Identification of Single and Combined Serum Metabolites Associated with Food Intake.

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

Urine Metabolite Profiles and Nutrient Intake Based on 4-Day Weighed Food Diary in Habitual Vegans, Vegetarians, and Omnivores.

BACKGROUND: Increasing interest in diets excluding meat and other products of animal origin emphasizes the importance of objective and reliable methods to measure dietary exposure, to evaluate associations and causation between diet and health, and to quantify nutrient intakes in different diets. OBJECTIVES: This study aimed to investigate if NMR analysis of urine samples can serve as an objective method to discriminate vegan, vegetarian with or without fish, and omnivore diets. A secondary aim was to assess the influence of dietary nutrient intake on the metabolomics results. METHODS: Healthy individuals (43 men and 75 women, age 19-57 y) complying with habitual vegan (n = 42), vegetarian (n = 25), vegetarian + fish (n = 13), or omnivore (n = 38) diets were enrolled. Data were collected on clinical phenotype and lifestyle including a 4-d weighed food diary. Urine was analyzed for metabolites by NMR spectroscopy and data normalized using probabilistic quotient normalization and Pareto-scaled before multivariate analysis. Before orthogonal projections to latent structures with discriminant analysis, participants were assigned as meat consumers or nonmeat consumers (vegans and vegetarians), vegans or nonvegans (omnivores, vegetarian, and vegetarian + fish). RESULTS: The main results showed that it was possible to discriminate meat and nonmeat consumers (91% correctly classified), but discrimination between vegans and nonvegans was less rigorous (75% correctly classified). Secondary outcomes showed that reported intake of protein was higher in omnivores, and saturated fat lower and fiber higher in vegans, compared with the other groups. Discriminating metabolites were mainly related to differences in protein intake. CONCLUSIONS: NMR urine metabolomics appears suitable to objectively identify and predict habitual intake of meat in healthy individuals, but results should be interpreted with caution because not only food groups but also specific foods contribute to the patterns.This trial was registered at clinicaltrials.gov as NCT02039609.

Serum metabolite profiles of habitual diet: evaluation by 1H-nuclear magnetic resonance analysis.

BACKGROUND: Objective and reliable methods to measure dietary exposure and prove associations and causation between diet and health are desirable. OBJECTIVE: The aim of this study was to investigate if 1H-nuclear magnetic resonance (1H-NMR) analysis of serum samples may be used as an objective method to discriminate vegan, vegetarian, and omnivore diets. Specifically, the aim was to identify a metabolite pattern that separated meat-eaters from non-meat-eaters and vegans from nonvegans. METHODS: Healthy volunteers (45 men and 75 women) complying with habitual vegan (n = 43), vegetarian (n = 24 + vegetarians adding fish n = 13), or omnivore (n = 40) diets were enrolled in the study. Data were collected on clinical phenotype, body composition, lifestyle including a food-frequency questionnaire (FFQ), and a 4-d weighed food diary. Serum samples were analyzed by routine clinical test and for metabolites by 1H-NMR spectroscopy. NMR data were nonnormalized, UV-scaled, and analyzed with multivariate data analysis [principal component analysis, orthogonal projections to latent structures (OPLS) and OPLS with discriminant analysis]. In the multivariate analysis volunteers were assigned as meat-eaters (omnivores), non-meat-eaters (vegans and vegetarians), vegans, or nonvegans (lacto-ovo-vegetarians, vegetarians adding fish, and omnivores). Metabolites were identified by line-fitting of 1D 1H-NMR spectra and the use of statistical total correlation spectroscopy. RESULTS: Although many metabolites differ in concentration between men and women as well as by age, body mass index, and body composition, it was possible to correctly classify 97.5% of the meat-eaters compared with non-meat-eaters and 92.5% of the vegans compared with nonvegans. The branched-chain amino acids, creatine, lysine, 2-aminobutyrate, glutamine, glycine, trimethylamine, and 1 unidentified metabolite were among the most important metabolites in the discriminating patterns in relation to intake of both meat and other animal products. CONCLUSIONS: 1H-NMR serum metabolomics appears to be a possible objective tool to identify and predict habitual intake of meat and other animal products in healthy subjects. These results should be confirmed in larger cohort studies or intervention trials. This trial was registered at clinicaltrials.gov as NCT02039609.

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.

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.

Metabolic profiles from two different breakfast meals characterized by 1H NMR-based metabolomics.

It is challenging to measure dietary exposure with techniques that are both accurate and applicable to free-living individuals. We performed a cross-over intervention, with 24 healthy individuals, to capture the acute metabolic response of a cereal breakfast (CB) and an egg and ham breakfast (EHB). Fasting and postprandial urine samples were analyzed using 1H nuclear magnetic resonance (NMR) spectroscopy and multivariate data analysis. Metabolic profiles were distinguished in relation to ingestion of either CB or EHB. Phosphocreatine/creatine and citrate were identified at higher concentrations after consumption of EHB. Beverage consumption (i.e., tea or coffee) could clearly be seen in the data. 2-furoylglycine and 5-hydroxymethyl-2-furoic acid - potential biomarkers for coffee consumption were identified at higher concentrations in coffee drinkers. Thus 1H NMR urine metabolomics is applicable in the characterization of acute metabolic fingerprints from meal consumption and in the identification of metabolites that may serve as potential biomarkers.