Discovery of Intake Biomarkers of Lentils, Chickpeas, and White Beans by Untargeted LC-MS Metabolomics in Serum and Urine

Plasma Metabolomics of Dietary Intake of Protein-Rich Foods and Kidney Disease Progression in Children

OBJECTIVE

Evidence regarding the efficacy of a low-protein diet for patients with CKD is inconsistent and recommending a low-protein diet for pediatric patients is controversial. There is also a lack of objective biomarkers of dietary intake. The purpose of this study was to identify plasma metabolites associated with dietary intake of protein and to assess whether protein-related metabolites are associated with CKD progression.

METHODS

Nontargeted metabolomics was conducted in plasma samples from 484 Chronic Kidney Disease in Children (CKiD) participants. Multivariable linear regression estimated the cross-sectional association between 949 known, nondrug metabolites and dietary intake of total protein, animal protein, plant protein, chicken, dairy, nuts and beans, red and processed meat, fish, and eggs, adjusting for demographic, clinical, and dietary covariates. Cox proportional hazards models assessed the prospective association between protein-related metabolites and CKD progression defined as the initiation of kidney replacement therapy or 50% eGFR reduction, adjusting for demographic and clinical covariates.

RESULTS

One hundred and twenty-seven (26%) children experienced CKD progression during 5 years of follow-up. Sixty metabolites were significantly associated with dietary protein intake. Among the 60 metabolites, 10 metabolites were significantly associated with CKD progression (animal protein: n = 1, dairy: n = 7, red and processed meat: n = 2, nuts and beans: n = 1), including one amino acid, one cofactor and vitamin, 4 lipids, 2 nucleotides, one peptide, and one xenobiotic. 1-(1-enyl-palmitoyl)-2-oleoyl-glycerophosphoethanolamine (GPE, P-16:0/18:1) was positively associated with dietary intake of red and processed meat, and a doubling of its abundance was associated with 88% higher risk of CKD progression. 3-ureidopropionate was inversely associated with dietary intake of red and processed meat, and a doubling of its abundance was associated with 48% lower risk of CKD progression.

CONCLUSIONS

Untargeted plasma metabolomic profiling revealed metabolites associated with dietary intake of protein and CKD progression in a pediatric population.

Plasma metabolite profile of legume consumption and future risk of type 2 diabetes and cardiovascular disease

BACKGROUND

Legume consumption has been linked to a reduced risk of type 2 diabetes (T2D) and cardiovascular disease (CVD), while the potential association between plasma metabolites associated with legume consumption and the risk of cardiometabolic diseases has never been explored. Therefore, we aimed to identify a metabolite signature of legume consumption, and subsequently investigate its potential association with the incidence of T2D and CVD.

METHODS

The current cross-sectional and longitudinal analysis was conducted in 1833 PREDIMED study participants (mean age 67 years, 57.6% women) with available baseline metabolomic data. A subset of these participants with 1-year follow-up metabolomics data (n = 1522) was used for internal validation. Plasma metabolites were assessed through liquid chromatography-tandem mass spectrometry. Cross-sectional associations between 382 different known metabolites and legume consumption were performed using elastic net regression. Associations between the identified metabolite profile and incident T2D and CVD were estimated using multivariable Cox regression models.

RESULTS

Specific metabolic signatures of legume consumption were identified, these included amino acids, cortisol, and various classes of lipid metabolites including diacylglycerols, triacylglycerols, plasmalogens, sphingomyelins and other metabolites. Among these identified metabolites, 22 were negatively and 18 were positively associated with legume consumption. After adjustment for recognized risk factors and legume consumption, the identified legume metabolite profile was inversely associated with T2D incidence (hazard ratio (HR) per 1 SD: 0.75, 95% CI 0.61-0.94; p = 0.017), but not with CVD incidence risk (1.01, 95% CI 0.86-1.19; p = 0.817) over the follow-up period.

CONCLUSIONS

This study identified a set of 40 metabolites associated with legume consumption and with a reduced risk of T2D development in a Mediterranean population at high risk of cardiovascular disease.

TRIAL REGISTRATION

ISRCTN35739639.

Plasma metabolomics profiles in Black and White participants of the Adventist Health Study-2 cohort

BACKGROUND

Black Americans suffer disparities in risk for cardiometabolic and other chronic diseases. Findings from the Adventist Health Study-2 (AHS-2) cohort have shown associations of plant-based dietary patterns and healthy lifestyle factors with prevention of such diseases. Hence, it is likely that racial differences in metabolic profiles correlating with disparities in chronic diseases are explained largely by diet and lifestyle, besides social determinants of health.

