Herbs and Spices- Biomarkers of Intake Based on Human Intervention Studies - A Systematic Review.

Culinary herbs and spices have been used as both food flavoring and food preservative agents for centuries. Moreover, due to their known and presumptive health benefits, herbs and spices have also been used in medical practices since ancient times. Some of the health effects attributed to herbs and spices include antioxidant, anti-microbial, and anti-inflammatory effects as well as potential protection against cardiovascular disease, neurodegeneration, type 2 diabetes, and cancer. While interest in herbs and spices as medicinal agents remains high and their use in foods continues to grow, there have been remarkably few studies that have attempted to track the dietary intake of herbs and spices and even fewer that have tried to find potential biomarkers of food intake (BFIs). The aim of the present review is to systematically survey the global literature on herbs and spices in an effort to identify and evaluate specific intake biomarkers for a representative set of common herbs and spices in humans. A total of 25 herbs and spices were initially chosen, including anise, basil, black pepper, caraway, chili pepper, cinnamon, clove, cumin, curcumin, dill, fennel, fenugreek, ginger, lemongrass, marjoram, nutmeg, oregano, parsley, peppermint and spearmint, rosemary, saffron, sage, tarragon, and thyme. However, only 17 of these herbs and spices had published, peer-reviewed studies describing potential biomarkers of intake. In many studies, the herb or spice of interest was administrated in the form of a capsule or extract and very few studies were performed with actual foods. A systematic assessment of the candidate biomarkers was also performed. Given the limitations in the experimental designs for many of the published studies, further work is needed to better evaluate the identified set of BFIs. Although the daily intake of herbs and spices is very low compared to most other foods, this important set of food seasoning agents should not be underestimated, especially given their potential benefits to human health.

Chemical Composition of Commercial Cow's Milk.

Bovine milk is a nutritionally rich, chemically complex biofluid consisting of hundreds of different components. While the chemical composition of cow's milk has been studied for decades, much of this information is fragmentary and very dated. In an effort to consolidate and update this information, we have applied modern, quantitative metabolomics techniques along with computer-aided literature mining to obtain the most comprehensive and up-to-date characterization of the chemical constituents in commercial cow's milk. Using nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography-mass spectrometry (LC-MS), and inductively coupled plasma-mass spectrometry (ICP-MS), we were able to identify and quantify 296 bovine milk metabolites or metabolite species (corresponding to 1447 unique structures) from a variety of commercial milk samples. Through our literature analysis, we also found another 676 metabolites or metabolite species (corresponding to 908 unique structures). Detailed information regarding all 2355 of the identified chemicals in bovine milk have been made freely available through a Web-accessible database called the Milk Composition Database or MCDB ( http://www.mcdb.ca/ ).

Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies.

The life sciences are currently being transformed by an unprecedented wave of developments in molecular analysis, which include important advances in instrumental analysis as well as biocomputing. In light of the central role played by metabolism in nutrition, metabolomics is rapidly being established as a key analytical tool in human nutritional studies. Consequently, an increasing number of nutritionists integrate metabolomics into their study designs. Within this dynamic landscape, the potential of nutritional metabolomics (nutrimetabolomics) to be translated into a science, which can impact on health policies, still needs to be realized. A key element to reach this goal is the ability of the research community to join, to collectively make the best use of the potential offered by nutritional metabolomics. This article, therefore, provides a methodological description of nutritional metabolomics that reflects on the state-of-the-art techniques used in the laboratories of the Food Biomarker Alliance (funded by the European Joint Programming Initiative "A Healthy Diet for a Healthy Life" (JPI HDHL)) as well as points of reflections to harmonize this field. It is not intended to be exhaustive but rather to present a pragmatic guidance on metabolomic methodologies, providing readers with useful "tips and tricks" along the analytical workflow.

Metabolic Signature of a Functional High-Catechin Tea after Acute and Sustained Consumption in Healthy Volunteers through 1H NMR Based Metabolomics Analysis of Urine.

Functional tea beverages have emerged as a novel approach to achieving health benefits associated with tea. The use of metabolomics may improve the evaluation of their consumption and their effects. The current study aimed at exploring the urinary signature of exposure to a functional high-catechin tea (HCT) using untargeted NMR-based metabolomics. Ten volunteers participated in a crossover intervention study. Individuals consumed an HCT or a control beverage over a period of 28 days. Multilevel partial least-squares discriminant analysis (ML-PLS-DA) was used for paired comparisons. A further crossover model was performed to assess the significant changes. The consumption of the HCT resulted in the excretion of theanine, epicatechin, pyrogallol sulfate, and higher levels of 3-methyl-2-oxovalerate and succinate, as well as unknown compounds. In conclusion, the present work established novel urinary signatures of a functional drink. Such signatures may be potential biomarkers and/or reflect certain benefits of functional tea beverages.

