ArticleFrontiers in endocrinology2022
The relationship between hair metabolites, air pollution exposure and gestational diabetes mellitus: A longitudinal study from pre-conception to third trimester.
Article in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 13 citations in OpenAlex.
- Unraveling the Molecular Links between Fine Particulate Matter Exposure and Early Birth Risks in African American Mothers: A Metabolomics Study in the Atlanta African American Maternal-Child Cohort.Environmental science & technology · 2025Article
- Assessment of Environmental Risk Factors for Gestational Diabetes Mellitus: A Ten-Year Systematic Review and Meta-Analysis.Journal of clinical medicine · 2025Review
- Critical Factors in Sample Collection and Preparation for Clinical Metabolomics of Underexplored Biological Specimens.Metabolites · 2024Review
- The maternal hair metabolome is capable of discriminating intrahepatic cholestasis of pregnancy from uncomplicated pregnancy.Frontiers in endocrinology · 2023Article
- Multigenerational diabetes mellitus.Frontiers in endocrinology · 2023Review
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Authors and funding
15 authors at 9 institutions in 4 countries.
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Abstract
Background: Gestational diabetes mellitus (GDM) is a metabolic condition defined as glucose intolerance with first presentation during pregnancy. Many studies suggest that environmental exposures, including air pollution, contribute to the pathogenesis of GDM. Although hair metabolite profiles have been shown to reflect pollution exposure, few studies have examined the link between environmental exposures, the maternal hair metabolome and GDM. The aim of this study was to investigate the longitudinal relationship (from pre-conception through to the third trimester) between air pollution exposure, the hair metabolome and GDM in a Chinese cohort. Methods: A total of 1020 women enrolled in the Complex Lipids in Mothers and Babies (CLIMB) birth cohort were included in our study. Metabolites from maternal hair segments collected pre-conception, and in the first, second, and third trimesters were analysed using gas chromatography-mass spectrometry (GC-MS). Maternal exposure to air pollution was estimated by two methods, namely proximal and land use regression (LUR) models, using air quality data from the air quality monitoring station nearest to the participant's home. Logistic regression and mixed models were applied to investigate associations between the air pollution exposure data and the GDM associated metabolites. Results: Of the 276 hair metabolites identified, the concentrations of fourteen were significantly different between GDM cases and non-GDM controls, including some amino acids and their derivatives, fatty acids, organic acids, and exogenous compounds. Three of the metabolites found in significantly lower concentrations in the hair of women with GDM (2-hydroxybutyric acid, citramalic acid, and myristic acid) were also negatively associated with daily average concentrations of PM Conclusions: This study demonstrated that the maternal hair metabolome reflects the longitudinal metabolic changes that occur in response to environmental exposures and the development of GDM.
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