Prediction models for the risk of gestational diabetes: a systematic review

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.

Adipokines in pregnancy

Reproductive success consists of a sequential events chronology, starting with the ovum fertilization, implantation of the embryo, placentation, and cellular processes like proliferation, apoptosis, angiogenesis, endocrinology, or metabolic changes, which taken together finally conduct the birth of healthy offspring. Currently, many factors are known that affect the regulation and proper maintenance of pregnancy in humans, domestic animals, or rodents. Among the determinants of reproductive success should be distinguished: the maternal microenvironment, genes, and proteins as well as numerous pregnancy hormones that regulate the most important processes and ensure organism homeostasis. It is well known that white adipose tissue, as the largest endocrine gland in our body, participates in the synthesis and secretion of numerous hormones belonging to the adipokine family, which also may regulate the course of pregnancy. Unfortunately, overweight and obesity lead to the expansion of adipose tissue in the body, and its excess in both women and animals contributes to changes in the synthesis and release of adipokines, which in turn translates into dramatic changes during pregnancy, including those taking place in the organ that is crucial for the proper progress of pregnancy, i.e. the placenta. In this chapter, we are summarizing the current knowledge about levels of adipokines and their role in the placenta, taking into account the physiological and pathological conditions of pregnancy, e.g. gestational diabetes mellitus, preeclampsia, or intrauterine growth restriction in humans, domestic animals, and rodents.

Early prediction of gestational diabetes mellitus using maternal demographic and clinical risk factors

OBJECTIVE

To build and validate an early risk prediction model for gestational diabetes mellitus (GDM) based on first-trimester electronic medical records including maternal demographic and clinical risk factors.

METHODS

To develop and validate a GDM prediction model, two datasets were used in this retrospective study. One included data of 14,015 pregnant women from Máxima Medical Center (MMC) in the Netherlands. The other was from an open-source database nuMoM2b including data of 10,038 nulliparous pregnant women, collected in the USA. Widely used maternal demographic and clinical risk factors were considered for modeling. A GDM prediction model based on elastic net logistic regression was trained from a subset of the MMC data. Internal validation was performed on the remaining MMC data to evaluate the model performance. For external validation, the prediction model was tested on an external test set from the nuMoM2b dataset.

RESULTS

An area under the receiver-operating-characteristic curve (AUC) of 0.81 was achieved for early prediction of GDM on the MMC test data, comparable to the performance reported in previous studies. While the performance markedly decreased to an AUC of 0.69 when testing the MMC-based model on the external nuMoM2b test data, close to the performance trained and tested on the nuMoM2b dataset only (AUC = 0.70).

Prediction of Gestational Diabetes Mellitus in the First Trimester of Pregnancy Based on Maternal Variables and Pregnancy Biomarkers

Gestational diabetes mellitus (GDM) is a significant health concern with adverse outcomes for both pregnant women and their offspring. Recognizing the need for early intervention, this study aimed to develop an early prediction model for GDM risk assessment during the first trimester. Utilizing a prospective cohort of 4917 pregnant women from the Third Department of Obstetrics and Gynecology, Aristotle University of Thessaloniki, Greece, the study sought to combine maternal characteristics, obstetric and medical history, and early pregnancy-specific biomarker concentrations into a predictive tool. The primary objective was to create a series of predictive models that could accurately identify women at high risk for developing GDM, thereby facilitating early and targeted interventions. To this end, maternal age, body mass index (BMI), obstetric and medical history, and biomarker concentrations were analyzed and incorporated into five distinct prediction models. The study's findings revealed that the models varied in effectiveness, with the most comprehensive model combining maternal characteristics, obstetric and medical history, and biomarkers showing the highest potential for early GDM prediction. The current research provides a foundation for future studies to refine and expand upon the predictive models, aiming for even earlier and more accurate detection methods.

Temporal validation and updating of a prediction model for the diagnosis of gestational diabetes mellitus

OBJECTIVE

The original Monash gestational diabetes mellitus (GDM) risk prediction in early pregnancy model is internationally externally validated and clinically implemented. We temporally validate and update this model in a contemporary population with a universal screening context and revised diagnostic criteria and ethnicity categories, thereby improving model performance and generalizability.

STUDY DESIGN AND SETTING

The updating dataset comprised of routinely collected health data for singleton pregnancies delivered in Melbourne, Australia from 2016 to 2018. Model predictors included age, body mass index, ethnicity, diabetes family history, GDM history, and poor obstetric outcome history. Model updating methods were recalibration-in-the-large (Model A), intercept and slope re-estimation (Model B), and coefficient revision using logistic regression (Model C1, original ethnicity categories; Model C2, revised ethnicity categories). Analysis included 10-fold cross-validation, assessment of performance measures (c-statistic, calibration-in-the-large, calibration slope, and expected-observed ratio), and a closed-loop testing procedure to compare models' log-likelihood and akaike information criterion scores.

RESULTS

In 26,474 singleton pregnancies (4,756, 18% with GDM), the original model demonstrated reasonable temporal validation (c-statistic = 0.698) but suboptimal calibration (expected-observed ratio = 0.485). Updated model C2 was preferred, with a high c-statistic (0.732) and significantly better performance in closed testing.

CONCLUSION

We demonstrated updating methods to sustain predictive performance in a contemporary population, highlighting the value and versatility of prediction models for guiding risk-stratified GDM care.

Diabetes detection in women with gestational diabetes and polycystic ovarian syndrome

Gestational diabetes mellitus (GDM) and polycystic ovarian syndrome (PCOS) represent two of the highest risk factors for development of type 2 diabetes mellitus in young women. As these increasingly common conditions generally affect younger women, early detection of dysglycemia is key if preventative measures are to be effective. While international guidance recommends screening for type 2 diabetes, current screening strategies suffer from significant challenges.First, guidance lacks consensus in defining which tests to use and frequency of monitoring, thereby sending mixed messages to healthcare professionals.Second, conformity to guidance is poor, with only a minority of women having tests at the recommended frequency (where specified). Approaches to improve conformity have focused on healthcare related factors (largely technology driven reminder systems), but patient factors such as convenience and clear messaging around risk have been neglected.Third, and most critically, current screening strategies are too generic and rely on tests that become abnormal far too late in the trajectory towards dysglycemia to offer opportunities for effective preventative measures. Risk factors show wide interindividual variation, and insulin sensitivity and β cell function are often abnormal during pre-diabetes stage, well before frank diabetes.New, consistent, targeted screening strategies are required that incorporate early, prevention focused testing and personalised risk stratification.

