The triad macrosomia, obesity, and hypertriglyceridemia in gestational diabetes

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.

Refining the diagnosis of gestational diabetes mellitus: a systematic review and meta-analysis

BACKGROUND

Perinatal outcomes vary for women with gestational diabetes mellitus (GDM). The precise factors beyond glycemic status that may refine GDM diagnosis remain unclear. We conducted a systematic review and meta-analysis of potential precision markers for GDM.

METHODS

Systematic literature searches were performed in PubMed and EMBASE from inception to March 2022 for studies comparing perinatal outcomes among women with GDM. We searched for precision markers in the following categories: maternal anthropometrics, clinical/sociocultural factors, non-glycemic biochemical markers, genetics/genomics or other -omics, and fetal biometry. We conducted post-hoc meta-analyses of a subset of studies with data on the association of maternal body mass index (BMI, kg/m2) with offspring macrosomia or large-for-gestational age (LGA).

RESULTS

A total of 5905 titles/abstracts were screened, 775 full-texts reviewed, and 137 studies synthesized. Maternal anthropometrics were the most frequent risk marker. Meta-analysis demonstrated that women with GDM and overweight/obesity vs. GDM with normal range BMI are at higher risk of offspring macrosomia (13 studies [n = 28,763]; odds ratio [OR] 2.65; 95% Confidence Interval [CI] 1.91, 3.68), and LGA (10 studies [n = 20,070]; OR 2.23; 95% CI 2.00, 2.49). Lipids and insulin resistance/secretion indices were the most studied non-glycemic biochemical markers, with increased triglycerides and insulin resistance generally associated with greater risk of offspring macrosomia or LGA. Studies evaluating other markers had inconsistent findings as to whether they could be used as precision markers.

CONCLUSIONS

Maternal overweight/obesity is associated with greater risk of offspring macrosomia or LGA in women with GDM. Pregnancy insulin resistance or hypertriglyceridemia may be useful in GDM risk stratification. Future studies examining non-glycemic biochemical, genetic, other -omic, or sociocultural precision markers among women with GDM are warranted.

Maternal AA/EPA Ratio and Triglycerides as Potential Biomarkers of Patients at Major Risk for Pharmacological Therapy in Gestational Diabetes

Gestational diabetes mellitus (GD) is characterized by glycemic and lipid metabolism alterations in an environment of low-grade inflammation. Our trial aimed to assess the effect of nutraceutical supplements (omega-3 fatty acids, anthocyanins, and alpha-cyclodextrins) in GD patients and evaluate the role of anthropometric, metabolic, and inflammatory parameters as biomarkers to identify subjects who require pharmacological hypoglycemic treatment during gestation. Pregnant women with GD at 24-28 weeks of gestation were enrolled in a double-blind trial and randomized to receive either nutraceutical supplements or a placebo for 12 weeks. No statistically significant differences were observed between the two groups in blood and urine measurements of metabolic, inflammatory, and antioxidant parameters. In the whole cohort, pre-pregnancy BMI and anthropometric measurements were significantly different in patients who required pharmacological intervention. These patients showed higher triglycerides, CRP, and insulin levels and gave birth to newborns with significantly higher weights. Subjects with a greater AA/EPA ratio had higher PAF levels and gave birth four days earlier. In conclusion, one-to-one nutritional coaching and poor compliance with nutraceutical supplementation might have outweighed the impact of this intervention. However, triglyceride concentration and the AA/EPA ratio seems to be a biomarker for higher inflammatory levels and GD candidates for pharmacological treatment. An adequate assumption of omega-3 in women with GD, either by a controlled diet or by nutraceutical supplementation, reduces the need for pharmacological therapy.

Maternal Glycemic Spectrum and Adverse Pregnancy and Perinatal Outcomes in a Multiracial US Cohort

Diabetes mellitus (pregestational (PDM) and gestational (GDM)) is associated with adverse pregnancy outcomes (APOs). However, studies exploring the association of APOs with maternal glycemia among women without PDM/GDM are limited. We utilized data from 4119 women (307-PDM; 582-GDM; 3230-non-PDM/GDM) in the Boston Birth Cohort (1998-2016). Women in the non-PDM/GDM group were subdivided by tertiles of 1 h, 50 g oral glucose load test at 24-32 weeks: T1: 50-95 mg/dL (n = 1166), T2: 96-116 mg/dL (n = 1151), T3: 117-201 mg/dL (n = 913). Using multivariable logistic regression, we examined the association of maternal glycemia with APOs-preterm birth (PTB) and hypertensive disorders of pregnancy (HDP)-and adverse perinatal outcomes-high birth weight (HBW), cesarean section (CS), and sub-analyses by race-ethnicity. Compared to women in T1, women in T2 and T3 had a higher prevalence of pre-existing hypertension (T1: 2.8% vs. T2: 5.2% vs. T3: 6.3%) and obesity (T1: 13.3% vs. T2: 18.1% vs. T3: 22.9%). Women in T2 and T3 had higher odds of HBW (adjusted odds ratio aOR T2: 1.47 [1.01-2.19] T3: 1.68 [1.13-2.50]) compared to women in T1. Additionally, women in T2, compared to T1, had higher odds of HDP (aOR 1.44 [1.10-1.88]). Among non-Hispanic Black (NHB) women, those in T2 and T3 had higher odds of HDP compared to T1 (aOR T2 1.67 [1.13-2.51]; T3: 1.68 [1.07-2.62]). GDM and PDM were associated with higher odds of HBW, CS, PTB, and HDP, compared to women in T1. In this predominantly NHB and Hispanic cohort, moderate maternal glycemia without PDM/GDM was associated with higher odds of HBW and HDP, even more strongly among NHB women. If confirmed, a review of current guidelines of glucose screening and risk stratification in pregnancy may be warranted.

