Startseite A systematic review on the association between exposure to air particulate matter during pregnancy and the development of hypertensive disorders of pregnancy and gestational diabetes mellitus
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A systematic review on the association between exposure to air particulate matter during pregnancy and the development of hypertensive disorders of pregnancy and gestational diabetes mellitus

  • Daniela Alvarado-Jiménez , Gabriele Donzelli ORCID logo EMAIL logo und María Morales-Suárez-Varela
Veröffentlicht/Copyright: 4. Mai 2023
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Reviews on Environmental Health
Aus der Zeitschrift Reviews on Environmental Health Band 39 Heft 4

Abstract

Particulate matter (PM) is considered an intrauterine toxin that can cross the blood-placental barrier and circulate in fetal blood, affecting fetal development, and implicating placental and intrauterine inflammation, and oxidative damage. However, the relationship between PM exposure and adverse pregnancy outcomes is still unclear and our aim was to systematically review toxicological evidence on the link between PM exposure during pregnancy and the development of gestational diabetes mellitus or hypertensive disorders of pregnancy, including gestational hypertension and pre-eclampsia. PubMed and Science Direct were searched until January 2022. Of the 204 studies identified, 168 were excluded. The remaining articles were assessed in full-text, and after evaluation, 27 were included in the review. Most of the studies showed an association between PM exposure and gestational hypertension, systolic and diastolic blood pressure, pre-eclampsia, and gestational diabetes mellitus. These results should be interpreted with caution due to the heterogeneity of baseline concentrations, which ranged from 3.3 μg/m3 to 85.9 μg/m3 and from 21.8 μg/m3 to 92.2 μg/m3, respectively for PM2.5 and PM10. Moreover, critical exposure periods were not consistent among studies, with five out of ten observational studies reporting the second trimester as the critical period for hypertensive disorders of pregnancy, and ten out of twelve observational studies reporting the first or second trimester as the critical period for gestational diabetes mellitus. Overall, the findings support an association between PM exposure during pregnancy and adverse pregnancy outcomes, highlighting the need for further research to identify the critical exposure periods and underlying mechanisms.

Keywords: epidemiological studies; gestational diabetes mellitus; hypertension disorders; in vivo studies; particulate matter; pregnancy

Introduction

Environmental air pollution is a serious problem in many cities around the world and it is a major cause of disease and death. Particulate matter (PM) is considered to be one of the most important factors causing urban air pollution, including particulate matter less than 2.5 μm in diameter (PM2.5) and particulate matter less than 10 μm (PM10) [1, 2]. Stanaway et al. [3] estimated that long-term exposure to ambient PM2.5 contributed to 2.9 million deaths (5.2 % of all global deaths).

Major sources of PM include industrial emissions, traffic, dust storms, cooking ovens, and other processes linked with fuel combustion; this makes PM a heterogeneous group composed of organic or inorganic compounds, ultrafine particle aggregates, and metals, which greatly impact human health [4].

Pregnant women are especially vulnerable to environmental air pollution due to the different physiologic changes in their body, like increased of oxygen consumption, cardiac circulation, and maternal blood flow [5]. PM is considered an intrauterine toxin that can cross the blood–placental barrier and circulate in the blood, affecting fetal development, and implicating placental inflammation and oxidative damage, intrauterine inflammation, and mitochondrial damage [6]. These phenomena could be responsible for the development of adverse pregnancy outcomes such as hypertensive disorders of pregnancy (HDP), including gestational hypertension (GH) (ICD-10-CM O13.9) and pre-eclampsia (PE) (ICD-10-CM O14), and gestational diabetes mellitus (GDM) (ICD-10-CM O24.92) [4].

HDP affect approximately 5–10 % of pregnancies worldwide [7]. Gestational hypertension is a form of high blood pressure (BP) in pregnancy. It is characterized by a systolic BP of 140 mm Hg and/or a diastolic BP of 90 mm Hg after 20 weeks of pregnancy in women with previously normal BP. Pre-eclampsia is a hypertensive disorder complicating pregnancy, most commonly diagnosed as having hypertension and proteinuria resulting from abnormal placentation after 20 weeks of pregnancy [1]. Besides that, approximately 7 % of all pregnancies are complicated by GDM, resulting in more than 200,000 cases annually. GDM is defined as any degree of glucose intolerance with onset or first recognition during pregnancy, and is one of the most common pregnancy complications [8].

GDM and HDP have serious short-term and long-term consequences for both mothers and newborns [9, 10]. Women with GDM are more susceptible to adverse perinatal outcomes such as pre-eclampsia [11]. Fetuses exposed to high glucose environments during pregnancy may have increased risks of stillbirth, macrosomia, hypoglycemia, hypocalcemia, respiratory distress syndrome, lifelong obesity, and glucose intolerance. They are also susceptible to metabolic syndrome and cardiovascular diseases throughout their lives [9]. In the case of HDP, it might increase the risks of cardiovascular diseases, liver and kidney diseases, and diabetes mellitus for mothers later; and newborns could have adverse birth outcomes like stillbirth, small for gestational age and low birth weight, neurodevelopmental disorders, type 2 diabetes, and elevated blood pressure [10].

Although the effects of these diseases in pregnant women are well known, their relationship with PM exposure remains unknown. The World Health Organization (WHO) [12] has determined reference values for PM10 and PM2.5 exposures. They recommended values of 15 and 45 μg/m3 per daily mean and 5 and 15 μg/m3 per annual mean concentrations for PM2.5 and PM10, respectively. These recommendations have the objective of protecting the general population’s health, even though there are no specific reference values for vulnerable populations such as pregnant women and neonates [13]. As well, according to WHO [12] there is no safe threshold identified for PM exposure.

In this regard, toxicological data can be used to support epidemiological findings by supplying mechanistic information, such as biological plausibility; combining toxicological and human data aids in establishing causality [14]. The evidence from epidemiological studies, clinical cohorts, and in vivo experimental studies indicates that initial environmental exposures could even influence an individual’s long-term health including cardiovascular, metabolic, and other diseases [15], [16], [17].

The objective of this systematic review was to assess the potential link between exposure to PM air pollution during pregnancy and the development of hypertensive disorders of pregnancy and gestational diabetes mellitus.

Methods

Search strategy and data extraction

We searched on PubMed (https://pubmed.ncbi.nlm.nih.gov/advanced/) and ScienceDirect (https://www.sciencedirect.com/) databases using the following search query: ((“particulate matter”[Title/Abstract]) AND (pregnant [Title/Abstract] OR neonates [Title/Abstract])). The results were filtered by year of publication (2018–2022), while we did not apply restrictions for article type, language, particulate size and components, and study population (we also include studies in pregnant animals). A total of 296 publications were identified from the search and storage in Zotero to make their management easier. 92 duplicate records were excluded. After reading 204 titles and abstracts, we decided to focus our investigation on hypertension disorders on pregnancy (preeclampsia, gestational hypertension and BP) and gestational diabetes mellitus. We used Population/Exposure/Comparison/Outcomes (PECO) [18] approach:

Inclusion and exclusion criteria

Studies were considered eligible for inclusion based on the following criteria:

  1. P (Populations): pregnant woman, mother-child pairs and gestating animals.

  2. E (Exposure): exposure to air pollution during pregnancy, including particulate matter such as PM2.5, PM10 and its components.

  3. C (Comparison): pregnant women not exposed to air pollution.

  4. O (Outcomes): after controlling the confounding factors, the OR/RR value of the correlation between air pollution HDP and GDM.