Machine Learning Methods for Pregnancy and Childbirth Risk Management

Machine learning methods enable medical systems to automatically generate data-driven decision support models using real-world data inputs, eliminating the need for explicit rule design. In this research, we investigated the application of machine learning methods in healthcare, specifically focusing on pregnancy and childbirth risks. The timely identification of risk factors during early pregnancy, along with risk management, mitigation, prevention, and adherence management, can significantly reduce adverse perinatal outcomes and complications for both mother and child. Given the existing burden on medical professionals, clinical decision support systems (CDSSs) can play a role in risk management. However, these systems require high-quality decision support models based on validated medical data that are also clinically interpretable. To develop models for predicting childbirth risks and due dates, we conducted a retrospective analysis of electronic health records from the perinatal Center of the Almazov Specialized Medical Center in Saint-Petersburg, Russia. The dataset, which was exported from the medical information system, consisted of structured and semi-structured data, encompassing a total of 73,115 lines for 12,989 female patients. Our proposed approach, which includes a detailed analysis of predictive model performance and interpretability, offers numerous opportunities for decision support in perinatal care provision. The high predictive performance achieved by our models ensures precise support for both individual patient care and overall health organization management.

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.

Lipolysis and gestational diabetes mellitus onset: a case-cohort genome-wide association study in Chinese

BACKGROUND

Genetic knowledge of gestational diabetes mellitus (GDM) in Chinese women is quite limited. This study aimed to identify the risk factors and mechanism of GDM at the genetic level in a Chinese population.

METHODS

We conducted a genome-wide association study (GWAS) based on single nucleotide polymorphism (SNP) array genotyping (ASA-CHIA Bead chip, Illumina) and a case-cohort study design. Variants including SNPs, copy number variants (CNVs), and insertions-deletions (InDels) were called from genotyping data. A total of 2232 pregnant women were enrolled in their first/second trimester between February 2018 and December 2020 from Anqing Municipal Hospital in Anhui Province, China. The GWAS included 193 GDM patients and 819 subjects without a diabetes diagnosis, and risk ratios (RRs) and their 95% confidence intervals (CIs) were estimated by a regression-based method conditional on the population structure. The calling and quality control of genotyping data were performed following published guidelines. CNVs were merged into CNV regions (CNVR) to simplify analyses. To interpret the GWAS results, gene mapping and overexpression analyses (ORAs) were further performed to prioritize the candidate genes and related biological mechanisms.

RESULTS

We identified 14 CNVRs (false discovery rate corrected P values < 0.05) and two suggestively significant SNPs (P value < 0.00001) associated with GDM, and a total of 19 candidate genes were mapped. Ten genes were significantly enriched in gene sets related to lipase (triglyceride lipase and lipoprotein lipase) activity (LIPF, LIPK, LIPN, and LIPJ genes), oxidoreductase activity (TPH1 and TPH2 genes), and cellular components beta-catenin destruction complex (APC and GSK3B genes), Wnt signalosome (APC and GSK3B genes), and lateral element in the Gene Ontology resource (BRCA1 and SYCP2 genes) by two ORA methods (adjusted P values < 0.05).

CONCLUSIONS

Genes related to lipolysis, redox reaction, and proliferation of islet β-cells are associated with GDM in Chinese women. Energy metabolism, particularly lipolysis, may play an important role in GDM aetiology and pathology, which needs further molecular studies to verify.

Methodological guidance for the evaluation and updating of clinical prediction models: a systematic review

BACKGROUND

Clinical prediction models are often not evaluated properly in specific settings or updated, for instance, with information from new markers. These key steps are needed such that models are fit for purpose and remain relevant in the long-term. We aimed to present an overview of methodological guidance for the evaluation (i.e., validation and impact assessment) and updating of clinical prediction models.

METHODS

We systematically searched nine databases from January 2000 to January 2022 for articles in English with methodological recommendations for the post-derivation stages of interest. Qualitative analysis was used to summarize the 70 selected guidance papers.

RESULTS

Key aspects for validation are the assessment of statistical performance using measures for discrimination (e.g., C-statistic) and calibration (e.g., calibration-in-the-large and calibration slope). For assessing impact or usefulness in clinical decision-making, recent papers advise using decision-analytic measures (e.g., the Net Benefit) over simplistic classification measures that ignore clinical consequences (e.g., accuracy, overall Net Reclassification Index). Commonly recommended methods for model updating are recalibration (i.e., adjustment of intercept or baseline hazard and/or slope), revision (i.e., re-estimation of individual predictor effects), and extension (i.e., addition of new markers). Additional methodological guidance is needed for newer types of updating (e.g., meta-model and dynamic updating) and machine learning-based models.

CONCLUSION

Substantial guidance was found for model evaluation and more conventional updating of regression-based models. An important development in model evaluation is the introduction of a decision-analytic framework for assessing clinical usefulness. Consensus is emerging on methods for model updating.

A Clinical Update on Gestational Diabetes Mellitus

Gestational diabetes mellitus (GDM) traditionally refers to abnormal glucose tolerance with onset or first recognition during pregnancy. GDM has long been associated with obstetric and neonatal complications primarily relating to higher infant birthweight and is increasingly recognized as a risk factor for future maternal and offspring cardiometabolic disease. The prevalence of GDM continues to rise internationally due to epidemiological factors including the increase in background rates of obesity in women of reproductive age and rising maternal age and the implementation of the revised International Association of the Diabetes and Pregnancy Study Groups' criteria and diagnostic procedures for GDM. The current lack of international consensus for the diagnosis of GDM reflects its complex historical evolution and pragmatic antenatal resource considerations given GDM is now 1 of the most common complications of pregnancy. Regardless, the contemporary clinical approach to GDM should be informed not only by its short-term complications but also by its longer term prognosis. Recent data demonstrate the effect of early in utero exposure to maternal hyperglycemia, with evidence for fetal overgrowth present prior to the traditional diagnosis of GDM from 24 weeks' gestation, as well as the durable adverse impact of maternal hyperglycemia on child and adolescent metabolism. The major contribution of GDM to the global epidemic of intergenerational cardiometabolic disease highlights the importance of identifying GDM as an early risk factor for type 2 diabetes and cardiovascular disease, broadening the prevailing clinical approach to address longer term maternal and offspring complications following a diagnosis of GDM.

