Development and validation of an early pregnancy risk score for the prediction of gestational diabetes mellitus in Chinese pregnant women

Nonlinear association between alanine aminotransferase to high-density lipoprotein cholesterol ratio and risk of gestational diabetes mellitus

Previous studies have indicated a potential association between the alanine aminotransferase to high-density lipoprotein cholesterol (ALT/HDL-C) ratio and the risk of Type 2 Diabetes Mellitus, but its relation with gestational diabetes mellitus (GDM) remains uncertain. This study aims to investigate the correlation between the ALT/HDL-C ratio in early pregnancy and the risk of GDM. This study is a secondary analysis based on an open-source cohort study. A total of 590 single pregnant women attending two hospitals in Korea up to 14 weeks gestation were included between November 2014 and July 2016. Logistic regression analysis, subgroup analysis, and smooth curve fitting were employed to explore the association between the ALT/HDL-C ratio and GDM risk. The predictive capability of the ALT/HDL-C ratio for GDM was assessed using ROC curve analysis. The average age of participants was 32.06 ± 3.80 years, with a GDM incidence rate of 6.27%. Multifactorial logistic regression analysis revealed that the serum ALT/HDL-C ratio is an independent influencing factor for GDM (OR = 1.08, 95% CI: 1.02-1.16). Furthermore, a non-linear relationship between the ALT/HDL-C ratio and GDM risk was observed, with a turning point at 5.51. The effect size (OR) on the left and right sides of the turning point were 0.75 (95% CI: 0.37-1.59) and 1.55 (95% CI: 1.18-2.00), respectively. Additionally, when combined with age, pre-pregnancy body mass index, parity, and insulin resistance index in a prediction model for GDM, the ALT/HDL-C ratio demonstrated improved sensitivity of prediction by reaching up to 67.6%, specificity of prediction by reaching up to 87.3%, and an area under curve value of 0.819 (95%CI: 0.743-0.894). In early pregnancy, the serum ALT/HDL-C ratio shows a positive correlation with maternal risk in a nonlinear manner. The combination of ALT/HDL-C ratio with maternal characteristics and metabolic indicators provides good predictive value for GDM. This study may facilitate optimization of GDM prevention in pregnant women and enable timely and effective intervention to enhance their prognosis.

Identifying Predictor Variables for a Composite Risk Prediction Tool for Gestational Diabetes and Hypertensive Disorders of Pregnancy: A Modified Delphi Study

A composite cardiometabolic risk prediction tool will support the systematic identification of women at increased cardiometabolic risk during pregnancy to enable early screening and intervention. This study aims to identify and select predictor variables for a composite risk prediction tool for cardiometabolic risk (gestational diabetes mellitus and/or hypertensive disorders of pregnancy) for use in the first trimester. A two-round modified online Delphi study was undertaken. A prior systematic literature review generated fifteen potential predictor variables for inclusion in the tool. Multidisciplinary experts (n = 31) rated the clinical importance of variables in an online survey and nominated additional variables for consideration (Round One). An online meeting (n = 14) was held to deliberate the importance, feasibility and acceptability of collecting variables in early pregnancy. Consensus was reached in a second online survey (Round Two). Overall, 24 variables were considered; 9 were eliminated, and 15 were selected for inclusion in the tool. The final 15 predictor variables related to maternal demographics (age, ethnicity/race), pre-pregnancy history (body mass index, height, history of chronic kidney disease/polycystic ovarian syndrome, family history of diabetes, pre-existing diabetes/hypertension), obstetric history (parity, history of macrosomia/pre-eclampsia/gestational diabetes mellitus), biochemical measures (blood glucose levels), hemodynamic measures (systolic blood pressure). Variables will inform the development of a cardiometabolic risk prediction tool in subsequent research. Evidence-based, clinically relevant and routinely collected variables were selected for a composite cardiometabolic risk prediction tool for early pregnancy.

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.

