Standardized measurement of abdominal muscle by computed tomography: association with cardiometabolic risk in the Framingham Heart Study

Exploring the Relationships between Sex Hormones and Abdominal Muscle Area and Radiodensity in Postmenopausal Women: Insights from the Multi-Ethnic Study of Atherosclerosis

The relationships between sex hormone levels and muscle composition in postmenopausal women remain underexplored. To address this gap, we conducted a cross-sectional observational study utilizing data from the Multi-Ethnic Study of Atherosclerosis. Our analysis included 682 postmenopausal women aged 45-84 years with complete data, with a mean age of 63.3 years. Using abdominal computed tomography, we assessed abdominal muscle area (cm2) and muscle radiodensity (Hounsfield units) in relation to serum levels of testosterone (total and free), estradiol, and sex hormone binding globulin (SHBG), measured in nmol/L. Multivariable linear regression models, adjusting for potential confounders, were employed to investigate these associations. In our fully adjusted models, higher levels of estradiol and free testosterone were found to be positively associated with total area of abdominal muscle (β = 1.41, 95 % CI 0.4, 2.4, p = 0.007 and β = 18.5, 95 % CI 4.0, 33.1, p = 0.004, respectively), but not with muscle radiodensity (p > 0.05). Conversely, elevated levels of SHBG were associated with a smaller total of area abdominal muscle and radiodensity (β = -2.1, 95 % CI -3.2, -0.9, p = 0.001 and β = -0.32, 95 % CI -0.6, -0.0, p = 0.07, respectively). Our study highlights significant associations between sex hormone levels and skeletal muscle area in postmenopausal women. Furthermore, the novel findings regarding SHBG and muscle composition suggest a potential previously unrecognized role of SHBG in the accumulation of skeletal muscle adipose tissue. However, further validation in other cohorts is necessary to elucidate the potential role of SHBG in body composition. Clinical Trial: NCT00005487.

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

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.