Association between early-pregnancy serum C-peptide and risk of gestational diabetes mellitus: a nested case-control study among Chinese women

OBJECTIVE

To examine the association of early-pregnancy serum C-peptide with incident gestational diabetes mellitus (GDM) and the predictive ability of maternal C-peptide for GDM.

METHODS

A nested case-control study of 332 GDM cases and 664 controls was established based on the Tongji-Shuangliu Birth Cohort. The GDM cases and controls were matched at 1:2 on maternal age (± 3 years) and gestational age (± 4 weeks). Multivariable conditional logistic regression was applied to assess the association of C-peptide with risk of GDM. Partial Spearman's correlation coefficients were estimated for the correlations between C-peptide and multiple metabolic biomarkers. C-statistics were calculated to assess the predictive ability of early-pregnancy C-peptide for GDM.

RESULTS

Of 996 pregnant women, median maternal age was 28.0 years old and median gestational age was 11.0 weeks. After adjustment for potential confounders, the odds ratio of GDM comparing the extreme quartiles of C-peptide was 2.28 (95% confidence interval, 1.43, 3.62; P for trend < 0.001). Partial correlation coefficients ranged between 0.07 and 0.77 for the correlations of C-peptide with fasting insulin, homeostatic model of insulin resistance, leptin, fasting blood glucose, triglycerides, glycosylated hemoglobin, waist-hip ratio, systolic blood pressure, and low-density lipoprotein cholesterol (P ≤ 0.025), and were - 0.11 and - 0.17 for high-density lipoprotein cholesterol and adiponectin (P < 0.001). Serum C-peptide slightly improved the predictive performance of the model with conventional predictive factors (0.66 vs. 0.63; P = 0.008).

CONCLUSION

While the predictive value for subsequent GDM should be validated, early-pregnancy serum C-peptide may be positively associated with risk of GDM.

Development of type 2 diabetes in women with comorbid gestational diabetes and common mental disorders in the Born in Bradford cohort

OBJECTIVES

To compare, in a population of women with gestational diabetes mellitus (GDM), the time to diagnosis of Type 2 diabetes in those with and without common mental disorder (CMD) (depression and/or anxiety) during pregnancy.

DESIGN AND SETTING

prospective study of the Born in Bradford cohort in Bradford, UK.

PARTICIPANTS

909 women diagnosed with GDM between 2007 and 2010, with linkage to their primary care records until 2017. The exposed population were women with an indicator of CMD during pregnancy in primary care records. The unexposed were those without an indicator.

OUTCOME MEASURES

Time to diagnosis of type 2 diabetes as indicated by a diagnosis in primary care records.

ANALYSIS

time to event analysis using Cox regression was employed. Multiple imputation by chained equations was implemented to handle missing data. Models were adjusted for maternal age, ethnicity, education, preconception CMD and tobacco smoking during pregnancy.

RESULTS

165 women (18%) were diagnosed with type 2 diabetes over a follow-up period of around 10 years. There was no evidence of an effect of antenatal CMD on the development of type 2 diabetes following GDM (adjusted HR 0.95; 95% CI 0.57 to 1.57).

CONCLUSIONS

Women with CMD were not at an increased risk of type 2 diabetes following GDM. This is reassuring for women with these co-morbidities but requires replication in other study populations.

The Neuropeptide-Related HERC5/TAC1 Interactions May Be Associated with the Dysregulation of lncRNA GAS5 Expression in Gestational Diabetes Mellitus Exosomes

Objective

The goal of this work was to look at the expression and probable role of exosomal long noncoding RNA (lncRNA) GAS5 in gestational diabetes mellitus (GDM), as well as forecast the importance of its interaction with neuropeptides in the progression of the disease.

Methods

We divided 44 pregnant women visiting the obstetric outpatient clinics at the Affiliated Hospital of Guilin Medical College from January 2021 to December 2021 into healthy and GDM groups. We measured the expression levels of the lncRNA GAS5 in peripheral blood using PCR and compared the expression levels between the 2 groups. The Gene Expression Omnibus (GEO) database and the R software were used to analyse the differences in the genes expressed in the amniotic fluid cells in the GDM and normal groups. catRAPID was used to identify potential target proteins for GAS5. Key neuropeptide-related proteins and potential target proteins of GAS5 were extracted, and protein interaction networks were mapped. AlphaFold 2 was used to predict the structure of the target protein. The ClusPro tool was used to predict protein-protein interactions. ZDOCK was used to further confirm the protein–nucleic acid docking.

Results

The lncRNA GAS5 was downregulated in the peripheral blood of pregnant women with GDM compared with normal pregnant women. The subcellular localization sites of GAS5 were the nucleus, cytoplasm, and ribosome; in addition, GAS5 was present in exosomes. Intercellular interactions, including neuropeptide receptors, were increased in the amniotic fluid cells of patients with GDM. Venn diagram analysis yielded seven neuropeptide-related proteins and three GAS5 target proteins. Among them, HERC5/TAC1 interacted and GAS5 docked well with HERC5.

Conclusion

The lncRNA GAS5 in the peripheral blood exosomes in patients with GDM may be a new target for the detection of GDM, and the interaction between GAS5 and HERC5/TAC1 may be involved in the pathogenesis of GDM.

Use and performance of machine learning models for type 2 diabetes prediction in clinical and community care settings: Protocol for a systematic review and meta-analysis of predictive modeling studies

Objective

Machine learning involves the use of algorithms without explicit instructions. Of late, machine learning models have been widely applied for the prediction of type 2 diabetes. However, no evidence synthesis of the performance of these prediction models of type 2 diabetes is available. We aim to identify machine learning prediction models for type 2 diabetes in clinical and community care settings and determine their predictive performance.

Methods

The systematic review of English language machine learning predictive modeling studies in 12 databases will be conducted. Studies predicting type 2 diabetes in predefined clinical or community settings are eligible. Standard CHARMS and TRIPOD guidelines will guide data extraction. Methodological quality will be assessed using a predefined risk of bias assessment tool. The extent of validation will be categorized by Reilly–Evans levels. Primary outcomes include model performance metrics of discrimination ability, calibration, and classification accuracy. Secondary outcomes include candidate predictors, algorithms used, level of validation, and intended use of models. The random-effects meta-analysis of c-indices will be performed to evaluate discrimination abilities. The c-indices will be pooled per prediction model, per model type, and per algorithm. Publication bias will be assessed through funnel plots and regression tests. Sensitivity analysis will be conducted to estimate the effects of study quality and missing data on primary outcome. The sources of heterogeneity will be assessed through meta-regression. Subgroup analyses will be performed for primary outcomes.

