Skip to main content
Download PDF
Download ePub

Maternal metabolomics linking prenatal exposure to fine particulate matter and birth weight: a cross-sectional analysis of the MADRES cohort

Environmental Health volume 24, Article number: 14 (2025) Cite this article

Abstract

Background

Prenatal exposure to air pollution has been associated with an increased risk of low birth weight. Disrupted metabolism may serve as an underlying mechanism, but the specific metabolic pathways involved remain unclear.

Methods

In the Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) study, 382 third-trimester maternal serum samples were analyzed for untargeted metabolomics using liquid chromatography with Fourier transform high-resolution mass spectrometry. Ambient concentrations of fine particulate matter (PM2.5), particulate matter ≤ 10 μm in diameter (PM10), nitrogen dioxide (NO2), and ozone (O3) were estimated using inverse-distance-squared weighted spatial interpolation based on daily residential histories. Birth weight was retrieved from medical records. Linear regression identified metabolomic features associated with air pollution exposure or birth weight, followed by Mummichog pathway enrichment and mediation analyses for the selected features.

Results

Second-trimester PM2.5 exposure was associated with lower birth weight. Fourteen metabolic pathways were significantly associated with second-trimester PM2.5 exposure, with C21-steroid hormone biosynthesis and metabolism showing the most significant association. Sixteen metabolic pathways were significantly associated with birth weight, with vitamin A (retinol) metabolism being the most significantly enriched pathway. Seven pathways were associated with both PM2.5 exposure and birth weight, including C21-steroid hormone biosynthesis and metabolism, bile acid biosynthesis, tyrosine metabolism, ascorbate (vitamin C) and aldarate metabolism, vitamin D3 (cholecalciferol) metabolism, vitamin A (retinol) metabolism, and pyrimidine metabolism. Overweight or obese women exhibited more metabolomic features and metabolic pathways associated with PM2.5 exposure compared to underweight or normal-weight women. No associations were observed between PM10, NO2, or O3 and birth weight.

Conclusions

Maternal metabolic pathways involving steroid metabolism, oxidative stress and inflammation, vitamin metabolism, and DNA damage may link prenatal PM2.5 exposure to lower birth weight, with overweight or obese women potentially more susceptible to these metabolic disruptions.

Peer Review reports

Background

Low birth weight is a public health concern worldwide [1]. It contributes to 34% of global mortality in children under 5 [2] and has enduring consequences for both developmental and physical health [1, 3]. Mounting evidence has identified a link between prenatal exposure to ambient air pollution, particularly fine particulate matter (PM2.5), and low birth weight [4,5,6]. Recently, Niu et al. replicated previous findings using data from the Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) study [7], finding that exposure to PM2.5 and nitrogen dioxide (NO2) during sensitive windows primarily in early to mid-pregnancy was associated with lower birth weight [8].

However, the biological mechanisms through which air pollution exposure influences birth weight remain unclear. It is hypothesized that oxidative stress, inflammation, blood coagulation, endothelial function, and hemodynamic responses are potential contributors [9], but evidence of the exact biological pathways responsible for air pollution-related adverse pregnancy outcomes is limited. Metabolism, which encompasses all the chemical reactions that occur within organisms to maintain life, plays an essential role in fetal development and can be influenced by various external factors, including air pollution [10]. Some studies have explored metabolic pathways associated with either air pollution exposure or birth weight separately [11,12,13,14,15,16,17,18,19,20]. However, few studies have examined the impact of air pollution exposure on maternal metabolism and their consequent relation to birth weight [21, 22].

Furthermore, although previous studies have shown the modifying effect of maternal body mass index (BMI) on the association between air pollution exposure and birth weight [23, 24], the metabolic basis of these associations remains underexplored. Therefore, it is crucial to further investigate how maternal BMI may affect metabolic changes in response to prenatal air pollution exposure.

This study aims to identify the metabolic pathways linking prenatal air pollution exposure to birth weight using data from the MADRES cohort study and an untargeted metabolomics approach. In addition, we explored whether the different pathways of air pollution effects on newborn birth weight among pregnant women varied by BMI level.

Methods

Study population

The MADRES study is an ongoing prospective pregnancy cohort study initiated in 2015, primarily focusing on lower-income Hispanic women in Los Angeles, California. Details of the study design and participant recruitment were previously reported [7]. Briefly, eligibility criteria included being within 30 gestational weeks, at least 18 years of age, and fluent in either English or Spanish. Women were excluded if they tested positive for HIV, had physical, mental, or cognitive disability preventing participation or consent, were currently incarcerated, or had a multifetal pregnancy. The MADRES study was approved by the Institutional Review Board of University of Southern California. Written informed consent and Health Insurance Portability and Accountability Act of 1996 (HIPAA) authorization was obtained from each participant at enrollment.

As of May 10, 2020, 424 women had reached delivery and had fasting blood samples collected in the third trimester (≥ 26 gestational week) available for untargeted metabolomics analysis. After excluding three participants without qualified untargeted metabolomics data, 38 participants who delivered prematurely (< 37 gestational week), and one participant without air pollution exposure data, there were 382 participants remaining (Figure S1).

Air pollution exposure assessment

Participants self-reported their residential address histories from one year prior to conception through the follow-up period. These along with participant contact tracking information were used to construct daily residential timelines that capture all residential moves with high spatial and temporal resolution and served as the basis for exposure linkages. Residential daily estimates of 24-h average concentrations of PM2.5, particulate matter ≤ 10 μm in diameter (PM10), and NO2, along with 8-h maximum ozone (O3) concentrations, were calculated using inverse-distance-squared weighted spatial interpolation based on ambient air quality data from the United Stated Environmental Protection Agency Air Quality System. Trimester-specific concentrations of ambient air pollutants for the preconception period (defined as 12 weeks prior to conception), and the first (0 < gestational week < 14) and second (14 ≤ gestational week < 26) trimesters were calculated based on daily estimates.

Newborn birth weight Z-score

Newborn birth weight and sex were obtained from the delivery medical records. Gestational age at delivery was estimated using a hierarchical approach, incorporating fetal crown-rump length measured by ultrasound during the first and second trimesters, the obstetrical medical record, or the self-reported date of the last menstrual period. Birth weight was then transformed into a sex- and gestational age-specific birth weight Z-score [25].

