Increased risk of gestational hypertension by periconceptional exposure to ambient air pollution and effect modification by prenatal depression

Abstract

Background:

Air pollution has been associated with gestational hypertension and pre-eclampsia, but susceptible windows of exposure and potential vulnerability by comorbidities, such as prenatal depression, remain unclear.

Methods:

We ascertained gestational hypertension and pre-eclampsia cases in a prospective pregnancy cohort in Los Angeles, California. Daily levels of ambient particles (with a diameter ≤ 10 micrometers, PM₁₀, or ≤ 2.5 micrometers, PM_2.5), nitrogen dioxide, and ozone were averaged for each week from 12 weeks pre-conception to 20 gestational weeks. We used distributed lag models to identify susceptible exposure windows, adjusting for potential confounders. Analyses were additionally stratified by probable prenatal depression to explore population vulnerability.

Results:

Among 619 participants, 60 developed pre-eclampsia and 42 developed gestational hypertension. We identified a susceptible window for exposure to PM_2.5 from 1 week pre-conception to 11 weeks post-conception: higher exposure (5 μg/m³) within this window was associated with an average of 8% (95% confidence interval: 1%–15%) higher risk of gestational hypertension. Among participants with probable prenatal depression (n=179; 32%), overlapping sensitive windows were observed for all pollutants from 8 weeks before to 10 weeks post-conception with increased risk of gestational hypertension (PM_2.5: 16% [3%–31%]; PM₁₀: 39% [13%–72%]; nitrogen dioxide: 65% [17%–134%]; ozone: 45% [9%–93%]), while the associations were close to null among those without prenatal depression. Air pollutants were not associated with pre-eclampsia in any analyses.

Conclusions:

We identified periconception through early pregnancy as a susceptible window of air pollution exposure with increased risk of gestational hypertension. Prenatal depression increased vulnerability to air pollution exposure on gestational hypertension.

Graphical Abstract

INTRODUCTION

Gestational hypertension (GH) and pre-eclampsia (PE) are leading causes of maternal and neonatal mortality and morbidity, affecting 5% to 20% of all pregnancies worldwide, with disparities in the US, such as a high prevalence among low-income populations (16.4%) than others (12.7%).^1,2 GH/PE increases risks of short- and long-term consequences for both pregnant individuals and their newborns, including preterm birth and hypertension later in life.^3,4 While traditional risk factors for GH/PE include nulliparity, obesity, and gestational diabetes, primary prevention of GH/PE should consider targeting modifiable risk factors, such as environmental pollution, as recent studies have linked GH/PE to exposure to ambient air pollution.^5,6

Air pollution exposure during pregnancy has been associated with increased risk of GH/PE in numerous studies. A multi-site US study found that exposure to particulate matter with diameters<2.5 μm (PM_2.5) and <10 μm (PM₁₀) in the first 20 gestational weeks was associated with an increased risk of GH.⁷ In a regional study in Utah, PM_2.5 and PM₁₀ exposures during the second trimester were associated with increased risk of GH, but the association was not significant in the first trimester.⁸ This timing-sensitive exposure effect also seemed to vary depending on the type of air pollutants. In a Florida birth registry study, nitrogen dioxide (NO₂) exposure in the first trimester and PM_2.5 exposure in the second trimester were associated with increased risk of GH.⁹ In a recent systematic review of 33 cohort studies including over 22 million participants, PM_2.5 and PM₁₀ exposure over the whole pregnancy was associated with increased risk of GH, while PM_2.5 exposure in the first trimester was associated a lower risk of PE, and there were no significant associations with NO₂ exposure.¹⁰ Overall, while strong evidence suggests that air pollution can increase the risk of GH/PE, and the timing of exposure plays a critical role, the sensitive exposure windows remain elusive.

