Development of biomarkers based on diet-dependent metabolic serotypes: characteristics of component-based models of metabolic serotypes.

Our research seeks to identify a serum profile, or serotype, that reflects the systemic physiologic modifications resultant from dietary restriction (DR), in part such that this knowledge can be applied for biomarker studies. Direct comparison suggests that component-based classification algorithms consistently out-perform distance-based metrics for studies of nutritional modulation of metabolic serotype, but are subject to over-fitting concerns. Intercohort differences in the sera metabolome could partially obscure the effects of DR. Further analysis now shows that implementation of component-based approaches (also called projection methods) optimized for class separation and controlled for over-fitting have >97% accuracy for distinguishing sera from control or DR rats. DR's effect on the metabolome is shown to be robust across cohorts, but differs in males and females (although some metabolites are affected in both). We demonstrate the utility of projection-based methods for both sample and variable diagnostics, including identification of critical metabolites and samples that are atypical with respect to both class and variable models. Inclusion of non-statistically different variables enhances classification models. Variables that contribute to these models are sharply dependent on mathematical processing techniques; some variables that do not contribute under one paradigm are powerful under alternative mathematical paradigms. In practical terms, this information may find purpose in other endeavors, such as mechanistic studies of DR. Application of these approaches confirms the utility of megavariate data analysis techniques for optimal generation of biomarkers based on nutritional modulation of physiological processes.