Unbiased and biased chemometric analysis of LC-MS data from human urine following coffee intake.

We carried out a human volunteer study with 14 participants, eight of whom were asked to consume one cup of coffee at four different time points. Urine samples were collected at eight time points and analyzed by HPLC-MS analysis. The LC-MS data were subjected to unsupervised multivariate statistical analysis (principal component analysis) followed by supervised multivariate analysis (linear discriminant analysis). In an unbiased approach, in the absence of data preselection and filtering, the most important features explaining differences between coffee consumers and the control group observed showed variations in endogenous human hormonal steroid metabolites as well as xanthine derivatives. Only after a biased data treatment data revealed differences between the sample groups based on literature reported chlorogenic acid metabolites resulting directly from coffee intake. Such analysis could confirm the presence of 21 previously reported chlorogenic acid plasma metabolites as urinary metabolites. The application of tandem MS molecular networking revealed the presence of five bioavailable chlorogenic acid derivatives in urine previously not reported, including both quinic acid lactone and dimethoxy caffeoyl esters. Selected cinnamic acids were quantified in urine.

LC-MS based metabolomic approach for the efficient identification and relative quantification of bioavailable cocoa phenolics in human urine.

This study was designed to investigate the rate and extent of urinary excretion of cocoa phenolic metabolites after human intake using metabolomics approach. In this context, a feeding trial was conducted where urine samples were collected at different time points over 48-h period. Several biomarkers were highlighted in LC-MS based chemometrics using principal component (PCA) and partial least squares discriminant analysis (PLS-DA), which revealed the presence of both epicatechin and gut microbial phenyl-γ-valerolactones (PVLs) conjugated analogues. The presences of these metabolites segregated and grouped the samples based on cocoa and non-cocoa ingestion. Furthermore, semi quantification of major bioavailable metabolites was performed to determine the interindividual differences and assess the relative bioavailability of cocoa compounds in the human body. Our approach presented here is unique in displaying a combination of LC-MS based chemometrics visualization strategies, which revealed and identified significant biomarkers that could reduce the problems associated with data screening complexity.

LC-MS/MS based molecular networking approach for the identification of cocoa phenolic metabolites in human urine.

Dietary phenolic compounds are often transformed by gut microbiota prior to absorption. This transformation may modify their structures, producing novel gut flora metabolites associated with numerous health benefits. Traditional mass spectrometry (MS) based approaches for assessing dietary exposure of cocotea (cocoa, coffee and tea) products provided very little information about the modification and fate of dietary phenolics after ingestion, mainly due to limitation of complex sample nature and their data analyses. Mass spectrometry techniques are well-suited to a high-throughput characterization of natural products, however, analyzing MS based data of complex biological matrix is still considered a challenge. In order to overcome such limitations and simplify the analysis of complex MS data, a cocotea based human trial was conducted where MS based molecular networking approach was implemented. To demonstrate the utility of this approach in one of the specific cocotea diets, we have applied it to a diverse collection of human (n = 15) urine samples, who consumed cocoa rich in polyphenols over a 48-h period. This approach illustrated the power of the new strategy, allowing the rapid identification of new analogues of cocoa metabolites after human consumption. Analysis of human urine samples after cocoa consumption revealed (by assignment of unknown metabolites based on the network similarities) that monomeric flavanols are mainly absorbed and transformed directly into their glucuronide and sulfated moieties. Subsequently, the hydroxy and methoxy phenyl-g-velerolactone as well as their smaller metabolites (such as hydroxyphenyl valeric acids, hydroxy and methoxy phenyl propionic acids and their derivates) are indicative of bacterial metabolism of cocoa major flavanols. For the first time, our study exemplifies and highlight the implementation of MS based molecular networking approach in illustrating the tracking of various structural motifs of ingested cocoa phenolics in human based study.