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Use of Metabolomics in Improving Assessment of Dietary Intake.

Recent high-throughput technologic developments in molecular biology, namely, genomics, transcriptomics, proteomics, and metabolomics, are leading us toward a new era in epidemiologic research. In the past few years, the scientific community has focused on a more integrated systems epidemiology approach, in which several fields converge to integrate traditional knowledge with novel -omics techniques (1). Nutritional epidemiology has not been the exception, and several studies in this field have incorporated -omics data in the past decade (1). Along these lines, the concept of precision nutrition has recently emerged (2) and refers to the integration of -omics techniques to personalize diets based on individual genetic makeup to achieve better prevention or management of disease. Among all the -omics, metabolomics plays a crucial role in the field of nutrition because it is more time sensitive than other -omics and can reflect the current biological status of an individual (3). The human metabolome can be influenced by several factors, such as age, diseases, drugs, environment, genetic factors, lifestyle, and nutrition (3).

Metabolomics can provide a comprehensive picture of overall dietary intake by measuring the full profile of small molecule metabolites in biological samples such as saliva, blood, and urine. Thus, it could help deepen our knowledge of metabolic pathways relevant to human nutrition (3). Importantly, because nutritional epidemiologic studies frequently rely on self-reported dietary assessment methods that are subject to recall bias and measurement error and because objective biomarkers do not exist for all nutrients and foods (4), metabolomics can be a promising technique to objectively identify dietary biomarkers. Metabolite profiling accounts for intrinsic variability in metabolism by measuring downstream components or metabolic products of foods, and might therefore accurately reflect true exposure as compared with traditional methods that measure individual food intake (5). Thereby, nutritional metabolomics, which refers to the integration of metabolic profiling with nutrition in complex biosystems, can be applied to discover new biomarkers of nutritional exposure and status and can help disentangle the molecular mechanisms by which diet affects health and disease. Diet can have effects on 2 different components of the metabolome: the endogenous metabolome, referring to all metabolites present in a biological sample of the host, and the food metabolome, which includes metabolites that are derived from food consumption and their subsequent metabolism in the human body (6). Food metabolome not only includes metabolites of known micro- and macronutrients but also nonnutrient food compounds with biological roles yet to be elucidated.

To date, several studies have identified metabolomic signatures associated with the intake of specific foods and food groups, including fruits, vegetables, meat, fish, nuts, whole-grain bread, wine, coffee, and cocoa (6). A growing body of evidence has also emerged relating metabolic profiles with overall dietary patterns. Therefore, the purpose of this review is 2-fold: (a) to provide a broad overview of the metabolite measurement techniques, study designs, and statistical approaches used in nutrition metabolomics studies, and (b) to describe the current knowledge from epidemiologic studies identifying metabolite profiles associated with the intake of individual nutrients, foods, and dietary patterns.

Methods and Approaches in Studies of Nutritional Metabolomics

MEASUREMENT OF METABOLITES

Metabolomics refers to the systematic analysis of low molecular weight biochemical compounds in a biological sample. Urine, serum, and plasma samples are the most common biofluids used in nutritional metabolomics studies. One of the major differences between urine and plasma is that urine contains a higher concentration of nonmetabolites and nonnutrient compounds (representing noncontributory information or noise) that are derived from food phytochemicals and other chemicals. However, most of the metabolites in urine are excreted faster than those from plasma and can serve as acute markers of frequently consumed foods. For example, urine excretion of proline betaine is known to peak within a few hours after intake and be almost completely excreted within 24 h (7). Blood, on the other hand, contains a higher concentration of metabolically active compounds, and lipid-soluble metabolites are present only in plasma, not in urine (3).

In general, 2 different metabolomics techniques have been applied: (a) mass spectrometry coupled with gas- or liquid-phase chromatography, and (b) proton ([sup.1]H) nuclear magnetic resonance (NMR) [4] spectroscopy (8). Other platforms, such as inductively coupled plasma mass spectrometry, are also used to detect trace minerals and other electrolytes in biological samples (9). Targeted approaches that focus on a specific subset of predefined metabolites, as well as more agnostic untargeted approaches that analyze many measurable compounds in the sample, including chemical unknowns, have been implemented (8). Although targeted approaches are usually less expensive and follow a hypothesis-driven approach of metabolites of known identity, untargeted metabolomics detect thousands of unknown metabolites that may provide novel information on biological pathways with clinical relevance. However, because of the higher cost of the latter approach, the high density of the data acquired, and the methods required for complex statistical analysis, several epidemiological studies so far have relied solely on targeted metabolite profiling. Human metabolic databases, such as the Human Metabolome Database, can be a useful resource for nutritional metabolomics. The Human Metabolome Database includes more than 6800 fully annotated metabolites, such as metabolic intermediates, hormones, drugs, and food components (10). Despite the existence of many metabolites, the nutrition community is particularly interested in metabolic pathways in which nutrients are involved, including carbohydrate, lipids, amino acids, and energy metabolism pathways, along with mineral, trace elements, and vitamin metabolism pathways (3).

