# Identifying ADRD intervention targets by characterizing neurobiological mechanisms of social isolation, loneliness, and social environment using novel imaging, molecular markers, and machine learning

> **NIH NIH R01** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2024 · $667,131

## Abstract

PROJECT SUMMARY
Social isolation, loneliness, and social environment continue to emerge as important factors in Alzheimer’s
disease and related dementias (ADRD), more so with the COVID-19 pandemic and observed trends in the
prevalence of social isolation, loneliness, and ADRD. Although there is increasing recognition these factors can
impact the aging brain, represent early expression of ADRD neuropathological changes, and influence health
behaviors and resource access, less is known about the biological mechanisms involved. Our overarching
hypothesis is that social isolation, loneliness, and social environment are distinct factors that alter brain biology
and influence trajectories of healthy neurocognitive aging and ADRD vulnerability. Because understanding
causal pathways and the cumulative role of these critical psychosocial factors through decades-long human
experimental trials is infeasible, here we propose a unique and innovative approach to comprehensively assess
these psychosocial determinants and temporally relate them to dynamic profiles of ADRD vulnerability,
leveraging one of the largest biologically well-characterized community-based cohorts in the US, the
Framingham Study (FS). Since 1948, FS has enrolled 3 generations of participants (ages 20-50) and 2 multi-
ethnic cohorts, examined them regularly for cognitive decline and dementia, and collected an exquisite array of
in-depth and cutting-edge “multi-omic”, imaging, and other data over their lifespan and before clinical ADRD
onset. The FS has a 70-year legacy of unique contributions to public health, and will continue make
breakthroughs in ADRD through its Brain Aging Program (3U19AG068753-02S1). We seek to leverage these
resources through the following specific aims: AIM 1 is to examine and explain associations of social
isolation, loneliness, and social network structure with ADRD vulnerability over a lifetime. Our prior work
in FS suggests a molecular pathway related to neural plasticity/repair and cognitive resilience might be involved
in these mechanisms. We will collect a new wave of data on these factors—partially harmonized with the NIH
Toolbox and integrate this data with relevant psychosocial information at multiple previous exams. AIM 2 is to
identify and validate these biological pathways using causal inference analyses and machine-learning
methods on the extensive multi-omics data available. AIM 3 is to characterize social environment’s
cumulative role in psychosocial mechanisms of ADRD risk across the adult lifespan by developing a latent
social environment index with a validated geocode-based method and conducting sophisticated analyses. We
will validate our findings with other multi-ethnic cohort datasets and share all data through dbGaP and bioLINCC.
We expect to meaningfully evaluate whether and how these psychosocial factors influence the biology of healthy
neurocognitive aging and ADRD vulnerability and identify new pathways that may serve as targets for
inter...

## Key facts

- **NIH application ID:** 10888281
- **Project number:** 5R01AG079282-03
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Hugo Javier Aparicio
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $667,131
- **Award type:** 5
- **Project period:** 2022-09-15 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10888281

## Citation

> US National Institutes of Health, RePORTER application 10888281, Identifying ADRD intervention targets by characterizing neurobiological mechanisms of social isolation, loneliness, and social environment using novel imaging, molecular markers, and machine learning (5R01AG079282-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10888281. Licensed CC0.

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