Herring and Beef Meals Lead to Differences in Plasma 2-Aminoadipic Acid, β-Alanine, 4-Hydroxyproline, Cetoleic Acid, and Docosahexaenoic Acid Concentrations in Overweight Men

Comparative Impact of Organic Grass-Fed and Conventional Cattle-Feeding Systems on Beef and Human Postprandial Metabolomics-A Randomized Clinical Trial

BACKGROUND/OBJECTIVES

Cattle-feeding systems may have health implications for consumers of beef products. Organic grass-fed (GRA) and conventional (CON) cattle-feeding systems may result in beef products with differing metabolite profiles and therefore could impact the postprandial metabolomic response of consumers. This study aims to measure whole beef metabolomics and postprandial metabolomic response of consumers between GRA and CON beef to elucidate potential health implications.

METHODS

This study followed a randomized double-blind crossover design with healthy male and female subjects (n = 10). Plasma samples were taken at fasting (0) and postprandially for four hours after consumption of a steak from each condition. Untargeted metabolomic analysis of whole beef and human plasma samples used LC/MS. Multivariate and pathway enrichment analysis in MetaboAnalyst was used to investigate metabolite and biochemical pathways that distinguished CON and GRA.

RESULTS

Cattle-feeding systems impacted both postprandial and whole beef steak metabolomic profiles. Metabolites that contributed to this variation included carnitine species (Proionylcarnitine), fatty acids, amino acids (L-valine), and Calamendiol. These metabolites have been associated with oxidative stress, inflammation, and cardiovascular health. Functional pathway enrichment analysis revealed numerous amino acid degradation pathways, especially branched-chain amino acids, and fatty acid degradation that changed throughout the postprandial time course.

CONCLUSIONS

These findings suggest that CON and GRA cattle-feeding systems differentially impact whole beef metabolomics, as well as consumer postprandial metabolic responses and the associated health implications.

The consumption of animal products is associated with plasma levels of alpha-aminoadipic acid (2-AAA)

BACKGROUND AND AIMS

The cardiometabolic disease-associated metabolite, alpha-aminoadipic acid (2-AAA) is formed from the breakdown of the essential dietary amino acid lysine. However, it was not known whether elevated plasma levels of 2-AAA are related to dietary nutrient intake. We aimed to determine whether diet is a determinant of circulating 2-AAA in healthy individuals, and whether 2-AAA is altered in response to dietary modification.

METHODS AND RESULTS

We investigated the association between 2-AAA and dietary nutrient intake in a cross-sectional study of healthy individuals (N = 254). We then performed a randomized cross-over dietary intervention trial to investigate the effect of lysine supplementation (1 week) on 2-AAA in healthy individuals (N = 40). We further assessed the effect of a vegetarian diet on 2-AAA in a short-term (4-day) dietary intervention trial in healthy omnivorous women (N = 35). We found that self-reported dietary intake of animal products, including meat, poultry, and seafood, was associated with higher plasma 2-AAA cross-sectionally (P < 0.0001). Supplementary dietary lysine (5g/day) caused no significant increase in plasma 2-AAA; however, plasma 2-AAA was altered by general dietary modification. Further, plasma 2-AAA was significantly reduced by a short-term vegetarian diet (P = 0.003).

CONCLUSION

We identified associations between plasma 2-AAA and consumption of animal products, which were validated in a vegetarian dietary intervention trial, but not in a trial designed to specifically increase the 2-AAA amino acid precursor lysine. Further studies are warranted to investigate whether implementation of a vegetarian diet improves cardiometabolic risk in individuals with elevated 2-AAA.

Trimethylamine N-oxide, β-alanine, tryptophan index, and vitamin B6-related dietary patterns in association with stroke risk

BACKGROUND AND AIMS

The aim of this study was to examine the associations of dietary patterns derived by reduced-rank regression (RRR) model reflecting variation in novel biomarkers (trimethylamine N-oxide, β-alanine, tryptophan index, and vitamin B6) with stroke risk.

METHODS AND RESULTS

We performed analyses based on a community-based cohort study "the Prospective Follow-up Study on Cardiovascular Morbidity and Mortality in China (PFS-CMMC)". Factor loadings were calculated by RRR using 11 food groups collected via a validated food frequency questionnaire and the four response variables based on its nested case-control data (393 cases of stroke vs. 393 matched controls). Dietary pattern scores were derived by applying the factor loadings to the food groups in the entire cohort (n = 15,518). The associations of dietary pattern with the stroke risk were assessed using Cox proportional hazards models. The dietary pattern characterized with higher intakes of red meat and poultry but lower intakes of fresh vegetables, fresh fruits, and fish/seafoods were identified for further analyses. The hazard ratios (HR) for the highest vs. lowest quartile was 1.55 [95 % confidence interval (CI): 1.18-2.03, P trend = 0.001] for total stroke, 2.96 [95 % CI: 1.53-5.71, P trend <0.001] for non-ischemic stroke, after adjustment for sex, age, educational attainment, current smoking, current drinking, body mass index, total energy intake, family history of stroke, hypertension, diabetes, hyperlipidemia, and estimated glomerular filtration rate.

CONCLUSION

Our findings highlight the importance of limited meat intake and increased intakes of fresh vegetables, fruits, and fish/seafoods in the prevention of stroke among Chinese adults.

