Systematic review and meta-analysis on the association between seasonal variation and gestational diabetes mellitus

Relationship between gut microbiota and the pathogenesis of gestational diabetes mellitus: a systematic review

Introduction

Gestational diabetes mellitus (GDM) is a form of gestational diabetes mellitus characterized by insulin resistance and abnormal function of pancreatic beta cells. In recent years, genomic association studies have revealed risk and susceptibility genes associated with genetic susceptibility to GDM. However, genetic predisposition cannot explain the rising global incidence of GDM, which may be related to the increased influence of environmental factors, especially the gut microbiome. Studies have shown that gut microbiota is closely related to the occurrence and development of GDM. This paper reviews the relationship between gut microbiota and the pathological mechanism of GDM, in order to better understand the role of gut microbiota in GDM, and to provide a theoretical basis for clinical application of gut microbiota in the treatment of related diseases.

Methods

The current research results on the interaction between GDM and gut microbiota were collected and analyzed through literature review. Keywords such as "GDM", "gut microbiota" and "insulin resistance" were used for literature search, and the methodology, findings and potential impact on the pathophysiology of GDM were systematically evaluated.

Results

It was found that the composition and diversity of gut microbiota were significantly associated with the occurrence and development of GDM. Specifically, the abundance of certain gut bacteria is associated with an increased risk of GDM, while other changes in the microbiome may be associated with improved insulin sensitivity. In addition, alterations in the gut microbiota may affect blood glucose control through a variety of mechanisms, including the production of short-chain fatty acids, activation of inflammatory pathways, and metabolism of the B vitamin group.

Discussion

The results of this paper highlight the importance of gut microbiota in the pathogenesis of GDM. The regulation of the gut microbiota may provide new directions for the treatment of GDM, including improving insulin sensitivity and blood sugar control through the use of probiotics and prebiotics. However, more research is needed to confirm the generality and exact mechanisms of these findings and to explore potential clinical applications of the gut microbiota in the management of gestational diabetes. In addition, future studies should consider the interaction between environmental and genetic factors and how together they affect the risk of GDM.

Climate change, air pollution and maternal and newborn health: An overview of reviews of health outcomes

Background

Climate change represents a fundamental threat to human health, with pregnant women and newborns being more susceptible than other populations. In this review, we aimed to describe the current landscape of available epidemiological evidence on key climate risks on maternal and newborn health (MNH).

Methods

We sought to identify published systematic and scoping reviews investigating the impact of different climate hazards and air pollution on MNH outcomes. With this in mind, we developed a systematic search strategy based on the concepts of 'climate/air pollution hazards, 'maternal health,' and 'newborn health,' with restrictions to reviews published between 1 January 2010 and 6 February 2023, but without geographical or language restriction. Following full text screening and data extraction, we synthesised the results using narrative synthesis.

Results

We found 79 reviews investigating the effects of climate hazards on MNH, mainly focussing on outdoor air pollution (n = 47, 59%), heat (n = 24, 30%), and flood/storm disasters (n = 7, 9%). Most were published after 2015 (n = 60, 76%). These reviews had consistent findings regarding the positive association of exposure to heat and to air pollution with adverse birth outcomes, particularly preterm birth. We found limited evidence for impacts of climate-related food and water security on MNH and did not identify any reviews on climate-sensitive infectious diseases and MNH.

Conclusions

Climate change could undermine recent improvements in maternal and newborn health. Our review provides an overview of key climate risks to MNH. It could therefore be useful to the MNH community to better understand the MNH needs for each climate hazard and to strengthen discussions on evidence and research gaps and potential actions. Despite the lack of comprehensive evidence for some climate hazards and for many maternal, perinatal, and newborn outcomes, we observed repeated findings of the impact of heat and air pollutants on birth outcomes, particularly preterm birth. It is time for policy dialogue to follow to specifically design climate policy and actions to protect the needs of MNH.

