Associations between the chemical composition of PM2.5 and gestational diabetes mellitus. Academic Article uri icon

Overview

abstract

  • BACKGROUND: Fine particulate matter (PM2.5) is a complex mixture of fine particulates with large spatiotemporal heterogeneities in chemical compositions. While PM2.5 has been associated with gestational diabetes mellitus (GDM), little is known about the relationship between specific chemical components of PM2.5 and GDM. We examined the associations between GDM and pregnancy exposures to PM2.5 and its compositions, including sulfate (SO42-), ammonium (NH4+), nitrate (NO3-), organic matter (OM), black carbon (BC), mineral dust (DUST), and sea-salt (SS), and to identify critical windows of exposure. METHODS: We used data from the 2005-2015 Florida Vital Statistics Birth Records. A well-validated geoscience-derived model was used to estimate women's pregnancy exposures to PM2.5 and its compositions. Distributed lag models were used to examine the associations and to identify the critical windows of exposure. RESULTS: A total of 2,078,669 women were included. In single-pollutant models, after controlling for potential confounders, positive associations between PM2.5 and GDM were observed during the second trimester of pregnancy. We found positive associations between SO42-, NH4+, NO3-, OM and BC, with largest effect sizes observed in the 21-24 weeks of pregnancy. Negative associations were observed for DUST and SS. Consistent results for NH4+, OM, DUST and SS were observed in the multi-pollutant models. CONCLUSIONS: Exposures to PM2.5 and its compositions (mainly NH4+, OM) during the second trimester are positively associated with GDM, especially for exposures during the 21-24 weeks of pregnancy. Further studies are needed to confirm the findings and examine the underlying mechanisms.

publication date

  • November 18, 2020

Research

keywords

  • Air Pollutants
  • Air Pollution
  • Diabetes, Gestational

Identity

Scopus Document Identifier

  • 85096472370

Digital Object Identifier (DOI)

  • 10.1016/j.envres.2020.110470

PubMed ID

  • 33217440

Additional Document Info

volume

  • 198