Identifying susceptible windows of exposure—a period when air pollution may exert a stronger effect on GH/PE due to physiological adaptations during pregnancy—is crucial for understanding the health impacts of environmental pollution and guiding timely preventive measures. During pregnancy, blood pressure dynamically adapts to a coordinated endocrine signal from the mother, the placenta, and the fetus.⁵ These finely-tuned biological and physiological changes in blood pressure ensure the placenta receives adequate blood flow to transport oxygen and nutrients to the fetus. However, these changes may also increase the mother’s vulnerability to environmental exposures.¹¹ Interestingly, the period before conception, marked by dynamic metabolic and hormonal changes associated with menstrual cycle, has also been suggested to be a susceptible window for environmental insults.⁸ While most previous studies focused on trimester averages of air pollution exposure, the true susceptible window could be shorter and needs to be more precisely defined. Moreover, the autocorrelation of air pollution across adjacent periods presents a challenge in accurately identifying a true sensitive window.¹² In this study, we aimed to identify a precise preconception and prenatal susceptible windows of air pollution exposure on GH/PE by employing an advanced statistical model (i.e., distributed lag model)¹³ to analyze temporally refined air pollution exposures at the weekly level.

Moreover, risk of GH/PE can increase with comorbidities. Depression is a common psychological condition before and during pregnancy. Prenatal depression could predispose pregnant individuals to environmental insults due to depressed immune function and related behavioral changes (e.g., less physical activity, increased sleep, changes in appetite).¹⁴ We previously reported a stronger effect and wider susceptible window of air pollution exposure on gestational diabetes among pregnant individuals with prenatal depression compared to those without.¹⁵ Building on this, we hypothesized that prenatal depression may also augment the association of air pollution exposure with GH/PE and impact the length of the susceptible window. Understanding such effect modification by prenatal depression could help to identify novel vulnerable populations to formulate targeted interventions and ultimately mitigate environmental health disparities.

Methods

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Study population

The Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) study is an ongoing prospective pregnancy cohort established in 2015 in Los Angeles, California, USA.¹⁶ Participants were recruited from clinical sites that serve low-income populations. At each recruitment site, healthcare providers identify potentially eligible participants who were referred to research staff. Research staff then approached potential participants to explain the study procedures and goals and obtained informed consent. Inclusion criteria were being 18 years or older, with a gestational age ≤ 30 weeks, carrying a singleton pregnancy, and fluency in English or Spanish. Exclusion criteria included human immunodeficiency virus infection, having a physical, mental, or cognitive disability that would prevent participation, or current incarceration. The Institutional Review Board at the University of Southern California approved all aspects of this study.

A flow chart is provided in Figure S1. As of September 1, 2021, 721 participants had reached delivery. We excluded 33 participants with pre-existing hypertension, defined as hypertension diagnosed before 20 gestational weeks,² and 14 participants whose GH/PE status could not be confirmed. Another 43 participants with missing information on maternal socioeconomic status, and 12 participants who reported smoking during pregnancy were excluded. No material differences were seen between the overall and analytical sample (Table S1).

Ambient air pollutant exposures

Details on ambient air pollutant exposure assessment in the MADRES study have been published.¹⁷ Briefly, daily geocoded residential histories were assembled for each participant, covering a period from one year prior to conception until delivery. Date of conception was estimated based on an ultrasound measurement in the first (<14 gestational weeks, 61.9%) or the second trimester (14–28 gestational weeks, 25.7%), medical records consensus (11.5%), or last menstrual period (0.9%). We estimated daily levels of 24-hour average PM_2.5, PM₁₀, NO₂, and daily maximum 8-hour average ground-level of ozone (O₃) at each participant’s residential locations using inverse-distance-squared weighted spatial interpolation from ambient air quality monitoring stations (U.S. EPA Air Quality System). The study area (Southern California) features the most extensive air pollution monitoring network in the US, enabling assignments of four closest monitoring stations within 8–14 kilometers for each estimation (Table S2). To smooth daily variabilities, we calculated weekly average air pollution levels from 12 weeks before to 20 weeks after conception. We truncated the exposure period at 20 weeks to ensure that exposures temporally preceded the outcome, given GH/PE is defined as incident hypertension after 20 gestational weeks.²

GH/PE Ascertainment

All PE cases and most GH cases (77%) were ascertained using physician diagnosis from maternal electronic medical records (EMR). Additionally presumed GH cases (23%) were identified by reviewing blood pressure monitoring data from the EMR where systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg was observed on at least two consecutive prenatal visits after 20 gestational weeks.

