Development of Biomarkers Based on Diet-Dependent Metabolic Serotypes: Characteristics of Component-Based Models of Metabolic Serotypes

This is the publisher's version, also available electronically from: http://online.liebertpub.com/doi/pdfplus/10.1089/omi.2004Our research seeks to identify a scrum 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 arc 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

Development of Biomarkers Based on Diet-Dependent Metabolic Serotypes: Practical Issues in Development of Expert System-Based Classification Models in Metabolomic Studies

This is the publisher's official version, also available electronically from: http://online.liebertpub.com/doi/pdfplus/10.1089/omi.2004.8.197Dietary 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 IHCAJ; principal components analysis [PCAJ) 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

Polyphenols in Almond Skins after Blanching Modulate Plasma Biomarkers of Oxidative Stress in Healthy Humans

Almond skins are a waste byproduct of blanched almond production. Polyphenols extracted from almond skins possess antioxidant activities in vitro and in vivo. Thus, we examined the pharmacokinetic profile of almond skin polyphenols (ASP) and their effect on measures of oxidative stress. In a randomized crossover trial, seven adults consumed two acute ASP doses (225 mg (low, L) or 450 mg (high, H) total phenols) in skim milk or milk alone. Plasma flavonoids, glutathione peroxidase (GPx), glutathione (GSH), oxidized GSH (GSSG), and resistance of low- density lipoprotein (LDL) to oxidation were measured over 10 h. The H dose increased catechin and naringenin in plasma, with maximum concentrations of 44.3 and 19.3 ng/mL, respectively. The GSH/GSSG ratio at 3 h after the H doses was 212% of the baseline value, as compared to 82% after milk (p = 0.003). Both ASP doses upregulated GPx activity by 26–35% from the baseline at 15, 30, 45, and 120 min after consumption. The in vitro addition of α-tocopherol extended the lag time of LDL oxidation at 3 h after L and H consumption by 144.7% and 165.2% of that at 0 h compared to no change after milk (p ≤ 0.05). In conclusion, ASP are bioavailable and modulate GSH status, GPx activity, and the resistance of LDL to oxidation

Effects of cranberry juice consumption on vascular function in patients with coronary artery disease123

Background: Cranberry juice contains polyphenolic compounds that could improve endothelial function and reduce cardiovascular disease risk