A new strategy of enteral nutrition intervention for ICU patients targeting intestinal flora. Issue 47 (24th November 2021)

• Record Type: Journal Article
• Authors: Guo, Yangyang; Xu, Ming; Shi, Guangzhi; Zhang, Jindong
• Is Part Of: Medicine. Volume 100:Issue 47(2021)
• Publication Date: 2021-11-24
• Subjects: enteral nutrition -- gut microbiota -- intensive care unit -- microbial marker; Medicine -- Periodicals; Medicine -- Periodicals; Médecine -- Périodiques; Geneeskunde; Medicine; Periodicals; Periodicals; 610.5
• DOI: 10.1097/MD.0000000000027763
• Languages: English
• ISSNs: 0025-7974

Abstract

Abstract: Background: Enteral nutrition (EN) therapy is a routine supportive method for patients in the intensive care unit (ICU). However, the incidence of EN intolerance is prevalent, because most ICU patients suffer intestinal mucosal barrier damage and gastrointestinal motility disorder. There is no definite index to predict EN intolerance, and the current treatment methods are not effective in alleviating EN intolerance. Gut microbiota is an important component of the intestinal micro-ecological environment, and alterations in its structure and composition can reflect changes in intestinal function and microenvironment. The purpose of this study is to investigate the effect of EN on the gut microbiota of ICU patients by monitoring the dynamic alterations of gut microbiota and to screen out the microbial markers that can be used to predict the incidence of EN intolerance. Methods: One hundred ICU patients with trauma or in a period of acute stress after surgery will be enrolled, and their fecal samples will be collected at different timepoints for microbial sequencing and analysis. General clinical data (demographic information, surgical data, laboratory parameters, illness severity scores, and therapeutic drugs), nutritional status data (nutritional status assessment and nutrition therapy monitoring data), as well as clinical outcomes, will be recorded. The microbial and clinical data will be combined to analyze the baseline characteristics and dynamic alterations of gut microbiota along with the incidence of EN intolerance. Data related to the gut microbiota will be statistically analyzed by R software, and other data performed by SPSS23.0 software. Conclusions: The effect of EN on gut microbiota and microbial markers predicting the intolerance of EN will lead us to develop a new nutrition intervention strategy for ICU patients. Furthermore, the results of this study will provide a basis for the discovery of potential probiotics used for the prevention and treatment of EN intolerance.