Biomarker of food intake for assessing the consumption of dairy and egg products.

Dairy and egg products constitute an important part of Western diets as they represent an excellent source of high-quality proteins, vitamins, minerals and fats. Dairy and egg products are highly diverse and their associations with a range of nutritional and health outcomes are therefore heterogeneous. Such associations are also often weak or debated due to the difficulty in establishing correct assessments of dietary intake. Therefore, in order to better characterize associations between the consumption of these foods and health outcomes, it is important to identify reliable biomarkers of their intake. Biomarkers of food intake (BFIs) provide an accurate measure of intake, which is independent of the memory and sincerity of the subjects as well as of their knowledge about the consumed foods. We have, therefore, conducted a systematic search of the scientific literature to evaluate the current status of potential BFIs for dairy products and BFIs for egg products commonly consumed in Europe. Strikingly, only a limited number of compounds have been reported as markers for the intake of these products and none of them have been sufficiently validated. A series of challenges hinders the identification and validation of BFI for dairy and egg products, in particular, the heterogeneous composition of these foods and the lack of specificity of the markers identified so far. Further studies are, therefore, necessary to validate these compounds and to discover new candidate BFIs. Untargeted metabolomic strategies may allow the identification of novel biomarkers, which, when taken separately or in combination, could be used to assess the intake of dairy and egg products.

Pasture Feeding Changes the Bovine Rumen and Milk Metabolome.

The purpose of this study was to examine the effects of two pasture feeding systems-perennial ryegrass (GRS) and perennial ryegrass and white clover (CLV)-and an indoor total mixed ration (TMR) system on the (a) rumen microbiome; (b) rumen fluid and milk metabolome; and (c) to assess the potential to distinguish milk from different feeding systems by their respective metabolomes. Rumen fluid was collected from nine rumen cannulated cows under the different feeding systems in early, mid and late lactation, and raw milk samples were collected from ten non-cannulated cows in mid-lactation from each of the feeding systems. The microbiota present in rumen liquid and solid portions were analysed using 16S rRNA gene sequencing, while ¹H-NMR untargeted metabolomic analysis was performed on rumen fluid and raw milk samples. The rumen microbiota composition was not found to be significantly altered by any feeding system in this study, likely as a result of a shortened adaptation period (two weeks' exposure time). In contrast, feeding system had a significant effect on both the rumen and milk metabolome. Increased concentrations of volatile fatty acids including acetic acid, an important source of energy for the cow, were detected in the rumen of TMR and CLV-fed cows. Pasture feeding resulted in significantly higher concentrations of isoacids in the rumen. The ruminal fluids of both CLV and GRS-fed cows were found to have increased concentrations of p-cresol, a product of microbiome metabolism. CLV feeding resulted in increased rumen concentrations of formate, a substrate compound for methanogenesis. The TMR feeding resulted in significantly higher rumen choline content, which contributes to animal health and milk production, and succinate, a product of carbohydrate metabolism. Milk and rumen-fluids were shown to have varying levels of dimethyl sulfone in each feeding system, which was found to be an important compound for distinguishing between the diets. CLV feeding resulted in increased concentrations of milk urea. Milk from pasture-based feeding systems was shown to have significantly higher concentrations of hippuric acid, a potential biomarker of pasture-derived milk. This study has demonstrated that ¹H-NMR metabolomics coupled with multivariate analysis is capable of distinguishing both rumen-fluid and milk derived from cows on different feeding systems, specifically between indoor TMR and pasture-based diets used in this study.

Guidelines for Biomarker of Food Intake Reviews (BFIRev): how to conduct an extensive literature search for biomarker of food intake discovery.

Identification of new biomarkers of food and nutrient intake has developed fast over the past two decades and could potentially provide important new tools for compliance monitoring and dietary intake assessment in nutrition and health science. In recent years, metabolomics has played an important role in identifying a large number of putative biomarkers of food intake (BFIs). However, the large body of scientific literature on potential BFIs outside the metabolomics area should also be taken into account. In particular, we believe that extensive literature reviews should be conducted and that the quality of all suggested biomarkers should be systematically evaluated. In order to cover the literature on BFIs in the most appropriate and consistent manner, there is a need for appropriate guidelines on this topic. These guidelines should build upon guidelines in related areas of science while targeting the special needs of biomarker methodology. This document provides a guideline for conducting an extensive literature search on BFIs, which will provide the basis to systematically validate BFIs. This procedure will help to prioritize future work on the identification of new potential biomarkers and on validating these as well as other biomarker candidates, thereby providing better tools for future studies in nutrition and health.