Passive smoking and risk of gestational diabetes mellitus: A systematic review and meta-analysis

INTRODUCTION

Pregestational smoking increases the risk of gestational diabetes mellitus (GDM) and is a common health problem during pregnancy, with its incidence on the rise worldwide, especially in China. This study is a meta-analysis of passive smoking as a risk factor associated with GDM.

METHODS

Two independent reviewers searched passive smoking and the risk of GDM in PubMed, Medline, Web of Knowledge, Science Direct, China National Knowledge Internet (CNKI) and Wanfang databases (up to May 2023). The authors extracted the study data independently and used the Newcastle-Ottawa scale (NOS) to evaluate the quality of the included articles. A meta-analysis was conducted using a random effects model depending on the size of the heterogeneity. Begg's and Egger's tests were performed to assess publication bias.

RESULTS

The overall relative risk for GDM caused by passive smoking was 1.47 (95% CI: 1.31-1.64), with moderate heterogeneity between studies (I2=41.7%, p=0.079). Subgroup and sensitivity analyses were stable, and no evidence of publication bias was found.

CONCLUSIONS

Passive smoking is a risk factor for GDM, even in those who are not active smokers. To eliminate the effects of other confounding factors, larger prospective cohort studies are required to clarify the relationship between passive smoking and the occurrence of GDM.

Development and validation of a screening questionnaire for early identification of pregnant women at risk for excessive gestational weight gain

BACKGROUND

Excessive weight gain during pregnancy is associated with adverse health outcomes for mother and child. Intervention strategies to prevent excessive gestational weight gain (GWG) should consider women's individual risk profile, however, no tool exists for identifying women at risk at an early stage. The aim of the present study was to develop and validate a screening questionnaire based on early risk factors for excessive GWG.

METHODS

The cohort from the German "Gesund leben in der Schwangerschaft"/ "healthy living in pregnancy" (GeliS) trial was used to derive a risk score predicting excessive GWG. Sociodemographics, anthropometrics, smoking behaviour and mental health status were collected before week 12^th of gestation. GWG was calculated using the last and the first weight measured during routine antenatal care. The data were randomly split into development and validation datasets with an 80:20 ratio. Using the development dataset, a multivariate logistic regression model with stepwise backward elimination was performed to identify salient risk factors associated with excessive GWG. The β coefficients of the variables were translated into a score. The risk score was validated by an internal cross-validation and externally with data from the FeLIPO study (GeliS pilot study). The area under the receiver operating characteristic curve (AUC ROC) was used to estimate the predictive power of the score.

RESULTS

1790 women were included in the analysis, of whom 45.6% showed excessive GWG. High pre-pregnancy body mass index, intermediate educational level, being born in a foreign country, primiparity, smoking, and signs of depressive disorder were associated with the risk of excessive GWG and included in the screening questionnaire. The developed score varied from 0-15 and divided the women´s risk for excessive GWG into low (0-5), moderate (6-10) and high (11-15). The cross-validation and the external validation yielded a moderate predictive power with an AUC of 0.709 and 0.738, respectively.

CONCLUSIONS

Our screening questionnaire is a simple and valid tool to identify pregnant women at risk for excessive GWG at an early stage. It could be used in routine care to provide targeted primary prevention measures to women at particular risk to gain excessive gestational weight.

TRIAL REGISTRATION

NCT01958307, ClinicalTrials.gov, retrospectively registered 9 October 2013.

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.

Development and validation of a multivariable genotype-informed gestational diabetes prediction algorithm for clinical use in the Mexican population: insights into susceptibility mechanisms

INTRODUCTION

Gestational diabetes mellitus (GDM) is underdiagnosed in Mexico. Early GDM risk stratification through prediction modeling is expected to improve preventative care. We developed a GDM risk assessment model that integrates both genetic and clinical variables.

