Abstract：
BACKGROUND: Reduced muscle mass is a criterion for diagnosing malnutrition using the Global Leadership Initiative on Malnutrition (GLIM) criteria; however, the choice of muscle-mass indicators within the GLIM criteria remains contentious. This study aimed to establish muscle-measurement-based GLIM criteria using data from bio-electrical impedance analysis (BIA) and anthropometric evaluations and evaluate their ability to predict overall survival (OS), short-term outcomes, and healthcare burden in patients with cancer.
METHODS: This was a multicenter, prospective study that commenced in 2013 and enrolled participants from various clinical centers across China. We constructed GLIM criteria based on various muscle measurements, including fat-free mass index (FFMI), skeletal muscle index (SMI), calf circumference (CC), midarm circumference (MAC), midarm muscle circumference (MAMC), and midarm muscle area (MAMA). Survival was estimated using the Kaplan-Meier method and survival curves were compared using the log-rank test. Cox proportional hazards regression was used to assess the independent association between the GLIM criteria and OS. The discriminatory performance of different muscle-measurement-based GLIM criteria for mortality was evaluated using Harrell\'s concordance index (C-index). Logistic regression was used to evaluate the association of the GLIM criteria with short-term outcomes and healthcare burden.
RESULTS: A total of 4769 patients were included in the analysis, of whom 1659 (34.8%) died during the study period. The Kaplan-Meier curves demonstrated that all muscle-measurement-based GLIM criteria significantly predicted survival in patients with cancer (all p < 0.001). The survival rate of malnourished patients was approximately 10% lower than that of non-malnourished patients. Cox proportional hazards regression showed that all the muscle-measurement-based GLIM could independently predict the OS of patients (all p < 0.001). The prognostic discrimination was as follows: MAMC (Chi-square: 79.61) > MAMA (Chi-square: 79.10) > MAC (Chi-square: 64.09) > FFMI (Chi-square: 62.33) > CC (Chi-square: 58.62) > ASMI (Chi-square: 57.29). In comparison to the FFMI-based GLIM criteria, the ASMI-based criteria (-0.002, 95% CI: -0.006 to 0.002, p = 0.334) and CC-based criteria (-0.003, 95% CI: -0.007 to 0.002, p = 0.227) did not exhibit a significant advantage. However, the MAC-based criteria (0.001, 95% CI: -0.003 to 0.004, p = 0.776), MAMA-based criteria (0.004, 95% CI: 0.000-0.007, p = 0.035), and MAMC-based criteria (0.005, 95% CI: 0.000-0.007, p = 0.030) outperformed the FFMI-based GLIM criteria. Logistic regression showed that muscle measurement-based GLIM criteria predicted short-term outcomes and length of hospital stay in patients with cancer.
CONCLUSIONS: All muscle measurement-based GLIM criteria can effectively predict OS, short-term outcomes, and healthcare burden in patients with cancer. Anthropometric measurement-based GLIM criteria have potential for clinical application as an alternative to BIA-based measurement.
METHODS: This was a multicenter, prospective study that commenced in 2013 and enrolled participants from various clinical centers across China. We constructed GLIM criteria based on various muscle measurements, including fat-free mass index (FFMI), skeletal muscle index (SMI), calf circumference (CC), midarm circumference (MAC), midarm muscle circumference (MAMC), and midarm muscle area (MAMA). Survival was estimated using the Kaplan-Meier method and survival curves were compared using the log-rank test. Cox proportional hazards regression was used to assess the independent association between the GLIM criteria and OS. The discriminatory performance of different muscle-measurement-based GLIM criteria for mortality was evaluated using Harrell\'s concordance index (C-index). Logistic regression was used to evaluate the association of the GLIM criteria with short-term outcomes and healthcare burden.
RESULTS: A total of 4769 patients were included in the analysis, of whom 1659 (34.8%) died during the study period. The Kaplan-Meier curves demonstrated that all muscle-measurement-based GLIM criteria significantly predicted survival in patients with cancer (all p < 0.001). The survival rate of malnourished patients was approximately 10% lower than that of non-malnourished patients. Cox proportional hazards regression showed that all the muscle-measurement-based GLIM could independently predict the OS of patients (all p < 0.001). The prognostic discrimination was as follows: MAMC (Chi-square: 79.61) > MAMA (Chi-square: 79.10) > MAC (Chi-square: 64.09) > FFMI (Chi-square: 62.33) > CC (Chi-square: 58.62) > ASMI (Chi-square: 57.29). In comparison to the FFMI-based GLIM criteria, the ASMI-based criteria (-0.002, 95% CI: -0.006 to 0.002, p = 0.334) and CC-based criteria (-0.003, 95% CI: -0.007 to 0.002, p = 0.227) did not exhibit a significant advantage. However, the MAC-based criteria (0.001, 95% CI: -0.003 to 0.004, p = 0.776), MAMA-based criteria (0.004, 95% CI: 0.000-0.007, p = 0.035), and MAMC-based criteria (0.005, 95% CI: 0.000-0.007, p = 0.030) outperformed the FFMI-based GLIM criteria. Logistic regression showed that muscle measurement-based GLIM criteria predicted short-term outcomes and length of hospital stay in patients with cancer.