Implications of metabolism on multi-systems healthy aging across the lifespan

Aging is increasingly thought to involve dysregulation of metabolism in multiple organ systems that culminate in decreased functional capacity and morbidity. Here, we seek to understand complex interactions among metabolism, aging, and systems-wide phenotypes across the lifespan. Among 2469 adults (mean age 74.7 years; 38% Black) in the Health, Aging and Body Composition study we identified metabolic cross-sectionally correlates across 20 multi-dimensional aging-related phenotypes spanning seven domains. We used LASSO-PCA and bioinformatic techniques to summarize metabolome-phenome relationships and derive metabolic scores, which were subsequently linked to healthy aging, mortality, and incident outcomes (cardiovascular disease, disability, dementia, and cancer) over 9 years. To clarify the relationship of metabolism in early adulthood to aging, we tested association of these metabolic scores with aging phenotypes/outcomes in 2320 participants (mean age 32.1, 44% Black) of the Coronary Artery Risk Development in Young Adults (CARDIA) study. We observed significant overlap in metabolic correlates across the seven aging domains, specifying pathways of mitochondrial/cellular energetics, host-commensal metabolism, inflammation, and oxidative stress. Across four metabolic scores (body composition, mental-physical performance, muscle strength, and physical activity), we found strong associations with healthy aging and incident outcomes, robust to adjustment for risk factors. Metabolic scores for participants four decades younger in CARDIA were related to incident cardiovascular, metabolic, and neurocognitive performance, as well as long-term cardiovascular disease and mortality over three decades. Conserved metabolic states are strongly related to domain-specific aging and outcomes over the life-course relevant to energetics, host-commensal interactions, and mechanisms of innate immunity.

Metabolomic Biomarkers of Dietary Approaches to Stop Hypertension (DASH) Dietary Patterns in Pregnant Women

Objective: the aim of this study was to identify plasma metabolomic markers of Dietary Approaches to Stop Hypertension (DASH) dietary patterns in pregnant women. Methods: This study included 186 women who had both dietary intake and metabolome measured from a nested case-control study within the NICHD Fetal Growth Studies-Singletons cohort (FGS). Dietary intakes were ascertained at 8-13 gestational weeks (GW) using the Food Frequency Questionnaire (FFQ) and DASH scores were calculated based on eight food and nutrient components. Fasting plasma samples were collected at 15-26 GW and untargeted metabolomic profiling was performed. Multivariable linear regression models were used to examine the association of individual metabolites with the DASH score. Least absolute shrinkage and selection operator (LASSO) regression was used to select a panel of metabolites jointly associated with the DASH score. Results: Of the total 460 known metabolites, 92 were individually associated with DASH score in linear regressions, 25 were selected as a panel by LASSO regressions, and 18 were identified by both methods. Among the top 18 metabolites, there were 11 lipids and lipid-like molecules (i.e., TG (49:1), TG (52:2), PC (31:0), PC (35:3), PC (36:4) C, PC (36:5) B, PC (38:4) B, PC (42:6), SM (d32:0), gamma-tocopherol, and dodecanoic acid), 5 organic acids and derivatives (i.e., asparagine, beta-alanine, glycine, taurine, and hydroxycarbamate), 1 organic oxygen compound (i.e., xylitol), and 1 organoheterocyclic compound (i.e., maleimide). Conclusions: our study identified plasma metabolomic markers for DASH dietary patterns in pregnant women, with most of being lipids and lipid-like molecules.

Data-driven analysis and prediction of dynamic postprandial metabolic response to multiple dietary challenges using dynamic mode decomposition

Motivation

In the field of precision nutrition, predicting metabolic response to diet and identifying groups of differential responders are two highly desirable steps toward developing tailored dietary strategies. However, data analysis tools are currently lacking, especially for complex settings such as crossover studies with repeated measures.Current methods of analysis often rely on matrix or tensor decompositions, which are well suited for identifying differential responders but lacking in predictive power, or on dynamical systems modeling, which may be used for prediction but typically requires detailed mechanistic knowledge of the system under study. To remedy these shortcomings, we explored dynamic mode decomposition (DMD), which is a recent, data-driven method for deriving low-rank linear dynamical systems from high dimensional data.Combining the two recent developments "parametric DMD" (pDMD) and "DMD with control" (DMDc) enabled us to (i) integrate multiple dietary challenges, (ii) predict the dynamic response in all measured metabolites to new diets from only the metabolite baseline and dietary input, and (iii) identify inter-individual metabolic differences, i.e., metabotypes. To our knowledge, this is the first time DMD has been applied to analyze time-resolved metabolomics data.

Results

We demonstrate the potential of pDMDc in a crossover study setting. We could predict the metabolite response to unseen dietary exposures on both measured (R2 = 0.40) and simulated data of increasing size (R max 2 = 0.65), as well as recover clusters of dynamic metabolite responses. We conclude that this method has potential for applications in personalized nutrition and could be useful in guiding metabolite response to target levels.

Availability and implementation

The measured data analyzed in this study can be provided upon reasonable request. The simulated data along with a MATLAB implementation of pDMDc is available at https://github.com/FraunhoferChalmersCentre/pDMDc.

Plasma β-Alanine is Positively Associated With Risk of Ischemic Stroke: a Nested Case-Control Study

BACKGROUND

Previous studies suggested that β-alanine as a neurotransmitter could affect the pathogenesis of ischemic damage. However, the association between circulating β-alanine and risk of ischemic stroke (IS) has not been evaluated in populations.

OBJECTIVES

We aimed to examine the association between β-alanine and IS risk in a nested case-control study.

METHODS

We performed a case-control study nested within a prospective community-based cohort (n = 16457; median follow-up time: 5.3 y), which included 321 incident IS cases and 321 controls matched by age and sex. Β-alanine and other metabolites were measured in plasma after overnight fasting by LC-MS/MS. The association of β-alanine with risk of IS was evaluated by conditional logistic regression. BMI, current smoking, educational attainment, physical activity, total energy intake, family history of stroke, hypertension, diabetes, hyperlipidemia, and estimated GFR were adjusted in multivariable models.

RESULTS

There was a significant Spearman partial correlation between β-alanine and 4-pyridoxic acid (ρ = 0.239; P < 0.001). Participants with elevated β-alanine levels were more likely to develop IS with an adjusted OR of 1.26 (95% CI: 1.06-1.51; P = 0.011) (per standard deviation increment). This association remained significant after excluding the first 2 y of follow-up, and after further adjustment for red meat intake, total protein intake, medication use, or vitamin B6 indicators.

