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.
Sci Rep. 2024 Nov 6;14(1):27022. doi: 10.1038/s41598-024-76280-6.
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.
临床上，体重指数仍然是最常用的整体肥胖度量标准，尽管它存在缺陷，无法考虑到不同的脂肪（即内脏、皮下和肌间/肌内）隔室以及肌肉质量。许多先前的研究已经证明了特定的脂肪或肌肉隔室与多种疾病的结果之间存在联系。尽管目前还没有普遍接受的身体成分测量标准，但许多研究使用 L3 椎骨水平的单个切片。在这项研究中，我们使用来自癌症基因组图谱 (TCGA) 患者的计算机断层扫描 (CT) 研究，将当前基于 L3 的技术与体积技术进行比较，证明了基于水平的方法在评估结果方面存在潜在的局限性。此外，我们确定了与脂肪和肌肉身体成分特征相关的正常肾脏中的基因表达特征，这些特征可用于预测肾细胞癌中特定性别的结果。
