Early Antenatal Prediction of Gestational Diabetes in Obese Women: Development of Prediction Tools for Targeted Intervention

Genetic and Biotechnological Approaches to Gestational Diabetes Mellitus: Advancing Diagnostics, Treatment Strategies, and Public Health Implications

Background Gestational diabetes mellitus (GDM) is a disorder where pregnant women have difficulty processing glucose, which influences the mother's and the fetus's health. The rising prevalence of GDM, often linked to obesity, highlights the need to study its processes and develop appropriate treatment techniques. Conventional diagnostic approaches may not accurately anticipate or address genetic predispositions, emphasizing the need for further investigation. Objective This study aims to explore genetic and biotechnological approaches to improve diagnostics, treatment, and public health strategies for GDM. Methodology This research was carried out in two hospitals in Pakistan over a period of 12 months, from January to December 2023. A sample size of 260 was determined using an anticipated GDM prevalence of 15%. The data were obtained by administering structured questionnaires, doing anthropometric measures, and analyzing blood samples for genetic information. The statistical study included the use of descriptive statistics, chi-square tests, and logistic regression to examine the relationships between genetic markers and the risk of GDM. The significance level of these associations was assessed using a p-value of <0.05. Results Among the 260 individuals with GDM included in the research, 52 (20.00%) and 73 (28.08%) patients had the TCF7L2 risk variation. The study found that the TCF7L2 and FTO risk polymorphisms were linked to a higher likelihood of developing GDM among the participants. The association was statistically significant, with p-values of 0.012 and 0.045, respectively. Logistic regression analysis showed that the TCF7L2 risk variant had an odds ratio of 2.35 (95% confidence interval [CI]: 1.21-4.56, p = 0.012), while the FTO risk variant had an odds ratio of 1.97 (95% CI: 1.05-3.70, p = 0.045). There is a substantial association between consuming a large amount of sugar (OR: 2.10, 95% CI: 1.15-3.82, p = 0.017) and engaging in little physical activity (OR: 1.85, 95% CI: 1.07-3.22, p = 0.025) and an increased risk of GDM. Treatment brought the glucose levels of people with GDM down to normal in 68.49% of cases. Conclusion Integrating genetic screening for TCF7L2 and FTO variants with lifestyle modifications may enhance early detection and personalized management of GDM, leading to improved maternal and fetal health outcomes.

Vitamin D status of pregnant women with obesity in the UK and its association with pregnancy outcomes: a secondary analysis of the UK Pregnancies Better Eating and Activity Trial (UPBEAT) study

Prenatal vitamin D deficiency is widely reported and may affect perinatal outcomes. In this secondary analysis of the UK Pregnancies Better Eating and Activity Trial, we examined vitamin D status and its relationship with selected pregnancy outcomes in women with obesity (BMI ≥ 30 kg/m2) from multi-ethnic inner-city settings in the UK. Determinants of vitamin D status at a mean of 17 ± 1 weeks' gestation were assessed using multivariable linear regression and reported as percent differences in serum 25-hydroxyvitamin D (25(OH)D). Associations between 25(OH)D and clinical outcomes were examined using logistic regression. Among 1089 participants, 67 % had 25(OH)D < 50 nmol/l and 26 % had concentrations < 25 nmol/l. In fully adjusted models accounting for socio-demographic and anthropometric characteristics, 25(OH)D was lower among women of Black (% difference = -33; 95 % CI: -39, -27), Asian (% difference = -43; 95 % CI: -51, -35) and other non-White (% difference = -26; 95 % CI: -35, -14) ethnicity compared with women of White ethnicity (n 1086; P < 0·001 for all). In unadjusted analysis, risk of gestational diabetes was greater in women with 25(OH)D < 25 nmol/l compared with ≥ 50 nmol/l (OR = 1·58; 95 % CI: 1·09, 2·31), but the magnitude of effect estimates was attenuated in the multivariable model (OR = 1·33; 95 % CI: 0·88, 2·00). There were no associations between 25(OH)D and risk of preeclampsia, preterm birth or small for gestational age or large-for-gestational-age delivery. These findings demonstrate low 25(OH)D among pregnant women with obesity and highlight ethnic disparities in vitamin D status in the UK. However, evidence for a greater risk of adverse perinatal outcomes among women with vitamin D deficiency was limited.

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.

Mid-upper arm circumference predicts the risk of gestational diabetes in early pregnancy

BACKGROUND

The present work aimed to assess the value of mid-upper arm circumference (MUAC) at 8 to 12 weeks in predicting the occurrence of gestational diabetes mellitus (GDM).

METHODS

According to eligibility criteria, 328 women with singleton pregnancies who underwent routine antenatal check-ups at Qinhuangdao Maternal and Child Health Hospital from September 2017 to September 2020 were included. The patients were divided into the gestational diabetes mellitus (GDM) and non-GDM groups according to oral glucose tolerance test (OGTT) data from gestation weeks 24 to 28. Clinical data were compared between the two groups. Logistic regression analysis was performed to determine factors independently predicting GDM. Receiver operating characteristic (ROC) curve analysis was employed to analyze the value of MUAC in predicting the occurrence of GDM. The optimal cut-off points were calculated.

RESULTS

In logistic regression analysis, pre-pregnancy weight, waist circumference, MUAC, UA, TG, and HDL-C independently predicted the occurrence of GDM (P < 0.05). MUAC retained statistical significance upon adjustment for various confounders (OR = 8.851, 95%CI: 3.907-20.048; P < 0.001). ROC curve analysis revealed good diagnostic potential for MUAC in GDM (AUC = 0.742, 95%CI: 0.684-0.800, P < 0.001), with a cut-off of 28.5 cm, sensitivity and specificity were 61% and 77%, respectively.

CONCLUSION

Pregnant women with MUAC >28.5 cm are prone to develop GDM during pregnancy, indicating that MUAC as an important predictive factor of GDM in early pregnancy.

Development and validation of a clinical model to predict preconception risk of gestational diabetes mellitus in nulliparous women: A retrospective cohort study

OBJECTIVE

To develop and validate a model to predict the preconception risk of gestational diabetes mellitus (GDM) in nulliparous women.

METHODS

This was a retrospective cohort study. A total of 1565 women in early pregnancy who underwent preconception health examinations in the Women and Children's Hospital of Chongqing Medical University between January 2020 and June 2021 were invited to participate in a questionnaire survey. Logistic regression analysis was performed to determine the preconception risk factors for GDM. These factors were used to construct a model to predict GDM risk in nulliparous women. Then, the model was used to assess the preconception risk of GDM in 1060 nulliparous women.

RESULTS

Independent preconception risk factors for GDM included the following: age 35 years or greater, diastolic blood pressure 80 mm Hg or greater, fasting plasma glucose 5.1 mmol/L or greater, body mass index (BMI, calculated as weight in kilograms divided by the square of height in meters) 24 or greater, weight gain 10 kg or greater in the year before pregnancy, age of menarche 15 years or greater, three or more previous pregnancies, daily staple food intake 300 g or greater, fondness for sweets, and family history of diabetes. BMI less than 18.5, daily physical activity duration 1 h or greater, and high-intensity physical activity were protective factors. These factors were used to construct a model to predict GDM risk in nulliparous women, and the incidence of GDM significantly increased as the risk score increased. The area under the curve of the prediction model was 0.82 (95% confidence interval 0.80-0.85).

CONCLUSION

The preconception GDM risk prediction model demonstrated good predictive efficacy and can be used to identify populations at high risk of GDM before pregnancy, which provides the possibility for preconception intervention.

Interventions in preconception and pregnant women at risk of gestational diabetes; a systematic review and meta-analysis of randomised controlled trials

BACKGROUND

Women at risk of gestational diabetes mellitus (GDM) need preventative interventions.

OBJECTIVE

To evaluate targeted interventions before and during pregnancy for women identified as being at risk of developing GDM.

METHODS

Systematic review and meta-analysis conducted following PRISMA guidelines. MEDLINE, EMBASE and the Cochrane Library in addition to reference and citation lists were searched to identify eligible randomised controlled trials (RCTs) utilising risk stratification during the preconception period or in the first/early second trimester. Screening and data extraction were carried out by the authors independently. Quality assessment was conducted based on the Cochrane risk-of-bias tool. Random effects meta-analysis and narrative synthesis were performed.

RESULTS

Eighty-four RCTs were included: two during preconception and 82 in pregnancy, with a pooled sample of 22,568 women. Interventions were behavioural (n = 54), dietary supplementation (n = 19) and pharmacological (n = 11). Predictive factors for risk assessment varied; only one study utilised a validated prediction model. Gestational diabetes was reduced in diet and physical activity interventions (risk difference - 0.03, 95% CI 0.06, - 0.01; I2 58.69%), inositol (risk difference - 0.19, 95% CI 0.33, - 0.06; I2 92.19%), and vitamin D supplements (risk difference - 0.16, 95% CI 0.25, - 0.06; I2 32.27%). Subgroup analysis showed that diet and physical activity interventions were beneficial in women with ≥ 2 GDM risk factors (risk difference - 0.16, 95% CI 0.25, - 0.07; I2 11.23%) while inositol supplementation was effective in women with overweight or obesity (risk difference - 0.17, 95% CI 0.22, - 0.11; I2 0.01%). Effectiveness of all other interventions were not statistically significant.