METHODS

Untargeted plasma metabolomics screening was performed on plasma samples from 350 participants of the AHS-2, including 171 Black and 179 White participants, using ultrahigh-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and a global platform of 892 metabolites. Differences in metabolites or biochemical subclasses by race were analyzed using linear regression, considering various models adjusted for known confounders, dietary and/or other lifestyle behaviors, social vulnerability, and psychosocial stress. The Storey permutation approach was used to adjust for false discovery at FDR < 0.05.

RESULTS

Linear regression revealed differential abundance of over 40% of individual metabolites or biochemical subclasses when comparing Black with White participants after adjustment for false discovery (FDR < 0.05), with the vast majority showing lower abundance in Blacks. Associations were not appreciably altered with adjustment for dietary patterns and socioeconomic or psychosocial stress. Metabolite subclasses showing consistently lower abundance in Black participants included various lipids, such as lysophospholipids, phosphatidylethanolamines, monoacylglycerols, diacylglycerols, and long-chain monounsaturated fatty acids, among other subclasses or lipid categories. Among all biochemical subclasses, creatine metabolism exclusively showed higher abundance in Black participants, although among metabolites within this subclass, only creatine showed differential abundance after adjustment for glomerular filtration rate. Notable metabolites in higher abundance in Black participants included methyl and propyl paraben sulfates, piperine metabolites, and a considerable proportion of acetylated amino acids, including many previously found associated with glomerular filtration rate.

CONCLUSIONS

Differences in metabolic profiles were evident when comparing Black and White participants of the AHS-2 cohort. These differences are likely attributed in part to dietary behaviors not adequately explained by dietary pattern covariates, besides other environmental or genetic factors. Alterations in these metabolites and associated subclasses may have implications for the prevention of chronic diseases in Black Americans.

Differences in the Stool Metabolome between Vegans and Omnivores: Analyzing the NIST Stool Reference Material

To gain confidence in results of omic-data acquisitions, methods must be benchmarked using validated quality control materials. We report data combining both untargeted and targeted metabolomics assays for the analysis of four new human fecal reference materials developed by the U.S. National Institute of Standards and Technologies (NIST) for metagenomics and metabolomics measurements. These reference grade test materials (RGTM) were established by NIST based on two different diets and two different samples treatments, as follows: firstly, homogenized fecal matter from subjects eating vegan diets, stored and submitted in either lyophilized (RGTM 10162) or aqueous form (RGTM 10171); secondly, homogenized fecal matter from subjects eating omnivore diets, stored and submitted in either lyophilized (RGTM 10172) or aqueous form (RGTM 10173). We used four untargeted metabolomics assays (lipidomics, primary metabolites, biogenic amines and polyphenols) and one targeted assay on bile acids. A total of 3563 compounds were annotated by mass spectrometry, including 353 compounds that were annotated in more than one assay. Almost half of all compounds were annotated using hydrophilic interaction chromatography/accurate mass spectrometry, followed by the lipidomics and the polyphenol assays. In total, 910 metabolites were found in at least 4-fold different levels in fecal matter from vegans versus omnivores, specifically for peptides, amino acids and lipids. In comparison, only 251 compounds showed 4-fold differences between lyophilized and aqueous fecal samples, including DG O-34:0 and methionine sulfoxide. A range of diet-specific metabolites were identified to be significantly different between vegans and omnivores, exemplified by citrinin and C17:0-acylcarnitine for omnivores, and curcumin and lenticin for vegans. Bioactive molecules like acyl alpha-hydroxy-fatty acids (AAHFA) were differentially regulated in vegan versus omnivore fecal materials, highlighting the importance of diet-specific reference materials for dietary biomarker studies.

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.

Comparative flavonoid profile of orange (Citrus sinensis) flavedo and albedo extracted by conventional and emerging techniques using UPLC-IMS-MS, chemometrics and antioxidant effects

Introduction

Citrus fruits are one of the most frequently counterfeited processed products in the world. In the juice production alone, the peels, divided into flavedo and albedo, are the main waste product. The extracts of this by-product are enriched with many bioactive substances. Newer extraction techniques generally have milder extraction conditions with simultaneous improvement of the extraction process.

