Predictive Metabolite Signature for Alzheimer's Disease - a Nested Case-Control Study

Project Summary/Abstract Alzheimer?s Disease (AD) is the leading cause of for cognitive impairment and dementia among older adults in the U.S. Further, evidence suggests there is a strong association between cognitive dementia vascular disease and regardless of subtype, with vascular pathology estimated to contribute to at least half of all diagnosed dementia cases. In the absence of curative treatments it is critical to identify high risk individuals and determine efficacious targets for interventions to slow or arrest progression as early as possible. Within the context of this background, we propose to expand the scope of the Einstein Aging Study (EAS) by utilizing biorepository samples to measure serum metabolite concentrations and combine these data with existing EAS longitudinal measures of vascular disease risk to determine their predictive value and identify individuals at risk for incident AD. The EAS is a well characterized community based cohort of adults aged ?70 years in Bronx County, NY. Metabolomics is an emerging field that shows great promise in identifying biomarkers associated with preclinical abnormalities and subsequent onset of clinical outcomes. The overarching aim of the proposed work is to identify serum metabolites/metabolic signatures that will predict the onset of AD with the ultimate goal of facilitating the development of early intervention strategies to prevent or slow progression. Proposed is a nested case-control study of 90 incident cases who developed AD at least 2-years post EAS enrollment and 90 cognitively normal controls, who remained free of dementia during follow-up, matched for sex, age, education, race/ethnicity and length of follow-up. We will measure serum metabolite concentrations at two time points; study entry (baseline) and time of AD diagnosis, using a combination of untargeted and targeted methodologies. Metabolite panels will include primary metabolites (carbohydrates and sugar phosphates, amino acids, hydroxyl acids, purines, pyrimidines, aromatics, fatty acids); biogenic amines (trimethylamine-N- oxide, s-adenosyl methionine, s-adenosyl homocysteine, nucleotides and nucleosides, methylated and acetylated amines, dipeptides and oligopeptides); and complex lipids (acyl carnitines, ceramides, sphingolipids, phospholipids; non-cholesterol sterols). We will then determine whether change in serum metabolite concentrations or metabolite signatures between baseline and time of AD diagnosis will discriminate between AD cases and cognitively normal controls. For those metabolites that appear to be candidate biomarkers on the basis of this change we will then determine whether these serum metabolite concentrations or metabolite signatures measured in the baseline samples are significantly different between cases and controls, and whether already available cardiometabolic risk factor data improves the predictive capacity of the measures. Combining these new measures with the rich EAS dataset provides a unique opportunity to identify of pathways that link the exposures of interest in the current EAS projects to AD and lays the groundwork for future studies in the EAS linking serum metabolites to longitudinal cognitive change prior to AD onset.