The relationship between hair metabolites, air pollution exposure and gestational diabetes mellitus: A longitudinal study from pre-conception to third trimester

Multigenerational diabetes mellitus

Gestational diabetes (GDM) changes the maternal metabolic and uterine environment, thus increasing the risk of short- and long-term adverse outcomes for both mother and child. Children of mothers who have GDM during their pregnancy are more likely to develop Type 2 Diabetes (T2D), early-onset cardiovascular disease and GDM when they themselves become pregnant, perpetuating a multigenerational increased risk of metabolic disease. The negative effect of GDM is exacerbated by maternal obesity, which induces a greater derangement of fetal adipogenesis and growth. Multiple factors, including genetic, epigenetic and metabolic, which interact with lifestyle factors and the environment, are likely to contribute to the development of GDM. Genetic factors are particularly important, with 30% of women with GDM having at least one parent with T2D. Fetal epigenetic modifications occur in response to maternal GDM, and may mediate both multi- and transgenerational risk. Changes to the maternal metabolome in GDM are primarily related to fatty acid oxidation, inflammation and insulin resistance. These might be effective early biomarkers allowing the identification of women at risk of GDM prior to the development of hyperglycaemia. The impact of the intra-uterine environment on the developing fetus, "developmental programming", has a multisystem effect, but its influence on adipogenesis is particularly important as it will determine baseline insulin sensitivity, and the response to future metabolic challenges. Identifying the critical window of metabolic development and developing effective interventions are key to our ability to improve population metabolic health.

The maternal hair metabolome is capable of discriminating intrahepatic cholestasis of pregnancy from uncomplicated pregnancy

Introduction

Intrahepatic cholestasis of pregnancy (ICP) is a pregnancy-specific liver disease associated with elevated bile acids in the blood. Diagnosis typically only occurs after the manifestation of clinical symptoms and the metabolic mechanisms underlying its development remain unclear. The aim of this study was to investigate potential specific metabolites and the underlying metabolic changes occurring during the development of ICP in the maternal plasma and hair metabolomes of women diagnosed with either ICP or having a healthy pregnancy.

Methods

A total of 35 Chinese women with ICP and 42 healthy pregnancies were enrolled in our study. Plasma and hair samples, total bile acid levels (TBA), alanine transaminase levels (ALT), aspartate aminotransferase levels (AST), and additional clinical information were collected during the third trimester. Metabolites from maternal plasma and hair segments collected pre-conception and analyzed using gas chromatography-mass spectrometry (GC-MS).

Results

Three plasma metabolites (p < 0.05, q < 0.38) and 21 hair metabolites (p < 0.05, q < 0.05) were significantly different between ICP and healthy pregnancies. A combination of the eight most significant hair metabolites in a multivariate receiver operating characteristic curve model showed the best area under the curve (AUC) was 0.885, whereas the highest AUC using metabolites from plasma samples was only 0.74. Metabolic pathway analysis revealed 32 pathways were significantly (p and q values < 0.05) affected in the hair samples of patients with ICP. Pathways associated with glutathione metabolism and ABC transporters were affected. No metabolic pathways were significantly affected in plasma.

Discussion

Overall, this study showed that the hair metabolome could be more useful than the plasma metabolome for distinguishing ICP from normal pregnancy.

Critical Factors in Sample Collection and Preparation for Clinical Metabolomics of Underexplored Biological Specimens

This review article compiles critical pre-analytical factors for sample collection and extraction of eight uncommon or underexplored biological specimens (human breast milk, ocular fluids, sebum, seminal plasma, sweat, hair, saliva, and cerebrospinal fluid) under the perspective of clinical metabolomics. These samples are interesting for metabolomics studies as they reflect the status of living organisms and can be applied for diagnostic purposes and biomarker discovery. Pre-collection and collection procedures are critical, requiring protocols to be standardized to avoid contamination and bias. Such procedures must consider cleaning the collection area, sample stimulation, diet, and food and drug intake, among other factors that impact the lack of homogeneity of the sample group. Precipitation of proteins and removal of salts and cell debris are the most used sample preparation procedures. This review intends to provide a global view of the practical aspects that most impact results, serving as a starting point for the designing of metabolomic experiments.