Measurement of prenatal depression

We a priori selected prenatal depression as a potential effect modifier to assess vulnerability of GH/PE risk with air pollution exposure.^18–20 Depressive symptoms were measured repeatedly in each trimester using the Center for Epidemiological Studies Depression (CES-D) Scale, a validated, widely-used instrument to screen for depression symptoms.²¹ Participants had an average of 2.24 measurements of CES-D over pregnancy. We defined probable prenatal depression if any CES-D score was ≥ 16, following CES-D scale recommendation.²¹

Measurement of potential confounders

Potential confounders were a priori selected based on literature review and analyses of the causal structure using a directed acyclic graph (Figure S2).^{6,8,10,22–24} In brief, participants self-reported their age, pre-pregnancy weight, race, ethnicity, birth country, marital status, annual household income, and birth order. Standing height was measured twice by a stadiometer, and pre-pregnancy BMI was calculated using self-reported pre-pregnancy weight and measured height (kg/m²). Marital status was combined into cohabitation status: cohabitate, do not cohabitate, and decline to respond. Parity was coded with 3 categories: first, second, and third or more. Weekly temperature was calculated as the average of daily maximum and minimum temperatures extracted from a 4km×4km gridded meteorological dataset.²⁵ Recruitment timepoint was dichotomized based on when participants signed an informed consent (<20 or 20–30 gestational weeks). Season of conception was coded as spring (March - May), summer (June - August), fall (September - November), and winter (December - February). These potential confounders were included in the final adjusted model (details below).

Statistical analysis

We calculated the cumulative incidence of GH and PE in the overall cohort and subgroups by prenatal depression. GH cases, as being at risk of PE, were included in the denominator for PE risk calculation. Log-linearity of the relationship between air pollution exposure and disease risks was first confirmed with a cubic spline for each air pollutant. Then we used distributed lag models (DLM)²⁶ with Poisson Regression with a robust error variance²⁷ to examine the association of weekly exposure to each air pollutant (i.e., PM_2.5, PM₁₀, NO₂, and O₃) with GH/PE risk. Specifically, we fit a model $log\left({\pi }_i\right)={\beta }_0+\sum _{j=1}^n\left[{\alpha }_j{AP}_{ij}\right]+{\beta }_1{x}_{1i}+\dots +{\beta }_p{x}_{pi}$, where ${\pi }_i$ is the probability of one specific disease (GH/PE) for subject $i,x_{1i},\dots ,x_{pi}$ are abovementioned confounders, ${AP}_{ij}$ is the estimated air pollutant level for week $j$ from 12 weeks before to 20 weeks after pregnancy, and ${\alpha }_j$ is the effect of air pollutant in week $j$ estimated using a “cross-basis”.²⁶ Specifically, the “cross-basis” combines a log-linear dose-response function and a nonlinear lag-response function where the lag-specific effect ${\alpha }_j=h\left(j\right)$. We treated $h\left(j\right)$ as a natural cubic spline smooth function, $S(j,df)$, with pollutant-specific parameters (i.e., $df$ for number of spline knots), which was determined by comparisons of model fitting with various numbers of knots (from 0 to 5 knots, shown in Figure S3–4) using quasi-information criterion (QIC). The final spline was parameterized as $df$ equal to 3, 2, 4, and 2 for PM_2.5, PM₁₀, NO₂, and O₃, respectively.²⁶ Details on DLM and R codes are provided in Online Supplement Text. We also included weekly temperature of a cross-basis function with a 4-df natural cubic spline for the dose-response function and a 5-df natural cubic spline for the lag-response function. Final estimates were presented as risk ratio (RR) for a specific outcome by each interquartile range (IQR) increase in weekly air pollutant concentration (i.e., 5 μg/m³ for PM_2.5, 12 μg/m³ for PM_10, 11 ppb for NO₂, and 15 ppb for O₃). Susceptible exposure windows were identified as the weeks during which the RR’s 95% confidence interval did not include 1. Heterogeneity by prenatal depression was assessed using Bayesian distributed lag interaction models (Table S3),²⁸ followed by stratification analysis.