  5. S (Study design): the types of included studies were cohort study, cross-sectional study, case control study and propensity matching study, reviews, experimental in vivo studies.

We excluded investigations without the outcomes related to HDP and GDM. More specifically, 168 papers were excluded because they were unrelated to outcomes. Of that search we considered 36 articles by reading full paper and, after assessing them, we finally include 27 articles in the review (Figure 1).

Figure 1: Flow diagram of study selection process review [19].
Figure 1:

Flow diagram of study selection process review [19].

Data collection

As part of this review, we collected all relevant information of different studies in order to summarize it. More specifically, we collected from each paper the following information: year, study country, data collection period, exposure period, outcome, prevalence, baseline concentration, main findings and conclusion. Regarding the main findings, we reported the estimates reported by the authors, such as odds ratios (ORs), relative risk (RRs) and other measure of associations between an exposure and an outcome.

Risk of bias assessment

Two researchers (D.A and G.D.) independently assessed the risk of bias (RoB) at the study-and item-level using a modified version of the ROBINS-I instrument tailored for non-randomized studies (NRS) addressing environmental exposures [20]. More specifically, we evaluated the RoB in the following seven items: (1) bias due to confounding, (2) bias in selection of participants into the study, (3) bias in classification of interventions, (4) bias due to departures from intended interventions, (5) bias due to missing data, (6) bias in measurement of outcomes, and (7) bias in selection of reported results. Judgments for each RoB item can be: ‘Low RoB’, ‘Moderate RoB’, ‘Serious RoB’, or ‘Critical RoB’. Similarly, an overall judgment about the bias at the study- and item level is either ‘Low RoB’, ‘Moderate RoB’, ‘Serious RoB’, or ‘Critical RoB’ [21].

Results

Twenty-seven studies met our inclusion criteria. Sixteen are related to gestational hypertensive disorders and eleven are about gestational diabetes mellitus. There is an article that was included in both topics due to it having GDM and GH outcomes.

Hypertensive disorders of pregnancy

We included sixteen works about hypertension disorders in pregnancy (10 observational studies, 2 reviews and 4 in vivo experimental studies).

As shown in Table 1, of those ten observational studies, five were prospective cohorts, four were retrospective cohorts and one was a cross-sectional study, 60 % of those papers were developed in China. On the other hand, Table 2 show the main information collected by the systematic review and the systematic review and meta-analysis included in our study.

Table 1:

Observational studies of association between PM exposure and hypertensive disorders during pregnancy.

Reference, study design Country, subjects (n), data collection period Exposure period(s) cited by authors Outcome Prevalence, % Conclusion by authors Main findingsa Critical exposure period Reference concentration level mean (standard deviation) Adjusted for
Mozzoni et al. [22] prospective cohort Shangai, China 198 pregnant women, 2017–2018 72-h Personal air sampling in 15–19, 24–28 and 31–35 pregnant weeks MAP DBP, SBP No information Results showed PM2.5 exposure was significantly associated with 4.7 (95 % CIs: 0.2, 9.2) mm Hg increase in SBP, 4.3 (95 % CIs: 0.3, 8.3) mm Hg increase in DBP and 4.0 (95 % CIs: 0.1, 7.8) mm Hg increase in MAP during the first follow-up period, respectively. No significant association was observed between PM2.5 exposure and BP alteration in the second or third trimesters. ↑↑SBP

↑↑DBP

↑↑MAP		 First trimester Personal exposure PM2.5 44.6 (41.9) µg/m3 Age, weeks of gestation, prepregnancy body mass index, gestational weight gain (GWG), daily sleeping duration, relative humidity and temperature, parity, passive smoking, physical activity, working status, education level, economic level, season, month, and hour-of day, day-of-study and a squared day-of-study.
Kaali et al. [23], prospective cohort Ghanaian 97 mother-child 2013–2016 Pregnant women with 24 weeks of gestation until child 1 year old CBMC rTL, SBP N.A Higher prenatal PM2.5 exposure was associated with reduced TL (4.9 % (95 % CI 8.6, 0.4), p=0.03, per 10 μg/m3 increase in PM2.5). In all children, shorter TL was associated with higher systolic BP (SBP) (0.35 mmHg (95 % CI 0.001, 0.71), p=0.05, per 10 % decrease in TL). ↑↑ SBP

↑↑ TL		 No information Prenatal average 79.2 μg/m3 Maternal age, maternal education, ethnicity, second hand smoke, index household level, socioeconomic status, BMI, date of delivery, child sex.
Yan et al. [24], prospective cohort China 3,754 pregnant women, 2017–2018 First, second and third trimester GH, GDM, PE Prevalence of GH, GDM, and PE were 2.6 %, 11.2 %, and 0.7 %, respectively. Elevated risks for GH from exposure to PM2.5 OR 1.119 (0.915, 1.368) and PM10 OR 1.138 (1.014, 1.278) in all three trimesters. The association between PM and GH was nearly log-linear, indicating a higher GH risk at a higher PM concentration. Increased risk of PE was associated with PM10 exposures in the late stages of pregnancy; the risk of PE increased by 20 % (95 % CI, 0.3 %, 43.5 %) and 19.3 % (−1.6 %, 44.6 %) per 10 μg/m3 increase in PM10 levels in the second and third trimester, respectively. PM2.5: ↑↑GH

↑→PE

PM10: ↑↑GH

↑↑PE		 GH: PM2.5 and PM10 in third trimester PE: PM2.5 in third trimester and PM10 in second with a little difference in third. Prenatal average PM2.5 47.4 (14.4) µg/m3 PM10 89.7(26.4) µg/m3 Maternal age (years), maternal body mass index before pregnancy, pre-pregnancy hypertension (yes/no), pre-pregnancy, diabetes mellitus (yes/no), weight gains (kg), newborn’s sex (male/female), residential region (mid-west, north, or southeast), and season of conception (spring, summer, autumn, and winter).
Ye et al. [25], cross-sectional study Tamil Nadu, India. 799 pregnant women, 2018–2022 24-h Personal exposure starting at 9-and 20-weeks of gestation DBP, SBP 13 pregnant women (2 %) were considered as at hypertension stage 1, defined as SBP between 130 and 139 mmHg or DBP between 80 and 89 mmHg. Two individuals (<1 % of the participants) were categorized as overly hypertensive, defined as SBP ≥140 mmHg or DBP ≥90 mmHg. Women with PM2.5 exposures in the highest quartile had a marginal significant association with DBP (1.50 mmHg, 95 % CI: 0.03, 3.06) compared to those who had PM2.5 exposures in the lowest quartile. ↑→DBP

↓↓SBP		 No information Personal exposure PM2.5 75.5 (82.2) µg/m3 Age, BMI, mother’s highest level of education, household wealth index at national quintile, gestational age, and season of measurement.
Luo et al. [26], prospective cohort Shanghai, China 176 pregnant women, 2017–2018 Second trimester: 17.3 ± 1.6 weeks and third trimester: 32.3 ± 2.2 weeks SBP, DBP N.A IQR increases in exposure to OC and PM2.5 from SOC were associated with decreases in SBP and DBP. PM2.5 source SOC and OC