CONCLUSIONS

This review provides evidence that interventions targeted at women at risk of GDM may be an effective strategy for prevention. Further studies using validated prediction tools or multiple risk factors to target high-risk women for intervention before and during pregnancy are warranted.

The effect of circulating adiponectin levels on incident gestational diabetes mellitus: systematic review and meta‑analysis

BACKGROUND

To quantitatively synthesize evidence from prospective observational studies regarding the mean levels of circulating adiponectin in patients with gestational diabetes mellitus (GDM) and the association between adiponectin levels and GDM risk.

METHODS

PubMed, EMBASE and Web of Science were searched from their inception until November 8th, 2022, for nested case-control studies and cohort studies. Random-effect models were applied to the synthesized effect sizes. The difference in circulating adiponectin levels between the GDM and control groups was measured using the pooled standardized mean difference (SMD) and 95% confidence interval (CI). The relationship between circulating adiponectin levels and GDM risk was examined using the combined odds ratio (OR) and 95% CI. Subgroup analyses were performed according to the study continent, GDM risk in the study population, study design, gestational weeks of circulating adiponectin detection, GDM diagnostic criteria, and study quality. Sensitivity and cumulative analyses were performed to evaluate the stability of the meta-analysis. Publication bias was assessed by funnel plots and Egger's test.

RESULTS

The 28 studies included 13 cohort studies and 15 nested case-control studies, containing 12,256 pregnant women in total. The mean adiponectin level in GDM patients was significantly lower than in controls (SMD = -1.514, 95% CI = -2.400 to -0.628, p = .001, I² = 99%). The risk of GDM was significantly decreased among pregnant women with increasing levels of circulating adiponectin (OR = 0.368, 95% CI = 0.271-0.500, p < .001, I²=83%). There were no significant differences between the subgroups.

CONCLUSIONS

Our findings indicate that increasing circulating adiponectin levels were inversely associated with the risk of GDM. Given the inherent heterogeneity and publication bias of the included studies, further well-designed large-scale prospective cohort or intervention studies are needed to confirm our finding.

A Simplified Screening Model to Predict the Risk of Gestational Diabetes Mellitus in Pregnant Chinese Women

INTRODUCTION

This study aimed to develop a simplified screening model to identify pregnant Chinese women at risk of gestational diabetes mellitus (GDM) in the first trimester.

METHODS

This prospective study included 1289 pregnant women in their first trimester (6-12 weeks of gestation) with clinical parameters and laboratory data. Logistic regression was performed to extract coefficients and select predictors. The performance of the prediction model was assessed in terms of discrimination and calibration. Internal validation was performed through bootstrapping (1000 random samples).

RESULTS

The prevalence of GDM in our study cohort was 21.1%. Maternal age, prepregnancy body mass index (BMI), a family history of diabetes, fasting blood glucose levels, the alanine transaminase to aspartate aminotransferase ratio (ALT/AST), and the triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C) were selected for inclusion in the prediction model. The Hosmer-Lemeshow goodness-of-fit test showed good consistency between prediction and actual observation, and bootstrapping indicated good internal performance. The area under the receiver operating characteristic curve (ROC-AUC) of the multivariate logistic regression model and the simplified clinical screening model was 0.825 (95% confidence interval [CI] 0.797-0.853, P < 0.001) and 0.784 (95% CI 0.750-0.818, P < 0.001), respectively. The performance of our prediction model was superior to that of three other published models.

CONCLUSION

We developed a simplified clinical screening model for predicting the risk of GDM in pregnant Chinese women. The model provides a feasible and convenient protocol to identify women at high risk of GDM in early pregnancy. Further validations are needed to evaluate the performance of the model in other populations.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT03246295.

An early prediction model for gestational diabetes mellitus based on metabolomic biomarkers

BACKGROUND

Gestational diabetes mellitus (GDM) represents the main metabolic alteration during pregnancy. The available methods for diagnosing GDM identify women when the disease is established, and pancreatic beta-cell insufficiency has occurred.The present study aimed to generate an early prediction model (under 18 weeks of gestation) to identify those women who will later be diagnosed with GDM.

METHODS

A cohort of 75 pregnant women was followed during gestation, of which 62 underwent normal term pregnancy and 13 were diagnosed with GDM. Targeted metabolomics was used to select serum biomarkers with predictive power to identify women who will later be diagnosed with GDM.

RESULTS

Candidate metabolites were selected to generate an early identification model employing a criterion used when performing Random Forest decision tree analysis. A model composed of two short-chain acylcarnitines was generated: isovalerylcarnitine (C5) and tiglylcarnitine (C5:1). An analysis by ROC curves was performed to determine the classification performance of the acylcarnitines identified in the study, obtaining an area under the curve (AUC) of 0.934 (0.873-0.995, 95% CI). The model correctly classified all cases with GDM, while it misclassified ten controls as in the GDM group. An analysis was also carried out to establish the concentrations of the acylcarnitines for the identification of the GDM group, obtaining concentrations of C5 in a range of 0.015-0.25 μmol/L and of C5:1 with a range of 0.015-0.19 μmol/L.

CONCLUSION

Early pregnancy maternal metabolites can be used to screen and identify pregnant women who will later develop GDM. Regardless of their gestational body mass index, lipid metabolism is impaired even in the early stages of pregnancy in women who develop GDM.

A mixed-methods study exploring women's perceptions and recommendations for a pregnancy app with monitoring tools

Digital health tools such as apps are being increasingly used by women to access pregnancy-related information. Conducted during the COVID-19 pandemic, this study investigated: (i) pregnant women's current usage of digital health tools to self-monitor and (ii) their interest in theoretical pregnancy app features (a direct patient-to-healthcare-professional communication tool and a body measurement tool). Using a mixed methods approach, 108 pregnant women were surveyed and 15 currently or recently pregnant women were interviewed online. We found that pregnant women used digital health tools to mainly access pregnancy related information and less so to self-monitor. Most participants were interested and enthusiastic about a patient-to-healthcare-professional communication tool. About half of the survey participants (49%) felt comfortable using a body measurement tool to monitor their body parts and 80% of interview participants were interested in using the body measurement to track leg/ankle swelling. Participants also shared additional pregnancy app features that they thought would be beneficial such as a "Digital Wallet" and a desire for a holistic pregnancy app that allowed for more continuous and personalised care. This study highlights the gaps and needs of pregnant women and should inform all stakeholders designing pregnancy digital healthcare. This study offers a unique insight into the needs of pregnant women during a very particular and unique period in human history.

Identification of potential biomarkers and metabolic insights for gestational diabetes prevention: A review of evidence contrasting gestational diabetes versus weight loss studies that may direct future nutritional metabolomics studies

Gestational diabetes mellitus (GDM) significantly increases maternal health risks and adverse effects for the offspring. Observational studies suggest that weight loss before pregnancy may be a promising GDM prevention method. Still, biochemical pathways linking preconception weight changes with subsequent development of GDM among women who are overweight or obese remain unclear. Metabolomic assessment is a powerful approach for understanding the global biochemical pathways linking preconception weight changes and subsequent GDM. We hypothesize that many of the alterations of metabolite levels associated with GDM will change in one direction in GDM studies but will change in the opposite direction in studies focusing on lifestyle interventions for weight loss. The present review summarizes available evidence from 21 studies comparing women with GDM with healthy participants and 12 intervention studies that investigated metabolite changes that occurred during weight loss using caloric restriction and behavioral interventions. We discuss 15 metabolites, including amino acids, lipids, amines, carbohydrates, and carbohydrate derivatives. Of particular note are the altered levels of branched-chain amino acids, alanine, palmitoleic acid, lysophosphatidylcholine 18:1, and hypoxanthine because of their mechanistic links to insulin resistance and weight change. Mechanisms that may explain how these metabolite modifications contribute to GDM development in those who are overweight or obese are proposed, including insulin resistance pathways. Future nutritional metabolomics preconception intervention studies in overweight or obese are necessary to investigate whether weight loss through lifestyle intervention can reduce GDM occurrence in association with these metabolite alterations and to test the value of these metabolites as potential diagnostic biomarkers of GDM development.

Establishment and validation of a predictive nomogram for gestational diabetes mellitus during early pregnancy term: A retrospective study

Objective

This study aims to develop and evaluate a predictive nomogram for early assessment risk factors of gestational diabetes mellitus (GDM) during early pregnancy term, so as to help early clinical management and intervention.

Methods

A total of 824 pregnant women at Zhongnan Hospital of Wuhan University and Maternal and Child Health Hospital of Hubei Province from 1 February 2020 to 30 April 2020 were enrolled in a retrospective observational study and comprised the training dataset. Routine clinical and laboratory information was collected; we applied least absolute shrinkage and selection operator (LASSO) logistic regression and multivariate ROC risk analysis to determine significant predictors and establish the nomogram, and the early pregnancy files (gestational weeks 12-16, n = 392) at the same hospital were collected as a validation dataset. We evaluated the nomogram via the receiver operating characteristic (ROC) curve, C-index, calibration curve, and decision curve analysis (DCA).

