Metabolomic-Based Approach to Identify Biomarkers of Apple Intake

Divergent metabolism of two lychee (Litchi chinensis Sonn.) pulp flavonols and their modulatory effects on gut microbiota: Discovery of hydroxyethylation in vitro colonic fermentation

Quercetin 3-O-rutinose-7-O-α-l-rhamnoside (QRR), a characteristic lychee pulp flavonoid, has been linked to diverse bioactivities involving microbial metabolism. By integrating colonic fermentation and mass spectrometry, the catabolites including 7-O-hydroxyethyl-isorhamnetin and 3'-amino-4'-O-methyl-7-O-hydroxyethyl-isorhamnetin were unprecedently identified and unique to QRR metabolism, relative to the structural analog quercetin 3-O-rutinoside (QR) metabolism. These above-described metabolites highlighted a special biotransformation hydroxyethylation in QRR catabolism. QRR was partially deglycosylated into quercetin 3-O-glucoside-7-O-α-l-rhamnoside potentially catalyzed by Bacteroides. QR was more directly degradable to aglycone during colonic fermentation than are QRR. Unlike with QR fermentation, equivalent QRR effectively upregulated concentrations of propionic and butyric acids that were highly relevant with Faecalibacterium and Coprococcus. After fermentation, the relative abundances of Bacteroides uniformis (0.03%) and Akkermansia muciniphila (0.13%) were only upregulated by QRR among all fermentation groups, leading to the enrichments of the corresponding genera. These results further reveal the relationship between flavonoid structures and metabolic characteristics.

Dietary biomarkers-an update on their validity and applicability in epidemiological studies

The aim of this literature review was to identify and provide a summary update on the validity and applicability of the most promising dietary biomarkers reflecting the intake of important foods in the Western diet for application in epidemiological studies. Many dietary biomarker candidates, reflecting intake of common foods and their specific constituents, have been discovered from intervention and observational studies in humans, but few have been validated. The literature search was targeted for biomarker candidates previously reported to reflect intakes of specific food groups or components that are of major importance in health and disease. Their validity was evaluated according to 8 predefined validation criteria and adapted to epidemiological studies; we summarized the findings and listed the most promising food intake biomarkers based on the evaluation. Biomarker candidates for alcohol, cereals, coffee, dairy, fats and oils, fruits, legumes, meat, seafood, sugar, tea, and vegetables were identified. Top candidates for all categories are specific to certain foods, have defined parent compounds, and their concentrations are unaffected by nonfood determinants. The correlations of candidate dietary biomarkers with habitual food intake were moderate to strong and their reproducibility over time ranged from low to high. For many biomarker candidates, critical information regarding dose response, correlation with habitual food intake, and reproducibility over time is yet unknown. The nutritional epidemiology field will benefit from the development of novel methods to combine single biomarkers to generate biomarker panels in combination with self-reported data. The most promising dietary biomarker candidates that reflect commonly consumed foods and food components for application in epidemiological studies were identified, and research required for their full validation was summarized.

Metabolomic based approach to identify biomarkers of broccoli intake

It is well-established that consumption of cruciferous and brassica vegetables has a correlation with reduced rates of many negative health outcomes. There is an increased interest in identifying food intake biomarkers to address limitations related to self-reported dietary assessment. The study aims to identify biomarkers of broccoli intake using metabolomic approaches, examine the dose-response relationship, and predict the intake by multimarker panel. Eighteen volunteers consumed cooked broccoli in A-Diet Discovery study and fasting and postprandial urine samples were collected at 2, 4 and 24 hours. Subsequently the A-Diet Dose-response study was performed where volunteers consumed different portions of broccoli (49, 101 or 153 g) and urine samples were collected at the end of each intervention week. Urine samples were analysed by 1H-NMR and LC-MS. Multivariate data analysis and one-way ANOVA were performed to identify discriminating biomarkers. A panel of putative biomarkers was examined for its ability to predict intake through a multiMarker model. Multivariate analysis revealed discriminatory spectral regions between fasting and fed metabolic profiles. Subsequent time-series plots revealed multiple features increased in concentration following the consumption. Urinary S-methyl cysteine sulfoxide (SMCSO) increased as broccoli intake increased (0.17-0.24 μM per mOSM per kg, p < 0.001). Similarly from LC-MS data genipin, dihydro-β-tubaic acid and sinapic acid increased with increasing portions of intake. A panel of 8 features displayed good ability to predict intake from biomarker data only. In conclusion, urinary SMCSO and several LC-MS features appeared as potentially promising biomarkers of broccoli consumption and demonstrated dose-response relationship. Future work should focus on validating these compounds as food intake biomarkers.