We conducted several sensitivity analyses to test results robustness, such as excluding participants with a history of GH/PE in previous pregnancies to assess its potential confounding effect; excluding GH cases that were determined from blood pressure measures at prenatal visits to assess the potential impact of differential assessment of the outcome; excluding GH from non-PE cases in the model with PE as the outcome. Considering prenatal depression was measured after pre-conception exposure, we adjusted for prenatal depression and assessed its potential role as a mediator. We also ran two-pollutant models to assess the impact of potential confounding effects of one pollutant on another’s association with GH/PE, where NO₂ and O₃ were adjusted for PM_2.5 and PM₁₀, respectively, and vice versa.

RESULTS

Among 619 eligible participants, 102 (16.5%) developed GH/PE, including 60 cases of PE and 42 cases of GH. Population characteristics are summarized in Table 1. Most characteristics were comparable among PE cases, GH cases, and non-GH/PE participants, including age, enrollment timepoint, nativity, education, and GH/PE in previous pregnancy. For a few exceptions, pre-pregnancy obesity was more prevalent among PE (52%, P=0.02) and GH (50%, P=0.03) cases than non-cases (32%). There were more male newborns among PE cases than non-cases (57% vs 48%, P=0.03). Prenatal depression prevalence was not significantly different among PE (27%), GH (31%), and non-PE/GH (29%) groups. Table S4 summarized air pollutant levels, and Figure S5 provided a Pearson correlation matrix of ambient air pollutants. Shown in Table S5, population characteristics were mostly comparable by probable prenatal depression.

Table 1.