Maternal untargeted metabolomics

Fasting blood samples were collected at the third trimester. The mean sample collection time was 31.53 ± 1.94 gestational weeks, and the duration from sample collection to delivery was 7.82 ± 2.14 weeks. Serum was isolated and stored at −80 °C until transported to the laboratory at Emory University on dry ice. High-resolution untargeted metabolomics analysis was performed using liquid chromatography and Fourier transform high-resolution mass spectrometry (LC-HRMS, Dionex Ultimate 3000, HF Q-Exactive, Thermo Scientific) [26]. Each serum sample was analyzed in triplicate using hydrophilic interaction liquid chromatography (HILIC) coupled with an electrospray ionization (ESI) source in positive mode, and C18 hydrophobic reversed-phase chromatography followed by negative ESI mode (hereafter abbreviate as HILIC-positive mode and C18-negative mode, respectively). A detailed protocol of sample processing and detection is provided in the Supplementary Material.

Each detected chemical signal, consisting of mass-to-charge ratio (m/z), retention time, and ion abundance, was referred to as a metabolomic feature. A total of 10800 and 10578 metabolomic features were identified using HILIC-positive mode and C18-negative mode, respectively. Metabolomic features detected in ≤ 50% of all samples, as well as samples in which ≤ 50% of metabolomic features were detected, were excluded from the final analysis. Missingness in intensity data was imputed by assigning half of the minimum intensity of a specific metabolomic feature. Quantile normalization [27] and log2 transformation were performed to normalize the intensity data. Additionally, metabolomic features with coefficients of variation (CVs) ≥ 30% across Qstd3 samples were further excluded [26, 28,29,30,31]. Qstd3 is pooled EDTA plasma obtained from 50 healthy donors purchased from Equitech-Bio (SHP45) without information on drug use or fasting status [26]. It was analyzed in duplicate at the beginning, middle, and end of each of the 18 batches. Detailed quality control and data transformation procedures are shown in Figure S1, and the distribution of CVs for untargeted metabolomics data is presented in Figure S2. 7393 metabolomic features in 377 serum samples detected with HILIC-positive mode and 8928 metabolomic features in 382 serum samples analyzed with C18-negative mode were included in the statistical analysis.

Covariates

Maternal race/ethnicity, education, household income, marital status, and smoking history, as well as newborn race/ethnicity were obtained using interviewer-administrated questionnaires. Pre-pregnancy BMI was calculated as the ratio of self-reported pre-pregnancy weight (kg) to the square of standing height (m2), measured with a stadiometer at onsite study visits. Participants were divided into two BMI categories, namely “Underweight or normal” versus “Overweight or obese”, using a cutoff of pre-pregnancy BMI = 25 kg/m2. Maternal age at delivery was calculated as the difference between the delivery date and mother’s birth date. Conception date was estimated with the same hierarchical information used for gestational age estimation and was further categorized into warm (April-September) and cold (October–March) conception seasons. The enrollment time point indicated in which gestational period the participants were enrolled (regular entry: < 20 gestational weeks versus late entry: 20–30 gestational weeks). Missingness of categorical variables was categorized as “Unknown”.

Statistical analysis

Descriptive data are reported as mean ± standard deviation (SD) or number (%), as appropriate. The associations between covariates and birth weight Z-score were examined using linear regression. Students’ t-test and chi-square test were used to compare continuous and categorial covariates between pre-pregnancy BMI subgroups, respectively.

A previous study of 628 MADRES participants found that PM2.5 exposure during 14–22 gestational weeks and NO2 exposure during 9–14 gestational weeks were significantly associated with lower birth weight [8]. To validate these findings, we used linear regression models to examine the associations of trimester-specific concentrations of PM2.5, PM10, NO2, and O3, as well as PM2.5 exposure during 14–22 gestational weeks and NO2 exposure during 9–14 gestational weeks, with birth weight Z-score in a subset of 382 MADRES participants with metabolomics data. The models were adjusted for maternal age at delivery, pre-pregnancy BMI, smoking history, education, household income, marital status, newborn ethnicity, conception season, and enrollment time point. We selected the air pollution exposure that was significantly associated with birth weight in subsequent analyses. To investigate the modifying effect of pre-pregnancy BMI on the association between prenatal air pollution exposure and birth weight, an interaction term between pre-pregnancy BMI categories and air pollution exposure levels was added to the linear regression models.

We used a meet-in-the-middle strategy in metabolome-wide association study (MWAS). MWAS analyses were performed using linear regression models to identify third-trimester metabolomic features in significant association with either air pollution exposure or birth weight Z-score. All models were adjusted for maternal age at delivery, smoking, education, income, marital status, ethnicity (maternal ethnicity in air pollution model and newborn ethnicity in birth weight model, respectively), conception season (air pollution model only), and enrollment time point. Stratified MWAS analyses were further conducted to examine the associations between metabolomic features and air pollution exposure among different BMI subgroups. Results with p-value < 0.05 were considered statistically significant. False discovery rate (FDR) calculated using Benjamini–Hochberg method [32] was applied to address multiple testing. Statistical analysis was conducted using R software (version 4.2.2; R Foundation for Statistical Computing, Vienna, Austria).

Mummichog pathway enrichment analysis [33] was conducted to identify metabolic pathways associated with air pollution exposure and birth weight, respectively. Metabolomic features detected with HILIC-positive mode and C18-negative mode were analyzed separately using Mummichog version 2.6.1 and metabolic model MFN_1.10.4, with a mass tolerance of 5 ppm and a p-value threshold of 0.05. Pathways with p-value < 0.05 based on Fisher’s exact test was considered significantly enriched. Only pathways containing at least three metabolites were interpreted. Mummichog annotated metabolomic features were further compared with a laboratory-developed reference of 467 chemical compounds using MS2 spectra [26] based on the criteria of mass difference ≤ 5 ppm, retention time difference ≤ 30 s, and having the same adduct. Metabolomic features matched to the compound reference were considered as metabolites with confirmed identity, following the Metabolomics Standard Initiative level 1 criteria [34].

Mediation analyses were conducted using R package “mediation” [35] to examine whether selected metabolomic features mediated the associations between air pollution exposure and birth weight. Two types of metabolomic features were included in the analyses: 1) A single metabolomic feature with a Mummichog annotation which was significantly associated with both air pollution exposure and birth weight Z-score. 2) Multiple metabolomic features sharing the same Mummichog annotation, where some features were significantly associated with air pollution exposure, while others were linked to birth weight Z-score. Maternal age at delivery, smoking, education, income, marital status, maternal ethnicity, conception season, and enrollment time point were adjusted for as covariates in the models.

Results

The characteristics of 382 MADRES participants are summarized in Table 1. The mean maternal age at delivery was 29.4 ± 5.9 years. Participants predominantly self-identified as Hispanic (80% newborns and 78% mothers) and the majority of women were overweight or obese (68%). 267 (70%) women never smoked and 109 (29%) smoked before pregnancy but stopped smoking during pregnancy. Among the 382 women, 24% completed less than 12th grade of education, 45% had household income < $30,000 per year, and 23% were single or divorced. Additionally, overweight or obesity was significantly associated with higher birth weight Z-score, while reporting unknown income and living together were each associated with lower birth weight Z-score. Table S1 shows the characteristics of participants stratified by pre-pregnancy BMI. Overweight or obese women were older, more likely to be Hispanic, and had lower education levels compared to those who were underweight or of normal BMI.

