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Abstract
Clinically, the body mass index remains the most frequently used metric of overall obesity, although it is flawed by its inability to account for different adipose (i.e., visceral, subcutaneous, and inter/intramuscular) compartments, as well as muscle mass. Numerous prior studies have demonstrated linkages between specific adipose or muscle compartments to outcomes of multiple diseases. Although there are no universally accepted standards for body composition measurement, many studies use a single slice at the L3 vertebral level. In this study, we use computed tomography (CT) studies from patients in The Cancer Genome Atlas (TCGA) to compare current L3-based techniques with volumetric techniques, demonstrating potential limitations with level-based approaches for assessing outcomes. In addition, we identify gene expression signatures in normal kidney that correlate with fat and muscle body composition traits that can be used to predict sex-specific outcomes in renal cell carcinoma.
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Details
1 University of Washington School of Medicine, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657)
2 Washington University in St. Louis School of Medicine, Mallinckrodt Institute of Radiology, St. Louis, USA (GRID:grid.4367.6) (ISNI:0000 0001 2355 7002)
3 Wake Forest University School of Medicine, Department of Internal Medicine, Section of Gerontology and Geriatric Medicine, Winston-Salem, USA (GRID:grid.241167.7) (ISNI:0000 0001 2185 3318)
4 Memorial University of Newfoundland, Department of Computer Science, St. John’s, Canada (GRID:grid.25055.37) (ISNI:0000 0000 9130 6822)
5 Simon Fraser University, School of Engineering Science, Burnaby, Canada (GRID:grid.61971.38) (ISNI:0000 0004 1936 7494)
6 Washington University in St. Louis School of Medicine, Division of Public Health Sciences, Department of Surgery, Siteman Cancer Center Biostatistics and Qualitative Research Shared Resource, St. Louis, USA (GRID:grid.4367.6) (ISNI:0000 0001 2355 7002)
7 Washington University in St. Louis School of Medicine, Mallinckrodt Institute of Radiology, St. Louis, USA (GRID:grid.4367.6) (ISNI:0000 0001 2355 7002); Washington University in St. Louis School of Medicine, Department of Biochemistry and Molecular Biophysics, St. Louis, USA (GRID:grid.4367.6) (ISNI:0000 0001 2355 7002)