调查肌肉及体重对结直肠癌患者预后的影响:纵向队列研究。
Investigation of the Trajectory of Muscle and Body Mass as a Prognostic Factor in Patients With Colorectal Cancer: Longitudinal Cohort Study.
发表日期:2023 Mar 22
作者:
Dongjin Seo, Han Sang Kim, Joong Bae Ahn, Yu Rang Park
来源:
Brain Structure & Function
摘要:
骨骼肌和体重指数(BMI)是结直肠癌(CRC)患者生存的重要预测因素。然而,由于这些变量的连续方面研究不足,缺乏理解。本研究旨在评估初始状态和肌肉量、BMI的轨迹对总生存率(OS)的预后影响,并评估这些1年内的4个配置文件是否可以代表6年后的配置文件。我们分析了2010年至2020年间,4056名CRC新诊断患者的数据。使用预先训练的深度学习算法测量了第三腰椎带5毫米厚度的肌肉体积。骨骼肌体积指数(SMVI)定义为肌肉体积除以身高的平方。分析了诊断前3年和6年的BMI和肌肉状态之间的相关性,并对肌肉量轨迹进行类似评估。通过受限制的立方样条分析和生存分析评估了基线BMI和SMVI及其1年内的变化轨迹对OS的预测重要性。患者基于这4个维度被分类,并使用热图预测和展示预后风险。SMVI的轨迹被分为减少(812/4056,20%)、稳定(2014/4056,49.7%)或增加(1230/4056,30.3%)。同样,BMI的轨迹被分为减少(792/4056,19.5%)、稳定(2253/4056,55.5%)或增加(1011/4056,24.9%)。诊断后第一年的BMI和SMVI值与第三年和第六年的值显示出统计学显着的相关性(P <0.001)。受限制的立方样条分析显示基线BMI和SMVI变化比和OS之间呈非线性关系;具体来说,BMI呈U型相关。根据生存分析,增加的BMI(危险比[HR] 0.83; P = .02)、高基线SMVI(HR 0.82; P = .04)和肥胖1期(HR 0.80; P = .02)呈有利影响,而减少的SMVI轨迹(HR 1.31; P=.001)、减少的BMI(HR 1.23; P=.02)和初始的低体重(HR 1.38; P = .02)或肥胖2-3期(HR 1.79; P = .01)是OS的负面预测因子。同时考虑,BMI> 30 kg/m²且诊断时SMVI较低的患者具有最高的死亡风险。相比于肌肉量和BMI稳定的患者,具有增加的肌肉量且没有BMI减少的患者存活率更高。 BMI和肌肉1年内的轨迹是预测后续轨迹的代理指标。体重和肌肉质量的连续轨迹是CRC患者独立的预后因素。自动算法提供了进行身体成分的纵向评估的独特机会。进一步研究了解肌肉量和脂肪量的复杂自然进程,以供临床应用参考。 © Dongjin Seo,Kim Han Sang,Ahn Joong Bae,Park Yu Rang。 最初发表于《JMIR公共卫生与监测》(https://publichealth.jmir.org),2023年3月22日。
Skeletal muscle and BMI are essential prognostic factors for survival in colorectal cancer (CRC). However, there is a lack of understanding due to scarce studies on the continuous aspects of these variables.This study aimed to evaluate the prognostic impact of the initial status and trajectories of muscle and BMI on overall survival (OS) and assess whether these 4 profiles within 1 year can represent the profiles 6 years later.We analyzed 4056 newly diagnosed patients with CRC between 2010 to 2020. The volume of the muscle with 5-mm thickness at the third lumbar spine level was measured using a pretrained deep learning algorithm. The skeletal muscle volume index (SMVI) was defined as the muscle volume divided by the square of the height. The correlation between BMI status at the first, third, and sixth years of diagnosis was analyzed and assessed similarly for muscle profiles. Prognostic significances of baseline BMI and SMVI and their 1-year trajectories for OS were evaluated by restricted cubic spline analysis and survival analysis. Patients were categorized based on these 4 dimensions, and prognostic risks were predicted and demonstrated using heat maps.Trajectories of SMVI were categorized as decreased (812/4056, 20%), steady (2014/4056, 49.7%), or increased (1230/4056, 30.3%). Similarly, BMI trajectories were categorized as decreased (792/4056, 19.5%), steady (2253/4056, 55.5%), or increased (1011/4056, 24.9%). BMI and SMVI values in the first year after diagnosis showed a statistically significant correlation with those in the third and sixth years (P<.001). Restricted cubic spline analysis showed a nonlinear relationship between baseline BMI and SMVI change ratio and OS; BMI, in particular, showed a U-shaped correlation. According to survival analysis, increased BMI (hazard ratio [HR] 0.83; P=.02), high baseline SMVI (HR 0.82; P=.04), and obesity stage 1 (HR 0.80; P=.02) showed a favorable impact, whereas decreased SMVI trajectory (HR 1.31; P=.001), decreased BMI (HR 1.23; P=.02), and initial underweight (HR 1.38; P=.02) or obesity stages 2-3 (HR 1.79; P=.01) were negative prognostic factors for OS. Considered simultaneously, BMI >30 kg/m2 with a low SMVI at the time of diagnosis resulted in the highest mortality risk. We observed improved survival in patients with increased muscle mass without BMI loss compared to those with steady muscle mass and BMI.Profiles within 1 year of both BMI and muscle were surrogate indicators for predicting the later profiles. Continuous trajectories of body and muscle mass are independent prognostic factors of patients with CRC. An automatic algorithm provides a unique opportunity to conduct longitudinal evaluations of body compositions. Further studies to understand the complicated natural courses of muscularity and adiposity are necessary for clinical application.©Dongjin Seo, Han Sang Kim, Joong Bae Ahn, Yu Rang Park. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 22.03.2023.