# A population-based study of deep learning derived organ and tissue measures for accelerated aging using repurposed abdominal CT images

> **NIH NIH R01** · MAYO CLINIC ROCHESTER · 2024 · $670,589

## Abstract

PROJECT SUMMARY
There has been a dramatic increase in the number of persons living with reduced physical function and with
aging-related chronic conditions. If we compare chronological age (calendar-based age) with biological age
(changes at the cellular, tissue, organ, and system levels), we can classify persons as aging faster
(accelerated aging) or slower (successful aging) than their peers. Methods have been developed to measure
biological age based on DNA methylation, telomere length, and blood biomarkers. However, such measures
may not accurately reflect organ- and tissue-level changes from aging. A multi-organ/tissue approach is
needed to identify comprehensive age-related structural changes before signs, symptoms, or clinical
diagnoses occur. Abdominal computed tomography (CT) has widespread use in the general population (35%
of adults ages 20-89 years in an 11-year period). Quantitative measures of the organs and tissues on
abdominal CT may predict organ-specific diseases, or in combination, may be used to calculate biological age
and predict the more global outcomes of hospitalization and mortality. Therefore, our central hypothesis is that
deep learning (DL) models applied to abdominal CTs can quantify structural features of the organs and tissues
to identify persons with accelerated aging at high-risk for organ-specific disease, hospitalization, and death.
The Rochester Epidemiology Project record-linkage system provides access to a general population archive of
images for 423,081 abdominal CTs and to comprehensive medical record data among 181,187 adults (ages
20-89 years) between 2010-2020. Our team has already developed and validated DL tools to measure liver,
kidney, aorta, fat, muscle, and bone on abdominal CT images. We will leverage these resources to 1) establish
percentiles of abdominal CT biomarkers from both healthy and general population samples; 2) determine the
risk of organ-specific clinical disease by abdominal CT biomarkers in the general population; and 3) determine
the risk of hospitalization and death associated with abdominal CT measures in the general population. If
successful, application of DL tools to abdominal CT images will enrich the characterization of age-related
health risks without additional testing burden. Subclinical abdominal CT biomarkers may also inform the
biology of aging and early disease, improve disease classification, and provide opportunities for early
intervention.

## Key facts

- **NIH application ID:** 10932977
- **Project number:** 5R01AG081223-02
- **Recipient organization:** MAYO CLINIC ROCHESTER
- **Principal Investigator:** ANDREW David RULE
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $670,589
- **Award type:** 5
- **Project period:** 2023-09-30 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10932977

## Citation

> US National Institutes of Health, RePORTER application 10932977, A population-based study of deep learning derived organ and tissue measures for accelerated aging using repurposed abdominal CT images (5R01AG081223-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10932977. Licensed CC0.

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