The impact of co-exposure to air and noise pollution on the incidence of metabolic syndrome from a health checkup cohort

• Published in: Scientific reports 2024-04, Vol.14 (1), p.8841-8841, Article 8841
• Main Authors: Tang, Jia-Hong, Jian, Hong-Lian, Chan, Ta-Chien
• Format: Article
• Language: English
• Subjects: 692/308/174; 704/172/4081; Air exposure; Air pollution; Chronic disease; Environmental exposure; Health impact; Humanities and Social Sciences; Metabolic disorders; Metabolic syndrome; multidisciplinary; Noise; Noise pollution; Particulate matter; Regression analysis; Regression models; Risk reduction; Roads; Satellite imagery; Science; Science (multidisciplinary); Traffic; Transportation noise
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-59576-5

Previous studies have found associations between the incidence of metabolic syndrome (MetS) and exposure to air pollution or road traffic noise. However, investigations on environmental co-exposures are limited. This study aimed to investigate the association between co-exposure to air pollution and road traffic noise and MetS and its subcomponents. Participants living in Taipei City who underwent at least two health checkups between 2010 and 2016 were included in the study. Data were sourced from the MJ Health database, a longitudinal, large-scale cohort in Taiwan. The monthly traffic noise exposure (L den and L night ) was computed using a dynamic noise map. Monthly fine particulate data at one kilometer resolution were computed from satellite imagery data. Cox proportional hazards regression models with month as the underlying time scale were used to estimate hazard ratios (HRs) for the impact of PM 2.5 and road traffic noise exposure on the risk of developing MetS or its subcomponents. Data from 10,773 participants were included. We found significant positive associations between incident MetS and PM 2.5 (HR: 1.88; 95% CI 1.67, 2.12), L den (HR: 1.10; 95% CI 1.06, 1.15), and L night (HR: 1.07; 95% CI 1.02, 1.13) in single exposure models. Results further showed significant associations with an elevated risk of incident MetS in co-exposure models, with HRs of 1.91 (95% CI 1.69, 2.16) and 1.11 (95% CI 1.06, 1.16) for co-exposure to PM 2.5 and L den , and 1.90 (95% CI 1.68, 2.14) and 1.08 (95% CI 1.02, 1.13) for co-exposure to PM 2.5 and L night . The HRs for the co-exposure models were higher than those for models with only a single exposure. This study provides evidence that PM 2.5 and noise exposure may elevate the risk of incident MetS and its components in both single and co-exposure models. Therefore, preventive approaches to mitigate the risk of MetS and its subcomponents should consider reducing exposure to PM 2.5 and noise pollution.