SummaryGlobal population growth and aging are driving a rise in age-related diseases, with neurodegenerative disorders like Alzheimer's expected to become increasingly prevalent by 2050. While advances in cardiovascular disease and cancer treatments may reduce their relative impact, the absence of curative approaches for neurodegenerative diseases remains a significant challenge for the German healthcare system. Air pollution, particularly fine particulate matter (PM2.5), is recognized as a major environmental risk factor for neurodegenerative diseases, with some studies confirming its link to Alzheimer's and dementia. However, the precise mechanisms through which PM2.5 contributes to neurodegeneration and the vulnerable subpopulations for these neurotoxic effects remain poorly understood. Thus, AIRCODE investigates the effects of PM2.5 on mild cognitive impairment (MCI) on a large scale by including several comprehensive data and statistical analysis approaches addressing two key gaps in previous research. First, we focus on the early stages of MCI before the onset of manifest neurodegenerative diseases. From a toxicological and causal inference perspective, it is unlikely that PM2.5 directly triggers neurodegenerative diseases in the short term. However, it may contribute to early pathological changes or intermediate stages of these conditions. Following the life course approach to chronic disease epidemiology, PM2.5-exposure could impair cognitive function earlier in life, even before the age of 60. AIRCODE is particularly interested in the dose-response relationships between PM2.5 and MCI across age groups and in the timing of effects, including the investigation of cumulative exposure. Using Distributed Lag Non-Linear Models (DLNM), AIRCODE aims to assess whether long-term, low-level exposure to PM2.5 may be as detrimental to cognitive health as short-term, high-level exposure, providing insights into both immediate and delayed impacts of air pollution on mild cognitive impairment. Second, we investigate the vulnerability of specific sociodemographic subpopulations, examining whether older adults or men and women differ in their susceptibility to PM2.5-exposure and its effects via systemic inflammation. Research on how PM2.5 affects cognitive impairment at the population level remains limited. For example, a U.S. cohort study found no association between PM2.5 exposure and cognitive impairment among individuals aged 60 and older with 13 or more years of education, while those with eight or fewer years of education showed an increased risk (Ailshire & Walsemann, 2020). These findings suggest that education may serve as a protective factor, highlighting the need for large-scale studies to identify and support high-risk groups. To better capture these disparities, Generalized Additive Models for Location, Scale and Shape (GAMLSS) will be used to analyze dispersion measures such as variance and skewness, which can reveal extreme cases where PM2.5 exposure has disproportionate effects. In further analyses, AIRCODE will also incorporate mortality follow-up data to assess whether PM2.5-exposure contributes to increased mortality risk. Initial evidence from Dutch cohort data suggests that systemic inflammation plays a role in PM2.5-induced neurotoxicity, but these findings are largely based on a region-specific cohort. By linking NAKO data with Germany-wide, high-resolution long-term air pollution data from EXPANSE and Copernicus Atmosphere Monitoring Service (CAMS), this project aims to model the relationship between systemic inflammation and cognitive function while identifying sociodemographic subpopulations most vulnerable to these effects.

Keywords PM25 air-pollution cognitive-function systemic-inflammation

InstitutionsUniklinikum Bonn, Universität Rostock, Helmholtz Zentrum München, Universität Bonn, University of Luxembourg