Combined Predictive Value of GLIM-Defined Malnutrition and Preoperative Adipose Tissue 18F-FDG Uptake for Recurrence-Free Survival After Radical Gastrectomy in Patients with Gastric Cancer.

BACKGROUND

The Global Leadership Initiative on Malnutrition (GLIM) criteria provide a standardized approach for assessing the nutritional status of patients and demonstrate strong predictive value for the prognosis of patients with gastric cancer. However, these criteria do not incorporate indicators of adipose tissue metabolic activity, which may reflect pro-tumor microenvironmental factors. This study investigated the combined predictive value of malnutrition, defined by the GLIM criteria, and preoperative adipose tissue 18F-fluorodeoxyglucose (18F-FDG) uptake for recurrence-free survival (RFS) in patients with gastric cancer following radical surgery.

METHODS

A total of 105 patients were retrospectively enrolled and classified into malnourished and non-malnourished groups based on the GLIM criteria. Preoperative 18F-FDG positron emission tomography/computed tomography (18F-FDG PET/CT) was used to measure the mean standardized uptake value (SUVmean) of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). The predictive values of these indicators for RFS in patients with gastric cancer were assessed.

RESULTS

Multivariate survival analysis was used to identify GLIM-defined malnutrition (p = 0.020) and increased preoperative VAT SUVmean (p = 0.042) as independent risk factors for RFS. The combined analysis revealed that patients with both malnutrition and a high preoperative VAT SUVmean had the poorest RFS (HR = 18.41, p < 0.001). The predictive model integrating GLIM criteria and VAT SUVmean outperformed the GLIM criteria alone.

CONCLUSIONS

This study demonstrated that combining malnutrition defined by the GLIM criteria with preoperative visceral adipose tissue 18F-FDG uptake optimizes recurrence risk stratification and exhibits superior prognostic predictive efficacy compared to using the GLIM criteria alone. This approach provides new insights into individualized prognostic assessment and intervention strategies.