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

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.

Grant Number: 1F99AG095040-01
NIH Institute/Center: NIH
Principal Investigator: Jiachen Chen