AUTHOR=Tang Xinyu , Zhang Changying , Xiao Feng , Yang Fang , Zhu Xiaoya , Gao Yunlai 

TITLE=A predictive model based on the GLIM diagnosis for malnutrition in older adult heart failure patients

JOURNAL=Frontiers in Nutrition

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1551483

DOI=10.3389/fnut.2025.1551483

ISSN=2296-861X

ABSTRACT=Background and aimsMalnutrition is closely associated with adverse clinical outcomes in older adult heart failure (HF) patients. Currently, there is a distinct absence of specific diagnostic tools to identify malnutrition within this particular population. Therefore, this study aims to analyze the factors influencing malnutrition in older adult HF patients based on the Global Leadership Initiative on Malnutrition (GLIM) criteria, with the goal of developing a rapid and accurate diagnostic method to identify malnutrition.MethodsThe research incorporated a primary cohort study of 163 HF patients aged 65 and above and a validation cohort of 69 patients. The nutritional status of these patients was assessed according to the GLIM criteria. Logistic regression analysis was conducted to determine the independent risk factors of malnutrition. Subsequently, a nomogram model was developed and validated.ResultsAccording to the GLIM criteria, 54 patients (33.1%) and 22 patients (32.4%) in two patient cohorts were suffering from malnutrition. The logistic analyses revealed that body mass index (BMI), grip strength, mid-upper arm circumference (MUAC), fat-free mass (FFM), and albumin independently serve as risk factors of malnutrition in older adult HF patients. The nomogram model demonstrates excellent discriminative ability, with an area under the curve (AUC) of 0.921 (95% CI: 0.881–0.962). While the AUC of validation cohort is 0.899 (95% CI: 0.827–0.972).ConclusionIn older adult HF patients, BMI, grip strength, FFM, MUAC and albumin are identified as independent risk factors for malnutrition. The constructed nomogram based on these factors can accurately predict malnutrition and holds significant practical value.