Journal of neurology, neurosurgery, and psychiatry · 2025
Long-term exposure to multiple air pollutants and risk of Parkinson's disease: a population-based multipollutant model study.
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Chen Szu-Ju, Pan Shih-Chun, Wu Chih-Da, Li Hsun, Guo Yue Leon, Lin Chin-Hsien
Аннотация
BACKGROUND: Recent evidence suggests brain-first Parkinson's disease (PD) may start from the olfactory system, indicating potential inhalational exposure to causal agents. We investigated the impact of long-term exposure to various air pollutants on PD incidence using both single- and multi-pollutant models to account for interactions between pollutants.
METHODS: This retrospective population study used data from Taiwan's National Health Insurance Research Database (2006 and 2018) and included individuals aged 40-65 without PD. Personal exposure levels to various air pollutants, including PM2.5, PM10, NO2, O3, SO2 and CO, were calculated using the hybrid Kriging/land-use regression method. Cox regression models were used to analyse the association between pollutants and PD incidence, adjusting for covariates.
RESULTS: A total of 5 113 322 individuals without PD (mean age 50.1±6.9 years, 47.3% men) were followed for an average of 11.2±2.4 years, during which 20 694 incident cases of PD were identified. In the single-pollutant model, exposure to PM2.5 (HR 2.65 (95% CI 2.59 to 2.72)), PM10 (HR 3.13 (3.04 to 3.22)), NO2 (HR 1.74 (1.68 to 1.80)) and SO2 (HR 1.68 (1.65 to 1.71)) was associated with an increased risk of PD. These associations remained robust in the multipollutant model. A positive association between exposure to O3 and an increased risk of PD (HR 1.29 (1.25-1.33)) was observed after adjusting for co-pollutants.
CONCLUSIONS: This nationwide cohort study employing multiple-pollutant models for considering the interaction effects revealed an association between exposure to multiple air pollutants and the risk of PD, emphasising the need for early prevention strategies.
METHODS: This retrospective population study used data from Taiwan's National Health Insurance Research Database (2006 and 2018) and included individuals aged 40-65 without PD. Personal exposure levels to various air pollutants, including PM2.5, PM10, NO2, O3, SO2 and CO, were calculated using the hybrid Kriging/land-use regression method. Cox regression models were used to analyse the association between pollutants and PD incidence, adjusting for covariates.
RESULTS: A total of 5 113 322 individuals without PD (mean age 50.1±6.9 years, 47.3% men) were followed for an average of 11.2±2.4 years, during which 20 694 incident cases of PD were identified. In the single-pollutant model, exposure to PM2.5 (HR 2.65 (95% CI 2.59 to 2.72)), PM10 (HR 3.13 (3.04 to 3.22)), NO2 (HR 1.74 (1.68 to 1.80)) and SO2 (HR 1.68 (1.65 to 1.71)) was associated with an increased risk of PD. These associations remained robust in the multipollutant model. A positive association between exposure to O3 and an increased risk of PD (HR 1.29 (1.25-1.33)) was observed after adjusting for co-pollutants.
CONCLUSIONS: This nationwide cohort study employing multiple-pollutant models for considering the interaction effects revealed an association between exposure to multiple air pollutants and the risk of PD, emphasising the need for early prevention strategies.