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.

Adipokines in Pregnancy: A Systematic Review of Clinical Data

Adipokines are signaling proteins involved in metabolic, endocrinological, vascular and immunogenic processes. Associations of various adipokines with not only insulin resistance but also with increased insulin sensitivity, increased systolic blood pressure, and atherosclerosis highlight the significance of adipokines in several components of metabolic syndrome and metabolic diseases in general. As pregnancy presents a unique metabolic state, the role of adipokines in pregnancy, and even in various pregnancy complications, appears to be key to elucidating these metabolic processes. Many studies in recent years have attempted to clarify the role of adipokines in pregnancy and gestational pathologies. In this review, we aim to investigate the changes in maternal adipokine levels in physiological gestation, as well as the association of adipokines with pregnancy pathologies, such as gestational diabetes mellitus (GDM) and preeclampsia (PE). Furthermore, we will analyze the association of adipokines in both maternal serum and cord blood with parameters of intrauterine growth and various pregnancy outcomes.

Risk prediction models of gestational diabetes mellitus before 16 gestational weeks

BACKGROUND

Gestational diabetes mellitus (GDM) can lead to adverse maternal and fetal outcomes, and early prevention is particularly important for their health, but there is no widely accepted approach to predict it in the early pregnancy. The aim of the present study is to build and evaluate predictive models for GDM using routine indexes, including maternal clinical characteristics and laboratory biomarkers, before 16 gestational weeks.

METHODS

A total of 2895 pregnant women were recruited and maternal clinical characteristics and laboratory biomarkers before 16 weeks of gestation were collected from two hospitals. All participants were randomly stratified into the training cohort and the internal validation cohort by the ratio of 7:3. Using multivariable logistic regression analysis, two nomogram models, including a basic model and an extended model, were built. The discrimination, calibration, and clinical validity were used to evaluate the models in the internal validation cohort.

RESULTS

The area under the receiver operating characteristic curve of the basic and the extended model was 0.736 and 0.756 in the training cohort, and was 0.736 and 0.763 in the validation cohort, respectively. The calibration curve analysis showed that the predicted values of the two models were not significantly different from the actual observations (p = 0.289 and 0.636 in the training cohort, p = 0.684 and 0.635 in the internal validation cohort, respectively). The decision-curve analysis showed a good clinical application value of the models.

CONCLUSIONS

The present study built simple and effective models, indicating that routine clinical and laboratory parameters can be used to predict the risk of GDM in the early pregnancy, and providing a novel reference for studying the prediction of GDM.

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.

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 .

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.

AST-to-ALT ratio in the first trimester and the risk of gestational diabetes mellitus

BACKGROUND

Aspartate aminotransferase-to-alanine transaminase ratio (AST/ALT) has been reported affect the risk of type 2 diabetes (T2DM), but it is uncertain if it has relationship with gestational diabetes mellitus (GDM).

OBJECTIVES

Our study aimed to investigate the association between AST/ALT ratio in the first trimester and the risk of subsequent development of GDM.

METHOD

This prospective cohort study enrolling 870 pregnant women, 204 pregnant women with missing data or liver diseases were excluded, 666 pregnant women were included in this study containing 94 GDM women. Blood samples were collected in the first trimester. Univariate analysis and multivariate logistic regression were used to evaluate the association between AST/ALT and GDM. Nomogram was established based on the results of multivariate logistic analysis. Receiver Operating Characteristic (ROC) curves and calibration curves were used to evaluate the predictive ability of this nomogram model for GDM. Decision curve analysis (DCA) was used to examine the clinical net benefit of predictive model.

RESULTS

AST/ALT ratio (RR:0.228; 95% CI:0.107-0.488) was associated with lower risk of GDM after adjusting for confounding factors. Indicators used in nomogram including AST/ALT, maternal age, preBMI, waist circumference, glucose, triglycerides, high density lipoprotein cholesterol and parity. The area under the ROC curve (AUC) value of this predictive model was 0.778, 95% CI (0.724, 0.832). Calibration curves for GDM probabilities showed acceptable agreement between nomogram predictions and observations. The DCA curve demonstrated a good positive net benefit in the predictive model.

CONCLUSIONS

The early AST/ALT level of pregnant women negatively correlated with the risk of GDM. The nomogram including AST/ALT at early pregnancy shows good predictive ability for the occurrence of GDM.

Users' Perceptions Toward mHealth Technologies for Health and Well-being Monitoring in Pregnancy Care: Qualitative Interview Study

Background

Mobile health (mHealth) technologies, such as wearable sensors, smart health devices, and mobile apps, that are capable of supporting pregnancy care are emerging. Although mHealth could be used to facilitate the tracking of health changes during pregnancy, challenges remain in data collection compliance and technology engagement among pregnant women. Understanding the interests, preferences, and requirements of pregnant women and those of clinicians is needed when designing and introducing mHealth solutions for supporting pregnant women’s monitoring of health and risk factors throughout their pregnancy journey.