Ethics and dissemination

No ethics approval is required, as no primary or personal data are collected. Findings will be disseminated through scientific sessions and peer-reviewed journals.

PROSPERO registration number

CRD42019130886

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.

The influence of maternal blood glucose during pregnancy on weight outcomes at birth and preschool age in offspring exposed to hyperglycemia first detected during pregnancy, in a South African cohort

Introduction

Little is known about the influence of hyperglycemia first detected in pregnancy (HFDP) on weight outcomes in exposed offspring in Africa. We investigated the influence of maternal blood glucose concentrations during pregnancy on offspring weight outcomes at birth and preschool age, in offspring exposed to HFDP, in South Africa.

Research design and methods

Women diagnosed with HFDP had data routinely collected during the pregnancy and at delivery, at a referral hospital, and the offspring followed up at preschool age. Maternal fasting, oral glucose tolerance test 1 and 2-hour blood glucose were measured at diagnosis of HFDP and 2-hour postprandial blood glucose during the third trimester. Offspring were classified as either those exposed to diabetes first recognized in pregnancy (DIP) or gestational diabetes (GDM). At birth, neonates were classified into macrosomia, low birth weight (LBW), large for gestational age (LGA), appropriate (AGA) and small for gestational age (SGA)groups. At preschool age, offspring had height and weight measured and Z-scores for weight, height and BMI calculated.

Results

Four hundred and forty-three neonates were included in the study at birth, with 165 exposed to DIP and 278 exposed to GDM. At birth, the prevalence of LGA, macrosomia and LBW were 29.6%, 12.2% and 7.5%, respectively, with a higher prevalence of LGA and macrosomia in neonates exposed to DIP. At pre-school age, the combined prevalence of overweight and obesity was 26.5%. Maternal third trimester 2-hour postprandial blood glucose was significantly associated with z-scores for weight at birth and preschool age, and both SGA and LGA at birth.

Conclusion

In offspring exposed to HFDP, there is a high prevalence of LGA and macrosomia at birth, and overweight and obesity at preschool age, with higher prevalence in those exposed to DIP, compared to GDM. Maternal blood glucose control during the pregnancy influences offspring weight at birth and preschool age.

Relative importance of metabolic syndrome components for developing gestational diabetes

PURPOSE

To assess the independent and joint contribution of the individual components of metabolic syndrome, and known risk factors for gestational diabetes (GDM), on risk of GDM.

METHODS

Two thousand nine hundred and fifteen women from Australia and New Zealand, who participated in The Screening for Pregnancy Endpoints Study (SCOPE), were included. Using the SCOPE clinical data set and biomarkers obtained at 14-16 weeks' gestation, a logistic regression model was fitted to the binary outcome GDM, with individual metabolic syndrome components (waist circumference, blood pressure, glucose, HDL-C, triglycerides), recruitment site, and other established factors associated with GDM. Hierarchical partitioning was used to assess the relative contribution of each variable.

RESULTS

Of the 2915 women, 103 women (3.5%) developed GDM. The deviance explained by the logistic regression model containing all variables was 18.65% and the AUC was 0.809. Seventy percent of the independent effect was accounted for by metabolic syndrome components. The highest independent relative contribution to GDM was circulating triglycerides (17 ± 3%), followed by waist circumference (13 ± 3%). Glucose and maternal BMI contributed 12 ± 2% and 12 ± 3%, respectively. The remaining factors had an independent relative contribution of < 10%.

CONCLUSION

Triglyceride concentrations had the highest independent relative importance for risk of GDM. Increased focus for lowering triglycerides as an important risk factor for GDM is warranted.

Lipid Metabolism Is Dysregulated before, during and after Pregnancy in a Mouse Model of Gestational Diabetes

The aim of the current study was to test the hypothesis that maternal lipid metabolism was modulated during normal pregnancy and that these modulations are altered in gestational diabetes mellitus (GDM). We tested this hypothesis using an established mouse model of diet-induced obesity with pregnancy-associated loss of glucose tolerance and a novel lipid analysis tool, Lipid Traffic Analysis, that uses the temporal distribution of lipids to identify differences in the control of lipid metabolism through a time course. Our results suggest that the start of pregnancy is associated with several changes in lipid metabolism, including fewer variables associated with de novo lipogenesis and fewer PUFA-containing lipids in the circulation. Several of the changes in lipid metabolism in healthy pregnancies were less apparent or occurred later in dams who developed GDM. Some changes in maternal lipid metabolism in the obese-GDM group were so late as to only occur as the control dams' systems began to switch back towards the non-pregnant state. These results demonstrate that lipid metabolism is modulated in healthy pregnancy and the timing of these changes is altered in GDM pregnancies. These findings raise important questions about how lipid metabolism contributes to changes in metabolism during healthy pregnancies. Furthermore, as alterations in the lipidome are present before the loss of glucose tolerance, they could contribute to the development of GDM mechanistically.

A Novel and Precise Profiling Tool to Predict Gestational Diabetes

BACKGROUND

There is a trend in healthcare for developing models for predictions of disease to enable early intervention and improve outcome.

INSTRUMENT

We present the use of artificial intelligence algorithms that were developed by Gynisus Ltd. using mathematical algorithms.

EXPERIENCE

Data were retrospectively collected on pregnant women that delivered at a single institution. Hundreds of parameters were collected and found to have different importance and correlation with the likelihood to develop gestational diabetes mellitus (GDM). We highlight 3 of 29 specific parameters that were important in pregestation and in early pregnancy, which have not been previously correlated with GDM.

CONCLUSION

This predictive tool identified parameters that are not currently being used as predictors in GDM, even before pregnancy. This tool opens the possibility of intervening on patients identified at risk for GDM and its complications. Future prospective studies are needed.

Machine learning risk score for prediction of gestational diabetes in early pregnancy in Tianjin, China

AIMS

This study aimed to develop a machine learning-based prediction model for gestational diabetes mellitus (GDM) in early pregnancy in Chinese women.

