A unified modeling framework for metabonomic profile development and covariate selection for acute trauma subjects.

Acute trauma is often associated with progressive deterioration of multiple organ systems in humans and is the leading cause of death in trauma care units. Identification of specific organ failure in a non-invasive manner and the contribution of different demographic factors on the casual progression of acute trauma are of supreme interests for successful diagnosis, prognosis or monitoring of trauma status. Recently, electrospray ionization and matrix-assisted laser desorption time-of-flight mass spectrometry have been used to identify biomarkers in both proteomics and metabonomics studies. Data sets generated from mass spectrometers in such studies are generally very large in size and thus require the use of sophisticated statistical techniques to glean useful information. In a recent development, Ghosh et al. (BMC Bioinformatics 2008; 9:38) suggested a unified semiparametric approach to distinguish urinary metabolic profiles in a group of traumatic subjects from those of a control group consisting of normal individuals. In this study we have extended their approach by combining available covariate information in the development of metabonomic profile of acute trauma. We have shown that age is a statistically significant covariate across trauma and control group, thus pointing out the fact that prognosis of trauma may be acutely linked with subjects' age.