Methods

This study presents a combinatorial approach utilizing data-independent acquisition-based ion mobility spectrometry coupled to tandem mass spectrometry. Integrating orthogonal collision cross section (CCS) data matching simultaneously improves the confidence in metabolite identification in flavedo and albedo tissues from Citrus sinensis. Furthermore, four different extraction approaches [conventional, ultrasonic, High Hydrostatic Pressure (HHP) and Pulsed Electric Field (PEF)] with various optimized processing conditions were compared in terms of antioxidant effects and flavonoid profile particularly polymethoxy flavones (PMFs).

Results

A total number of 57 metabolites were identified, 15 of which were present in both flavedo and albedo, forming a good qualitative overlapping of distributed flavonoids. For flavedo samples, the antioxidant activity was higher for PEF and HHP treated samples compared to other extraction methods. However, ethyl acetate extract exhibited the highest antioxidant effects in albedo samples attributed to different qualitative composition content rather than various quantities of same metabolites. The optimum processing conditions for albedo extraction using HHP and PEF were 200 MPa and 15 kJ/kg at 10 kV, respectively. While, HHP at medium pressure (400 MPa) and PEF at 15 kJ/kg/3 kV were the optimum conditions for flavedo extraction.

Conclusion

Chemometric analysis of the dataset indicated that orange flavedo can be a valid source of soluble phenolic compounds especially PMFs. In order to achieve cross-application of production, future study should concentrate on how citrus PMFs correlate with biological engineering techniques such as breeding, genetic engineering, and fermentation engineering.

Metabolomics of Dietary Intake of Total, Animal, and Plant Protein: Results from the Atherosclerosis Risk in Communities (ARIC) Study

Background

Dietary consumption has traditionally been studied through food intake questionnaires. Metabolomics can be used to identify blood markers of dietary protein that may complement existing dietary assessment tools.

Objectives

We aimed to identify associations between 3 dietary protein sources (total protein, animal protein, and plant protein) and serum metabolites using data from the Atherosclerosis Risk in Communities Study.

Methods

Participants' dietary protein intake was derived from a food frequency questionnaire administered by an interviewer, and fasting serum samples were collected at study visit 1 (1987-1989). Untargeted metabolomic profiling was performed in 2 subgroups (subgroup 1: n = 1842; subgroup 2: n = 2072). Multivariable linear regression models were used to assess associations between 3 dietary protein sources and 360 metabolites, adjusting for demographic factors and other participant characteristics. Analyses were performed separately within each subgroup and meta-analyzed with fixed-effects models.

Results

In this study of 3914 middle-aged adults, the mean (SD) age was 54 (6) y, 60% were women, and 61% were Black. We identified 41 metabolites significantly associated with dietary protein intake. Twenty-six metabolite associations overlapped between total protein and animal protein, such as pyroglutamine, creatine, 3-methylhistidine, and 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid. Plant protein was uniquely associated with 11 metabolites, such as tryptophan betaine, 4-vinylphenol sulfate, N-δ-acetylornithine, and pipecolate.

Conclusions

The results of 17 of the 41 metabolites (41%) were consistent with those of previous nutritional metabolomic studies and specific protein-rich food items. We discovered 24 metabolites that had not been previously associated with dietary protein intake. These results enhance the validity of candidate markers of dietary protein intake and introduce novel metabolomic markers of dietary protein intake.

Metabolomics signatures of plant protein intake: effects of amino acids and compounds associated with plant protein on cardiometabolic health

PURPOSE OF REVIEW

An increase in the plant-based characteristics of the diet is now recommended for human and planetary health. There is growing evidence that plant protein (PP) intake has beneficial effects on cardiometabolic risk. However, proteins are not consumed isolated and the protein package (lipid species, fiber, vitamins, phytochemicals, etc) may contribute, besides the protein effects per se, to explain the beneficial effects associated with PP-rich diets.

RECENT FINDINGS

Recent studies have shown the potential of nutrimetabolomics to apprehend the complexity of both the human metabolism and the dietary habits, by providing signatures associated to the consumption of PP-rich diets. Those signatures comprised an important proportion of metabolites that were representative of the protein package, including specific amino acids (branched-chain amino acids and their derivates, glycine, lysine), but also lipid species (lysophosphatidylcholine, phosphatidylcholine, plasmalogens) and polyphenol metabolites (catechin sulfate, conjugated valerolactones and phenolic acids).

SUMMARY

Further studies are needed to go deeper in the identification of all metabolites making part of the specific metabolomic signatures, associated to the large range of protein package constituents and their effects on the endogenous metabolism, rather than to the protein fraction itself. The objective is to determine the bioactive metabolites, as well as the modulated metabolic pathways and the mechanisms responsible for the observed effects on cardiometabolic health.