CONCLUSIONS: All muscle measurement-based GLIM criteria can effectively predict OS, short-term outcomes, and healthcare burden in patients with cancer. Anthropometric measurement-based GLIM criteria have potential for clinical application as an alternative to BIA-based measurement.
摘要：
背景：使用全球营养不良领导力倡议（GLIM）标准，肌肉质量减少是诊断营养不良的标准；然而，GLIM标准中肌肉质量指标的选择仍存在争议.本研究旨在使用生物电阻抗分析（BIA）和人体测量评估的数据建立基于肌肉测量的GLIM标准，并评估其预测总体生存（OS）的能力。短期结果，以及癌症患者的医疗负担。
方法：这是一个多中心，前瞻性研究于2013年开始，招募了来自中国各个临床中心的参与者。我们根据各种肌肉测量结果构建了GLIM标准，包括无脂肪质量指数(FFMI)，骨骼肌指数（SMI），小腿周长(CC),中臂周长（MAC），中臂肌围(MAMC),和中臂肌肉面积（MAMA）。使用Kaplan-Meier方法估计存活率,并使用对数秩检验比较存活曲线。Cox比例风险回归用于评估GLIM标准与OS之间的独立关联。使用Harrell一致性指数（C指数）评估基于不同肌肉测量的GLIM死亡率标准的辨别性能。使用Logistic回归评估GLIM标准与短期结果和医疗负担的相关性。
结果：总共4769名患者被纳入分析，其中1659人(34.8%)在研究期间死亡。Kaplan-Meier曲线表明，所有基于肌肉测量的GLIM标准均显着预测癌症患者的生存（所有p<0.001）。营养不良患者的生存率比非营养不良患者低约10％。Cox比例风险回归分析显示，所有基于肌肉测量的GLIM均可独立预测患者的OS（均p<0.001）。预后判断为：MAMC（卡方：79.61）>MAMA（卡方：79.10）>MAC（卡方：64.09）>FFMI（卡方：62.33）>CC（卡方：58.62）>ASMI（卡方：57.29）。与基于FFMI的GLIM标准相比，基于ASMI的标准(-0.002,95%CI:-0.006~0.002,p=0.334)和基于CC的标准(-0.003,95%CI:-0.007~0.002,p=0.227)没有显著优势.然而,基于MAC的标准(0.001，95%CI：-0.003至0.004，p=0.776)，基于MAMA的标准（0.004，95%CI：0.000-0.007，p=0.035），基于MAMC的标准(0.005，95%CI:0.000-0.007，p=0.030)优于基于FFMI的GLIM标准。Logistic回归显示，基于肌肉测量的GLIM标准可预测癌症患者的短期预后和住院时间。
结论：所有基于肌肉测量的GLIM标准都可以有效地预测OS，短期结果，以及癌症患者的医疗负担。基于人体测量的GLIM标准具有临床应用潜力，可替代基于BIA的测量。
方法：这是一个多中心，前瞻性研究于2013年开始，招募了来自中国各个临床中心的参与者。我们根据各种肌肉测量结果构建了GLIM标准，包括无脂肪质量指数(FFMI)，骨骼肌指数（SMI），小腿周长(CC),中臂周长（MAC），中臂肌围(MAMC),和中臂肌肉面积（MAMA）。使用Kaplan-Meier方法估计存活率,并使用对数秩检验比较存活曲线。Cox比例风险回归用于评估GLIM标准与OS之间的独立关联。使用Harrell一致性指数（C指数）评估基于不同肌肉测量的GLIM死亡率标准的辨别性能。使用Logistic回归评估GLIM标准与短期结果和医疗负担的相关性。
结果：总共4769名患者被纳入分析，其中1659人(34.8%)在研究期间死亡。Kaplan-Meier曲线表明，所有基于肌肉测量的GLIM标准均显着预测癌症患者的生存（所有p<0.001）。营养不良患者的生存率比非营养不良患者低约10％。Cox比例风险回归分析显示，所有基于肌肉测量的GLIM均可独立预测患者的OS（均p<0.001）。预后判断为：MAMC（卡方：79.61）>MAMA（卡方：79.10）>MAC（卡方：64.09）>FFMI（卡方：62.33）>CC（卡方：58.62）>ASMI（卡方：57.29）。与基于FFMI的GLIM标准相比，基于ASMI的标准(-0.002,95%CI:-0.006~0.002,p=0.334)和基于CC的标准(-0.003,95%CI:-0.007~0.002,p=0.227)没有显著优势.然而,基于MAC的标准(0.001，95%CI：-0.003至0.004，p=0.776)，基于MAMA的标准（0.004，95%CI：0.000-0.007，p=0.035），基于MAMC的标准(0.005，95%CI:0.000-0.007，p=0.030)优于基于FFMI的GLIM标准。Logistic回归显示，基于肌肉测量的GLIM标准可预测癌症患者的短期预后和住院时间。
结论：所有基于肌肉测量的GLIM标准都可以有效地预测OS，短期结果，以及癌症患者的医疗负担。基于人体测量的GLIM标准具有临床应用潜力，可替代基于BIA的测量。
