Maternal air pollution exposure increases the risk of preterm birth: Evidence from the meta-analysis of cohort studies

Ju, Liangliang; Li, Changlian; Yang, Mei; Sun, Shuai; Zhang, Qi; Cao, Jiyu; Ding, Rui

doi:10.1016/j.envres.2021.111654

Cited by 38 publications

(20 citation statements)

References 86 publications

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“…Environmental changes according to the month such as air pollution, ambient temperature, the synthesis of vitamin D from sunlight could affect the adverse birth outcomes. Recently, existing literature reports the positive association of adverse birth outcomes with maternal exposure to PM during specific trimester or entire pregnancy 21 , 22 . A meta-analysis study reported that PM 10 was found to increase extremely preterm birth 21 , and in particular, the incidence of preterm birth increased in Asia with high concentration of air pollution 22 .…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based risk factor analysis of adverse birth outcomes in very low birth weight infants

Cho

Lee

et al. 2022

Sci Rep

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This study aimed to analyze major predictors of adverse birth outcomes in very low birth weight (VLBW) infants including particulate matter concentration (PM10), using machine learning and the national prospective cohort. Data consisted of 10,423 VLBW infants from the Korean Neonatal Network database during January 2013–December 2017. Five adverse birth outcomes were considered as the dependent variables, i.e., gestational age less than 28 weeks, gestational age less than 26 weeks, birth weight less than 1000 g, birth weight less than 750 g and small-for-gestational age. Thirty-three predictors were included and the artificial neural network, the decision tree, the logistic regression, the Naïve Bayes, the random forest and the support vector machine were used for predicting the dependent variables. Among the six prediction models, the random forest had the best performance (accuracy 0.79, area under the receiver-operating-characteristic curve 0.72). According to the random forest variable importance, major predictors of adverse birth outcomes were maternal age (0.2131), birth-month (0.0767), PM10 month (0.0656), sex (0.0428), number of fetuses (0.0424), primipara (0.0395), maternal education (0.0352), pregnancy-induced hypertension (0.0347), chorioamnionitis (0.0336) and antenatal steroid (0.0318). In conclusion, adverse birth outcomes had strong associations with PM10 month as well as maternal and fetal factors.

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Section: Discussionmentioning

confidence: 99%

Machine learning-based risk factor analysis of adverse birth outcomes in very low birth weight infants

Cho

Lee

et al. 2022

Sci Rep

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“…The studies included were cohort studies on the association between preterm birth and PM 2.5 that were selected in a recent systematic review and meta-analysis (SRMA) [ 8 ]. This SRMA was selected because it was the most recent (included peer-reviewed English-language articles published to October 2020) and employed restrictions to promote quality and minimise heterogeneity in design.…”

Section: Methodsmentioning

confidence: 99%

“…There were 31 primary studies, all of which were cohort studies with Newcastle–Ottawa quality Scale scores ≥ 7. The need for the SRMA was conducted based on the premise that “preterm birth has been shown to be associated with prenatal air pollution exposure, but the results are still inconsistent” [ 8 ]. The authors of the SRMA reported that the pooled relative risk (RR) of preterm birth was 1.07 (95% CI, 1.05, 1.10) per 10 μg/m 3 increase in whole-pregnancy exposure to PM 2.5 .…”

Section: Methodsmentioning

confidence: 99%

“…Study-level characteristics of the total sample size, outcome prevalence, exposure distribution (mean, standard deviation, median, 25th centile, 75th centile and interquartile range) and year of publication/online release were extracted. Data were extracted by the author independently of the SRMA [ 8 ], then extracted in duplicate from the SRMA for validation. Minor differences were encountered.…”

Section: Methodsmentioning

confidence: 99%

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A Simple Method to Establish Sufficiency and Stability in Meta-Analyses: With Application to Fine Particulate Matter Air Pollution and Preterm Birth

