Urinary polycyclic aromatic hydrocarbon metabolites and their association with oxidative stress among pregnant women in Los Angeles

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Methods We measured a total of 7 PAH metabolites and 2 oxidative stress biomarkers (malondialdehyde (MDA), 8-hydroxy-2’-deoxyguanosine (8-OHdG)) in urine samples collected up to three times during pregnancy in 159 women enrolled at antenatal clinics at the University of California Los Angeles during 2016–2019. Using multiple linear regression models, we estimated the percentage change (%) and 95% confidence interval (CI) in 8-OHdG and MDA measured at each sample collection time per doubling of PAH metabolite concentrations. Results Most PAH metabolites were positively associated with both urinary oxidative stress biomarkers, MDA and 8-OHdG, with stronger associations in early and late pregnancy. Women pregnant with male fetuses exhibited slightly larger increases in both MDA and 8-OHdG in association with PAH exposures in early and late pregnancy. Conclusion Urinary OH-PAH biomarkers are associated with increases in oxidative stress during pregnancy, especially in early and late pregnancy. Sex differences in associations between PAH exposures and oxidative stress need to be further explored in the future. PAH (polycyclic aromatic hydrocarbons) Oxidative stress Pregnancy Figures Figure 1 Figure 2 1 Background Polycyclic aromatic hydrocarbons (PAHs) are widespread environmental pollutants resulting from incomplete combustion or pyrolysis of organic matter [ 1 ]. Due to their ubiquity and toxicity, PAHs are of global public health concern [ 2 ]. The sources of human exposures to PAHs are diverse and include tobacco smoke, industrial sites, wildfires, residential wood fires, ambient air pollution, and dietary items such a charbroiled meat [ 1 , 3 ]. Some PAHs, such as Benzo[a]pyrene and Benzo[b]fluoranthene, have been listed as carcinogens by IARC [ 4 ]. Moreover, PAHs can act as endocrine disrupters [ 5 ]. Prenatal exposures to PAHs have been linked to adverse birth outcomes such as fetal growth restriction and preterm birth [ 6 , 7 ]. They have also been related to neurodevelopment [ 8 ] and childhood asthma [ 9 ], yet the biological mechanisms through which PAHs exhibit such toxicity are still debated. PAHs have been reported to induce oxidative stress, inflammation and endocrine disruptions, which may each individually or jointly have adverse effects on the pregnancy [ 10 – 12 ]. Specifically, PAHs can induce an excess of reactive oxygen species (ROS), which would target DNA, proteins and lipids, resulting in their oxidation [ 12 ]. Well-established biomarkers of oxidative stress include products of oxidation processes such as malondialdehyde (MDA) and 8-hydroxy-2’-deoxyguanosine (8-OHdG), which represents lipid peroxidation and oxidative DNA damage, respectively [ 13 ]. Urinary concentrations of hydroxylated metabolites of PAHs (OH-PAHs) have been widely used as biomarkers of PAH exposure, as they represent the integrated levels of exposure across multiple pathways in the hours or days prior to sampling [ 14 ]. Nevertheless, few studies up to date used urinary PAH biomarkers to examine the effect of PAH exposures on oxidative stress levels among pregnant women. A cohort study of 200 pregnant women in Boston collected urine samples in the 3rd trimester of pregnancy and found increases in urinary 8-hydroxydeoxyguanosine (8-OHdG), among women with higher concentrations of the PAH metabolite, 2-hydroxynapthalene (2-NAP) [ 15 ]. Similarly, several PAH metabolites including 2-NAP, fluorene, and phenanthrene were reported to be correlated with urinary concentrations of 8-OHdG in a cohort of 188 pregnant Chinese women at different times in pregnancy [ 16 ]. Finally, a larger study of 715 South Korean pregnant women also reported that urinary concentrations of 2-NAP and pyrene were associated with increases in MDA at 12–28 weeks of gestation [ 17 ]. These studies, however, did not collect multiple samples during pregnancy and, thus, could not control for intra-individual variability in urinary PAH metabolites. Some also focused on a specific gestational window but none could address whether critical windows exist in which PAH exposures increase oxidative stress during pregnancy. Here, we collected urine samples from pregnant women enrolled at UCLA antenatal clinics for up to three times during pregnancy and evaluated associations between urinary PAH metabolite concentrations and oxidative stress biomarkers in different gestational periods during pregnancy. 2 Methods 2.1 Study population The Imaging Innovations for Placental Assessment in Response to Environmental Exposures (PARENTs) study recruited a cohort of 199 women early in pregnancy from antenatal clinics at the University of California Los Angeles during 2016–2019 [ 18 ]. Women were enrolled as early as the 10th week of gestation and were asked to participate in a once-per-trimester and at-birth study visit that included urinary sample collections we used for oxidative stress biomarkers and metabolites of hydroxyl polycyclic aromatic hydrocarbons (OH-PAHs) assessment. Phone interviews were conducted at three timepoints during pregnancy and at birth to collect environmental and behavioral risk factor data. Our study population consists of 159 women enrolled in the PARENTs study for whom at least one useable urine sample was available at the time of laboratory analysis supported by the Emory Children’s Health Exposure Analysis Resource (CHEAR) program. 2.2 Biomarkers assessment We focused on two oxidative stress biomarkers, malondialdehyde (MDA) and 8-hydroxy-2’-deoxyguanosine (8-OHdG), and a total of 7 hydroxyl PAH metabolites (OH-PAHs). Specifically, we examined measures for the combined 2-hydroxyfluorene + 3-hydroxyfluorene (2&3-FLUO) metabolites, the single metabolites 2-hydroxynaphthalene (2-NAP), 1-hydroxyphenanthrene (1-PHEN), 2-hydroxyphenanthrene (2-PHEN), 3-hydroxyphenanthrene (3-PHEN), 4-hydroxyphenanthrene (4-PHEN), and 1-hydroxypyrene (1-PYR), and also the sum of 1-, 2-, 3-, and 4- hydroxyphenanthrene (Σ 4 OH-PHEN), and of all 7 analytes (Σ 7 OH-PAH), respectively. Study visits were timed to be optimal for Magnetic resonance imaging (MRI) evaluations (1st MRI 14th -18th gestational week, 2nd MRI 19th -24th gestational week) in the PARENTs cohort study, and sample collection followed this schedule with the 1st sample being collected in the 10-17th gestational week, the 2nd sample collection in the 18-29th gestational week, and the 3rd sample collection after the 30th gestational week and prior to delivery. Maternal urinary samples were collected at each study visit, and we collected at least one and at most three urine samples from all participants during pregnancy. Specifically, multiple urine samples were collected among 146 out of 159 participants (92%). The urine samples were stored at -80°C after collection at UCLA and were shipped on dry ice to the CHEAR Laboratory Hub to measure both the OH-PAHs and the oxidative stress biomarkers. All samples were randomized using a Fisher-Yates shuffling algorithm prior to laboratory analysis to reduce any potential batch effects [ 19 , 20 ]. The OH-PAHs were measured by tandem mass spectrometry (MS/MS) [ 21 ], and the oxidative stress biomarkers (MDA and 8-OHdG) were measured by liquid chromatography-mass spectrometry (LC-MS) [ 22 ]. Samples with measures below the limit of detection (LOD) values were replaced with the LOD/√2 [ 23 ]. Concentrations were further corrected for urine dilution by adjusting for specific gravity (SG) measured with a Reichert AR200 refractometer. We excluded samples with an invalid SG value below 1 (N = 14, 18 and 16 samples during the 1st, 2nd and 3rd sample collection interval, respectively), resulting in a total of 391 samples available for analysis. To correct for the hydration status of pregnant women, SG-standardized biomarker concentrations were calculated using the following formula [ 24 ]: $${CHEM}_{SG\_Adj }={CHEM}_{i}*\left[\right({SG}_{m}-1)/({SG}_{i}-1)]$$ where CHEM SG_Adj is the specific gravity-standardized biomarker concentration (nmol/L for MDA, ng/mL for 8-OHdG, ng/L for OH-PAHs), CHEM i is the observed biomarker concentration, SG i is the specific gravity of the urine sample and SG m is the median specific gravity for the total samples with valid SG values. 2.3 Covariates Information on maternal age (years), parity (continuous), maternal pre-pregnancy body mass index (BMI) (< 18.5, 18.5–24.9, 25.0–30.0 and ≥ 30.0), maternal race/ethnicity (White, non-White), smoking (yes, no), maternal educational attainment (bachelor’s degree or less, master’s degree, doctoral/professional degree) were collected in interviews. Gestational age (based on the best obstetric estimate obtained during a 1st trimester ultrasound exam), as well as information about pregnancy complications including gestational diabetes, gestational hypertension, and pre-eclampsia were obtained from medical records. Season of sample collection was categorized based on the month of sample collection, as spring (March, April, May), summer (June, July, August), fall (September, October, November) and winter (December, January, February). 