title: Using metabolomics to monitor kidney transplantation patients by means of clustering to spot anomalous patient behavior.
abstract: BACKGROUND
  NMR spectroscopy-based metabolomics is a system approach used to investigate the metabolic profile of biological fluids with multivariate data analysis tools. The aim of this study was to examine the kidney graft recovery process noninvasively through the examinations of urine samples using (1)H NMR spectroscopy combined with chemometric tools.
  
  
  METHODS
  Urine samples were treated as the source of metabolites reflecting the pathological and clinical conditions of patients with transplanted kidneys. We observed 15 subjects (9 males and 6 females) during the graft recovery process and initial days thereafter. The patients provided at least 9 samples each, applying advanced statistical methods of analysis: Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis PLS-DA).
  
  
  RESULTS
  The PCA model (for all subjects exp. var. PC1 13.96% and PC2 9.88%) allowed us to clearly designate 3 stages of recovery: initially the kidney is not working; in the second stage, it regains functions, and the third stage includes follow-up during hospitalization. PCA analysis of a single patient follows graft recovery based on biochemical (metabolites) information, assigning the appropriate recuperation stage.
  
  
  CONCLUSIONS
  NMR spectroscopy together with chemometric analysis allow monitoring of kidney graft recovery to identify patients who are not progressing within the normal range.
authors.affiliation: NMR Center University of Siena, Siena, Italy.
authors.forename: M
authors.initials: M
authors.lastname: Calderisi
authors.forename: A
authors.initials: A
authors.lastname: Vivi
authors.forename: P
authors.initials: P
authors.lastname: Mlynarz
authors.forename: M
authors.initials: M
authors.lastname: Tassin
authors.forename: M
authors.initials: M
authors.lastname: Banasik
authors.forename: T
authors.initials: T
authors.lastname: Dawiskiba
authors.forename: M
authors.initials: M
authors.lastname: Carmellini
mesh_terms: D016000:Cluster Analysis; D006801:Humans; D016030:Kidney Transplantation; D016018:Least-Squares Analysis; D009682:Magnetic Resonance Spectroscopy; D055432:Metabolomics; D008991:Monitoring, Physiologic / Q000379:methods*; D025341:Principal Component Analysis