MATERIALS AND METHODS

We used an established population-based prospective cohort of 19,331 pregnant women registered as pregnant before the 15th gestational week in Tianjin, China, from October 2010 to August 2012. The dataset was randomly divided into a training set (70%) and a test set (30%). Risk factors collected at registration were examined and used to construct the prediction model in the training dataset. Machine learning, that is, the extreme gradient boosting (XGBoost) method, was employed to develop the model, while a traditional logistic model was also developed for comparison purposes. In the test dataset, the performance of the developed prediction model was assessed by calibration plots for calibration and area under the receiver operating characteristic curve (AUR) for discrimination.

RESULTS

In total, 1484 (7.6%) women developed GDM. Pre-pregnancy body mass index, maternal age, fasting plasma glucose at registration, and alanine aminotransferase were selected as risk factors. The machine learning XGBoost model-predicted probability of GDM was similar to the observed probability in the test data set, while the logistic model tended to overestimate the risk at the highest risk level (Hosmer-Lemeshow test p value: 0.243 vs. 0.099). The XGBoost model achieved a higher AUR than the logistic model (0.742 vs. 0.663, p < 0.001). This XGBoost model was deployed through a free, publicly available software interface (https://liuhongwei.shinyapps.io/gdm_risk_calculator/).

CONCLUSION

The XGBoost model achieved better performance than the logistic model.

Accounting for Repeat Pregnancies in Risk Prediction Models

BACKGROUND

In perinatal epidemiology, the development of risk prediction models is complicated by parity; how repeat pregnancies influence the predictive accuracy of models that include obstetrical history is unclear.

METHODS

To assess the influence of repeat pregnancies on the association between predictors and the outcomes, as well as the influence of ignoring the nonindependence between pregnancies, we created four analytical cohorts using the Clinical Practice Research Datalink. The cohorts included (1) first deliveries, (2) a random sample of one delivery per woman, (3) all eligible deliveries per woman, and (4) all eligible deliveries and censoring of follow-up at subsequent pregnancies. Using Plasmode simulations, we varied the predictor-outcome association across cohorts.

RESULTS

We found minimal differences in the relative contribution of predictors to the overall predictions and the discriminative accuracy of models in the cohort of randomly sampled deliveries versus the all deliveries cohort (C-statistic: 0.62 vs. 0.63; Nagelkerke's R2: 0.03 for both). Accounting for clustering and censoring upon subsequent pregnancies also had negligible influence on model performance. We found important differences in model performance between the models developed in the cohort of first deliveries and the random sample of deliveries.

CONCLUSIONS

In our study, a model including first deliveries had the best predictive accuracy but was not generalizable to women of varying parities. Moreover, including repeat pregnancies did not improve the predictive accuracy of the models. Multiple models may be needed to improve the transportability and accuracy of prediction models when the outcome of interest is influenced by parity.

Association of pre- and early-pregnancy factors with the risk for gestational diabetes mellitus in a large Chinese population

Gestational diabetes mellitus (GDM) has aroused wide public concern, as it affects approximately 1.8-25.1% of pregnancies worldwide. This study aimed to examine the association of pre-pregnancy demographic parameters and early-pregnancy laboratory biomarkers with later GDM risk, and further to establish a nomogram prediction model. This study is based on the big obstetric data from 10 "AAA" hospitals in Xiamen. GDM was diagnosed according to the International Association of Diabetes and Pregnancy Study Group (IADPSG) criteria. Data are analyzed using Stata (v14.1) and R (v3.5.2). Total 187,432 gestational women free of pre-pregnancy diabetes mellitus were eligible for analysis, including 49,611 women with GDM and 137,821 women without GDM. Irrespective of confounding adjustment, eight independent factors were consistently and significantly associated with GDM, including pre-pregnancy body mass index (BMI), pre-pregnancy intake of folic acid, white cell count, platelet count, alanine transaminase, albumin, direct bilirubin, and creatinine (p < 0.001). Notably, per 3 kg/m² increment in pre-pregnancy BMI was associated with 22% increased risk [adjusted odds ratio (OR) 1.22, 95% confidence interval (CI) 1.21-1.24, p < 0.001], and pre-pregnancy intake of folic acid can reduce GDM risk by 27% (adjusted OR 0.73, 95% CI 0.69-0.79, p < 0.001). The eight significant factors exhibited decent prediction performance as reflected by calibration and discrimination statistics and decision curve analysis. To enhance clinical application, a nomogram model was established by incorporating age and above eight factors, and importantly this model had a prediction accuracy of 87%. Taken together, eight independent pre-/early-pregnancy predictors were identified in significant association with later GDM risk, and importantly a nomogram modeling these predictors has over 85% accuracy in early detecting pregnant women who will progress to GDM later.

The Need for Personalized Risk-Stratified Approaches to Treatment for Gestational Diabetes: A Narrative Review

Gestational diabetes mellitus (GDM) is common and is associated with an increased risk of adverse pregnancy outcomes. However, the prevailing one-size-fits-all approach that treats all women with GDM as having equivalent risk needs revision, given the clinical heterogeneity of GDM, the limitations of a population-based approach to risk, and the need to move beyond a glucocentric focus to address other intersecting risk factors. To address these challenges, we propose using a clinical prediction model for adverse pregnancy outcomes to guide risk-stratified approaches to treatment tailored to the individual needs of women with GDM. This will allow preventative and therapeutic interventions to be delivered to those who will maximally benefit, sparing expense, and harm for those at a lower risk.

Models for estimating and projecting global, regional and national prevalence and disease burden of asthma: a systematic review

Background

Statistical models are increasingly being used to estimate and project the prevalence and burden of asthma. Given substantial variations in these estimates, there is a need to critically assess the properties of these models and assess their transparency and reproducibility. We aimed to critically appraise the strengths, limitations and reproducibility of existing models for estimating and projecting the global, regional and national prevalence and burden of asthma.

Methods

We undertook a systematic review, which involved searching Medline, Embase, World Health Organization Library and Information Services (WHOLIS) and Web of Science from 1980 to 2017 for modelling studies. Two reviewers independently assessed the eligibility of studies for inclusion and then assessed their strengths, limitations and reproducibility using pre-defined quality criteria. Data were descriptively and narratively synthesised.