Results

We conducted LASSO analysis and multivariate regression to establish a GDM nomogram during the early pregnancy term; the five selected risk predictors are as follows: age, blood urea nitrogen (BUN), fibrinogen-to-albumin ratio (FAR), blood urea nitrogen-to-creatinine ratio (BUN/Cr), and blood urea nitrogen-to-albumin ratio (BUN/ALB). The calibration curve and DCA present optimal predictive power. DCA demonstrates that the nomogram could be applied clinically.

Conclusion

An effective nomogram that predicts GDM should be established in order to help clinical management and intervention at the early gestational stage.

Metabolic Profiling of Pregnant Women with Obesity: An Exploratory Study in Women at Greater Risk of Gestational Diabetes

Gestational diabetes mellitus (GDM) is one of the most prevalent obstetric conditions, particularly among women with obesity. Pathways to hyperglycaemia remain obscure and a better understanding of the pathophysiology would facilitate early detection and targeted intervention. Among obese women from the UK Pregnancies Better Eating and Activity Trial (UPBEAT), we aimed to compare metabolic profiles early and mid-pregnancy in women identified as high-risk of developing GDM, stratified by GDM diagnosis. Using a GDM prediction model combining maternal age, mid-arm circumference, systolic blood pressure, glucose, triglycerides and HbA1c, 231 women were identified as being at higher-risk, of whom 119 women developed GDM. Analyte data (nuclear magnetic resonance and conventional) were compared between higher-risk women who developed GDM and those who did not at timepoint 1 (15+0−18+6 weeks) and at timepoint 2 (23+2−30+0 weeks). The adjusted regression analyses revealed some differences in the early second trimester between those who developed GDM and those who did not, including lower adiponectin and glutamine concentrations, and higher C-peptide concentrations (FDR-adjusted p < 0.005, < 0.05, < 0.05 respectively). More differences were evident at the time of GDM diagnosis (timepoint 2) including greater impairment in β-cell function (as assessed by HOMA2-%B), an increase in the glycolysis-intermediate pyruvate (FDR-adjusted p < 0.001, < 0.05 respectively) and differing lipid profiles. The liver function marker γ-glutamyl transferase was higher at both timepoints (FDR-adjusted p < 0.05). This exploratory study underlines the difficulty in early prediction of GDM development in high-risk women but adds to the evidence that among pregnant women with obesity, insulin secretory dysfunction may be an important discriminator for those who develop GDM.

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 maternal adiposity measures and adverse maternal outcomes of pregnancy: Systematic review and meta-analysis

Maternal obesity increases pregnancy-related risks. Women with a body mass index (BMI) ≥ 30 kg/m2 are considered to be at risk and should receive additional care, although approximately half will have uncomplicated pregnancies. This systematic review aimed to identify early pregnancy measures of adiposity associated with adverse maternal health outcomes. Searches included six databases, reference lists, citations, and contacting authors. Screening and quality assessment were carried out by two authors independently. Random effects meta-analysis and narrative synthesis were conducted. Seventy studies were included with a pooled sample of 89,588 women. Meta-analysis showed significantly increased odds of gestational diabetes mellitus (GDM) with higher waist circumference (WC) categories (1.40, 95% confidence interval [CI] 1.04, 1.88) and per unit increase in WC (1.31, 95% CI 1.03, 1.67). Women with GDM had higher WC than controls (mean difference [MD] 6.18 cm, 95% CI 3.92, 8.44). WC was significantly associated with hypertensive disorders, delivery-related outcomes, metabolic syndrome, and composite pregnancy outcomes. Waist to hip ratio was significantly associated with GDM, hypertensive disorders, and delivery-related outcomes. Fat mass, neck circumference, skinfolds, and visceral fat were significantly associated with adverse outcomes, although limited data were available. Our findings identify the need to explore how useful adiposity measures are at predicting risk in pregnancy, compared with BMI, to direct care to women with the greatest need.

Neck circumference as a predictor of gestational diabetes and risk of adverse outcomes in pregnancy of Brazilian woman with overweight and obesity

Objective

To evaluate the association of neck circumference (NC) with gestational diabetes (GDM) and adverse outcomes in women with overweight and obesity.

Subjects and methods

This prospective study included 132 (BMI > 25 kg/m2) pregnant women without and with GDM. Standardized questionnaire and biochemical/physical evaluation were performed during the 1st to 3rd trimester. Fifth-five women were evaluated regarding hypertension in pregnancy, type of delivery and neonatal complications (death, intensive care unit admission and hypoglycemia).

Results

Women with (n = 61) and without (n = 71) GDM had similar mean (SD) pre-gestational BMI [30.3 (4.0) vs. 29.4 (3.5) kg/m2, p = 0.16]. Women with GDM were older [32 (6) vs. 28 (6) yrs, p < 0.001] and had greater NC [36.0 (2.7) vs. 34.5 (1.8) cm, p < 0.001]. NC was similar in women with GDM diagnosed in first or third trimester [p = 0.4] and was correlated with FPG [r 0.29, p = 0.01] and systolic [r 0.28, p = 0.001] and diastolic [r 0.25, p = 0.004] blood pressure. NC was associated with GDM [OR 1.25, 95%CI 1.03-1.52] adjusted for age, physical activity, education and familiar history of diabetes. In ROC analysis, the area under the curve was 0.655 and the cut-off value of 34.5 cm had 0.70 of sensitivity and 0.51 of specificity for GDM. Women who had NC ≥ 34.5 vs. < 34.5 cm had higher frequencies of hypertension [32.3 vs. 4.2%, p = 0.01].

Conclusion

In a group of pregnant women with overweight or obesity, NC can be a useful tool for identifying risk of GDM and obstetric adverse outcomes.

Prevention of gestational diabetes in pregnant women with obesity: protocol for a pilot randomised controlled trial

BACKGROUND

Obesity in pregnancy increases the risk of gestational diabetes mellitus (GDM) and associated adverse outcomes. Despite metabolic differences, all pregnant women with obesity are considered to have the same risk of developing GDM. Improved risk stratification is required to enable targeted intervention in women with obesity who would benefit the most. The aim of this study is to identify pregnant women with obesity at higher risk of developing GDM and, in a pilot randomised controlled trial (RCT), test feasibility and assess the efficacy of a lifestyle intervention and/or metformin to improve glycaemic control.

METHODS

Women aged 18 years or older with a singleton pregnancy and body mass index (BMI) ≥ 30kg/m² will be recruited from one maternity unit in London, UK. The risk of GDM will be assessed using a multivariable GDM prediction model combining maternal age, mid-arm circumference, systolic blood pressure, glucose, triglycerides and HbA1c. Women identified at a higher risk of developing GDM will be randomly allocated to one of two intervention groups (lifestyle advice with or without metformin) or standard antenatal care. The primary feasibility outcomes are study recruitment, retention rate and intervention adherence and to collect information needed for the sample size calculation for the definitive trial. A process evaluation will assess the acceptability of study processes and procedures to women. Secondary patient-centred outcomes include a reduction in mean glucose/24h of 0.5mmol/l as assessed by continuous glucose monitoring and changes in a targeted maternal metabolome, dietary intake and physical activity. A sample of 60 high-risk women is required.

DISCUSSION

Early risk stratification of GDM in pregnant women with obesity and targeted intervention using lifestyle advice with or without metformin could improve glucose tolerance compared to standard antenatal care. The results from this feasibility study will inform a larger adequately powered RCT should the intervention show trends for potential effectiveness.

TRIAL REGISTRATION

This study has been approved by the NHS Research Ethics Committee (UK IRAS integrated research application system; reference 18/LO/1500). EudraCT number 2018-000003-16 .

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

AIMS

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

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

Gestational diabetes in women with obesity; an analysis of clinical history and simple clinical/anthropometric measures

AIM

We assessed clinical risk factors, anthropometric measures of adiposity and weight gain to determine associations with development of GDM in a cohort of pregnant women with obesity.

METHODS

This was a secondary analysis of the UPBEAT trial of a complex lifestyle intervention in pregnant women with obesity (ISRCTN89971375). Clinical risk factors, and measures of adiposity and weight were assessed in the early 2nd trimester (mean 17 +0 weeks), and adiposity and weight repeated in the early 3rd trimester (mean 27 +5 weeks').

RESULTS

Of the 1117 women (median BMI 35.0 kg/m2) with complete data, 25.8% (n = 304) developed GDM (IADPSG criteria, OGTT 24-28weeks). Using multivariable analysis, early clinical risk factors associated with later development of GDM included age (adj OR 1.06 per year; 95% CI 1.04-1.09), previous GDM (3.27; 1.34-7.93) and systolic blood pressure (per 10mmHg, 1.34; 1.18-1.53). Anthropometric measures positively associated with GDM included second trimester (mean 17+0 weeks) subscapular skinfold thickness, (per 5mm, 1.12; 1.05-1.21), and neck circumference (per cm, 1.11; 1.05-1.18). GDM was not associated with gestational weight gain, or changes in skinfolds thicknesses or circumferences between visits.