CONCLUSIONS

Our novel findings revealed that plasma β-alanine at baseline were positively associated with risk of IS and may function as an early biomarker of IS risk.

Dietary metabolic signatures and cardiometabolic risk

AIMS

Observational studies of diet in cardiometabolic-cardiovascular disease (CM-CVD) focus on self-reported consumption of food or dietary pattern, with limited information on individual metabolic responses to dietary intake linked to CM-CVD. Here, machine learning approaches were used to identify individual metabolic patterns related to diet and relation to long-term CM-CVD in early adulthood.

METHODS AND RESULTS

In 2259 White and Black adults (age 32.1 ± 3.6 years, 45% women, 44% Black) in the Coronary Artery Risk Development in Young Adults (CARDIA) study, multivariate models were employed to identify metabolite signatures of food group and composite dietary intake across 17 food groups, 2 nutrient groups, and healthy eating index-2015 (HEI2015) diet quality score. A broad array of metabolites associated with diet were uncovered, reflecting food-related components/catabolites (e.g. fish and long-chain unsaturated triacylglycerols), interactions with host features (microbiome), or pathways broadly implicated in CM-CVD (e.g. ceramide/sphingomyelin lipid metabolism). To integrate diet with metabolism, penalized machine learning models were used to define a metabolite signature linked to a putative CM-CVD-adverse diet (e.g. high in red/processed meat, refined grains), which was subsequently associated with long-term diabetes and CVD risk numerically more strongly than HEI2015 in CARDIA [e.g. diabetes: standardized hazard ratio (HR): 1.62, 95% confidence interval (CI): 1.32-1.97, P < 0.0001; CVD: HR: 1.55, 95% CI: 1.12-2.14, P = 0.008], with associations replicated for diabetes (P < 0.0001) in the Framingham Heart Study.

CONCLUSION

Metabolic signatures of diet are associated with long-term CM-CVD independent of lifestyle and traditional risk factors. Metabolomics improves precision to identify adverse consequences and pathways of diet-related CM-CVD.

Amino Acids and Lipids Associated with Long-Term and Short-Term Red Meat Consumption in the Chinese Population: An Untargeted Metabolomics Study

Red meat (RM) consumption is correlated with multiple health outcomes. This study aims to identify potential biomarkers of RM consumption in the Chinese population and evaluate their predictive ability. We selected 500 adults who participated in the 2015 China Health and Nutrition Survey and examined their overall metabolome differences by RM consumption by using elastic-net regression, then evaluate the predictivity of a combination of filtered metabolites; 1108 metabolites were detected. In the long-term RM consumption analysis 12,13-DiHOME, androstenediol (3α, 17α) monosulfate 2, and gamma-Glutamyl-2-aminobutyrate were positively associated, 2-naphthol sulfate and S-methylcysteine were negatively associated with long-term high RM consumption, the combination of metabolites prediction model evaluated by area under the receiver operating characteristic curve (AUC) was 70.4% (95% CI: 59.9–80.9%). In the short-term RM consumption analysis, asparagine, 4-hydroxyproline, and 3-hydroxyisobutyrate were positively associated, behenoyl sphingomyelin (d18:1/22:0) was negatively associated with short-term high RM consumption. Combination prediction model AUC was 75.6% (95% CI: 65.5–85.6%). We identified 10 and 11 serum metabolites that differed according to LT and ST RM consumption which mainly involved branch-chained amino acids, arginine and proline, urea cycle and polyunsaturated fatty acid metabolism. These metabolites may become a mediator of some chronic diseases among high RM consumers and provide new evidence for RM biomarkers.

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.

High-Coverage Metabolome Analysis Reveals Significant Diet Effects of Autoclaved and Irradiated Feed on Mouse Fecal and Urine Metabolomics

SCOPE

Using metabolomics to study the relations of nutrition and health requires stringent control of the experimental conditions used in an animal model. This work investigates the diet effects of autoclaved and irradiated feed on mouse urine and fecal metabolomics.

METHODS AND RESULTS

C57BL/6 mice are fed normal-irradiation sterilized diet (n = 9), autoclave sterilized diet (n = 9), and high-irradiation sterilized diet (n = 9) for 4 weeks. Differential chemical isotope labeling liquid chromatography mass spectrometry is used to quantify the metabolome variations of urine and feces collected at five time points. Significant differences are observed in urine or fecal metabolomes of mice fed autoclaved diet versus mice fed high-irradiation diet or fed normal-irradiation diet, while the differences are small between the mice fed normal-irradiation and high-irradiation diet. Correlation studies of metabolite changes of diet- and aging-related biomarkers indicate a large overlap of significantly affected metabolites by the two factors.

CONCLUSIONS

Diet can be a confounding factor that needs to be carefully considered when a metabolomics study is designed and metabolomic results of a mouse model of nutritional or other biological study are interpreted. Using the same sterilized diet for a given metabolomics project is essential to control the diet effect.

Metabolomic Markers of Southern Dietary Patterns in the Jackson Heart Study

SCOPE

New biomarkers are needed that are representative of dietary intake.

METHODS AND RESULTS

We assess metabolites associated with Southern dietary patterns in 1401 Jackson Heart Study participants. Three dietary patterns are empirically derived using principal component analysis: meat and fast food, fish and vegetables, and starchy foods. We randomly select two subsets of the study population: two-third sample for discovery (n = 934) and one-third sample for replication (n = 467). Among the 327 metabolites analyzed, 14 are significantly associated with the meat and fast food dietary pattern, four are significantly associated with the fish and vegetables dietary pattern, and none are associated with the starchy foods dietary pattern in the discovery sample. In the replication sample, nine remain associated with the meat and fast food dietary pattern [indole-3-propanoic acid, C24:0 lysophosphatidylcholine (LPC), N-methyl proline, proline betaine, C34:2 phosphatidylethanolamine (PE) plasmalogen, C36:5 PE plasmalogen, C38:5 PE plasmalogen, cotinine, hydroxyproline] and three remain associated with the fish and vegetables dietary pattern [1,7-dimethyluric acid, C22:6 lysophosphatidylethanolamine, docosahexaenoic acid (DHA)].

