CELLULAR AND MOLECULAR GEROSCIENCE CoBRE

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ABSTRACT This project aims to advance aging research by developing a biomarker for aging using a model based upon the epigenetic clock, a machine learning model that predicts age from DNA methylation changes in any human tissue. We propose to test these clocks’ association to cognitive and vascular pathologies, as well as to generate new clocks designed to predict age- related dysfunction. We propose to evaluate the performance of epigenetic clocks in clinical samples by correlating cognitive function and vascular-cognitive impairment measures with DNA methylation data. Our goal is to determine if epigenetic clocks can serve as an aging biomarker using accessible tissue (blood) to predict age-related vascular and cognitive dysfunction. Aim 1 of the project will determine the ability of epigenetic clocks to predict age-associated vascular and cognitive impairment from blood samples. We will test the association between Horvath, Levine, and our own epigenetic clocks’ “age acceleration” and functional data on cognition and brain vascular health from human samples. We will generate methylation data from previously collected white blood cells, which will be paired with cognitive assessment using the Cambridge Neuropsychological Test Automated Battery (CANTAB) of cognitive tests, cerebrovascular health using transcranial doppler (TCD), functional near-infrared spectroscopy (fNIRS), dynamic retinal vessel analysis, and peripheral vascular health using the flow mediated dilation approach in large (ultrasonography) and small vessels (laser speckle contrast imaging). We hypothesize that age acceleration should be negatively correlated with cognitive and vascular function both in central circulation and in the periphery. These analyses will provide evidence critical to determining if epigenetic clocks can be a clinically relevant biomarker for aging in the brain. In the Aim 2, we will design new epigenetic clocks to predict vascular and cognitive impairment and evaluate their association to AD and cognitive impairment in a meta-analysis of public data. Using the same epigenetic data generated in aim 1, we will train new epigenetic clocks that predict functional impairment instead of chronological age. We hypothesize that epigenetic clocks trained to predict vascular and cognitive dysfunction will also be able to predict age-related disease status.