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. 2022 Dec 2:13:1060309.
doi: 10.3389/fendo.2022.1060309. eCollection 2022.

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

Xuyang Chen  1   2 , Xue Zhao  1   2 , Mary Beatrix Jones  3 , Alexander Harper  4 , Jamie V de Seymour  5 , Yang Yang  1   2 , Yinyin Xia  6 , Ting Zhang  1   2   7 , Hongbo Qi  1   2   8 , John Gulliver  9 , Richard D Cannon  10 , Richard Saffery  11 , Hua Zhang  1   2 , Ting-Li Han  2   12   13 , Philip N Baker  14
Affiliations

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

Xuyang Chen et al. Front Endocrinol (Lausanne). .

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 PM2.5, PM10, SO2, NO2, CO and the exposure estimates of PM2.5 and NO2, and positively associated with O3.

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.

Keywords: air pollution; cohort study; gestational diabetes mellitus; hair analysis; metabolomics.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor RZ declared a shared affiliation with the authors XC, XZ, YY, YX, TZ, HQ, HZ, T-LH at the time of review.

Figures

Figure 1
Figure 1
Flowchart of recruited participants. In total, 1500 pregnant women were enrolled between 11-14 gestational weeks. Forty-nine women were lost to follow up, five women with diabetes, one woman with a history of GDM, three women dyed hair, 207 women provided insufficient hair samples and 215 women did not provide a hair sample. A 75-g OGTT was conducted to diagnose GDM according to the IADPSG guidelines. Two hundred and seventy-two GDM women and 748 non-GDM women were included in the final analysis.
Figure 2
Figure 2
Map of Chongqing showing the location of the monitoring sites and participants’ residential locations.
Figure 3
Figure 3
UMAP projection and heatmap of the metabolites across pregnancy. (A) UMAP clustering of all participants labelled and colored by GDM status and different periods of gestation. Groupings include pre-conception GDM [pre (GDM)], first trimester GDM [1st T (GDM)], second trimester GDM [2nd T (GDM)], third trimester GDM [3rd T (GDM)], pre-conception non-GDM [pre (non-GDM)], first trimester non-GDM [1st T (non-GDM)], second trimester non-GDM [2nd T (non-GDM)], and third trimester non-GDM (3rd T (non-GDM)). (B) The ratio of fourteen metabolite levels significantly different between GDM case and control groups. Red color indicates higher metabolite levels in the GDM group than the control group, while blue color indicates lower metabolite levels in the GDM group than the control group. Metabolites with both p-value and q-value less than 0.05 in the logistic regression adjusted for age and BMI are marked with *p-value and q-value less than 0.01 are marked with **p-value and q-value less than 0.001 are marked with ***.
Figure 4
Figure 4
Line plots and UMAP of GDM-associated metabolites across pregnancy. (A) shows unadjusted models while (B) shows the results of linear mixed models adjusted for maternal age and BMI. Triangles represent the fitted effects in GDM maternal hair and squares represent non-GDM. Blue denotes a consistent effect across gestation in the GDM group (no significant interaction with time), while red corresponds to a time-specific effect. (C, D) UMAP representation of myristic acid and 2-hydroxybutyric acid from data in Figure 3 (A). Red dots indicate higher metabolite levels in GDM women compared with the non-GDM women, while blue dots indicate lower levels. Data are visualized after log transformation.
Figure 5
Figure 5
Odds ratios between air pollution exposures and GDM occurrence. Green indicates the unadjusted odds ratio (95% CI); blue indicates the adjusted odds ratio (95% CI). Hollow shapes represent statistical significance, while solid shapes are non-significant.
Figure 6
Figure 6
The association between GDM related metabolites and specific air pollutants. The grey numbers are standardized regression coefficients that show the effect of one standard derivation (SD) pollutant change on the standardized log metabolite concentration. The red right-handed ellipses indicate positive relationships between metabolites and air pollutants, while the blue left-handed ellipses indicate negative relationships. Only the significant associations (q-values < 0.05) between metabolites and pollutants are displayed by ellipses.
Figure 7
Figure 7
The potential pathways that air pollution exposure influences GDM development through fatty acids and glutathione metabolism. Higher PM2.5 and NO2 air pollution exposure from pre-conception to early pregnancy are likely to increase pregnant women’s susceptibility to GDM through induced oxidative stress and inflammation. Myristic acid could promote the production of EPA and DHA to compete against inflammation. Methionine is catablised into cysteine and 2-ketobutyric acid (2-KB), which in turn is converted into glutathione and 2-hydroxybutyric acid (2-HB) respectively. The lower level of myristic acid and 2-HB in hair could be related to poor antioxidant and anti-inflammatory capacity in women with GDM. Therefore, increased exposure to air pollutants might promote GDM pathogenesis by downregulating fatty acids and glutathione metabolism. Abbreviations as follows: ALA, α-linolenic acid; EPA, eicosapentaenoic acid; DHA, docosahexaenoic acid; D6D, delta-6 desaturase; 2-HB, 2-hydroxybutyric acid; 2-KB, 2-ketobutyric acid.
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