The Relationship Between Diet, Gut Microbiota, and Serum Metabolome of South Asian Infants at 1 Year

BACKGROUND

Diet is known to affect the gut microbiota and the serum metabolome in adults, but this has not been fully explored in infants. Infancy is an important developmental period that may influence a person's long-term health. Infant development can be affected by diet, which also interacts with the developing gut microbiota.

OBJECTIVES

This study aimed to explore the associations between diet, the gut microbiota, and the serum metabolome of 1-y-old infants with the overarching goal of identifying serum biomarkers of diet and/or the gut microbiota.

METHODS

We derived dietary patterns of 1-y-old infants (n = 182) participating in the Canadian South Asian Birth Cohort (START) study. We compared gut microbiota α-diversity and β-diversity and taxa relative abundance from 16S rRNA gene profiles with dietary patterns (PERMANOVA, Envfit) and investigated diet-serum metabolite associations using a multivariate analysis (partial least squares-discriminant analysis) and univariate analysis (t test). We explored the effect of nondietary factors on diet-serum metabolite relationships by incorporating diet, the gut microbiota, and maternal, perinatal, and infant characteristics in a multivariable forward stepwise regression. We replicated this analysis in White European infants, from the CHILD Cohort Study (n = 81).

RESULTS

A dietary pattern characterized by formula consumption and negatively associated with breastfeeding most strongly predicted variation in the gut microbiota (R² = 0.109) and serum metabolome (R² = 0.547). Breastfed participants showed higher abundance of microbes from the genera Bifidobacterium (3.29 log2-fold) and Lactobacillus (7.93 log2-fold) and higher median concentrations of the metabolites S-methylcysteine (1.38 μM) and tryptophan betaine (0.43 μM) than nonbreastfed participants. Formula consuming infants showed higher median concentrations of branched-chain/aromatic amino acids (average 48.3 μM) than non-formula-consuming infants.

CONCLUSIONS

Formula consumption and breastfeeding most strongly predicted the serum metabolites of 1-y-old infants, even when the gut microbiota, solid food consumption, and other covariates were considered.

Integrated metabolomics and network analysis reveal changes in lipid metabolisms of tripterygium glycosides tablets in rats with collagen-induced arthritis

Tripterygium glycosides tablets (TGT) are the commonly used preparation for rheumatoid arthritis (RA). However, the changes in TGT on RA are still unclear at the metabolic level. This study aimed to reveal the biological processes of TGT in collagen-induced arthritis (CIA) rats through integrated metabolomics and network analysis. First, the CIA model in rats was established, and the CIA rats were given three doses of TGT. Then, the endogenous metabolites in the serum from normal rats, CIA rats, and CIA rats treated with varying doses of TGT were detected by UHPLC-QTOF-MS/MS. Next, univariate and multivariate statistical analyses were performed to find the differential metabolites. Finally, differential metabolites, metabolic pathways, and hub genes were analyzed integrally to reveal the biological processes of TGT in CIA rats. The paw diameter, arthritis score, immunoglobulin G (IgG) concentration, CT image, and histological assay showed that TGT had evident therapeutic effects on CIA rats. Untargeted metabolomics revealed that TGT could ameliorate the down-regulation of lipid levels in CIA rats. Four key differential metabolites were found including LysoP(18:0), LysoPA(20:4), LysoPA(18:2), and PS(O-20:0/17:1). The glycerophospholipid metabolic pathway was perturbed in treating CIA with TGT. A total of 24 genes, including PLD1, LPCAT4, AGPAT1, and PLA2G4A, were found to be the hub genes of TGT in CIA rats. In conclusion, the integrated analysis provided a novel and holistic perspective on the biological processes of TGT in CIA rats, which could give helpful guidance for further TGT on RA. Future studies based on human samples are necessary.

Combining Feature-Based Molecular Networking and Contextual Mass Spectral Libraries to Decipher Nutrimetabolomics Profiles

Untargeted metabolomics approaches deal with complex data hindering structural information for the comprehensive analysis of unknown metabolite features. We investigated the metabolite discovery capacity and the possible extension of the annotation coverage of the Feature-Based Molecular Networking (FBMN) approach by adding two novel nutritionally-relevant (contextual) mass spectral libraries to the existing public ones, as compared to widely-used open-source annotation protocols. Two contextual mass spectral libraries in positive and negative ionization mode of ~300 reference molecules relevant for plant-based nutrikinetic studies were created and made publicly available through the GNPS platform. The postprandial urinary metabolome analysis within the intervention of Vaccinium supplements was selected as a case study. Following the FBMN approach in combination with the added contextual mass spectral libraries, 67 berry-related and human endogenous metabolites were annotated, achieving a structural annotation coverage comparable to or higher than existing non-commercial annotation workflows. To further exploit the quantitative data obtained within the FBMN environment, the postprandial behavior of the annotated metabolites was analyzed with Pearson product-moment correlation. This simple chemometric tool linked several molecular families with phase II and phase I metabolism. The proposed approach is a powerful strategy to employ in longitudinal studies since it reduces the unknown chemical space by boosting the annotation power to characterize biochemically relevant metabolites in human biofluids.