RESEARCH DESIGN AND METHODS

Data from pregnant Mexican women enrolled in the 'Cuido mi Embarazo' (CME) cohort were used for development (107 cases, 469 controls) and data from the 'Mónica Pretelini Sáenz' Maternal Perinatal Hospital (HMPMPS) cohort were used for external validation (32 cases, 199 controls). A 2-hour oral glucose tolerance test (OGTT) with 75 g glucose performed at 24-28 gestational weeks was used to diagnose GDM. A total of 114 single-nucleotide polymorphisms (SNPs) with reported predictive power were selected for evaluation. Blood samples collected during the OGTT were used for SNP analysis. The CME cohort was randomly divided into training (70% of the cohort) and testing datasets (30% of the cohort). The training dataset was divided into 10 groups, 9 to build the predictive model and 1 for validation. The model was further validated using the testing dataset and the HMPMPS cohort.

RESULTS

Nineteen attributes (14 SNPs and 5 clinical variables) were significantly associated with the outcome; 11 SNPs and 4 clinical variables were included in the GDM prediction regression model and applied to the training dataset. The algorithm was highly predictive, with an area under the curve (AUC) of 0.7507, 79% sensitivity, and 71% specificity and adequately powered to discriminate between cases and controls. On further validation, the training dataset and HMPMPS cohort had AUCs of 0.8256 and 0.8001, respectively.

CONCLUSIONS

We developed a predictive model using both genetic and clinical factors to identify Mexican women at risk of developing GDM. These findings may contribute to a greater understanding of metabolic functions that underlie elevated GDM risk and support personalized patient recommendations.

Utilizing fog computing and explainable deep learning techniques for gestational diabetes prediction

Gestational diabetes mellitus (GDM) is one of the pregnancy complications that poses a significant risk on mothers and babies as well. GDM usually diagnosed at 22–26 of gestation. However, the early prediction is desirable as it may contribute to decrease the risk. The continuous monitoring for mother’s vital signs helps in predicting any deterioration during pregnancy. The originality of this paper is to provide comprehensive framework for pregnancy women monitoring. The proposed Data Replacement and Prediction Framework consists of three layers which are: (i) IoT Layer, (ii) Fog Layer, and (iii) Cloud Layer. The first layer used IOT sensors to aggregate vital sings from pregnancies using invasive and noninvasive sensors. Then the vital signs transmitted to fog nodes to processed and finally stored in the cloud layer. The main contribution in this paper is located in the fog layer producing GDM module to implement two influential tasks which are: (i) Data Finding Methodology (DFM), and (ii) Explainable Prediction Algorithm (EPM) using DNN. First, the DFM is used to replace the unused data to free the cache space for the new incoming data items. The cache replacement is very important in the case of healthcare system as the incoming vital signs are frequent and must be replaced continuously. Second, the EPM is used to predict the incidence of GDM that may occur in the second trimester of the pregnancy. To evaluate our model, we extract data of 16,354 pregnancy women from medical information mart for intensive care (MIMIC III) benchmark dataset. For each woman, vital signs, demographic data and laboratory tests was aggregated. The results of the prediction model superior the state of the art (ACC = 0.957, AUC = 0.942). Regarding to explainability, we utilized Shapley additive explanation framework to provide local and global explanation for the developed models. Overall, the proposed framework is medically intuitive, allow the early prediction of GDM with cost effective solution.

Stacking Ensemble Method for Gestational Diabetes Mellitus Prediction in Chinese Pregnant Women: A Prospective Cohort Study