CONCLUSIONS

In this cohort of women with obesity, we confirmed clinical risk factors for GDM, (age, systolic blood pressure) previously identified in heterogeneous weight women but add to these indices of adiposity which may provide a discriminatory approach to GDM risk assessment in this group. This study also underscores the need to focus on modifiable factors pre-pregnancy as an opportunity for GDM prevention, as targeting gestational weight gain and adiposity during pregnancy is likely to be less effective.

First Trimester Plasma MicroRNA Levels Predict Risk of Developing Gestational Diabetes Mellitus

AIMS

Our objective is to identify first-trimester plasmatic miRNAs associated with and predictive of GDM.

METHODS

We quantified miRNA using next-generation sequencing in discovery (Gen3G: n = 443/GDM = 56) and replication (3D: n = 139/GDM = 76) cohorts. We have diagnosed GDM using a 75-g oral glucose tolerance test and the IADPSG criteria. We applied stepwise logistic regression analysis among replicated miRNAs to build prediction models.

RESULTS

We identified 17 miRNAs associated with GDM development in both cohorts. The prediction performance of hsa-miR-517a-3p|hsa-miR-517b-3p, hsa-miR-218-5p, and hsa-let7a-3p was slightly better than GDM classic risk factors (age, BMI, familial history of type 2 diabetes, history of GDM or macrosomia, and HbA1c) (AUC 0.78 vs. 0.75). MiRNAs and GDM classic risk factors together further improved the prediction values [AUC 0.84 (95% CI 0.73-0.94)]. These results were replicated in 3D, although weaker predictive values were obtained. We suggest very low and higher risk GDM thresholds, which could be used to identify women who could do without a diagnostic test for GDM and women most likely to benefit from an early GDM prevention program.

CONCLUSIONS

In summary, three miRNAs combined with classic GDM risk factors provide excellent prediction values, potentially strong enough to improve early detection and prevention of GDM.

Gestational Diabetes Mellitus in Europe: A Systematic Review and Meta-Analysis of Prevalence Studies

Background

Gestational Diabetes Mellitus (GDM) is defined as the type of hyperglycemia diagnosed for the first-time during pregnancy, presenting with intermediate glucose levels between normal levels for pregnancy and glucose levels diagnostic of diabetes in the non-pregnant state. We aimed to systematically review and meta-analyze studies of prevalence of GDM in European countries at regional and sub-regional levels, according to age, trimester, body weight, and GDM diagnostic criteria.

Methods

Systematic search was conducted in five databases to retrieve studies from 2014 to 2019 reporting the prevalence of GDM in Europe. Two authors have independently screened titles and abstracts and full text according to eligibility using Covidence software. A random-effects model was used to quantify weighted GDM prevalence estimates. The National Heart, Lung, and Blood Institute criteria was used to assess the risk of bias.

Results

From the searched databases, 133 research reports were deemed eligible and included in the meta-analysis. The research reports yielded 254 GDM-prevalence studies that tested 15,572,847 pregnant women between 2014 and 2019. The 133 research reports were from 24 countries in Northern Europe (44.4%), Southern Europe (27.1%), Western Europe (24.1%), and Eastern Europe (4.5%). The overall weighted GDM prevalence in the 24 European countries was estimated at 10.9% (95% CI: 10.0-11.8, I2 : 100%). The weighted GDM prevalence was highest in the Eastern Europe (31.5%, 95% CI: 19.8-44.6, I2 : 98.9%), followed by in Southern Europe (12.3%, 95% CI: 10.9-13.9, I2 : 99.6%), Western Europe (10.7%, 95% CI: 9.5-12.0, I2 : 99.9%), and Northern Europe (8.9%, 95% CI: 7.9-10.0, I2 : 100). GDM prevalence was 2.14-fold increased in pregnant women with maternal age ≥30 years (versus 15-29 years old), 1.47-fold if the diagnosis was made in the third trimester (versus second trimester), and 6.79- fold in obese and 2.29-fold in overweight women (versus normal weight).

Conclusions

In Europe, GDM is significant in pregnant women, around 11%, with the highest prevalence in pregnant women of Eastern European countries (31.5%). Findings have implications to guide vigilant public health awareness campaigns about the risk factors associated with developing GDM.

Systematic Review Registration

PROSPERO [https://www.crd.york.ac.uk/PROSPERO/], identifier CRD42020161857.

Longitudinal Metabolic Profiling of Maternal Obesity, Gestational Diabetes, and Hypertensive Pregnancy Disorders

CONTEXT

Comprehensive assessment of metabolism in maternal obesity and pregnancy disorders can provide information about the shared maternal-fetal milieu and give insight into both maternal long-term health and intergenerational transmission of disease burden.

OBJECTIVE

To assess levels, profiles, and change in the levels of metabolic measures during pregnancies complicated by obesity, gestational diabetes (GDM), or hypertensive disorders.

DESIGN, SETTING AND PARTICIPANTS

A secondary analysis of 2 study cohorts, PREDO and RADIEL, including 741 pregnant women.

MAIN OUTCOME MEASURES

We assessed 225 metabolic measures by nuclear magnetic resonance in blood samples collected at median 13 [interquartile range (IQR) 12.4-13.7], 20 (IQR 19.3-23.0), and 28 (27.0-35.0) weeks of gestation.

RESULTS

Across all 3 time points women with obesity [body mass index (BMI) ≥ 30kg/m2] in comparison to normal weight (BMI 18.5-24.99 kg/m2) had significantly higher levels of most very-low-density lipoprotein-related measures, many fatty and most amino acids, and more adverse metabolic profiles. The change in the levels of most metabolic measures during pregnancy was smaller in obese than in normal weight women. GDM, preeclampsia, and chronic hypertension were associated with metabolic alterations similar to obesity. The associations of obesity held after adjustment for GDM and hypertensive disorders, but many of the associations with GDM and hypertensive disorders were rendered nonsignificant after adjustment for BMI and the other pregnancy disorders.

CONCLUSIONS

This study shows that the pregnancy-related metabolic change is smaller in women with obesity, who display metabolic perturbations already in early pregnancy. Metabolic alterations of obesity and pregnancy disorders resembled each other suggesting a shared metabolic origin.

Protocol for a randomized controlled trial of pre-pregnancy lifestyle intervention to reduce recurrence of gestational diabetes: Gestational Diabetes Prevention/Prevención de la Diabetes Gestacional

BACKGROUND

Gestational diabetes mellitus (GDM) is associated with several maternal complications in pregnancy, including preeclampsia, preterm labor, need for induction of labor, and cesarean delivery as well as increased long-term risks of type 2 diabetes, metabolic syndrome, and cardiovascular disease. Intrauterine exposure to GDM raises the risk for complications in offspring as well, including stillbirth, macrosomia, and birth trauma, and long-term risk of metabolic disease. One of the strongest risk factors for GDM is the occurrence of GDM in a prior pregnancy. Preliminary data from epidemiologic and bariatric surgery studies suggest that reducing body weight before pregnancy can prevent the development of GDM, but no adequately powered trial has tested the effects of a maternal lifestyle intervention before pregnancy to reduce body weight and prevent GDM recurrence.

METHODS

The principal aim of the Gestational Diabetes Prevention/Prevención de la Diabetes Gestacional is to determine whether a lifestyle intervention to reduce body weight before pregnancy can reduce GDM recurrence. This two-site trial targets recruitment of 252 women with overweight and obesity who have previous histories of GDM and who plan to have another pregnancy in the next 1-3 years. Women are randomized within site to a comprehensive pre-pregnancy lifestyle intervention to promote weight loss with ongoing treatment until conception or an educational control group. Participants are assessed preconceptionally (at study entry, after 4 months, and at brief quarterly visits until conception), during pregnancy (at 26 weeks' gestation), and at 6 weeks postpartum. The primary outcome is GDM recurrence, and secondary outcomes include fasting glucose, biomarkers of cardiometabolic disease, prenatal and perinatal complications, and changes over time in weight, diet, physical activity, and psychosocial measures.

DISCUSSION

The Gestational Diabetes Prevention /Prevención de la Diabetes Gestacional is the first randomized controlled trial to evaluate the effects of a lifestyle intervention delivered before pregnancy to prevent GDM recurrence. If found effective, the proposed lifestyle intervention could lay the groundwork for shifting current treatment practices towards the interconception period and provide evidence-based preconception counseling to optimize reproductive outcomes and prevent GDM and associated health risks.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02763150 . Registered on May 5, 2016.

Risk Factors Screening for Gestational Diabetes Mellitus Heterogeneity in Chinese Pregnant Women: A Case-Control Study

PURPOSE

To study the risk factors of gestational diabetes mellitus (GDM) heterogeneity, and to evaluate the correlation between the risk factors and obesity.

METHODS

We performed a case-control study of 452 women with GDM and 516 women with normal glucose tolerance (NGT) at the first and second trimester. We defined GDM women as GDM-resistance subtype, GDM-dysfunction subtype, and GDM-mixed subtype, according to their simultaneous insulin-release test with predominant insulin-sensitivity defect, insulin-secretion defect, or both defects.

