Long-term exposure to ambient air pollution mixtures and incident cardiovascular–liver–metabolic multimorbidity: a prospective cohort study
Abstract
Background: Ambient air pollution is associated with cardiovascular, metabolic, and liver diseases, but evidence for cardiovascular–liver–metabolic multimorbidity (CLMM) and pollutant mixtures is limited. We examined these associations in middle-aged and older adults in China.
Methods: We analyzed 4,650 adults from 119 cities in the China Health and Retirement Longitudinal Study at the 2011 baseline and 2015 follow-up. Incident CLMM was defined as the new coexistence of at least two domains: cardiovascular disease, diabetes, and operationally defined probable metabolic dysfunction-associated steatotic liver disease. City-level concentrations of seven pollutants were obtained from the China High Air Pollutants database. Logistic generalized linear mixed models with a city-level random intercept were supplemented by weighted quantile sum (WQS) regression, interaction analyses, and city-grouped machine-learning models.
Results: During follow-up, 757 participants (16.3%) developed CLMM. In fully adjusted single-pollutant models, the odds ratios per interquartile-range increase were 1.33 (95% CI 1.10–1.61) for PM₂.₅, 1.42 (1.13–1.77) for PM₁₀, 1.30 (1.08–1.56) for CO, and 1.30 (1.06–1.58) for nitrogen dioxide. Estimates were attenuated after co-pollutant adjustment: only PM₁₀ remained associated with CLMM after adjustment for CO; and no association remained statistically significant in the three-pollutant models. The positive WQS index was associated with CLMM (OR 1.25, 95% CI 1.09–1.42). No multiplicative interaction was detected by glycemic or blood-pressure status. Odds-ratio-based additive interaction signals were observed only for PM₂.₅ and CO with elevated blood pressure. In the selected random-forest model, the mean absolute Shapley additive explanations (SHAP) value was largest for CO (0.0128), followed by PM₁₀ (0.0110) and O₃ (0.0090).
Conclusions: Long-term exposure to PM₂.₅, PM₁₀, CO, nitrogen dioxide and the positive pollutant-mixture index was associated with incident CLMM. The findings support further evaluation of air pollution as a potentially modifiable environmental correlate of multimorbidity. Studies with finer exposure assessment and external validation are needed, particularly for adults with elevated blood pressure.
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BibTeX
@article{wang2026longterm,
title = {{Long-term exposure to ambient air pollution mixtures and incident cardiovascular–liver–metabolic multimorbidity: a prospective cohort study}},
author = {Miyuan Wang and Fuxia Wu and Jing Zhang and Jian Yang},
journal = {BMC Public Health},
year = {2026},
month = oct,
doi = {10.1186/s12889-026-29891-9},
url = {https://link.springer.com/10.1186/s12889-026-29891-9},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Long-term exposure to ambient air pollution mixtures and incident cardiovascular–liver–metabolic multimorbidity: a prospective cohort study
AU - Miyuan Wang
AU - Fuxia Wu
AU - Jing Zhang
AU - Jian Yang
PY - 2026
DA - 2026/10/13
JO - BMC Public Health
DO - 10.1186/s12889-026-29891-9
UR - https://link.springer.com/10.1186/s12889-026-29891-9
AB - Background: Ambient air pollution is associated with cardiovascular, metabolic, and liver diseases, but evidence for cardiovascular–liver–metabolic multimorbidity (CLMM) and pollutant mixtures is limited. We examined these associations in middle-aged and older adults in China. Methods: We analyzed 4,650 adults from 119 cities in the China Health and Retirement Longitudinal Study at the 2011 baseline and 2015 follow-up. Incident CLMM was defined as the new coexistence of at least two domains: cardiovascular disease, diabetes, and operationally defined probable metabolic dysfunction-associated steatotic liver disease. City-level concentrations of seven pollutants were obtained from the China High Air Pollutants database. Logistic generalized linear mixed models with a city-level random intercept were supplemented by weighted quantile sum (WQS) regression, interaction analyses, and city-grouped machine-learning models. Results: During follow-up, 757 participants (16.3%) developed CLMM. In fully adjusted single-pollutant models, the odds ratios per interquartile-range increase were 1.33 (95% CI 1.10–1.61) for PM₂.₅, 1.42 (1.13–1.77) for PM₁₀, 1.30 (1.08–1.56) for CO, and 1.30 (1.06–1.58) for nitrogen dioxide. Estimates were attenuated after co-pollutant adjustment: only PM₁₀ remained associated with CLMM after adjustment for CO; and no association remained statistically significant in the three-pollutant models. The positive WQS index was associated with CLMM (OR 1.25, 95% CI 1.09–1.42). No multiplicative interaction was detected by glycemic or blood-pressure status. Odds-ratio-based additive interaction signals were observed only for PM₂.₅ and CO with elevated blood pressure. In the selected random-forest model, the mean absolute Shapley additive explanations (SHAP) value was largest for CO (0.0128), followed by PM₁₀ (0.0110) and O₃ (0.0090). Conclusions: Long-term exposure to PM₂.₅, PM₁₀, CO, nitrogen dioxide and the positive pollutant-mixture index was associated with incident CLMM. The findings support further evaluation of air pollution as a potentially modifiable environmental correlate of multimorbidity. Studies with finer exposure assessment and external validation are needed, particularly for adults with elevated blood pressure.
SN - 1471-2458
ER -