Acute Consumption of Flavan-3-ol-Enriched Dark Chocolate Affects Human Endogenous Metabolism

Flavan-3-ols and methylxanthines have potential beneficial effects on human health including reducing cardiovascular risk. We performed a randomized controlled crossover intervention trial to assess the acute effects of consumption of flavan-3-ol-enriched dark chocolate, compared with standard dark chocolate and white chocolate, on the human metabolome. We assessed the metabolome in urine and blood plasma samples collected before and at 2 and 6 h after consumption of chocolates in 42 healthy volunteers using a nontargeted metabolomics approach. Plasma samples were assessed and showed differentiation between time points with no further separation among the three chocolate treatments. Multivariate statistics applied to urine samples could readily separate the postprandial time points and distinguish between the treatments. Most of the markers responsible for the multivariate discrimination between the chocolates were of dietary origin. Interestingly, small but significant level changes were also observed for a subset of endogenous metabolites. H-1 NMR revealed that flavan-3-ol-enriched dark chocolate and standard dark chocolate reduced urinary levels of creatinine, lactate, some amino acids, and related degradation products and increased the levels of pyruvate and 4-hydroxyphenylacetate, a phenolic compound of bacterial origin. This study demonstrates that an acute chocolate intervention can significantly affect human metabolism

A comparison of variate pre-selection methods for use in partial least squares regression: a case study on NIR spectroscopy applied to monitoring beer fermentation

This work investigates four methods of selecting variates from near-infrared (NIR) spectra for use in partial least squares (PLS) regression models to predict biomass and chemical changes during beer fermentation. The fermentation parameters studied were ethanol concentration, specific gravity (SG), optical density (OD) and dry cell weight (DCW). The four selection methods investigated were: Simple, where a fingerprint region is chosen manually; CovProc, a covariance procedure where variates are introduced based on the magnitude of the 1st PLS vector coefficients; CovProc-SavGo, a modification to CovProc where the window size of a Savitzky-Golay filter applied to the spectra is also optimised; and Genetic Algorithm (GA), where variates are selected based on the frequency of appearance in 8-variate multiple linear regression models found from repeated execution of the GA routine. The analysis found that all four methods produced good predictive models. The GA approach produced the lowest standard error in prediction (SEP) based on leave-one-out cross validation (LOO-CV), although this advantage was not reflected in the standard error in validation values, SEV, where all four models performed comparably. From this work, we would recommend using the Simple approach if a suitable fingerprint region can be identified, and using CovProc otherwise

Lack of acute or chronic effects of epicatechin-rich and procyanidin-rich apple extracts on blood pressure and cardiometabolic biomarkers in adults with moderately elevated blood pressure: a randomized, placebo-controlled crossover trial

Background: The reported effects of flavanol-rich foods such as cocoa, dark chocolate, and apples on blood pressure and endothelial function may be due to the monomeric flavanols [mainly (–)-epicatechin (EC)], the oligomeric flavanols [procyanidins (PCs)], or other components. Reports of well-controlled intervention studies that test the effects of isolated oligomeric flavanols on biomarkers of cardiovascular health are lacking. Objective: We studied the acute and chronic effects of an EC-rich apple flavanol extract and isolated apple PCs on systolic blood pressure (BP) and other cardiometabolic biomarkers. Design: Forty-two healthy men and women with moderately elevated BP completed this randomized, double-blind, placebo-controlled, 4-arm crossover trial. Participants ingested a single dose of an apple flavanol extract (70 mg monomeric flavanols, 65 mg PCs), a double dose of this extract (140 mg monomeric flavanols, 130 mg PCs), an apple PC extract (130 mg PCs, 6.5 mg monomeric flavanols), or placebo capsules once daily for 4 wk, in random order. Biomarkers of cardiovascular disease risk and vascular function were measured before and 2 h after ingestion of the first dose and after the 4-wk intervention. Results: Compared with placebo, none of the isolated flavanol treatments significantly (P < 0.05) changed systolic or diastolic BP (peripheral and aortic), plasma nitric oxide (NO) reaction products, or measures of arterial stiffness (carotid femoral pulse-wave velocity, brachial-ankle pulse-wave velocity, or Augmentation Index) after 2 h or 4 wk of the intervention. There were no changes in plasma endogenous metabolite profiles or circulating NO; endothelin 1; total, HDL, or LDL cholesterol; triglycerides; fasting glucose; fructosamine; or insulin after 4 wk of the intervention. Conclusions: Our data suggest that, in isolation, neither monomeric flavanols nor PCs affect BP, blood lipid profiles, endothelial function, or glucose control in individuals with moderately elevated BP. The reported benefits of consuming flavanol-rich cocoa, chocolate, and apple products appear to be dependent on other components, which may work in combination with monomeric flavanols and PCs. This trial was registered at www.clinicaltrials.gov as NCT02013856