RESULTS

We found that higher maternal age, family history of diabetes, the elevated level of fasting blood glucose in the first trimester (≥5.1 mmol/L) were risk factors of all GDM subtypes. Pre-pregnancy overweight/obesity and the increased gestational weight gain (GWG) in the first-trimester are risk factors of the GDM-resistance subtype. Indicators including younger age at first menstruation, the elevated levels of alanine aminotransferase (ALT), total bile acid (TBA), triglyceride (TG), and the decreased level of high-density lipoprotein cholesterol (HDL-C) are risk factors of the GDM-resistance subtype. However, the associations between those risk factors and GDM-resistance subtype attenuated after adjusted by pre-pregnancy body mass index (pre-BMI) and gestational weight gain (GWG) in the first trimester. Nonalcoholic fatty liver disease (NAFLD) and the improved level of TG are independent risk factors for the GDM-resistance subtype and the GDM-mixed subtype, respectively.

CONCLUSION

Women with GDM exhibited heterogeneity based on glycemic physiology and their risk factors are not all the same. Some obesity-related risk factors are specific to the GDM-resistance subtype, which are mediated by pre-pregnancy overweight/obesity and the elevated GWG the first-trimester.

Do nuclear magnetic resonance (NMR)-based metabolomics improve the prediction of pregnancy-related disorders? Findings from a UK birth cohort with independent validation

BACKGROUND

Prediction of pregnancy-related disorders is usually done based on established and easily measured risk factors. Recent advances in metabolomics may provide earlier and more accurate prediction of women at risk of pregnancy-related disorders.

METHODS

We used data collected from women in the Born in Bradford (BiB; n = 8212) and UK Pregnancies Better Eating and Activity Trial (UPBEAT; n = 859) studies to create and validate prediction models for pregnancy-related disorders. These were gestational diabetes mellitus (GDM), hypertensive disorders of pregnancy (HDP), small for gestational age (SGA), large for gestational age (LGA) and preterm birth (PTB). We used ten-fold cross-validation and penalised regression to create prediction models. We compared the predictive performance of (1) risk factors (maternal age, pregnancy smoking, body mass index (BMI), ethnicity and parity) to (2) nuclear magnetic resonance-derived metabolites (N = 156 quantified metabolites, collected at 24-28 weeks gestation) and (3) combined risk factors and metabolites. The multi-ethnic BiB cohort was used for training and testing the models, with independent validation conducted in UPBEAT, a multi-ethnic study of obese pregnant women.

RESULTS

Maternal age, pregnancy smoking, BMI, ethnicity and parity were retained in the combined risk factor and metabolite models for all outcomes apart from PTB, which did not include maternal age. In addition, 147, 33, 96, 51 and 14 of the 156 metabolite traits were retained in the combined risk factor and metabolite model for GDM, HDP, SGA, LGA and PTB, respectively. These include cholesterol and triglycerides in very low-density lipoproteins (VLDL) in the models predicting GDM, HDP, SGA and LGA, and monounsaturated fatty acids (MUFA), ratios of MUFA to omega 3 fatty acids and total fatty acids, and a ratio of apolipoprotein B to apolipoprotein A-1 (APOA:APOB1) were retained predictors for GDM and LGA. In BiB, discrimination for GDM, HDP, LGA and SGA was improved in the combined risk factors and metabolites models. Risk factor area under the curve (AUC 95% confidence interval (CI)): GDM (0.69 (0.64, 0.73)), HDP (0.74 (0.70, 0.78)) and LGA (0.71 (0.66, 0.75)), and SGA (0.59 (0.56, 0.63)). Combined risk factor and metabolite models AUC 95% (CI): GDM (0.78 (0.74, 0.81)), HDP (0.76 (0.73, 0.79)) and LGA (0.75 (0.70, 0.79)), and SGA (0.66 (0.63, 0.70)). For GDM, HDP and LGA, but not SGA, calibration was good for a combined risk factor and metabolite model. Prediction of PTB was poor for all models. Independent validation in UPBEAT at 24-28 weeks and 15-18 weeks gestation confirmed similar patterns of results, but AUCs were attenuated.

CONCLUSIONS

Our results suggest a combined risk factor and metabolite model improves prediction of GDM, HDP and LGA, and SGA, when compared to risk factors alone. They also highlight the difficulty of predicting PTB, with all models performing poorly.

Gestational diabetes: opportunities for improving maternal and child health

Gestational diabetes, the most common medical disorder in pregnancy, is defined as glucose intolerance resulting in hyperglycaemia that begins or is first diagnosed in pregnancy. Gestational diabetes is associated with increased pregnancy complications and long-term metabolic risks for the woman and the offspring. However, the current diagnostic and management strategies recommended by national and international guidelines are mainly focused on short-term risks during pregnancy and delivery, except the Carpenter-Coustan criteria, which were based on the risk of future incidence of type 2 diabetes post-gestational diabetes. In this Personal View, first, we summarise the evidence for long-term risk in women with gestational diabetes and their offspring. Second, we suggest that a shift is needed in the thinking about gestational diabetes; moving from the perception of a short-term condition that confers increased risks of large babies to a potentially modifiable long-term condition that contributes to the growing burden of childhood obesity and cardiometabolic disorders in women and the future generation. Third, we propose how the current clinical practice might be improved. Finally, we outline and justify priorities for future research.

Sex Hormone-binding Globulin, Cardiometabolic Biomarkers, and Gestational Diabetes: A Longitudinal Study and Meta-analysis

Objective

This study investigated the prospective associations of circulating levels of sex hormone-binding globulin (SHBG) levels with cardiometabolic biomarkers and risk of gestational diabetes (GDM) during pregnancy. It also examines the longitudinal trajectory of SHBG in women with and without GDM.

Methods

We conducted a nested case-control study of 107 incident GDM cases and 214 matched controls within the Eunice Kennedy Shriver National Institute of Child Health and Human Development Fetal Growth Studies-Singleton Cohort. The cohort enrolled non-obese and obese women aged 18-40 years with a singleton pregnancy between 8 and 13 weeks of gestation from 2009 to 2013. GDM was ascertained via medical records review. Blood samples were drawn four times at gestational weeks 10-14, 15-26, 23-31, and 33-39. The prospective associations between SHBG levels and cardiometabolic biomarkers were examined using the Spearman partial correlation among the controls. The longitudinal trajectories of SHBG levels were examined among the cases and the controls. Meta-analysis of prospective studies were performed to examine the association between SHBG levels and GDM risk.

Results

SHBG levels at gestational weeks 10-14 were significantly inversely associated with fasting insulin (r = -0.17, P = 0.01) and insulin resistance as measured by HOMA-IR (r = -0.17, P = 0.01) at gestational week 15-26. SHBG at gestational weeks 10-14 and 15-26 was lower in cases than controls (mean ± standard deviation: (204.0 ± 97.6) vs. (220.9 ± 102.5) nmol/L, P = 0.16 and (305.6 ± 124.3) vs. (322.7 ± 105.1) nmol/L, P = 0.14, respectively), yet the differences were not significant. In the meta-analysis, SHBG was 41.5 nmol/L (95% confidence interval: 23.9, 59.1, P < 0.01) significantly lower among women with GDM than without, and each 50 nmol/L increase in SHBG was significantly associated with an odds ratio of 0.85 (95% confidence interval: 0.76-0.95, P = 0.01) for GDM.

Conclusion

Lower SHBG levels in early pregnancy were prospectively associated with higher high insulin levels and insulin resistance in mid-pregnancy and subsequent risk of GDM, independent of adiposity. SHBG may serve as a marker for the identification of high-risk pregnancies during early pregnancy.

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.

The plasma metabolome of women in early pregnancy differs from that of non-pregnant women

BACKGROUND

In comparison to the non-pregnant state, the first trimester of pregnancy is characterized by systemic adaptation of the mother. The extent to which these adaptive processes are reflected in the maternal blood metabolome is not well characterized.

OBJECTIVE

To determine the differences between the plasma metabolome of non-pregnant and pregnant women before 16 weeks gestation.

STUDY DESIGN

This study included plasma samples from 21 non-pregnant women and 50 women with a normal pregnancy (8-16 weeks of gestation). Combined measurements by ultrahigh performance liquid chromatography/tandem mass spectrometry and by gas chromatography/mass spectrometry generated molecular abundance measurements for each sample. Molecular species detected in at least 10 samples were included in the analysis. Differential abundance was inferred based on false discovery adjusted p-values (FDR) from Mann-Whitney-Wilcoxon U tests <0.1 and a minimum median abundance ratio (fold change) of 1.5. Alternatively, metabolic data were quantile normalized to remove sample-to-sample differences in the overall metabolite abundance (adjusted analysis).

RESULTS

Overall, 637 small molecules met the inclusion criteria and were tested for association with pregnancy; 44% (281/637) of small molecules had significantly different abundance, of which 81% (229/281) were less abundant in pregnant than in non-pregnant women. Eight percent (14/169) of the metabolites that remained significant in the adjusted analysis also changed as a function of gestational age. A pathway analysis revealed enrichment in steroid metabolites related to sex hormones, caffeine metabolites, lysolipids, dipeptides, and polypeptide bradykinin derivatives (all, FDR < 0.1).