2.4 Statistical analysis First, we estimated effects for different time intervals during pregnancy by first conducting multiple linear regression analyses and calculated the expected percentage of change in each oxidative stress biomarker concentration according to PAH metabolite levels in each sample collection interval, separately. The oxidative stress biomarker concentrations were treated as continuous variables and log-transformed for statistical analyses. For all OH-PAHs, we log-transformed (base 2) the values such that in statistical model the exposure effect estimate represents an increase per doubling of the OH-PAHs concentration (ng/L). Second, we also used linear mixed models with a random intercept for participant to take repeated measurements into account, i.e., we relied on up to 3 samples collected across pregnancy for each exposure and biomarker and assessed associations between OH-PAHs exposure urine measures and oxidative stress concentrations across multiple time points in pregnancy. In one-point-in-time linear and in longitudinal linear mixed regression models, we adjusted for maternal age, maternal race/ethnicity, maternal education, parity, pre-pregnancy BMI, and season of sampling. We also conducted stratified one-point-in-time linear regression analyses by season of sampling, fetal sex, and maternal race/ethnicity to evaluate potential effect measure modification. Sensitivity analyses were conducted by additionally adjusting for gestational day at sample collection. Furthermore, as women experiencing pregnancy complications are likely to have higher oxidative stress levels due to these conditions [ 25 ], we also restricted some analyses to women without pregnancy complications specifically gestational diabetes, gestational hypertension, or pre-eclampsia. All statistical analyses were performed using SAS 9.4 (SAS Institute Inc., Cary, NC, USA). 3 Results Most of our study participants were ≥ 30 years old and highly educated, more than half were parous, and almost a third (31%) were overweight or obese (BMI ≥ 25) (Table 1 ). Approximately half of the mothers reported their race/ethnicity as non-Hispanic White; 18% as Hispanic and 28% as Asian/Pacific Islander. The demographics characteristics of the entire PARENTs cohort are very similar to those of the subpopulation used in this study (Table S1 ). Table 1 Characteristics of the study population (N = 159) Characteristics N % Maternal age (years) ≤ 24 3 1.9 25–29 22 13.8 30–34 77 48.4 ≥ 35 57 35.9 Parity 0 74 46.5 ≥ 1 85 53.5 Maternal race Asian or Pacific islander 45 28.3 Black, non-Hispanic 11 6.9 Hispanic 29 18.2 White, non-Hispanic 73 45.9 American Indian or Alaskan Native 1 0.6 Maternal education Bachelor's degree or less 72 46.5 Master's degree 45 29.0 Doctoral degree or professional degree 38 24.5 Missing 4 Employment Status Employed or student 139 89.7 Not employed 16 10.3 Missing 4 Pre-pregnancy BMI Underweight 6 3.8 Normal 103 64.8 Overweight 32 20.1 Obese 18 11.3 Gestational Diabetes Yes 20 12.6 No 139 87.4 Gestational Hypertension Yes 15 9.4 No 144 90.6 Pre-eclampsia Yes 16 10.1 No 143 89.9 Season of Conception Spring 35 22.0 Summer 42 26.4 Fall 36 22.6 Winter 46 28.9 The average MDA concentrations decreased from the 1st to 2nd sample collection and increased in the 3rd time interval (Table S2). The concentrations of 8-OHdG decreased throughout the three sampling periods over pregnancy. While the hydroxy PAH metabolites, 2-NAP and 2-PYR levels, increased throughout the three sampling periods, the 2&3-FLUO and all OH-PHEN isomers decreased slightly from the 1st to 2nd sample collection but increased again at the 3rd sample collection time. For both oxidative stress biomarkers we measured the lowest average concentrations in summer and relatively higher concentrations in fall and winter (Table S3). For the PAH metabolites including 2-NAP and 1-PYR we also saw relatively higher concentration levels in fall and winter, while 2&3-FLUO and all the OH-PHEN isomers increased from spring to summer, then decreased in fall and increased again in winter, thus exhibiting relatively higher levels in summer and in winter. In each pregnancy sample collection period, the two oxidative stress biomarkers were highly correlated with each other (r = 0.8) as were the OH-PHEN isomers and 1-PYR (r = 0.6–0.9) (Figure S1 ), and the OH-PHEN isomers were moderately to highly correlated with the two oxidative stress biomarkers, except in the 2nd pregnancy sampling period. In one-pregnancy period at a time linear regression models, a doubling of each urinary PAH metabolite concentration increased the MDA (a lipid peroxidation marker) concentrations by 5.8%-41.1%, with the lowest effects estimated in the 2nd sampling period (Fig. 1 ; Table S4), and some of the 95% CIs including the null value. MDA concentration also increased by 8.7%-23.6% in each of the three sampling periods with a doubling of the Σ 7 OH-PAH. Specifically, a doubling of 2&3-FLUO concentrations increased MDA levels by 13.7%-41.1% and of 1-PYR by 17.7%-39.9%. 2-NAP concentrations were associated with a 21.8% (95% CI: 9.2%, 35.8%) increase in MDA measured in the 3rd sampling period, and were also increased in the first two periods, but the 95% CIs included the null. MDA increases ranged from 14.5–46.0% per doubling in Σ 4 OH-PHEN; and each individual OH-PHEN isomer exhibited a similar pattern as the summary measure, with the smallest effects estimated for 4-PHEN. Figure 1 Linear regression for percentage changes of oxidative stress biomarker concentrations per doubling concentration of different PAH metabolites a . a. Adjusted for maternal age, maternal race/ethnicity, maternal education, pre−pregnancy BMI, and sampling season . Positive associations were also observed for 8-OHdG (a DNA damage marker) with each PAH exposure in simple one-pregnancy period only linear regression models. Although effect estimates were smaller in the 2nd gestational sampling period, most of the 95% CIs overlapped for the three sampling periods (Fig. 1 ; Table S4). A doubling of Σ 7 OH-PAH concentration was associated with a 22.2%, 15.5% and 34.5% increase in urinary 8-OHdG in the 1st, 2nd and 3rd sampling period, respectively. In all three sampling periods, concentrations of 8-OHdG increased with a doubling in urinary concentrations of 2&3-FLUO by 25.1%-42.0%, 2-NAP by 13.8%-31.6%, and 1-PYR by 23.8%-44.6%, respectively. Σ 4 OH-PHEN were also found to increase 8-OHdG in all three sampling periods by 33.9%-58.6%; and similar to MDA, smaller effect estimates were observed for 4-PHEN. Linear mixed models that included all three pregnancy sample collection periods showed consistent patterns with those from separate linear regression models (Fig. 2 ; Table S5). We estimated a 15.5% (95% CI: 8.5%, 22.9%) increase of MDA levels and a 22.1% (95% CI: 15.9%, 28.5%) increase of 8-OHdG levels associated with a doubling of Σ 7 OH-PAH concentration. The effect estimates for specific PAH metabolites ranged from 13.1–30.3% for MDA concentrations, and from 19.6–39.1% for 8-OHdG concentrations, with somewhat less strong associations for 2-NAP and 4-PHEN. When stratifying by sampling season in simple one-pregnancy sampling period linear regression models, stronger effect estimates were generally observed in fall and winter for most PAH exposures and both oxidative stress biomarkers, especially for the 3rd gestational period (Figure S2). When stratifying by fetal sex, we estimated slightly larger effect estimates among male fetuses for most PAH exposures and lipid peroxidation or DNA damage in early and late pregnancy, although the 95% CIs for male and female fetuses generally overlapped especially for MDA in the 3rd sampling period (Figure S3). Compared to non-White women, we saw that for White women PAH exposures were associated with higher oxidative stress biomarker levels in the 1st sampling period, except for 2&3-FLUO and 8-OHdG (Figure S4). Finally, in one-pregnancy-time-point linear regression, effect estimates slightly increased after restricting to women without pregnancy complications (Table S6). Results changed minimally after additionally adjusting for gestational days at sample collection date (data not shown). 4 Discussion In our study of pregnant women, we found consistent associations between several urinary PAH metabolites and two oxidative stress biomarkers for lipid peroxidation, MDA, and DNA damage, 8-OHdG. Stronger associations were seen in early and late pregnancy, especially for urine samples collected in the fall or winter season. Effect estimates in different gestational period also differed by fetal sex with slightly stronger associations seen in early and late pregnancy in mothers carrying male fetuses. To our knowledge, this is the first longitudinal study with repeated urinary measures to assess associations between urinary PAH metabolites and two oxidative stress biomarkers during pregnancy. This also allowed us to additionally investigate whether there are susceptible windows for PAH exposures across pregnancy and according to fetal sex. The metabolism of PAHs depends on the cytochrome P450-mediated mixed function oxidase system that produces enormous quantities of reactive intermediates involved in lipid peroxidation, protein modification, DNA damage, and the depletion of endogenous antioxidants [ 26 ]. Our findings for PAH metabolites including 2&3-FLUO, 2-NAP, 1-PYR, and OH-PHENs being associated with increases in urinary 8-OHdG and MDA during pregnancy are consistent with the few previously published reports investigating this subject [ 15 – 17 ]. However, studies in pregnant women that investigate associations between PAH exposures and oxidative stress in different