Francois Faitot1, Camille Besch2, Stephanie Battini3, Elisa Ruhland3, Mihaela Onea4, Pietro Addeo2, Marie-Lorraine Woehl-Jaeglé2, Bernard Ellero2, Philippe Bachellier2, Izzie-Jacques Namer5. 1. Hepatobiliopancreatic Surgery and Transplantation Department, Hopital de Hautepierre, CHU de Strasbourg, France; Laboratoire ICube, UMR7357, University of Strasbourg, France. 2. Hepatobiliopancreatic Surgery and Transplantation Department, Hopital de Hautepierre, CHU de Strasbourg, France. 3. Laboratoire ICube, UMR7357, University of Strasbourg, France. 4. Pathology Department, Hopital de Hautepierre, CHU de Strasbourg, France. 5. Laboratoire ICube, UMR7357, University of Strasbourg, France; Nuclear Medicine Department, Hôpital de Hautepierre, CHU de Strasbourg, France. Electronic address: izzie.jacques.namer@chru-strasbourg.fr.
Abstract
BACKGROUND & AIMS: There is an emerging need to assess the metabolic state of liver allografts especially in the novel setting of machine perfusion preservation and donor in cardiac death (DCD) grafts. High-resolution magic-angle-spinning nuclear magnetic resonance (HR-MAS-NMR) could be a useful tool in this setting as it can extemporaneously provide untargeted metabolic profiling. The purpose of this study was to evaluate the potential value of HR-MAS-NMR metabolomic analysis of back-table biopsies for the prediction of early allograft dysfunction (EAD) and donor-recipient matching. METHOD: The metabolic profiles of back-table biopsies obtained by HR-MAS-NMR, were compared according to the presence of EAD using partial least squares discriminant analysis. Network analysis was used to identify metabolites which changed significantly. The profiles were compared to native livers to identify metabolites for donor-recipient matching. RESULTS: The metabolic profiles were significantly different in grafts that caused EAD compared to those that did not. The constructed model can be used to predict the graft outcome with excellent accuracy. The metabolites showing the most significant differences were lactate level >8.3 mmol/g and phosphocholine content >0.646 mmol/g, which were significantly associated with graft dysfunction with an excellent accuracy (AUROClactates = 0.906; AUROCphosphocholine = 0.816). Native livers from patients with sarcopenia had low lactate and glycerophosphocholine content. In patients with sarcopenia, the risk of EAD was significantly higher when transplanting a graft with a high-risk graft metabolic score. CONCLUSION: This study underlines the cost of metabolic adaptation, identifying lactate and choline-derived metabolites as predictors of poor graft function in both native livers and liver grafts. HR-MAS-NMR seems a valid technique to evaluate graft quality and the consequences of cold ischemia on the graft. It could be used to assess the efficiency of graft resuscitation on machine perfusion in future studies. LAY SUMMARY: Real-time metabolomic profiles of human grafts during back-table can accurately predict graft dysfunction. High lactate and phosphocholine content are highly predictive of graft dysfunction whereas low lactate and phosphocholine content characterize patients with sarcopenia. In these patients, the cost of metabolic adaptation may explain the poor outcomes.
BACKGROUND & AIMS: There is an emerging need to assess the metabolic state of liver allografts especially in the novel setting of machine perfusion preservation and donor in cardiac death (DCD) grafts. High-resolution magic-angle-spinning nuclear magnetic resonance (HR-MAS-NMR) could be a useful tool in this setting as it can extemporaneously provide untargeted metabolic profiling. The purpose of this study was to evaluate the potential value of HR-MAS-NMR metabolomic analysis of back-table biopsies for the prediction of early allograft dysfunction (EAD) and donor-recipient matching. METHOD: The metabolic profiles of back-table biopsies obtained by HR-MAS-NMR, were compared according to the presence of EAD using partial least squares discriminant analysis. Network analysis was used to identify metabolites which changed significantly. The profiles were compared to native livers to identify metabolites for donor-recipient matching. RESULTS: The metabolic profiles were significantly different in grafts that caused EAD compared to those that did not. The constructed model can be used to predict the graft outcome with excellent accuracy. The metabolites showing the most significant differences were lactate level >8.3 mmol/g and phosphocholine content >0.646 mmol/g, which were significantly associated with graft dysfunction with an excellent accuracy (AUROClactates = 0.906; AUROCphosphocholine = 0.816). Native livers from patients with sarcopenia had low lactate and glycerophosphocholine content. In patients with sarcopenia, the risk of EAD was significantly higher when transplanting a graft with a high-risk graft metabolic score. CONCLUSION: This study underlines the cost of metabolic adaptation, identifying lactate and choline-derived metabolites as predictors of poor graft function in both native livers and liver grafts. HR-MAS-NMR seems a valid technique to evaluate graft quality and the consequences of cold ischemia on the graft. It could be used to assess the efficiency of graft resuscitation on machine perfusion in future studies. LAY SUMMARY: Real-time metabolomic profiles of human grafts during back-table can accurately predict graft dysfunction. High lactate and phosphocholine content are highly predictive of graft dysfunction whereas low lactate and phosphocholine content characterize patients with sarcopenia. In these patients, the cost of metabolic adaptation may explain the poor outcomes.
Authors: Alyssa Ward; David K Klassen; Kate M Franz; Sebastian Giwa; Jedediah K Lewis Journal: Curr Opin Organ Transplant Date: 2018-06 Impact factor: 2.640
Authors: Hynek Mergental; Barnaby T F Stephenson; Richard W Laing; Amanda J Kirkham; Desley A H Neil; Lorraine L Wallace; Yuri L Boteon; Jeannette Widmer; Ricky H Bhogal; M Thamara P R Perera; Amanda Smith; Gary M Reynolds; Christina Yap; Stefan G Hübscher; Darius F Mirza; Simon C Afford Journal: Liver Transpl Date: 2018-10 Impact factor: 5.799