CONCLUSION

Twelve metabolites are discovered and replicated in association with dietary patterns detected in a Southern U.S. African-American population, which could be useful as biomarkers of Southern dietary patterns.

Charged metabolite biomarkers of food intake assessed via plasma metabolomics in a population-based observational study in Japan

Food intake biomarkers can be critical tools that can be used to objectively assess dietary exposure for both epidemiological and clinical nutrition studies. While an accurate estimation of food intake is essential to unravel associations between the intake and specific health conditions, random and systematic errors affect self-reported assessments. This study aimed to clarify how habitual food intake influences the circulating plasma metabolome in a free-living Japanese regional population and to identify potential food intake biomarkers. To achieve this aim, we conducted a cross-sectional analysis as part of a large cohort study. From a baseline survey of the Tsuruoka Metabolome Cohort Study, 7,012 eligible male and female participants aged 40-69 years were chosen for this study. All data on patients' health status and dietary intake were assessed via a food frequency questionnaire, and plasma samples were obtained during an annual physical examination. Ninety-four charged plasma metabolites were measured using capillary electrophoresis mass spectrometry, by a non-targeted approach. Statistical analysis was performed using partial-least-square regression. A total of 21 plasma metabolites were likely to be associated with long-term food intake of nine food groups. In particular, the influential compounds in each food group were hydroxyproline for meat, trimethylamine-N-oxide for fish, choline for eggs, galactarate for dairy, cystine and betaine for soy products, threonate and galactarate for carotenoid-rich vegetables, proline betaine for fruits, quinate and trigonelline for coffee, and pipecolate for alcohol, and these were considered as prominent food intake markers in Japanese eating habits. A set of circulating plasma metabolites was identified as potential food intake biomarkers in the Japanese community-dwelling population. These results will open the way for the application of new reliable dietary assessment tools not by self-reported measurements but through objective quantification of biofluids.

Identification of Milk and Cheese Intake Biomarkers in Healthy Adults Reveals High Interindividual Variability of Lewis System-Related Oligosaccharides

BACKGROUND

The use of biomarkers of food intake (BFIs) in blood and urine has shown great promise for assessing dietary intake and complementing traditional dietary assessment tools whose use is prone to misreporting.

OBJECTIVE

Untargeted LC-MS metabolomics was applied to identify candidate BFIs for assessing the intake of milk and cheese and to explore the metabolic response to the ingestion of these foods.

METHODS

A randomized controlled crossover study was conducted in healthy adults [5 women, 6 men; age: 23.6 ± 5.0 y; BMI (kg/m2): 22.1 ± 1.7].  After a single isocaloric intake of milk (600 mL), cheese (100 g), or soy-based drink (600 mL), serum and urine samples were collected postprandially up to 6 h and after fasting after 24 h. Untargeted metabolomics was conducted using LC-MS. Discriminant metabolites were selected in serum by multivariate statistical analysis, and their mass distribution and postprandial kinetics were compared.

RESULTS

Serum metabolites discriminant for cheese intake had a significantly lower mass distribution than metabolites characterizing milk intake (P = 4.1 × 10-4). Candidate BFIs for milk or cheese included saccharides, a hydroxy acid, amino acids, amino acid derivatives, and dipeptides. Two serum oligosaccharides, blood group H disaccharide (BGH) and Lewis A trisaccharide (LeA), specifically reflected milk intake but with high interindividual variability. The 2 oligosaccharides showed related but opposing trends: subjects showing an increase in either oligosaccharide did not show any increase in the other oligosaccharide. This result was confirmed in urine.

CONCLUSIONS

New candidate BFIs for milk or cheese could be identified in healthy adults, most of which were related to protein metabolism. The increase in serum of LeA and BGH after cow-milk intake in adults calls for further investigations considering the beneficial health effects on newborns of such oligosaccharides in maternal milk. The trial is registered at clinicaltrials.gov as NCT02705560.

Food intake biomarkers for green leafy vegetables, bulb vegetables, and stem vegetables: a review

BACKGROUND

Numerous studies acknowledged the importance of an adequate vegetable consumption for human health. However, current methods to estimate vegetable intake are often prone to measurement errors due to self-reporting and/or insufficient detail. More objective intake biomarkers for vegetables, using biological specimens, are preferred. The only concentration biomarkers currently available are blood carotenoids and vitamin C, covering total fruit and vegetable intake. Identification of biomarkers for specific vegetables is needed for a better understanding of their relative importance for human health. Within the FoodBAll Project under the Joint Programming Initiative "A Healthy Diet for a Healthy Life", an ambitious action was undertaken to identify candidate intake biomarkers for all major food groups consumed in Europe by systematically reviewing the existent literature. This study describes the review on candidate biomarkers of food intake (BFIs) for leafy, bulb, and stem vegetables, which was conducted within PubMed, Scopus and Web of Science for studies published through March 2019.

RESULTS

In total, 65 full-text articles were assessed for eligibility for leafy vegetables, and 6 full-text articles were screened for bulb and stem vegetables. Putative BFIs were identified for spinach, lettuce, endive, asparagus, artichoke, and celery, but not for rocket salad. However, after critical evaluation through a validation scheme developed by the FoodBAll consortium, none of the putative biomarkers appeared to be a promising BFI. The food chemistry data indicate that some candidate BFIs may be revealed by further studies.

CONCLUSION

Future randomized controlled feeding studies combined with observational studies, applying a non-targeted metabolomics approach, are needed in order to identify valuable BFIs for the intake of leafy, bulb, and stem vegetables.