Acute effect of high-intensity interval training exercise on redox status in the ovaries of rats fed a high-fat diet

This study demonstrates the effect of a single high-intensity interval training (HIIT) session on the redox status of rat ovaries with excess adiposity. Forty Wistar female rats (mean (±s.e.m.) weight 94.40 ± 13.40 g) were divided into two groups and fed either a standard diet (SD) or a high-fat diet (HFD) for 62 days. At the end of this period, the rats were subjected to a single HIIT session and were killed 24 h after exercise. Both groups subjected to exercise (SDex and HFDex) generated a significantly higher antioxidant environment by presenting a higher thiol content, which represents a lower oxidation rate of GSH than their respective controls (SD and HFD). The percentage of morphologically normal primary follicles decreased, whereas that of antral follicles increased, in the SDex group. In addition, the HFD group had a higher percentage of degenerated antral follicles than the SD and SDex groups. Cells immunoreactive for α-smooth muscle actin were seen in the cortical stroma and thecal layer enclosing late secondary and tertiary follicles in all groups. Moreover, heme oxygenase and cytochrome P450 family 19 subfamily A member 1 (Cyp19A1) labelling was seen in all antral follicles. Progesterone concentrations were significantly higher in the HFDex than SDex group. In conclusion, this study indicates that a single session of HIIT may result in an improvement in ovary redox status because of metabolic muscle activity by inducing physiological adaptation after exercise in a paracrine manner.

Establishment of a nomogram model to predict macrosomia in pregnant women with gestational diabetes mellitus

AIM

To establish a nomogram model to predict the risk of macrosomia in pregnant women with gestational diabetes mellitus in China.

METHODS

We retrospectively collected the medical records of 783 pregnant women with gestational diabetes who underwent prenatal examinations and delivered at the Affiliated Hospital of Qingdao University from October 2019 to October 2020. The pregnant women were randomly divided into two groups in a 4:1 ratio to generate and validate the model. The independent risk factors for macrosomia in pregnant women with gestational diabetes mellitus were analyzed by multivariate logistic regression, and the nomogram model to predict the risk of macrosomia in pregnant women with gestational diabetes mellitus was established and verified by R software.

RESULTS

Logistic regression analysis showed that prepregnancy body mass index, weight gain during pregnancy, fasting plasma glucose, triglycerides, biparietal diameter and amniotic fluid index were independent risk factors for macrosomia (P < 0.05). The areas under the ROC curve for internal and external validation of the model were 0.813 (95 % confidence interval 0.754-0.862) and 0.903 (95 % confidence interval 0.588-0.967), respectively. The calibration curve was a straight line with a slope close to 1.

CONCLUSIONS

In this study, we constructed a nomogram model to predict the risk of macrosomia in pregnant women with gestational diabetes mellitus. The model has good discrimination and calibration abilities, which can help clinical healthcare staff accurately predict macrosomia in pregnant women with gestational diabetes mellitus.

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.

The Placenta as a Target of Epigenetic Alterations in Women with Gestational Diabetes Mellitus and Potential Implications for the Offspring

Gestational diabetes mellitus (GDM) is a pregnancy complication first detected in the second or third trimester in women that did not show evident glucose intolerance or diabetes before gestation. In 2019, the International Diabetes Federation reported that 15.8% of live births were affected by hyperglycemia during pregnancy, of which 83.6% were due to gestational diabetes mellitus, 8.5% were due to diabetes first detected in pregnancy, and 7.9% were due to diabetes detected before pregnancy. GDM increases the susceptibility to developing chronic diseases for both the mother and the baby later in life. Under GDM conditions, the intrauterine environment becomes hyperglycemic, while also showing high concentrations of fatty acids and proinflammatory cytokines, producing morphological, structural, and molecular modifications in the placenta, affecting its function; these alterations may predispose the baby to disease in adult life. Molecular alterations include epigenetic mechanisms such as DNA and RNA methylation, chromatin remodeling, histone modifications, and expression of noncoding RNAs (ncRNAs). The placenta is a unique organ that originates only in pregnancy, and its main function is communication between the mother and the fetus, ensuring healthy development. Thus, this review provides up-to-date information regarding two of the best-documented (epigenetic) mechanisms (DNA methylation and miRNA expression) altered in the human placenta under GDM conditions, as well as potential implications for the offspring.

