Prediction of large-for-gestational-age infants in pregnancies complicated by obesity: A population-based cohort study

A critique of measurement of defective insulin secretion and insulin sensitivity as a precision approach to gestational diabetes

AIMS/HYPOTHESIS

Precision medicine approaches to gestational diabetes mellitus (GDM) have categorised patients according to disease pathophysiology (insulin resistance, insulin insufficiency or both), and demonstrated associations with clinical outcomes. We aimed to assess whether using enhanced processing to determine indices of insulin secretion and sensitivity is analytically robust, reproducible in a different population, and useful diagnostically and prognostically in clinical practice.

METHODS

A total of 1308 pregnant women with one or more risk factors for GDM who underwent a 75 g OGTT at one of nine hospital sites were recruited to this observational study. Specimens were collected for determination of glucose levels using standard and enhanced procedures, HbA1c and insulin analysis. GDM diagnosis and management followed National Institute for Health and Care Excellence guidance. We categorised women into pathophysiological subtypes: insulin-resistant GDM (HOMA2-S < 25th centile of the population with normal glucose tolerance [NGT]), insulin-insufficient GDM (HOMA2-B < 25th centile), both or neither. We assessed associations with pregnancy outcomes using logistic regression.

RESULTS

Using enhanced specimen handling, 1027/1308 (78.5%) women had NGT, with 281/1308 (21.5%) being classified as having GDM. Of this group, 135/281 (48.0%) had insulin-resistant GDM, 73/281 (26.0%) had insulin-insufficient GDM and 2/281 (0.7%) had both insulin-resistant and insulin-insufficient GDM. Unexpectedly, 71 patients (25.3%) had GDM with both HOMA2-S and HOMA2-B ≥ 25th centile (GDM-neither). This novel subgroup appeared to be relatively insulin-sensitive in the fasting state but developed marked post-load hyperglycaemia and hyperinsulinaemia, suggesting an isolated postprandial defect in insulin sensitivity that was not captured by HOMA2-B or HOMA2-S. Women within most GDM subgroups had comparable pregnancy outcomes to those of normoglycaemic women, and HOMA2-B and HOMA2-S were weak predictors of pregnancy outcomes. Maternal BMI predicted a similar number of outcomes to HOMA2-S, suggesting that there was no additional predictive value in adding HOMA2-S. Similar findings were obtained when using different indices and standard specimen handling techniques.

CONCLUSIONS/INTERPRETATION

Precision categorisation of GDM using HOMA2-S and HOMA2-B does not provide useful diagnostic or prognostic information, but did distinguish a novel subgroup of patients with GDM, characterised by an isolated postprandial defect in insulin sensitivity.

Risk factors of large for gestational age among pregnant women with gestational diabetes mellitus: a protocol for systematic review and meta-analysis

INTRODUCTION

Women with gestational diabetes mellitus (GDM) are more likely to give birth to large for gestational age (LGA) infants, due to abnormalities in glucose metabolism during pregnancy. Although previous studies have explored the risk factors for LGA delivery in GDM women, the results are quite different and still lack of unified understanding.

OBJECTIVE

To explore the elements linked to LGA delivery in GDM women, and thus provide a reference for medical staff to formulate relevant clinical interventions.

METHODS AND ANALYSIS

Systematic search of seven electronic databases (PubMed, Scopus, Cochrane Library, Web of Science, EMBASE, OVID and CINAHL) will be undertaken between the inception of the database to 1 August 2024. Quantitative studies published in English and focused on the risk factors for LGA delivery in GDM women will be included. Two researchers will independently screen the literature and any disagreements will be resolved by a third-party researcher. Joanna Briggs's Institutional Critical Appraisal Tools will be used for the quality assessment of included studies. RevMan V.5.4 software will be used for data processing and summarising. To ensure the reliability and stability of the results, Q test and I2 test will be used to identify the heterogeneity between studies, while subgroup analysis and sensitivity analysis will be performed based on study quality.

ETHICS AND DISSEMINATION

This systematic review and meta-analysis will be based on published literature, and the findings will be published in a peer-reviewed journal and presented at major conferences focused on clinical nursing.

PROSPERO REGISTRATION NUMBER

CRD42024559013.

Multivariable prediction models for fetal macrosomia and large for gestational age: A systematic review

BACKGROUND

The identification of large for gestational age (LGA) and macrosomic fetuses is essential for counselling and managing these pregnancies.

OBJECTIVES

To systematically review the literature for multivariable prediction models for LGA and macrosomia, assessing the performance, quality and applicability of the included model in clinical practice.

SEARCH STRATEGY

MEDLINE, EMBASE and Cochrane Library were searched until June 2022.

