SummaryAir pollution is a serious environmental health concern worldwide. Empirical studies suggest that harmful air pollutants may reach beyond the cerebral blood barrier and contribute to the development of neurodegenerative diseases. Long-term air pollution exposure has been linked to various neurological disorders, including Parkinson’s disease and dementia. Since early manifestations of these disorders often include subtle cognitive impairments, it is crucial to detect and monitor cognitive changes at an early stage. This proposal aims to investigate the association between long-term exposure to air pollutants and cognitive impairment among participants in the German National Cohort (NAKO) , using both cross-sectional and longitudinal data. Specifically, we will analyze the baseline data to evaluate the independent and combined associations of long-term exposure to air pollutants with cognitive function. Furthermore, using repeated cognitive assessments during follow-up, we will assess how air pollution exposure influences cognitive decline over time. To achieve this, we will use detailed long-term data on key air pollutants—including particulate matter with a diameter ≤2.5 μm (PM2.5), particulate matter with a diameter ≤10 μm (PM10), black carbon, and nitrogen dioxide (NO2)— assigned to each participant based on their baseline residential addresses by the environmental data unit (EDU). For the cross-sectional analysis, we will apply Generalized Additive Models (GAMs) to examine potential non-linear associations between each pollutant and cognitive performance at baseline. We will also assess interaction effects with age, sex, unhealthy lifestyle, metabolic disorders, and cardiovascular diseases. To further investigate the combined effects of multiple pollutants at baseline, we will use advanced mixture models, including Weighted Quantile Sum (WQS) regression and Bayesian Kernel Machine Regression (BKMR), which allow for the modeling of complex interactions and non-linear relationships among pollutants. For the longitudinal analysis, we will employ mixed-effects models to account for within-person variability and to estimate the association between long-term air pollution exposure and the trajectory of cognitive function over time. This holistic approach, incorporating both cross-sectional and longitudinal analyses, aims to provide robust evidence on the cognitive health effects of long-term air pollution exposure, thereby supporting the development of effective public health strategies for disease prevention and risk reduction.

Keywords Air-pollution Longitudinal cognitive-impairment cross-sectional long-term-exposure

InstitutionsHelmholtz Zentrum München, Institut für Epidemiologie der Universität Münster, Helmholtz Munich, Martin-Luther-Universität Halle-Wittenberg, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt, IUF - Leibniz Institut für umweltmedizinische Forschung