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.
Scientific reports. 2024 Nov 06*** epublish ***
Olesya Mironchuk, Andrew L Chang, Farzaneh Rahmani, Kaitlyn Portell, Elena Nunez, Zack Nigogosyan, Da Ma, Karteek Popuri, Vincent Tze Yang Chow, Mirza Faisal Beg, Jingqin Luo, Joseph E Ippolito
University of Washington School of Medicine, Seattle, WA, USA., Mallinckrodt Institute of Radiology, Washington University in St. Louis School of Medicine, Mail Stop Code: 8131, 4559 Scott Ave, St. Louis, MO, 63110, USA., Department of Internal Medicine, Section of Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA., Department of Computer Science, Memorial University of Newfoundland, St. John's, NL, Canada., School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada., Division of Public Health Sciences, Department of Surgery, Siteman Cancer Center Biostatistics and Qualitative Research Shared Resource, Washington University in St. Louis School of Medicine, St. Louis, MO, USA., Mallinckrodt Institute of Radiology, Washington University in St. Louis School of Medicine, Mail Stop Code: 8131, 4559 Scott Ave, St. Louis, MO, 63110, USA. .