CONCLUSIONS

This high-throughput mass spectrometry study identified: 1) differences between pregnant vs. non-pregnant women in the abundance of 44% of the profiled plasma metabolites, including known and novel molecules and pathways; and 2) specific metabolites that changed with gestational age.

Effect of maternal obesity on pregnancy outcomes in women delivering singleton babies: a historical cohort study

Background Obesity in pregnancy is increasing worldwide, reaching epidemic proportions in many countries and frequently creating challenges for obstetricians. We conducted this study to assess the effects of maternal obesity on maternal and perinatal outcomes. Methods A historical cohort study was performed on 16,609 women who delivered singleton babies in a 5-year period (2013-2017). Data were retrieved from the Cruces Perinatal Database (CPD) and only women whose prepregnancy body mass index (BMI) was known were included. Women were categorized according to the World Health Organization (WHO) classification: normal weight (BMI 20-24.9 kg/m2) and obesity (BMI ≥ 30 kg/m2). Obstetric, perinatal and neonatal outcomes were compared, and adjusted odds ratios (aORs) and 95% confidence intervals (95% CIs) were calculated using the normal-weight group as the reference. Results Compared to women of normal weight (n = 9778), obese women (n = 2207) had a higher risk of preeclampsia (aOR 2.199, 95% CI: 1.46-3.29), rectovaginal group B streptococcus colonization (aOR 1.299, 95% CI: 1.14-1.47), induction of labor (aOR 1.593, 95% CI: 1.44-1.75), cesarean section (aOR 2.755, 95% CI: 2.46-3.08), cesarean section in women with a history of cesarean delivery (aOR 1.409, 95% CI: 1.03-1.92), fetal weight ≥4000 g (aOR 2.090, 95% CI: 1.803-2.422) and admission to the neonatal intensive care unit (NICU) (aOR 1.341, 95% CI: 1.12-1.59). No association was found with preterm birth (aOR 0.936, 95% CI: 0.77-1.13), stillbirth (aOR 0.921, 95% CI: 0.41-2.02) or neonatal mortality (aOR 2.205, 95% CI: 0.86-5.62). Conclusion Maternal obesity is associated with a higher risk of adverse pregnancy and perinatal outcomes. Pregnancy in this population of women should be considered and managed as high risk.

Can a Simple Dietary Screening in Early Pregnancy Identify Dietary Habits Associated with Gestational Diabetes?

Gestational diabetes mellitus (GDM) is predominantly a lifestyle disease, with diet being an important modifiable risk factor. A major obstacle for the prevention in clinical practice is the complexity of assessing diet. In a cohort of 1651 Icelandic women, this study examined whether a short 40-item dietary screening questionnaire administered in the 1st trimester could identify dietary habits associated with GDM. The dietary variables were aggregated into predefined binary factors reflecting inadequate or optimal intake and stepwise backward elimination was used to identify a reduced set of factors that best predicted GDM. Those binary factors were then aggregated into a risk score (range: 0–7), that was mostly characterised by frequent consumption of soft drinks, sweets, cookies, ice creams and processed meat. The women with poor dietary habits (score ≥ 5, n = 302), had a higher risk of GDM (RR = 1.38; 95%CI = 3, 85) compared with women with a more optimal diet (score ≤ 2, n = 407). In parallel, a pilot (n = 100) intervention was conducted among overweight and obese women examining the effect of internet-based personalized feedback on diet quality. Simple feedback was given in accordance with the answers provided in the screening questionnaire in 1st trimester. At the endpoint, the improvements in diet quality were observed by, as an example, soft drink consumption being reduced by ~1 L/week on average in the intervention group compared to the controls. Our results suggest that a simple dietary screening tool administered in the 1st trimester could identify dietary habits associated with GMD. This tool should be easy to use in a clinical setting, and with simple individualized feedback, improvements in diet may be achieved.

A simple model to predict risk of gestational diabetes mellitus from 8 to 20 weeks of gestation in Chinese women

Background

Gestational diabetes mellitus (GDM) is associated with adverse perinatal outcomes. Screening for GDM and applying adequate interventions may reduce the risk of adverse outcomes. However, the diagnosis of GDM depends largely on tests performed in late second trimester. The aim of the present study was to bulid a simple model to predict GDM in early pregnancy in Chinese women using biochemical markers and machine learning algorithm.

Methods

Data on a total of 4771 pregnant women in early gestation were used to fit the GDM risk-prediction model. Predictive maternal factors were selected through Bayesian adaptive sampling. Selected maternal factors were incorporated into a multivariate Bayesian logistic regression using Markov Chain Monte Carlo simulation. The area under receiver operating characteristic curve (AUC) was used to assess discrimination.

Results

The prevalence of GDM was 12.8%. From 8th to 20th week of gestation fasting plasma glucose (FPG) levels decreased slightly and triglyceride (TG) levels increased slightly. These levels were correlated with those of other lipid metabolites. The risk of GDM could be predicted with maternal age, prepregnancy body mass index (BMI), FPG and TG with a predictive accuracy of 0.64 and an AUC of 0.766 (95% CI 0.731, 0.801).

Conclusions

This GDM prediction model is simple and potentially applicable in Chinese women. Further validation is necessary.

Electronic supplementary material

The online version of this article (10.1186/s12884-019-2374-8) contains supplementary material, which is available to authorized users.

Sex hormone binding globulin for prediction of gestational diabetes mellitus in pre-conception and pregnancy: A systematic review

AIM

The purpose of the present study was to assess the relationship of sex hormone binding globulin (SHBG) and gestational diabetes mellitus (GDM).

METHODS

The Cochrane Library, Medline, ScienceDirect, and Web of Science were searched for studies published from the inception of the databases up to February 2019. Our inclusion criteria were published observational full-text articles. All data were analyzed using Review Manager 5.3. Of 208 papers reviewed, 26 studies (n = 6668) were considered for meta-analysis.

RESULTS

The SHBG level was significantly lower in women with GDM compared to healthy women (MD = -11.86; 95% CI: [-13.02, -10.71]). Also, SHBG in women with PCOS and GDM and obesity was significantly lower than women with PCOS without GDM (MD = -38.14; 95% CI: [-56.79, -19.48]) and normal weight women (MD: -58.96; 95% CI: [-79.32, -38.59]). SHBG in the second trimester was lower than that in the first trimester and pre-conception.

CONCLUSIONS

This systematic review showed that the level of SHBG is significantly lower in GDM pregnant women than that in healthy women. The results of this systematic review about the relationship of GDM and SHBG and suggestion to assess this marker in early pregnancy should be considered with caution.

Urinary metabolic variation analysis during pregnancy and application in Gestational Diabetes Mellitus and spontaneous abortion biomarker discovery

Pregnancy is associated with the onset of many adaptation processes that are likely to change over the course of gestation. Understanding normal metabolites’ variation with pregnancy progression is crucial for gaining insights of the key nutrients for normal fetal growth, and for comparative research of pregnancy-related complications. This work presents liquid chromatography-mass spectrum-based urine metabolomics study of 50 health pregnant women at three time points during pregnancy. The influence of maternal physiological factors, including age, BMI, parity and gravity to urine metabolome was explored. Additionally, urine metabolomics was applied for early prediction of two pregnancy complications, gestational diabetes mellitus and spontaneous abortion. Our results suggested that during normal pregnancy progression, pathways of steroid hormone biosynthesis and tyrosine metabolism were significantly regulated. BMI is a factor that should be considered during cross-section analysis. Application analysis discovered potential biomarkers for GDM in the first trimester with AUC of 0.89, and potential biomarkers for SA in the first trimester with AUC of 0.90. In conclusion, our study indicated that urine metabolome could reflect variations during pregnancy progression, and has potential value for pregnancy complications early prediction. The clinical trial number for this study is NCT03246295.

Heterogeneity of gestational diabetes (GDM) and challenges in developing a GDM risk score

AIMS

Gestational diabetes (GDM) affects a growing number of women and identification of individuals at risk, e.g., with risk prediction models, would be important. However, the performance of GDM risk scores has not been optimal. Here, we assess the impact of GDM heterogeneity on the performance of two top-rated GDM risk scores.

METHODS

This is a substudy of the RADIEL trial-a lifestyle intervention study including women at high GDM risk. We assessed the GDM risk score by Teede and that developed by Van Leeuwen in our high-risk cohort of 510 women. To investigate the heterogeneity of GDM, we further divided the women according to GDM history, BMI, and parity. With the goal of identifying novel predictors of GDM, we further analyzed 319 women with normal glucose tolerance in the first trimester.

RESULTS

Both risk scores underestimated GDM incidence in our high-risk cohort. Among women with a BMI ≥ 30 kg/m² and/or previous GDM, 49.4% developed GDM and 37.4% received the diagnosis already in the first trimester. Van Leeuwen score estimated a 19% probability of GDM and Teede succeeded in risk identification in 61%. The lowest performance of the risk scores was seen among the non-obese women. Fasting plasma glucose, HbA_1c, and family history of diabetes were predictors of GDM in the total study population. Analysis of subgroups did not provide any further information.

CONCLUSIONS

Our findings suggest that the marked heterogeneity of GDM challenges the development of risk scores for detection of GDM.

