# Machine learning for brain and epigenetic aging in neurodegenerative diseases

> **NIH NIH K99** · UNIVERSITY OF PENNSYLVANIA · 2021 · $119,508

## Abstract

Project Summary/Abstract
Accurate quantification of aging for different organ systems enables detection of any deviation from typical aging
and identification of early onset of a disease characterized by accelerated aging, and thus provides opportunities
for early intervention. The brain-Predicted Age Difference (brainPAD) is a new clinical informatics framework
defined as the difference between the brain-derived age and the chronological age of the individual. It has been
suggested that brainPAD correlates with physical fitness, cognitive performance, mild cognitive impairment and
Alzheimer's disease (AD). However, several limitations remain in current brainPAD models. Models with large
age range yield prediction error larger than 5 years and are often derived from a single-modal imaging feature
set (e.g. only structural data). Moreover, due to known influence of sex differences on brain morphology, existing
models are trained on male and female separately, which may pose challenges in studies with small sample size
and in interpretation across models. Finally, because of the phenomenon of regression to the mean, predicted age
is overestimated in younger individuals and underestimated in older, which can lead to false positive associations
of brainPAD with variables of interest, such as disease status.
 To address these limitations, this proposal aims to develop and apply novel machine learning algorithms
and biomedical software to increase the model accuracy and robustness in three Specific Aims: 1) Develop
and evaluate a novel feature selection method to identify brain features influencing brainPAD; 2) Apply the novel
machine learning framework to explore different data types and feature types provided by the Alzheimer's Disease
Neuroimaging Initiative (ADNI); 3) Develop and validate integrative methods to create new measures of clinically
abnormal aging and compute these measures on the ADNI longitudinal data. Addressing the existing bias and
accounting for confounding effects, the new and more accurate measures of a person's age from their brain
and epigenetic signatures will provide insights into what causes atypical aging and help predict the onset and
individual trajectory of progression in specific neurodegenerative diseases such as AD.

## Key facts

- **NIH application ID:** 10127233
- **Project number:** 1K99AG066947-01A1
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Trang Thao Le
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $119,508
- **Award type:** 1
- **Project period:** 2021-02-15 → 2021-10-03

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10127233

## Citation

> US National Institutes of Health, RePORTER application 10127233, Machine learning for brain and epigenetic aging in neurodegenerative diseases (1K99AG066947-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10127233. Licensed CC0.

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