Population characteristics by GH/PE status among 619 MADRES participants

Characteristic	Overall, N = 619	Non-GH/PE, N = 517	GH, N = 42	P value*	PE, N = 60	P value*
Maternal age (in years) at consent	28.4 (6.0)	28.3 (5.9)	28.2 (6.3)	>0.90	29.5 (6.4)	0.13
Enrollment timepoint				0.70		0.07
<20 weeks	463 (75%)	393 (76%)	31 (74%)		39 (65%)
20–30 weeks	156 (25%)	124 (24%)	11 (26%)		21 (35%)
Preferred language				0.60		0.70
English	415 (67%)	346 (67%)	30 (71%)		39 (65%)
Spanish	202 (33%)	169 (33%)	12 (29%)		21 (35%)
Maternal country of birth				>0.90		>0.90
Latin America	230 (37%)	193 (37%)	15 (36%)		22 (37%)
US or Canada	302 (49%)	251 (49%)	21 (50%)		30 (50%)
Others †	87 (14%)	73 (14%)	6 (14%)		8 (13%)
Maternal race/ethnicity				0.20		0.70
White, non-Hispanic	35 (6%)	28 (5%)	4 (10%)		3 (5%)
Black, non-Hispanic	70 (11%)	56 (11%)	7 (17%)		7 (12%)
Hispanic	492 (79%)	413 (80%)	30 (71%)		49 (82%)
Multiracial, non-Hispanic	7 (1%)	7 (1%)	0 (0%)		0 (0%)
Other, non-Hispanic	15 (2%)	13 (3%)	1 (2%)		1 (2%)
Cohabitation status				>0.90		0.20
Cohabitate with spouse/partner	391 (63%)	332 (64%)	26 (62%)		33 (55%)
Non-cohabitating	139 (22%)	110 (21%)	10 (24%)		19 (32%)
Decline/missing	89 (14%)	75 (15%)	6 (14%)		8 (13%)
Annual household income				0.20		0.11
<$15,000	121 (20%)	99 (19%)	4 (10%)		18 (30%)
$15,000-$29,000	154 (25%)	129 (25%)	12 (29%)		13 (22%)
≥$30,000	144 (23%)	127 (25%)	8 (19%)		9 (15%)
Unknown	200 (32%)	162 (31%)	18 (43%)		20 (33%)
Education				0.70		0.70
Below 12th grade	157 (25%)	132 (26%)	10 (24%)		15 (25%)
Completed 12th grade	192 (31%)	160 (31%)	10 (24%)		22 (37%)
Some college	165 (27%)	136 (26%)	14 (33%)		15 (25%)
College or above	105 (17%)	89 (17%)	8 (19%)		8 (13%)
Pre-pregnancy BMI				0.02		0.03
Normal/underweight	202 (33%)	178 (35%)	12 (29%)		12 (20%)
Overweight	196 (32%)	170 (33%)	8 (19%)		18 (30%)
Obese	218 (35%)	166 (32%)	22 (52%)		30 (50%)
GH/PE in previous pregnancies				0.40		0.40
No	599 (97%)	502 (97%)	40 (95%)		57 (95%)
Yes	20 (3%)	15 (3%)	2 (5%)		3 (5%)
Prenatal depression				>0.90		0.70
No	387 (62%)	322 (62%)	27 (64%)		38 (63%)
Yes	179 (29%)	150 (29%)	13 (31%)		16 (27%)
Missing	53 (9%)	45 (9%)	2 (5%)		6 (10%)
Parity				0.07		0.09
First born	200 (32%)	152 (29%)	21 (50%)		27 (45%)
Second born	174 (28%)	154 (30%)	10 (24%)		10 (17%)
Third or later born	176 (28%)	151 (29%)	8 (19%)		17 (28%)
Missing	69 (11%)	60 (12%)	3 (7%)		6 (10%)
Newborn sex assigned at birth				0.03		>0.90
Female	316 (51%)	267 (52%)	18 (43%)		31 (52%)
Male	303 (49%)	250 (48%)	24 (57%)		29 (48%)

^*P value from Chi-square test comparing to the Non-GH/PE group;

^†including all regions out of Americas; MADRES = Maternal and Developmental Risks from Environmental and Social Stressors; BMI = body mass index; GH = Gestational hypertension; PE = pre-eclampsia.

Shown in Figure 1 and Table S6, we identified a susceptible window for PM_2.5 exposure from one week before to 11 weeks after conception when each IQR higher PM_2.5 exposure (5 μg/m³) was associated with an average of 8% (95% CI: 1%–16%) higher risk of GH. No susceptible windows were observed for the other three air pollutants with GH. For PE, no susceptible window was found for any air pollutants (Figure 2).

Figure 1. Associations of pre-conception and prenatal weekly exposure to PM_2.5, PM₁₀, NO₂, and O₃ with risk of gestational hypertension in the MADRES cohort.

All results were from DLMs adjusted for maternal age, ethnicity and nativity, marital status, pre-pregnancy BMI, annual household income, parity, fetal sex, temperature, year and season of conception, and recruitment timepoint. Susceptible windows were marked with a star on the top. Effect estimation was based on per IQR increases in weekly air pollutant concentrations (i.e., 5 μg/m³, 12 μg/m³, 11 ppb, and 15 ppb for 24-hour average PM_2.5, PM₁₀, NO₂, and 8-hour maximum O₃, respectively).

Figure 2. Associations of pre-conception and prenatal weekly exposure to PM_2.5, PM₁₀, NO₂, and O₃ with risk of pre-eclampsia in the MADRES cohort.