Age at menarche and the future risk of gestational diabetes: a systematic review and dose response meta-analysis

Published studies show an inconsistent association between age at menarche and the subsequent risk of developing gestational diabetes mellitus when pregnant. This systematic review and meta-analysis was performed to clarify any trends in this association in published observational population studies. We searched online databases for relevant studies, entered into them up until June 21st 2017. Five eligible studies were found and a pooled random effects dose response meta-analysis of results from these was conducted. This included coverage of 58,133 pregnancies, from which 3,035 women developed gestational diabetes. There was evidence of a non-linear association between age at menarche and gestational diabetes (overall p = 1.4 × 10^-8; p for non-linearity = 2.4 × 10^-4), along with evidence of relatively low heterogeneity (I² = 25.5%). The largest predicted risk of gestational diabetes was associated with having a low age at menarche; the mean (95% confidence interval) risk relative to that associated with menarche at age 13 years being: 9 years 2.0 (1.6, 2.4), 10 years 1.6 (1.4, 1.9), 11 years 1.3 (1.2, 1.4), 12 years 1.1 (1.1, 1.1), 13 years was the reference, 14 years 1.0 (1.0, 1.0), 15 years 1.1 (0.9, 1.2), 16 years 1.1 (0.9, 1.4). There was evidence of potential publication bias, such that the maximal true relative risk of gestational diabetes, associated with an age at menarche of 9 years, may be closer to 1.6 than 2. Nevertheless, the curvilinear relationship between age at menarche and the future risk of gestational diabetes in pregnancy appears robust.

Preconception risk of gestational diabetes: Development of a prediction model in nulliparous Australian women

AIM

To develop a prediction model for preconception identification of women at risk of gestational diabetes mellitus (GDM).

METHODS

Data from a prospective cohort, the Australian Longitudinal Study on Women's Health, were used. Nulliparous women aged 18-23 who reported a pregnancy up to age 37-42 were included. Preconception predictors of GDM during a first pregnancy were selected using logistic regression. Regression coefficients were multiplied by a shrinkage factor estimated with bootstrapping to improve prediction in external populations.

RESULTS

Among 6504 women, 314 (4.8%) developed GDM during their first pregnancy. The final prediction model included age at menarche, proposed age at future first pregnancy, ethnicity, body mass index, diet, physical activity, polycystic ovary syndrome, and family histories of type 1 or 2 diabetes and GDM. The model showed good discriminative ability with a C-statistic of 0.79 (95% CI 0.76, 0.83) after internal validation. More than half of the women (58%) were classified to be at risk of GDM (>2% predicted risk), with corresponding sensitivity and specificity values of 91% and 43%.

CONCLUSIONS

Nulliparous women at risk of GDM in a future first pregnancy can be accurately identified based on preconception lifestyle and health-related characteristics. Further studies are needed to test our model in other populations.

Metabolic syndrome in pregnancy and risk for adverse pregnancy outcomes: A prospective cohort of nulliparous women

BACKGROUND

Obesity increases the risk for developing gestational diabetes mellitus (GDM) and preeclampsia (PE), which both associate with increased risk for type 2 diabetes mellitus (T2DM) and cardiovascular disease (CVD) in women in later life. In the general population, metabolic syndrome (MetS) associates with T2DM and CVD. The impact of maternal MetS on pregnancy outcomes, in nulliparous pregnant women, has not been investigated.

METHODS AND FINDINGS

Low-risk, nulliparous women were recruited to the multi-centre, international prospective Screening for Pregnancy Endpoints (SCOPE) cohort between 11 November 2004 and 28 February 2011. Women were assessed for a range of demographic, lifestyle, and metabolic health variables at 15 ± 1 weeks' gestation. MetS was defined according to International Diabetes Federation (IDF) criteria for adults: waist circumference ≥80 cm, along with any 2 of the following: raised trigycerides (≥1.70 mmol/l [≥150 mg/dl]), reduced high-density lipoprotein cholesterol (<1.29 mmol/l [<50 mg/dl]), raised blood pressure (BP) (i.e., systolic BP ≥130 mm Hg or diastolic BP ≥85 mm Hg), or raised plasma glucose (≥5.6 mmol/l). Log-binomial regression analyses were used to examine the risk for each pregnancy outcome (GDM, PE, large for gestational age [LGA], small for gestational age [SGA], and spontaneous preterm birth [sPTB]) with each of the 5 individual components for MetS and as a composite measure (i.e., MetS, as defined by the IDF). The relative risks, adjusted for maternal BMI, age, study centre, ethnicity, socioeconomic index, physical activity, smoking status, depression status, and fetal sex, are reported. A total of 5,530 women were included, and 12.3% (n = 684) had MetS. Women with MetS were at an increased risk for PE by a factor of 1.63 (95% CI 1.23 to 2.15) and for GDM by 3.71 (95% CI 2.42 to 5.67). In absolute terms, for PE, women with MetS had an adjusted excess risk of 2.52% (95% CI 1.51% to 4.11%) and, for GDM, had an adjusted excess risk of 8.66% (95% CI 5.38% to 13.94%). Diagnosis of MetS was not associated with increased risk for LGA, SGA, or sPTB. Increasing BMI in combination with MetS increased the estimated probability for GDM and decreased the probability of an uncomplicated pregnancy. Limitations of this study are that there are several different definitions for MetS in the adult population, and as there are none for pregnancy, we cannot be sure that the IDF criteria are the most appropriate definition for pregnancy. Furthermore, MetS was assessed in the first trimester and may not reflect pre-pregnancy metabolic health status.

CONCLUSIONS

We did not compare the impact of individual metabolic components with that of MetS as a composite, and therefore cannot conclude that MetS is better at identifying women at risk. However, more than half of the women who had MetS in early pregnancy developed a pregnancy complication compared with just over a third of women who did not have MetS. Furthermore, while increasing BMI increases the probability of GDM, the addition of MetS exacerbates this probability. Further studies are required to determine if individual MetS components act synergistically or independently.

A Pre-Pregnancy Biomarker Risk Score Improves Prediction of Future Gestational Diabetes

CONTEXT

Previous studies have not examined the ability of multiple preconception biomarkers, considered together, to improve prediction of gestational diabetes mellitus (GDM).

OBJECTIVE

To develop a preconception biomarker risk score and assess its association with subsequent GDM.

DESIGN

A nested case-control study among a cohort of women with serum collected as part of a health examination (1984 to 1996) and subsequent pregnancy (1984 to 2009). Biomarkers associated with GDM were dichotomized into high/low risk.

SETTING

Integrated health care system.

PARTICIPANTS

Two controls were matched to each GDM case (n = 256 cases) on year and age at examination, age at pregnancy, and number of pregnancies between examination and index pregnancy.

MAIN OUTCOME MEASURE

GDM.

RESULTS

High-risk levels of sex hormone-binding globulin (SHBG; <44.2 nM), glucose (>90 mg/dL), total adiponectin (<7.2 μg/mL), and homeostasis model assessment-estimated insulin resistance (>3.9) were independently associated with 2.34 [95% confidence interval (CI): 1.50, 3.63], 2.03 (95% CI: 1.29, 3.19), 1.83 (95% CI: 1.16, 2.90), and 1.67 (95% CI: 1.07, 2.62) times the odds of GDM and included in the biomarker risk score. For each unit increase in the biomarker risk score, odds of GDM were 1.94 times greater (95% CI: 1.59, 2.36). A biomarker risk score including only SHBG and glucose was sufficient to improve prediction beyond established risk factors (age, race/ethnicity, body mass index, family history of diabetes, previous GDM; area under the curve = 0.73 vs 0.67, P = 0.002).

CONCLUSIONS

The improved, predictive ability of the biomarker risk score beyond established risk factors suggests clinical use of the biomarker risk score in identifying women at risk for GDM before conception for targeted prevention strategies.

Proinsulin in the identification and risk stratification of gestational diabetes mellitus: study protocol for a prospective, longitudinal cohort study

INTRODUCTION

Gestational diabetes mellitus (GDM) is a common metabolic disorder occurring in up to 10% of pregnancies in the western world. Most women with GDM are asymptomatic; therefore, it is important to screen, diagnose and manage the condition as it is associated with an increased risk of maternal and perinatal complications. Diagnosis of GDM is made in the late second trimester or early third trimester because accurate diagnosis or risk stratification in the first trimester is still lacking. An increase in serum proinsulin may be seen earlier in pregnancy and before a change in glycaemic control can be identified. This study will aim to establish if fasting proinsulin concentrations at 16-18 weeks gestation will help to identify or risk stratify high-risk pregnant women with GDM.

METHODS AND ANALYSIS

This is a prospective, longitudinal cohort study. Two oral glucose tolerance tests will be carried out at 16-18 and 24-28 weeks gestation in 200 pregnant women with at least one risk factor for GDM (body mass index>30 kg/m², previous macrosomic baby (>4.5 kg), previous gestational diabetes, first degree relative with type 2 diabetes mellitus) recruited from antenatal clinics. Blood samples will be taken fasting and at 30 min, 1 and 2 hours following the 75 g glucose load. In addition, a fasting blood sample will be taken 6-weeks post delivery. All samples will be analysed for glucose, insulin, C peptide and proinsulin. Recruitment began in November 2017. Optimal cut-off points for proinsulin to diagnose gestational diabetes according to National Institute for Health and Care Excellence (2015) criteria will be established by the receiver operating characteristic plot and sensitivity and specificity will be calculated to assess the diagnostic accuracy of proinsulin at 16-18 weeks gestation.

