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Comparison of three objective nutritional screening tools for identifying GLIM-defined malnutrition in patients with gastric cancer

European Journal of Clinical Nutrition volume 79, pages 64–70 (2025)Cite this article

Subjects

Abstract

Objective

This study aimed to compare three objective nutritional screening tools for identifying GLIM-defined malnutrition in patients with gastric cancer (GC).

Method

Objective nutritional screening tools including geriatric nutritional risk index (GNRI), prognostic nutritional index (PNI), and controlling nutritional status (CONUT) score, were evaluated in patients with GC at our institution. Malnutrition was diagnosed according to the GLIM criteria. The diagnostic value of GNRI, PNI, and COUNT scores in identifying GLIM-defined malnutrition was assessed by conducting Receiver Operating Characteristic (ROC) curves and calculating the area under the curve (AUC). Additionally, sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were determined. The Kappa coefficient (k) was used to assess agreement between three objective nutritional screening tools and GLIM criteria.

Results

A total of 316 patients were enrolled in this study, and malnutrition was diagnosed in 151 (47.8%) patients based on the GLIM criteria. The GNRI demonstrated good diagnostic accuracy (AUC = 0.805, 95% CI: 0.758–0.852) for detecting GLIM-defined malnutrition, while the PNI and COUNT score showed poor diagnostic accuracy with AUCs of 0.699 (95% CI: 0.641–0.757) and 0.665 (95% CI: 0.605–0.725) respectively. Among these objective nutritional screening tools, the GNRI-based malnutrition risk assessment demonstrated the highest specificity (80.0%), accuracy (72.8%), PPV (74.8%), NPV (71.4%), and consistency (k = 0.452) with GLIM-defined malnutrition.

Conclusions

Compared to PNI and COUNT scores, GNRI demonstrated superior performance as an objective nutritional screening tool for identifying GLIM-defined malnutrition in GC patients.

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Fig. 1: Flow diagram showing patients enrollment process.
Fig. 2: Univariable and multivariable logistic regression analyses assessing the association between objective nutritional indicators and GLIM malnutrition.
Fig. 3: ROC curves for objective nutritional screening tools in identifying GLIM-defined malnutrition.

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Data availability

Applications for data access for non-commercial use can be submitted to author Junbo Zuo, [email protected].

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Acknowledgements

We would like to thank all the physicians and nurses in general surgery and patients for their great cooperation.

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Author notes

  1. These authors contributed equally: Junbo Zuo, Yan Huang.

Authors and Affiliations

  1. Department of General Surgery, The Affiliated People’s Hospital of Jiangsu University, Zhenjiang, China

    Junbo Zuo, Zhenhua Huang, Jingxin Zhang, Wenji Hou, Chen Wang & Xuefeng Bu

  2. Department of Nutrition, The Affiliated People’s Hospital of Jiangsu University, Zhenjiang, China

    Junbo Zuo, Yan Huang & Xiuhua Wang

  3. Department of Cardiovascular Medicine, The Affiliated People’s Hospital of Jiangsu University, Zhenjiang, China

    Yan Huang

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Contributions

Xuefeng Bu and Junbo Zuo designed the study. Zhenhua Huang, JingXin Zhang, Wenji Hou, Chen Wang, and Xiuhua Wang collected the data. Junbo Zuo, Yan Huang, and Xuefeng Bu analyzed and interpreted the data. Junbo Zuo and Yan Huang wrote the manuscript. All the authors critically reviewed and approved the final manuscript.

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Correspondence to Xuefeng Bu.

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Zuo, J., Huang, Y., Huang, Z. et al. Comparison of three objective nutritional screening tools for identifying GLIM-defined malnutrition in patients with gastric cancer. Eur J Clin Nutr 79, 64–70 (2025). https://doi.org/10.1038/s41430-024-01514-9

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