Cardiometabolic Health Effects of Air Pollution, Noise, Green Space, and Socioeconomic Status: The HERMES Study.

Abstract

Introduction: We conducted the HERMES study to address the role of source-specific air pollution and the independent effects of air pollution, noise, and green space as well as the identification of susceptible subgroups defined by sociodemographic characteristics, stress conditions, and comorbidity in relation to cardiometabolic health. We studied three cohorts, a chemistry transport model (CTM) system, a noise model, a high-resolution land use map, and Danish registries on health and sociodemographic variables at individual and small-area levels.

Methods: Using Danish registries we defined a cohort of about 2 million persons living in Denmark. We also used data from the Danish National Health Survey (DNHS) (n = 246,766) and the Diet Cancer and Health - Next Generations cohort (DCH-NG) (n = 32,851). The Danish registries provided sociodemographic data at individual and small-area levels and allowed identification of medical diagnoses, comorbidity, and financial stress. The other two cohorts included information on lifestyle habits and measurements of blood pressure and biomarkers. We used Cox models for analyses of associations between exposures and type 2 diabetes, myocardial infarction (MI), and stroke. For analyses of interactions, we used both Cox and Aalen models and multivariate linear regression models for the analyses of air pollution and biomarkers.

Results: Air pollution concentrations correlated well with measurements. Analyses of associations between air pollution and type 2 diabetes, MI, and stroke adjusted for individual and area-level sociodemographic variables showed that further adjustment for individual lifestyle had minimal effect on the risk estimates. All four air pollutants were associated with a higher risk of each of the three endpoints. The local traffic contribution to air pollution seemed more important for risk of type 2 diabetes than the contribution from all other sources combined, whereas for MI and stroke, the contribution from all other sources seemed most important. The most consistent interaction was a stronger association between air pollution and type 2 diabetes, MI, and stroke among those with comorbidity. For MI and stroke, we found several interactions on the absolute scale that could not be detected on the relative scale. In multiexposure analyses, we found that particulate matter ≤2.5 μm in aerodynamic diameter (PM_2.5) was most important for cardiovascular diseases, and ultrafine particles (UFPs) were most important for type 2 diabetes. We also found that noise and lack of green space were associated with all three endpoints. Analyses of the DCH-NG cohort showed associations between exposure to air pollution and higher concentrations of non-high-density lipoprotein, lower concentrations of high-density lipoprotein, and higher blood pressure. The contribution to air pollution from sources other than local traffic seemed mainly responsible for these associations.

Conclusions: We found that PM_2.5, UFPs, elemental carbon (EC), and nitrogen dioxide (NO₂) were all associated with type 2 diabetes, MI, and stroke in single-pollutant models. However, in multiexposure analyses that included noise and green space, only UFPs for type 2 diabetes and PM_2.5 for MI and stroke remained associated, suggesting that these are the main air pollutants responsible for increasing the risk of cardiometabolic disease. Noise and lack of green space were also associated with cardiometabolic diseases in multiexposure models. We found that air pollution from local traffic was most important for risk of type 2 diabetes, whereas air pollution from other sources was most important for the risk of MI and stroke, which could relate to different air pollution mixtures and/or different biological pathways. Associations between air pollution and type 2 diabetes, MI, and stroke were consistently stronger among individuals with comorbidity, indicating higher susceptibility to negative air pollution effects in this subpopulation. The results of the interaction analyses showed that higher risk estimates among those of low socioeconomic status could be detected when estimating absolute risk but not when estimating relative risk, indicating that the best picture of effect modification is provided when expressed by both relative and absolute risk. The biomarker study showed expected associations between exposure to air pollution and blood lipid levels and blood pressure.