All results were from DLMs adjusted for maternal age, ethnicity and nativity, marital status, pre-pregnancy BMI, annual household income, parity, fetal sex, temperature, year and season of conception, and recruitment timepoint. Effect estimation was based on per IQR increases in weekly air pollutant concentrations (i.e., 5 μg/m³, 12 μg/m³, 11 ppb, and 15 ppb for 24-hour average PM_2.5, PM₁₀, NO₂, and 8-hour maximum O₃, respectively).

Shown in Figure 3 and Table S6, we observed a heightened vulnerability by prenatal depression. Among participants with prenatal depression, there was a stronger association of air pollution with GH, and the susceptible exposure window was wider and consistent across the four air pollutants. Specifically, all four pollutants had a notably overlapping periconception window of susceptibility spanning from 5 weeks before to 3 weeks after conception, with small variations in the specific window for each air pollutant. Per IQR higher PM_2.5 during the susceptible window from 5 weeks before to 4 weeks after conception was associated with an average of 16% (95% CI: 3%–31%) higher risk of GH. Per IQR higher PM₁₀ during the susceptible window from 8 weeks before to 3 weeks after conception was associated with an average of 39% (95% CI: 13%–72%) higher risk of GH. Per IQR higher NO₂ during the susceptible window from 9 weeks before to 3 weeks after conception was associated with an average of 65% (95% CI: 17%–134%) higher risk of GH. Outside of the overlapped window, NO₂ exposure from 8 to 17 gestational weeks was also associated with increased risk of GH (RR: 1.58, 95%CI: 1.14–2.18, per 11 ppb increase). For O₃ (per 15 ppb increase), the susceptible window spanned from 5 weeks before conception to 10 weeks after conception on the risk of GH (RR: 1.45, 95%CI: 1.09–1.93). In the absence of prenatal depression, the associations between these pollutants and GH risk were essentially null.

Figure 3. Stratified analyses by prenatal depression for the associations of pre-conception and prenatal weekly exposure to PM_2.5, PM₁₀, NO₂, and O₃ with risk of gestational hypertension in the MADRES cohort.

All results were from DLMs adjusted for maternal age, ethnicity and nativity, marital status, pre-pregnancy BMI, annual household income, parity, fetal sex, temperature, year and season of conception, and recruitment timepoint. Susceptible windows were marked with a star on the top. Triangles and bars in orange indicated the RR and 95% CI among people with prenatal depression (n=163). Dots and bars in blue indicated the RR and 95% CI among people without prenatal depression (n=349). Effect estimation was scaled by IQR increases in weekly air pollutant concentrations (i.e., 5 μg/m³, 12 μg/m³, 11 ppb, and 15 ppb for 24-hour average PM_2.5, PM₁₀, NO₂, and 8-hour maximum O₃, respectively).

Our findings were robust in sensitivity analyses. Overall findings remained mostly unchanged when women with GH/PE in previous pregnancies were excluded (Figure S6). Case definition had no meaningful impact on the results when we excluded GH cases that were determined based on blood pressure measures (Figure S7). Excluding GH cases in the model with PE as the outcome did not meaningfully change the results (Figure S8). Additional adjustment for prenatal depression also had minimal impact on the results (Figure S9), suggesting it is not playing an important role as a confounder or a mediator. Results from the two-pollutant models (Figure S10) remained relatively unchanged, except for slight changes for PM_2.5, where the susceptible window for PM_2.5 narrowed to between 5 to 11 weeks post-conception after adjusting for O₃. After adjusting for NO₂, the association between PM_2.5 and GH risk maintained a similar magnitude, although it was not statistically significant (Figure S10).

Discussion

In a prospective pregnancy cohort of predominately low-income Hispanics with a high prevalence of GH/PE (16.5%, versus 10.6% among US Hispanics),¹ we identified a susceptible window for PM_2.5 exposure, spanning from one week before to 11 weeks after conception, in association with increased GH risk. In the subgroup with prenatal depression, susceptible exposure windows overlapped for all four pollutants (PM_2.5, PM₁₀, NO₂, and O₃) spanning from 5 weeks before to 3 weeks after conception, while significant associations were not observed in the subgroup without prenatal depression. Our results provide a nuanced understanding of susceptibility windows for environmental air pollutants and vulnerability by probable depression on the risk of GH.