Table 1 Characteristics of 382 MADRES participants
Full size table

Table S2 presents the levels of PM2.5, PM10, NO2, and O3 in the preconception period, the first and second trimesters, and the time window during which air pollution exposure was significantly associated with birth weight in previous MADRES analysis [8]. Among all the air pollutants we investigated, only PM2.5 exposure in the second trimester was significantly associated with lower birth weight Z-score [−0.18 (95% confidence interval (CI): −0.33, −0.03)]. There were no significant associations observed for PM10, NO2, or O3 (Table S3). Additionally, among women who were underweight or had normal BMI, an interquartile range increase in second-trimester PM2.5 exposure was associated with a −0.24 (95% CI: −0.43, −0.04) change in birth weight Z-score. In contrast, this association was not significant among overweight or obese women [−0.12 (95% CI: −0.32, 0.08)], with a p-value for the interaction term of 0.37.

In the overall population, 691 and 755 metabolomic features detected in HILIC-positive and C18-negative mode were significantly associated with second-trimester PM2.5 exposure, respectively (p < 0.05). After FDR-adjustment, the MWAS associations remained significant for three C18-negative metabolomic features (FDR < 0.05) (Figure S3). Metabolic pathways in significant association with second-trimester PM2.5 exposure are shown in Fig. 1 and Table S4. Fourteen metabolic pathways were significantly associated with second-trimester PM2.5 exposure and C21-steroid hormone biosynthesis and metabolism was the top significantly enriched pathway (p = 0.001). 324 HILIC-positive and 513 C18-negative metabolomic features were found to be significantly associated with birth weight Z-score (p < 0.05), and the MWAS association was significant at FDR < 0.05 for one C18-negative metabolomic feature (Figure S3). These metabolomic features were significantly enriched in sixteen metabolic pathways (Fig. 1 and Table S4). Among them, vitamin A (retinol) metabolism showed the most significant association, as indicated by the smallest p-value from Fisher’s exact test (p = 0.004). There were seven common metabolic pathways associated with both PM2.5 exposure and birth weight, including C21-steroid hormone biosynthesis and metabolism, bile acid biosynthesis, tyrosine metabolism, ascorbate (vitamin C) and aldarate metabolism, vitamin D3 (cholecalciferol) metabolism, vitamin A (retinol) metabolism, and pyrimidine metabolism. Pathways related to amino acid, carbohydrate, and nucleotide metabolism were predominantly identified in the C18-negative mode, while pathways associated with lipid and vitamin metabolism were primarily identified in the HILIC-positive mode.

Fig. 1
figure 1

Metabolic pathways significantly associated with second-trimester PM2.5 exposure and birth weight Z-score. Triangles denote common pathways in at least two columns

Full size image

241 HILIC-positive and 687 C18-negative metabolomic features were significantly associated with second-trimester PM2.5 exposure among underweight or normal-weight participants (p < 0.05), and one C18-negative metabolomic feature was significant at FDR < 0.05; among overweight or obese participants, the number of metabolomic features associated with PM2.5 exposure was 772 for HILIC-positive mode and 742 for C18-negative mode (p < 0.05), and the associations remained significant at FDR < 0.05 for 24 HILIC-positive metabolomic features (Figure S4). Notably, metabolic pathways associated with second-trimester PM2.5 exposure differed by BMI status. As shown in Fig. 1 and Table S4, among women who were underweight or of normal BMI, four lipid metabolism pathways and one glycan biosynthesis and metabolism pathway were significantly associated with PM2.5 exposure, while more metabolic pathways were changed among overweight or obese women, including ten lipid metabolism pathways, one amino acid metabolism pathway, one carbohydrate metabolism pathway, two glycan metabolism pathways, three vitamin metabolism pathways, two nucleotide metabolism pathways, and one hyaluronan metabolism pathway. C21-steroid hormone biosynthesis and metabolism, bile acid biosynthesis, and fatty acid activation were common metabolic pathways in significant association with PM2.5 exposure in both BMI groups. Moreover, out of the seven common metabolic pathways associated with both PM2.5 exposure and birth weight among the overall population, two pathways showed significant association with PM2.5 exposure among underweight or normal-weight women and six showed significant association with PM2.5 exposure among overweight or obese women.

Metabolomic features having common Mummichog annotations that were associated with second-trimester PM2.5 exposure or birth weight Z-score are summarized in Table S5. These common annotations were involved in C21-steroid hormone biosynthesis and metabolism, ascorbate (vitamin C) and aldarate metabolism, glycolysis and gluconeogenesis, glycosphingolipid biosynthesis – globoseries, N-glycan degradation, and biosynthesis of unsaturated fatty acids. A metabolomic feature was matched to confirmed identity, that is FA 20:5 (eicosapentaenoic acid) involved in biosynthesis of unsaturated fatty acids. A metabolomic feature without confirmed identity (m/z = 177.0413, retention time = 227, mode = C18-negative) and FA 20:5 (eicosapentaenoic acid) were both significantly associated with both PM2.5 exposure and birth weight Z-score among the overall population. They also mediated the association between second-trimester PM2.5 exposure and birth weight Z-score [βindirect = 0.01 (95% CI: 0, 0.02), p = 0.02 and βindirect = −0.01 (95% CI: −0.02, 0), p = 0.07, respectively]. Additionally, a significant mediating effect was observed for the same metabolomic feature m/z = 177.0413 (retention time = 227, mode = C18-negative) among overweight and obese women [βindirect = 0.01 (95% CI: 0, 0.03), p = 0.04].

Discussion

In the MADRES pregnancy cohort study, only second-trimester PM2.5 exposure was significantly associated with lower birth weight, while no significant associations were observed for the other ambient air pollutants, including PM10, NO2, and O3. Through high-resolution untargeted metabolomics analysis, we identified metabolic pathways potentially underlying the associations between prenatal exposure to PM2.5 and lower birth weight, including maternal metabolism of C21-steroid hormones, bile acids, tyrosine, ascorbate and aldarate, vitamin D3, vitamin A, and pyrimidine during late pregnancy. Additionally, women who were overweight or obese exhibited more pronounced metabolic perturbations by prenatal PM2.5 exposure.