↓↓SBP

↓↓DBP		 No information Personal exposure 38.97 μg/m3 Age, gestational age, season of conception, reproductive history, education attainment, pre pregnancy (BMI, and GWG), working status, average daily sleep duration, physical activities, cooking, sweeping, purifier use and passive smoking, temperature and relative humidity.
Jia et al. [27], retrospective cohort Hebei Province, China 116,042 pregnant women, 2015–2017 LMP to second trimester PE 2,988 (2.57 %) pregnant women were diagnosed with preeclampsia Exposure to PM2.5 was an independent risk factor for preeclampsia, and the OR (95 % CI) were 1.014 (1.000–1.028) and 1.026 (1.012–1.049) in the first and second trimester of pregnancy respectively. Exposure to PM10 was also an independent risk factor for preeclampsia, and the OR (95 % CI) were 1.013 (1.002–1.023) and 1.016 (1.005–1.027) in the first and second trimester of pregnancy respectively. PM2.5 and PM10: ↑↑PE PM2.5 first trimester and second. PM10 third trimester PM2.5. Trimester 1: 71.33 (55.67, 107.67) μg/m3. Trimester 2: 68.67 (54.67, 99.67) μg/m3 PM10. Trimester 1: 135.33 (101.00, 169.67) μg/m3. Trimester 2: 134.67 (99.00, 160.67) μg/m3 Trimester, gravidity, parity, maternal age, maternal education, hospital level, conception and delivery season.
Rosa et al. [28], prospective cohort Ciudad de México, México 537 mother-child, 2007–2011 First trimester (1–13 gestation), second trimester (14–27 gestation) and third (28 weeks’ gestation to delivery) SBP, DBP Three children (1 %) had BPs above the 90th percentile for either SBP or DBP. PM2.5 exposures between weeks 11–32 of gestation (days 80–226) was significantly associated with children’s increased SBP. Similarly, PM2.5 exposures between weeks 9–25 of gestation (days 63–176) was significantly associated with increased DBP. A constant 10 μg/m3 increase in PM2.5 would predict a cumulative increase of 2.6 mmHg (CI: 0.5, 4.6) in SBP and 0.88 mmHg (CI: 0.1, 1.6) in DBP at ages 4–6 years. ↑↑SBP

↑↑DBP		 Second trimester (weeks 11–32 of gestation) associated with children’s increased SBP, as well as between weeks 9–25 associated with increased DBP. Prenatal average PM2.5 22.6 (2.62) µg/m3 Child’s age, sex and BMI, as well as maternal education, preeclampsia and indoor smoking report during the second and third trimester, seasonality and average postnatal year 1 PM2.5 exposure.
Yang et al. [10], retrospective cohort Wuhan, China 38,115 pregnant women, 2014 Whole pregnant (LMP to delivery) GH 4.7 % pregnant women were diagnosticate with GH Compared with the reference group, pregnant women with the highest quartile of PM2.5 exposure had higher odds of GH (OR=1.59, 95 % CI: 1.18, 2.14). ↑↑ GH No information Prenatal average 83.4 (28.6) μg/m3 Maternal age, pre-pregnancy body mass index (BMI), gestational age at diagnosis, maternal education level (middle school or below, high school, college, bachelor’s or higher degree), parity (1, ≥2), gravidity (1, ≥2), season of conception, age, pre-pregnancy BMI, and gestational age at diagnosis were treated as continuous variables; maternal education level; parity and gravidity.
Choe et al. [29], retrospective cohort Seul, Korea 18,835 pregnant women, 2001–2013 Four exposure periods as 12 (preconception), 6, 3, and 1 (prenatal) month to birth. GH, PE, Mg-preeclampsia 0.6 , 0.5 and 0.4 % of women developed GH, PE, and Mg-preeclampsia, respectively Average concentrations of PM10 during 12, 6, and 3 months before birth were significantly or marginally associated with gestational hypertension (OR=1.67 [95 % CI, 0.91–3.04], 1.68 [1.09–2.58], and 1.35 [0.91–1.99], respectively). There was no association with preeclampsia or Mg-preeclampsia. ↑↑GH

↔PE

↔PE-Mg		 Especially 6 months before delivery. 12 month 57.78 (10.31) µg/m3. 6 month 57.78 (10.31) µg/m3. 3 month 56.34 (16.68) µg/m3. 1 month 56.68 (20.40) µg/m3 Age, parity, plurality, paid employment, presence of diabetes (gestational or non-gestational) and relative level of household income.
Wang et al. [30], retrospective cohort Shenzhen, China 1,208,559 mother-child, 2005–2012 First and second trimester PE 1.16 % mothers were diagnosed as preeclampsia. Positive gradient of increasing preeclampsia risk with increasing quartiles of PM10 and SO2 exposure. ↑↑PE Second trimester Trimester 1:61.2 (16.2) μg/m3. Trimester 2:r: 58.0 (14.69) μg/m3. Entire pregnancy: 59.1 (7.0) μg/m3 Maternal age, pre-pregnancy BMI, education levels, times of prenatal examination, parity, gestational age at birth, date of birth, birth weight, and infant sex.

  1. a↑↑At least one model with a positive association where lower confidence bound does not include 1.0 OR or RR. ↑→Positive association where lower confidence bound includes 1.0 for OR or RR. ↓↓Negative association where lower confidence bound does not includes 1.0 for OR or RR. ↓→Negative association where lower confidence bound includes 1.0 for OR or RR. ↔Null association. LMP, last menstrual period; GH, hypertension; PE, preeclampsia; Mg-PE, preeclampsia requiring magnesium sulfate; BMI, body mass index; GWG, gestational weight gain; DBP, diastolic blood pressure; SBP, systolic blood pressure; MAP, mean arterial pressure; SOC, secondary organic carbon; N.A, not applicable.

Table 2:

Reviews on the association between PM and hypertensive disorders during pregnancy.

Reference, study design Data collection period No. Of studies included Exposure period, s Outcome Country, subjects, n Conclusions by authors Main findingsa Critical exposure period Reference concentration level (µg/m3)
Koman et al. [5], systematic review January 2012–June 2018 11 Exposure period varied from the entire pregnancy to different trimesters, or using monthly and hourly exposures. HDP.GH, BP, PE U. S 1,3 million of pregnant women Results for the main adjusted effect of PM2.5 or PM10 exposure on HDP among pregnant women in the United States yielded generally positive but not always significant associations. Six analyses of PM exposure with various measures of HDP yielded positive significant associations and eight analyses reported positive associations but the lower 95 percent confidence interval includes 1.0. ↑→ HDP First No information
Yu et al. [31], systematic review: meta-analysis random effect model From inception to March 23, 2020. 9 Different periods. Entire pregnant or first and second PE 6 in USA, 2 in Spain and one in Sweden. 592,891 pregnant women Meta-analysis showed that maternal exposure to PM2.5 (per 10 μg/m3 increment) elevated the risk of preeclampsia (OR=1.32, 95 % CI 1.10–1.58 %). Compared with other pregnancy trimesters, the third trimester of pregnancy seems to be the period in which women are more susceptible to PM2.5. ↑↑PE Third trimester Mean pregnancy PM2.5 exposure ranged from 10.1 (Rudra et al. 2011) to 16.5 μg/m3 (Dadvand et al. 2013).

  1. a↑↑At least one model with a positive association where lower confidence bound does not include 1.0 OR or RR. ↑→Positive association where lower confidence bound includes 1.0 for OR or RR. ↓↓Negative association where lower confidence bound does not includes 1.0 for OR or RR. ↓→Negative association where lower confidence bound includes 1.0 for OR or RR. ↔Null association. LMP, last menstrual period; GH, hypertension; PE, preeclampsia; Mg-PE, preeclampsia requiring magnesium sulfate; BMI, body mass index; GWG, gestational weight gain; DBP, diastolic blood pressure; SBP, systolic blood pressure; MAP, mean arterial pressure; SOC, secondary organic carbon; N.A, not applicable.

