ABSTRACT In response to the NOT-AG-18-008, we are submitting a supplement to our parent R01 5R01GM120033-02 to extend the proposed work to Alzheimer's disease (AD). Within our R01 award, we are developing two powerful new automated algorithms to capture biomarkers of cognitive decline in Alzheimer?s Disease. Both of these tools capitalize on recent developments in machine learning; one of them, NMRQuant, has already been validated on simulated and phantom nuclear magnetic resonance (NMR) data and is ready to be applied to biological samples. We propose to examine the plasma samples obtained from the Texas Alzheimer?s Research and Care Consortium (TARCC), which prospectively collects demographic, environmental, neuropsychosocial, and genetic data along with the biofluid samples, in consecutive 1-year follow-up analyses that track various health outcomes. We hypothesize that progression of cognitive dysfunction from mild cognitive impairment to AD are accompanied by quantifiable changes in small molecules and metabolites in peripheral plasma. We will test this hypothesis on patients with AD, those with mild cognitive impairment, and healthy controls. We are uniquely positioned to advance biomarker and diagnostics tools as well as screening methods for cognitive deficits in AD, given that we have access to the state-of-the-art equipment, data collection expertise, and new analytical algorithms with superb sensitivity and specificity for NMR spectral data. Application of NMR metabolomics to AD could provide diagnostic and prognostic biomarkers of cognitive status, which is necessary for measuring both disease progression and treatment response. This work may thus hold a promise to bring transformative data to the field of AD.