Gestational diabetes mellitus (GDM) is closely related to adverse pregnancy outcomes and other diseases. Early intervention in pregnant women who are at high risk of developing GDM could help prevent adverse health consequences. The study aims to develop a simple model using the stacking ensemble method to predict GDM for women in the first trimester based on easily available factors. We used the data from the Chinese Pregnant Women Cohort Study from July 2017 to November 2018. A total of 6,848 pregnant women in the first trimester were included in the analysis. Logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost) were considered as base learners. Optimal feature subsets for each learner were chosen by using recursive feature elimination cross-validation. Then, we built a pipeline to process imbalance data, tune hyperparameters, and evaluate model performance. The learners with the best hyperparameters were employed in the first layer of the proposed stacking method. Their predictions were obtained using optimal feature subsets and served as meta-learner's inputs. Another LR was used as a meta-learner to obtain the final prediction results. Accuracy, specificity, error rate, and other metrics were calculated to evaluate the performance of the models. A paired samples t-test was performed to compare the model performance. In total, 967 (14.12%) women developed GDM. For base learners, the RF model had the highest accuracy (0.638 (95% confidence interval (CI) 0.628-0.648)) and specificity (0.683 (0.669-0.698)) and lowest error rate (0.362 (0.352-0.372)). The stacking method effectively improved the accuracy (0.666 (95% CI 0.663-0.670)) and specificity (0.725 (0.721-0.729)) and decreased the error rate (0.333 (0.330-0.337)). The differences in the performance between the stacking method and RF were statistically significant. Our proposed stacking method based on easily available factors has better performance than other learners such as RF.

Development of Clinical Risk Scores for Detection of COVID-19 in Suspected Patients During a Local Outbreak in China: A Retrospective Cohort Study

Objectives: To develop and internally validate two clinical risk scores to detect coronavirus disease 2019 (COVID-19) during local outbreaks.

Methods: Medical records were extracted for a retrospective cohort of 336 suspected patients admitted to Baodi hospital between 27 January to 20 February 2020. Multivariate logistic regression was applied to develop the risk-scoring models, which were internally validated using a 5-fold cross-validation method and Hosmer-Lemeshow (H-L) tests.

Results: Fifty-six cases were diagnosed from the cohort. The first model was developed based on seven significant predictors, including age, close contact with confirmed/suspected cases, same location of exposure, temperature, leukocyte counts, radiological findings of pneumonia and bilateral involvement (the mean area under the receiver operating characteristic curve [AUC]:0.88, 95% CI: 0.84–0.93). The second model had the same predictors except leukocyte and radiological findings (AUC: 0.84, 95% CI: 0.78–0.89, Z = 2.56, p = 0.01). Both were internally validated using H-L tests and showed good calibration (both p > 0.10).

Conclusion: Two clinical risk scores to detect COVID-19 in local outbreaks were developed with excellent predictive performances, using commonly measured clinical variables. Further external validations in new outbreaks are warranted.

Association between body mass index in the first half of pregnancy and gestational diabetes: A systematic review

Gestational diabetes mellitus is a more common complication in pregnancy and rising worldwide and screening for treating gestational diabetes mellitus is an opportunity for preventing its complications. Abnormal body mass index is the cause of many complications in pregnancy that is one of the major and modifiable risk factors in pregnancy too. This systematic review aimed to define the association between body mass index in the first half of pregnancy (before 20 weeks of gestation) and gestational diabetes mellitus. Web of Science, PubMed/Medline, Embase, Scopus, ProQuest, Cochrane library, and Google Scholar databases were systematically explored for articles published until April 31, 2022. Participation, exposure, comparators, outcomes, study design criteria include pregnant women (P), body mass index (E), healthy pregnant women (C), gestational diabetes mellitus (O), and study design (cohort, case–control, and cross-sectional). Newcastle–Ottawa scale checklists were used to report the quality of the studies. Eighteen quality studies were analyzed. A total of 41,017 pregnant women were in the gestational diabetes mellitus group and 285,351 pregnant women in the normal glucose tolerance group. Studies have reported an association between increased body mass index and gestational diabetes mellitus. Women who had a higher body mass index in the first half of pregnancy were at higher risk for gestational diabetes mellitus. In the first half of pregnancy, body mass index can be used as a reliable and available risk factor to assess gestational diabetes mellitus, especially in some situations where the pre-pregnancy body mass index is not available.