HMDB 4.0: the human metabolome database for 2018.

The Human Metabolome Database or HMDB (www.hmdb.ca) is a web-enabled metabolomic database containing comprehensive information about human metabolites along with their biological roles, physiological concentrations, disease associations, chemical reactions, metabolic pathways, and reference spectra. First described in 2007, the HMDB is now considered the standard metabolomic resource for human metabolic studies. Over the past decade the HMDB has continued to grow and evolve in response to emerging needs for metabolomics researchers and continuing changes in web standards. This year's update, HMDB 4.0, represents the most significant upgrade to the database in its history. For instance, the number of fully annotated metabolites has increased by nearly threefold, the number of experimental spectra has grown by almost fourfold and the number of illustrated metabolic pathways has grown by a factor of almost 60. Significant improvements have also been made to the HMDB's chemical taxonomy, chemical ontology, spectral viewing, and spectral/text searching tools. A great deal of brand new data has also been added to HMDB 4.0. This includes large quantities of predicted MS/MS and GC-MS reference spectral data as well as predicted (physiologically feasible) metabolite structures to facilitate novel metabolite identification. Additional information on metabolite-SNP interactions and the influence of drugs on metabolite levels (pharmacometabolomics) has also been added. Many other important improvements in the content, the interface, and the performance of the HMDB website have been made and these should greatly enhance its ease of use and its potential applications in nutrition, biochemistry, clinical chemistry, clinical genetics, medicine, and metabolomics science.

Microbial metabolites are associated with a high adherence to a Mediterranean dietary pattern using a 1H-NMR-based untargeted metabolomics approach.

The study of biomarkers of dietary patterns including the Mediterranean diet (MedDiet) is scarce and could improve the assessment of these patterns. Moreover, it could provide a better understanding of health benefits of dietary patterns in nutritional epidemiology. We aimed to determine a robust and accurate biomarker associated with a high adherence to a MedDiet pattern that included dietary assessment and its biological effect. In this cross-sectional study, we included 56 and 63 individuals with high (H-MDA) and low (L-MDA) MedDiet adherence categories, respectively, all from the Prevención con Dieta Mediterránea trial. A 1H-NMR-based untargeted metabolomics approach was applied to urine samples. Multivariate statistical analyses were conducted to determine the metabolite differences between groups. A stepwise logistic regression and receiver operating characteristic curves were used to build and evaluate the prediction model for H-MDA. Thirty-four metabolites were identified as discriminant between H-MDA and L-MDA. The fingerprint associated with H-MDA included higher excretion of proline betaine and phenylacetylglutamine, among others, and decreased amounts of metabolites related to glucose metabolism. Three microbial metabolites - phenylacetylglutamine, p-cresol and 4-hydroxyphenylacetate - were included in the prediction model of H-MDA (95% specificity, 95% sensitivity and 97% area under the curve). The model composed of microbial metabolites was the biomarker that defined high adherence to a Mediterranean dietary pattern. The overall metabolite profiling identified reflects the metabolic modulation produced by H-MDA. The proposed biomarker may be a better tool for assessing and aiding nutritional epidemiology in future associations between H-MDA and the prevention or amelioration of chronic diseases.

Urinary 1H Nuclear Magnetic Resonance Metabolomic Fingerprinting Reveals Biomarkers of Pulse Consumption Related to Energy-Metabolism Modulation in a Subcohort from the PREDIMED study.

Little is known about the metabolome fingerprint of pulse consumption. The study of robust and accurate biomarkers for pulse dietary assessment has great value for nutritional epidemiology regarding health benefits and their mechanisms. To characterize the fingerprinting of dietary pulses (chickpeas, lentils, and beans), spot urine samples from a subcohort from the PREDIMED study were stratified using a validated food frequency questionnaire. Urine samples of nonpulse consumers (≤4 g/day of pulse intake) and habitual pulse consumers (≥25 g/day of pulse intake) were analyzed using a 1H nuclear magnetic resonance (NMR) metabolomics approach combined with multi- and univariate data analysis. Pulse consumption showed differences through 16 metabolites coming from (i) choline metabolism, (ii) protein-related compounds, and (iii) energy metabolism (including lower urinary glucose). Stepwise logistic regression analysis was applied to design a combined model of pulse exposure, which resulted in glutamine, dimethylamine, and 3-methylhistidine. This model was evaluated by a receiver operating characteristic curve (AUC > 90% in both training and validation sets). The application of NMR-based metabolomics to reported pulse exposure highlighted new candidates for biomarkers of pulse consumption and the impact on energy metabolism, generating new hypotheses on energy modulation. Further intervention studies will confirm these findings.

