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Combined Assessment of Gastrointestinal Hormones and Metabolomic Profiling Following Mixed-Meal Tolerance Tests in Patients at High Risk of Refeeding Syndrome: Study Protocol
ABSTRACT
Introduction: Refeeding syndrome (RS) is a life-threatening metabolic complication of nutritional support. Prolonged fasting, frequently observed in malnourished patients referred for percutaneous endoscopic gastrostomy (PEG), induces histological and ultrastructural changes in the intestinal mucosa, potentially influencing RS development. Mixed-Meal Tolerance Tests (MMTT) combined with metabolomics allow dynamic assessment of serum glucose, gastrointestinal hormones and cellular metabolites, which may help to elucidate RS pathogenesis and assess patient risk.
Objective:
The present study aims to perform MMTT in PEG-fed patients to evaluate enteroendocrine hormone responses and metabolomic profiles following a prolonged fasting period and subsequent enteral refeeding.
Methods:
This prospective, single-center study includes adults referred for PEG after at least one month of oral intake below 50% of energy needs. The MMTT will be performed at PEG placement and after 3–6 months of enteral nutrition. Serial blood samples will be collected from baseline up to 120 minutes post-meal to measure serum glucose, insulin, C-peptide, electrolytes and gastrointestinal hormones (GLP-1/GIP/Ghrelin/Peptide YY), and for metabolomic profiling by spectroscopy. Clinical and nutritional data will be prospectively recorded. Statistical and multivariate analyses will assess metabolic and hormonal changes over time and their potential association with patient refeeding syndrome risk and clinical outcome. The study was approved by the institutional ethics committee, and patient informed consent will be obtained. Conclusion: This study integrates MMTT and metabolomic profiling to characterize hormonal and metabolic responses during fasting and refeeding in high-risk PEG patients. Findings are expected to reveal underlying mechanisms of RS, identify potential predictive biomarkers, and improve individualized risk stratification and nutritional management.
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