Most of the observational studies (7 of 9) regarding to GH or SBP/DBP outcomes found a positive association with an increase in PM. The results were similar in the cause of PE, where only the study made by Choe et al. [29], did not have any association with particulate matter (Table 1). Similar results were described in systematic reviews and meta-analyses (Table 2); Koman et al. [5], concluded there is a positive but not significant association between PM and hypertensive disorder during pregnancy. With regard to PE, the meta-analysis done by Yu et al. [31], highlighted a positive significant association between PM and PE.

Furthermore, the period with the highest risk of PM explosion is not consistent among investigations. We observed that 5 of the 10 observational studies report the critical exposure period for PM pollutants in the second trimester. However, the findings in the systematic review made by of Koman et al. [5] pointed out the first period as the critical one; in contrast to Yu et al. [31] who reported the third trimester.

The population’s mean exposure concentration varied across studies, with a minimum mean concentration of 22.6 g/m3 and a maximum of 83.4 g/m3. PM10 had a maximum average of 135.3 g/m3 and a minimum of 56.3 g/m3.

In vivo experimental studies

As shown in Table 3, we identified four in vivo experimental studies. A significant percentage of animal studies, three out of four, indicate a positive and significative correlation between a rise in blood pressure or decreased blood flow and high PM exposure levels, except for the study made by Gao et al. [32], which found a positive but insignificant relationship.

Table 3:

In vivo experimental studies on the association between PM and hypertensive disorders in pregnant animals.

Reference Species, strain Treatment period Dose, route End point Conclusions by authors Main findingsa
Gao et al. [32] Female sprague Dawley (SD) rats weighing 200–250 g w During the whole pregnancy period 4 h per day and 7 days per week Filtered air (FA) with an average PM2.5 concentration of 7.29 μg/m3 and the other with an ambient air in the presence of 17–148 μg/m3 BP, PE, SBP The SBP of the expose group slightly increased from 111.20 ± 3.96 mmHg on gestation day (GD), GD8 to 126.60 ± 6.34 mmHg on GD17; however, there was no significant difference between the control group. These findings indicate that maternal PM2.5 exposure during pregnancy did not increase the blood pressure of either pregnant or preeclamptic rats. ↑→SBP
Jia et al. [33] Zebrafish Embryos/larvae 24 to 120 hpf (hours post-fertilization). 100, 200, and 400 μg/mL of TSP and PM2.5 Cardiovascular and neurodevelopmental toxicity, blood flow This study provides evidence for acute exposure to TSP and PM2.5-induced cardiovascular and neurodevelopmental toxicity, attributed to enhanced oxidative stress and aberrant gene expression. Comparatively, the effects of PM2.5 were more pronounced than TSP. The velocity of blood flow decreased significantly upon TSP exposure only with 200 μg/mL as compared with control group. Such a decreasing trend in the velocity of blood flow was also noticed with PM2.5 exposure at 50, 100 and 200 μg/mL concentrations as compared with control larvae. ↓↓Blood flow
Morales-Rubio et al. [34] Pregnant C57BL/6J pun/pun female mice and offspring Six times during the fetal development after implantation on gestation day (GD) 6.5, 8.5, 10.5, 12.5, 14.5 and 16.5 day of pregnancy. The total dose per animal was 12 μg or 400 μg, instilled through the trachea SBP, DBP.MAP In utero UFP exposure induced increases in the PAH-bio transforming enzymes, intrauterine oxidative damage and inflammation and stimulated programming and activation of receptor angiotensina II tipo 1 (AT1R) and angiotensin I-converting enzyme (ACE), which resulted in increased blood pressure in the PND 50 male offspring. Systolic and mean arterial pressure were statistically increased in the offspring male exposed in utero to UFP compared to both control groups. ↑↑SBP

↑↑DBP

↑↑MAP
Ye et al. [35] Pregnant Sprague–Dawley rats and offspring Gestation day 8, 10, and 12 PM2.5 suspension of 1.0 mg/kg in 25 μL. SBP, renal D1R expression, GRK4 expression Systolic blood pressures were significantly higher in the offspring of PM2.5-treated dams than the offspring of vehicle-treated dams at 10 weeks of age and remained significantly elevated until the end of the study (14 weeks of age). Also, 24 h urine volume and sodium excretion were both lower in the offspring of the PM2.5-treated dams than the offspring of the control dams. Our present study found that renal D1R expression was lower in the offspring of PM2.5-treated dams than the offspring of vehicle-treated dams Moreover, renal. GRK4 expression was higher in the offspring of PM2.5-treated dams than the offspring of vehicle-treated dams, indicating that GRK4 may be involved in the dysfunction of renal D1R in PM2.5-induced hypertension. ↑↑SBP

↓↓Renal D1R expression

↑↑GRK4

  1. a↑↑At least one model with a positive association where lower confidence bound does not include 1.0 OR or RR. ↑→Positive association where lower confidence bound includes 1.0 for OR or RR. ↓↓Negative association where lower confidence bound does not includes 1.0 for OR or RR. ↓→Negative association where lower confidence bound includes 1.0 for OR or RR. ↔Null association. TSP, total suspended particles; UFP, ultrafine particles; LMP, last menstrual period; GH, hypertension; PE, preeclampsia; Mg-PE, preeclampsia requiring magnesium sulfate; BMI, body mass index; GWG, gestational weight gain; DBP, diastolic blood pressure; SBP, systolic blood pressure; MAP, mean arterial pressure; SOC, secondary organic carbon; N.A, not applicable.

Gestational diabetes mellitus

We identified twelve investigations related to gestational diabetes mellitus. Eight studies were prospective cohorts and three retrospective cohorts. Also, we found a cross-sectional study. It is important to note that 58 % of the papers were developed with China’s population, 16 % of the USA, and the other 25 % correspond to countries like Iran, Australia and México (Table 4).

Table 4:

Observational studies on the association between PM and gestational diabetes mellitus.

Reference, study design Country, subjects (n), data collection period Exposure period(s) cited by authors Outcome Prevalence, % Conclusion by authors Main findings Critical exposure period Reference concentration level Adjusted for
Liu et al. [36], prospective cohort Guangzhou, China 20,113 pregnant women Four stages before pregnancy: Stage 1 (271–360 days before LMP), stage 2 (181–270 days before the LMP), stage 3 (91–270 days before the LMP), stage 4 (0–90 days before the LMP), and two stages during pregnancy: The first trimester (1–13 gestational weeks), and the second trimester (14–27 gestational weeks). GDM Of the 20,113 participants, 3,440 women (17.1 %) were diagnosed with GDM In the adjusted model, increased concentrations of O3 and PM10 were associated with GDM occurrence in the 1st trimester, the adjusted relative risk (95 % confident intervals) (RRs [95 % CI]) ranged from 1.05 (1.00, 1.09) to 1.21 (1.04, 1.40). The largest JRR for GDM was the combination of SO2, NO2, and PM10 in the 1st trimester (JRR=1.32, 95 % CI: 1.10, 1.59). ↑↑ GDM First PM10: First trimester: 57.73 μg/m3