Optimising Cardiometabolic Risk Factors in Pregnancy: A Review of Risk Prediction Models Targeting Gestational Diabetes and Hypertensive Disorders

Cardiovascular disease, especially coronary heart disease and cerebrovascular disease, is a leading cause of mortality and morbidity in women globally. The development of cardiometabolic conditions in pregnancy, such as gestational diabetes mellitus and hypertensive disorders of pregnancy, portend an increased risk of future cardiovascular disease in women. Pregnancy therefore represents a unique opportunity to detect and manage risk factors, prior to the development of cardiovascular sequelae. Risk prediction models for gestational diabetes mellitus and hypertensive disorders of pregnancy can help identify at-risk women in early pregnancy, allowing timely intervention to mitigate both short- and long-term adverse outcomes. In this narrative review, we outline the shared pathophysiological pathways for gestational diabetes mellitus and hypertensive disorders of pregnancy, summarise contemporary risk prediction models and candidate predictors for these conditions, and discuss the utility of these models in clinical application.

Plasma lipidomics in early pregnancy and risk of gestational diabetes mellitus: a prospective nested case-control study in Chinese women

BACKGROUND

Lipid metabolism plays an important role in the pathogenesis of diabetes. There is little evidence regarding the prospective association of the maternal lipidome with gestational diabetes mellitus (GDM), especially in Chinese populations.

OBJECTIVES

We aimed to identify novel lipid species associated with GDM risk in Chinese women, and assess the incremental predictive capacity of the lipids for GDM.

METHODS

We conducted a nested case-control study using the Tongji-Shuangliu Birth Cohort with 336 GDM cases and 672 controls, 1:2 matched on age and week of gestation. Maternal blood samples were collected at 6-15 wk, and lipidomes were profiled by targeted ultra-HPLC-tandem MS. GDM was diagnosed by oral-glucose-tolerance test at 24-28 wk. The least absolute shrinkage and selection operator is a regression analysis method that was used to select novel biomarkers. Multivariable conditional logistic regression was used to estimate the associations.

RESULTS

Of 366 detected lipids, 10 were selected and found to be significantly associated with GDM independently of confounders: there were positive associations with phosphatidylinositol 40:6, alkylphosphatidylcholine 36:1, phosphatidylethanolamine plasmalogen 38:6, diacylglyceride 18:0/18:1, and alkylphosphatidylethanolamine 40:5 (adjusted ORs per 1 log-SD increment range: 1.34-2.86), whereas there were inverse associations with sphingomyelin 34:1, dihexosyl ceramide 24:0, mono hexosyl ceramide 18:0, dihexosyl ceramide 24:1, and phosphatidylcholine 40:7 (adjusted ORs range: 0.48-0.68). Addition of these novel lipids to the classical GDM prediction model resulted in a significant improvement in the C-statistic (discriminatory power of the model) to 0.801 (95% CI: 0.772, 0.829). For every 1-point increase in the lipid risk score of the 10 lipids, the OR of GDM was 1.66 (95% CI: 1.50, 1.85). Mediation analysis suggested the associations between specific lipid species and GDM were partially explained by glycemic and insulin-related indicators.

CONCLUSIONS

Specific plasma lipid biomarkers in early pregnancy were associated with GDM in Chinese women, and significantly improved the prediction for GDM.

Extracellular vesicle-associated miRNAs are an adaptive response to gestational diabetes mellitus

BACKGROUND

Gestational diabetes mellitus (GDM) is a serious public health issue affecting 9-15% of all pregnancies worldwide. Recently, it has been suggested that extracellular vesicles (EVs) play a role throughout gestation, including mediating a placental response to hyperglycaemia. Here, we investigated the EV-associated miRNA profile across gestation in GDM, assessed their utility in developing accurate, multivariate classification models, and determined the signaling pathways in skeletal muscle proteome associated with the changes in the EV miRNA profile.

METHODS

Discovery: A retrospective, case-control study design was used to identify EV-associated miRNAs that vary across pregnancy and clinical status (i.e. GDM or Normal Glucose Tolerance, NGT). EVs were isolated from maternal plasma obtained at early, mid and late gestation (n = 29) and small RNA sequencing was performed. Validation: A longitudinal study design was used to quantify expression of selected miRNAs. EV miRNAs were quantified by real-time PCR (cases = 8, control = 14, samples at three times during pregnancy) and their individual and combined classification efficiencies were evaluated. Quantitative, data-independent acquisition mass spectrometry was use to establish the protein profile in skeletal muscle biopsies from normal and GDM.