Profiling of Endogenous and Gut Microbial Metabolites to Indicate Metabotype-Specific Dietary Responses: A Systematic Review

Upon dietary exposure, the endogenous metabolism responds to the diet-derived nutrients and bioactive compounds, such as phytochemicals. However, the responses vary remarkably due to the interplay with other dietary components, lifestyle exposures, and intrinsic factors, which lead to differences in endogenous regulatory metabolism. These physiological processes are evidenced as a signature profile composed of various metabolites constituting metabolic phenotypes, or metabotypes. The metabolic profiling of biological samples following dietary intake hence would provide information about diet-that is, as the intake biomarkers and the ongoing physiological reactions triggered by this intake-thereby enable evaluation of the metabolic basis required to distinguish the different metabotypes. The capacity of nontargeted metabolomics to also encompass the unprecedented metabolite species has enabled the profiling of multiple metabolites and the corresponding metabotypes with a single analysis, decoding the complex interplay between diet, other relevant factors, and health. In this systematic review, we screened 345 articles published in English in January 2007-July 2018, which applied the metabolomics approach to profile the changes of endogenous metabolites in the blood related to dietary interventions, either derived by metabolism of gut microbiota or the human host. We excluded all the compounds that were directly derived from diet, and also the dietary interventions focusing on supplementation with individual compounds. After the removal of less relevant studies and assessment of eligibility, 49 articles were included in this review. First, we mention the contribution of individual factors, either modifiable or nonmodifiable factors, in shaping metabolic profile. Then, how different aspects of the diet would affect the metabolic profiles are disentangled. Next, the classes of endogenous metabolites altered following included dietary interventions are listed. We also discuss the current challenges in the field, along with future research opportunities.

Circulating Pro- and Anti-Inflammatory Metabolites and Its Potential Role in Rheumatoid Arthritis Pathogenesis

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease that affects synovial joints, leading to inflammation, joint destruction, loss of function, and disability. Although recent pharmaceutical advances have improved the treatment of RA, patients often inquire about dietary interventions to improve RA symptoms, as they perceive pain and/or swelling after the consumption or avoidance of certain foods. There is evidence that some foods have pro- or anti-inflammatory effects mediated by diet-related metabolites. In addition, recent literature has shown a link between diet-related metabolites and microbiome changes, since the gut microbiome is involved in the metabolism of some dietary ingredients. But diet and the gut microbiome are not the only factors linked to circulating pro- and anti-inflammatory metabolites. Other factors including smoking, associated comorbidities, and therapeutic drugs might also modify the circulating metabolomic profile and play a role in RA pathogenesis. This article summarizes what is known about circulating pro- and anti-inflammatory metabolites in RA. It also emphasizes factors that might be involved in their circulating concentrations and diet-related metabolites with a beneficial effect in RA.

The Relationship between Fish Intake and Urinary Trimethylamine-N-Oxide

SCOPE

Fish intake is reported to be associated with certain health benefits; however, accurate assessment of fish intake is still problematic. The objective of this study is to identify fish intake biomarkers and examine relationships with health parameters in a free-living population.

METHODS AND RESULTS

In the NutriTech study, ten participants randomized into the fish group consume increasing quantities of fish for 3 days per week for 3 weeks. Urine is analyzed by NMR spectroscopy. Trimethylamine-N-oxide (TMAO), dimethylamine, and dimethyl sulfone are identified and display significant dose-response with intake (p < 0.05). Fish consumption yields a greater increase in urinary TMAO compared to red meat. Biomarker-derived fish intake is calculated in the National Adult Nutrition Survey cross-sectional study. However, the correlation between fish intake and TMAO (r = 0.148, p < 0.01) and that between fish intake and calculated fish intake (r = 0.142, p < 0.01) are poor. In addition, TMAO shows significantly positive correlation with serum insulin and insulin resistance in males and the relationship is more pronounced for males with high dietary fat intake.

CONCLUSION

Urinary TMAO displays a strong dose-response relationship with fish intake; however, use of TMAO alone is insufficient to determine fish intake in a free-living population.

Biomarkers of meat and seafood intake: an extensive literature review

Meat, including fish and shellfish, represents a valuable constituent of most balanced diets. Consumption of different types of meat and fish has been associated with both beneficial and adverse health effects. While white meats and fish are generally associated with positive health outcomes, red and especially processed meats have been associated with colorectal cancer and other diseases. The contribution of these foods to the development or prevention of chronic diseases is still not fully elucidated. One of the main problems is the difficulty in properly evaluating meat intake, as the existing self-reporting tools for dietary assessment may be imprecise and therefore affected by systematic and random errors. Dietary biomarkers measured in biological fluids have been proposed as possible objective measurements of the actual intake of specific foods and as a support for classical assessment methods. Good biomarkers for meat intake should reflect total dietary intake of meat, independent of source or processing and should be able to differentiate meat consumption from that of other protein-rich foods; alternatively, meat intake biomarkers should be specific to each of the different meat sources (e.g., red vs. white; fish, bird, or mammal) and/or cooking methods. In this paper, we present a systematic investigation of the scientific literature while providing a comprehensive overview of the possible biomarker(s) for the intake of different types of meat, including fish and shellfish, and processed and heated meats according to published guidelines for biomarker reviews (BFIrev). The most promising biomarkers are further validated for their usefulness for dietary assessment by published validation criteria.

Lysine pathway metabolites and the risk of type 2 diabetes and cardiovascular disease in the PREDIMED study: results from two case-cohort studies

Background

The pandemic of cardiovascular disease (CVD) and type 2 diabetes (T2D) requires the identification of new predictor biomarkers. Biomarkers potentially modifiable with lifestyle changes deserve a special interest. Our aims were to analyze: (a) The associations of lysine, 2-aminoadipic acid (2-AAA) or pipecolic acid with the risk of T2D or CVD in the PREDIMED trial; (b) the effect of the dietary intervention on 1-year changes in these metabolites, and (c) whether the Mediterranean diet (MedDiet) interventions can modify the effects of these metabolites on CVD or T2D risk.

