Summary/Abstract Alzheimer’s Disease (AD), the most common cause of dementia, affects over 6.2 million Americans and 50 million individuals worldwide. Ten to fifteen years prior to the onset of cognitive symptoms, AD pathology begins to appear in the brain. While the primary pathology associated with AD include amyloid beta and hyperphosphorylated tau, the pathogenesis of AD remains elusive. Other changes in the AD brain include derangements in cerebral glucose metabolism. Recent epidemiological studies have highlighted an association between AD and systemic metabolic impairment (i.e. diabetes). This indicates a major role of altered metabolism in AD, and warrants examination of how metabolic risk may contribute to brain health and dementia risk. Biomarker studies of brain structure and function offer potential opportunities for early detection of AD, and for improving our understanding of factors relating to disease risk. Functional connectivity, a measure of correlated neural activity in two brain regions can be measured by resting-state functional magnetic resonance imaging (rsfMRI). This functional neuroimaging approach has emerged as a potential biomarker for AD diagnosis and disease monitoring. Importantly, metabolic risk has previously been associated with altered functional connectivity. However, data regarding early forms of metabolic risk, like prediabetes, is lacking. Additionally, functional connectivity studies often suffer from limited sample size and a lack of diversity. We intend to analyze the existing rsfMRI data of individuals recruited into the Health and Aging Brain Among Latino Elders (HABLE) Study, a diverse cohort of approximately 2,000 individuals. In our first aim, we will characterize the effect of metabolic risk (prediabetes or Type 2 Diabetes status assessed by blood glucose, diagnosis, or medication use) on functional connectivity in cognitively healthy individuals. As part of this aim, we will also examine the role of APOE4, a foremost genetic risk factor of AD that is closely related to cerebrovascular dysfunction. In our second aim, we will examine functional networks across the AD diagnostic spectrum, and examine to what degree metabolic risk mediates changes in these networks. We will then assess the relationship between cardiometabolic risk score and functional connectivity across the entire cohort.