Pereira

2022

IJERPH

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Fine particulate matter air pollution (PM2.5) is a potential cause of preterm birth. Inconsistent findings from observational studies have motivated researchers to conduct more studies, but some degree of study heterogeneity is inevitable. The consequence of this feedback is a burgeoning research effort that results in marginal gains. The aim of this study was to develop and apply a method to establish the sufficiency and stability of estimates of associations as they have been published over time. Cohort studies identified in a recent systematic review and meta-analysis on the association between preterm birth and whole-pregnancy exposure to PM2.5 were selected. The estimates of the cohort studies were pooled with cumulative meta-analysis, whereby a new meta-analysis was run for each new study published over time. The relative risks (RR) and 95% confidence interval (CI) limits needed for a new study to move the cumulative RR to 1.00 were calculated. Findings indicate that the cumulative relative risks (cRR) for PM2.5 (cRR 1.07, 95% CI 1.03, 1.12) converged in 2015 (RR 1.07, 95% CI 1.01, 1.14). To change conclusions to a null association, a new study would need to observe a protective RR of 0.93 (95% CI limit 1.02) with precision equivalent to that achieved by all past 24 cohort studies combined. Preterm birth is associated with elevated PM2.5, and it is highly unlikely that any new observational study will alter this conclusion. Consequently, establishing whether an observational association exists is now less relevant an objective for future studies than characterising risk (magnitude, impact, pathways, populations and potential bias) and interventions. Sufficiency and stability can be effectively applied in meta-analyses and have the potential to reduce research waste.

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“…Risk factors for preterm birth include advanced maternal age 29 , greater maternal body mass index 30 , 31 , substance use during pregnancy (tobacco or alcohol use) 32 , 33 , exposure to violence (physical or emotional) 34 – 36 , and psychosocial stress 37 , 38 . In addition, clinical and obstetrical characteristics, such as a history of previous preterm birth 39 – 41 , gestational diabetes and chronic hypertension 42 , short inter-pregnancy interval 43 , 44 , infection and inflammation 45 – 50 , genetic factors 51 – 54 , and environmental pollutants 55 – 59 , have also been linked to an increased risk of preterm birth. Race/ethnicity-related disparities in preterm birth rates were also reported 60 – 62 .…”

Section: Introductionmentioning

confidence: 99%

The amniotic fluid proteome predicts imminent preterm delivery in asymptomatic women with a short cervix

Gudicha

Romero

Gomez‐Lopez

et al. 2022

Sci Rep

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Preterm birth, the leading cause of perinatal morbidity and mortality, is associated with increased risk of short- and long-term adverse outcomes. For women identified as at risk for preterm birth attributable to a sonographic short cervix, the determination of imminent delivery is crucial for patient management. The current study aimed to identify amniotic fluid (AF) proteins that could predict imminent delivery in asymptomatic patients with a short cervix. This retrospective cohort study included women enrolled between May 2002 and September 2015 who were diagnosed with a sonographic short cervix (< 25 mm) at 16–32 weeks of gestation. Amniocenteses were performed to exclude intra-amniotic infection; none of the women included had clinical signs of infection or labor at the time of amniocentesis. An aptamer-based multiplex platform was used to profile 1310 AF proteins, and the differential protein abundance between women who delivered within two weeks from amniocentesis, and those who did not, was determined. The analysis included adjustment for quantitative cervical length and control of the false-positive rate at 10%. The area under the receiver operating characteristic curve was calculated to determine whether protein abundance in combination with cervical length improved the prediction of imminent preterm delivery as compared to cervical length alone. Of the 1,310 proteins profiled in AF, 17 were differentially abundant in women destined to deliver within two weeks of amniocentesis independently of the cervical length (adjusted p-value < 0.10). The decreased abundance of SNAP25 and the increased abundance of GPI, PTPN11, OLR1, ENO1, GAPDH, CHI3L1, RETN, CSF3, LCN2, CXCL1, CXCL8, PGLYRP1, LDHB, IL6, MMP8, and PRTN3 were associated with an increased risk of imminent delivery (odds ratio > 1.5 for each). The sensitivity at a 10% false-positive rate for the prediction of imminent delivery by a quantitative cervical length alone was 38%, yet it increased to 79% when combined with the abundance of four AF proteins (CXCL8, SNAP25, PTPN11, and MMP8). Neutrophil-mediated immunity, neutrophil activation, granulocyte activation, myeloid leukocyte activation, and myeloid leukocyte-mediated immunity were biological processes impacted by protein dysregulation in women destined to deliver within two weeks of diagnosis. The combination of AF protein abundance and quantitative cervical length improves prediction of the timing of delivery compared to cervical length alone, among women with a sonographic short cervix.

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Maternal air pollution exposure increases the risk of preterm birth: Evidence from the meta-analysis of cohort studies

Cited by 38 publications

References 86 publications

Machine learning-based risk factor analysis of adverse birth outcomes in very low birth weight infants

Machine learning-based risk factor analysis of adverse birth outcomes in very low birth weight infants

A Simple Method to Establish Sufficiency and Stability in Meta-Analyses: With Application to Fine Particulate Matter Air Pollution and Preterm Birth

The amniotic fluid proteome predicts imminent preterm delivery in asymptomatic women with a short cervix

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