Several studies have found associations between PM_2.5 and GH or PE, but most studies examined air pollution in specific trimesters or three months before pregnancy, with inconsistent findings. In analyses of Florida birth record data (2004–2005), PM_2.5 exposure in the second trimester increased the combined risk of GH and PE, which had much lower prevalence (4.7%) than ours (16.5%).⁹ Using California birth record data, pregnancy averaged PM_2.5 increased the risk of PE.²⁹ Although different levels and composition of PM_2.5 could have contributed to discrepancies among different studies, another key reason could be a high temporal correlation of PM_2.5 exposure in short periods of time (weeks within trimesters), which may have induced residual bias on one another.¹² Thus, appropriate adjustment for PM_2.5 in adjoining exposure windows while addressing collinearity issue, such as with advanced statistical DLM modeling, is essential to avoid confounded results and the misidentification of susceptible windows.¹² Using DLM, we identified a precise susceptible window of PM_2.5 exposure on the risk of GH, which is consistent with a handful of other recent DLM studies. Deneil et al. found PM_2.5 exposure at 7–14 gestational weeks was associated with PE among Jewish women.²³ Yuan et al. found PM_2.5 exposure from 1 week before to 6 weeks post conception was associated with hypertensive disorders of pregnancy among Chinese women.³⁰

Our findings underscore the critical timing of air pollution exposure in pre-conception and early pregnancy for its effect on the risk of GH. While mechanisms underlying this susceptible window remain unclear, the dynamic physiological adaptations and hormonal changes from the last menstruation to early pregnancy are important for regulating blood pressure and determining GH risk. Estrogen, a vasodilating hormone, drops after ovulation, while progesterone starts to increase and continues increasing in early pregnancy.³¹ Progesterone plays a critical role in signaling the maternal circulatory system during pregnancy to relax vascular smooth muscle, thus decreasing peripheral resistance and blood pressure.³¹ When pregnant individuals are exposed to air pollution, their blood pressure regulation could be disrupted through upregulated sympathetic nervous system and basal systemic vascular tone, heightened oxidative stress, and chronic inflammation.¹¹ These environmentally-induced disruptions may influence the dynamic hormonal regulation from the time of last menstrual period to the first trimester to increase the risk of GH.

We found that PM_2.5 had a stronger effect on GH, compared to other pollutants in the overall population, which may be explained by two characteristics of PM_2.5. First, compared to PM₁₀ and gaseous pollutants (NO₂ and O₃), PM_2.5 has a larger superficial area to which more toxic elements such as black carbon and heavy metals can be attached.³² Moreover, PM_2.5 can remain suspended for longer periods, thus more easily inhaled.³² Second, PM_2.5 can infiltrate deep in the lungs, where it may directly modulate blood pressure, such as through stimulating endothelial function. PM₁₀, NO₂, and O₃ are posited to exert a more indirect influence on blood pressure, such as through inducing oxidative stress and systemic inflammation.³³ This may also explain why we see significant effect of all pollutants on GH among those with prenatal depression that can also increase chronic inflammation.³⁴

To the best of our knowledge, our study is the first to report that prenatal depression heightened vulnerability of air pollution on GH. Whereas little is known about the biological mechanism of how prenatal depression magnifies the effect of air pollution on GH, several biologically plausible pathways warrant further investigation. Specifically, depression has been shown to affect cardiovascular health through increased arterial stiffness, endothelial dysfunction, and chronic inflammation.³⁴ These changes are shared pathways by which air pollution exposure may induce GH, as discussed above. Our finding that all four air pollutants had overlapping susceptible exposure windows among pregnant individuals with prenatal depression further support a possible disruption on the cardiovascular system by prenatal depression. Therefore, prenatal depression, with its own adverse effect on the cardiovascular system and heightened chronic inflammation, may render pregnant individuals more vulnerable to the adverse effects of various air pollutants. It is worth noting that the independent association of prenatal depression with GH/PE was not significant in our study, and previous studies on such an association remain inconclusive.³⁴ Nevertheless, our findings suggest that early screening and providing timely treatment for prenatal depression could protect pregnant individuals against increased risk of pregnancy complications, such as GH, from environmental insults.