Objective

This study aims to understand clinicians’ and pregnant women’s perceptions on the potential use of mHealth, including factors that may influence their engagement with mHealth technologies and the implications for technology design and implementation.

Methods

A qualitative study using semistructured interviews was conducted with 4 pregnant women, 4 postnatal women, and 13 clinicians working in perinatal care.

Results

Clinicians perceived the potential benefit of mHealth in supporting different levels of health and well-being monitoring, risk assessment, and care provision in pregnancy care. Most pregnant and postnatal female participants were open to the use of wearables and health monitoring devices and were more likely to use these technologies if they knew that clinicians were monitoring their data. Although it was acknowledged that some pregnancy-related medical conditions are suitable for an mHealth model of remote monitoring, the clinical and technical challenges in the introduction of mHealth for pregnancy care were also identified. Incorporating appropriate health and well-being measures, intelligently detecting any abnormalities, and providing tailored information for pregnant women were the critical aspects, whereas usability and data privacy were among the main concerns of the participants. Moreover, this study highlighted the challenges of engaging pregnant women in longitudinal mHealth monitoring, the additional work required for clinicians to monitor the data, and the need for an evidence-based technical solution.

Conclusions

Clinical, technical, and practical factors associated with the use of mHealth to monitor health and well-being in pregnant women need to be considered during the design and feasibility evaluation stages. Technical solutions and appropriate strategies for motivating pregnant women are critical to supporting their long-term data collection compliance and engagement with mHealth technology during pregnancy.

Putrescine as a Novel Biomarker of Maternal Serum in First Trimester for the Prediction of Gestational Diabetes Mellitus: A Nested Case-Control Study

AIMS

Early identification of gestational diabetes mellitus (GDM) aims to reduce the risk of adverse maternal and perinatal outcomes. Currently, no acknowledged biomarker has proven clinically useful for the accurate prediction of GDM. In this study, we tested whether serum putrescine level changed in the first trimester and could improve the prediction of GDM.

METHODS

This study is a nested case-control study conducted in Beijing Obstetrics and Gynecology Hospital. We examined serum putrescine at 8-12 weeks pregnancy in 47 women with GDM and 47 age- and body mass index (BMI)-matched normoglycaemic women. Anthropometric, clinical and laboratory variables were obtained during the same period. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to assess the discrimination and calibration of the prediction models.

RESULTS

Serum putrescine in the first trimester was significantly higher in women who later developed GDM. When using putrescine alone to predict the risk of GDM, the AUC of the nomogram was 0.904 (sensitivity of 100% and specificity of 83%, 95% CI=0.832-0.976, P<0.001). When combined with traditional risk factors (prepregnant BMI and fasting blood glucose), the AUC was 0.951 (sensitivity of 89.4% and specificity of 91.5%, 95% CI=0.906-0.995, P<0.001).

CONCLUSION

This study revealed that GDM women had an elevated level of serum putrescine in the first trimester. Circulating putrescine may serve as a valuable predictive biomarker for GDM.

First trimester secreted Frizzled-Related Protein 4 and other adipokine serum concentrations in women developing gestational diabetes mellitus

Background

The aim of this study was to evaluate whether soluble frizzled-related protein 4 (sFRP4) concentration in the first trimester of pregnancy is individually, or in combination with Leptin, Chemerin and/or Adiponectin, associated with the development of gestational diabetes (GDM).

Methods

In a nested case-control study, 50 women with GDM who spontaneously conceived and delivered a live-born infant were matched with a total of 100 uncomplicated singleton control pregnancies based on body mass index (± 2 kg/m²), gestational age at sampling (exact day) and maternal age (± 2 years). In serum samples, obtained between 70–90 days gestational age, sFRP4, Chemerin, Leptin and Adiponectin concentrations were determined by ELISA. Statistical comparisons were performed using univariate and multi-variate logistic regression analysis after logarithmic transformation of the concentrations. Discrimination of the models was assessed by the area under the curve (AUC).

Results

First trimester sFRP4 concentrations were significantly increased in GDM cases (2.04 vs 1.93 ng/ml; p<0.05), just as Chemerin (3.19 vs 3.15 ng/ml; p<0.05) and Leptin (1.44 vs 1.32 ng/ml; p<0.01). Adiponectin concentrations were significantly decreased (2.83 vs 2.94 ng/ml; p<0.01) in GDM cases. Further analysis only showed a weak, though significant, correlation of sFRP4 with Chemerin (R² = 0.124; p<0.001) and Leptin (R² = 0.145; p<0.001), and Chemerin with Leptin (R² = 0.282; p<0.001) in the control group. In a multivariate logistic regression model of these four markers, only Adiponectin showed to be significantly associated with GDM (odds ratio 0.12, 95%CI 0.02–0.68). The AUC of this model was 0.699 (95%CI 0.605–0.793).