Results

We identified 108 eligible studies, which employed a total of 51 models: 42 models were used to derive national level estimates, two models for regional estimates, four models for global and regional estimates and three models for global, regional and national estimates. Ten models were used to estimate the prevalence of asthma, 27 models estimated the burden of asthma – including, health care service utilisation, disability-adjusted life years, mortality and direct and indirect costs of asthma – and 14 models estimated both the prevalence and burden of asthma. Logistic and linear regression models were most widely used for national estimates. Different versions of the DisMod-MR- Bayesian meta-regression models and Cause Of Death Ensemble model (CODEm) were predominantly used for global, regional and national estimates. Most models suffered from a number of methodological limitations – in particular, poor reporting, insufficient quality and lack of reproducibility.

Conclusions

Whilst global, regional and national estimates of asthma prevalence and burden continue to inform health policy and investment decisions on asthma, most models used to derive these estimates lack the required reproducibility. There is a need for better-constructed models for estimating and projecting the prevalence and disease burden of asthma and a related need for better reporting of models, and making data and code available to facilitate replication.

Use and performance of machine learning models for type 2 diabetes prediction in community settings: A systematic review and meta-analysis

OBJECTIVE

We aimed to identify machine learning (ML) models for type 2 diabetes (T2DM) prediction in community settings and determine their predictive performance.

METHOD

Systematic review of ML predictive modelling studies in 13 databases since 2009 was conducted. Primary outcomes included metrics of discrimination, calibration, and classification. Secondary outcomes included important variables, level of validation, and intended use of models. Meta-analysis of c-indices, subgroup analyses, meta-regression, publication bias assessments and sensitivity analyses were conducted.

RESULTS

Twenty-three studies (40 prediction models) were included. Studies with high-, moderate-, and low- risk of bias were 3, 14, and 6 respectively. All studies conducted internal validation whereas none conducted external validation of their models. Twenty studies provided classification metrics to varying extents whereas only 7 studies performed model calibration. Eighteen studies reported information on both the variables used for model development and the feature importance. Twelve studies highlighted potential applicability of their models for T2DM screening. Meta-analysis produced a good pooled c-index (0.812). Sources of heterogeneity were identified through subgroup analyses and meta-regression. Issues pertaining to methodological quality and reporting were observed.

CONCLUSIONS

We found evidence of good performance of ML models for T2DM prediction in the community. Improvements to methodology, reporting and validation are needed before they can be used at scale.

Measuring maternal body composition by biomedical impedance can predict risk for gestational diabetes mellitus: a retrospective study among 22,223 women

OBJECTIVES

This study aimed to identify which element of body composition measurements taken before 17th week gestation was the strongest risk factor for gestational diabetes mellitus (GDM) in Chinese pregnant women.

DESIGN AND SETTING

A retrospective study was performed using data retrieved from the Electronic Medical Record database of Chongqing Health Center for Women and Children (China) from January 2014 to December 2015.

PARTICIPANTS

A total of 22,223 women were included with singleton pregnancies and no preexisting diabetes who underwent bioelectrical impedance analysis (BIA) before 17 gestational weeks and 75-g OGTT at 24-28 gestational weeks.

RESULTS

The prevalence of GDM from 2014 to 2015 was 27.13% (IADPSG). All indicators of BIA (total body water, fat mass, fat-free mass, percent body fat, muscle mass, visceral fat levels, proteins, bone minerals, basal metabolic rate, lean trunk mass), age, weight and body mass index (BMI) were risk factors that significantly increased the occurrence of GDM (p < .001 for all). Women older than 30 years or with a BMI more than 23, had a significantly higher GDM prevalence (34.89% and 34.77%). After adjusted covariates, visceral fat levels at the third quartile, the ORs of GDM were 1.142 (95% CI 1.032-1.263) in model I and 1.419 (95% CI 1.274-1.581) in model II used the first quartile as reference (p < .05 for both); bone minerals at the third quartile, the ORs of GDM were 1.124 (95% CI 1.020-1.238) in model I and 1.311 (95% CI 1.192-1.442) in model II (p < .05 for both). After adjusted for age, visceral fat levels and bone minerals, OR of GDM for percent body fat more than 28.77% at the third quartile was 1.334 (95% CI 1.201-1.482) in model II (p < .05 for both).

CONCLUSIONS

Visceral fat levels, bone minerals and percent body fat were significantly associated with an increased risk of GDM, providing the reference ranges of visceral fat levels, bone minerals and percent body fat as predictive factors for Chinese women to estimate the risk of GDM by BIA during pregnancy.

Occupational exposure to whole-body vibrations and pregnancy complications: a nationwide cohort study in Sweden

Objectives

Pregnancy complications are common contributors to perinatal mortality and morbidity. Still, the cause(s) of gestational hypertensive disorders and diabetes are largely unknown. Some occupational exposures have been inconsistently associated with pregnancy complications, but exposure to whole-body vibrations (WBV) has been largely overlooked even though it has been associated with adverse birth outcomes. Therefore, the aim was to assess whether occupational WBV exposure during pregnancy is associated with pregnancy complications in a nationwide, prospective cohort study.

Methods

The Fetal Air Pollution Exposure cohort was formed by merging multiple Swedish, national registers containing information on occupation during pregnancy and diagnosis codes, and includes all working women who gave birth between 1994 and 2014 (n=1 091 044). WBV exposure was derived from a job-exposure matrix and was divided into categories (0, 0.1–0.2, 0.3–0.4 and ≥0.5 m/s²). ORs with 95% CIs were calculated using logistic regression adjusted for potential confounders.

Results

Among women working full time (n=646 490), we found increased risks of all pregnancy complications in the highest exposure group (≥0.5 m/s²), compared with the lowest. The adjusted ORs were 1.76 (95% CI 1.41 to 2.20), 1.55 (95% CI 1.26 to 1.91) and 1.62 (95% CI 1.07 to 2.46) for preeclampsia, gestational hypertension and gestational diabetes, respectively, and were similar in all sensitivity analyses. There were no clear associations for part-time workers.

Conclusions

The results suggest that women should not be exposed to WBV at/above the action limit value of 0.5 m/s² (European directive) continuously through pregnancy. However, these results need further confirmation.