time periods during pregnancy are rare. The stronger associations we observed in early and late pregnancy were consistent with the sensitive window for adverse birth outcomes such as preterm birth and term low birth weight, which have been widely reported to be associated with prenatal exposures to environmental pollutants and the oxidative stress they induce [ 27 – 30 ]. Stronger associations between PAH exposures and oxidative stress biomarkers have also been detected in the winter season, which may be explained by seasonally higher atmospheric PAH concentrations in winter than summer for most compounds due to an increased production from heating related combustion and adverse meteorological conditions [ 31 ], suggesting higher toxicity of the mixture from combustion sources. Apart from smoking and ambient or indoor air pollution from traffic and combustion including open fireplaces and wood burning, dietary intake is one of the main sources of PAH exposures in the general population, including food contamination and food processing procedures such as smoking, drying, and frying of foods [ 26 ], which makes diet a potential confounder of the association between PAH exposures and oxidative stress. In our study, we adjusted for Alternate Mediterranean Diet scores (aMED) as an indicator for antioxidant intake in a subgroup of 125 women for whom we had collected dietary data and found that our results were robust. Maternal employment conditions may also confound the associations, as work-related stress was reported to be associated with urinary concentrations of 8-OHdG in female workers [ 32 , 33 ]. Our results, however, were robust after adjustment for maternal employment status, possibly because most of the study participants were students or employees of UCLA. Oxidative stress is one of the main mechanism hypothesized to lead to suboptimal placentation and in turn affect pregnancy success [ 38 ]. Our previous study found oxidative stress from air pollution to be associated with adverse birth outcomes [ 39 ] as well as with oxidative stress biomarker concentrations in pregnant women [ 40 ]. Previous studies have also linked ambient PAH pollutants with adverse birth outcomes [ 41 , 42 ]. A study of 1,677 women measured urinary PAH metabolites in the 2nd trimester and linked these to preterm birth; female fetuses were found to be more susceptible to 2-PHEN and 1-PYR exposures than the males [ 7 ]. Furthermore, a recent study reported sex-modification of associations between urinary OH-PAH concentrations in mid-pregnancy and the placental transcriptome, with more affected transcripts identified in females than males [ 43 ]. Oxidative stress has also emerged as one of the underlying mechanism contributing to the toxicity of endocrine disrupting chemicals [ 44 ]. In addition, hormonal imbalance leads to compromised antioxidant status and oxidative stress in tissues induces various pathophysiological conditions [ 45 ]. Thus, altered hormonal signaling by PAH exposures may contribute to sex differences in oxidative stress levels. A recent cohort study of pregnant women has observed the endocrine disrupting potential of PAH exposures with sex-specific changes in hormone concentrations in pregnancy, which can result in adverse birth outcomes [ 10 , 46 ]. Oxidative stress pathways may mediate the impact of PAH exposures on adverse birth outcomes via these biological pathways. Our study indicated that fetal sex may modify the relationship between PAH exposures and oxidative stress during pregnancy, with slightly stronger associations seen for males in early and late pregnancy. Very few studies that investigated PAH exposures and relied on oxidative stress biomarkers among pregnant women examined fetal sex differences, although sex-specific adverse birth outcomes have been well documented, with males more likely to be displaying earlier onset of more severe neonatal complications, which may be due to the sex difference of the placenta [ 34 ]. Specifically, male fetuses are more vulnerable to the environmental hazards as they grow faster than females during early placental development [ 35 ], which may explain the slightly stronger effect estimates of most PAH exposures on oxidative stress in early pregnancy in our study. Furthermore, PAHs not only can cross the placental barrier, but are also metabolized by the placenta [ 36 , 37 ]. Thus, our sex-specific associations between PAH and oxidative stress may be indicative of the sex difference in placental response to the PAH exposures. Given the limited sample size and the general overlap of 95% CIs for the sex-specific effect estimates, we cannot exclude the possibility of chance findings. Further studies are necessary to document and understand placental sex differences in response to PAH exposures. Our study has several strengths. First, the repeated measures in different pregnancy periods made it possible to investigate how the PAH metabolite concentrations throughout pregnancy influence oxidative stress. In addition, repeated urinary measures also made it possible to account for intra-individual variability in oxidative stress biomarkers. Second, the detailed data we collected for environmental and medical covariates enabled sensitivity analyses, such as excluding women more likely to exhibit higher oxidative stress levels such as due to pregnancy disorders. Third, our study population is highly educated and mostly of high socioeconomic status, such that occupational exposures, which are often higher than environmental exposures, are unlikely to have played a role as confounders. Lastly, we used two different oxidative stress biomarkers and evaluated both DNA oxidative damage and lipid peroxidation. Some limitations need to also be acknowledged. First, the short biological half-live of PAHs limits the informativeness of urinary PAH metabolites as they only reflect recent exposures and to imply that they represent longer term exposures we have to assume relatively constancy of exposures over time [ 12 , 47 ]. We analyzed urine samples for both the exposure and the outcomes, thus technically using a multiple time point cross-sectional design. Nevertheless, reverse-causation is likely not a concern as PAHs are environmental toxicants that the body metabolizes but does not produce and oxidative stress is a likely consequence, not cause of PAH exposures. Second, even though we collected up to three samples in pregnancy for the participants, our sample size was limited, especially for subgroup analyses. Larger prospective studies with multiple sample collection time points would be helpful to better understand these mechanisms. 5 Conclusion We found that PAH exposures measured with urinary OH-PAH biomarkers are associated with increased oxidative stress generation during pregnancy, which has been previously linked to adverse birth outcomes. We observed potential sex differences that may suggest differential vulnerability during different pregnancy periods for PAH exposures resulting in oxidative stress. Larger investigations are necessary to corroborate these findings. Abbreviations PAH : Polycyclic aromatic hydrocarbons ROS : reactive oxygen species 8-OHdG : 8-hydroxy-2’-deoxyguanosine MDA : malondialdehyde OH-PAHs : hydroxylated metabolites of PAHs 2-NAP : 2-hydroxynapthalene PARENTs : Imaging Innovations for Placental Assessment in Response to Environmental Exposures study OH-PAHs : hydroxyl polycyclic aromatic hydrocarbons CHEAR : Children’s Health Exposure Analysis Resource 2&3-FLUO : combined 2-hydroxyfluorene + 3-hydroxyfluorene metabolites 1-PHEN : 1-hydroxyphenanthrene 2-PHEN : 2-hydroxyphenanthrene 3-PHEN : 3-hydroxyphenanthrene 4-PHEN : 4-hydroxyphenanthrene 1-PYR : 1-hydroxypyrene Σ4OH-PHEN : sum of 1-, 2-, 3-, and 4- hydroxyphenanthrene Σ7OH-PAH : sum of combined 2-hydroxyfluorene + 3-hydroxyfluorene metabolites, 2-hydroxynapthalene, 1-hydroxypyrene, 1-hydroxyphenanthrene, 2-hydroxyphenanthrene, 3-hydroxyphenanthrene, and 4-hydroxyphenanthrene MRI : Magnetic resonance imaging LOD : limit of detection SG : specific gravity BMI : body mass index aMED : Alternate Mediterranean Diet scores Declarations Ethics approval and consent to participate : The research protocol for this study was approved by the UCLA Institutional Review Board and registered on the ClinicalTrials.gov Website. Consent for publication : Not applicable Availability of data and materials : Data required to reproduce the above findings will be shared via the Human Health Exposure Analysis Resource [HHEAR, formerly Children’s Health Exposure Analysis Resource (CHEAR)] platform maintained by NIEHS contractors. Competing interests : The authors declare that they have no competing interests. Funding : This work was partly supported by the following grants from NICHD: U01HD087221 (to BR, SUD, CJ), R01HD089714 (to SUD) and R01HD100015 (to SUD). This work was partly supported by the California Air Resources Board (contract No.17RD012). Authors' contributions : Conceptualization, BR and QM; Methodology, BR and MJ; Validation: BR, QM and SM; Formal analysis, QM; Investigation, BR, QM, IDR; Data Curation: QM, SM, IDR; Writing – original draft, QM; Writing – review & editing, all authors; Visualization, QM; Supervision, BR; Project administration: BR, QM, IDR; Funding acquisition, BR, SUD, CJ, MJ. 