Utilising the precision nutrition toolkit in the path towards precision medicine

The overall aim of precision nutrition is to replace the 'one size fits all' approach to dietary advice with recommendations that are more specific to the individual in order to improve the prevention or management of chronic disease. Interest in precision nutrition has grown with advancements in technologies such as genomics, proteomics, metabolomics and measurement of the gut microbiome. Precision nutrition initiatives have three major applications in precision medicine. First, they aim to provide more 'precision' dietary assessments through artificial intelligence, wearable devices or by employing omic technologies to characterise diet more precisely. Secondly, precision nutrition allows us to understand the underlying mechanisms of how diet influences disease risk and identify individuals who are more susceptible to disease due to gene-diet or microbiota-diet interactions. Third, precision nutrition can be used for 'personalised nutrition' advice where machine-learning algorithms can integrate data from omic profiles with other personal and clinical measures to improve disease risk. Proteomics and metabolomics especially provide the ability to discover new biomarkers of food or nutrient intake, proteomic or metabolomic signatures of diet and disease, and discover potential mechanisms of diet-disease interactions. Although there are several challenges that must be overcome to improve the reproducibility, cost-effectiveness and efficacy of these approaches, precision nutrition methodologies have great potential for nutrition research and clinical application.

Urine Metabolites and Bioactive Compounds from Functional Food: Applications of Liquid Chromatography Mass Spectrometry

Bioactive compounds in functional foods, medicinal plants and others are considered attractive value-added molecules based on their wide range of bioactivity. It is clear that an important role is occupied by polyphenol, phenolic compounds and others. Urine is an effective biofluid to evaluate and monitor alterations in homeostasis and other processes related to metabolism. The current review provides a detailed description of the formation of urine in human body, various aspects relevant to sampling and analysis of urinary metabolites before presenting recent developments leveraging on metabolite profiling of urine. For the profiling of small molecules in urine, advancement of liquid chromatography mass tandem spectrometry (LC/MS/MS), establishment of standardized chemical fragmentation libraries, computational resources, data-analysis approaches with pattern recognition tools have made it an attractive option. The profiling of urinary metabolites gives an overview of the biomarkers associated with the diet and evaluates its biological effects. Metabolic pathways such as glycolysis, tricarboxylic acid cycle, amino acid metabolism, energy metabolism, purine metabolism and others can be evaluated. Finally, a combination of metabolite profiling with chemical standardization and bioassay in functional food and medicinal plants will likely lead to the identification of new biomarkers and novel biochemical insights.

NMR foodomics in the assessment of diet and effects beyond nutrients

PURPOSE OF REVIEW

This review provides an overview of most recent research studies employing nuclear magnetic resonance (NMR)-based metabolomics in the assessment of effects of diet and food ingestion.

RECENT FINDINGS

NMR metabolomics is a useful tool in the elucidation of specific diets, for example, the Mediterranean diet, the New Nordic diet types, and also for comparing vegan, vegetarian and omnivore diets where specific diet-linked metabolite perturbations have been identified. Another core area where NMR metabolomics is employed involves research focused on examining specific food components or ingredients, including dietary fibers and other functional components. In several cases, NMR metabolomics has aided to document how specific food components exert effects on the metabolic activity of the gut microbiota. Research has also demonstrated the potential use of NMR metabolomics in assessing diet quality and interactions between specific food components such as meat and diet quality. The implications of these findings are important as they address that background diet can be decisive for if food items turn out to exert either harmful or health-promoting effects.

SUMMARY

NMR metabolomics can provide important mechanistic insight and aid to biomarker discovery with implications for compliance and food registration purposes.

Identification of Single and Combined Serum Metabolites Associated with Food Intake

Assessment of dietary intake is challenging. Traditional methods suffer from both random and systematic errors; thus objective measures are important complements in monitoring dietary exposure. The study presented here aims to identify serum metabolites associated with reported food intake and to explore whether combinations of metabolites may improve predictive models. Fasting blood samples and a 4-day weighed food diary were collected from healthy Swedish subjects (n = 119) self-defined as having habitual vegan, vegetarian, vegetarian + fish, or omnivore diets. Serum was analyzed for metabolites by 1H-nuclear magnetic resonance spectroscopy. Associations between single and combined metabolites and 39 foods and food groups were explored. Area under the curve (AUC) was calculated for prediction models. In total, 24 foods or food groups associated with serum metabolites using the criteria of rho > 0.2, p < 0.01 and AUC ≥ 0.7 were identified. For the consumption of soybeans, citrus fruits and marmalade, nuts and almonds, green tea, red meat, poultry, total fish and shellfish, dairy, fermented dairy, cheese, eggs, and beer the final models included two or more metabolites. Our results indicate that a combination of metabolites improve the possibilities to use metabolites to identify several foods included in the current diet. Combined metabolite models should be confirmed in dose−response intervention studies.