ETHICS AND DISSEMINATION

This study received ethical approval from the Wales Research Ethics Committee (Panel 6) (Ref. 17/WA/0194). Data will be presented at international conferences and published in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

ISRCTN16416602; Pre-results.

Gestational diabetes modifies the association between PlGF in early pregnancy and preeclampsia in women with obesity

Objective

To identify clinical and biomarker risk factors for preeclampsia in women with obesity and to explore interactions with gestational diabetes, a condition associated with preeclampsia.

Study design

In women with obesity (body mass index ≥ 30 kg/m²) from the UK Pregnancies Better Eating and Activity Trial (UPBEAT), we examined 8 clinical factors (socio-demographic characteristics, BMI, waist circumference and clinical variables) and 7 biomarkers (HDL cholesterol, hemoglobin A1c, adiponectin, interleukin-6, high sensitivity C-reactive protein, and placental growth factor (PlGF)) in the early second trimester for association with later development of preeclampsia using logistic regression. Factors were selected based on prior association with preeclampsia. Interaction with gestational diabetes was assessed.

Main outcome measure

Preeclampsia.

Results

Prevalence of preeclampsia was 7.3% (59/824). Factors independently associated with preeclampsia were higher mean arterial blood pressure (Odds Ratio (OR) 2.22; 95% Confidence Interval (CI) 1.58–3.12, per 10 mmHg) and lower PlGF (OR 1.39; 95% CI 1.03–1.87, per each lower 1 log2). The association of PlGF with preeclampsia was present amongst obese women without gestational diabetes (OR 1.91; 95% CI 1.32–2.78), but not in those with GDM (OR 1.05; 95% CI 0.67–1.63), p = 0.04 for interaction.

Conclusion

The relationship between PlGF and preeclampsia differed in women with obesity according to gestational diabetes status, which may suggest different mechanistic pathways to preeclampsia. Whilst replication is required in other populations, this study suggests that performance of prediction models for preeclampsia should be confirmed in pre-specified subgroups.

Risk factors for hyperglycemia in pregnancy in the DALI study differ by period of pregnancy and OGTT time point

OBJECTIVE

Risk factors are widely used to identify women at risk for gestational diabetes mellitus (GDM) without clear distinction by pregnancy period or oral glucose tolerance test (OGTT) time points. We aimed to assess the clinical risk factors for Hyperglycemia in pregnancy (HiP) differentiating by these two aspects.

DESIGN AND METHODS

Nine hundred seventy-one overweight/obese pregnant women, enrolled in the DALI study for preventing GDM. OGTTs were performed at ≤19 + 6, 24-28 and 35-37 weeks (IADPSG/WHO2013 criteria). Women with GDM or overt diabetes at one time point did not proceed to further OGTTs. Potential independent variables included baseline maternal and current pregnancy characteristics.

STATISTICAL ANALYSIS

Multivariate logistic regression.

RESULTS

Clinical characteristics independently associated with GDM/overt diabetes were at ≤19 + 6 weeks, previous abnormal glucose tolerance (odds ratio (OR): 3.11; 95% CI: 1.41-6.85), previous GDM (OR: 2.22; 95% CI: 1.20-4.11), neck circumference (NC) (OR: 1.58; 95% CI: 1.06-2.36 for the upper tertile), resting heart rate (RHR, OR: 1.99; 95% CI: 1.31-3.00 for the upper tertile) and recruitment site; at 24-28 weeks, previous stillbirth (OR: 2.92; 95% CI: 1.18-7.22), RHR (OR: 3.32; 95% CI: 1.70-6.49 for the upper tertile) and recruitment site; at 35-37 weeks, maternal height (OR: 0.41; 95% CI: 0.20-0.87 for upper tertile). Clinical characteristics independently associated with GDM/overt diabetes differed by OGTT time point (e.g. at ≤19 + 6 weeks, NC was associated with abnormal fasting but not postchallenge glucose).

CONCLUSION

In this population, most clinical characteristics associated with GDM/overt diabetes were non-modifiable and differed by pregnancy period and OGTT time point. The identified risk factors can help define the target population for future intervention trials.

A prospective cohort study of early-pregnancy risk factors for gestational diabetes in polycystic ovarian syndrome

BACKGROUND

Polycystic ovarian syndrome (PCOS) is a strong risk factor for gestational diabetes (GDM). However, the association between features of PCOS during early pregnancy and the risk of GDM is not clearly characterized. In this prospective cohort study, we seek to identify early-pregnancy risk factors for GDM in PCOS women.

METHODS

Between 2011 and 2013, 248 women with PCOS were followed from their first prenatal visit to delivery. Multiple early-pregnancy metabolic factors were evaluated for their association with the risk of GDM.

RESULTS

Among 248 subjects, 75 (30.2%) developed GDM. Single factor analysis identified a number of metabolic risk factors for GDM, including higher body mass index, fasting plasma glucose (FPG) and insulin resistance; abnormal cholesterol; elevated blood pressure and free androgen index; lower level of sex-hormone binding globulin (SHBG); and less gestational weight gain. Multivariate analysis showed that FPG, non-high-density lipoprotein-cholesterol and SHBG are independent predictive factors for GDM.

CONCLUSIONS

Our study established strong association of multiple early-pregnancy risk factors with development of GDM in PCOS women. These risk factors are predominantly related to the regulation of glucose, lipid, and androgen metabolism. Among these factors, FPG, non-high-density lipoprotein-cholesterol, and SHBG, predict incident GDM.

A Novel Early Pregnancy Risk Prediction Model for Gestational Diabetes Mellitus

INTRODUCTION

Accurate early risk prediction for gestational diabetes mellitus (GDM) would target intervention and prevention in women at the highest risk. We evaluated novel biomarker predictors to develop a first-trimester risk prediction model in a large multiethnic cohort.

METHODS

Maternal clinical, aneuploidy and pre-eclampsia screening markers (PAPP-A, free hCGβ, mean arterial pressure, uterine artery pulsatility index) were measured prospectively at 11-13+6 weeks' gestation in 980 women (248 with GDM; 732 controls). Nonfasting glucose, lipids, adiponectin, leptin, lipocalin-2, and plasminogen activator inhibitor-2 were measured on banked serum. The relationship between marker multiples-of-the-median and GDM was examined with multivariate regression. Model predictive performance for early (< 24 weeks' gestation) and overall GDM diagnosis was evaluated by receiver operating characteristic curves.

RESULTS

Glucose, triglycerides, leptin, and lipocalin-2 were higher, while adiponectin was lower, in GDM (p < 0.05). Lipocalin-2 performed best in Caucasians, and triglycerides in South Asians with GDM. Family history of diabetes, previous GDM, South/East Asian ethnicity, parity, BMI, PAPP-A, triglycerides, and lipocalin-2 were significant independent GDM predictors (all p < 0.01), achieving an area under the curve of 0.91 (95% confidence interval [CI] 0.89-0.94) overall, and 0.93 (95% CI 0.89-0.96) for early GDM, in a combined multivariate prediction model.

CONCLUSIONS

A first-trimester risk prediction model, which incorporates novel maternal lipid markers, accurately identifies women at high risk of GDM, including early GDM.

Risk of gestational diabetes mellitus in relation to plasma concentrations of amino acids and acylcarnitines: A nested case-control study

AIMS

Gestational diabetes mellitus (GDM) affects between 5 and 10% of all pregnancies in Canada and can lead to adverse health outcomes in both the mother and fetus. Amino acids (AA) and acylcarnitines (AC) have been identified as early biomarkers of type 2 diabetes but their usefulness in screening for GDM has yet to be demonstrated.

METHODS

We conducted a nested case-control study involving 50 controls and 50 GDM cases diagnosed between the 24th and 28th week of gestation. Heparinized plasma samples were obtained during the first and early second trimester of pregnancy. Case and controls were matched according to date of recruitment, maternal age, gestational age at blood sampling as well as pre-pregnancy body mass index. Eight AA and eight AC were quantified using an ultra-high pressure liquid-chromatography quadrupole time-of-flight mass spectrometry platform. Conditional regression analyses adjusted for matching factors and smoking habits during pregnancy were performed to identify plasma metabolites associated with GDM risk.

RESULTS

Odds ratio (OR) and 95% confidence interval (CI) for the prediction of GDM per one standard deviation increase of AA or AC in plasma levels were 0.25 (0.08-0.79) for butyrylcarnitine, 0.31 (0.12-0.79) for glutamic acid, 2.5 (1.2-5.3) for acetylcarnitine, 2.9 (1.3-6.8) for isobutyrylcarnitine and 5.3 (1.7-17.0) for leucine. These five metabolites were selected by stepwise conditional logistic regression to create a predictive model with an OR of 2.7 (1.5-4.9).

CONCLUSION

Whether the identified metabolites can predict the risk of developing GDM requires additional studies in a larger sample of pregnant women.