Our study highlighted the important role of steroid metabolism, including C21-steroid hormone, bile acid, and vitamin D3, in linking prenatal PM2.5 exposure to lower birth weight. We observed that disturbance in C21-steroid hormone biosynthesis and metabolism was associated with altered birth weight, which is consistent with previous studies profiling metabolomics in maternal or newborn blood samples [11, 12, 18]. C21-steroid hormones, systematically known as pregnanes and including progesterone and corticosteroids, play pivotal roles in all stages of pregnancy [36, 37]. Progesterone is implicated profoundly in initiation and maintenance of pregnancy, fetal growth and development, as well as parturition, through immune modulation [38,39,40]. Progesterone has also been associated with alternation in brown adipose tissue that can affect gestational metabolism and fetal growth [41] and a lower level of maternal progesterone is a predictor of low birth weight [42,43,44]. Additionally, prenatal PM2.5 exposure has been associated with lower steroid hormones in pregnant women and newborns [45, 46], potentially by affecting related enzymes [47]. A metabolomic study also demonstrated that air pollution exposure interfered with C21-steroid hormone metabolism in follicular fluid of women undergoing in vitro fertilization [14]. In summary, by employing a meet-in-the-middle strategy, our study bridges previous findings, providing evidence on the potential role of C21-steroid hormone metabolism in linking prenatal PM2.5 exposure to lower birth weight.

Bile acids are steroid acids involved in intestinal fat absorption and triglyceride, cholesterol, glucose, and energy homeostasis regulation, which have critical roles in pregnancy [48]. Dysregulated bile acid metabolism is linked to gestational diseases such as intrahepatic cholestasis of pregnancy, gestational diabetes mellitus, and asymptomatic hypercholanemia, and can detrimentally impact the mother, the placenta, and the developing fetus [49]. Previous studies have shown the associations between excessive bile acids and lower birth weight as well as an increased risk of small for gestational age [50,51,52,53]. Consistent with our finding, a study among 214 mothers living in California’s Central Valley found that traffic-related air pollution exposure during pregnancy was associated with perturbations in bile acid metabolism [54]. PM2.5 exposure may detrimentally affect bile acid synthesis through downregulated expression of farnesoid X receptor [55], which is a nuclear receptor repressing bile acid synthesis through a feedback regulatory pathway [56].

Additionally, we found that vitamin D3 metabolism, which is a secosteroid hormone originating from cholesterol, might serve as an underlying mechanism linking prenatal PM2.5 exposure to low birth weight. Vitamin D3 metabolism is essential in fetal skeletal growth, particularly during the third trimester when the fetus undergoes accelerated skeleton accumulation [57]. Deficiency of vitamin D3 has long been linked to an increased risk of low birth weight [58,59,60]. A study enrolling 125 women undergoing in vitro fertilization reported that exposure to NO2 and PM2.5 could disrupt vitamin D3 metabolism in follicular fluid, implying a link between vitamin D3 metabolism and reproductive impacts of air pollution exposure [14]. Notably, a complex interplay exists between bile acid and vitamin D3 metabolism. Vitamin D regulates bile acid production and transport, and conversely bile acids act as ligands for vitamin D receptors [61, 62]. Circulating levels of vitamin D metabolites have been inversely associated with fecal bile acid concentrations [63]. Our finding, which aligns with prior research and exhibits biological plausibility, supports the notion that bile acid biosynthesis and vitamin D3 metabolism may serve as pathways through which prenatal PM2.5 exposure contributes to reduced birth weight.

Pathway enrichment results suggest that oxidative stress and inflammation may be underlying mechanisms contributing to abnormal birth weight related to PM2.5 exposure. Tyrosine, an aromatic amino acid, has been closely linked to oxidative stress and inflammation [64, 65], and is a well-established risk factor for obesity, insulin resistance, type 2 diabetes, and gestational diabetes mellitus [66,67,68]. Our study and prior research demonstrated that exposure to ambient and traffic-related air pollution could disrupt tyrosine metabolism [13, 15, 16, 19], which may partly attribute to oxidative stress [65]. The disruption may further lead to lower birth weight, owing to the multifaceted role of tyrosine in oxidative stress, inflammatory responses, and glucose metabolism. Furthermore, ascorbate (vitamin C) is a potent antioxidant with significant implications in oxidative stress and inflammation [69, 70]. Studies have consistently shown the relationship between vitamin C deficiency and an elevated risk of low birth weight [71,72,73], while separate findings have linked alterations in ascorbate and aldarate metabolism to particulate air pollution exposure [14, 16, 17, 20]. Our research not only corroborates prior observations but also provides a clear and direct insight into the potential role of ascorbate and aldarate metabolism in linking prenatal PM2.5 exposure and low birth weight. Additionally, we identified a metabolite, FA 20:5 (eicosapentaenoic acid), that was associated with both prenatal PM2.5 exposure and birth weight. Notably, FA 20:5 (eicosapentaenoic acid) exhibited a mediating role in a mediation testing framework. FA 20:5 (eicosapentaenoic acid), part of the α-linolenic acid (n-3 fatty acid) family, is known as an anti-inflammatory agent and a cardiovascular protective compound [74,75,76]. These observations further underscore the critical role of inflammation.

Vitamin A is an essential micronutrient for fetal growth and development. It is required for normal visual functioning, immune responses, gene expression, embryogenesis, and hematopoiesis, as well as lung growth and maintenance of the integrity of respiratory tract epithelial cells [77, 78]. Previous metabolomic studies have independently illustrated the associations between vitamin A metabolism and two distinct factors: birth weight [18], and air pollution exposure [14, 15], while our meet-in-the-middle analysis further indicates that vitamin A metabolism may serve as a pathway linking these two factors. Additionally, pyrimidine metabolism was associated with both prenatal PM2.5 exposure and birth weight, suggesting that prenatal PM2.5 exposure may adversely affect birth weight by inducing DNA damage [15, 16].

Overweight or obese women may experience more pronounced metabolic disruptions in response to prenatal PM2.5 exposure, as demonstrated by our findings. Similarly, a study reported near-roadway air pollution exposure had a stronger association with non-esterified fatty acids and branched-chain amino acids among adolescents and young adults who were overweight or obese than normal-weight participants [79]. Previous studies have shown that pregnant women who were overweight and obese demonstrated metabolic alterations, characterized by dysregulation in pathways related to the antioxidant defense system, nucleotide production, lipid metabolism, and energy production [80, 81], while PM2.5 exposure has been linked to metabolic perturbations in similar pathways [15,16,17]. Consequently, it is plausible that overweight or obese women may exacerbate the metabolic disruptions of PM2.5 exposure.