Hair and cord blood element levels and their relationship with air pollution, dietary intake, gestational diabetes mellitus, and infant neurodevelopment

BACKGROUND & AIMS

Exposure to a range of elements, air pollution, and specific dietary components in pregnancy has variously been associated with gestational diabetes mellitus (GDM) risk or infant neurodevelopmental problems. We measured a range of pregnancy exposures in maternal hair and/or infant cord serum and tested their relationship to GDM and infant neurodevelopment.

METHODS

A total of 843 pregnant women (GDM = 224, Non-GDM = 619) were selected from the Complex Lipids in Mothers and Babies cohort study. Forty-eight elements in hair and cord serum were quantified using inductively coupled plasma-mass spectrometry analysis. Binary logistic regression was used to estimate the associations between hair element concentrations and GDM risk, while multiple linear regression was performed to analyze the relationship between hair/cord serum elements and air pollutants, diet exposures, and Bayley Scales of infant neurodevelopment at 12 months of age.

RESULTS

After adjusting for maternal age, BMI, and primiparity, we observed that fourteen elements in maternal hair were associated with a significantly increased risk of GDM, particularly Ta (OR = 9.49, 95% CI: 6.71, 13.42), Re (OR = 5.21, 95% CI: 3.84, 7.07), and Se (OR = 5.37, 95% CI: 3.48, 8.28). In the adjusted linear regression model, three elements (Rb, Er, and Tm) in maternal hair and infant cord serum were negatively associated with Mental Development Index scores. For dietary exposures, elements were positively associated with noodles (Nb), sweetened beverages (Rb), poultry (Cs), oils and condiments (Ca), and other seafood (Gd). In addition, air pollutants PM2.5 (LUR) and PM10 were negatively associated with Ta and Re in maternal hair.

CONCLUSIONS

Our findings highlight the potential influence of maternal element exposure on GDM risk and infant neurodevelopment. We identified links between levels of these elements in both maternal hair and infant cord serum related to air pollutants and dietary factors.

Ambient air pollution and gestational diabetes mellitus: An updated systematic review and meta-analysis

OBJECTIVE

We aimed to evaluate the relationship between the composition of particulate matter (PM) and gestational diabetes mellitus (GDM) by a comprehensively review of epidemiological studies.

METHODS

We systematically identified cohort studies related to air pollution and GDM risk before February 8, 2023 from six databases (PubMed, Embase, Web of Science Core Collection, China National Knowledge Infrastructure, Wanfang Data Knowledge Service Platform and Chongqing VIP Chinese Science and Technology Periodical databases). We calculated the relative risk (RR) and its 95% confidence intervals (CIs) to assess the overall effect by using a random effects model.

RESULTS

This meta-analysis of 31 eligible cohort studies showed that exposure to PM_2.5, PM₁₀, SO₂, and NO₂ was associated with a significantly increased risk of GDM, especially in preconception and first trimester. Analysis of the components of PM_2.5 found that the risk of GDM was strongly linked to black carbon (BC) and nitrates (NO₃^-). Specifically, BC exposure in the second trimester and NO₃^- exposure in the first trimester elevated the risk of GDM, with the RR of 1.128 (1.032-1.231) and 1.128 (1.032-1.231), respectively. The stratified analysis showed stronger correlations of GDM risk with higher levels of pollutants in Asia, except for PM_2.5 and BC, which suggested that the specific composition of particulate pollutants had a greater effect on the exposure-outcome association than the concentration.

CONCLUSIONS

Our study found that ambient air pollutant is a critical factor for GDM and further studies on specific particulate matter components should be considered in the future.