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March 22, 2026Scientific Reports0 citationsOpen Access

A case-control study identifying critical exposure windows in the association between ambient air pollution and spontaneous abortion

YZYingying ZhangShanxi Medical University ZZZhiwei ZouShanxi Medical University HDHaonan DaiShanxi Medical University

Key Points

The aim is to examine the relationship between early-pregnancy exposure to ambient air pollutants and spontaneous abortion while identifying significant exposure timeframes.
Conducted a hospital-based case-control study in Changzhi, Shanxi Province.
Estimated individual daily exposures using inverse distance weighting based on residential location and monitoring data.
Applied multivariable logistic regression to control for various factors like BMI and gestational age.
Utilized distributed lag non-linear models to investigate effects of significant pollutants.
Employed network toxicology for mechanistic insights, including PPI networks and enrichment analyses.
Ozone exposure showed an inverse association with spontaneous abortion (OR = 0.42).
Sulfur dioxide displayed positive associations with increasing exposure (Q3 vs. Q1: OR = 2.124; Q4 vs. Q1: OR = 2.992).
Lag analysis indicated SO₂ effects peaked at 28 days (OR = 1.242), with significant associations at lag 22-28 days.
Network analysis pointed to inflammation-related pathways as potential mechanisms involved.

Abstract

To examine associations between early-pregnancy exposure to ambient air pollutants and spontaneous abortion (SA), identify lag windows using distributed lag non-linear models (DLNM), and explore the potential mechanisms via network toxicology. A hospital-based case-control study was conducted in Changzhi, Shanxi Province. Individual daily exposures were estimated using inverse distance weighting (IDW) based on residential geocoding and monitoring data. Multivariable logistic regression models were adjusted for BMI, gestational age, gravidity, and parity. DLNM were applied to pollutants showing statistical significance in the logistic model. Network toxicology was used to identify overlapping targets, construct PPI networks, and perform GO/KEGG enrichment analyses. O₃ Q3 (vs. Q1) was inversely associated with SA (OR = 0.42, 95% CI: 0.211–0.824). SO₂ showed positive gradient associations (Q3 vs. Q1: OR = 2.124, 95% CI: 1.068–4.291; Q4 vs. Q1: OR = 2.992, 95% CI: 1.536–5.946). DLNM suggested a lag-dependent SO₂ effect, peaking at lag 28 days (OR = 1.242, 95% CI: 1.0077–1.5363), with a significant association observed during lag 22–28 days, whereas O₃ displayed weak and inconsistent lag patterns. Network toxicology highlighted inflammation/immune-related pathways (e.g., Toll-like receptor and IL-17 signaling). SO₂ exposure was associated with increased SA risk with a more evident late-lag effect, while the inverse association for O₃ should be interpreted cautiously. Inflammation-related immune activation may be a plausible mechanistic pathway.

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