Clinical phenotype clustering in cardiovascular risk patients for the identification of responsive metabotypes after red wine polyphenol intake.

This study aims to evaluate the robustness of clinical and metabolic phenotyping through, for the first time, the identification of differential responsiveness to dietary strategies in the improvement of cardiometabolic risk conditions. Clinical phenotyping of 57 volunteers with cardiovascular risk factors was achieved using k-means cluster analysis based on 69 biochemical and anthropometric parameters. Cluster validation based on Dunn and Figure of Merit analysis for internal coherence and external homogeneity were employed. k-Means produced four clusters with particular clinical profiles. Differences on urine metabolomic profiles among clinical phenotypes were explored and validated by multivariate orthogonal signal correction partial least-squares discriminant analysis (OSC-PLS-DA) models. OSC-PLS-DA of (1)H-NMR data revealed that model comparing "obese and diabetic cluster" (OD-c) against "healthier cluster" (H-c) showed the best predictability and robustness in terms of explaining the pairwise differences between clusters. Considering these two clusters, distinct groups of metabolites were observed following an intervention with wine polyphenol intake (WPI; 733 equivalents of gallic acid/day) per 28days. Glucose was significantly linked to OD-c metabotype (P<.01), and lactate, betaine and dimethylamine showed a significant trend. Tartrate (P<.001) was associated with wine polyphenol intervention (OD-c_WPI and H-c_WPI), whereas mannitol, threonine methanol, fucose and 3-hydroxyphenylacetate showed a significant trend. Interestingly, 4-hydroxyphenylacetate significantly increased in H-c_WPI compared to OD-c_WPI and to basal groups (P<.05)-gut microbial-derived metabolite after polyphenol intake-, thereby exhibiting a clear metabotypic intervention effect. Results revealed gut microbiota responsive phenotypes to wine polyphenols intervention. Overall, this study illustrates a novel metabolomic strategy for characterizing interindividual responsiveness to dietary intervention and identification of health benefits.

Metabolic fingerprint after acute and under sustained consumption of a functional beverage based on grape skin extract in healthy human subjects.

Grape-derived polyphenols are considered to be one of the most promising ingredients for functional foods due to their health-promoting activities. We applied a HPLC-MS-based untargeted metabolomic approach in order to evaluate the impact of a functional food based on grape skin polyphenols on the urinary metabolome of healthy subjects. Thirty-one volunteers participated in two dietary crossover randomized intervention studies: with a single-dose intake (187 mL) and with a 15-day sustained consumption (twice per day, 187 mL per day in total) of a functional beverage (FB). Postprandial (4-hour) and 24-hour urine samples collected after acute consumption and on the last day of sustained FB consumption, respectively, were analysed using an untargeted HPLC-qTOF-MS approach. Multivariate modelling with subsequent application of an S-plot revealed differential mass features related to acute and prolonged consumption of FB. More than half of the mass features were shared between the two types of samples, among which several phase II metabolites of grape-derived polyphenols were identified at confidence level II. Prolonged consumption of FB was specifically reflected in urine metabolome by the presence of first-stage microbial metabolites of flavanols: hydroxyvaleric acid and hydroxyvalerolactone derivatives. Overall, several epicatechin and phenolic acid metabolites both of tissular and microbiota origin were the most representative markers of FB consumption. To our knowledge, this is one of the first studies where an untargeted LC-MS metabolomic approach has been applied in nutrition research on a grape-derived FB.

Metabolomic pattern analysis after mediterranean diet intervention in a nondiabetic population: a 1- and 3-year follow-up in the PREDIMED study.

