1MG, BMG, CysC, CO2, TBA, FPG, and CREA. Our assessment of GDMPredictor's effectiveness involved training it on the dataset of 3467 pregnant women and measuring its ability to predict GDM risk using an AUC and auPRC.

RESULTS

GDMPredictor demonstrated an impressive level of precision by achieving an AUC score of 0.967. To tailor our treatment recommendations, we use the GDM risk level to identify higher risk candidates who require more intensive care. The GDMPredictor can accept biochemical indicators for predicting the risk of GDM at any period from 1 to 24 weeks, providing healthcare professionals with an intuitive interface to identify high-risk patients and give optimal treatment recommendations.

CONCLUSIONS

The GDMPredictor presents a valuable asset for clinical practice, with the potential to change the management of GDM in pregnant women. Its high accuracy and efficiency make it a reliable tool for doctors to improve patient outcomes. Early identification of high-risk individuals and tailored treatment can improve maternal and fetal health outcomes http://www.bioinfogenetics.info/GDM/ .

The Association of the Triglyceride and Muscle to Fat Ratio During Early Pregnancy with the Development of Gestational Diabetes Mellitus

Objective

This study explored the association between metabolic factors and body composition during the first trimester of gestational diabetes mellitus (GDM).

Methods

This prospective study recruited pregnant women in their first trimester. Clinical information and glucose and lipid measurements were collected, and body composition was assessed using multifrequency bioelectrical impedance analysis. GDM was diagnosed on the basis of an oral glucose tolerance test at 24-28 gestational week. Factors related to GDM were investigated using correlation, and risk ratios (RRs) and 95% CIs of potential risk factors with GDM were estimated using Poisson regression. The area under the receiver operating characteristic (ROC) curve was used to determine predictive effects.

Results

59/302 women (19.5%) developed GDM. Older (RR 1.076, 95% CI 1.005-1.152), higher body mass index (BMI) before pregnancy (pre-BMI) (RR 1.012, 95% CI 1.005-1.063), triglycerides (RR 4.052, 95% CI 1.641-6.741), and lower skeletal muscle mass (SMM) to fat mass (FM) ratio (SMM/FM) (RR 0.213, 95% CI 0.051-0890) in the first trimester, and family history of type 2 diabetes (RR 1.496, 95% CI 1.014-2.667) significantly associated with the risk of GDM, but neither fasting plasma glucose nor glycated albumin was associated with GDM. The combined multivariate prediction model achieved good discrimination with an AUC of 0.806 (95% CI 0.737-0.895, P<0.001). According to ROC curve, the cut-off values of TG and SMM/FM were 0.925 mmol/L and 1.305.

Conclusion

Reduced SMM/FM and elevated triglyceride (TG) levels in the first trimester are associated with GDM development, and should be screened in early pregnancy to identify high-risk subjects for GDM.

Clinical First-Trimester Prediction Models for Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis

BACKGROUND

Gestational diabetes mellitus (GDM) is a common pregnancy complication that negatively impacts the health of both the mother and child. Early prediction of the risk of GDM may permit prompt and effective interventions. This systematic review and meta-analysis aimed to summarize the study characteristics, methodological quality, and model performance of first-trimester prediction model studies for GDM.

METHODS

Five electronic databases, one clinical trial register, and gray literature were searched from the inception date to March 19, 2022. Studies developing or validating a first-trimester prediction model for GDM were included. Two reviewers independently extracted data according to an established checklist and assessed the risk of bias by the Prediction Model Risk of Bias Assessment Tool (PROBAST). We used a random-effects model to perform a quantitative meta-analysis of the predictive power of models that were externally validated at least three times.

RESULTS

We identified 43 model development studies, six model development and external validation studies, and five external validation-only studies. Body mass index, maternal age, and fasting plasma glucose were the most commonly included predictors across all models. Multiple estimates of performance measures were available for eight of the models. Summary estimates range from 0.68 to 0.78 (I² ranged from 0% to 97%).

CONCLUSION

Most studies were assessed as having a high overall risk of bias. Only eight prediction models for GDM have been externally validated at least three times. Future research needs to focus on updating and externally validating existing models.