Interactions between Prepregnancy Overweight and Passive Smoking for Macrosomia and Large for Gestational Age in Chinese Pregnant Women

INTRODUCTION

Previous analysis showed that passive smoking and overweight were associated with an increased risk of gestational diabetes mellitus (GDM) in a synergistic manner, while GDM increased the risk of macrosomia/large for gestational age (LGA). This study aimed to examine any interactive effects between passive smoking and overweight/obesity on risk of macrosomia/LGA.

METHODS

From 2010 to 2012, 22,302 pregnant women registered for pregnancy at a primary hospital in Tianjin, China. Data were collected longitudinally; that is, from their first antenatal care visit, at the glucose challenge test (GCT) time (24-28 weeks of gestation) and at delivery. Passive smoking was self-reported. Macrosomia was defined as birth weight ≥4,000 g. Binary logistic regression was used to obtain odds ratios (ORs) and 95% confidence intervals (CIs). Additive interaction was used to test the synergistic effect.

RESULTS

Passive smokers accounted for 57.4% of women (n = 8,230). Using nonpassive smoking and prepregnancy body mass index (BMI) <24.0 kg/m2 as the reference, the adjusted ORs of overweight alone and passive smoking alone for macrosomia were 2.39 (95% CI: 2.11-2.71) and 1.17 (95% CI: 1.04-1.32). Copresence of passive smoking and prepregnancy BMI ≥24.0 kg/m2 increased the OR to 2.70 (95% CI: 2.28-3.20), with a significant additive interaction. After further adjustment for GDM or GCT, the OR of copresence of both risk factors was slightly attenuated to 2.52 (2.13-3.00) and 2.51 (2.11-2.98), with significant additive interaction. However, the additive interaction between prepregnancy overweight/obesity and passive smoking for LGA was nonsignificant.

CONCLUSIONS

Prepregnancy overweight/obesity was associated with an increased risk of macrosomia in Chinese women synergistically with passive smoking during pregnancy, and most of the association was not modified by hyperglycemia during pregnancy.

The combination of symphysis-fundal height and abdominal circumference as a novel predictor of macrosomia in GDM and normal pregnancy

Background

Macrosomia is a major adverse pregnancy outcome of gestational diabetes mellitus (GDM). Although BMI, symphysis-fundal height (SFH) and abdominal circumference (AC) are associated with foetal weight, there are some limitations to their use, especially for the prediction of macrosomia. This study aimed to identify a novel predictive methodology to improve the prediction of high-risk macrosomia.

Methods

Clinical information was collected from 3730 patients. The association between the ISFHAC (index of the SFH algorithm multiplied by the square of AC) and foetal weight was determined and validated. A new index, the ISFHAC, was evaluated by area under the curve (AUC) analysis.

Results

A total of 1087 GDM and 657 normal singleton pregnancies were analysed. The ISFHAC was positively correlated with foetal weight in GDM pregnancies and normal pregnancies (NPs). The AUCs of the ISFHAC were 0.815 in the GDM group and 0.804 in the NP group, which were higher than those of BMI, SFH, AC and GA. The ISFHAC cut-off points were 41.7 and 37 in the GDM and NP groups, respectively. The sensitivity values for the prediction of macrosomia with high ISFHAC values were 75.9 and 81.3% in the GDM and NP groups, respectively, which were higher than those with BMI. Regarding the validation data, the sensitivity values for prediction with high ISFHAC values were 78.9% (559 GDM pregnancies) and 78.3% (1427 NPs).

Conclusions

The ISFHAC can be regarded as a new predictor of and risk factor for macrosomia in GDM pregnancy and NP.

Low Serum Vitamin B12 Levels Are Associated with Adverse Lipid Profiles in Apparently Healthy Young Saudi Women

An abnormal lipid profile is an independent risk factor for cardiovascular diseases. The relationship between vitamin B12 deficiency and lipid profile is inconclusive, with most studies conducted in unhealthy populations. In this study, we aimed to assess the relationship between serum vitamin B12 levels and lipid profiles in a cross-sectional study that included 341 apparently healthy Saudi women, aged 19-30 years, from different colleges at King Saud University, Saudi Arabia. Sociodemographic, anthropometric, biochemical, and lifestyle data were collected, including diet and physical activity. Serum vitamin B12 deficiency was defined as serum B12 level of <148 pmol/L. The prevalence of vitamin B12 deficiency was approximately 0.6%. Using multivariable linear regression models, serum vitamin B12 levels were found to be inversely associated with total cholesterol (B = -0.26; p < 0.001), low-density lipoprotein cholesterol levels (B = -0.30; p < 0.001), and triglyceride (B = -0.16; p < 0.01) after adjusting for potential confounders, while obesity indices of body mass index, central obesity, and fat percentage showed no association. Therefore, we conclude that low serum vitamin B12 levels are independently associated with abnormal lipid profiles in healthy young Saudi women. Further interventional studies are needed to determine whether improving serum vitamin B12 levels in a healthy population can improve lipid profiles.