Methods

Two unstratified case-cohort studies nested within the PREDIMED trial were used. For CVD analyses, we selected 696 non-cases and 221 incident CVD cases; for T2D, we included 610 non-cases and 243 type 2 diabetes incident cases. Metabolites were quantified using liquid chromatography–tandem mass spectrometry, at baseline and after 1-year of intervention.

Results

In weighted Cox regression models, we found that baseline lysine (HR_{+1 SD increase} = 1.26; 95% CI 1.06–1.51) and 2-AAA (HR_{+1 SD increase} = 1.28; 95% CI 1.05–1.55) were both associated with a higher risk of T2D, but not with CVD. A significant interaction (p = 0.032) between baseline lysine and T2D on the risk of CVD was observed: subjects with prevalent T2D and high levels of lysine exhibited the highest risk of CVD. The intervention with MedDiet did not have a significant effect on 1-year changes of the metabolites.

Conclusions

Our results provide an independent prospective replication of the association of 2-AAA with future risk of T2D. We show an association of lysine with subsequent CVD risk, which is apparently diabetes-dependent. No evidence of effects of MedDiet intervention on lysine, 2-AAA or pipecolic acid changes was found.

Trial registration ISRCTN35739639; registration date: 05/10/2005; recruitment start date 01/10/2003

The 1H NMR serum metabolomics response to a two meal challenge: a cross-over dietary intervention study in healthy human volunteers

BACKGROUND

Metabolomics represents a powerful tool for exploring modulation of the human metabolome in response to food intake. However, the choice of multivariate statistical approach is not always evident, especially for complex experimental designs with repeated measurements per individual. Here we have investigated the serum metabolic responses to two breakfast meals: an egg and ham based breakfast and a cereal based breakfast using three different multivariate approaches based on the Projections to Latent Structures framework.

METHODS

In a cross over design, 24 healthy volunteers ate the egg and ham breakfast and cereal breakfast on four occasions each. Postprandial serum samples were subjected to metabolite profiling using ¹H nuclear magnetic resonance spectroscopy and metabolites were identified using 2D nuclear magnetic resonance spectroscopy. Metabolic profiles were analyzed using Orthogonal Projections to Latent Structures with Discriminant Analysis and Effect Projections and ANOVA-decomposed Projections to Latent Structures.

RESULTS

The Orthogonal Projections to Latent Structures with Discriminant Analysis model correctly classified 92 and 90% of the samples from the cereal breakfast and egg and ham breakfast, respectively, but confounded dietary effects with inter-personal variability. Orthogonal Projections to Latent Structures with Effect Projections removed inter-personal variability and performed perfect classification between breakfasts, however at the expense of comparing means of respective breakfasts instead of all samples. ANOVA-decomposed Projections to Latent Structures managed to remove inter-personal variability and predicted 99% of all individual samples correctly. Proline, tyrosine, and N-acetylated amino acids were found in higher concentration after consumption of the cereal breakfast while creatine, methanol, and isoleucine were found in higher concentration after the egg and ham breakfast.

CONCLUSIONS

Our results demonstrate that the choice of statistical method will influence the results and adequate methods need to be employed to manage sample dependency and repeated measurements in cross-over studies. In addition, ¹H nuclear magnetic resonance serum metabolomics could reproducibly characterize postprandial metabolic profiles and identify discriminatory metabolites largely reflecting dietary composition.

TRIAL REGISTRATION

Registered with ClinicalTrials.gov, identifier: NCT02039596 . Date of registration: January 17, 2014.

Recent Advances in the Application of Metabolomics for Nutrition and Health

Metabolomics is the study of small molecules called metabolites in biological samples. Application of metabolomics to nutrition research has expanded in recent years, with emerging literature supporting multiple applications. Key examples include applications of metabolomics in the identification and development of objective biomarkers of dietary intake, in developing personalized nutrition strategies, and in large-scale epidemiology studies to understand the link between diet and health. In this review, we provide an overview of the current applications and identify key challenges that need to be addressed for the further development of the field. Successful development of metabolomics for nutrition research has the potential to improve dietary assessment, help deliver personalized nutrition, and enhance our understanding of the link between diet and health.

Nutritional Metabolomics: Postprandial Response of Meals Relating to Vegan, Lacto-Ovo Vegetarian, and Omnivore Diets

Metabolomics provide an unbiased tool for exploring the modulation of the human metabolome in response to food intake. This study applied metabolomics to capture the postprandial metabolic response to breakfast meals corresponding to vegan (VE), lacto ovo-vegetarian (LOV), and omnivore (OM) diets. In a cross over design 32 healthy volunteers (16 men and 16 females) consumed breakfast meals in a randomized order during three consecutive days. Fasting and 3 h postprandial serum samples were collected and then subjected to metabolite profiling using ¹H-nuclear magnetic resonance (NMR) spectroscopy. Changes in concentration of identified and discriminating metabolites, between fasting and postprandial state, were compared across meals. Betaine, choline, and creatine displayed higher concentration in the OM breakfast, while 3-hydroxyisobutyrate, carnitine, proline, and tyrosine showed an increase for the LOV and unidentified free fatty acids displayed a higher concentration after the VE breakfast. Using ¹H NMR metabolomics it was possible to detect and distinguish the metabolic response of three different breakfast meals corresponding to vegan, lacto-ovo vegetarian, and omnivore diets in serum.