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Lipid Peroxidation-Coupled Co-Oxygenation of Benzo(a)Pyrene and Benzo(a)Pyrene-7,8-Dihydrodiol in Human Term Placental Microsomes. Placenta 1990 , 11 , 17–26, doi:10.1016/S0143-4004(05)80439-4. Vadillo-Ortega, F.; Osornio-Vargas, A.; Buxton, M.A.; Sánchez, B.N.; Rojas-Bracho, L.; Viveros-Alcaráz, M.; Castillo-Castrejón, M.; Beltrán-Montoya, J.; Brown, D.G.; O´Neill, M.S. AIR POLLUTION, INFLAMMATION AND PRETERM BIRTH: A POTENTIAL MECHANISTIC LINK. Med Hypotheses 2014 , 82 , 219–224, doi:10.1016/j.mehy.2013.11.042. Meng, Q.; Liu, J.; Shen, J.; Del Rosario, I.; Lakey, P.S.J.; Shiraiwa, M.; Su, J.; Weichenthal, S.; Zhu, Y.; Oroumiyeh, F.; et al. Fine Particulate Matter Metal Composition, Oxidative Potential, and Adverse Birth Outcomes in Los Angeles. Environmental Health Perspectives 2023 , 131 , 107012, doi:10.1289/EHP12196. Meng, Q.; Liu, J.; Shen, J.; Del Rosario, I.; Janzen, C.; Devaskar, S.U.; Lakey, P.S.J.; Shiraiwa, M.; Weichenthal, S.; Zhu, Y.; et al. Ambient Exposure to Fine Particulate Matter with Oxidative Potential Affects Oxidative Stress Biomarkers in Pregnancy. AJE 2024 . In Press . Wilhelm, M.; Ghosh, J.K.; Su, J.; Cockburn, M.; Jerrett, M.; Ritz, B. Traffic-Related Air Toxics and Preterm Birth: A Population-Based Case-Control Study in Los Angeles County, California. Environmental Health 2011 , 10 , 89, doi:10.1186/1476-069X-10-89. Padula, A.M.; Noth, E.M.; Hammond, S.K.; Lurmann, F.W.; Yang, W.; Tager, I.B.; Shaw, G.M. Exposure to Airborne Polycyclic Aromatic Hydrocarbons during Pregnancy and Risk of Preterm Birth. Environmental Research 2014 , 135 , 221–226, doi:10.1016/j.envres.2014.09.014. Paquette, A.G.; Lapehn, S.; Freije, S.; MacDonald, J.; Bammler, T.; Day, D.B.; Loftus, C.T.; Kannan, K.; Alex Mason, W.; Bush, N.R.; et al. Placental Transcriptomic Signatures of Prenatal Exposure to Hydroxy-Polycyclic Aromatic Hydrocarbons. Environ Int 2023 , 172 , 107763, doi:10.1016/j.envint.2023.107763. Neier, K.; Marchlewicz, E.H.; Dolinoy, D.C.; Padmanabhan, V. Assessing Human Health Risk to Endocrine Disrupting Chemicals: A Focus on Prenatal Exposures and Oxidative Stress. Endocr Disruptors (Austin) 2015 , 3 , e1069916, doi:10.1080/23273747.2015.1069916. Chainy, G.B.N.; Sahoo, D.K. Hormones and Oxidative Stress: An Overview. Free Radical Research 2020 , 54 , 1–26, doi:10.1080/10715762.2019.1702656. Cathey, A.L.; Watkins, D.J.; Rosario, Z.Y.; Vega, C.M.V.; Mukherjee, B.; O’Neill, M.S.; Loch-Caruso, R.; Alshawabkeh, A.N.; Cordero, J.F.; Meeker, J.D. Gestational Hormone Concentrations Are Associated With Timing of Delivery in a Fetal Sex-Dependent Manner. Front Endocrinol (Lausanne) 2021 , 12 , 742145, doi:10.3389/fendo.2021.742145. Li, Z.; Romanoff, L.; Bartell, S.; Pittman, E.N.; Trinidad, D.A.; McClean, M.; Webster, T.F.; Sjödin, A. Excretion Profiles and Half-Lives of Ten Urinary Polycyclic Aromatic Hydrocarbon Metabolites after Dietary Exposure. Chem Res Toxicol 2012 , 25 , 1452–1461, doi:10.1021/tx300108e. Additional Declarations No competing interests reported. Supplementary Files SupplementarymaterialPAHandoxidativestress0317.docx Cite Share Download PDF Status: Published Journal Publication published 13 Aug, 2024 Read the published version in Environmental Health → Version 1 posted Editorial decision: Revision requested 24 Apr, 2024 Reviews received at journal 18 Apr, 2024 Reviews received at journal 04 Apr, 2024 Reviewers agreed at journal 25 Mar, 2024 Reviewers agreed at journal 22 Mar, 2024 Reviewers invited by journal 20 Mar, 2024 Editor assigned by journal 18 Mar, 2024 Submission checks completed at journal 18 Mar, 2024 First submitted to journal 17 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4119505","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":281011372,"identity":"13862ce3-25cd-4e1c-b645-984f1284c324","order_by":0,"name":"Qi Meng","email":"","orcid":"","institution":"University of California, Los Angeles","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Meng","suffix":""},{"id":281011374,"identity":"e043de81-b41e-4cdf-9016-a82fe5e91af9","order_by":1,"name":"Sanjali Mitra","email":"","orcid":"","institution":"University of California, Los Angeles","correspondingAuthor":false,"prefix":"","firstName":"Sanjali","middleName":"","lastName":"Mitra","suffix":""},{"id":281011376,"identity":"c61e4ad0-a5de-4fdd-8949-afacfc4665ba","order_by":2,"name":"Irish Del Rosario","email":"","orcid":"","institution":"University of California, Los Angeles","correspondingAuthor":false,"prefix":"","firstName":"Irish","middleName":"Del","lastName":"Rosario","suffix":""},{"id":281011378,"identity":"abce5869-c1d9-477a-8b6e-de6490ca8492","order_by":3,"name":"Michael Jerrett","email":"","orcid":"","institution":"University of California, Los Angeles","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Jerrett","suffix":""},{"id":281011379,"identity":"bbbe432f-71ad-41d2-809a-ddf1cc777e37","order_by":4,"name":"Carla Janzen","email":"","orcid":"","institution":"University of California, Los Angeles","correspondingAuthor":false,"prefix":"","firstName":"Carla","middleName":"","lastName":"Janzen","suffix":""},{"id":281011380,"identity":"e5387b48-7248-4569-8024-bbc5bbe0766a","order_by":5,"name":"Sherin U. Devaskar","email":"","orcid":"","institution":"University of California, Los Angeles","correspondingAuthor":false,"prefix":"","firstName":"Sherin","middleName":"U.","lastName":"Devaskar","suffix":""},{"id":281011381,"identity":"d9e398eb-11f7-4324-bc45-f03768bfb75d","order_by":6,"name":"Beate Ritz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYBACAwbGBgaGCgYGPhCPh0gtjQ0MZxgY2EjQArSGsY0ULebSh9sf/px3WJ6N/QDjg7dtRGix7EtsbJDcdtiwjSeB2XAuMVoMzgD9YrjtcAIbQwKbNC/RWhLnALXwP2D/TbyWgw1ALRIJbMxEabHsYWyc2XAs3bBN4mGz5JxzRGgx52F/8PFHjbU8P3/ywQ9vyojQggRAyWAUjIJRMApGAXUAAPo2M3HeGMsTAAAAAElFTkSuQmCC","orcid":"","institution":"University of California, Los Angeles","correspondingAuthor":true,"prefix":"","firstName":"Beate","middleName":"","lastName":"Ritz","suffix":""}],"badges":[],"createdAt":"2024-03-18 02:59:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4119505/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4119505/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12940-024-01107-w","type":"published","date":"2024-08-13T15:57:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":53082950,"identity":"d5740920-3ae5-4439-9f4d-a7d5ee1be023","added_by":"auto","created_at":"2024-03-20 11:09:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":140298,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLinear regression for percentage changes of oxidative stress biomarker concentrations per doubling concentration of different PAH metabolites \u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea. Adjusted for maternal age, maternal race/ethnicity, maternal education, pre-pregnancy BMI, and sampling season.\u003c/sup\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4119505/v1/c2a86a5c12eb8ebaf870cba0.png"},{"id":53082949,"identity":"d5dcba24-6971-4a0a-9805-a72c51324f1d","added_by":"auto","created_at":"2024-03-20 11:09:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":87259,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLinear mixed model for percentage changes of oxidative stress biomarker concentrations per doubling concentration of different PAH metabolites \u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea. \u003c/sup\u003eAdjusted for maternal age, maternal race/ethnicity, maternal education, pre-pregnancy BMI, and sampling season.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4119505/v1/2563e98ff60ddaec20d343f1.png"},{"id":63071431,"identity":"cdc8a8be-04fb-4905-8964-578ff2913fb3","added_by":"auto","created_at":"2024-08-22 20:07:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1011688,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4119505/v1/b8cccda5-7dbb-4811-aed8-79e2f316ff27.pdf"},{"id":53082951,"identity":"a4701bce-1f00-45c3-80ee-5b1b681a248b","added_by":"auto","created_at":"2024-03-20 11:09:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":683879,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialPAHandoxidativestress0317.docx","url":"https://assets-eu.researchsquare.com/files/rs-4119505/v1/43dde92a1698a212f9b0ef9f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Urinary polycyclic aromatic hydrocarbon metabolites and their association with oxidative stress among pregnant women in Los Angeles","fulltext":[{"header":"1 Background","content":"\u003cp\u003ePolycyclic aromatic hydrocarbons (PAHs) are widespread environmental pollutants resulting from incomplete combustion or pyrolysis of organic matter [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Due to their ubiquity and toxicity, PAHs are of global public health concern [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The sources of human exposures to PAHs are diverse and include tobacco smoke, industrial sites, wildfires, residential wood fires, ambient air pollution, and dietary items such a charbroiled meat [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Some PAHs, such as Benzo[a]pyrene and Benzo[b]fluoranthene, have been listed as carcinogens by IARC [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Moreover, PAHs can act as endocrine disrupters [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrenatal exposures to PAHs have been linked to adverse birth outcomes such as fetal growth restriction and preterm birth [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. They have also been related to neurodevelopment [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and childhood asthma [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], yet the biological mechanisms through which PAHs exhibit such toxicity are still debated. PAHs have been reported to induce oxidative stress, inflammation and endocrine disruptions, which may each individually or jointly have adverse effects on the pregnancy [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Specifically, PAHs can induce an excess of reactive oxygen species (ROS), which would target DNA, proteins and lipids, resulting in their oxidation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Well-established biomarkers of oxidative stress include products of oxidation processes such as malondialdehyde (MDA) and 8-hydroxy-2\u0026rsquo;-deoxyguanosine (8-OHdG), which represents lipid peroxidation and oxidative DNA damage, respectively [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUrinary concentrations of hydroxylated metabolites of PAHs (OH-PAHs) have been widely used as biomarkers of PAH exposure, as they represent the integrated levels of exposure across multiple pathways in the hours or days prior to sampling [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Nevertheless, few studies up to date used urinary PAH biomarkers to examine the effect of PAH exposures on oxidative stress levels among pregnant women. A cohort study of 200 pregnant women in Boston collected urine samples in the 3rd trimester of pregnancy and found increases in urinary 8-hydroxydeoxyguanosine (8-OHdG), among women with higher concentrations of the PAH metabolite, 2-hydroxynapthalene (2-NAP) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Similarly, several PAH metabolites including 2-NAP, fluorene, and phenanthrene were reported to be correlated with urinary concentrations of 8-OHdG in a cohort of 188 pregnant Chinese women at different times in pregnancy [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Finally, a larger study of 715 South Korean pregnant women also reported that urinary concentrations of 2-NAP and pyrene were associated with increases in MDA at 12\u0026ndash;28 weeks of gestation [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These studies, however, did not collect multiple samples during pregnancy and, thus, could not control for intra-individual variability in urinary PAH metabolites. Some also focused on a specific gestational window but none could address whether critical windows exist in which PAH exposures increase oxidative stress during pregnancy.\u003c/p\u003e \u003cp\u003eHere, we collected urine samples from pregnant women enrolled at UCLA antenatal clinics for up to three times during pregnancy and evaluated associations between urinary PAH metabolite concentrations and oxidative stress biomarkers in different gestational periods during pregnancy.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cp\u003e2.1 Study population\u003c/p\u003e\n\u003cp\u003eThe Imaging Innovations for Placental Assessment in Response to Environmental Exposures (PARENTs) study recruited a cohort of 199 women early in pregnancy from antenatal clinics at the University of California Los Angeles during 2016\u0026ndash;2019 [\u003cspan\u003e18\u003c/span\u003e]. Women were enrolled as early as the 10th week of gestation and were asked to participate in a once-per-trimester and at-birth study visit that included urinary sample collections we used for oxidative stress biomarkers and metabolites of hydroxyl polycyclic aromatic hydrocarbons (OH-PAHs) assessment. Phone interviews were conducted at three timepoints during pregnancy and at birth to collect environmental and behavioral risk factor data.\u003c/p\u003e\n\u003cp\u003eOur study population consists of 159 women enrolled in the PARENTs study for whom at least one useable urine sample was available at the time of laboratory analysis supported by the Emory Children\u0026rsquo;s Health Exposure Analysis Resource (CHEAR) program.\u003c/p\u003e\n\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.2 Biomarkers assessment\u003c/h2\u003e\n \u003cp\u003eWe focused on two oxidative stress biomarkers, malondialdehyde (MDA) and 8-hydroxy-2\u0026rsquo;-deoxyguanosine (8-OHdG), and a total of 7 hydroxyl PAH metabolites (OH-PAHs). Specifically, we examined measures for the combined 2-hydroxyfluorene\u0026thinsp;+\u0026thinsp;3-hydroxyfluorene (2\u0026amp;3-FLUO) metabolites, the single metabolites 2-hydroxynaphthalene (2-NAP), 1-hydroxyphenanthrene (1-PHEN), 2-hydroxyphenanthrene (2-PHEN), 3-hydroxyphenanthrene (3-PHEN), 4-hydroxyphenanthrene (4-PHEN), and 1-hydroxypyrene (1-PYR), and also the sum of 1-, 2-, 3-, and 4- hydroxyphenanthrene (\u0026Sigma;\u003csub\u003e4\u003c/sub\u003eOH-PHEN), and of all 7 analytes (\u0026Sigma;\u003csub\u003e7\u003c/sub\u003eOH-PAH), respectively.\u003c/p\u003e\n \u003cp\u003eStudy visits were timed to be optimal for Magnetic resonance imaging (MRI) evaluations (1st MRI 14th -18th gestational week, 2nd MRI 19th -24th gestational week) in the PARENTs cohort study, and sample collection followed this schedule with the 1st sample being collected in the 10-17th gestational week, the 2nd sample collection in the 18-29th gestational week, and the 3rd sample collection after the 30th gestational week and prior to delivery. Maternal urinary samples were collected at each study visit, and we collected at least one and at most three urine samples from all participants during pregnancy. Specifically, multiple urine samples were collected among 146 out of 159 participants (92%).\u003c/p\u003e\n \u003cp\u003eThe urine samples were stored at -80\u0026deg;C after collection at UCLA and were shipped on dry ice to the CHEAR Laboratory Hub to measure both the OH-PAHs and the oxidative stress biomarkers. All samples were randomized using a Fisher-Yates shuffling algorithm prior to laboratory analysis to reduce any potential batch effects [\u003cspan\u003e19\u003c/span\u003e, \u003cspan\u003e20\u003c/span\u003e]. The OH-PAHs were measured by tandem mass spectrometry (MS/MS) [\u003cspan\u003e21\u003c/span\u003e], and the oxidative stress biomarkers (MDA and 8-OHdG) were measured by liquid chromatography-mass spectrometry (LC-MS) [\u003cspan\u003e22\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eSamples with measures below the limit of detection (LOD) values were replaced with the LOD/\u0026radic;2 [\u003cspan\u003e23\u003c/span\u003e]. Concentrations were further corrected for urine dilution by adjusting for specific gravity (SG) measured with a Reichert AR200 refractometer. We excluded samples with an invalid SG value below 1 (N\u0026thinsp;=\u0026thinsp;14, 18 and 16 samples during the 1st, 2nd and 3rd sample collection interval, respectively), resulting in a total of 391 samples available for analysis. To correct for the hydration status of pregnant women, SG-standardized biomarker concentrations were calculated using the following formula [\u003cspan\u003e24\u003c/span\u003e]:\u003c/p\u003e\n \u003cdiv id=\"Equa\"\u003e\n \u003cdiv id=\"FileID_Equa\" name=\"EquationSource\"\u003e$${CHEM}_{SG\\_Adj }={CHEM}_{i}*\\left[\\right({SG}_{m}-1)/({SG}_{i}-1)]$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere CHEM\u003csub\u003eSG_Adj\u003c/sub\u003e is the specific gravity-standardized biomarker concentration (nmol/L for MDA, ng/mL for 8-OHdG, ng/L for OH-PAHs), CHEM\u003csub\u003ei\u003c/sub\u003e is the observed biomarker concentration, SG\u003csub\u003ei\u003c/sub\u003e is the specific gravity of the urine sample and SG\u003csub\u003em\u003c/sub\u003e is the median specific gravity for the total samples with valid SG values.\u003c/p\u003e\n \u003cp\u003e2.3 Covariates\u003c/p\u003e\n \u003cp\u003eInformation on maternal age (years), parity (continuous), maternal pre-pregnancy body mass index (BMI) (\u0026lt;\u0026thinsp;18.5, 18.5\u0026ndash;24.9, 25.0\u0026ndash;30.0 and \u0026ge;\u0026thinsp;30.0), maternal race/ethnicity (White, non-White), smoking (yes, no), maternal educational attainment (bachelor\u0026rsquo;s degree or less, master\u0026rsquo;s degree, doctoral/professional degree) were collected in interviews. Gestational age (based on the best obstetric estimate obtained during a 1st trimester ultrasound exam), as well as information about pregnancy complications including gestational diabetes, gestational hypertension, and pre-eclampsia were obtained from medical records. Season of sample collection was categorized based on the month of sample collection, as spring (March, April, May), summer (June, July, August), fall (September, October, November) and winter (December, January, February).\u003c/p\u003e\n \u003cp\u003e2.4 Statistical analysis\u003c/p\u003e\n \u003cp\u003eFirst, we estimated effects for different time intervals during pregnancy by first conducting multiple linear regression analyses and calculated the expected percentage of change in each oxidative stress biomarker concentration according to PAH metabolite levels in each sample collection interval, separately. The oxidative stress biomarker concentrations were treated as continuous variables and log-transformed for statistical analyses. For all OH-PAHs, we log-transformed (base 2) the values such that in statistical model the exposure effect estimate represents an increase per doubling of the OH-PAHs concentration (ng/L). Second, we also used linear mixed models with a random intercept for participant to take repeated measurements into account, i.e., we relied on up to 3 samples collected across pregnancy for each exposure and biomarker and assessed associations between OH-PAHs exposure urine measures and oxidative stress concentrations across multiple time points in pregnancy.