The Mediterranean diet (MD) is considered a dietary pattern with beneficial effects on human health. The aim of this study was to assess the effect of an MD on urinary metabolome by comparing subjects at 1 and 3 years of follow-up, after an MD supplemented with either extra-virgin olive oil (MD + EVOO) or nuts (MD + Nuts), to those on advice to follow a control low-fat diet (LFD). Ninety-eight nondiabetic volunteers were evaluated, using metabolomic approaches, corresponding to MD + EVOO (n = 41), MD + Nuts (n = 27), or LFD (n = 30) groups. The (1)H NMR urinary profiles were examined at baseline and after 1 and 3 years of follow-up. Multivariate data analysis (OSC-PLS-DA and HCA) methods were used to identify the potential biomarker discriminating groups, exhibiting a urinary metabolome separation between MD groups against baseline and LFD. Results revealed that the most prominent hallmarks concerning MD groups were related to the metabolism of carbohydrates (3-hydroxybutyrate, citrate, and cis-aconitate), creatine, creatinine, amino acids (proline, N-acetylglutamine, glycine, branched-chain amino acids, and derived metabolites), lipids (oleic and suberic acids), and microbial cometabolites (phenylacetylglutamine and p-cresol). Otherwise, hippurate, trimethylamine-N-oxide, histidine and derivates (methylhistidines, carnosine, and anserine), and xanthosine were predominant after LFD. The application of NMR-based metabolomics enabled the classification of individuals regarding their dietary pattern and highlights the potential of this approach for evaluating changes in the urinary metabolome at different time points of follow-up in response to specific dietary interventions.

Urinary metabolomic fingerprinting after consumption of a probiotic strain in women with mastitis.

Infectious mastitis is a common condition among lactating women, with staphylococci and streptococci being the main aetiological agents. In this context, some lactobacilli strains isolated from breast milk appear to be particularly effective for treating mastitis and, therefore, constitute an attractive alternative to antibiotherapy. A (1)H NMR-based metabolomic approach was applied to detect metabolomic differences after consuming a probiotic strain (Lactobacillus salivarius PS2) in women with mastitis. 24h urine of women with lactational mastitis was collected at baseline and after 21 days of probiotic (PB) administration. Multivariate analysis (OSC-PLS-DA and hierarchical clustering) showed metabolome differences after PB treatment. The discriminant metabolites detected at baseline were lactose, and ibuprofen and acetaminophen (two pharmacological drugs commonly used for mastitis pain), while, after PB intake, creatine and the gut microbial co-metabolites hippurate and TMAO were detected. In addition, a voluntary desertion of the pharmacological drugs ibuprofen and acetaminophen was observed after probiotic administration. The application of NMR-based metabolomics enabled the identification of the overall effects of probiotic consumption among women suffering from mastitis and highlighted the potential of this approach in evaluating the outcomes of probiotics consumption. To our knowledge, this is the first time that this approach has been applied in women with mastitis during lactation.

(1)H-NMR-based metabolomic analysis of the effect of moderate wine consumption on subjects with cardiovascular risk factors.

Moderate wine consumption is associated with health-promoting activities. An H-NMR-based metabolomic approach was used to identify urinary metabolomic differences of moderate wine intake in the setting of a prospective, randomized, crossover, and controlled trial. Sixty-one male volunteers with high cardiovascular risk factors followed three dietary interventions (28 days): dealcoholized red wine (RWD) (272mL/day, polyphenol control), alcoholized red wine (RWA) (272mL/day) and gin (GIN) (100mL/day, alcohol control). After each period, 24-h urine samples were collected and analyzed by (1) H-NMR. According to the results of a one-way ANOVA, significant markers were grouped in four categories: alcohol-related markers (ethanol); gin-related markers; wine-related markers; and gut microbiota markers (hippurate and 4-hydroxphenylacetic acid). Wine metabolites were classified into two groups; first, metabolites of food metabolome: tartrate (RWA and RWD), ethanol, and mannitol (RWA); and second, biomarkers that relates to endogenous modifications after wine consumption, comprising branched-chain amino acid (BCAA) metabolite (3-methyl-oxovalerate). Additionally, a possible interaction between alcohol and gut-related biomarkers has been identified. To our knowledge, this is the first time that this approach has been applied in a nutritional intervention with red wine. The results show the capacity of this approach to obtain a comprehensive metabolome picture including food metabolome and endogenous biomarkers of moderate wine intake.

Nutrimetabolomic strategies to develop new biomarkers of intake and health effects.

Correctly assessing the metabolic status of subjects after consumption of specific diets is an important challenge for modern nutrition. Recently, metabolomics has been proposed as a powerful tool for exploring the complex relationship between nutrition and health. Nutritional metabolomics, through investigating the role that dietary components play in the maintenance of health and development of risk disease, aims to identify new biomarkers that allow the intake of these compounds to be monitored and related to their expected biological effects. This review offers an overview of the application of nutrimetabolomic strategies in the discovery of new biomarkers in human nutritional research, suggesting three main categories: (1) assessment of nutritional and dietary interventions; (2) diet exposure and food consumption monitoring; and (3) health phenotype and metabolic impact of diet. For this purpose, several examples of these applications will be used to provide evidence and to discuss the advantages and drawbacks of these nutrimetabolomic strategies.