PROJECT SUMMARY/ABSTRACT While it is widely acknowledged that Alzheimer’s disease and related dementias (AD/ADRD) have an extended preclinical phase, there are sparse data on the cognitive and pathobiological changes in middle-ages, a critical window for preventative intervention in people likely to progress to full-blown clinical disease. South Asians, a population with heightened risk of AD/ADRD and vascular/metabolic risks at younger ages, offer a unique opportunity to investigate preclinical AD/ADRD and related modifiable risk factors of its progression. Predictive models leveraging multimodal data could transform prospects for early AD/ADRD detection and phenotype identification in middle-aged people, but are currently lacking. To address these gaps, we propose to efficiently leverage the NHLBI-funded PPG, the Precision-CARRS cohort (P01HL154996; n=21,864), which has ~15 years of detailed, repeated measures of socio-behavioral, environmental, and vascular/metabolic data. Our specific aims are: (Aim-1) Perform multimodal AD/ADRD phenotyping and genetically characterize the risk of AD/ADRD across the continuum; (Aim-2) Investigate modifiable risk factors (socio-behavioral and vascular/metabolic factors) associated with the AD/ADRD continuum; and (Aim-3) Develop and validate a longitudinal machine learning network by employing high-dimensional data to distinguish AD/ADRD risk. We will achieve these aims by augmenting the cohort with AD/ADRD phenotyping and genotyping protocols aligned with the NIA-funded Alzheimer's Disease Research Centers (P50 AG025688). This study will focus on characterizing risks and outcomes in individuals who were enrolled in P-CARRS in two waves (2010, 2015) at ages ≥40 years (n=8,142, 51% female, 78% literate, 38% diabetes, and 48% hypertension). We will characterize middle-life AD/ADRD risks through newly measuring: (a) Using baseline stored samples (2010 or 2015) to perform plasma AD/ ADRD biomarkers (pTau217, NfL, and GFAP) and whole genome sequencing and (b) At a new follow-up examination, assess plasma AD biomarkers and administer sensitive AI-driven digital cognitive measures; and c) only in those >50 years (n=4,132), we will additionally obtain comprehensive cognitive battery, retinal, and neuroimaging. In preliminary studies, we have successfully demonstrated the feasibility of administering novel AI-enabled digital cognitive assessment tools, collecting and processing plasma AD biomarkers, and pilot standardization of MRI neuroimaging biomarkers. This project will advance progress in understanding the natural history of the AD/ADRD along the life course and promote early detection, precise diagnosis and tailored therapeutic strategies for this high-risk, understudied population and beyond.