Second trimester: 56.47 μg/m3

PM2.5. First trimester: 36.84 μg/m3

Second trimester: 35.33 μg/m3 		Maternal age, pre-pregnancy BMI, maternal education, family history of diabetes, parity, the season of LMP and temperature.
Rezaei et al. [37], cross-sectional Birjand, Iran 102 pregnant women Pregnant woman was carried out between 24 and 28 weeks of gestation. GDM Diabetic n=60 and healthy n=42 Concentrations of as (8.58 vs. 3.15 μg/L), Cd (6.74 vs. 0.52 μg/L), and Hg (2.60 vs. 0.90 μg/L) were significantly higher in women with GDM. Risk difference (RD) estimation showed that as, 0.516 (0.355, 0.677); Cd, 0.719 (0.534, 0.904); and Hg, 0.505 (0.276, 0.735) increase GDM probability, while V lower than risk, −0.139 (−0.237, −0.042). GDM subjects presented higher levels of As, Cd, and Hg, indicating that these elements may disturb insulin metabolism and promote the development of GDM. ↑↑GDM N.A N.A Age and previous GDM confounders
Yan et al. [24], prospective cohort China 3,754 pregnant women First, second and third trimester GDM The prevalence of GH, GDM, and PE were 2.6 %, 11.2 %, and 0.7 %, respectively Ambient PM2.5 and PM10 exposure in the first trimester were significantly associated with an increased risk of GDM. PM associated risk of GDM was more pronounced for PM2.5 than PM10, with the estimated ORs of 1.093 (95 % CI, 1.035–1.155) and 1.038 (1.001–1.076). We observed nonlinear effects of PM on the risk of GDM, with the most pronounced effects at moderate exposure levels. ↑↑GDM GDM PM2.5 Y PM10 first trimester Entire pregnancy PM2.5 47.4 (14.4) μg/m. PM10 89.7(26.4) μg/m Maternal age, maternal body mass index before pregnancy, pre-pregnancy hypertension, pre-pregnancy diabetes mellitus, weight gains (kg), newborn’s sex (male/female), residential region (mid-west, north, or southeast), and season of conception.
Zou et al. [38], retrospective cohort Beijing, China 24,001 pregnant women Preconception (90 days prior to las menstrual period (LMP)), first trimester (LMP to the 12th gestational weeks) and second trimester (the 13th to the 26th gestational weeks). GDM 3,168 (13.2 %) pregnant women were diagnosed with GDM, including 1,206 with isolated fasting hyperglycemia (GDM-IFH). IQR increase in maternal PM2.5 exposure during the first trimester (OR=1.56; 95 % CIs: 1.19, 2.04) and second trimester (OR=1.70; 95 % CIs: 1.37, 2.10) was significantly associated with increased GDM risk. An increased GDM-IFH risk with per interquartile range shows an increase in first trimester exposures to PM2.5 (OR=1.94; 95 % confidence Intervals: 1.23–3.07). ↑↑ GDM First trimester Preconception 81.8 (23.4) μg/m3. First trimester 85.9 (27.3) μg/m3. Second trimester 84.9 (25.1) μg/m3 Maternal age at pregnancy, maternal ethnicity, gravidity, parity, the season of conception, the year of conception and fetal sex (girl or boy), maternal weight at the 12th week of gestation and gestational and GWG from 12th to 26th gestational weeks.
Hu et al. [39], prospective cohort Guangzhou and Heshan, the Pearl River Delta region (PRD), China. 2,326 pregnant women First trimester (estimated date of conception to 13 weeks + 6 days gestation), second trimester (14th week to 27 weeks + 6 days gestation), the first two trimesters (estimated date of conception to 27 weeks + 6 days gestation). Fasting glucose 256 (11 %) pregnant women had GDM Fasting glucose levels were significantly associated with PM10 and BC exposure during the second trimester and the first two trimesters. Corresponding estimates for PM10 and BC exposure (per IQR increase) during the second trimester were slightly stronger [for PM10: 1.86 mg/dL (95 % CI: 1.19, 2.53). A statistically significant association between elevated fasting glucose levels and PM2.5 exposure was only observed in the second trimester [2.12 mg/dL (95 % CI: 1.22, 3.02) increase in fasting glucose per IQR increase in PM2.5]. ↑↑Fasting glucose Second semester PM10: First trimester 51 μg/m3, second trimester 53 μg/m3 and first two 52 μg/m3. PM2.5: First trimester 37 μg/m3, second trimester 39 μg/m3 and first two 38 μg/m3. Maternal age, pre-pregnancy BMI, income levels, city, natural cubic splines of temperature and relative humidity during the same exposure periods as the air pollutant.
Wang et al. [40], prospective cohort Massachusetts. USA 103 pregnant women Four exposure windows: Moving averages of same-day, one-week, first-trimester, and second-trimester PM gross β-activity. Glucose concentration Eight participants (7.7 %) had a glucose level above 140 mg/dL, one standard GCT clinical cut-off for additional GDM screening, and one participant (1 %) was later diagnosed with GDM. An interquartile range increase in average PM gross β-activity during the second trimester of pregnancy was associated with an increase of 17.5 (95 % CI: 0.8, 34.3) mg/dL in glucose concentration. No associations of glucose were observed with PM gross β-activity during same-day and first-trimester exposure windows. PM2.5 was not associated with glucose levels during any exposure window in our data. ↑→Glucose concentration Second trimester PM gross β-activity: 2.0 (0.3, 1.5–2.8) × 10− 4 Bq/m. PM2.5 o 6.9 (1.6, 3.6–10.1) μg/m3. Pre-pregnancy BMI, age at enrollment, race/ethnicity, educational attainment and insurance status (private vs. other).
Lin et al. [41], prospective cohort Foshan, China 12,842 pregnant women First trimester, second trimester and first two trimesters (both). OGTT-fasting glucose, GDM 3,055 (23.8 %) pregnant women had GDM. A 10 μg/m3 increase in PM2.5, PM10 and SO2 during trimester 1, trimester 2 and two trimesters were associated with 0.07 mmol/L to 0.29 mmol/L increment in Oral Glucose tolerance test (OGTT)-fasting glucose levels in single-pollutant model. Moreover, exposure to PM2.5, PM10 and SO2 were associated with increased risk of GDM in both single- and two-pollutant models. In single-pollutant models, increased odds of GDM for a 10 μg/m3 increase in PM2.5 were observed during the first two trimesters (OR 1=1.17 [95 % CI: 1.06, 1.29)] for trimester 1, OR=1.37 (95 % CI: 1.20, 1.56) for trimester 2 and OR=1.43 ([95 % CI: 1.22, 1.68] for two trimesters). ↑↑GDM

↑↑OGTT-fasting glucose		 First and second The concentrations of PM2.5 ranged from 3.32 μg/m3 to 223.57 μg/m3, and the interquartile range was 34.39 μg/m3 in the six cities. Maternal age, pre-pregnancy BMI, education level, marital status, occupation status, conception season, hospital, temperature and humidity, diet, physical activity and gestational weight gaining.
Melody et al. [42], retrospective cohort Latrobe valley in regional Australia. 3,612 pregnant women Pregnant days under the fire event GDM, HDP Approximately six percent of women had a diagnosis of gestational diabetes mellitus (n=224 (6.2 %). An interquartile range increase in peak PM2.5 was associated with a 16 % increased likelihood of gestational diabetes mellitus (95 % CI 1.09, 1.22; <0.0001). Whereas, an interquartile range increase in average PM2.5 was associated with a 7 % increased likelihood of gestational diabetes mellitus (95 % CI 1.03, 1.10; <0.0001). Second trimester exposure was of critical importance. ↑↑GDM