We applied a meet-in-the-middle strategy in MWAS analysis to investigate the maternal metabolic pathways linking prenatal PM2.5 exposure to lower birth weight. The novel insight on biological pathways may illuminate promising intervention and prevention targets to mitigate the detrimental effects of prenatal PM2.5 exposure. The study had some limitations. First, air pollution exposure was estimated using inverse-distance-squared weighted spatial interpolation, which may not fully capture the fine spatial variability of air pollution. However, Southern California has the densest ambient air pollution monitoring network in the United States, providing excellent data for capturing spatial variability. In addition, we developed detailed daily level residential timelines that accounted for all residential mobility with high spatial and temporal resolution, allowing for more accurate estimates of daily residential exposure to ambient air pollution. Second, the sample size may have limited statistical power due to the large number of metabolomic features analyzed, and chemical annotation remains a great challenge in untargeted metabolomics research. However, in our main results, we focused primarily on presenting air pollution-related metabolic pathways that convened information from multiple metabolites within a pathway. The pathway findings provide more robust evidence than individual metabolites. Third, while our study adds knowledge on the metabolic pathways through which prenatal PM2.5 exposure adversely affect fetal growth in an underrepresented population [82], more studies in diverse populations remain imperative. Fourth, the presence of uncontrolled confounding factors in this observational study limits the establishment of causality, which highlights the need for evidence from interventional or experimental studies.

Conclusions

In the MADRES pregnancy cohort, we applied a meet-in-the-middle strategy in an untargeted metabolomics study to investigate the metabolic mechanisms linking prenatal PM2.5 exposure to lower birth weight. We found that metabolic pathways in relation to steroid metabolism including C21-steroid hormone biosynthesis and metabolism, bile acid biosynthesis, and vitamin D3 (cholecalciferol) metabolism; oxidative stress and inflammation including tyrosine metabolism and ascorbate (vitamin C) and aldarate metabolism; vitamin metabolism including vitamin A (retinol) metabolism; and DNA damage including pyrimidine metabolism, may serve as underlying mechanisms. Additionally, overweight or obese women may have enhanced susceptibility to metabolic disturbances of prenatal PM2.5 exposure. Further studies are essential to explore the potential of these biological pathways as intervention targets for mitigating the detrimental impacts of PM2.5 exposure and unravel the intricate interplay of various risk factors in influencing adverse birth outcomes.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

BMI:

Body mass index

CV:

Coefficient of variation

ESI:

Electrospray ionization

FDR:

False discovery rate

HILIC:

Hydrophilic interaction liquid chromatography

LC-HRMS:

Liquid chromatography coupled with Fourier transform high-resolution mass spectrometry

MADRES:

Maternal and Developmental Risks from Environmental and Social Stressors

MWAS:

Metabolome-wide association study

m/z:

Mass-to-charge ratio

NO2 :

Nitrogen dioxide

O3 :

Ozone

PM2.5 :

Fine particulate matter

PM10 :

Particulate matter ≤ 10 μm in diameter

SD:

Standard deviation

References

  1. Blencowe H, Krasevec J, de Onis M, Black RE, An X, Stevens GA, et al. National, regional, and worldwide estimates of low birthweight in 2015, with trends from 2000: A systematic analysis. Lancet Glob Health. 2019;7:e849–60.

    Article  Google Scholar 

  2. GBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396:1223–49.

    Article  Google Scholar 

  3. Gluckman PD, Hanson MA, Beedle AS. Early life events and their consequences for later disease: A life history and evolutionary perspective. Am J Hum Biol. 2007;19:1–19.

    Article  Google Scholar 

  4. Bekkar B, Pacheco S, Basu R, DeNicola N. Association of air pollution and heat exposure with preterm birth, low birth weight, and stillbirth in the US: A systematic review. JAMA Netw Open. 2020;3:e208243.

  5. Li C, Yang M, Zhu Z, Sun S, Zhang Q, Cao J, et al. Maternal exposure to air pollution and the risk of low birth weight: A meta-analysis of cohort studies. Environ Res. 2020;190: 109970.

    Article  CAS  Google Scholar 

  6. Stieb DM, Chen L, Eshoul M, Judek S. Ambient air pollution, birth weight and preterm birth: A systematic review and meta-analysis. Environ Res. 2012;117:100–11.

    Article  CAS  Google Scholar 

  7. Bastain TM, Chavez T, Habre R, Girguis MS, Grubbs B, Toledo-Corral C, et al. Study design, protocol and profile of the Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) pregnancy cohort: A prospective cohort study in predominantly low-income Hispanic women in urban Los Angeles. BMC Pregnancy Childbirth. 2019;19:189.

  8. Niu Z, Habre R, Chavez TA, Yang T, Grubbs BH, Eckel SP, et al. Association between ambient air pollution and birth weight by maternal individual- and neighborhood-level stressors. JAMA Netw Open. 2022;5: e2238174.

    Article  Google Scholar 

  9. Kannan S, Misra DP, Dvonch JT, Krishnakumar A. Exposures to airborne particulate matter and adverse perinatal outcomes: A biologically plausible mechanistic framework for exploring potential effect modification by nutrition. Environ Health Perspect. 2006;114:1636–42.

    Article  CAS  Google Scholar 

  10. Sferruzzi-Perri AN, Lopez-Tello J, Napso T, Yong HEJ. Exploring the causes and consequences of maternal metabolic maladaptations during pregnancy: Lessons from animal models. Placenta. 2020;98:43–51.

    Article  CAS  Google Scholar 

  11. Heazell AE, Bernatavicius G, Warrander L, Brown MC, Dunn WB. A metabolomic approach identifies differences in maternal serum in third trimester pregnancies that end in poor perinatal outcome. Reprod Sci. 2012;19:863–75.

    Article  Google Scholar 

  12. Horgan RP, Broadhurst DI, Walsh SK, Dunn WB, Brown M, Roberts CT, et al. Metabolic profiling uncovers a phenotypic signature of small for gestational age in early pregnancy. J Proteome Res. 2011;10:3660–73.

    Article  CAS  Google Scholar 

  13. Hu X, Yan M, He L, Qiu X, Zhang J, Zhang Y, et al. Associations between time-weighted personal air pollution exposure and amino acid metabolism in healthy adults. Environ Int. 2021;156: 106623.

    Article  CAS  Google Scholar 

  14. Hwang S, Hood RB, Hauser R, Schwartz J, Laden F, Jones D, et al. Using follicular fluid metabolomics to investigate the association between air pollution and oocyte quality. Environ Int. 2022;169: 107552.

    Article  CAS  Google Scholar 

  15. Li Z, Liang D, Ye D, Chang HH, Ziegler TR, Jones DP, et al. Application of high-resolution metabolomics to identify biological pathways perturbed by traffic-related air pollution. Environ Res. 2021;193: 110506.