The Unrealised Potential for Predicting Pregnancy Complications in Women with Gestational Diabetes: A Systematic Review and Critical Appraisal

Gestational diabetes (GDM) increases the risk of pregnancy complications. However, these risks are not the same for all affected women and may be mediated by inter-related factors including ethnicity, body mass index and gestational weight gain. This study was conducted to identify, compare, and critically appraise prognostic prediction models for pregnancy complications in women with gestational diabetes (GDM). A systematic review of prognostic prediction models for pregnancy complications in women with GDM was conducted. Critical appraisal was conducted using the prediction model risk of bias assessment tool (PROBAST). Five prediction modelling studies were identified, from which ten prognostic models primarily intended to predict pregnancy complications related to GDM were developed. While the composition of the pregnancy complications predicted varied, the delivery of a large-for-gestational age neonate was the subject of prediction in four studies, either alone or as a component of a composite outcome. Glycaemic measures and body mass index were selected as predictors in four studies. Model evaluation was limited to internal validation in four studies and not reported in the fifth. Performance was inadequately reported with no useful measures of calibration nor formal evaluation of clinical usefulness. Critical appraisal using PROBAST revealed that all studies were subject to a high risk of bias overall driven by methodologic limitations in statistical analysis. This review demonstrates the potential for prediction models to provide an individualised absolute risk of pregnancy complications for women affected by GDM. However, at present, a lack of external validation and high risk of bias limit clinical application. Future model development and validation should utilise the latest methodological advances in prediction modelling to achieve the evolution required to create a useful clinical tool. Such a tool may enhance clinical decision-making and support a risk-stratified approach to the management of GDM. Systematic review registration: PROSPERO CRD42019115223.

External validation and clinical utility of prognostic prediction models for gestational diabetes mellitus: A prospective cohort study

Introduction

We performed an independent validation study of all published first trimester prediction models, containing non‐invasive predictors, for the risk of gestational diabetes mellitus. Furthermore, the clinical potential of the best performing models was evaluated.

Material and methods

Systemically selected prediction models from the literature were validated in a Dutch prospective cohort using data from Expect Study I and PRIDE Study. The predictive performance of the models was evaluated by discrimination and calibration. Clinical utility was assessed using decision curve analysis. Screening performance measures were calculated at different risk thresholds for the best model and compared with current selective screening strategies.

Results

The validation cohort included 5260 women. Gestational diabetes mellitus was diagnosed in 127 women (2.4%). The discriminative performance of the 12 included models ranged from 68% to 75%. Nearly all models overestimated the risk. After recalibration, agreement between the observed outcomes and predicted probabilities improved for most models.

Conclusions

The best performing prediction models showed acceptable performance measures and may enable more personalized medicine‐based antenatal care for women at risk of developing gestational diabetes mellitus compared with current applied strategies.

Altered triglyceride and phospholipid metabolism predates the diagnosis of gestational diabetes in obese pregnancy

Gestational diabetes (GDM), a common pregnancy complication associated with obesity and long-term health risks, is usually diagnosed at approximately 28 weeks of gestation. An understanding of lipid metabolism in women at risk of GDM could contribute to earlier diagnosis and treatment. We tested the hypothesis that altered lipid metabolism at the beginning of the second trimester in obese pregnant women is associated with a diagnosis of GDM. Plasma samples from 831 participants (16-45 years, 15-18 weeks gestation, BMI ≥ 30) from the UPBEAT study of obese pregnant women were used. The lipid, sterol and glyceride fraction was isolated and analysed in a semi-quantitative fashion using direct infusion mass spectrometry. A combination of uni-, multi-variate and multi-variable statistical analyses was used to identify candidate biomarkers in plasma associated with a diagnosis of GDM (early third trimester; IADPSG criteria). Multivariable adjusted analyses showed that participants who later developed GDM had a greater abundance of several triglycerides (48:0, 50:1, 50:2, 51:5, 53:4) and phosphatidylcholine (38:5). In contrast sphingomyelins (32:1, 41:2, 42:3), lyso-phosphatidylcholine (16:0, 18:1), phosphatidylcholines (35:2, 40:7, 40:10), two polyunsaturated triglycerides (46:5, 48:6) and several oxidised triglycerides (48:6, 54:4, 56:4, 58:6) were less abundant. We concluded that both lipid and triglyceride metabolism were altered at least 10 weeks before diagnosis of GDM. Further investigation is required to determine the functional consequences of these differences and the mechanisms by which they arise.

Prognostic prediction models for pregnancy complications in women with gestational diabetes: a protocol for systematic review, critical appraisal and meta-analysis

BACKGROUND

Gestational diabetes (GDM) is increasingly common and has significant implications during pregnancy and for the long-term health of the mother and offspring. However, it is a heterogeneous condition with inter-related factors including ethnicity, body mass index and gestational weight gain significantly modifying the absolute risk of complications at an individual level. Predicting the risk of pregnancy complications for an individual woman with GDM presents a useful adjunct to therapeutic decision-making and patient education. Diagnostic prediction models for GDM are prevalent. In contrast, prediction models for risk of complications in those with GDM are relatively novel. This study will systematically review published prognostic prediction models for pregnancy complications in women with GDM, describe their characteristics, compare performance and assess methodological quality and applicability.

METHODS

Studies will be identified by searching MEDLINE and Embase electronic databases. Title and abstract screening, full-text review and data extraction will be completed independently by two reviewers. The included studies will be systematically assessed for risk of bias and applicability using appropriate tools designed for prediction modelling studies. Extracted data will be tabulated to facilitate qualitative comparison of published prediction models. Quantitative data on predictive performance of these models will be synthesised with meta-analyses if appropriate.

DISCUSSION

This review will identify and summarise all published prognostic prediction models for pregnancy complications in women with GDM. We will compare model performance across different settings and populations with meta-analysis if appropriate. This work will guide subsequent phases in the prognosis research framework: further model development, external validation and model updating, and impact assessment. The ultimate model will estimate the absolute risk of pregnancy complications for women with GDM and will be implemented into routine care as an evidence-based GDM complication risk prediction model. It is anticipated to offer value to women and their clinicians with individualised risk assessment and may assist decision-making. Ultimately, this systematic review is an important step towards a personalised risk-stratified model-of-care for GDM to allow preventative and therapeutic interventions for the maximal benefit to women and their offspring, whilst sparing expense and harm for those at low risk.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO registration number CRD42019115223.

