1H NMR metabolic signature of cerebrospinal fluid following repetitive lower-limb remote ischemia preconditioning. (June 2018)

• Record Type: Journal Article
• Authors: Wang, Hailian; He, Zhiyong; Zhang, Yijue; Zhang, Jun
• Is Part Of: Neurochemistry international. Volume 116(2018)
• Page Start: 95
• Page End: 103
• Publication Date: 2018-06
• Subjects: Remote ischemia preconditioning (RIPC) -- Cerebrospinal fluid(CSF) -- Metabolites -- Metabolomics -- NMR spectroscopy; Neurochemistry -- Periodicals; Neurochemistry -- Periodicals; Neurochimie -- Périodiques; Neurochemistry; Periodicals; 612.804205
• DOI: 10.1016/j.neuint.2018.02.009
• Languages: English
• ISSNs: 0197-0186

Abstract

Abstract: Background Objective: The cerebral ischemia/reperfusion greatly influences brain metabolism. Remote ischemia preconditioning (RIPC) is reported to confer neuroprotective effects against cerebral ischemia in animal models and human. This study aims to investigate the metabolomic profiles of cerebrospinal fluid (CSF) in patients treated with repetitive lower limb RIPC and provides an insight into possible mechanism underlying RIPC-induced neuroprotection. Method: Fifty healthy patients undergoing minor surgery under spinal anesthesia were randomly allocated to 2 groups: control group (Group C, n = 25) and RIPC treatment group (Group T, n = 25). Repetitive limb RIPC were performed 3 sessions, consisting of three 5-min cycles per session from the day before surgery to the morning on the surgery day. The CSF samples were collected from 48 patients before intrathecal injection of local anesthetic. A proton nuclear magnetic resonance ( 1 H NMR)-based metabonomics approach was used to obtain the CSF metabolic profiles of the samples (n = 24 each). The acquired data were processed with MestReNova and followed by statistical analysis with SIMCA-P. Results: The model obtained with the orthogonal partial least-squares discriminant analysis (OPLS-DA) identified difference of metabolite profiles between two groups. The validation of the discriminant analysis showed that the accuracy of the OPLS-DA model was 81.3%. Sixteen metabolites including glucose, amino-acids and organic acids et al. were identified as the most influential CSF biomarkers for the discrimination between two groups, which are involved in pathways of energy metabolism and amino-acids metabolism. Conclusion: 1 H NMR spectra combined with pattern recognition analysis offers a new and promising platform to investigate metabolic signatures in patients treated with RIPC. Our results suggest repetitive RIPC mainly changes energy metabolism and amino-acid metabolism in brain, which provides a potential mechanistic understanding of RIPC-induced tolerance to cerebral ischemia. Highlights: 1 H NMR-based CSF metabolomics could be a valuable method to study underlying mechanism of RIPC-induced neuroprotection. Specific metabolites involved in energy & amino-acid metabolism pathways following RIPC may contribute to metabolic adaption. OPLS-DA model identified difference of metabolite profiles between RIPC treatment and control, showing the accuracy is 81.3%.