Performance of anthropometry-based and bio-electrical impedance-based muscle-mass indicators in the Global Leadership Initiative on Malnutrition criteria for predicting prognosis in patients with cancer.

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. CONCLUSION: 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.