LB005 - BIOIMPEDANCE VS CT SCAN FOR MUSCLE MASS ASSESSMENT IN MALNUTRITION ACCORDING TO GLIM CRITERIA: IS IT TIME TO MOVE BEYOND BIA?

LB005

BIOIMPEDANCE VS CT SCAN FOR MUSCLE MASS ASSESSMENT IN MALNUTRITION ACCORDING TO GLIM CRITERIA: IS IT TIME TO MOVE BEYOND BIA?

C. Mañas Ortiz^1,*, J. Ruiz Berjaga^1,2, A. Artero Fullana^1,2, A. Jimenez Portilla¹, C. González Blanco¹, L. Fernández Salvago¹, C. Sánchez Juan^1,2

¹Endocrinology and Nutrition, General University Hospital of Valencia, ²Medicine , University of Valencia, Valencia, Spain

Rationale: Skeletal muscle mass plays a critical role in metabolic regulation, immune response, and overall functional capacity, making it a cornerstone in the assessment of disease-related malnutrition. Within the Global Leadership Initiative on Malnutrition (GLIM) framework, reduced muscle mass is a key phenotypic criterion for diagnosing malnutrition, given its strong association with increased morbidity, prolonged hospitalization, and mortality. Accurate evaluation of muscle mass is thus essential to identify patients at nutritional risk. Various methodologies are available for this purpose, including bioelectrical impedance analysis (BIA) and computed tomography (CT). Each method presents unique strengths and limitations regarding accessibility, precision, and clinical applicability. Therefore, selecting an optimal and reliable assessment technique is vital to ensure accurate diagnosis and to guide effective nutritional interventions in clinical practice.

Methods: A retrospective observational study was conducted on 16 adult oncology patients diagnosed with malnutrition according to GLIM criteria. Data were collected between January and May 2025 and included age, sex, weight, height, and BMI. Muscle mass was assessed using two methods: bioelectrical impedance analysis (BIA) with the InBody S10 device, estimating the appendicular skeletal muscle mass index (ASMI), and computed tomography (CT), measuring cross-sectional muscle area at the L3 vertebra with FocusedOn software.

Malnutrition was diagnosed using the GLIM two-step approach, requiring at least one phenotypic and one etiologic criterion. Statistical analysis was performed using SPSS version 28, including descriptive statistics, Shapiro-Wilk test for normality, Spearman’s rho for correlation, and Bland-Altman analysis to evaluate agreement between BIA and CT measurements. Significance was set at p < 0.05.

Results: Sixteen oncology patients diagnosed with malnutrition according to GLIM criteria were included. The mean age was 64.4 ± 10.4 years, with 43.8% being female. The average BMI was 23.9 ± 4.5 kg/m² and mean body weight was 64.0 ± 11.5 kg. Muscle mass assessed by computed tomography (CT) at the L3 vertebra had a mean value of 120.7 ± 38.3 units. The appendicular skeletal muscle mass index (ASMI) obtained by bioelectrical impedance analysis (BIA) showed a mean of 7.74 ± 2.07 kg/m².

Normality testing with the Shapiro-Wilk test indicated non-normal distribution for ASMI (p = 0.094). Spearman’s rank correlation coefficient between ASMI and CT-derived muscle mass was 0.172 (p = 0.524). The Bland-Altman analysis revealed a mean difference of –112.91 units between BIA and CT measurements, with a standard deviation of 38.33. The calculated 95% limits of agreement ranged from –188.04 to –37.78. All data points were contained within these limits.

Conclusion: In malnourished oncology patients defined by GLIM criteria, ASMI measured by BIA does not correlate well with CT-based muscle mass and consistently overestimates it. Due to the observed variability and lack of agreement, CT cannot be considered a clinically interchangeable method with BIA for muscle mass assessment in this population. Further studies with larger samples are needed to explore potential adjustments or combined use of both methods.

References: Jensen GL, et al. GLIM criteria for the diagnosis of malnutrition: a consensus report from the global clinical nutrition community. Clin Nutr. 2019;38(1):1-9.

Kyle UG, et al. Bioelectrical impedance analysis—part II: utilization in clinical practice. Clin Nutr. 2004;23(6):1430-1453.

Prado CM, et al. CT-defined muscle and fat wasting are associated with cancer outcomes. Clin Cancer Res. 2008;14(18):5669-5674.

Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1(8476):307–310.

Disclosure of Interest: None declared