RESULTS

A total of 2822 miRNAs were analyzed using a small RNA library, and a total of 563 miRNAs that significantly changed (p < 0.05) across gestation and 101 miRNAs were significantly changed between NGT and GDM. Analysis of the miRNA changes in NGT and GDM separately identified a total of 256 (NGT-group), and 302 (GDM-group) miRNAs that change across gestation. A multivariate classification model was developed, based on the quantitative expression of EV-associated miRNAs, and the accuracy to correctly assign samples was > 90%. We identified a set of proteins in skeletal muscle biopsies from women with GDM associated with JAK-STAT signaling which could be targeted by the miRNA-92a-3p within circulating EVs. Interestingly, overexpression of miRNA-92a-3p in primary skeletal muscle cells increase insulin-stimulated glucose uptake.

CONCLUSIONS

During early pregnancy, differently-expressed, EV-associated miRNAs may be of clinical utility in identifying presymptomatic women who will subsequently develop GDM later in gestation. We suggest that miRNA-92a-3p within EVs might be a protected mechanism to increase skeletal muscle insulin sensitivity in GDM.

The overall plant-based diet index during pregnancy and risk of gestational diabetes mellitus: a prospective cohort study in China

The high overall plant-based diet index (PDI) is considered to protect against type 2 diabetes in the general population. However, whether the PDI affects gestational diabetes mellitus (GDM) risk among pregnant women is still unclear. We evaluated the association between PDI and GDM risk based on a Chinese large prospective cohort - the Tongji Maternal and Child Health Cohort. Dietary data were collected at 13-28 weeks of pregnancy by a validated semi-quantitative FFQ. The PDI was obtained by assigning plant food groups positive scores while assigning animal food groups reverse scores. GDM was diagnosed by a 75 g 2-h oral glucose tolerance test at 24-28 weeks of gestation. Logistic regression models were fitted to estimate OR of GDM, with associated 95 % CI, comparing women in different PDI quartiles. Among the total 2099 participants, 169 (8·1 %) were diagnosed with GDM. The PDI ranged from 21·0 to 52·0 with a median of 36·0 (interquartile range (IQR) 33·0-39·0). After adjusting for social-demographic characteristics and lifestyle factors etc., the participants with the highest quartile of PDI were associated with 57 % reduced odds of GDM compared with women in the lowest quartile of PDI (adjusted OR 0·43; 95 % CI 0·24, 0·77; Pfor trend = 0·005). An IQR increment in PDI was associated with 29 % decreased odds of GDM (adjusted OR 0·71; 95 % CI 0·56, 0·90). Findings suggest that adopting a plant-based diet during pregnancy could reduce GDM risk among Chinese women, which may be valuable for dietary counselling during pregnancy.

FGF21 Serum Levels in the Early Second Trimester Are Positively Correlated With the Risk of Subsequent Gestational Diabetes Mellitus: A Propensity-Matched Nested Case-Control Study

BACKGROUND

As an important endocrine hormone regulating glucose metabolism, fibroblast growth factor 21 (FGF21) is increased in individuals with gestational diabetes mellitus (GDM) after 24 gestational weeks. However, it is unknown whether the increase in FGF21 precedes the diagnosis of GDM.

METHODS

In this nested case-control study, 133 pregnant women with GDM and 133 pregnant women with normal glucose tolerance (NGT) were identified through propensity score matching, and serum FGF21 levels were measured at 14 to 21 gestational weeks, before GDM is routinely identified. The differences in FGF21 levels were compared. The association between FGF21 and the occurrence of GDM was evaluated using logistic regression models with adjustment for confounders.

