Supplemental parenteral nutrition improves immunity with unchanged carbohydrate and protein metabolism in critically ill patients: The SPN2 randomized tracer study.

BACKGROUND & AIMS: Individualized supplemental parenteral nutrition (SPN) providing measured energy expenditure from day 4 reduced infectious complications in a previous study including 305 intensive care (ICU) patients. The study aimed at investigating the metabolic, and immune responses underlying the clinical response of the previous trial. METHODS: Randomized controlled trial enrolling 23 critically ill patients on day 3 (D3) of admission to the ICU who were fed less than 60% of their energy target by the enteral nutrition (EN) alone: allocation to either continued EN or to SPN to a target validated by indirect calorimetry. Protein and glucose metabolism (primary endpoint) were investigated with tracer isotopes on D4 and D9. Secondary endpoints: 1) immune response, investigated in serum and in stimulated peripheral blood mononuclear cells (PMBC), by dosing a panel of cytokines (infectious complications were recorded), and 2) Muscle mass was assessed by ultrasound of the thigh. RESULTS: Comparable at baseline, the SPN group (n = 11) received more energy (median 24.3 versus 17.8 kcal/kg/day: p < 0.001) and proteins (1.11 versus 0.69 g/kg/day: p < 0.001) than the control group during the five days' intervention, resulting in a less negative energy balance by D9 (p = 0.0027). Net protein breakdown and Glucose kinetics on D9 did not differ, within or between groups. In agreement with a decrease in infection rate, immune response in the SPN group showed decreased serum IL-6 (p = 0.024), IL-1ß, IL-10 levels and TNF-α secretion by PBMC (p = 0.018) at D9. Muscle mass loss from D4 to D15 tended to be less in the SPN group (-16% versus -23%: p = 0.06). Clinical course by D28 did not differ. CONCLUSIONS: Feeding patients to cover an individualised measured energy target with SPN from D4 to cover needs, was associated with improved immunity, less systemic inflammation and a trend to less muscle mass loss. CLINICAL TRIAL REGISTRY: NCT02022813 at https://clinicaltrials.gov/.

[Body composition: decision making support in oncology, current and future practices]. / Composition corporelle: outil d'aide d la décision en oncologie, pratiques actuelles et perspectives.

This review aims to highlight the synergies between clinical nutrition, body composition and cancer treatment. Body composition is already a powerful tool to diagnose cachexia and determine response to nutritionnal intervention. It may be used in the future to fine tune body surface area (BSA) based drug dose determination thanks to its capacity to predict chemotoxicity. The overall aim of nutritionnal intervention is to optimize the oncological care by reducing treatment interruptions and improving the quality of life. However, to achieve this goal, nutritionnal intervention has to be very accurate as most of the failures result from inappropriate intervention.

Clinical nutrition, body composition and oncology: a critical literature review of the synergies.

PURPOSE OF THE RESEARCH: Review the oncology and clinical nutrition literature to highlight the synergies between those two subjects. This review focuses on diagnostic of lean body wasting and the recent improvements in measuring body composition to monitor the response to nutrition during optimal oncology treatment. PRINCIPAL RESULTS: Nutrition support in cancer patients has made major progresses. A variety of advanced tools allow monitoring and explaining weight loss, body composition changes and metabolic alterations. Body composition is more accurate than body surface area to determine chemotherapeutic drug dosing. As with any therapeutic approach, clinical nutrition has a better risk-benefit ratio if implemented when indicated rather than used routinely. Body composition measurements are helpful for a better understanding of the host-tumor interactions during cancer treatment and nutrition support. MAJOR CONCLUSIONS: Nutrition support based on body composition analysis may significantly contribute to optimize current oncology treatment and clinical outcomes.