↔HDP		 Second trimester Average maternal PM2.5 exposures was 4.4 (7.7) μg/m3. Average peak PM2.5 exposure was 44.9 (57.1) μg/m3 Parity; smoking in early pregnancy and smoking in late pregnancy, meteorological characteristics, season of conception (winter, spring, summer and autumn) and year of conception.
Yao et al. [43], prospective cohort Hebei, China 5,427 pregnant women First trimester (1–13 weeks) and 12-week period prior to pregnancy. We also calculated the different time windows of exposure to air pollution (“moving averages”) from 1 to 12 months before the LMP and from 1 to 3 months after the LMP. OGTT-fasting glucose, GDM Of the 5,427 participants, 1,119 (20.6 %) had GDM The single pollutant model shows a significant risk between GDM and PM2.5 (OR=IC 95 % 1.24 [1.06–1.45]) and 1.42 (1.26–1.59) for PM10. The time windows of the maximum effect of PM2.5, PM10, and SO2 were 6 months, 5 months, and 1 month before the last menstrual period (LMP) and 3 months after the LMP, respectively.PM10 exposure during prepregnancy had significant effect on OGTT fasting, 1-h, and 2-h blood glucose levels both in single- and two-pollutant models. The time window of the maximum effect of PM10 on fasting OGTT glucose levels was similar to that of PM2.5. ↑↑GDM

↑↑OGTT-fasting glucose		 PM2.5 6 months before LMP. PM10 5 months before LMP Preconception PM2.5 61.54 (18.42)

PM10 92.24 (17.08)

First trimester

PM2.5 60.38 (18.33)

PM10 90.59 (16.44).		 Maternal age, maternal education, husband’s monthly income, prepreg Nancy BMI, parity, fruit intake frequency, physical activity frequency, and family history of diabetes.
Ye et al. [44], prospective cohort Wuhan, China 3,967 pregnant women First trimester (gestational weeks 0–13), second trimester (gestational weeks 14–26), and the first 2 trimesters (gestational weeks 0–26). Fasting glucose, GDM A total of 332 (8.4 %) pregnant women were diagnosed with GDM An interquartile-range increase in PM2.5 exposures (33.84 μg/m3 for trimester 1 and 33.23 μg/m3 for trimester 2) was associated with 36 % (95 % confidence interval (CI): 1.15, 1.61) and 23 % (95 % CI: 1.01, 1.50) increased odds of GDM during trimester 1 and trimester 2, respectively. In the adjusted models, for each IQR increment in PM2.5 exposures during trimester 1, 1-h and 2-h blood glucose levels increased by 1.40 % (95 % CI: 0.42, 2.37) and 1.82 % (95 % CI: 0.98, 2.66), respectively. An IQR increase in PM2.5 exposure during trimester 2 increased fasting blood glucose level by 0.85 % (95 % CI: 0.41, 1.29). ↑↑GDM

↑↑ OGTT-fasting glucose		 GDM not adjusted: Second trimester. Adjusted: First trimester. 2 h blood glucose=first trimester. First trimester: 85.19 (27.52) μg/m3. Second trimester: 76.01 (24.00) μg/m3. First 2 trimesters: 80.60 (20.48) μg/m3. Age, education, income, smoking status, alcohol drinking status, exercise during pregnancy, ethnicity, parity, preconception BMI, family history of diabetes mellitus (all types) and season of conception.
Choe et al. [45], retrospective cohort New York city, USA. 256,372 pregnant women First trimester (weeks 1–12) and second trimester (weeks 13–26) GDM There were 17,065 (6.7 %) women identified as having GDM. In models mutually adjusting for PM2.5 levels in both trimesters, GDM was associated with PM2.5 levels in the 2nd trimester (OR: 1.06, 95 % CI: 1.02, 1.10 per interquartile range increase in PM2.5), but not the 1st trimester (OR: 0.99, 95 % CI: 0.96, 1.02). In fully adjusted model’s 2nd trimester PM2.5 was associated with higher odds of GDM, with an OR of 1.10 (95 % confidence intervals (CI): 1.03, 1.18) comparing the highest vs. lowest quartiles. The association between 1st trimester PM2.5 and GDM was close to null. ↑↑ GDM Second trimester First trimester: 12.0 (2.5) μg/m3 a. second trimester 11.9 (2.4) μg/m3. BMI, maternal age, ethnicity, parity, and season of conception.
Moody et al. [46], prospective cohort Mexico city, Mexico. 365 mother-child pairs 365 mother-child pairs From 4 weeks prior to mothers’ date of last menstrual period (LMP), to 12 weeks after the delivery date and followed up until the child was approximately 7 years of age HbA1c levels N. A The mean prenatal PM2.5 exposures (22.4 μg/m3 [2.7 μg/m3]) was associated with an annual increase in hemoglobin A1C test (HbA1c) levels of 0.25 % (95 % CI, 0.004–0.50 %) from age 4–5 years, to 6–7 years compared with exposure at 12 μg/m3, the national regulatory standard in Mexico. Sex-specific effect estimates were statistically significant for girls (β=0.21 %; 95 % CI, 0.10–0.32 %) but not for boys (β=0.31 %; 95 % CI, −0.09–0.72 %). There was no significant association between PM2.5 exposure and HbA1c level at age 6–7 years in any group. ↑↑HbA1c levels Week 28–50.6 after the mother’s LMP for the overall cohort and from week 11 to the end of the study period for girls 23.0 3 (2.7) μg/m Child sex, exact child age at visit 1, maternal pre pregnancy BMI, maternal age at delivery, maternal educational level, SES category, and season of LMP.

  1. a↑↑At least one model iastolic Blood Press with a positive association where lower confidence bound does not include 1.0 OR RR. ↑→Positive association where lower confidence bound includes 1.0 for OR or RR. ↓↓Negative association where lower confidence bound does not includes 1.0 for OR or RR. ↓→Negative association where lower confidence bound includes 1.0 for OR or RR. ↔Null association. LMP, last menstrual period; GH, hypertension; PE, preeclampsia; Mg-PE, preeclampsia requiring magnesium sulfate; BMI, body mass index; GWG, gestational weight gain; DBP, diastolic blood pressure; SBP, systolic blood pressure; MAP, mean arterial pressure; SOC, secondary organic carbon); N.A, not applicable.

Only one study, published by Wang et al. [40], did not find a significant association between PM and GDM or increase in glucose concentration. Concerning critical exposure periods, we observed that 10 out of 12 observational studies report the critical exposure periods in the first or second trimester for the contaminants (PM2.5 or PM10).

Baseline concentrations were heterogeneous across the studies; for PM2.5, the minimum mean concentration was 3.3 μg/m3 and the maximum 85.9 μg/m3. On the other hand, for PM10 the minimum mean concentration was 21.8 μg/m3 and the maximum 92.2 μg/m3.