\u003c/p\u003e\n \u003cp\u003eIn one-point-in-time linear and in longitudinal linear mixed regression models, we adjusted for maternal age, maternal race/ethnicity, maternal education, parity, pre-pregnancy BMI, and season of sampling. We also conducted stratified one-point-in-time linear regression analyses by season of sampling, fetal sex, and maternal race/ethnicity to evaluate potential effect measure modification. Sensitivity analyses were conducted by additionally adjusting for gestational day at sample collection. Furthermore, as women experiencing pregnancy complications are likely to have higher oxidative stress levels due to these conditions [\u003cspan\u003e25\u003c/span\u003e], we also restricted some analyses to women without pregnancy complications specifically gestational diabetes, gestational hypertension, or pre-eclampsia. All statistical analyses were performed using SAS 9.4 (SAS Institute Inc., Cary, NC, USA).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003eMost of our study participants were \u0026ge;\u0026thinsp;30 years old and highly educated, more than half were parous, and almost a third (31%) were overweight or obese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;25) (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Approximately half of the mothers reported their race/ethnicity as non-Hispanic White; 18% as Hispanic and 28% as Asian/Pacific Islander. The demographics characteristics of the entire PARENTs cohort are very similar to those of the subpopulation used in this study (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of the study population (N\u0026thinsp;=\u0026thinsp;159)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMaternal age (years)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eParity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal race\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsian or Pacific islander\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack, non-Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite, non-Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmerican Indian or Alaskan Native\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal education\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBachelor\u0026apos;s degree or less\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaster\u0026apos;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDoctoral degree or professional degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployed or student\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-pregnancy BMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGestational Diabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGestational Hypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-eclampsia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSeason of Conception\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSummer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWinter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThe average MDA concentrations decreased from the 1st to 2nd sample collection and increased in the 3rd time interval (Table S2). The concentrations of 8-OHdG decreased throughout the three sampling periods over pregnancy. While the hydroxy PAH metabolites, 2-NAP and 2-PYR levels, increased throughout the three sampling periods, the 2\u0026amp;3-FLUO and all OH-PHEN isomers decreased slightly from the 1st to 2nd sample collection but increased again at the 3rd sample collection time. For both oxidative stress biomarkers we measured the lowest average concentrations in summer and relatively higher concentrations in fall and winter (Table S3). For the PAH metabolites including 2-NAP and 1-PYR we also saw relatively higher concentration levels in fall and winter, while 2\u0026amp;3-FLUO and all the OH-PHEN isomers increased from spring to summer, then decreased in fall and increased again in winter, thus exhibiting relatively higher levels in summer and in winter. In each pregnancy sample collection period, the two oxidative stress biomarkers were highly correlated with each other (r\u0026thinsp;=\u0026thinsp;0.8) as were the OH-PHEN isomers and 1-PYR (r\u0026thinsp;=\u0026thinsp;0.6\u0026ndash;0.9) (Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e), and the OH-PHEN isomers were moderately to highly correlated with the two oxidative stress biomarkers, except in the 2nd pregnancy sampling period.\u003c/p\u003e\n\u003cp\u003eIn one-pregnancy period at a time linear regression models, a doubling of each urinary PAH metabolite concentration increased the MDA (a lipid peroxidation marker) concentrations by 5.8%-41.1%, with the lowest effects estimated in the 2nd sampling period (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e; Table S4), and some of the 95% CIs including the null value. MDA concentration also increased by 8.7%-23.6% in each of the three sampling periods with a doubling of the \u0026Sigma;\u003csub\u003e7\u003c/sub\u003eOH-PAH. Specifically, a doubling of 2\u0026amp;3-FLUO concentrations increased MDA levels by 13.7%-41.1% and of 1-PYR by 17.7%-39.9%. 2-NAP concentrations were associated with a 21.8% (95% CI: 9.2%, 35.8%) increase in MDA measured in the 3rd sampling period, and were also increased in the first two periods, but the 95% CIs included the null. MDA increases ranged from 14.5\u0026ndash;46.0% per doubling in \u0026Sigma;\u003csub\u003e4\u003c/sub\u003eOH-PHEN; and each individual OH-PHEN isomer exhibited a similar pattern as the summary measure, with the smallest effects estimated for 4-PHEN.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cstrong\u003eLinear regression for percentage changes of oxidative stress biomarker concentrations per doubling concentration of different PAH metabolites\u003c/strong\u003e \u003csup\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea. Adjusted for maternal age, maternal race/ethnicity, maternal education, pre\u0026minus;pregnancy BMI, and sampling season\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003ePositive associations were also observed for 8-OHdG (a DNA damage marker) with each PAH exposure in simple one-pregnancy period only linear regression models. Although effect estimates were smaller in the 2nd gestational sampling period, most of the 95% CIs overlapped for the three sampling periods (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e; Table S4). A doubling of \u0026Sigma;\u003csub\u003e7\u003c/sub\u003eOH-PAH concentration was associated with a 22.2%, 15.5% and 34.5% increase in urinary 8-OHdG in the 1st, 2nd and 3rd sampling period, respectively. In all three sampling periods, concentrations of 8-OHdG increased with a doubling in urinary concentrations of 2\u0026amp;3-FLUO by 25.1%-42.0%, 2-NAP by 13.8%-31.6%, and 1-PYR by 23.8%-44.6%, respectively. \u0026Sigma;\u003csub\u003e4\u003c/sub\u003eOH-PHEN were also found to increase 8-OHdG in all three sampling periods by 33.9%-58.6%; and similar to MDA, smaller effect estimates were observed for 4-PHEN.\u003c/p\u003e\n\u003cp\u003eLinear mixed models that included all three pregnancy sample collection periods showed consistent patterns with those from separate linear regression models (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e; Table S5). We estimated a 15.5% (95% CI: 8.5%, 22.9%) increase of MDA levels and a 22.1% (95% CI: 15.9%, 28.5%) increase of 8-OHdG levels associated with a doubling of \u0026Sigma;\u003csub\u003e7\u003c/sub\u003eOH-PAH concentration. The effect estimates for specific PAH metabolites ranged from 13.1\u0026ndash;30.3% for MDA concentrations, and from 19.6\u0026ndash;39.1% for 8-OHdG concentrations, with somewhat less strong associations for 2-NAP and 4-PHEN.\u003c/p\u003e\n\u003cp\u003eWhen stratifying by sampling season in simple one-pregnancy sampling period linear regression models, stronger effect estimates were generally observed in fall and winter for most PAH exposures and both oxidative stress biomarkers, especially for the 3rd gestational period (Figure S2). When stratifying by fetal sex, we estimated slightly larger effect estimates among male fetuses for most PAH exposures and lipid peroxidation or DNA damage in early and late pregnancy, although the 95% CIs for male and female fetuses generally overlapped especially for MDA in the 3rd sampling period (Figure S3). Compared to non-White women, we saw that for White women PAH exposures were associated with higher oxidative stress biomarker levels in the 1st sampling period, except for 2\u0026amp;3-FLUO and 8-OHdG (Figure S4).\u003c/p\u003e\n\u003cp\u003eFinally, in one-pregnancy-time-point linear regression, effect estimates slightly increased after restricting to women without pregnancy complications (Table S6). Results changed minimally after additionally adjusting for gestational days at sample collection date (data not shown).\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIn our study of pregnant women, we found consistent associations between several urinary PAH metabolites and two oxidative stress biomarkers for lipid peroxidation, MDA, and DNA damage, 8-OHdG. Stronger associations were seen in early and late pregnancy, especially for urine samples collected in the fall or winter season. Effect estimates in different gestational period also differed by fetal sex with slightly stronger associations seen in early and late pregnancy in mothers carrying male fetuses. To our knowledge, this is the first longitudinal study with repeated urinary measures to assess associations between urinary PAH metabolites and two oxidative stress biomarkers during pregnancy. This also allowed us to additionally investigate whether there are susceptible windows for PAH exposures across pregnancy and according to fetal sex.