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Our research seeks to identify a serum profile, or serotype, that reflects substantial changes in food intake. Earlier studies demonstrated that a number of low-molecular-weight, redox-active compounds of metabolome were sufficiently stable analytically and biologically to identify biomarkers of dietary restriction (DR, restriction of total food intake) in rats. A second initial requirement is to demonstrate feasibility, i.e., that concentration changes in selected serum metabolites can contain sufficient information to classify rats by diet. The current study distinguished 101 (female) and 112 (male) chromatographically identifiable compounds that differ between ad libitum (AL) consumption and DR 6-mo-old rats. In a cohort of female rats, both hierarchical cluster analysis (HCA) and principal component analyses (PCA) could distinguish dietary groups with 100% efficiency (101 metabolites). Repeating the classification studies using the 63 biologically and analytically most robust metabolites decreased noise without affecting categorical separation. In a cohort of male rats, PCA, but not HCA, distinguished the original dietary groups with 100% accuracy (112 metabolites). A subset of 52 of the 112 metabolites enabled both HCA and PCA to group the male rats with 100% accuracy. These data demonstrate that quantitative analysis of selected serum metabolites can yield sufficient information by which to classify the dietary intake of a group of rats, identify such markers chromatographically and set the stage for validation of these metabolic serotypes in independent datasets. J. Nutr..132: 1031-1038, 2002. KEY WORDS: * dietary restriction * serum metabolite * HPLC * multivariate analysis * biomarker * rats

Presents a study that investigated serum metabolites that differentiate dietary groups utilizing both male and female rats to establish serotypes that reflect substantial changes in food intake. Details on the principal component analysis of the dietary restriction serotype; Validation of the male serotype; Comparison between the principal component analysis and the hierarchical cluster analysis.

Presents a study that investigated a metabolic serum profile or serotype that reflects changes in food intake. Selection of metabolites of interest by statistical comparison; Examination of the datasets using hierarchical cluster analysis to determine whether the metabolite distinguishes the ad libitum and dietary restriction in rats; Discussion on the principal component analysis of ad libitum and dietary restriction serotypes.

Dietary restriction (DR)-induced changes in the serum metabolome may be biomarkers for physiological status (e.g., relative risk of developing age-related diseases such as cancer). Megavariate analysis (unsupervised hierarchical cluster analysis [HCA]; principal components analysis [PCA]) of serum metabolites reproducibly distinguish DR from ad libitum fed rats. Component-based approaches (i.e., PCA) consistently perform as well as or better than distance-based metrics (i.e., HCA). We therefore tested the following: (A) Do identified subsets of serum metabolites contain sufficient information to construct mathematical models of class membership (i.e., expert systems)? (B) Do component-based metrics out-perform distance-based metrics? Testing was conducted using KNN (k-nearest neighbors, supervised HCA) and SIMCA (soft independent modeling of class analogy, supervised PCA). Models were built with single cohorts, combined cohorts or mixed samples from previously studied cohorts as training sets. Both algorithms over-fit models based on single cohort training sets. KNN models had >85% accuracy within training/test sets, but were unstable (i.e., values of k could not be accurately set in advance). SIMCA models had 100% accuracy within all training sets, 89 % accuracy in test sets, did not appear to over-fit mixed cohort training sets, and did not require post-hoc modeling adjustments. These data indicate that (i) previously defined metabolites are robust enough to construct classification models (expert systems) with SIMCA that can predict unknowns by dietary category; (ii) component-based analyses outperformed distance-based metrics; (iii) use of over-fitting controls is essential; and (iv) subtle inter-cohort variability may be a critical issue for high data density biomarker studies that lack state markers.

Mathematical models that reflect the effects of dietary restriction (DR) on the sera metabolome may have utility in understanding the mechanisms of DR and in applying this knowledge to human epidemiological studies. Previous studies demonstrated both the feasibility of identifying biomarkers through metabolome analysis and the validity of our approach in independent cohorts of 6-month-old male and female ad libitum fed or DR rats. Cross-cohort studies showed that cohort-specific effects distorted the dataset. The present study extends these observations across the entire sample set, thereby validating our markers independently of specific cohorts. Metabolites originally identified in males were examined in females and vice-versa. DR's effect on the metabolome is partially gender-specific and is modulated by environmental factors. DR reduces inter-gender differences in the metabolome. Univariate statistical methods showed that 56/93 metabolites in the female samples and 39/93 metabolites in the male samples were significantly altered (using our previous cut-off criteria of p < or = 0.2) by DR. The metabolites modulated by DR present a wide spectrum of concentration, redox reactivity and hydrophilicity, suggesting that our serotype is broadly representative of the metabolome and that DR has broad effects on the metabolome. These studies, coupled with those in the preceding and following reports, also highlight the utility for consideration of the metabolome as a network of metabolites using appropriate data analysis approaches. The inter-cohort and inter-gender differences addressed herein suggest potential cautions, and potential approaches, for identification of multivariate biomarker profiles that reflect changes in physiological status, such as a metabolism that predisposes to increased risk of neoplasia.

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.