Conclusion

In the first trimester of pregnancy, a multi-marker model with sFRP4, Leptin, Chemerin and Adiponectin is associated with the development of GDM. Therefore, this panel seems to be an interesting candidate to further evaluate for prediction of GDM in a prospective study.

Prognostic models versus single risk factor approach in first-trimester selective screening for gestational diabetes mellitus: a prospective population-based multicentre cohort study

Objectives

To evaluate whether (1) first‐trimester prognostic models for gestational diabetes mellitus (GDM) outperform the currently used single risk factor approach, and (2) a first‐trimester random venous glucose measurement improves model performance.

Design

Prospective population‐based multicentre cohort.

Setting

Thirty‐one independent midwifery practices and six hospitals in the Netherlands.

Population

Women recruited before 14 weeks of gestation without pre‐existing diabetes.

Methods

The single risk factor approach (presence of at least one risk factor: BMI ≥30 kg/m², previous macrosomia, history of GDM, positive first‐degree family history of diabetes, non‐western ethnicity) was compared with the four best performing models in our previously published external validation study (Gabbay‐Benziv 2014, Nanda 2011, Teede 2011, van Leeuwen 2010) with and without the addition of glucose.

Main outcome measures

Discrimination was assessed by c‐statistics, calibration by calibration plots, added value of glucose by the likelihood ratio chi‐square test, net benefit by decision curve analysis and reclassification by reclassification plots.

Results

Of the 3723 women included, a total of 181 (4.9%) developed GDM. The c‐statistics of the prognostic models were higher, ranging from 0.74 to 0.78 without glucose and from 0.78 to 0.80 with glucose, compared with the single risk factor approach (0.72). Models showed adequate calibration, and yielded a higher net benefit than the single risk factor approach for most threshold probabilities. Teede 2011 performed best in the reclassification analysis.

Conclusions

First‐trimester prognostic models seem to outperform the currently used single risk factor approach in screening for GDM, particularly when glucose was added as a predictor.

Tweetable abstract

Prognostic models seem to outperform the currently used single risk factor approach in screening for gestational diabetes.

Prognostic models seem to outperform the currently used single risk factor approach in screening for gestational diabetes.

Nomogram for prediction of gestational diabetes mellitus in urban, Chinese, pregnant women

Background

This study sought to develop and validate a nomogram for prediction of gestational diabetes mellitus (GDM) in an urban, Chinese, antenatal population.

Methods

Age, pre-pregnancy body mass index (BMI), fasting plasma glucose (FPG) in the first trimester and diabetes in first degree relatives were incorporated as validated risk factors. A prediction model (nomogram) for GDM was developed using multiple logistic regression analysis, from a retrospective study conducted on 3956 women who underwent their first antenatal visit during 2015 in Shanghai. Performance of the nomogram was assessed through discrimination and calibration. We refined the predicting model with t-distributed stochastic neighbor embedding (t-SNE) to distinguish GDM from non-GDM. The results were validated using bootstrap resampling and a prospective cohort of 6572 women during 2016 at the same institution.

Results

Advanced age, pre-pregnancy BMI, high first-trimester, fasting, plasma glucose, and, a family history of diabetes were positively correlated with the development of GDM. This model had an area under the receiver operating characteristic (ROC) curve of 0.69 [95% CI:0.67–0.72, p < 0.0001]. The calibration curve for probability of GDM showed good consistency between nomogram prediction and actual observation. In the validation cohort, the ROC curve was 0.70 [95% CI: 0.68–0.72, p < 0.0001] and the calibration plot was well calibrated. In exploratory and validation cohorts, the distinct regions of GDM and non-GDM were distinctly separated in the t-SNE, generating transitional boundaries in the image by color difference. Decision curve analysis showed that the model had a positive net benefit at threshold between 0.05 and 0.78.

Conclusions

This study demonstrates the ability of our model to predict the development of GDM in women, during early stage of pregnancy.

The Role of Inflammation in the Development of GDM and the Use of Markers of Inflammation in GDM Screening

Gestational diabetes mellitus is a hyperglycaemic state first recognised in pregnancy. GDM affects both mother and child. Women with GDM and their new-borns are at risk of developing type 2 diabetes in the future. The screening and diagnostic criteria for GDM are inconsistent and thus novel biomarkers of GDM are required to strengthen the screening and diagnostic processes in GDM. Chronic low-grade inflammation is linked to the majority of the well-established risk factors of GDM such as old age, obesity and PCOS. This review provides an overview of the present knowledge on the pathology of GDM, the screening criteria applied, the role of inflammation in the development of GDM and the use of markers of inflammation namely cytokines, oxidative stress markers, lipids, amino acids and iron markers in screening and diagnosis of GDM.