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. 2024 Apr 17;14(1):8841.
doi: 10.1038/s41598-024-59576-5.

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

Jia-Hong Tang  1 , Hong-Lian Jian  1 , Ta-Chien Chan  2   3   4   5
Affiliations

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

Jia-Hong Tang et al. Sci Rep. .

Abstract

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 (Lden and Lnight) 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 PM2.5 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 PM2.5 (HR: 1.88; 95% CI 1.67, 2.12), Lden (HR: 1.10; 95% CI 1.06, 1.15), and Lnight (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 PM2.5 and Lden, and 1.90 (95% CI 1.68, 2.14) and 1.08 (95% CI 1.02, 1.13) for co-exposure to PM2.5 and Lnight. The HRs for the co-exposure models were higher than those for models with only a single exposure. This study provides evidence that PM2.5 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 PM2.5 and noise pollution.

Keywords: Air pollution; Chronic disease; Environmental exposure; Health impact; Metabolic syndrome; Noise.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
The flowchart of participants’ selection. The flowchart shows the exclusion criteria for participant selection and shows the final number of participants included (N = 10,773 and their distribution across the different subcomponents of the metabolic syndrome).
Figure 2
Figure 2
Effect estimates from the co-exposure models with interaction terms for PM2.5 and all-day traffic noise. Effect estimates are evaluated by hazard ratios. The crude model examines how PM2.5, all-day traffic noise, and the interaction term affect MetS and its subcomponents and ignores potential covariates. The adjusted model incorporates other potential covariates. The forest plot is used to visualize effect estimates and display the observed effects and confidence intervals.
Figure 3
Figure 3
Effect estimates from the co-exposure models with interaction terms for PM2.5 and nighttime traffic noise. Effect estimates are evaluated by hazard ratios. The crude model examines how PM2.5, nighttime traffic noise, and the interaction term affect MetS and its subcomponents and ignores potential covariates. The adjusted model incorporates other potential covariates. The forest plot is used to visualize effect estimates and display the observed effects and confidence intervals.
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