SELECTION CRITERIA

We included observational and experimental studies reporting the development and/or validation of any multivariable prediction model for fetal macrosomia and/or LGA. We excluded studies that used a single variable or did not evaluate model performance.

DATA COLLECTION AND ANALYSIS

Data were extracted using the Checklist for critical appraisal and data extraction for systematic reviews of prediction modelling studies checklist. The model performance measures discrimination, calibration and validation were extracted. The quality and completion of reporting within each study was assessed by its adherence to the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) checklist. The risk of bias and applicability were measured using PROBAST (Prediction model Risk Of Bias Assessment Tool).

MAIN RESULTS

A total of 8442 citations were identified, with 58 included in the analysis: 32/58 (55.2%) developed, 21/58 (36.2%) developed and internally validated and 2/58 (3.4%) developed and externally validated a model. Only three studies externally validated pre-existing models. Macrosomia and LGA were differentially defined by many studies. In total, 111 multivariable prediction models were developed using 112 different variables. Model discrimination was wide ranging area under the receiver operating characteristics curve (AUROC 0.56-0.96) and few studies reported calibration (11/58, 19.0%). Only 5/58 (8.6%) studies had a low risk of bias.

CONCLUSIONS

There are currently no multivariable prediction models for macrosomia/LGA that are ready for clinical implementation.

Developing and validating a predictive model of delivering large-for-gestational-age infants among women with gestational diabetes mellitus

BACKGROUND

The birth of large-for-gestational-age (LGA) infants is associated with many short-term adverse pregnancy outcomes. It has been observed that the proportion of LGA infants born to pregnant women with gestational diabetes mellitus (GDM) is significantly higher than that born to healthy pregnant women. However, traditional methods for the diagnosis of LGA have limitations. Therefore, this study aims to establish a predictive model that can effectively identify women with GDM who are at risk of delivering LGA infants.

AIM

To develop and validate a nomogram prediction model of delivering LGA infants among pregnant women with GDM, and provide strategies for the effective prevention and timely intervention of LGA.

METHODS

The multivariable prediction model was developed by carrying out the following steps. First, the variables that were associated with LGA risk in pregnant women with GDM were screened by univariate analyses, for which the P value was < 0.10. Subsequently, Least Absolute Shrinkage and Selection Operator regression was fit using ten cross-validations, and the optimal combination factors were selected by choosing lambda 1se as the criterion. The final predictors were determined by multiple backward stepwise logistic regression analysis, in which only the independent variables were associated with LGA risk, with a P value < 0.05. Finally, a risk prediction model was established and subsequently evaluated by using area under the receiver operating characteristic curve, calibration curve and decision curve analyses.

RESULTS

After using a multistep screening method, we establish a predictive model. Several risk factors for delivering an LGA infant were identified (P < 0.01), including weight gain during pregnancy, parity, triglyceride-glucose index, free tetraiodothyronine level, abdominal circumference, alanine transaminase-aspartate aminotransferase ratio and weight at 24 gestational weeks. The nomogram's prediction ability was supported by the area under the curve (0.703, 0.709, and 0.699 for the training cohort, validation cohort, and test cohort, respectively). The calibration curves of the three cohorts displayed good agreement. The decision curve showed that the use of the 10%-60% threshold for identifying pregnant women with GDM who are at risk of delivering an LGA infant would result in a positive net benefit.

CONCLUSION

Our nomogram incorporated easily accessible risk factors, facilitating individualized prediction of pregnant women with GDM who are likely to deliver an LGA infant.

Effects of prepregnancy dietary patterns on infant birth weight: a prospective cohort study

BACKGROUND

Maternal nutrition can have a profound effect on fetal growth, development, and subsequent infant birth weight. However, little is known regarding the influence of prepregnancy dietary patterns.

OBJECTIVES

This study aimed to explore the effects between prepregnancy dietary patterns on birth weight.

METHODS

This study included 911 singleton live-born infants from the Taicang and Wuqiang Mother-Child Cohort Study (TAWS). Baseline information and prepregnancy diet data were collected during early pregnancy. Newborn birth information was obtained from the Wuqiang County Hospital. Macrosomia, defined as a birth weight of ≥4000 g, and large for gestational age (LGA), defined as a birth weight higher than the 90th percentile for the same sex and gestational age, were the outcomes of interest. The dietary patterns were extracted using principal component analysis. Logistic regression models were used to investigate the association between prepregnancy dietary patterns (in tertiles) and macrosomia and LGA, and subgroup analysis was further explored by pre-pregnancy body mass index (BMI).

