Background: Exposure to air pollution is associated with increased blood pressure (BP) in adults and children. Some evidence suggests that air pollution exposure during the prenatal period may contribute to adverse cardiorenal health later in life. Here we apply a distributed lag model (DLM) approach to identify critical windows that may underlie the association between prenatal particulate matter ≤ 2.5 μm in diameter (PM2.5) exposure and children's BP at ages 4–6 years. Methods: Participants included 537 mother-child dyads enrolled in the Programming Research in Obesity, GRowth Environment, and Social Stress (PROGRESS) longitudinal birth cohort study based in Mexico City. Prenatal daily PM2.5 exposure was estimated using a validated satellite-based spatio-temporal model and BP was measured using the automated Spacelabs system with a sized cuff. We used distributed lag models (DLMs) to examine associations between daily PM2.5 exposure and systolic and diastolic BP (SBP and DBP), adjusting for 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. Results: We found that PM2.5 exposure between weeks 11–32 of gestation (days 80–226) was significantly associated with children's increased SBP. Similarly, PM2.5 exposure between weeks 9–25 of gestation (days 63–176) was significantly associated with increased DBP. To place this into context, a constant 10 μg/m3 increase in PM2.5 sustained throughout this critical window 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. In a stratified analysis by sex, this association persisted in boys but not in girls. Conclusions: Second and third trimester PM2.5 exposure may increase children's BP in early life. Further work investigating PM2.5 exposure with BP trajectories later in childhood will be important to understanding cardiorenal trajectories that may predict adult disease. Our results underscore the importance of reducing air pollution exposure among susceptible populations, including pregnant women.
- Bayesian distributed lag interaction models
- Blood pressure
- Distributive lag models
- Particulate matter
- Prenatal exposure