# Identifying molecular traits associated with extreme human longevity using an AI based integrative approach

> **NIH NIH R21** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2023 · $238,102

## Abstract

PROJECT SUMMARY/ABSTRACT
Life expectancy is increasing, and consequently, the burden of chronic age-related disease is also increasing.
Interventions and treatments that target the fundamental biological process of human aging have the potential
to mitigate risk of multiple diseases faced by our aging population. To develop interventions targeting the aging
process, one must identify predictive factors and biomarkers associated with the aging clinical endpoints. By
definition, the development of aging-based conditions and diseases takes time and requires a great deal of
follow-up time. To accelerate research in human aging, biomarkers of human aging and prognostic biomarkers
of healthy human aging are desperately needed. Without reliable biomarkers, early-stage drug development is
severely limited. In this application, we propose a framework to identify biomarkers of healthy human aging
using advanced Artificial Intelligence (AI) methods applied to a wide range of deeply phenotyped studies that
collected data from humans and non-humans. We have assembled a team with deep expertise in clinical
research of aging, genetic epidemiology, biology of aging, and AI. To identify biomarkers of aging through the
integrative analysis of omic data with AI, we propose the following specific aims: Aim 1 (R21, first stage).
Assemble datasets from the Framingham Heart Study (FHS) and the Longevity Consortium (LC) with multiple
omics to test AI methods and to identify biomarkers associated with human aging. Aim 2 (R21, first stage).
Test biologically informed AI Deep Neural Network (DNN) models with FHS and LC data to integrate omic
data, predict outcomes, and identify predictive omic features. Aim 3 (R33, second stage). Apply models from
public data onto exceptional longevity (EL) data. Aim 4 (R33, second stage). Establishing causal relationship
between biomarkers and longevity phenotypes through Mendelian Randomization (MR) analysis and cell
culture experiments.

## Key facts

- **NIH application ID:** 10745015
- **Project number:** 1R21AG078793-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Daniel Spencer Evans
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $238,102
- **Award type:** 1
- **Project period:** 2023-09-01 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10745015

## Citation

> US National Institutes of Health, RePORTER application 10745015, Identifying molecular traits associated with extreme human longevity using an AI based integrative approach (1R21AG078793-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10745015. Licensed CC0.

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