Strengths and Limitations

Our study is strengthened by the novel application of DLM in the well-characterized MADRES cohort, particularly the comprehensive residential history data and a high temporal resolution of air pollution exposure. Measures on prenatal depression enabled us to investigate individual vulnerability by psychological distress.

Our study has limitations. First, we estimated outdoor air pollution exposure based on residential address, which does not directly measure personal exposure. Also, we used the inverse-distance-squared weighted (IDW2) spatial interpolation to estimate ambient air pollutant levels, which is limited in spatial resolution and could introduce exposure measurement error. However, IDW2 provides high temporal resolution, which is critical to accurately identify sensitive exposure windows by aligning exposures more precisely with gestational timeline. In addition, the high monitoring network density in Southern California further provided adequate prediction of regional, background air pollutant exposures. Secondly, we had no information on the exact date of GH/PE diagnosis and thus restricted air pollution exposure to the first 20 gestational weeks (prior to most diagnoses). This may partially explain why we did not find significant associations of air pollution with PE, because for PE cases that occurred in later pregnancy, we may have missed potential susceptible exposure windows after 20 weeks. Other potential susceptible life stages, such as participants’ own in-utero or pubertal exposures, were not considered in this study. Third, we only measured depression during the prenatal period. Future research should consider measuring preconception depression. Fourth, we lacked data on other environmental risk factors, such as noise and greenspaces, and time-varying physical activities, which has been associated with hypertension and thus act as potential residual confounders.

Perspectives

As a modifiable risk factor, air pollution has been suggested as a prevention target to protect pregnant individuals from gestational hypertension, one of the leading causes of maternal mortality. Our findings of a susceptible window from five weeks before to three weeks after conception for air pollution exposure on GH risk suggest that preventive measures during this period may be more effective. Our identification of the preconception period as a susceptible window further emphasizes that air pollution could affect child-bearing women’s health even before pregnancy. Because many pregnancies are not planned, strengthening air quality control could protect a large proportion of child-bearing age women from pregnancy complications. Moreover, our findings of a stronger effect of air pollution on GH among participants with prenatal depression highlight the potential benefit of early screening and treatment of prenatal depression as a potential approach to protect women from environmental insults, and to mitigate the disparity in developing GH.

Conclusions

Risk of gestational hypertension is increased with air pollution exposure in a susceptible window between pre-conception and early pregnancy. Prenatal depression increases vulnerability to air pollution exposure effects on gestational hypertension.

Novelty and Relevance:

What Is New?

• We identified the two months around conception as a susceptible window when air pollution exposure can increase the risk of gestational hypertension.

• Pregnant individuals with prenatal depression are more vulnerable to the effects of air pollution on gestational hypertension.

What Is Relevant?

• Gestational hypertension increases maternal mortality and long-term risk of cardiovascular diseases. While air pollution control is beneficial to everyone, our findings identified a periconceptional period when protecting pregnant individuals from air pollution may be most effective in reducing risk of gestational hypertension.

Clinical/Pathophysiological Implications?

• Moreover, we found a stronger effect of air pollution on gestational hypertension among participants with prenatal depression. Thus, early screening and providing timely treatment for prenatal depression could help protect pregnant individuals against environmental exposure related pregnancy complications, such as gestational hypertension.

Nonstandard abbreviation:

GH

gestational hypertension

PE

pre-eclampsia

CES-D

Center for Epidemiological Studies Depression

DLM

distributed lag models

QIC

quasi-information criterion

EPA

Environmental Protection Agency

IDW2

inverse-distance-squared weighted