Integrating BIA-derived body composition parameters with GLIM criteria for survival prediction in patients with solid...

To evaluate whether integrating Global Leadership Initiative on Malnutrition (GLIM) criteria with advanced body composition parameters improves the prediction of 6-mo mortality in patients with active solid tumors and suspected disease-related malnutrition (DRM).

A prospective, observational cohort study was conducted in adult patients with active solid malignancy referred to the Endocrinology and Nutrition Department of Virgen Macarena University Hospital (Spain) for suspected malnutrition between April 2021 and December 2023. Nutritional status was assessed using the Mini Nutritional Assessment-Short Form (MNA-SF), GLIM diagnostic criteria, anthropometry, handgrip strength, and bioelectrical impedance analysis (BIA). Body composition parameters included fat-free mass percentage (FFM%), fat mass percentage (FM%), phase angle (PA), standardized PA (SPA), body cell mass index (BCMI), and extracellular water percentage (ECW%). Survival analysis was performed using Cox regression models adjusted for age and sex, and discriminative ability was evaluated with the area under the curve (AUC).

A total of 209 patients were included: 11% at risk of malnutrition, 27.3% with moderate DRM, and 61.7% with severe DRM according to GLIM criteria. Head and neck, lung, and hepatobiliary cancers were the most frequent tumor types. Severe DRM was associated with greater weight loss and lower muscle-related and body composition indices. In multivariate survival models, BCMI (HR: 0.862; P = 0.036), PA (HR: 0.733; P = 0.039), and FFM% (HR: 0.957; P = 0.032) were protective factors, whereas ECW% (HR: 1.066; P = 0.026), advanced disease stage, and prolonged hospitalizations predicted higher mortality. The best predictive models combined BCMI, clinical variables, and nutritional factors (AUC: 0.851), followed by ECW% (AUC: 0.856) and FFM% (AUC: 0.828).

GLIM-defined malnutrition is highly prevalent in oncology patients and strongly associated with mortality. Incorporating body composition parameters-particularly BCMI, PA, and FFM%-into prognostic models enhances survival prediction beyond GLIM criteria alone. These findings support the integration of advanced morphofunctional assessment into routine nutritional evaluation to improve risk stratification and guide personalized interventions in cancer care.