An evaluation of adherence to folic acid supplementation in pregnant women during early gestation for the prevention of neural tube defects

OBJECTIVE

Neural tube defects (NTD) are potentially preventable by periconceptual folic acid supplementation. Women with obesity are at higher risk of NTD, therefore, are recommended a higher dose of 5 mg folic acid to mitigate this risk. The aim of this study was to evaluate maternal practice of folic acid supplementation amongst the antenatal population in relation to maternal obesity status.

DESIGN

Prospective observational study.

SETTING

Women ≤18 weeks' gestation at their first antenatal appointment attending University Maternity Hospital Limerick (Ireland) were recruited. Maternal height and weight were measured. Obesity was defined at a threshold of ≥30·0 kg/m² and ≥27·5 kg/m² when adjusting for ethnicity. A two-part questionnaire captured maternal characteristics and assessed supplementation compliance, commencement and dosage. Fisher's exact test for independence analysed differences in variables. A P value of <0·05 was considered significant.

PARTICIPANTS

A total of 328 women participated over a duration of 6 weeks.

RESULTS

Mean gestational age was 12·4 ± 1·4 weeks and mean BMI 26·7 kg/m² ± 5·2 kg/m². 23·8 % (n 78) were classified as obese. 96·5 % (n 315) were taking folic acid and 95·7 % (n 314) supplemented daily. 30·2 % (n 99) commenced supplementation 12 weeks prior to conception. Overall, 57·9 % (n 190) of women met folic acid supplementation dose requirements. 89·1 % (n 55) of women with obesity did not. Women with obesity were less likely to meet the higher folic acid supplementation dose requirements (P =< 0·001).

CONCLUSION

Folic acid supplementation practices within this cohort were suboptimal to prevent their risk of NTD. This study showed inadequate compliance of folic acid supplementation, and inadequate dosage for women with obesity. Increased patient education and awareness are needed within the antenatal period of pregnancy to bring folic acid supplementation practices in line with best practice guidelines.

Cardiovascular Disease-Associated MicroRNAs as Novel Biomarkers of First-Trimester Screening for Gestational Diabetes Mellitus in the Absence of Other Pregnancy-Related Complications

We assessed the diagnostic potential of cardiovascular disease-associated microRNAs for the early prediction of gestational diabetes mellitus (GDM) in singleton pregnancies of Caucasian descent in the absence of other pregnancy-related complications. Whole peripheral venous blood samples were collected within 10 to 13 weeks of gestation. This retrospective study involved all pregnancies diagnosed with only GDM (n = 121) and 80 normal term pregnancies selected with regard to equality of sample storage time. Gene expression of 29 microRNAs was assessed using real-time RT-PCR. Upregulation of 11 microRNAs (miR-1-3p, miR-20a-5p, miR-20b-5p, miR-23a-3p, miR-100-5p, miR-125b-5p, miR-126-3p, miR-181a-5p, miR-195-5p, miR-499a-5p, and miR-574-3p) was observed in pregnancies destinated to develop GDM. Combined screening of all 11 dysregulated microRNAs showed the highest accuracy for the early identification of pregnancies destinated to develop GDM. This screening identified 47.93% of GDM pregnancies at a 10.0% false positive rate (FPR). The predictive model for GDM based on aberrant microRNA expression profile was further improved via the implementation of clinical characteristics (maternal age and BMI at early stages of gestation and an infertility treatment by assisted reproductive technology). Following this, 69.17% of GDM pregnancies were identified at a 10.0% FPR. The effective prediction model specifically for severe GDM requiring administration of therapy involved using a combination of these three clinical characteristics and three microRNA biomarkers (miR-20a-5p, miR-20b-5p, and miR-195-5p). This model identified 78.95% of cases at a 10.0% FPR. The effective prediction model for GDM managed by diet only required the involvement of these three clinical characteristics and eight microRNA biomarkers (miR-1-3p, miR-20a-5p, miR-20b-5p, miR-100-5p, miR-125b-5p, miR-195-5p, miR-499a-5p, and miR-574-3p). With this, the model identified 50.50% of GDM pregnancies managed by diet only at a 10.0% FPR. When other clinical variables such as history of miscarriage, the presence of trombophilic gene mutations, positive first-trimester screening for preeclampsia and/or fetal growth restriction by the Fetal Medicine Foundation algorithm, and family history of diabetes mellitus in first-degree relatives were included in the GDM prediction model, the predictive power was further increased at a 10.0% FPR (72.50% GDM in total, 89.47% GDM requiring therapy, and 56.44% GDM managed by diet only). Cardiovascular disease-associated microRNAs represent promising early biomarkers to be implemented into routine first-trimester screening programs with a very good predictive potential for GDM.

