Project Summary: Early identification of Alzheimer's disease (AD) is essential for preventing or delaying disease onset. There is currently no effective treatment for AD, which may be attributable to multiple underlying causes. Mounting evidence supports the notion that AD is, in part, a metabolic condition with an array of metabolic perturbations early in the disease, even at the prototypical prodromal stage, i.e. amnestic mild cognitive impairment (aMCI). Animal models indicate that gut- and neuropeptide Glucagon like peptide-1 (GLP-1) is a promising link within the gut-brain axis of AD, although this peripheral-central pathway has yet to be defined in AD. We propose a novel gut-GLP-1 interactome of prodromal AD. We hypothesize that gut specific bacterial strains will influence GLP-1 levels and produce small metabolites that influence brain connectivity patterns in patients with aMCI. We will test this scientific premise in two Specific Aims. In Aim 1, we will determine the relationship between GLP-1 levels and default mode network (DMN) connectivity patterns in aMCI compared to age-matched control cohorts. We will apply rigorous methodological design to measure pre- and post- meal testing GLP-1 levels and assess DMN connectivity patterns using a cutting-edge functional MRI method. In Aim 2, we seek to determine bacterial strains and metabolites associated with DMN connectivity patterns in aMCI and control groups. We will conduct metagenomic sequencing on stool samples collected at similar times from the same individuals assessed in Aim1. We will identify bacterial genes with potential to impact GLP-1 levels. Metabolic markers will be correlated with DMN connectivity patterns. Together, these Aims will elucidate bacterial metabolic biomarkers in a prodromal AD disease state that can negatively affect GLP-1 levels or activity to affect brain functioning. We will use the data collected here to initiate a longitudinal study to follow the progression of metabolic and neuroimaging data along with known AD pathologic biomarkers such as beta- amyloid and tau. Our goal is to determine the specific bacterial strains and their metabolic properties that drive the transition from normal cognitive aging to aMCI to AD.