PeakForest: a multi-platform digital infrastructure for interoperable metabolite spectral data and metadata management

INTRODUCTION

Accuracy of feature annotation and metabolite identification in biological samples is a key element in metabolomics research. However, the annotation process is often hampered by the lack of spectral reference data in experimental conditions, as well as logistical difficulties in the spectral data management and exchange of annotations between laboratories.

OBJECTIVES

To design an open-source infrastructure allowing hosting both nuclear magnetic resonance (NMR) and mass spectra (MS), with an ergonomic Web interface and Web services to support metabolite annotation and laboratory data management.

METHODS

We developed the PeakForest infrastructure, an open-source Java tool with automatic programming interfaces that can be deployed locally to organize spectral data for metabolome annotation in laboratories. Standardized operating procedures and formats were included to ensure data quality and interoperability, in line with international recommendations and FAIR principles.

RESULTS

PeakForest is able to capture and store experimental spectral MS and NMR metadata as well as collect and display signal annotations. This modular system provides a structured database with inbuilt tools to curate information, browse and reuse spectral information in data treatment. PeakForest offers data formalization and centralization at the laboratory level, facilitating shared spectral data across laboratories and integration into public databases.

CONCLUSION

PeakForest is a comprehensive resource which addresses a technical bottleneck, namely large-scale spectral data annotation and metabolite identification for metabolomics laboratories with multiple instruments. PeakForest databases can be used in conjunction with bespoke data analysis pipelines in the Galaxy environment, offering the opportunity to meet the evolving needs of metabolomics research. Developed and tested by the French metabolomics community, PeakForest is freely-available at https://github.com/peakforest .

Serum metabolomic signatures of plant-based diets and incident chronic kidney disease

BACKGROUND

Greater adherence to plant-based diets is associated with a lower risk of incident chronic kidney disease (CKD). Metabolomics can help identify blood biomarkers of plant-based diets and enhance understanding of underlying mechanisms.

OBJECTIVES

Using untargeted metabolomics, we aimed to identify metabolites associated with 4 plant-based diet indices (PDIs) (overall PDI, provegetarian diet, healthful PDI, and unhealthful PDI) and incident CKD in 2 subgroups within the Atherosclerosis Risk in Communities study.

METHODS

We calculated 4 PDIs based on participants' responses on an FFQ. We used multivariable linear regression to examine the association between 4 PDIs and 374 individual metabolites, adjusting for confounders. We used Cox proportional hazards regression to evaluate associations between PDI-related metabolites and incident CKD. Estimates were meta-analyzed across 2 subgroups (n1 = 1762; n2 = 1960). We calculated C-statistics to assess whether metabolites improved the prediction of those in the highest quintile compared to the lower 4 quintiles of PDIs, and whether PDI- and CKD-related metabolites predicted incident CKD beyond the CKD prediction model.

RESULTS

We identified 82 significant PDI-metabolite associations (overall PDI = 27; provegetarian = 17; healthful PDI = 20; unhealthful PDI = 18); 11 metabolites overlapped across the overall PDI, provegetarian diet, and healthful PDI. The addition of metabolites improved prediction of those in the highest quintile as opposed to the lower 4 quintiles of PDIs compared with participant characteristics alone (range of differences in C-statistics = 0.026-0.104; P value ≤ 0.001 for all tests). Six PDI-related metabolites (glycerate, 1,5-anhydroglucitol, γ-glutamylalanine, γ-glutamylglutamate, γ-glutamylleucine, γ-glutamylvaline), involved in glycolysis, gluconeogenesis, pyruvate metabolism, and γ-glutamyl peptide metabolism, were significantly associated with incident CKD and improved prediction of incident CKD beyond the CKD prediction model (difference in C-statistics for 6 metabolites = 0.005; P value = 0.006).

CONCLUSIONS

In a community-based study of US adults, we identified metabolites that were related to plant-based diets and predicted incident CKD. These metabolites highlight pathways through which plant-based diets are associated with incident CKD.