Association of body composition in early pregnancy with gestational diabetes mellitus: A meta-analysis

INTRODUCTION

Body composition as dynamic indices constantly changes in pregnancy. The use of body composition indices in the early stages of pregnancy has recently been considered. Therefore, the current meta-analysis study was conducted to investigate the relationship between body composition in the early stages of pregnancy and gestational diabetes.

METHOD

Valid databases searched for papers published from 2010 to December 2021 were based on PRISMA guideline. Newcastle Ottawa was used to assess the quality of the studies. For all analyses, STATA 14.0 was used. Mean difference (MD) of anthropometric indices was calculated between the GDM and Non-GDM groups. Pooled MD was estimated by "Metan" command, and heterogeneity was defined using Cochran's Q test of heterogeneity, and I 2 index was used to quantify heterogeneity.

RESULTS

Finally, 29 studies with a sample size of 56438 met the criteria for entering the meta-analysis. Pooled MD of neck circumference, hip circumference, waist hip ratio, and visceral adipose tissue depth were, respectively, 1.00 cm (95% CI: 0.79 to 1.20) [N = 5; I^2: 0%; p: 0.709], 7.79 cm (95% CI: 2.27 to 13.31) [N = 5; I2: 84.3%; P<0.001], 0.03 (95% CI: 0.02 to 0.04) [N = 9; I2: 89.2%; P<0.001], and 7.74 cm (95% CI: 0.11 to 1.36) [N = 4; I^2: 95.8%; P<0.001].

CONCLUSION

Increased neck circumference, waist circumference, hip circumference, arm circumference, waist to hip ratio, visceral fat depth, subcutaneous fat depth, and short stature increased the possibility of developing gestational diabetes. These indices can accurately, cost-effectively, and affordably assess the occurrence of gestational diabetes, thus preventing many consequences with early detection of gestational diabetes.

Relationship of anthropometric measurements with glycated hemoglobin and 1-h blood glucose after 50 g glucose challenge test in pregnant women: A longitudinal cohort study in Southern Thailand

AIMS

To assess correlations of anthropometric measurements with glycated hemoglobin (HbA1c) and 1-h blood glucose after a 50 g glucose challenge test during the first and late second trimesters and explore their relationships of anthropometric measurements with neonatal birth weight.

METHODS

A longitudinal study was conducted among pregnant Thai women with gestational age ≤14 weeks. Anthropometric measurements, using body mass index, body compositions, and circumferences, and skinfold thickness, were measured at four-time points: ≤14, 18-22, 24-28, and 30-34 weeks of gestation. HbA1c and 1-h blood glucose were examined at ≤14 and 24-28 weeks. Neonatal birth weight was recorded.

RESULTS

Of 312 women, HbA1c was more correlated with anthropometric measurements during pregnancy than 1-h blood glucose. At 24-28 weeks, women with high/very high body fat percentage were more likely to have higher HbA1c. Women with high subscapular skinfold thickness were more likely to have higher 1-h blood glucose at ≤14 and 24-28 weeks. High hip circumference significantly increased neonatal birth weights.

CONCLUSION

Anthropometric measurements were longitudinally correlated with HbA1c and 1-h blood glucose, higher in the late second than first trimesters, as well as neonatal birth weight. The mechanisms to explain the relationship of different anthropometric measurements are required to be further studied.