Abstract
Background: Although an increasing number of studies have shown air pollution exposure is associated with diabetes, the potential causal effects of air pollutants on incident diabetes and the joint effects of air pollutant mixtures remain unclear. Methods: We conducted a prospective cohort study that included 25,801 adults based on Chronic Disease of the Community Natural Population in the Beijing-Tianjin-Hebei region. Three-year mean concentrations of air pollutants (PM2.5, PM10, PM1, and NO2) and PM2.5 components (ammonium [NH4+], nitrate [NO3-], sulfate [SO42-], and chloride ion [Cl-]) were obtained from China High Air Pollutants database. Targeted maximum likelihood estimation was used to estimate potential causal relationships between long-term air pollution exposure and diabetes incidence. The joint effects of air pollutant mixtures on diabetes and the contribution of each pollutant were assessed using Quantile G-computation. Results: In single-pollutant models, moderate and high concentrations of PM2.5, PM10, PM1, NO2, NH4+, NO3-, SO42-, and Cl- exposure were significantly associated with diabetes risk compared with low concentrations of air pollutants. In multi-pollutant models, the joint effect of air pollutant mixture (PM2.5, PM10, PM1, and NO2) on diabetes was 1.006 (1.004, 1.009). After replacing PM2.5 with PM2.5 components in the mixture, the effect estimates remained robust at 1.015 (1.008, 1.021), and the positive effect was driven primarily by NH4+ at 43.66 %, followed by NO3- at 39.20 %. Conclusions: Our results revealed relationships between long-term air pollutant exposure and incident diabetes. Furthermore, NH4+ and NO3- might be strong contributors. These findings support targeted air quality interventions to reduce diabetes risk. © 2025 The Authors.
| Original language | English |
|---|---|
| Article number | 118652 |
| Journal | Ecotoxicology and Environmental Safety |
| Volume | 302 |
| Online published | 15 Jul 2025 |
| DOIs | |
| Publication status | Published - 1 Sept 2025 |
Funding
The study was supported by the National Key Research and Development Program of China (2016YFC0900603).
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Research Keywords
- Ambient air pollution
- Diabetes
- Fine particulate components
- Quantile G-computation
- Targeted maximum likelihood estimation
Publisher's Copyright Statement
- This full text is made available under CC-BY-NC-ND 4.0. https://creativecommons.org/licenses/by-nc-nd/4.0/
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