Plasma biochemical signals associated with exposure to PM2.5 in an ethnically diverse aging population with and without dementia

Abstract

BACKGROUND AND AIM: Advances in public health have increased the average human lifespan across the globe. As a result, we are observing a greater burden of age-related disorders. The elderly population is particularly vulnerable to the adverse effects of air pollution. Using data from participants of the Washington Heights and Inwood Community Aging Project (WHICAP) in New York City, we assessed the relationship between plasma biochemical signals and exposure to particulate matter less than 2.5 µm in diameter (PM2.5). METHODS: As a pilot study, for 107 participants, we generated plasma metabolomic profiles using an untargeted liquid chromatography coupled high-resolution mass spectrometry platform, operated in two modes, using a HILIC column under positive ESI (HILICpos) and a C18 column under negative ESI (C18neg). The annual average level of PM2.5 exposure was predicted based on residential address at the time of enrollment. We modeled the relationship between each plasma biochemical feature, that was present in at least 70% of the samples, and PM2.5 exposure in the year prior to plasma collection using a metabolome wide association framework. The model was adjusted to account for confounding by age, sex, race/ethnicity, the year of plasma collection, and whether or not they were diagnosed with dementia. RESULTS:From the HILICpos mode, 60 metabolic features were significantly associated (FDR 0.05) with PM2.5, including metabolites of cystine. Seventeen features from the C18neg mode, including metabolites of glutamic acid, were significantly associated with PM2.5. Pathway analysis performed using features associated with PM2.5 from both modes revealed changes in metabolism of amino acids, energy production, and oxidative stress response. CONCLUSIONS:Using an untargeted metabolomics approach, we found several plasma biochemical signals associated with annual PM2.5 levels in an ethnically diverse aging cohort. These signals could help understand the mechanisms through which PM2.5 exposure can lead to altered metabolic outcomes. KEYWORDS: Air pollution, Particulate matter, Metabolomics, Aging