Risk of bias

Tables 5 and 6 reported the results of the risk of bias assessment for hypertension disorders for pregnancy and gestational diabetes mellitus studies, respectively. Regarding the studies which investigated the hypertension disorders for pregnancy, 4 out of 10 were classified as serious risk of bias for confounding, while the other as moderate. The studies with a serious risk for confounding did not include in the analysis important risk factors, like smoke, diet and occupational exposure. Similarly, for gestational diabetes mellitus, 8 out of 12 studies did not consider these important confounders. Regarding selection, measurement of exposure and departures from exposure biases, we observed low and moderate risks, except for one study which was classified as serious due to the use of average concentrations of pollutants in the city as indicator of pregnant women exposure [30]. Overall, 4 out of 10 and 8 out of 12 studies were considered as serious risk of bias at the study-level judgement for hypertension disorders for pregnancy and gestational diabetes mellitus studies, respectively. On the other hand, at the item-level judgment, confounding and measurement of exposure highlighted a serious risk for hypertension disorders for pregnancy, while only confounding was considered as serious for gestational diabetes mellitus studies.

Table 5:

Risk of bias matrix for hypertension disorders for pregnancy.

First author, year Confounding Selection Measurement of exposure Departures from exposure Missing data Measurement of outcomes Reported results Study-level judgment
Mozzoni et al. [22] M M M M L L L M
Yan et al. [24] S M M M L L L S
Ye et al. [25] M L L L L L L M
Kaali et al. [23] M M L L L M L M
Luo et al. [26] M M L L L L L M
Jia et al. [27] S L M M L L L S
Rosa et al. [28] M L M M L L L M
Yang et al. [10] M L M M L L L M
Choe et al. [29] S M M M L L L S
Wang et al. [30] S M S M L L L S
Item level judgment S M S M L M L
Low Moderate Serious Critical
Table 6:

Risk of bias matrix for gestational diabetes mellitus.

First author, year Confounding Selection Measurement of exposure Departures from exposure Missing data Measurement of outcomes Reported results Study-level judgment
Liu et al. [36] M L M M L L L M
Rezaei et al. [37] S M L L L L M S
Yan et al. [24] S M M M L L L S
Zou et al. [38] S L M M L L L S
Hu et al. [39] M M M M L L L M
Wang et al. [40] S M M M L L L S
Lin et al. [41] S L M M L L L S
Melody et al. [42] S L M M L L L S
Yao et al. [43] M L M M L L L M
Ye et al. [44] S L M M L L L S
Choe et al. [45] S L M M L L L S
Moody et al. [46] M M M M L L L M
Item level judgment S M M M L L L
Low Moderate Serious Critical

Discussion

There is a large body of evidence showing that PM exposure is associated with health risks, including the development of cardiovascular and respiratory diseases. Smaller particles are substantially more toxic than larger particles, and it is known that particles less than 2.5 μm in diameter (PM2.5) pose the greatest risk to health [47]. Although numerous studies show an association between PM exposure and adverse pregnancy outcomes, it is still not clear what are the impacts of on mothers, the fetus and newborns.

Despite evidence on PM exposure and adverse pregnancy outcomes PM exposure and adverse pregnancy outcomes is not conclusive, it is known that pregnant women and newborns are more vulnerable to the adverse effects of ambient air pollution. One reason could be found in the respiratory minute ventilation during gestation (the volume of air inhaled/exhaled over 1 min), which increases by 40 percent via a growth in tidal volume (size of each breath). The oxygen consumption increases by 20 percent to accommodate the metabolic demands of pregnancy and oxygen transfer across the placenta [48, 49]. This race in oxygen consumption is maintained by a 50 percent increase in cardiac output, which is accompanied by increased maternal blood volume, heart rate, and pulmonary circulation and it is necessary to maintain sufficient blood flow to the uterus, lungs, kidneys, and skin [5].

Hence, compared to other adults, pregnant women have greater inhalation uptake on a body weight basis and corresponding blood concentration of inhaled pullulation, as volatile organic compounds [50], elemental carbon, dust, and other PM constituents [51].

The findings of this systematic review suggest a potential association between PM exposure and adverse pregnancy outcomes as HDP, including gestational hypertension and pre-eclampsia, and gestational diabetes mellitus.

The results related to HDP were consistent with those presented in the systematic review and meta-analysis published by Koman et al. [5] and Yu et al. [31], respectively. Only Luo et al. [26], out of 10 articles, did not found positive correlation between the increase in PM concentrations and HDP. However, not all of them presented significant differences. The same situation occurred for all the research conducted on PM and GDM; there was an association, but it was not always significant.

Regarding the critical exposure periods, there was no consensus in the research, neither by HDP nor GDM. For example, Liu et al. [36], Yan et al. [24], and Zou et al. [38], pointed to the firsts semesters as critical exposure period of risk for developing GDM. This view argued that this period has the potential to affect normal implantation and placental development through linked inflammation and oxidative stress [52]. In the case of HDP, Yan et al. [24], and Jia et al. [27], found an association between HDP and the third trimester. Yu et al. [31], explained this could be due to the decrease in total airway resistance caused by the relaxation of the tracheobronchial tree allowing PM2.5 to enter the lungs more easily. In addition, in this period the increased progesterone level caused bronchiectasis and congestion of the lung mucosa, which made easier the inhalation of PM2.5 into the lungs and the exchange with the blood with the passage in the systemic circulation. The lack of agreement on critical exposure periods could be due to the different periods included in the studies, as well as the missing critical peaks or shorter exposure periods when averaging air quality concentrations.

The inconsistency among studies may be due to the difference between baseline concentrations to which study subjects were exposed. For example, Jia et al. [27], reports 135.7 μg/m3 as the average concentration for PM10 during the second trimester of pregnancy, while Choe et al. [29], indicates 57.8 μg/m3, a lower concentration of 57 percent. In addition, it is important to mention that most of the studies reflect poor air quality, which is far from the values recommended by WHO [12].

Difference in exposure–response could be attributed to the heterogeneous regions that we found, because of other risk factors that vary due to geographical differences, such as climate, race, and social economy. Therefore, differences in selected covariates might also be one of the reasons for heterogeneity between studies [1].

In addition, studies which used models to assess exposures showed a more significant association between exposure to PM and the risk of HDP and PE [53]. It suggests that exposure assessment methods may be the cause of differences in results among studies. Madsen et al. [53], also pointed out that the use of models to assess exposures provided high spatial resolution but had limited ability to characterize time variability. Otherwise, monitoring stations had a direct classification of exposure, but they could include subjects located far away from the monitoring station, which introduces a risk of bias. In the same way, residential exposure to air pollution was not always accompanied by data on traveling or occupational exposures. Therefore, it is necessary to conduct research that considers these possible alternatives and include in the analysis the main risk factors that can affect the results of the studies, like diet, smoke and occupational exposures.

Concerning in vivo experimental studies, they agreed with the observational studies, indicating that animal models with carefully controlled cofounding conditions can be useful for examining the effects of early life pollution exposure, as well as determining mechanisms involved in outcomes such as GDM and GDP, filling a gap in prospective cohort studies [15–17, 54].

However, toxicological studies, on the other side, are not always good predictors of the impact of an exposure in humans, especially when other concurrent exposures (e.g., lifestyle factors, diet) or variability in toxicokinetic or the microbiome are present [14, 54], additionally, several factors influence the contribution of the evidence, including the impact of data strength or weakness, and data uncertainties [14].

Pathways and mechanisms underlying effects of particulate matter

Biological pathways and mechanisms underlying the effects of air pollution on pregnant and child health are thought to include endocrine disruption, oxidative stress, inflammatory response, and DNA damage [55]. The specific effects of particulate matter principally depend on the composition and the source. For example, Rezaei et al. [37], showed that PM2.5 components, as Cd and Hg, may disturb insulin metabolism and promote the development of GDM. Mendola et al. [51], found an association between PM constitutions as organic compounds, dust particles as well as elemental carbon with preeclampsia. They also point out that acute exposure to several volatile organic compounds and some polycyclic aromatic hydrocarbons elevates blood pressure among women with new onset hypertensive disorders of pregnancy.

