# Identifying Regulators of Cellular Aging that can Prevent Alzheimer's Disease

> **NIH NIH R43** · INTEGRAL MOLECULAR · 2022 · $358,588

## Abstract

ABSTRACT
Aging is the leading risk factor for Alzheimer’s Disease (AD) and many other chronic diseases, with more than
6.5 million cases of AD in the US. Regulating or slowing cellular aging, particularly in microglia and astrocytes
that regulate neuroimmunity, would have a major impact on the treatment of AD, but the proteins that control
cellular aging processes are poorly understood. Cellular aging has been characterized into 9 “hallmarks” or
phenotypes that define aged cells. Most hallmarks are a result of cellular stress, such as DNA damage and
oxidation, and are inter-related in both their causes and outcomes. The cellular aging process is linked to
dramatically altered gene expression, which is largely controlled by transcription factors (TFs). Longevity
studies between species also suggest that TF activity can define the rate of aging. We hypothesize that TFs
can modulate cellular aging and that identifying the TFs that play a role in aging processes will enable an
entirely new generation of therapeutic targets with the potential to treat, delay, and possibly even prevent AD.
The discovery of TFs that can improve the lifespan of patients would have a profound impact on the prevention
and treatment of AD and many other diseases.

## Key facts

- **NIH application ID:** 10383454
- **Project number:** 1R43AG076101-01
- **Recipient organization:** INTEGRAL MOLECULAR
- **Principal Investigator:** Benjamin J Doranz
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $358,588
- **Award type:** 1
- **Project period:** 2022-06-01 → 2023-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10383454

## Citation

> US National Institutes of Health, RePORTER application 10383454, Identifying Regulators of Cellular Aging that can Prevent Alzheimer's Disease (1R43AG076101-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10383454. Licensed CC0.

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