RESULTS

Four dietary patterns were identified based on 15 food groups. These patterns were named as "cereals-vegetables-fruits," "vegetables-poultry-aquatic products," "milk-meat-eggs," and "nuts-aquatic products-snacks." After adjusting for sociodemographic characteristics, pregnancy complications, and other dietary patterns, greater adherence to the "cereals-vegetables-fruits" pattern before pregnancy was associated with a higher risk of macrosomia (adjusted OR = 2.220, 95% CI: 1.018, 4.843), while greater adherence to the "nuts-aquatic products-snacks" pattern was associated with a lower risk of macrosomia (adjusted OR = 0.357, 95% CI: 0.175, 0.725) compared to the lowest tertile. No significant association was observed between prepregnancy dietary patterns and LGA. However, after subgroup analysis of pre-pregnancy BMI, "cereals-vegetables-fruits" pattern was associated with an increased risk of LGA in overweight and obese mothers (adjusted OR = 2.353, 95% CI: 1.010, 5.480).

CONCLUSIONS

An unbalanced pre-pregnancy diet increases the risk of macrosomia and LGA, especially in overweight or obese women before pre-pregnancy.

Combined Effect of Maternal Obesity and Diabetes on Excessive Fetal Growth: Pregnancy Risk Assessment Monitoring System (PRAMS), United States, 2012-2015

Introduction

Obesity and dysregulation in glucose metabolism are risk factors for excessive fetal growth, but their combined effects are not often examined in a single study.

Methods

Data from the Centers for Disease Control and Prevention's Pregnancy Risk Assessment Monitoring System Phase 7 (2012-2015) were used. Logistic regression was used to investigate the association between maternal prepregnancy BMI and pre-existing diabetes/gestational diabetes on the odds of delivering a large-for-gestational-age infant or an infant with macrosomia.

Results

Complete data for 128,199 singleton births were used. The proportions of large-for-gestational-age infants and infants with macrosomia increased with the degree of obesity (p<0.001) and were higher in women with diabetes than in those without (p<0.001). Compared with the AOR among normal-weight women, the AOR of delivering large-for-gestational-age infants and infants with macrosomia among women with morbid obesity (BMI≥40) were 2.82 (p<0.001) and 2.67 (p<0.001), respectively. Compared with the AOR among nondiabetic women, the AOR of delivering a large-for-gestational-age infant was 1.88 (p<0.001) among those with pre-existing diabetes and 1.49 (p<0.001) among those with gestational diabetes. Except for the underweight group, women with pre-existing diabetes were nearly twice as likely to deliver a large-for-gestational-age infant as those with similar BMI without diabetes. Women with morbid obesity and gestational diabetes were twice as likely to have a large-for-gestational-age infant and an infant with macrosomia as nondiabetic women with normal BMI.

Conclusions

We have shown that when maternal obesity and diabetes, particularly pre-existing diabetes, occur together, the risk of delivering large-for-gestational-age and macrosomia increases significantly. Our findings call for public health attention to address maternal obesity and diabetes to minimize suboptimal fetal growth.

Association between maternal adiposity measures and infant health outcomes: A systematic review and meta-analysis

Maternal obesity increases risks of adverse fetal and infant outcomes. Guidelines use body mass index to diagnose maternal obesity. Evidence suggests body fat distribution might better predict individual risk, but there is a lack of robust evidence during pregnancy. We explored associations between maternal adiposity and infant health. Searches included six databases, references, citations, and contacting authors. Screening and quality assessment were carried out by two authors independently. Random effects meta-analysis and narrative synthesis were conducted. We included 34 studies (n = 40,143 pregnancies). Meta-analysis showed a significant association between maternal fat-free mass and birthweight (average effect [AE] 18.07 g, 95%CI 12.75, 23.38) but not fat mass (AE 8.76 g, 95%CI -4.84, 22.36). Women with macrosomic infants had higher waist circumference than controls (mean difference 4.93 cm, 95% confidence interval [CI] 1.05, 8.82). There was no significant association between subcutaneous fat and large for gestational age (odds ratio 1.06 95% CI 0.91, 1.25). Waist-to-hip ratio, neck circumference, skinfolds, and visceral fat were significantly associated with several infant outcomes including small for gestational age, preterm delivery, neonatal morbidity, and mortality, although meta-analysis was not possible for these variables. Our findings suggest that some measures of maternal adiposity may be useful for risk prediction of infant outcomes. Individual participant data meta-analysis could overcome some limitations in our ability to pool published data.