Seasonal variation and its interaction with pre-pregnancy BMI for GDM: a large population-based study in Tianjin, China

To evaluate the independent association of seasonal variation with GDM incidence in Tianjin, China, and to test whether there is an additive interaction between seasonal variation and pre-pregnancy body mass index (BMI) on GDM incidence. A population-based observational cohort study was conducted using the healthcare records data from Tianjin, China. Logistic regression was used to obtain odds ratios (ORs) and 95% confidence intervals (CIs). Additive interaction between pre-pregnancy BMI groups and seasons was estimated by using relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP), and synergy index (S). Among the 112,639 pregnant women, 20.8% developed GDM at 24-28 weeks of gestation. The multivariable adjusted ORs and 95% CIs were 1.00, 1.00 (0.96-1.05), 1.15 (1.09-1.20) and 1.22 (1.16-1.29) respectively based on seasons (spring, summer, autumn and winter). Compared with the spring/summer and pre-pregnant BMI < 24 kg/m2 group, co-presence of autumn/winter and pre-pregnancy BMI ≥ 24 kg/m2 increased the OR from 1.00 to 2.70 (95% CI 2.28-3.20), with a significant additive interaction: RERI (0.32, 95% CI 0.19-0.45), S (1.21, 95% CI 1.12-1.31) and AP (0.11, 95% CI 0.07-0.16). Autumn/winter is an independent risk factor for GDM incidence, and can significantly amplify the obesity-associated risk for GDM incidence. The underlying mechanism warrants further investigations. We suggest that seasonality is an additional factor when interpreting OGTT results for the diagnosis of GDM.

Seasonal Pattern in Gestational Diabetes Mellitus in Poland: A Retrospective Cohort Study

The existing literature does not address the question of the seasonal impact on pregnancy in Central-Eastern Europe; therefore, this study was designed to investigate the seasonal variation in gestational diabetes mellitus (GDM) based on a recent Polish sample. The data of 30,205 newborns from singleton pregnancies and their mothers, including the date and gestational age of birth, neonatal sex and weight, maternal age and parity, mode of delivery, ethnicity, and a detailed list of comorbidities (including GDM), were retrospectively analysed. The prevalence of GDM was significantly (p < 0.0001) lower in spring (14.71%) than in the other seasons (16.78%). A higher incidence of GDM was observed for mothers who underwent an oral glucose tolerance test from June to August compared to those who were tested from December to February (17.34% vs. 14.75%, p < 0.0001). Similarly, there were significant differences between seasons with higher and lower insolation. The regression analysis revealed that seasonal patterns were significantly associated with the prevalence of GDM. In conclusion, this large retrospective cohort study demonstrated seasonal changes in GDM risk. The observed seasonal patterns may equally refer to mothers of babies born at term and prematurely. Further research concerning GDM risk and other seasonal and gender associations is warranted.

Causal Associations of PM2.5 and GDM: A Two-Sample Mendelian Randomization Study

Epidemiological studies have linked particulate matter (PM2.5) to gestational diabetes mellitus (GDM). However, the causality of this association has not been established; Mendelian randomization was carried out using summary data from genome-wide association studies (GWAS). For the analysis of the causal relationship between PM2.5 and GDM, the inverse variance weighted (IVW) method was used. The exposure data came from a GWAS dataset of IEU analysis of the United Kingdom Biobank phenotypes consisting of 423,796 European participants. The FinnGen consortium provided the GDM data, which included 6033 cases and 123,000 controls. We also performed multivariate MR (MVMR), adjusting for body mass index (BMI) and smoking. As a result, we found that each standard deviation increase in PM2.5 is associated with a 73.6% increase in the risk of GDM (OR: 1.736; 95%CI: 1.226-2.457). Multivariable MR analysis showed that the effect of PM2.5 on GDM remained after accounting for BMI and smoking. Our results demonstrate a causal relationship between PM2.5 and GDM.

A longitudinal cohort study of gestational diabetes mellitus and perinatal depression

BACKGROUND

The association between gestational diabetes mellitus (GDM) and perinatal depression (PND) remains controversial. Our study aimed to comprehensively assess this association in a longitudinal cohort study with repeated measurements of depression.