\u003c/p\u003e \u003cp\u003eThe metabolism of PAHs depends on the cytochrome P450-mediated mixed function oxidase system that produces enormous quantities of reactive intermediates involved in lipid peroxidation, protein modification, DNA damage, and the depletion of endogenous antioxidants [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Our findings for PAH metabolites including 2\u0026amp;3-FLUO, 2-NAP, 1-PYR, and OH-PHENs being associated with increases in urinary 8-OHdG and MDA during pregnancy are consistent with the few previously published reports investigating this subject [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, studies in pregnant women that investigate associations between PAH exposures and oxidative stress in different time periods during pregnancy are rare. The stronger associations we observed in early and late pregnancy were consistent with the sensitive window for adverse birth outcomes such as preterm birth and term low birth weight, which have been widely reported to be associated with prenatal exposures to environmental pollutants and the oxidative stress they induce [\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStronger associations between PAH exposures and oxidative stress biomarkers have also been detected in the winter season, which may be explained by seasonally higher atmospheric PAH concentrations in winter than summer for most compounds due to an increased production from heating related combustion and adverse meteorological conditions [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], suggesting higher toxicity of the mixture from combustion sources. Apart from smoking and ambient or indoor air pollution from traffic and combustion including open fireplaces and wood burning, dietary intake is one of the main sources of PAH exposures in the general population, including food contamination and food processing procedures such as smoking, drying, and frying of foods [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], which makes diet a potential confounder of the association between PAH exposures and oxidative stress. In our study, we adjusted for Alternate Mediterranean Diet scores (aMED) as an indicator for antioxidant intake in a subgroup of 125 women for whom we had collected dietary data and found that our results were robust. Maternal employment conditions may also confound the associations, as work-related stress was reported to be associated with urinary concentrations of 8-OHdG in female workers [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Our results, however, were robust after adjustment for maternal employment status, possibly because most of the study participants were students or employees of UCLA.\u003c/p\u003e \u003cp\u003eOxidative stress is one of the main mechanism hypothesized to lead to suboptimal placentation and in turn affect pregnancy success [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Our previous study found oxidative stress from air pollution to be associated with adverse birth outcomes [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] as well as with oxidative stress biomarker concentrations in pregnant women [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Previous studies have also linked ambient PAH pollutants with adverse birth outcomes [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. A study of 1,677 women measured urinary PAH metabolites in the 2nd trimester and linked these to preterm birth; female fetuses were found to be more susceptible to 2-PHEN and 1-PYR exposures than the males [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Furthermore, a recent study reported sex-modification of associations between urinary OH-PAH concentrations in mid-pregnancy and the placental transcriptome, with more affected transcripts identified in females than males [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOxidative stress has also emerged as one of the underlying mechanism contributing to the toxicity of endocrine disrupting chemicals [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In addition, hormonal imbalance leads to compromised antioxidant status and oxidative stress in tissues induces various pathophysiological conditions [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Thus, altered hormonal signaling by PAH exposures may contribute to sex differences in oxidative stress levels. A recent cohort study of pregnant women has observed the endocrine disrupting potential of PAH exposures with sex-specific changes in hormone concentrations in pregnancy, which can result in adverse birth outcomes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Oxidative stress pathways may mediate the impact of PAH exposures on adverse birth outcomes via these biological pathways.\u003c/p\u003e \u003cp\u003eOur study indicated that fetal sex may modify the relationship between PAH exposures and oxidative stress during pregnancy, with slightly stronger associations seen for males in early and late pregnancy. Very few studies that investigated PAH exposures and relied on oxidative stress biomarkers among pregnant women examined fetal sex differences, although sex-specific adverse birth outcomes have been well documented, with males more likely to be displaying earlier onset of more severe neonatal complications, which may be due to the sex difference of the placenta [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Specifically, male fetuses are more vulnerable to the environmental hazards as they grow faster than females during early placental development [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], which may explain the slightly stronger effect estimates of most PAH exposures on oxidative stress in early pregnancy in our study. Furthermore, PAHs not only can cross the placental barrier, but are also metabolized by the placenta [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Thus, our sex-specific associations between PAH and oxidative stress may be indicative of the sex difference in placental response to the PAH exposures. Given the limited sample size and the general overlap of 95% CIs for the sex-specific effect estimates, we cannot exclude the possibility of chance findings. Further studies are necessary to document and understand placental sex differences in response to PAH exposures.\u003c/p\u003e \u003cp\u003eOur study has several strengths. First, the repeated measures in different pregnancy periods made it possible to investigate how the PAH metabolite concentrations throughout pregnancy influence oxidative stress. In addition, repeated urinary measures also made it possible to account for intra-individual variability in oxidative stress biomarkers. Second, the detailed data we collected for environmental and medical covariates enabled sensitivity analyses, such as excluding women more likely to exhibit higher oxidative stress levels such as due to pregnancy disorders. Third, our study population is highly educated and mostly of high socioeconomic status, such that occupational exposures, which are often higher than environmental exposures, are unlikely to have played a role as confounders. Lastly, we used two different oxidative stress biomarkers and evaluated both DNA oxidative damage and lipid peroxidation.\u003c/p\u003e \u003cp\u003eSome limitations need to also be acknowledged. First, the short biological half-live of PAHs limits the informativeness of urinary PAH metabolites as they only reflect recent exposures and to imply that they represent longer term exposures we have to assume relatively constancy of exposures over time [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. We analyzed urine samples for both the exposure and the outcomes, thus technically using a multiple time point cross-sectional design. Nevertheless, reverse-causation is likely not a concern as PAHs are environmental toxicants that the body metabolizes but does not produce and oxidative stress is a likely consequence, not cause of PAH exposures. Second, even though we collected up to three samples in pregnancy for the participants, our sample size was limited, especially for subgroup analyses. Larger prospective studies with multiple sample collection time points would be helpful to better understand these mechanisms.