Nutritional Metabolomics and the Classification of Dietary Biomarker Candidates: A Critical Review

Recent advances in metabolomics allow for more objective assessment of contemporary food exposures, which have been proposed as an alternative or complement to self-reporting of food intake. However, the quality of evidence supporting the utility of dietary biomarkers as valid measures of habitual intake of foods or complex dietary patterns in diverse populations has not been systematically evaluated. We reviewed nutritional metabolomics studies reporting metabolites associated with specific foods or food groups; evaluated the interstudy repeatability of dietary biomarker candidates; and reported study design, metabolomic approach, analytical technique(s), and type of biofluid analyzed. A comprehensive literature search of 5 databases (PubMed, EMBASE, Web of Science, BIOSIS, and CINAHL) was conducted from inception through December 2020. This review included 244 studies, 169 (69%) of which were interventional studies (9 of these were replicated in free-living participants) and 151 (62%) of which measured the metabolomic profile of serum and/or plasma. Food-based metabolites identified in ≥1 study and/or biofluid were associated with 11 food-specific categories or dietary patterns: 1) fruits; 2) vegetables; 3) high-fiber foods (grain-rich); 4) meats; 5) seafood; 6) pulses, legumes, and nuts; 7) alcohol; 8) caffeinated beverages, teas, and cocoas; 9) dairy and soya; 10) sweet and sugary foods; and 11) complex dietary patterns and other foods. We conclude that 69 metabolites represent good candidate biomarkers of food intake. Quantitative measurement of these metabolites will advance our understanding of the relation between diet and chronic disease risk and support evidence-based dietary guidelines for global health.

multiMarker: software for modelling and prediction of continuous food intake using multiple biomarkers measurements

Background

Metabolomic biomarkers offer potential for objective and reliable food intake assessment, and there is growing interest in using biomarkers in place of or with traditional self-reported approaches. Ongoing research suggests that multiple biomarkers are associated with single foods, offering great sensitivity and specificity. However, currently there is a dearth of methods to model the relationship between multiple biomarkers and single food intake measurements.

Results

Here, we introduce multiMarker, a web-based application based on the homonymous R package, that enables one to infer the relationship between food intake and two or more metabolomic biomarkers. Furthermore, multiMarker allows prediction of food intake from biomarker data alone. multiMarker differs from previous approaches by providing distributions of predicted intakes, directly accounting for uncertainty in food intake quantification. Usage of both the R package and the web application is demonstrated using real data concerning three biomarkers for orange intake. Further, example data is pre-loaded in the web application to enable users to examine multiMarker’s functionality.

Conclusion

The proposed software advance the field of Food Intake Biomarkers providing researchers with a novel tool to perform continuous food intake quantification, and to assess its associated uncertainty, from multiple biomarkers. To facilitate widespread use of the framework, multiMarker has been implemented as an R package and a Shiny web application.

The Potential of Multi-Biomarker Panels in Nutrition Research: Total Fruit Intake as an Example

Dietary and food intake biomarkers offer the potential of improving the accuracy of dietary assessment. An extensive range of putative intake biomarkers of commonly consumed foods have been identified to date. As the field of food intake biomarkers progresses toward solving the complexities of dietary habits, combining biomarkers associated with single foods or food groups may be required. The objective of this work was to examine the ability of a multi-biomarker panel to classify individuals into categories of fruit intake. Biomarker data was measured using ¹H NMR spectroscopy in two studies: (1) An intervention study where varying amounts of fruit was consumed and (2) the National Adult Nutrition Survey (NANS). Using data from an intervention study a biomarker panel (Proline betaine, Hippurate, and Xylose) was constructed from three urinary biomarker concentrations. Biomarker cut-off values for three categories of fruit intake were developed. The biomarker sum cut-offs were ≤ 4.766, 4.766–5.976, >5.976 μM/mOsm/kg for <100, 101–160, and >160 g fruit intake. The ability of the biomarker sum to classify individuals into categories of fruit intake was examined in the cross-sectional study (NANS) (N = 565). Examination of results in the cross-sectional study revealed excellent agreement with self-reported intake: a similar number of participants were ranked into each category of fruit intake. The work illustrates the potential of multi-biomarker panels and paves the way forward for further development in the field. The use of such panels may be key to distinguishing foods and adding specificity to the predictions of food intake.