Plasma Metabolites Associated with a Protein-Rich Dietary Pattern: Results from the OmniHeart Trial

Scope

Lack of biomarkers is a challenge for the accurate assessment of protein intake and interpretation of observational study data. The study aims to identify biomarkers of a protein‐rich dietary pattern.

Methods and Results

The Optimal Macronutrient Intake Trial to Prevent Heart Disease (OmniHeart) trial is a randomized cross‐over feeding study which tested three dietary patterns with varied macronutrient content (carbohydrate‐rich; protein‐rich with about half from plant sources; and unsaturated fat‐rich). In 156 adults, differences in log‐transformed plasma metabolite levels at the end of the protein‐ and carbohydrate‐rich diet periods using paired t‐tests is examined. Partial least‐squares discriminant analysis is used to identify a set of metabolites which are influential in discriminating between the protein‐rich versus carbohydrate‐rich dietary patterns. Of 839 known metabolites, 102 metabolites differ significantly between the protein‐rich and the carbohydrate‐rich dietary patterns after Bonferroni correction, the majority of which are lipids (n = 35), amino acids (n = 27), and xenobiotics (n = 24). Metabolites which are the most influential in discriminating between the protein‐rich and the carbohydrate‐rich dietary patterns represent plant protein intake, food or beverage intake, and preparation methods.

Conclusions

The study identifies many plasma metabolites associated with the protein‐rich dietary pattern. If replicated, these metabolites may be used to assess level of adherence to a similar dietary pattern.

Comparison of chemometric strategies for potential exposure marker discovery and false-positive reduction in untargeted metabolomics: application to the serum analysis by LC-HRMS after intake of Vaccinium fruit supplements

Untargeted liquid chromatographic-high-resolution mass spectrometric (LC-HRMS) metabolomics for potential exposure marker (PEM) discovery in nutrikinetic studies generates complex outputs. The correct selection of statistically significant PEMs is a crucial analytical step for understanding nutrition-health interactions. Hence, in this paper, different chemometric selection workflows for PEM discovery, using multivariate or univariate parametric or non-parametric data analyses, were comparatively tested and evaluated. The PEM selection protocols were applied to a small-sample-size untargeted LC-HRMS study of a longitudinal set of serum samples from 20 volunteers after a single intake of (poly)phenolic-rich Vaccinium myrtillus and Vaccinium corymbosum supplements. The non-parametric Games-Howell test identified a restricted group of significant features, thus minimizing the risk of false-positive retention. Among the forty-seven PEMs exhibiting a statistically significant postprandial kinetics, twelve were successfully annotated as purine pathway metabolites, benzoic and benzodiol metabolites, indole alkaloids, and organic and fatty acids, and five (i.e. octahydro-methyl-β-carboline-dicarboxylic acid, tetrahydro-methyl-β-carboline-dicarboxylic acid, citric acid, caprylic acid, and azelaic acid) were associated to Vaccinium berry consumption for the first time. The analysis of the area under the curve of the longitudinal dataset highlighted thirteen statistically significant PEMs discriminating the two interventions, including four intra-intervention relevant metabolites (i.e. abscisic acid glucuronide, catechol sulphate, methyl-catechol sulphate, and α-hydroxy-hippuric acid). Principal component analysis and sample classification through linear discriminant analysis performed on PEM maximum intensity confirmed the discriminating role of these PEMs.

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.

Metabolomics analysis of time-series human small intestine lumen samples collected in vivo

The human small intestine remains an elusive organ to study due to the difficulty of retrieving samples in a non-invasive manner. Stool samples as a surrogate do not reflect events in the upper gut intestinal tract. As proof of concept, this study investigates time-series samples collected from the upper gastrointestinal tract of a single healthy subject. Samples were retrieved using a small diameter tube that collected samples in the stomach and duodenum as the tube progressed to the jejunum, and then remained positioned in the jejunum during the final 8.5 hours of the testing period. Lipidomics and metabolomics liquid chromatography tandem mass spectrometry (LC-MS/MS) assays were employed to annotate 828 unique metabolites using accurate mass with retention time and/or tandem MS library matches. Annotated metabolites were clustered based on correlation to reveal sets of biologically related metabolites. Typical clusters included bile metabolites, food metabolites, protein breakdown products, and endogenous lipids. Acylcarnitines and phospholipids were clustered with known human bile components supporting their presence in human bile, in addition to novel human bile compounds 4-hydroxyhippuric acid, N-acetylglucosaminoasparagine and 3-methoxy-4-hydroxyphenylglycol sulfate. Food metabolites were observed passing through the small intestine after meals. Acetaminophen and its human phase II metabolism products appeared for hours after the initial drug treatment, due to excretion back into the gastrointestinal tract after initial absorption. This exploratory study revealed novel trends in timing and chemical composition of the human jejunum under standard living conditions.