Childhood obesity and adverse cardiometabolic risk in large for gestational age infants and potential early preventive strategies: a narrative review

Accumulating evidence indicates that obesity and cardiometabolic risks become established early in life due to developmental programming and infants born as large for gestational age (LGA) are particularly at risk. This review summarizes the recent literature connecting LGA infants and early childhood obesity and cardiometabolic risk and explores potential preventive interventions in early infancy. With the rising obesity rates in women of childbearing age, the LGA birth rate is about 10%. Recent literature continues to support the higher rates of obesity in LGA infants. However, there is a knowledge gap for their lifetime risk for adverse cardiometabolic outcomes. Potential factors that may modify the risk in early infancy include catch-down early postnatal growth, reduction in body fat growth trajectory, longer breastfeeding duration, and presence of a healthy gut microbiome. The early postnatal period may be a critical window of opportunity for active interventions to mitigate or prevent obesity and potential adverse metabolic consequences in later life. A variety of promising candidate biomarkers for the early identification of metabolic alterations in LGA infants is also discussed. IMPACT: LGA infants are the greatest risk category for future obesity, especially if they experience rapid postnatal growth during infancy. Potential risk modifying secondary prevention strategies in early infancy in LGA infants include catch-down early postnatal growth, reduction in body fat growth trajectory, longer breastfeeding duration, and presence of a healthy gut microbiome. LGA infants may be potential low-hanging fruit targets for early preventive interventions in the fight against childhood obesity.

A first trimester prediction model for large for gestational age infants: a preliminary study

Background

Large for gestational age infants (LGA) have increased risk of adverse short-term perinatal outcomes. This study aims to develop a multivariable prediction model for the risk of giving birth to a LGA baby, by using biochemical, biophysical, anamnestic, and clinical maternal characteristics available at first trimester.

Methods

Prospective study that included all singleton pregnancies attending the first trimester aneuploidy screening at the Obstetric Unit of the University Hospital of Modena, in Northern Italy, between June 2018 and December 2019.

Results

A total of 503 consecutive women were included in the analysis. The final prediction model for LGA, included multiparity (OR = 2.8, 95% CI: 1.6–4.9, p = 0.001), pre-pregnancy BMI (OR = 1.08, 95% CI: 1.03–1.14, p = 0.002) and PAPP-A MoM (OR = 1.43, 95% CI: 1.08–1.90, p = 0.013). The area under the ROC curve was 70.5%, indicating a satisfactory predictive accuracy. The best predictive cut-off for this score was equal to − 1.378, which corresponds to a 20.1% probability of having a LGA infant. By using such a cut-off, the risk of LGA can be predicted in our sample with sensitivity of 55.2% and specificity of 79.0%.

Conclusion

At first trimester, a model including multiparity, pre-pregnancy BMI and PAPP-A satisfactorily predicted the risk of giving birth to a LGA infant. This promising tool, once applied early in pregnancy, would identify women deserving targeted interventions.

Trial registration

ClinicalTrials.gov NCT04838431, 09/04/2021.

Comparison of ultrasound with biomarkers to identify large-for-gestational age in women screened for gestational diabetes mellitus

OBJECTIVE

Large-for-gestational-age (LGA) is associated with both fetal and maternal complications. One of the few modifiable risk factors for LGA is Gestational Diabetes Mellitus (GDM); for this reason, fetal growth is usually monitored by ultrasound in the third trimester. This prospective study compared a panel of ten established biomarkers measured at the time of selective screening for GDM at 26-28 weeks gestation with the ultrasound prediction of LGA.

METHOD

Women were recruited using convenience sampling and consented at the first antenatal visit. Women with maternal risk factors for GDM attended for the one-step 75 g oral glucose tolerance test. An additional blood sample was taken for biomarker measurement. GDM was diagnosed according to the 2013 World Health Organization (WHO) criteria. Fetal biometry, including the abdominal circumference (AC) and the fetal abdominal subcutaneous tissue (FAST) thickness, were measured at 37 weeks gestation.

RESULTS

Of the 195 women included, 105 (53.8%) had GDM. Of the 195 babies, 36 (18.5%) were LGA. Whether the woman had GDM or not, fetal biometry was strongly predictive of LGA but none of the following biomarkers measured at 26-28 weeks gestation alone or in combination were predictive: c-peptide, ghrelin, gastric inhibitory polypeptide, glucagon-like peptide-1 (GLP-1), glucagon, insulin, leptin, plasminogen activator inhibitor-1, resistin and visfatin.

CONCLUSIONS

In women diagnosed with GDM, surveillance of fetal growth to identify LGA by ultrasound should continue in the third trimester. None of the ten established maternal biomarkers measured at the time of the OGTT was as strongly predictive of LGA as ultrasound.