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Updated: Jan 9, 2026

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
Published on: November 29, 2024
Nutritional therapy and a stepwise approach in critical illness: aligning provision with metabolic phases
Christian Stoppe1,2, Zheng-Yii Lee1,2,3, Yaseen M Arabi4
1Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, University Hospital, Würzburg.
Purpose Of Review:
Nutritional therapy is fundamental in critical care, yet conventional one-size-fits-all feeding strategies might overlook the dynamic metabolic shifts and patient variability inherent to critical illness. This review proposes a stepwise, phase-adapted strategy aiming to align nutrition therapy with three-step conceptual model of metabolic phases: acute catabolic, stabilization, and recovery.
Recent Findings:
Randomized controlled trials have consistently shown no benefit - and potential harm - from early full-dose energy or protein delivery, particularly in patients with severe organ failure. The acute catabolic phase is characterized by systemic inflammation, stress hormone surges, and insulin resistance, where permissive underfeeding minimizes metabolic burden. In the stabilization phase, declining inflammatory markers and improving organ functions support cautious escalation of nutrition, though biomarkers like hyperglycemia and hypophosphatemia may signal unreadiness to target nutrition. In the recovery phase, inflammation resolves, and mobility improves requiring higher energy and protein delivery alongside physical rehabilitation. Patients may regress to earlier phases, necessitating nutrition de-escalation.
Summary:
This stepwise, phase-adapted nutritional strategy - start low, advance judiciously, and individualize throughout - offers a precision framework aimed at aligning feeding with metabolic capacity and minimizes risks of over- or underfeeding. Future priorities include validating biomarkers, identifying metabolic phenotypes, and testing adaptive nutrition algorithms in optimizing nutritional therapy.
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