RESULTS

The serum FGF21 levels of the GDM group at 14 to 21 gestational weeks were significantly higher than those of the NGT group overall (P < 0.001), with similar results observed between the corresponding BMI subgroups (P < 0.05). The 2nd (OR 1.224, 95% CI 0.603-2.485), 3rd (OR 2.478, 1.229-5.000), and 4th (OR 3.419, 95% CI 1.626-7.188) FGF21 quartiles were associated with greater odds of GDM occurrence than the 1st quartile after multivariable adjustments.

CONCLUSIONS

The serum FGF21 levels in GDM groups increased in the early second trimester, regardless of whether participants were stratified according to BMI. After adjusting for confounding factors, the FGF21 levels in the highest quartile were associated with more than three times higher probability of the diagnosis of GDM in the pregnancy as compared to levels in the first quartile.

Machine Learning Prediction Models for Gestational Diabetes Mellitus: A meta- analysis (Preprint)

Background

Gestational diabetes mellitus (GDM) is a common endocrine metabolic disease, involving a carbohydrate intolerance of variable severity during pregnancy. The incidence of GDM-related complications and adverse pregnancy outcomes has declined, in part, due to early screening. Machine learning (ML) models are increasingly used to identify risk factors and enable the early prediction of GDM.

Objective

The aim of this study was to perform a meta-analysis and comparison of published prognostic models for predicting the risk of GDM and identify predictors applicable to the models.

Methods

Four reliable electronic databases were searched for studies that developed ML prediction models for GDM in the general population instead of among high-risk groups only. The novel Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias of the ML models. The Meta-DiSc software program (version 1.4) was used to perform the meta-analysis and determination of heterogeneity. To limit the influence of heterogeneity, we also performed sensitivity analyses, a meta-regression, and subgroup analysis.

Results

A total of 25 studies that included women older than 18 years without a history of vital disease were analyzed. The pooled area under the receiver operating characteristic curve (AUROC) for ML models predicting GDM was 0.8492; the pooled sensitivity was 0.69 (95% CI 0.68-0.69; P<.001; I²=99.6%) and the pooled specificity was 0.75 (95% CI 0.75-0.75; P<.001; I²=100%). As one of the most commonly employed ML methods, logistic regression achieved an overall pooled AUROC of 0.8151, while non–logistic regression models performed better, with an overall pooled AUROC of 0.8891. Additionally, maternal age, family history of diabetes, BMI, and fasting blood glucose were the four most commonly used features of models established by the various feature selection methods.

Conclusions

Compared to current screening strategies, ML methods are attractive for predicting GDM. To expand their use, the importance of quality assessments and unified diagnostic criteria should be further emphasized.

Transportation noise and gestational diabetes mellitus: A nationwide cohort study from Denmark

BACKGROUND

Few studies have investigated whether road traffic noise is associated with gestational diabetes mellitus (GDM), and have yielded inconsistent findings. We aimed to investigate whether maternal exposure to residential transportation noise, before and during pregnancy, was associated with GDM in a nationwide cohort.

METHODS

From the Danish population (2004-2017) we identified 629,254 pregnancies using the Danish Medical Birth Register. By linkage with the National Patient Registry, we identified 15,973 pregnancies complicated by GDM. Road traffic and railway noise (L_den) at the most and least exposed façades for all residential addresses from five years before pregnancy until birth were estimated for all. Analyses were conducted using generalized estimating equation models with adjustment for various individual and area-level sociodemographic covariates gathered from Danish registries, as well as green space and air pollution (PM_2.5) estimated for all addresses.

RESULTS

We found no positive associations between road traffic noise at either façade and GDM. For railway noise, a 10 dB increase in railway noise at the most and least exposed façades during the first trimester was associated with GDM, with an odds ratio (OR) of 1.06 (95% confidence interval (CI): 1.03-1.10) and 1.07 (95% CI: 1.02-1.13), respectively. We found indications of higher odds of GDM among women exposed to both high road traffic and railway noise at the least exposed facade during the first trimester (OR: 1.24; 95% CI: 1.07-1.44).

CONCLUSION

In conclusion, this nationwide study suggests that railway noise but not road traffic noise might be associated with GDM.