Limited Impact of Pivalate-Induced Secondary Carnitine Deficiency on Hepatic Transcriptome and Hepatic and Plasma Metabolome in Nursery Pigs

Administration of pivalate has been demonstrated to be suitable for the induction of secondary carnitine deficiency (CD) in pigs, as model objects for humans. In order to comprehensively characterize the metabolic effects of secondary CD in the liver of pigs, the present study aimed to carry out comparative analysis of the hepatic transcriptome and hepatic and plasma metabolome of a total of 12 male 5-week-old pigs administered either pivalate (group PIV, n = 6) or vehicle (group CON, n = 6) for 28 days. Pigs of group PIV had approximately 40-60% lower concentrations of free carnitine and acetylcarnitine in plasma, liver and different skeletal muscles than pigs of group CON (p < 0.05). Transcript profiling of the liver revealed 140 differentially expressed genes (DEGs) between group PIV and group CON (fold change > 1.2 or <-1.2, p-value < 0.05). Biological process terms dealing with the innate immune response were found to be enriched with the DEGs (p < 0.05). Using a targeted metabolomics approach for the simultaneous quantification of 630 metabolites, 9 liver metabolites and 18 plasma metabolites were identified to be different between group PIV and group CON (p < 0.05). Considering the limited alterations of the hepatic transcriptome and of the liver and plasma metabolome, it can be concluded that pivalate-induced secondary CD is not associated with significant hepatic metabolism changes in pigs.

Urine Metabolites Associated with the Dietary Approaches to Stop Hypertension (DASH) Diet: Results from the DASH-Sodium Trial

SCOPE

Serum metabolomic markers of the Dietary Approaches to Stop Hypertension (DASH) diet are previously reported. In an independent study, the similarity of urine metabolomic markers are investigated.

METHODS AND RESULTS

In the DASH-Sodium trial, participants are randomly assigned to the DASH diet or control diet, and received three sodium interventions (high, intermediate, low) within each randomized diet group in random order for 30 days each. Urine samples are collected at the end of each intervention period and analyzed for 938 metabolites. Two comparisons are conducted: 1) DASH-high sodium (n = 199) versus control-high sodium (n = 193), and 2) DASH-low sodium (n = 196) versus control-high sodium. Significant metabolites identified using multivariable linear regression are compared and the top 10 influential metabolites identified using partial least-squares discriminant analysis to the results from the DASH trial. Nine out of 10 predictive metabolites of the DASH-high sodium and DASH-low sodium diets are identical. Most candidate biomarkers from the DASH trial replicated. N-methylproline, chiro-inositol, stachydrine, and theobromine replicated as influential metabolites of DASH diets.

CONCLUSIONS

Candidate biomarkers of the DASH diet identified in serum replicated in urine. Replicated influential metabolites are likely to be objective biomarkers of the DASH diet.

The Gut Microbiome and Abiotic Factors as Potential Determinants of Postprandial Glucose Responses: A Single-Arm Meal Study

The gut microbiome has combined with other person-specific information, such as blood parameters, dietary habits, anthropometrics, and physical activity been found to predict personalized postprandial glucose responses (PPGRs) to various foods. Yet, the contributions of specific microbiome taxa, measures of fermentation, and abiotic factors in the colon to glycemic control remain elusive. We tested whether PPGRs 60 min after a standardized breakfast was associated with gut microbial α-diversity (primary outcome) and explored whether postprandial responses of glucose and insulin were associated with specific microbiome taxa, colonic fermentation as reflected by fecal short-chain fatty acids (SCFAs), and breath hydrogen and methane exhalation, as well as abiotic factors including fecal pH, fecal water content, fecal energy density, intestinal transit time (ITT), and stool consistency. A single-arm meal trial was conducted. A total of 31 healthy (24 female and seven male) subjects consumed a standardized evening meal and a subsequent standardized breakfast (1,499 kJ) where blood was collected for analysis of postprandial glucose and insulin responses. PPGRs to the same breakfast varied across the healthy subjects. The largest inter-individual variability in PPGRs was observed 60 min after the meal but was not associated with gut microbial α-diversity. In addition, no significant associations were observed between postprandial responses and specific taxa of the gut microbiome, measures of colonic fermentation, ITT, or other abiotic factors. However, fasting glucose concentrations were negatively associated with ITT, and fasting insulin was positively associated with fasting breath hydrogen. In conclusion, the gut microbiome, measures of colonic fermentation, and abiotic factors were not shown to be significantly associated with variability in postprandial responses, suggesting that contributions of the gut microbiome, colonic fermentation, and abiotic factors to PPGRs may be subtle in healthy adults.