    Article  CAS  Google Scholar 

  16. Liang D, Ladva CN, Golan R, Yu T, Walker DI, Sarnat SE, et al. Perturbations of the arginine metabolome following exposures to traffic-related air pollution in a panel of commuters with and without asthma. Environ Int. 2019;127:503–13.

    Article  CAS  Google Scholar 

  17. Menni C, Metrustry SJ, Mohney RP, Beevers S, Barratt B, Spector TD, et al. Circulating levels of antioxidant vitamins correlate with better lung function and reduced exposure to ambient pollution. Am J Respir Crit Care Med. 2015;191:1203–7.

    Article  CAS  Google Scholar 

  18. Robinson O, Keski-Rahkonen P, Chatzi L, Kogevinas M, Nawrot T, Pizzi C, et al. Cord blood metabolic signatures of birth weight: A population-based study. J Proteome Res. 2018;17:1235–47.

    Article  CAS  Google Scholar 

  19. Vlaanderen JJ, Janssen NA, Hoek G, Keski-Rahkonen P, Barupal DK, Cassee FR, et al. The impact of ambient air pollution on the human blood metabolome. Environ Res. 2017;156:341–8.

    Article  CAS  Google Scholar 

  20. Walker DI, Lane KJ, Liu K, Uppal K, Patton AP, Durant JL, et al. Metabolomic assessment of exposure to near-highway ultrafine particles. J Expo Sci Environ Epidemiol. 2019;29:469–83.

    Article  CAS  Google Scholar 

  21. Hood RB, Moyd S, Hoffman S, Chow SS, Tan Y, Bhanushali P, et al. 2024. Metabolomics application in understanding the link between air pollution and infant health outcomes: A narrative review. Current Pollution Reports:1–13.

  22. Inoue K, Yan Q, Arah OA, Paul K, Walker DI, Jones DP, et al. Air pollution and adverse pregnancy and birth outcomes: Mediation analysis using metabolomic profiles. Curr Environ Health Rep. 2020;7:231–42.

    Article  Google Scholar 

  23. Lakshmanan A, Chiu YH, Coull BA, Just AC, Maxwell SL, Schwartz J, et al. Associations between prenatal traffic-related air pollution exposure and birth weight: Modification by sex and maternal pre-pregnancy body mass index. Environ Res. 2015;137:268–77.

    Article  CAS  Google Scholar 

  24. Laurent O, Hu J, Li L, Cockburn M, Escobedo L, Kleeman MJ, et al. Sources and contents of air pollution affecting term low birth weight in Los Angeles County, California, 2001–2008. Environ Res. 2014;134:488–95.

    Article  CAS  Google Scholar 

  25. Aris IM, Kleinman KP, Belfort MB, Kaimal A, Oken E. A 2017 US reference for singleton birth weight percentiles using obstetric estimates of gestation. Pediatrics. 2019;144.

  26. Liu KH, Nellis M, Uppal K, Ma C, Tran V, Liang Y, et al. Reference standardization for quantification and harmonization of large-scale metabolomics. Anal Chem. 2020;92:8836–44.

    Article  CAS  Google Scholar 

  27. Lee J, Park J, Lim M-s, Seong SJ, Seo JJ, Park SM, et al. Quantile normalization approach for liquid chromatography–mass spectrometry-based metabolomic data from healthy human volunteers. Anal Sci. 2012;28:801–5.

    Article  CAS  Google Scholar 

  28. Broadhurst D, Goodacre R, Reinke SN, Kuligowski J, Wilson ID, Lewis MR, et al. Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies. Metabolomics. 2018;14:72.

    Article  Google Scholar 

  29. Vorkas PA, Isaac G, Anwar MA, Davies AH, Want EJ, Nicholson JK, et al. Untargeted UPLC-MS profiling pipeline to expand tissue metabolome coverage: Application to cardiovascular disease. Anal Chem. 2015;87:4184–93.

    Article  CAS  Google Scholar 

  30. Want EJ, Wilson ID, Gika H, Theodoridis G, Plumb RS, Shockcor J, et al. Global metabolic profiling procedures for urine using UPLC-MS. Nat Protoc. 2010;5:1005–18.

    Article  CAS  Google Scholar 

  31. Zhang X, Dong J, Raftery D. Five easy metrics of data quality for LC-MS-based global metabolomics. Anal Chem. 2020;92:12925–33.

    Article  CAS  Google Scholar 

  32. Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J Roy Stat Soc: Ser B (Methodol). 1995;57:289–300.

    Article  Google Scholar 

  33. Li S, Park Y, Duraisingham S, Strobel FH, Khan N, Soltow QA, et al. Predicting network activity from high throughput metabolomics. PLoS Comput Biol. 2013;9: e1003123.

    Article  CAS  Google Scholar 

  34. Domingo-Almenara X, Montenegro-Burke JR, Benton HP, Siuzdak G. Annotation: A computational solution for streamlining metabolomics analysis. Anal Chem. 2018;90:480–9.

    Article  CAS  Google Scholar 

  35. Tingley D, Yamamoto T, Hirose K, Keele L, Imai K. Mediation: R package for causal mediation analysis. J Stat Softw. 2014;59:1–38.

    Article  Google Scholar 

  36. Micks E, Raglan GB, Schulkin J. Bridging progestogens in pregnancy and pregnancy prevention. Endocr Connect. 2015;4:R81-92.

    Article  CAS  Google Scholar 

  37. Pasqualini JR. Enzymes involved in the formation and transformation of steroid hormones in the fetal and placental compartments. J Steroid Biochem Mol Biol. 2005;97:401–15.

    Article  CAS  Google Scholar 

  38. Arck PC, Hecher K. Fetomaternal immune cross-talk and its consequences for maternal and offspring’s health. Nat Med. 2013;19:548–56.

    Article  CAS  Google Scholar 

  39. Druckmann R, Druckmann MA. Progesterone and the immunology of pregnancy. J Steroid Biochem Mol Biol. 2005;97:389–96.

    Article  CAS  Google Scholar 

  40. Solano ME, Arck PC. Steroids, pregnancy and fetal development. Front Immunol. 2019;10:3017.

    Article  CAS  Google Scholar 

  41. McIlvride S, Mushtaq A, Papacleovoulou G, Hurling C, Steel J, Jansen E, et al. A progesterone-brown fat axis is involved in regulating fetal growth. Sci Rep. 2017;7:10671.

    Article  Google Scholar 

  42. Diemert A, Goletzke J, Barkmann C, Jung R, Hecher K, Arck P. Maternal progesterone levels are modulated by maternal BMI and predict birth weight sex-specifically in human pregnancies. J Reprod Immunol. 2017;121:49–55.