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eWe found that PAH exposures measured with urinary OH-PAH biomarkers are associated with increased oxidative stress generation during pregnancy, which has been previously linked to adverse birth outcomes. We observed potential sex differences that may suggest differential vulnerability during different pregnancy periods for PAH exposures resulting in oxidative stress. Larger investigations are necessary to corroborate these findings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003ePAH\u003c/strong\u003e: Polycyclic aromatic hydrocarbons\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eROS\u003c/strong\u003e: reactive oxygen species\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8-OHdG\u003c/strong\u003e: 8-hydroxy-2\u0026rsquo;-deoxyguanosine\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMDA\u003c/strong\u003e: malondialdehyde\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOH-PAHs\u003c/strong\u003e: hydroxylated metabolites of PAHs\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2-NAP\u003c/strong\u003e: 2-hydroxynapthalene\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePARENTs\u003c/strong\u003e: Imaging Innovations for Placental Assessment in Response to Environmental Exposures study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOH-PAHs\u003c/strong\u003e: hydroxyl polycyclic aromatic hydrocarbons\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCHEAR\u003c/strong\u003e: Children\u0026rsquo;s Health Exposure Analysis Resource\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2\u0026amp;3-FLUO\u003c/strong\u003e: combined 2-hydroxyfluorene + 3-hydroxyfluorene metabolites\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1-PHEN\u003c/strong\u003e: 1-hydroxyphenanthrene\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2-PHEN\u003c/strong\u003e: 2-hydroxyphenanthrene\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3-PHEN\u003c/strong\u003e: 3-hydroxyphenanthrene\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4-PHEN\u003c/strong\u003e: 4-hydroxyphenanthrene\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1-PYR\u003c/strong\u003e: 1-hydroxypyrene\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026Sigma;4OH-PHEN\u003c/strong\u003e: sum of 1-, 2-, 3-, and 4- hydroxyphenanthrene\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026Sigma;7OH-PAH\u003c/strong\u003e: sum of combined 2-hydroxyfluorene + 3-hydroxyfluorene metabolites, 2-hydroxynapthalene, 1-hydroxypyrene, 1-hydroxyphenanthrene, 2-hydroxyphenanthrene, 3-hydroxyphenanthrene, and 4-hydroxyphenanthrene\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMRI\u003c/strong\u003e: Magnetic resonance imaging\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLOD\u003c/strong\u003e: limit of detection\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSG\u003c/strong\u003e: specific gravity\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e: body mass index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eaMED\u003c/strong\u003e: Alternate Mediterranean Diet scores\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e:\u0026nbsp;The research protocol for this study was approved by the UCLA Institutional Review Board and registered on the ClinicalTrials.gov Website.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eData required to reproduce the above findings will be shared via the Human Health Exposure Analysis Resource [HHEAR, formerly Children\u0026rsquo;s Health Exposure Analysis Resource (CHEAR)] platform maintained by NIEHS contractors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThis work was partly supported by the following grants from NICHD: U01HD087221 (to BR, SUD, CJ), R01HD089714 (to SUD) and R01HD100015 (to SUD). This work was partly supported by the California Air Resources Board (contract No.17RD012).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e: Conceptualization, BR and QM; Methodology, BR and MJ; Validation: BR, QM and SM; Formal analysis, QM; Investigation, BR, QM, IDR; Data Curation: QM, SM, IDR; Writing \u0026ndash; original draft, QM; Writing \u0026ndash; review \u0026amp; editing, all authors; Visualization, QM; Supervision, BR; Project administration: BR, QM, IDR; Funding acquisition, BR, SUD, CJ, MJ.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e: Research reported in this publication was supported by the National Institute of Environmental Health Sciences of the National Institutes of Health under Award Number U2CES026560. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSrogi, K. Monitoring of Environmental Exposure to Polycyclic Aromatic Hydrocarbons: A Review. \u003cem\u003eEnviron Chem Lett\u003c/em\u003e \u003cstrong\u003e2007\u003c/strong\u003e, \u003cem\u003e5\u003c/em\u003e, 169\u0026ndash;195, doi:10.1007/s10311-007-0095-0.\u003c/li\u003e\n\u003cli\u003ePatel, A.B.; Shaikh, S.; Jain, K.R.; Desai, C.; Madamwar, D. 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AIR POLLUTION, INFLAMMATION AND PRETERM BIRTH: A POTENTIAL MECHANISTIC LINK. \u003cem\u003eMed Hypotheses\u003c/em\u003e \u003cstrong\u003e2014\u003c/strong\u003e, \u003cem\u003e82\u003c/em\u003e, 219\u0026ndash;224, doi:10.1016/j.mehy.2013.11.042.\u003c/li\u003e\n\u003cli\u003eMeng, Q.; Liu, J.; Shen, J.; Del Rosario, I.; Lakey, P.S.J.; Shiraiwa, M.; Su, J.; Weichenthal, S.; Zhu, Y.; Oroumiyeh, F.; et al. Fine Particulate Matter Metal Composition, Oxidative Potential, and Adverse Birth Outcomes in Los Angeles. \u003cem\u003eEnvironmental Health Perspectives\u003c/em\u003e \u003cstrong\u003e2023\u003c/strong\u003e, \u003cem\u003e131\u003c/em\u003e, 107012, doi:10.1289/EHP12196.\u003c/li\u003e\n\u003cli\u003eMeng, Q.; Liu, J.; Shen, J.; Del Rosario, I.; Janzen, C.; Devaskar, S.U.; Lakey, P.S.J.; Shiraiwa, M.; Weichenthal, S.; Zhu, Y.; et al. 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Excretion Profiles and Half-Lives of Ten Urinary Polycyclic Aromatic Hydrocarbon Metabolites after Dietary Exposure. \u003cem\u003eChem Res Toxicol\u003c/em\u003e \u003cstrong\u003e2012\u003c/strong\u003e, \u003cem\u003e25\u003c/em\u003e, 1452\u0026ndash;1461, doi:10.1021/tx300108e.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enhe","sideBox":"Learn more about [Environmental Health](http://ehjournal.biomedcentral.com)","snPcode":"12940","submissionUrl":"https://submission.nature.com/new-submission/12940/3","title":"Environmental Health","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"PAH (polycyclic aromatic hydrocarbons), Oxidative stress, Pregnancy","lastPublishedDoi":"10.21203/rs.3.rs-4119505/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4119505/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePolycyclic aromatic hydrocarbons (PAHs) have been linked to adverse birth outcomes, but few epidemiological studies to date have evaluated associations between urinary PAH metabolites and oxidative stress biomarkers in pregnancy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe measured a total of 7 PAH metabolites and 2 oxidative stress biomarkers (malondialdehyde (MDA), 8-hydroxy-2\u0026rsquo;-deoxyguanosine (8-OHdG)) in urine samples collected up to three times during pregnancy in 159 women enrolled at antenatal clinics at the University of California Los Angeles during 2016\u0026ndash;2019. Using multiple linear regression models, we estimated the percentage change (%) and 95% confidence interval (CI) in 8-OHdG and MDA measured at each sample collection time per doubling of PAH metabolite concentrations.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMost PAH metabolites were positively associated with both urinary oxidative stress biomarkers, MDA and 8-OHdG, with stronger associations in early and late pregnancy. Women pregnant with male fetuses exhibited slightly larger increases in both MDA and 8-OHdG in association with PAH exposures in early and late pregnancy.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eUrinary OH-PAH biomarkers are associated with increases in oxidative stress during pregnancy, especially in early and late pregnancy. Sex differences in associations between PAH exposures and oxidative stress need to be further explored in the future.\u003c/p\u003e","manuscriptTitle":"Urinary polycyclic aromatic hydrocarbon metabolites and their association with oxidative stress among pregnant women in Los Angeles","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-20 11:09:41","doi":"10.21203/rs.3.rs-4119505/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-24T13:35:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-18T20:42:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-04T18:25:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8db935b5-6e2c-45d2-8f31-cfbab7202058","date":"2024-03-25T16:24:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7a68ce79-460c-4bed-8574-d3d06517a722","date":"2024-03-22T15:56:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-20T12:53:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-18T12:19:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-18T12:19:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Health","date":"2024-03-18T02:57:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enhe","sideBox":"Learn more about [Environmental Health](http://ehjournal.biomedcentral.com)","snPcode":"12940","submissionUrl":"https://submission.nature.com/new-submission/12940/3","title":"Environmental Health","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"55ee3c01-2042-474f-82c9-6eff1bc0c493","owner":[],"postedDate":"March 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-22T19:39:54+00:00","versionOfRecord":{"articleIdentity":"rs-4119505","link":"https://doi.org/10.1186/s12940-024-01107-w","journal":{"identity":"environmental-health","isVorOnly":false,"title":"Environmental Health"},"publishedOn":"2024-08-13 15:57:50","publishedOnDateReadable":"August 13th, 2024"},"versionCreatedAt":"2024-03-20 11:09:41","video":"","vorDoi":"10.1186/s12940-024-01107-w","vorDoiUrl":"https://doi.org/10.1186/s12940-024-01107-w","workflowStages":[]},"version":"v1","identity":"rs-4119505","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4119505","identity":"rs-4119505","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}