Challenges Associated With the Design and Deployment of Food Intake Urine Biomarker Technology for Assessment of Habitual Diet in Free-Living Individuals and Populations-A Perspective

Improvement of diet at the population level is a cornerstone of national and international strategies for reducing chronic disease burden. A critical challenge in generating robust data on habitual dietary intake is accurate exposure assessment. Self-reporting instruments (e.g., food frequency questionnaires, dietary recall) are subject to reporting bias and serving size perceptions, while weighed dietary assessments are unfeasible in large-scale studies. However, secondary metabolites derived from individual foods/food groups and present in urine provide an opportunity to develop potential biomarkers of food intake (BFIs). Habitual dietary intake assessment in population surveys using biomarkers presents several challenges, including the need to develop affordable biofluid collection methods, acceptable to participants that allow collection of informative samples. Monitoring diet comprehensively using biomarkers requires analytical methods to quantify the structurally diverse mixture of target biomarkers, at a range of concentrations within urine. The present article provides a perspective on the challenges associated with the development of urine biomarker technology for monitoring diet exposure in free-living individuals with a view to its future deployment in "real world" situations. An observational study (n = 95), as part of a national survey on eating habits, provided an opportunity to explore biomarker measurement in a free-living population. In a second food intervention study (n = 15), individuals consumed a wide range of foods as a series of menus designed specifically to achieve exposure reflecting a diversity of foods commonly consumed in the UK, emulating normal eating patterns. First Morning Void urines were shown to be suitable samples for biomarker measurement. Triple quadrupole mass spectrometry, coupled with liquid chromatography, was used to assess simultaneously the behavior of a panel of 54 potential BFIs. This panel of chemically diverse biomarkers, reporting intake of a wide range of commonly-consumed foods, can be extended successfully as new biomarker leads are discovered. Towards validation, we demonstrate excellent discrimination of eating patterns and quantitative relationships between biomarker concentrations in urine and the intake of several foods. In conclusion, we believe that the integration of information from BFI technology and dietary self-reporting tools will expedite research on the complex interactions between dietary choices and health.

Design and Characterisation of a Randomized Food Intervention That Mimics Exposure to a Typical UK Diet to Provide Urine Samples for Identification and Validation of Metabolite Biomarkers of Food Intake

Poor dietary choices are major risk factors for obesity and non-communicable diseases, which places an increasing burden on healthcare systems worldwide. To monitor the effectiveness of healthy eating guidelines and strategies, there is a need for objective measures of dietary intake in community settings. Metabolites derived from specific foods present in urine samples can provide objective biomarkers of food intake (BFIs). Whilst the majority of biomarker discovery/validation studies have investigated potential biomarkers for single foods only, this study considered the whole diet by using menus that delivered a wide range of foods in meals that emulated conventional UK eating patterns. Fifty-one healthy participants (range 19-77 years; 57% female) followed a uniquely designed, randomized controlled dietary intervention, and provided spot urine samples suitable for discovery of BFIs within a real-world context. Free-living participants prepared and consumed all foods and drinks in their own homes and were asked to follow the protocols for meal consumption and home urine sample collection. This study also assessed the robustness, and impact on data quality, of a minimally invasive urine collection protocol. Overall the study design was well-accepted by participants and concluded successfully without any drop outs. Compliance for urine collection, adherence to menu plans, and observance of recommended meal timings, was shown to be very high. Metabolome analysis using mass spectrometry coupled with data mining demonstrated that the study protocol was well-suited for BFI discovery and validation. Novel, putative biomarkers for an extended range of foods were identified including legumes, curry, strongly-heated products, and artificially sweetened, low calorie beverages. In conclusion, aspects of this study design would help to overcome several current challenges in the development of BFI technology. One specific attribute was the examination of BFI generalizability across related food groups and across different preparations and cooking methods of foods. Furthermore, the collection of urine samples at multiple time points helped to determine which spot sample was optimal for identification and validation of BFIs in free-living individuals. A further valuable design feature centered on the comprehensiveness of the menu design which allowed the testing of biomarker specificity within a biobank of urine samples.