Integrating Multi-Omics to Uncover Shared Mechanisms Linking Physical Frailty and Alzheimer's Disease: Inflammation and Relevant Pathways

About This Grant

Project Summary Alzheimer’s disease and related dementias (ADRD), rank as the fifth-leading cause of death for Americans over age 65 and have no known treatments to prevent or cure. Physical frailty, a syndrome of physiological systems declines among individuals aged 65 and older, is recognized as a risk factor for cognitive impairment that is predictive of ADRD. Understanding the shared mechanisms for both physical frailty and ADRD is crucial for enhancing and understanding the link between these two complex and multifactorial conditions. Existing studies have focused only on a few inflammatory biomarkers involved in physical frailty and cognitive impairment. Few studies have comprehensively investigated the role of inflammation underlying physical frailty and ADRD by utilizing multi-omics data, which also allows for integrated analyses of other shared mechanisms and their interplay with inflammation. As high-throughput proteomics, circulating immune cell abundances, and metabolomics data are becoming more accessible, as well as employed with advanced data integration and artificial intelligence /machine learning (AI/ML) approaches, the proposed F99/K00 will be the first to explore the impact of a broad panel of inflammatory markers that link physical frailty and ADRD, extending the investigation beyond inflammation alone. The specific aims of this study are to: 1) establish frailty-implicated inflammatory signatures associated with cognitive decline and/or dementia risk, aiding in ADRD risk assessment (F99 phase); and 2) characterize the molecular basis among different biological components (multi-omics data) linking physical frailty and ADRD, with a focus on inflammation and relevant pathways (K00 phase). During my dissertation phase, I will train in multi-omics integrative analysis methods, machine learning, and predictive modeling, to develop frailty-implicated inflammatory signatures and examine the signatures as risk factors for ADRD. I will develop data integration methods that both incorporate cohort heterogeneity and are transferrable across cohorts to improve association detection and prediction accuracy. As a post-doctoral fellow, I will expand my training to ADRD pathogenesis leveraging multi-omics data and AI/ML-driven multimodal approaches to understand shared mechanisms underlying the link between physical frailty and ADRD including inflammation and relevant processes. This research will leverage the Framingham Heart Study, under the direction of the National Heart, Lung, and Blood Institute, and UK Biobank, including longitudinal cohorts with large-scale multi- omics data, physical frailty phenotypes, and surveillance for ADRD onset. This fellowship application aligns with NIA’s strategic goal D-2, “to identify and understand the genetic, molecular, and cellular mechanisms underlying the pathogenesis of AD/ADRD and other neurodegenerative disorders of aging.” As a result of this work, we will identify shared biological pathways, including inflammation, that contribute to the progression of physical frailty and ADRD, offering promising targets for ADRD early detection and intervention. This award will train me as an independent investigator launching a career in making novel contributions to ADRD pathogenesis and prediction.