Abdominal CT metrics in 17,646 patients reveal associations between myopenia, myosteatosis, and medical phenotypes: a phenome-wide association study

Juan M Zambrano Chaves; Leon Lenchik; Isabel O Gallegos; Louis Blankemeier; Daniel L Rubin; Marc H Willis; Akshay S Chaudhari; Robert D Boutin

doi:10.1016/j.ebiom.2024.105116

Abdominal CT metrics in 17,646 patients reveal associations between myopenia, myosteatosis, and medical phenotypes: a phenome-wide association study

EBioMedicine. 2024 Apr 17:103:105116. doi: 10.1016/j.ebiom.2024.105116. Online ahead of print.

Authors

Juan M Zambrano Chaves¹, Leon Lenchik², Isabel O Gallegos³, Louis Blankemeier⁴, Daniel L Rubin¹, Marc H Willis⁵, Akshay S Chaudhari¹, Robert D Boutin⁶

Affiliations

¹ Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA; Department of Radiology, Stanford University, Stanford, CA, USA.
² Department of Diagnostic Radiology, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA.
³ Department of Computer Science, (IOG), Stanford University, Stanford, CA, USA.
⁴ Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
⁵ Department of Radiology, Stanford University, Stanford, CA, USA.
⁶ Department of Radiology, Stanford University, Stanford, CA, USA. Electronic address: boutin@stanford.edu.

Abstract

Background: Deep learning facilitates large-scale automated imaging evaluation of body composition. However, associations of body composition biomarkers with medical phenotypes have been underexplored. Phenome-wide association study (PheWAS) techniques search for medical phenotypes associated with biomarkers. A PheWAS integrating large-scale analysis of imaging biomarkers and electronic health record (EHR) data could discover previously unreported associations and validate expected associations. Here we use PheWAS methodology to determine the association of abdominal CT-based skeletal muscle metrics with medical phenotypes in a large North American cohort.

Methods: An automated deep learning pipeline was used to measure skeletal muscle index (SMI; biomarker of myopenia) and skeletal muscle density (SMD; biomarker of myosteatosis) from abdominal CT scans of adults between 2012 and 2018. A PheWAS was performed with logistic regression using patient sex and age as covariates to assess for associations between CT-derived muscle metrics and 611 common EHR-derived medical phenotypes. PheWAS P values were considered significant at a Bonferroni corrected threshold (α = 0.05/1222).

Findings: 17,646 adults (mean age, 56 years ± 19 [SD]; 57.5% women) were included. CT-derived SMI was significantly associated with 268 medical phenotypes; SMD with 340 medical phenotypes. Previously unreported associations with the highest magnitude of significance included higher SMI with decreased cardiac dysrhythmias (OR [95% CI], 0.59 [0.55-0.64]; P < 0.0001), decreased epilepsy (OR, 0.59 [0.50-0.70]; P < 0.0001), and increased elevated prostate-specific antigen (OR, 1.84 [1.47-2.31]; P < 0.0001), and higher SMD with decreased decubitus ulcers (OR, 0.36 [0.31-0.42]; P < 0.0001), sleep disorders (OR, 0.39 [0.32-0.47]; P < 0.0001), and osteomyelitis (OR, 0.43 [0.36-0.52]; P < 0.0001).

Interpretation: PheWAS methodology reveals previously unreported associations between CT-derived biomarkers of myopenia and myosteatosis and EHR medical phenotypes. The high-throughput PheWAS technique applied on a population scale can generate research hypotheses related to myopenia and myosteatosis and can be adapted to research possible associations of other imaging biomarkers with hundreds of EHR medical phenotypes.

Funding: National Institutes of Health, Stanford AIMI-HAI pilot grant, Stanford Precision Health and Integrated Diagnostics, Stanford Cardiovascular Institute, Stanford Center for Digital Health, and Stanford Knight-Hennessy Scholars.

Keywords: Computed tomography; Myopenia; Myosteatosis; Phenome wide association study.