Nutrimetabolomics: integrating metabolomics in nutrition to disentangle intake of animal-based foods

Food intake and metabolization of foods is a complex and multi-facetted process that encompasses the introduction of new metabolite compounds in our body, initiation or alterations in endogenous metabolic processes and biochemical pathways, and likely also involving the activity of the gut microbial community that we host. The explorative nature of metabolomics makes it a superior tool for examining the whole response to food intake in a more thorough way and has led to the introduction of the term nutrimetabolomics. Protein derived from animal sources constitutes an important part of our diet, and there is therefore an interest in understanding how these animal-derived dietary sources influence us metabolically. This review aims to illuminate how the introduction of nutrimetabolomics has contributed to gain novel insight into metabolic and nutritional aspects related to intake of animal-based foods.

Higher intake of fish and fat is associated with lower plasma s-adenosylhomocysteine: a cross-sectional study

Several B-vitamins act as co-factors in one-carbon metabolism, a pathway that plays a central role in several chronic diseases. However, there is a lack of knowledge of how diet affects markers in one-carbon metabolism. The aim of this study was to explore dietary patterns and components associated with one-carbon metabolites. We hypothesized that intake of whole-grains and fish would be associated with lower Hcy, and higher SAM:SAH ratio due to their nutrient content. We assessed dietary information using a four-day dietary record in 118 men and women with features of the metabolic syndrome. In addition we assessed whole-blood fatty acid composition and plasma alkylresorcinols. Plasma s-adenosylmethionine (SAM), s-adenosylhomocysteine (SAH), homocysteine (Hcy) and vitamin B₁₂ was included as one-carbon metabolism markers. We used principal component analysis (PCA) to explore dietary patterns and multiple linear regression models to examine associations between dietary factors and one-carbon metabolites. PCA separated subjects based on prudent and unhealthy dietary patterns, but the dietary pattern score was not related to the one-carbon metabolites. Whole grain intake was found to be inversely associated to plasma Hcy (-4.7% (-9.3; 0.0), P=.05) and total grain intake tended to be positively associated with SAM and SAH (2.4% (-0.5; 5.5), P=.08; 5.8% (-0.2; 12.1), P=.06, respectively, per SD increase in cereal intake). Fish intake was inversely associated with plasma Hcy and SAH concentrations (-5.4% (-9.7; -0.8), P=.02 and -7.0% (-12.1; -1.5), P=.01, respectively) and positively associated with the SAM:SAH ratio (6.2% (1.6; 11.0), P=.008). In conclusion, intake and fish and whole-grain appear to be associated with a beneficial one-carbon metabolism profile. This indicates that dietary components could play a role in regulation of one-carbon metabolism with a potential impact on disease prevention.

Biomarkers of food intake and nutrient status are associated with glucose tolerance status and development of type 2 diabetes in older Swedish women

Background: Diet is frequently associated with both the development and prevention of type 2 diabetes (T2D), but there is a lack of objective tools for assessing the relation between diet and T2D. Biomarkers of dietary intake are unconfounded by recall and reporting bias, and using multiple dietary biomarkers could help strengthen the link between a healthy diet and the prevention of T2D.Objective: The objective of this study was to explore how diet is related to glucose tolerance status (GTS) and to future development of T2D irrespective of common T2D and cardiovascular disease risk factors by using multiple dietary biomarkers.Design: Dietary biomarkers were measured in plasma from 64-y-old Swedish women with different GTS [normal glucose tolerance (NGT; n = 190), impaired glucose tolerance (IGT; n = 209), and diabetes (n = 230)]. The same subjects were followed up after 5 y to determine changes in glucose tolerance (n = 167 for NGT, n = 174 for IGT, and n = 159 for diabetes). ANCOVA and logistic regression were used to explore baseline data for associations between dietary biomarkers, GTS, and new T2D cases at follow-up (n = 69).Results: Of the 10 dietary biomarkers analyzed, β-alanine (beef) (P-raw < 0.001), alkylresorcinols C17 and C19 (whole-grain wheat and rye) (P-raw = 0.003 and 0.011), eicosapentaenoic acid (fish) (P-raw = 0.041), 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF) (fish) (P-raw = 0.002), linoleic acid (P-raw < 0.001), oleic acid (P-raw = 0.003), and α-tocopherol (margarine and vegetable oil) (P-raw < 0.001) were associated with GTS, and CMPF (fish) (OR: 0.72; 95% CI: 0.56, 0.93; P-raw = 0.013) and α-tocopherol (OR: 0.71; 95% CI: 0.51, 0.98; P-raw = 0.041) were inversely associated with future T2D development.Conclusions: Several circulating dietary biomarkers were strongly associated with GTS after correction for known T2D risk factors, underlining the role of diet in the development and prevention of T2D. To our knowledge, this study is the first to use multiple dietary biomarkers to investigate the link between diet and disease risk.

Pre-diagnostic metabolite concentrations and prostate cancer risk in 1077 cases and 1077 matched controls in the European Prospective Investigation into Cancer and Nutrition

BACKGROUND

Little is known about how pre-diagnostic metabolites in blood relate to risk of prostate cancer. We aimed to investigate the prospective association between plasma metabolite concentrations and risk of prostate cancer overall, and by time to diagnosis and tumour characteristics, and risk of death from prostate cancer.

METHODS

In a case-control study nested in the European Prospective Investigation into Cancer and Nutrition, pre-diagnostic plasma concentrations of 122 metabolites (including acylcarnitines, amino acids, biogenic amines, glycerophospholipids, hexose and sphingolipids) were measured using targeted mass spectrometry (AbsoluteIDQ p180 Kit) and compared between 1077 prostate cancer cases and 1077 matched controls. Risk of prostate cancer associated with metabolite concentrations was estimated by multi-variable conditional logistic regression, and multiple testing was accounted for by using a false discovery rate controlling procedure.