    Article  CAS  Google Scholar 

  43. Hartwig IR, Pincus MK, Diemert A, Hecher K, Arck PC. Sex-specific effect of first-trimester maternal progesterone on birthweight. Hum Reprod. 2013;28:77–86.

    Article  CAS  Google Scholar 

  44. Mucci LA, Lagiou P, Tamimi RM, Hsieh CC, Adami HO, Trichopoulos D. Pregnancy estriol, estradiol, progesterone and prolactin in relation to birth weight and other birth size variables (United States). Cancer Causes Control. 2003;14:311–8.

    Article  Google Scholar 

  45. Colicino E, Cowell W, Foppa Pedretti N, Joshi A, Youssef O, Just AC, et al. Maternal steroids during pregnancy and their associations with ambient air pollution and temperature during preconception and early gestational periods. Environ Int. 2022;165: 107320.

    Article  CAS  Google Scholar 

  46. Plusquin M, Wang C, Cosemans C, Roels HA, Vangeneugden M, Lapauw B, et al. The association between newborn cord blood steroids and ambient prenatal exposure to air pollution: Findings from the ENVIRONAGE birth cohort. Environ Health. 2023;22:63.

    Article  CAS  Google Scholar 

  47. Ye L, Guo J, Ge RS. Environmental pollutants and hydroxysteroid dehydrogenases. Vitam Horm. 2014;94:349–90.

    Article  CAS  Google Scholar 

  48. Lefebvre P, Cariou B, Lien F, Kuipers F, Staels B. Role of bile acids and bile acid receptors in metabolic regulation. Physiol Rev. 2009;89:147–91.

    Article  CAS  Google Scholar 

  49. McIlvride S, Dixon PH, Williamson C. Bile acids and gestation. Mol Aspects Med. 2017;56:90–100.

    Article  CAS  Google Scholar 

  50. Li L, Chen W, Ma L, Liu ZB, Lu X, Gao XX, et al. Continuous association of total bile acid levels with the risk of small for gestational age infants. Sci Rep. 2020;10:9257.

  51. Liu W, Wang Q, Chang J, Bhetuwal A, Bhattarai N, Ni X. Circulatory metabolomics reveals the association of the metabolites with clinical features in the patients with intrahepatic cholestasis of pregnancy. Front Physiol. 2022;13: 848508.

    Article  Google Scholar 

  52. Song F, Chen Y, Chen L, Li H, Cheng X, Wu W. Association of elevated maternal serum total bile acids with low birth weight and intrauterine fetal growth restriction. JAMA Netw Open. 2021;4: e2117409.

    Article  Google Scholar 

  53. Uyar I, Gülhan I, Öztekin D, Gezer C, Ekin A, Karaca Kurtulmuş S, et al. Intrahepatic cholestasis of pregnancy may lead to low birth weight. Turk J Med Sci. 2015;45:723–8.

    Article  Google Scholar 

  54. Kim JH, Yan Q, Uppal K, Cui X, Ling C, Walker DI, et al. Metabolomics analysis of maternal serum exposed to high air pollution during pregnancy and risk of autism spectrum disorder in offspring. Environ Res. 2021;196: 110823.

    Article  CAS  Google Scholar 

  55. Wang M, Tan J, Zhou J, Yi B, Huang Z. Farnesoid X receptor mediates hepatic steatosis induced by PM2.5. Environ Sci Pollut Res Int. 2020;27:34412–20.

    Article  CAS  Google Scholar 

  56. Kim I, Ahn SH, Inagaki T, Choi M, Ito S, Guo GL, et al. Differential regulation of bile acid homeostasis by the farnesoid X receptor in liver and intestine. J Lipid Res. 2007;48:2664–72.

    Article  CAS  Google Scholar 

  57. Mulligan ML, Felton SK, Riek AE, Bernal-Mizrachi C. Implications of vitamin D deficiency in pregnancy and lactation. Am J Obstet Gynecol. 2010;202:429.e421–429.

    Article  Google Scholar 

  58. Chen YH, Fu L, Hao JH, Yu Z, Zhu P, Wang H, et al. Maternal vitamin D deficiency during pregnancy elevates the risks of small for gestational age and low birth weight infants in Chinese population. J Clin Endocrinol Metab. 2015;100:1912–9.

    Article  CAS  Google Scholar 

  59. Fang K, He Y, Mu M, Liu K. Maternal vitamin D deficiency during pregnancy and low birth weight: A systematic review and meta-analysis. J Matern Fetal Neonatal Med. 2021;34:1167–73.

    Article  Google Scholar 

  60. Khalessi N, Kalani M, Araghi M, Farahani Z. The relationship between maternal vitamin D deficiency and low birth weight neonates. J Family Reprod Health. 2015;9:113–7.

    Google Scholar 

  61. Bouillon R, Carmeliet G, Lieben L, Watanabe M, Perino A, Auwerx J, et al. Vitamin D and energy homeostasis - of mice and men. Nat Rev Endocrinol. 2014;10:79–87.

    Article  CAS  Google Scholar 

  62. Schmidt DR, Holmstrom SR, Fon Tacer K, Bookout AL, Kliewer SA, Mangelsdorf DJ. Regulation of bile acid synthesis by fat-soluble vitamins A and D. J Biol Chem. 2010;285:14486–94.

    Article  CAS  Google Scholar 

  63. Jacobs ET, Haussler MR, Alberts DS, Kohler LN, Lance P, Martínez ME, et al. Association between circulating vitamin D metabolites and fecal bile acid concentrations. Cancer Prev Res (Phila). 2016;9:589–97.

    Article  CAS  Google Scholar 

  64. Capuron L, Schroecksnadel S, Féart C, Aubert A, Higueret D, Barberger-Gateau P, et al. Chronic low-grade inflammation in elderly persons is associated with altered tryptophan and tyrosine metabolism: Role in neuropsychiatric symptoms. Biol Psychiatry. 2011;70:175–82.

    Article  CAS  Google Scholar 

  65. Stadtman ER. Protein oxidation and aging. Free Radic Res. 2006;40:1250–8.

    Article  CAS  Google Scholar 

  66. Guasch-Ferré M, Hruby A, Toledo E, Clish CB, Martínez-González MA, Salas-Salvadó J, et al. Metabolomics in prediabetes and diabetes: A systematic review and meta-analysis. Diabetes Care. 2016;39:833–46.

    Article  Google Scholar 

  67. Park S, Park JY, Lee JH, Kim SH. Plasma levels of lysine, tyrosine, and valine during pregnancy are independent risk factors of insulin resistance and gestational diabetes. Metab Syndr Relat Disord. 2015;13:64–70.

