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Body Composition Measurement Improved Performance of GLIM Criteria in Diagnosing Malnutrition Compared to PG-SGA in Ambulatory Cancer Patients: A Prospective Cross-Sectional Study

Background and aims: Muscle mass reduction (MMR) is one of the three etiologic criteria in the Global Leadership Initiative on Malnutrition (GLIM) framework. This study aimed to evaluate the value of MMR in GLIM criteria among ambulatory cancer patients. Methods: A single-center prospective cross-se...

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Detalles Bibliográficos
Autores principales: Wang, Yanfei, Chen, Xiaoyan, Wang, Yunyi, Liu, Ziqi, Fang, Yu, Peng, Zhi, Liu, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8400685/
https://www.ncbi.nlm.nih.gov/pubmed/34444902
http://dx.doi.org/10.3390/nu13082744
Descripción
Sumario:Background and aims: Muscle mass reduction (MMR) is one of the three etiologic criteria in the Global Leadership Initiative on Malnutrition (GLIM) framework. This study aimed to evaluate the value of MMR in GLIM criteria among ambulatory cancer patients. Methods: A single-center prospective cross-sectional study was conducted. All participants underwent calf circumference (CC) measurement and body composition measurement by bioelectrical impedance analysis (BIA). MMR was identified by CC, fat-free mass index (FFMI), appendicular skeletal muscle index (ASMI), or combinations of the above three indicators. Patients-generated Subjective Global Assessment (PG-SGA) was used as the comparator. Results: A total of 562 cancer patients receiving intravenous treatment were evaluated. Of the participants, 62.8% (355/562) were male. The median age of the patients was 59.0 years (range, 21–82 y). The median BMI was 22.8 kg/m(2) (range, 14.6–34.5 kg/m(2)). A total of 41.8% of patients were evaluated as malnutrition (PG-SGA ≥ 4), and 11.9% were diagnosed with severe malnutrition (PG-SGA ≥ 9). For the GLIM criteria, the prevalence of malnutrition was 26.9%, and severe malnutrition was 12.3%. For all criteria combinations of GLIM together versus PG-SGA, sensitivity was 60.4% (53.8–66.7), specificity was 97.9% (95.4–99.1), while the accordance between GLIM and PG-SGA was moderate (κ = 0.614). The performance of the GLIM worsened when MMR was excluded (κ = 0.515), with reduced sensitivity (50.2% (43.7–56.8)) and the same specificity (97.9% (95.4–99.1)). Including FFMI and ASMI by BIA can further improve the performance of GLIM than using CC alone (κ = 0.614 vs. κ = 0.565). Conclusions: It is important to include MMR in the GLIM framework. Using body composition measurement further improves the performance of the GLIM criteria than using anthropometric measurement alone.