# Sarcopenia: computable phenotypes and clinical outcomes.

> **NIH NIH R01** · INDIANA UNIVERSITY INDIANAPOLIS · 2022 · $167,151

## Abstract

PROJECT SUMMARY
 Sarcopenia is a generalized muscle condition that develops with aging and complicates many common
chronic diseases, resulting in low muscle mass, weakness, and impaired physical function. Sarcopenia
contributes to disability, increased hospitalizations, healthcare costs, and risk of death. Despite being under-
recognized clinically, sarcopenia is a major public health concern, with the worldwide prevalence projected to
increase by up to 72% in the next 30 years. However, limited knowledge of sarcopenia among clinicians,
combined with time pressures in clinical encounters delay its detection, and limit opportunity for intervention or
recruitment into clinical trials. To overcome this barrier to detecting sarcopenia, we propose to use advanced
big data and machine learning methods to identify additional component variables predicting sarcopenia
among the rich electronic health record (EHR) data and develop a validated and portable sarcopenia
computable phenotype (which uses a computer algorithm to detect patient characteristics or outcomes from
the EHR). This innovative proposal takes advantage of key resources at Indiana University and its affiliation
with the Regenstrief Institute and the Indiana Network for Patient Care (INPC), a statewide multi-health system
clinical data warehouse including >100 healthcare entities and >18 million unique patients with both coded and
text-based data, combined with the ability to perform comprehensive musculoskeletal measurements in the
Musculoskeletal Function Imaging and Tissue (MSK-FIT) Core funded through a NIAMS Core Center for
Clinical Research grant (P30AR072581). Our long-term goal is to accurately identify patients with, or at risk for,
sarcopenia and its consequences in order to provide targeted interventions. We hypothesize that by using
medical informatics and machine learning innovations, computable phenotypes can identify patients with
sarcopenia from the EHR, predict deficits in measured muscle strength and physical function, and
prospectively predict risk of hospitalization and death. In Aim 1, we will categorize >2000 adult participants in
the MSK-FIT Core with accessible EHR data, as either sarcopenic or nonsarcopenic according to
measurements of muscle strength, muscle mass and physical performance. We will then use 75% of the MSK-
FIT Core cohort to train machine deep learning algorithms to detect combinations of variables from these
subjects’ EHR predicting whether the patient is sarcopenic or not sarcopenic. The performance of the resulting
computable phenotype will then be tested in the remaining 25% of the MSK-FIT Core participants. In Aim 2, we
will test the performance of the sarcopenia computable phenotype to detect a clinically meaningful phenotype
in the entire INPC adult population (>18 million), by evaluating the ability to predict the rate of hospitalizations
and death among patients rated as sarcopenic versus matched controls. Such a computable phenotype will
the...

## Key facts

- **NIH application ID:** 10378772
- **Project number:** 5R01AR077273-03
- **Recipient organization:** INDIANA UNIVERSITY INDIANAPOLIS
- **Principal Investigator:** Erik Allen Imel
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $167,151
- **Award type:** 5
- **Project period:** 2020-04-20 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10378772

## Citation

> US National Institutes of Health, RePORTER application 10378772, Sarcopenia: computable phenotypes and clinical outcomes. (5R01AR077273-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10378772. Licensed CC0.

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