# Mathematical Models of Vulnerability and Cell-Type Specific Analysis of DNA Modifications in Aging

> **NIH NIH F31** · UNIVERSITY OF OKLAHOMA HLTH SCIENCES CTR · 2021 · $34,517

## Abstract

Project Summary
Understanding the biochemical processes underlying aging that give rise to age-related pathologies is the central
concept of geroscience research. A focus in geroscience is to identify interventions that slow/prevent aging.
These could both alter the trajectory of aging and have beneficial effects across a number of diseases, delaying
onset or slowing progression. However, evaluating anti-aging interventions requires years-long studies in
mammalian model organisms and is almost impossible in clinical trials that would require of multi-year studies
to evaluate lifespan and healthspan extensions. The solution to this problem lies in developing endpoints that
indicate biological age and the health-related changes that go with it with sufficient sensitivity. The recent
development of epigenetic clocks, chronological age-predictive machine learning models that use DNA cytosine
methylation data, are leading the field in age estimation accuracy and precision. The difference between a
person’s chronological age and the estimated “methylation age” has been proposed as a measure of aging
acceleration or deceleration – a ‘biological age’. Age acceleration predicted by methylation has shown robust
correlations to all-cause mortality risk, but weak correlations in age-related diseases. Age-related biological
outcomes such as cognitive decline have found little to no association with methylation age acceleration. Put
simply, epigenetic clocks have been developed to predict chronological age but have so far demonstrated
marginal utility to predict disease states. However, our preliminary results indicate that as many as 20% of
genomic cytosines have age-related methylation changes which may be leveraged to understand biological
aging and its outcomes simultaneously. Using the largest collection of methylation data with disease, age, tissue,
and sex labels from our previous work in natural language processing, we will create the first model of epigenetic
aging to identify “healthy” aging loci and disease-predictive loci. These loci will give us insight into the molecular
pathways disturbed by age-related methylation changes, providing targets for therapeutic intervention and a
predictor for patient disease risk. However, it is still unknown how epigenetic clocks ‘tick’, especially considering
their performance across tissues. We will test how epigenetic changes with age are distributed throughout
specific cell types in the central nervous system and cell types common across all tissues – such as vascular
endothelium and immune cells. Using single-cell bisulfite sequencing and cell-type specific promoter-driven
labelling, we can detect if the epigenetic clock changes are truly occurring in all cells or if they are restricted to
some cell types common across tissues. This information is critical for predicting the downstream effects of
changes identified by epigenetic clocks and interpreting the effects of ‘reversing’ epigenetic aging. These
experi...

## Key facts

- **NIH application ID:** 10295753
- **Project number:** 5F31AG063493-02
- **Recipient organization:** UNIVERSITY OF OKLAHOMA HLTH SCIENCES CTR
- **Principal Investigator:** Hunter L. Porter
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $34,517
- **Award type:** 5
- **Project period:** 2020-09-30 → 2022-09-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10295753

## Citation

> US National Institutes of Health, RePORTER application 10295753, Mathematical Models of Vulnerability and Cell-Type Specific Analysis of DNA Modifications in Aging (5F31AG063493-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10295753. Licensed CC0.

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