Prospective cohort study demonstrates that severe GLIM-defined malnutrition triples one-year mortality in lung cancer, highlighting the prognostic value of morphofunctional assessment.
Purpose Malnutrition is common in lung cancer and may contribute to impaired functional status and poor survival. This study evaluated the association between GLIM-defined malnutrition, morphofunctional nutritional parameters, and one-year mortality in patients with lung cancer. Methods We conducted a multicentre prospective cohort study including 374 patients with primary lung cancer referred for nutritional and morphofunctional assessment before oncological treatment. Nutritional status was classified according to GLIM criteria. Body composition was assessed by bioelectrical impedance analysis, muscle ultrasound, handgrip strength and Timed Up and Go. Associations with severe malnutrition were explored using logistic regression, ROC analyses, and random forest models. One-year survival was evaluated using Kaplan–Meier curves and Cox regression models adjusted for age, sex, BMI, and ECOG performance status. Results Severe malnutrition was present in 87 patients and was associated with lower albumin and prealbumin, higher C-reactive protein, poorer ECOG performance status, and higher one-year mortality. ECOG performance status worsened progressively across GLIM categories. Severe GLIM-defined malnutrition was associated with increased one-year mortality in unadjusted analysis (HR 4.31, 95% CI 2.73–6.80) and after adjustment for age, sex, BMI, and ECOG performance status (HR 3.32, 95% CI 1.96–5.61). Several morphofunctional markers, particularly phase angle, BCMI, BCM, FFMI, FFM, and ultrasound-derived rectus femoris parameters, showed discriminatory ability for severe malnutrition and exploratory associations with mortality. Conclusion Severe GLIM-defined malnutrition is associated with poorer functional status and one-year mortality in lung cancer. Morphofunctional assessment may help characterize nutritional impairment, although proposed cut-offs require external validation.
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Vera-Gómez et al. (2026) studied this question.
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