Development of Biomarkers Based on Diet-Dependent Metabolic Serotypes: Concerns and Approaches for Cohort and Gender Issues in Serum Metabolome Studies

This is the publisher's version, also available electronically from: http://online.liebertpub.com/doi/pdfplus/10.1089/omi.2004.8.209Mathematical 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-oId 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 metabolom e 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 ^ 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

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

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

Biological variability dominates and influences analytical variance in HPLC-ECD studies of the human plasma metabolome

<p>Abstract</p> <p>Background</p> <p>Biomarker-based assessments of biological samples are widespread in clinical, pre-clinical, and epidemiological investigations. We previously developed serum metabolomic profiles assessed by HPLC-separations coupled with coulometric array detection that can accurately identify <it>ad libitum </it>fed and caloric-restricted rats. These profiles are being adapted for human epidemiology studies, given the importance of energy balance in human disease.</p> <p>Methods</p> <p>Human plasma samples were biochemically analyzed using HPLC separations coupled with coulometric electrode array detection.</p> <p>Results</p> <p>We identified these markers/metabolites in human plasma, and then used them to determine which human samples represent blinded duplicates with 100% accuracy (N = 30 of 30). At least 47 of 61 metabolites tested were sufficiently stable for use even after 48 hours of exposure to shipping conditions. Stability of some metabolites differed between individuals (N = 10 at 0, 24, and 48 hours), suggesting the influence of some biological factors on parameters normally considered as analytical.</p> <p>Conclusion</p> <p>Overall analytical precision (mean median CV, ~9%) and total between-person variation (median CV, ~50–70%) appear well suited to enable use of metabolomics markers in human clinical trials and epidemiological studies, including studies of the effect of caloric intake and balance on long-term cancer risk.</p

Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research

Mass spectrometry (MS) techniques, because of their sensitivity and selectivity, have become methods of choice to characterize the human metabolome and MS-based metabolomics is increasingly used to characterize the complex metabolic effects of nutrients or foods. However progress is still hampered by many unsolved problems and most notably the lack of well established and standardized methods or procedures, and the difficulties still met in the identification of the metabolites influenced by a given nutritional intervention. The purpose of this paper is to review the main obstacles limiting progress and to make recommendations to overcome them. Propositions are made to improve the mode of collection and preparation of biological samples, the coverage and quality of mass spectrometry analyses, the extraction and exploitation of the raw data, the identification of the metabolites and the biological interpretation of the results

Determination of selected water-soluble vitamins using hydrophilic chromatography: A comparison of photodiode array, fluorescence, and coulometric detection, and validation in a breakfast cereal matrix

Water-soluble vitamins are an important class of compounds that require quantification from food sources to monitor nutritional value. In this study we have analysed six water-soluble B vitamins ([thiamine (B1), riboflavin (B2), nicotinic acid (B3, NAc), nicotinamide (B3, NAm), pyridoxal (B6), folic acid (B9)], and ascorbic acid (vit C) with hydrophilic interaction liquid chromatography (HILIC), and compared UV, fluorescent (FLD) and coulometric detection to optimise a method to quantitate the vitamins from food sources. Employing UV/diode array (DAD) and fluorimetric detection, six B vitamins were detected in a single run using gradient elution from 100% to 60% solvent B [10mM ammonium acetate, pH 5.0, in acetonitrile and water 95:5 (v:v)] over 18min. UV detection was performed at 268nm for B1, 260nm for both B3 species and 284nm for B9. FLD was employed for B2 at excitation wavelength of 268nm, emission of 513nm, and 284nm/317nm for B6. Coulometric detection can be used to detect B6 and B9, and vit C, and was performed isocratically at 75% and 85% of solvent B, respectively. B6 was analysed at a potential of 720mV, while B9 was analysed at 600mV, and vit C at 30mV. Retention times (0.96 to 11.81min), intra-day repeatability (CV 1.6 to 3.6), inter-day variability (CV 1.8 to 11.1), and linearity (R 0.9877 to 0.9995) remained good under these conditions with limits of detection varying from 6.6 to 164.6ngmL(-1), limits of quantification between 16.8 and 548.7ngmL(-1). The method was successfully applied for quantification of six B vitamins from a fortified food product and is, to our knowledge, the first to simultaneously determine multiple water-soluble vitamins extracted from a food matrix using HILIC