    Article  CAS  Google Scholar 

  68. Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, et al. Metabolite profiles and the risk of developing diabetes. Nat Med. 2011;17:448–53.

    Article  Google Scholar 

  69. Sánchez-Moreno C, Dashe JF, Scott T, Thaler D, Folstein MF, Martin A. Decreased levels of plasma vitamin C and increased concentrations of inflammatory and oxidative stress markers after stroke. Stroke. 2004;35:163–8.

    Article  Google Scholar 

  70. Smirnoff N. Ascorbic acid metabolism and functions: A comparison of plants and mammals. Free Radic Biol Med. 2018;122:116–29.

    Article  CAS  Google Scholar 

  71. Jain SK, Wise R, Yanamandra K, Dhanireddy R, Bocchini JA Jr. The effect of maternal and cord-blood vitamin C, vitamin E and lipid peroxide levels on newborn birth weight. Mol Cell Biochem. 2008;309:217–21.

    Article  CAS  Google Scholar 

  72. Lee BE, Hong YC, Lee KH, Kim YJ, Kim WK, Chang NS, et al. Influence of maternal serum levels of vitamins C and E during the second trimester on birth weight and length. Eur J Clin Nutr. 2004;58:1365–71.

    Article  CAS  Google Scholar 

  73. Saker M, Soulimane Mokhtari N, Merzouk SA, Merzouk H, Belarbi B, Narce M. Oxidant and antioxidant status in mothers and their newborns according to birthweight. Eur J Obstet Gynecol Reprod Biol. 2008;141:95–9.

    Article  CAS  Google Scholar 

  74. Fernández-Real JM, Broch M, Vendrell J, Ricart W. Insulin resistance, inflammation, and serum fatty acid composition. Diabetes Care. 2003;26:1362–8.

    Article  Google Scholar 

  75. Kalupahana NS, Claycombe K, Newman SJ, Stewart T, Siriwardhana N, Matthan N, et al. Eicosapentaenoic acid prevents and reverses insulin resistance in high-fat diet-induced obese mice via modulation of adipose tissue inflammation. J Nutr. 2010;140:1915–22.

    Article  CAS  Google Scholar 

  76. Mori TA, Woodman RJ, Burke V, Puddey IB, Croft KD, Beilin LJ. Effect of eicosapentaenoic acid and docosahexaenoic acid on oxidative stress and inflammatory markers in treated-hypertensive type 2 diabetic subjects. Free Radic Biol Med. 2003;35:772–81.

    Article  CAS  Google Scholar 

  77. Darlow BA, Graham PJ, Rojas-Reyes MX. 2016. Vitamin A supplementation to prevent mortality and short- and long-term morbidity in very low birth weight infants. Cochrane Database Syst Rev 2016:Cd000501.

  78. Haider BA, Sharma R, Bhutta ZA. 2017. Neonatal vitamin A supplementation for the prevention of mortality and morbidity in term neonates in low and middle income countries. Cochrane Database Syst Rev 2:Cd006980.

  79. Chen Z, Newgard CB, Kim JS, O II, Alderete TL, Thomas DC, et al. Near-roadway air pollution exposure and altered fatty acid oxidation among adolescents and young adults - the interplay with obesity. Environ Int. 2019;130: 104935.

    Article  CAS  Google Scholar 

  80. Fattuoni C, Mandò C, Palmas F, Anelli GM, Novielli C, Parejo Laudicina E, et al. Preliminary metabolomics analysis of placenta in maternal obesity. Placenta. 2018;61:89–95.

    Article  CAS  Google Scholar 

  81. Ruebel ML, Piccolo BD, Mercer KE, Pack L, Moutos D, Shankar K, et al. Obesity leads to distinct metabolomic signatures in follicular fluid of women undergoing in vitro fertilization. Am J Physiol Endocrinol Metab. 2019;316:E383-e396.

    Article  CAS  Google Scholar 

  82. Heo S, Fong KC, Bell ML. 2019. Risk of particulate matter on birth outcomes in relation to maternal socio-economic factors: A systematic review. Environ Res Lett 14.

Download references

Acknowledgements

Not applicable.

Funding

This work was supported by the National Institute of Health (NIH) grants P50MD015705, P50ES026086, UH3OD023287, P30ES007048, and R00ES027870, and Environmental Protection Agency (EPA) grant 83615801.

Author information

Authors and Affiliations

  1. Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Wu Chen, Chenyu Qiu, Jiayuan Hao, Jiawen Liao, Rima Habre, Theresa M. Bastain, Carrie V. Breton & Zhanghua Chen

  2. Sonoma Technology Inc, Petaluma, CA, USA

    Fred Lurmann & Nathan Pavlovic

  3. Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Department of Medicine, School of Medicine, Emory University, Atlanta, GA, USA

    Dean P. Jones

Authors
  1. Wu Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Chenyu Qiu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Jiayuan Hao
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Jiawen Liao
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Fred Lurmann
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Nathan Pavlovic
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Rima Habre
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Dean P. Jones
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Theresa M. Bastain
    View author publications

    You can also search for this author inPubMed Google Scholar

  10. Carrie V. Breton
    View author publications

    You can also search for this author inPubMed Google Scholar

  11. Zhanghua Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

WC: Methodology, Software, Formal Analysis, Writing-Original Draft, Writing—Review & Editing, Visualization; CQ: Methodology, Software, Formal Analysis, Data Curation, Writing—Review & Editing; JH: Writing—Original Draft, Writing—Review & Editing; JL: Software, Writing—Review & Editing; FL: Investigation, Writing—Review & Editing; NP: Investigation, Writing—Review & Editing; RH: Investigation, Writing—Review & Editing; DJ: Investigation, Resources, Writing—Review & Editing; TB: Investigation, Resources, Project administration, Writing—Review & Editing, Funding acquisition; CB: Investigation, Resources, Project administration, Writing—Review & Editing, Funding acquisition; and ZC: Conceptualization, Methodology, Project administration, Writing—Review & Editing, Supervision, Funding acquisition.

Corresponding author

Correspondence to Zhanghua Chen.

Ethics declarations

Ethics approval and consent to participate

Written informed consent and HIPAA authorization to obtain medical records from each participant is obtained at study entry for herself and her child. The University of Southern California’s Institutional Review Board approved the protocol (Protocol #HS-15–00498). The study was conducted in accordance with the Declaration of Helsinki.

Clinical trial number

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, W., Qiu, C., Hao, J. et al. Maternal metabolomics linking prenatal exposure to fine particulate matter and birth weight: a cross-sectional analysis of the MADRES cohort. Environ Health 24, 14 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12940-025-01162-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12940-025-01162-x

Keywords

Environmental Health

ISSN: 1476-069X