First Trimester Combined Test (FTCT) as a Predictor of Gestational Diabetes Mellitus

Background—The first trimester combined test (FTCT) is an effective screening tool to estimate the risk of fetal aneuploidy. It is obtained by the combination of maternal age, ultrasound fetal nuchal translucency (NT) measurement, and the maternal serum markers free β-human chorionic gonadotropin (β-hCG) and pregnancy-associated plasma protein A (PAPP-A). However, conflicting data have been reported about the association of FTCT, β-hCG, or PAPP-A with the subsequent diagnosis of gestational diabetes mellitus (GDM). Research design and methods—2410 consecutive singleton pregnant women were retrospectively enrolled in Calabria, Southern Italy. All participants underwent examinations for FTCT at 11–13 weeks (plus 6 days) of gestation, and screening for GDM at 16–18 and/or 24–28 weeks of gestation, in accordance with current Italian guidelines and the International Association Diabetes Pregnancy Study Groups (IADPSG) glycemic cut-offs. Data were examined by univariate and logistic regression analyses. Results—1814 (75.3%) pregnant women were normal glucose tolerant, while 596 (24.7%) were diagnosed with GDM. Spearman univariate analysis demonstrated a correlation between FTCT values and subsequent GDM diagnosis (ρ = 0.048, p = 0.018). The logistic regression analysis showed that women with a FTCT <1:10000 had a major GDM risk (p = 0.016), similar to women with a PAPP-A <1 multiple of the expected normal median (MoM, p = 0.014). Conversely, women with β-hCG ≥2.0 MoM had a reduced risk of GDM (p = 0.014). Conclusions—Our findings indicate that GDM susceptibility increases with fetal aneuploidy risk, and that FTCT and its related maternal serum parameters can be used as early predictors of GDM.

Performance of prediction models on survival outcomes of colorectal cancer with surgical resection: A systematic review and meta-analysis

Prediction models allow accurate estimate of individualized prognosis. Increasing numbers of models on survival of CRC patients with surgical resection are being published. However, their performance and potential clinical utility have been unclear. A systematic search in MEDLINE and Embase databases (until 9th April 2018) was performed. Original model development studies and external validation studies predicting any survival outcomes from CRC (follow-up ≥1 year after surgery) were included. We conducted random-effects meta-analyses in external validation studies to estimate the performance of each model. A total of 83 original prediction models and 52 separate external validation studies were identified. We identified five models (Basingstoke score, Fong score, Nordinger score, Peritoneal Surface Disease Severity Score and Valentini nomogram) that were validated in at least two external datasets with a median summarized C-statistic of 0.67 (range: 0.57-0.74). These models can potentially assist clinical decision-making. Besides developing new models, future research should also focus on validating existing prediction models and investigating their real-word impact and cost-effectiveness for CRC prognosis in clinical practice.

External validation and clinical usefulness of first-trimester prediction models for small- and large-for-gestational-age infants: a prospective cohort study

Objective

To assess the external validity of all published first‐trimester prediction models based on routinely collected maternal predictors for the risk of small‐ and large‐for‐gestational‐age (SGA and LGA) infants. Furthermore, the clinical potential of the best‐performing models was evaluated.

Design

Multicentre prospective cohort.

Setting

Thirty‐six midwifery practices and six hospitals (in the Netherlands).

Population

Pregnant women were recruited at <16 weeks of gestation between 1 July 2013 and 31 December 2015.

Methods

Prediction models were systematically selected from the literature. Information on predictors was obtained by a web‐based questionnaire. Birthweight centiles were corrected for gestational age, parity, fetal sex, and ethnicity.

Main outcome measures

Predictive performance was assessed by means of discrimination (C‐statistic) and calibration.

Results

The validation cohort consisted of 2582 pregnant women. The outcomes of SGA <10th percentile and LGA >90th percentile occurred in 203 and 224 women, respectively. The C‐statistics of the included models ranged from 0.52 to 0.64 for SGA (n = 6), and from 0.60 to 0.69 for LGA (n = 6). All models yielded higher C‐statistics for more severe cases of SGA (<5th percentile) and LGA (>95th percentile). Initial calibration showed poor‐to‐moderate agreement between the predicted probabilities and the observed outcomes, but this improved substantially after recalibration.

Conclusion

The clinical relevance of the models is limited because of their moderate predictive performance, and because the definitions of SGA and LGA do not exclude constitutionally small or large infants. As most clinically relevant fetal growth deviations are related to ‘vascular’ or ‘metabolic’ factors, models predicting hypertensive disorders and gestational diabetes are likely to be more specific.

Tweetable abstract

The clinical relevance of prediction models for the risk of small‐ and large‐for‐gestational‐age is limited.

The clinical relevance of prediction models for the risk of small‐ and large‐for‐gestational‐age is limited.

Predictive factors of gestational diabetes in pregnancies following assisted reproductive technology: a nested case-control study

PURPOSE

To evaluate predictive factors for gestational diabetes mellitus (GDM) in singleton pregnancy following assisted reproductive technology (ART).

METHODS

This nested case-control study was performed during October 2016-June 2017. Pregnant women who conceived following ART procedures referred to infertility clinic were selected and categorized into GDM and non-GDM based on ADA/IAPDSG criteria. The study variables including age, educational status, first-degree family history of chronic diseases, systolic and diastolic blood pressure, previous obstetric and perinatal outcomes, infertility history, and ART cycle characteristics were collected from medical records. Prediction model to develop GDM was employed by binary logistic regression analysis after adjustment for age and body mass index, family history of diabetes, and gravidity.

RESULTS

In total, 270 women with singleton pregnancies (consisted of 135 GDM and 135 non-GDM women) conceived were studied. According to the final model, significant predictors of GDM were history of polycystic ovarian syndrome (PCOS), previous ovarian hyper-stimulation syndrome (OHSS) risk and progesterone injections. Administration of injectable progesterone during the first 10-12 weeks of pregnancy was associated with an approximately twofold increased risk of developing GDM [odds ratio (OR) 2.28, 95% confidence interval (CI) 1.27-4.09)] compared to vaginal progesterone. In addition, the regression analysis revealed that previous OHSS risk (OR 2.40, 95% CI 1.34-4.31) and history of PCOS (OR 2.76, 95% CI 1.26-6.06) were other most important predictors of GDM.

CONCLUSIONS

The route of progesterone administration, previous OHSS risk and history of PCOS seem to be putative risk factors for GDM in women conceived by ART.