Evaluation of multiple variate selection methods from a biological perspective: a nutrigenomics case study

Genomics-based technologies produce large amounts of data. To interpret the results and identify the most important variates related to phenotypes of interest, various multivariate regression and variate selection methods are used. Although inspected for statistical performance, the relevance of multivariate models in interpreting biological data sets often remains elusive. We compare various multivariate regression and variate selection methods applied to a nutrigenomics data set in terms of performance, utility and biological interpretability. The studied data set comprised hepatic transcriptome (10,072 predictor variates) and plasma protein concentrations [2 dependent variates: Leptin (LEP) and Tissue inhibitor of metalloproteinase 1 (TIMP-1)] collected during a high-fat diet study in ApoE3Leiden mice. The multivariate regression methods used were: partial least squares “PLS”; a genetic algorithm-based multiple linear regression, “GA-MLR”; two least-angle shrinkage methods, “LASSO” and “ELASTIC NET”; and a variant of PLS that uses covariance-based variate selection, “CovProc.” Two methods of ranking the genes for Gene Set Enrichment Analysis (GSEA) were also investigated: either by their correlation with the protein data or by the stability of the PLS regression coefficients. The regression methods performed similarly, with CovProc and GA performing the best and worst, respectively (R-squared values based on “double cross-validation” predictions of 0.762 and 0.451 for LEP; and 0.701 and 0.482 for TIMP-1). CovProc, LASSO and ELASTIC NET all produced parsimonious regression models and consistently identified small subsets of variates, with high commonality between the methods. Comparison of the gene ranking approaches found a high degree of agreement, with PLS-based ranking finding fewer significant gene sets. We recommend the use of CovProc for variate selection, in tandem with univariate methods, and the use of correlation-based ranking for GSEA-like pathway analysis methods

Notes on the practical utility of OPLS

This article concerns two chemometric modeling methods - the well-known partial least squares regression and the comparatively recently-devised orthogonal projections to latent structures (OPLS). We discuss their similarities and differences with a focus on the usage of OPLS in the analytical-chemistry literature. (C) 2009 Elsevier Ltd. All rights reserved

Optimizing the efficiency of cross-validation in linear discriminant analysis through selective use of the Sherman-Morrison-Woodbury inversion formula

Cross-validation (CV) is a necessary stage in the development of multivariate discriminant models, but is potentially very time-consuming. Significant time saving is possible by employing update formula to avoid unnecessary recalculations. We show that using the Sherman-Morrison-Woodbury (SMW) inversion formula can sometimes provide additional speed gains. The potential gain depends on the structure of the dataset and CV approach. We recommend comparing rival schemes before starting long computational tasks. Datasets and Matlab® m-files are available at www.metabolomics-nrp.org.uk/ publications.htm

TinyLVR:A utility for viewing single predictor multivariate models in terms of a two factor latent vector model

This paper describes an adaptation of Ergon's 2PLS approach (Compression into two-component PLS factorizations. J. Chemom. 2003: 17: 303-312.) to represent a single predictor regression model in terms of a two-factor latent vector model. The purpose of this reduction is to aid model interpretation and diagnostics. Non-orthogonal score vectors are produced from two orthonormal loading vectors: one identical to the first PLS loading vector, and a second built from the regression vector. Using an invertible matrix, the factorization can be alternatively represented by two orthogonal score vectors, one of which is proportional to centred predictions. An auxiliary set of loadings is also calculated, which captures a different model space, but is provided since its associated residuals have useful properties. Identities connecting the two model spaces are provided. The latent vector regression coefficients are not always least-squares estimates but can be represented as the solution to a two-term generalized ridge regression. Consequences of this are addressed. The utility of TinyLVR is demonstrated with example models built using stepwise variate selection and ridge regression. (c) 2010 Elsevier B.V. All rights reserved