METHODS

Our cohort study was nested in a pilot study of an implementation study aiming to screen and manage perinatal depression within the primary health system in China. Women were recruited in the first trimester from May-September 2019 and followed four times up to 1 year postpartum. Data on sociodemographic characteristics and depression were collected using self-developed questionnaires incorporating the Edinburgh Postnatal Depression Scale (EPDS). Oral glucose tolerance test at 24 ~ 28 weeks and fasting plasma glucose (FPG) data were extracted from medical records. Depression throughout the whole period was divided into different trajectories. Associations of GDM with PND at different time periods and PND of different trajectories were determined by logistic regression. The path of association between blood glucose and depression over time was estimated with an autoregressive cross-lagged model.

RESULTS

In total, 1043 women were included in this analysis and 313 (30.0%) were diagnosed with GDM. The prevalence of depression in the first, second, and third trimesters and postpartum period were 17.2, 6.9, 6.8 and 9.0%, respectively. GDM was neither significantly associated with PND at any time point nor with any specific trajectory of depression. Except for autoregressive paths, no cross-lagged path of FPG and scores of EPDS was significant.

CONCLUSIONS

Our study indicates no association between GDM/blood glucose and PND.

Risk factor-based screening compared to universal screening for gestational diabetes mellitus in marginalized Burman and Karen populations on the Thailand-Myanmar border: An observational cohort

Background: Gestational diabetes mellitus (GDM) contributes significantly to maternal and neonatal morbidity, but data from marginalized populations remains scarce. This study aims to compare risk-factor-based screening to universal testing for GDM among migrants along the Thailand-Myanmar border. Methods: From the prospective cohort (September 2016, February 2019), 374 healthy pregnant women completed a 75g oral glucose tolerance test (OGTT) at 24-32 weeks gestation. Fasting, one hour and two hour cut-offs were based on Hyperglycaemia and Adverse Pregnancy Outcomes (HAPO trial) criteria and cases were treated. The sensitivity and specificity of risk-factor-based screening criteria was calculated using OGTT as the gold standard. Risk factors included at least one positive finding among 10 criteria, e.g., obesity (body mass index (BMI) ≥27.5kg/m ²), 1 ^st degree relative with diabetes etc. Adverse maternal and neonatal outcomes were compared by GDM status, and risk factors for GDM were explored. Results: GDM prevalence was 13.4% (50/374) (95% CI: 10.3-17.2). Risk-factors alone correctly identified 74.0% (37/50) OGTT positive cases: sensitivity 74.0% (59.7-85.4) and specificity 27.8% (3.0-33.0). Burman women accounted for 29.1% of the cohort population, but 38.0% of GDM cases. Percentiles for birthweight (p=0.004), head circumference (p=0.005), and weight-length ratio (p=0.010) were higher in newborns of GDM mothers compared with non-GDM, yet 21.7% (75/346) of newborns in the cohort were small-for-gestational age. In Burman women, overweight/obese BMI was associated with a significantly increased adjusted odds ratio 5.03 (95% CI: 1.43-17.64) for GDM compared to normal weight, whereas underweight and overweight/obese in Karen women were both associated with similarly elevated adjusted odds, approximately 2.4-fold (non-significant) for GDM. GDM diagnosis by OGTT was highest prior to peak rainfall. Conclusions: Risk-factor-based screening was not sufficiently sensitive or specific to be useful to diagnose GDM in this setting among a cohort of low-risk pregnant women. A two-step universal screening program has thus been implemented.

Current Resources for Evidence-Based Practice, January 2022

An extensive review of new resources to support the provision of evidence-based care for women and infants. The current column includes a discussion of breastfeeding while employed and commentaries on reviews focused on mammography test characteristics and sexual health for gynecologic cancer survivors. It also includes a quick update on a USPSTF review for aspirin as pre-eclampsia prophylaxis.