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Statistical approach to metabonomic analysis of rat urine following surgical trauma
Author(s) -
Ghosh Samiran,
Hill Dennis W.,
Petty Nathan M.,
Melchert Russell B.,
Luo Belinda,
Grant David F.,
Dey Dipak K.
Publication year - 2006
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.972
Subject(s) - logistic regression , medicine , urinary system , intensive care medicine , categorization , urine , artificial intelligence , computer science
Acute trauma is often associated with the progressive deterioration of multiple organ systems in humans and is the leading cause of death in trauma care units. Previous studies have suggested that multiple organ failure is likely related to uncontrolled systemic inflammation. However the causal mechanisms remain unknown. Current methods of assessing trauma patient status and predicting outcome are based on a variety of anatomical and/or physiological scoring models. While being useful, these are labor intensive and do not allow for real‐time analysis of patient status. In this study, we have developed a metabonomic based approach using a rodent model of acute trauma in order to determine whether statistically significant differences exist between the quantitative and qualitative profile of urinary metabolites of control rats and rats that have experienced surgical trauma. This approach incorporates statistical, analytical, and computational tools in order to identify metabolites that are unique to trauma and maybe used to predict trauma outcome. Statistical analysis showed significant differences between the trauma and the control urinary metabonomes. Partial least square (PLS) and Principle component analysis (PCA) combined with Logistic regression (LR) were used to categorize subjects into either control or trauma with greater than 80% accuracy. We have also shown that using Bayesian methods that we could classify subjects being traumatic or control with a given credible interval. These results suggest that metabonomics may prove useful for quantifying and identifying biomarkers of trauma status as well as trauma outcome in humans. Copyright © 2007 John Wiley & Sons, Ltd.