Department of Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
Yonsei Cancer Center, Division of Medical Oncology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
JMIR Public Health Surveill. 2023 Mar 22;9:e43409. doi: 10.2196/43409.
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/m 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.
骨骼肌和 BMI 是结直肠癌(CRC)生存的重要预后因素。然而,由于关于这些变量连续方面的研究很少,因此了解不足。
本研究旨在评估肌肉和 BMI 的初始状态和轨迹对总生存期(OS)的预后影响,并评估这 4 个在 1 年内的特征是否可以代表 6 年后的特征。
我们分析了 2010 年至 2020 年间新诊断的 4056 例 CRC 患者。使用经过预训练的深度学习算法测量第三腰椎水平 5mm 厚处的肌肉体积。定义骨骼肌体积指数(SMVI)为肌肉体积除以身高的平方。分析了诊断后第 1、3 和 6 年 BMI 状态之间的相关性,并对肌肉特征进行了类似的评估。通过限制性立方样条分析和生存分析评估基线 BMI 和 SMVI 及其 1 年轨迹对 OS 的预后意义。根据这些 4 个维度对患者进行分类,并使用热图预测和展示预后风险。
SMVI 轨迹分为下降(812/4056,20%)、稳定(2014/4056,49.7%)或增加(1230/4056,30.3%)。同样,BMI 轨迹分为下降(792/4056,19.5%)、稳定(2253/4056,55.5%)或增加(1011/4056,24.9%)。诊断后第 1 年的 BMI 和 SMVI 值与第 3 年和第 6 年的 BMI 和 SMVI 值具有统计学显著相关性(P<.001)。限制性立方样条分析显示,基线 BMI 和 SMVI 变化率与 OS 之间呈非线性关系;BMI 尤其呈 U 形相关。根据生存分析,升高的 BMI(HR 0.83;P=.02)、较高的基线 SMVI(HR 0.82;P=.04)和 1 期肥胖(HR 0.80;P=.02)表现出有利的影响,而下降的 SMVI 轨迹(HR 1.31;P=.001)、下降的 BMI(HR 1.23;P=.02)和初始体重不足(HR 1.38;P=.02)或肥胖 2-3 期(HR 1.79;P=.01)则是 OS 的负面预后因素。同时考虑,诊断时 BMI>30kg/m2 且 SMVI 较低的患者死亡率最高。与稳定肌肉质量和 BMI 的患者相比,我们观察到肌肉质量增加而 BMI 没有下降的患者生存率提高。
BMI 和肌肉的 1 年内特征是预测后续特征的替代指标。身体和肌肉质量的连续轨迹是 CRC 患者的独立预后因素。自动算法为身体成分的纵向评估提供了独特的机会。需要进一步研究以了解肌肉质量和脂肪质量的复杂自然过程,以便在临床应用中使用。