The littlest particulates, like PM2.5, can easily enter in the respiratory tract and disperse into blood circulation, then they reach the placenta and accumulate in the trophoblast cells, causing damage to trophoblast mitochondria and as consequence the release of inflammatory factors and other biological mediators [31]. Ritz et al. [6] describe the mechanism in which PM induces fatty acid metabolism and biosynthesis pathways, in the carnitine shuttle, glycerophospholipid metabolism, and disturbances in eicosanoids including prostaglandins, and methionine and cysteine metabolism. They suggest that air pollutants disrupt inflammatory signaling linked to the antioxidant-oxidant balance. Besides, PM can also cause dysfunction of mitochondria and activate inflammatory cells that produce reactive oxygen species and/or reactive nitrogen species.

Oxidative stress responses in pregnancy can damage the placenta and its function and can contribute to adverse birth outcomes such as spontaneous abortion, preeclampsia, intrauterine growth restriction, low birthweight, preterm delivery, and gestational hypertension [56].

The placental disruption causes a decrease in blood flow, and an imbalance between angiogenic placental growth factors and antiangiogenic proteins, such as vascular endothelial growth factor, placental growth factor, soluble fms-like tyrosine kinase-1, and soluble endoglin leading to develop preeclampsia and the damage of systemic vascular endothelial function [31].

Moreover, an altered placental function might contribute to the development of GDM. According to Kang et al. [9], exposure to PM2.5 may activate the innate immune system, cause lung inflammation and systemic inflammation to include adipose tissue, and thus lead to obesity-related insulin resistance and type 2 diabetes. Besides, the reactive oxygen species triggers oxidative stress in brown adipose tissue, and this might lead to metabolic dysfunction including reduced insulin sensitivity, endothelial dysfunction, and then to reduce intake of peripheral glucose [41].

Limitations of this review

One limitation of this review was the high level of study heterogeneity which did not permit to perform a meta-analysis and obtain a systematically synthesize of the findings of single studies obtaining an overall effect. The main differences were related to study design, exposure and outcome assessment, and statistical analysis. For this reason, we only provided a narrative summary of the evidence of effect modification based on the identified studies.

Moreover, we carried out our search focusing on two databases which include most of the studies that investigated the relationship between exposure to environmental pollutants and human health. containing a significant amount of research on pollution’s effects on human health. The exclusion of other databases may have led to missing some important studies and has to be considered as a potential limitation of this review.

Conclusions

This review tries to close the knowledge gap on the association between PM exposure and the development of hypertensive disorders of pregnancy and gestational diabetes mellitus. We considered human and non-human data, including animal experimental investigations. Most of the studies found a positive association confirming PM exposure is an important environmental risk factor for adverse pregnancy outcomes. Further research should focus on the identification of the critical exposure period. These results have implications and should guide policy actions on the protection of pregnant women in order to prevent adverse pregnancy outcomes and promote the health of offspring.

Corresponding author: Gabriele Donzelli, PhD, Department of Health Sciences, University of Florence, 50134 Florence, Italy, E-mail:

  1. Research funding: None declared.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: Authors state no competing of interest.

  4. Informed consent: Not applicable.

  5. Ethical approval: Not applicable.

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Received: 2022-12-19
Accepted: 2023-04-17
Published Online: 2023-05-04
Published in Print: 2024-12-17

© 2023 the author(s), published by De Gruyter, Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 International License.

Artikel in diesem Heft

  1. Frontmatter
  2. Reviews
  3. A systematic review on the association between exposure to air particulate matter during pregnancy and the development of hypertensive disorders of pregnancy and gestational diabetes mellitus
  4. Screen time and childhood attention deficit hyperactivity disorder: a meta-analysis
  5. The association between polycystic ovary syndrome and environmental pollutants based on animal and human study; a systematic review
  6. Residues of carcinogenic pesticides in food: a systematic review
  7. The concentration of Lithium in water resources: A systematic review, meta-analysis and health risk assessment
  8. Polychlorinated biphenyls and thyroid function: a scoping review
  9. The European Union assessments of radiofrequency radiation health risks – another hard nut to crack (Review)
  10. Research progresses on the effects of heavy metals on the circadian clock system
  11. Diagnosing and managing heat exhaustion: insights from a systematic review of cases in the desert climate of Mecca
  12. Para-occupational exposure to chemical substances: a systematic review
  13. Association of the ACE2-Angiotensin1-7–Mas axis with lung damage caused by cigarette smoke exposure: a systematic review
  14. Impacts and mechanisms of PM2.5 on bone
  15. Impacts and potential mechanisms of fine particulate matter (PM2.5) on male testosterone biosynthesis disruption
  16. Exposure to perfluoroalkyl and polyfluoroalkyl substances and risk of stroke in adults: a meta-analysis
  17. Prevalence and concentration of aflatoxin M1 and ochratoxin A in cheese: a global systematic review and meta-analysis and probabilistic risk assessment
  18. The effect of polycyclic aromatic hydrocarbon biomarkers on cardiovascular diseases
  19. Biological effects of electromagnetic fields on insects: a systematic review and meta-analysis
  20. Letter to the Editor
  21. Monkeypox and drug repurposing: seven potential antivirals to combat the viral disease
https://doi.org/10.1515/reveh-2022-0258
Schlagwörter für diesen Artikel
epidemiological studies; gestational diabetes mellitus; hypertension disorders; in vivo studies; particulate matter; pregnancy
Creative Commons
BY 4.0

Artikel in diesem Heft

  1. Frontmatter
  2. Reviews
  3. A systematic review on the association between exposure to air particulate matter during pregnancy and the development of hypertensive disorders of pregnancy and gestational diabetes mellitus
  4. Screen time and childhood attention deficit hyperactivity disorder: a meta-analysis
  5. The association between polycystic ovary syndrome and environmental pollutants based on animal and human study; a systematic review
  6. Residues of carcinogenic pesticides in food: a systematic review
  7. The concentration of Lithium in water resources: A systematic review, meta-analysis and health risk assessment
  8. Polychlorinated biphenyls and thyroid function: a scoping review
  9. The European Union assessments of radiofrequency radiation health risks – another hard nut to crack (Review)
  10. Research progresses on the effects of heavy metals on the circadian clock system
  11. Diagnosing and managing heat exhaustion: insights from a systematic review of cases in the desert climate of Mecca
  12. Para-occupational exposure to chemical substances: a systematic review
  13. Association of the ACE2-Angiotensin1-7–Mas axis with lung damage caused by cigarette smoke exposure: a systematic review
  14. Impacts and mechanisms of PM2.5 on bone
  15. Impacts and potential mechanisms of fine particulate matter (PM2.5) on male testosterone biosynthesis disruption
  16. Exposure to perfluoroalkyl and polyfluoroalkyl substances and risk of stroke in adults: a meta-analysis
  17. Prevalence and concentration of aflatoxin M1 and ochratoxin A in cheese: a global systematic review and meta-analysis and probabilistic risk assessment
  18. The effect of polycyclic aromatic hydrocarbon biomarkers on cardiovascular diseases
  19. Biological effects of electromagnetic fields on insects: a systematic review and meta-analysis
  20. Letter to the Editor
  21. Monkeypox and drug repurposing: seven potential antivirals to combat the viral disease
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