RESULTS

Seven metabolite concentrations, i.e. acylcarnitine C18:1, amino acids citrulline and trans-4-hydroxyproline, glycerophospholipids PC aa C28:1, PC ae C30:0 and PC ae C30:2, and sphingolipid SM (OH) C14:1, were associated with prostate cancer (p < 0.05), but none of the associations were statistically significant after controlling for multiple testing. Citrulline was associated with a decreased risk of prostate cancer (odds ratio (OR1SD) = 0.73; 95% confidence interval (CI) 0.62-0.86; p trend = 0.0002) in the first 5 years of follow-up after taking multiple testing into account, but not after longer follow-up; results for other metabolites did not vary by time to diagnosis. After controlling for multiple testing, 12 glycerophospholipids were inversely associated with advanced stage disease, with risk reduction up to 46% per standard deviation increase in concentration (OR1SD = 0.54; 95% CI 0.40-0.72; p trend = 0.00004 for PC aa C40:3). Death from prostate cancer was associated with higher concentrations of acylcarnitine C3, amino acids methionine and trans-4-hydroxyproline, biogenic amine ADMA, hexose and sphingolipid SM (OH) C14:1 and lower concentration of glycerophospholipid PC aa C42:4.

CONCLUSIONS

Several metabolites, i.e. C18:1, citrulline, trans-4-hydroxyproline, three glycerophospholipids and SM (OH) C14:1, might be related to prostate cancer. Analyses by time to diagnosis indicated that citrulline may be a marker of subclinical prostate cancer, while other metabolites might be related to aetiology. Several glycerophospholipids were inversely related to advanced stage disease. More prospective data are needed to confirm these associations.

Metabolic Phenotyping of Diet and Dietary Intake

Nutrition provides the building blocks for growth, repair, and maintenance of the body and is key to maintaining health. Exposure to fast foods, mass production of dietary components, and wider importation of goods have challenged the balance between diet and health in recent decades, and both scientists and clinicians struggle to characterize the relationship between this changing dietary landscape and human metabolism with its consequent impact on health. Metabolic phenotyping of foods, using high-density data-generating technologies to profile the biochemical composition of foods, meals, and human samples (pre- and postfood intake), can be used to map the complex interaction between the diet and human metabolism and also to assess food quality and safety. Here, we outline some of the techniques currently used for metabolic phenotyping and describe key applications in the food sciences, ending with a broad outlook at some of the newer technologies in the field with a view to exploring their potential to address some of the critical challenges in nutritional science.

Chemical Isotope Labeling LC-MS for Monitoring Disease Progression and Treatment in Animal Models: Plasma Metabolomics Study of Osteoarthritis Rat Model

We report a chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS) method generally applicable for tracking metabolomic changes from samples collected in an animal model for studying disease development and treatment. A rat model of surgically induced osteoarthritis (OA) was used as an example to illustrate the workflow and technical performance. Experimental duplicate analyses of 234 plasma samples were carried out using dansylation labeling LC-MS targeting the amine/phenol submetabolome. These samples composed of 39 groups (6 rats per group) were collected at multiple time points with sham operation, OA control group, and OA rats with treatment, separately, using glucosamine/Celecoxib and three traditional Chinese medicines (Epimedii folium, Chuanxiong Rhizoma and Bushen-Huoxue). In total, 3893 metabolites could be detected and 2923 of them were consistently detected in more than 50% of the runs. This high-coverage submetabolome dataset could be used to track OA progression and treatment. Many differentiating metabolites were found and 11 metabolites including 2-aminoadipic acid, saccharopine and GABA were selected as potential biomarkers of OA progression and OA treatment. This study illustrates that CIL LC-MS is a very useful technique for monitoring incremental metabolomic changes with high coverage and accuracy for studying disease progression and treatment in animal models.

Herring and chicken/pork meals lead to differences in plasma levels of TCA intermediates and arginine metabolites in overweight and obese men and women

SCOPE

What effect does replacing chicken or pork with herring as the main dietary source of protein have on the human plasma metabolome?

METHOD AND RESULTS

A randomised crossover trial with 15 healthy obese men and women (age 24-70 years). Subjects were randomly assigned to four weeks of herring diet or a reference diet of chicken and lean pork, five meals per week, followed by a washout and the other intervention arm. Fasting blood serum metabolites were analysed at 0, 2 and 4 weeks for eleven subjects with available samples, using GC-MS based metabolomics. The herring diet decreased plasma citrate, fumarate, isocitrate, glycolate, oxalate, agmatine and methyhistidine and increased asparagine, ornithine, glutamine and the hexosamine glucosamine. Modelling found that the tricarboxylic acid cycle, glyoxylate, and arginine metabolism were affected by the intervention. The effect on arginine metabolism was supported by an increase in blood nitric oxide in males on the herring diet.

CONCLUSION

The results suggest that eating herring instead of chicken and lean pork leads to important metabolic effects, particularly on energy and amino acid metabolism. Our findings support the hypothesis that there are metabolic effects of herring intake unrelated to the long chain n-3 polyunsaturated fatty acid content.

Metabolomics as a tool in the identification of dietary biomarkers

Current dietary assessment methods including FFQ, 24-h recalls and weighed food diaries are associated with many measurement errors. In an attempt to overcome some of these errors, dietary biomarkers have emerged as a complementary approach to these traditional methods. Metabolomics has developed as a key technology for the identification of new dietary biomarkers and to date, metabolomic-based approaches have led to the identification of a number of putative biomarkers. The three approaches generally employed when using metabolomics in dietary biomarker discovery are: (i) acute interventions where participants consume specific amounts of a test food, (ii) cohort studies where metabolic profiles are compared between consumers and non-consumers of a specific food and (iii) the analysis of dietary patterns and metabolic profiles to identify nutritypes and biomarkers. The present review critiques the current literature in terms of the approaches used for dietary biomarker discovery and gives a detailed overview of the currently proposed biomarkers, highlighting steps needed for their full validation. Furthermore, the present review also evaluates areas such as current databases and software tools, which are needed to advance the interpretation of results and therefore enhance the utility of dietary biomarkers in nutrition research.