STUDY DESIGNS USED IN NUTRITIONAL METABOLOMICS

Intervention studies. Metabolomics can be used as a key tool in the search for novel biomarkers of dietary intake. Studies of nutritional metabolomics need to account for intersubject metabolic variation and should be able to deal with measurements of subtle metabolic modulations against relatively low doses of bioactive food nutrients or supplements (11). One method is to conduct controlled dietary intervention trials; in acute feeding studies, participants consume the food of interest in a single meal. For short- to medium-term trials, participants typically consume the food of interest in repeated meals over a given period, ranging from a few days up to several months. For this purpose, a crossover study design has traditionally been favored over a parallel design because it effectively deals with intersubject variation as each participant serves as his or her own control. Biofluids can be collected before and after the consumption of the food of interest in acute studies; in short- and medium-term trials, biofluids are usually collected at baseline and at the end of the intervention period (6). Any biomarker identified in acute studies must ideally be validated with longer intervention studies and replicated in different populations. Given the need to detect accurate measurements of dietary exposures, nutritional metabolomics can be a useful tool for identifying objective markers of relatively short-term response to diet interventions, as well as compliance with such dietary interventions. In addition, well-conducted clinical trials are less prone to confounding and measurement errors compared with observational studies.

Observational studies. One of the advantages of nutritional metabolomics studies is the possibility to identify a wide range of dietary biomarkers instead of a single biomarker. Indeed, multimetabolite biomarker panels can offer a better estimation than single biomarkers and increase the accuracy and precision of dietary assessment when combined with a food frequency questionnaire (FFQ). In this context, observational studies with repeated measurements of diet over time, which provide information of usual diet, play an important role. Epidemiologic studies in this field compare low and high consumers of nutrients/foods using FFQ, food records, and other dietary assessment tools, and then characterize objective biomarkers that are reflective of habitual intake or related to the intake of specific nutrients and food groups. These studies can also be used to detect metabolite signatures associated with overall dietary patterns. It is important to note that many of the foods consumed are highly correlated, and there is a risk of identifying biomarkers that are not specific to the particular food of interest (6). For example, vitamin C, several carotenoids, and flavonoids are common to many fruit and vegetables; therefore, they can be used as generic biomarkers of total fruit and vegetable intake but not specific to individual fruits or vegetables (12).

A number of these observational studies have applied cross-sectional designs comparing groups of participants at a single time point (i.e., consumers vs nonconsumers). Another possible approach to identify dietary biomarkers through metabolomics is the study of longitudinal variations in metabolite concentrations and their associations with diet and particular health outcomes or markers in population-based studies and clinical trials (6). With these prospective designs, individuals or populations exposed to different environments, lifestyles, or dietary patterns can be distinguished and metabolic differences can be identified.

Nevertheless, it is worth mentioning that most of the observational studies in nutritional metabolomics have small sample sizes and have not been replicated. One of the reasons that may account for the lack of replication in metabolomics studies is the fact that such analyses have not yet been standardized and homogenized, especially compared with genome-wide, large-scale studies (13). Several differences across studies exist, including storage of biological samples, different platforms for analyses of specimens, quality control and data preparation, and relative vs absolute quantification of metabolites. Therefore, there is an urgent need to evaluate metabolite stability and biological variability in large populations before markers of food intake or dietary patterns can be properly validated (13).

STATISTICAL APPROACHES

Analyses of high-throughput nutritional metabolomics data require the use of advanced bioinformatics and computational tools. Challenges with metabolomics analyses include data preparation and normalization, data reduction into fewer dimensions, and interpretation. In the section below, we briefly provide an overview of the statistical approaches commonly used in nutritional metabolomics studies.

Data preparation. The first step in metabolomics analysis is to prepare the metabolic profile of raw data generated by the analysis of biological samples. Raw data undergo data preprocessing, data alignment, data normalization, and signal correction before application of statistical methods (14). The first step is preprocessing; in mass spectrometry, this step includes peak detection, peak matching, retention time alignment, peak integration, and peak filling (13). Data alignment consists of matching peaks (m/z) and retention times to standards to align the different sample profiles. Some software tools also include gap filling, which checks raw data for any peak that has not been detected in a sample but was found in others. Quality control is also undertaken in this phase with the use of repeatability filters (i.e., filtering out features with coefficient of variation >30% or lower cutpoints for targeted biomarkers) (13). In most laboratories, internal standards and pooled study samples are used to standardize data across data sets. In NMR, preprocessing needs to account for peak overlap and peak shift. Binning (or grouping) of metabolite signals based on correlation structure is commonly used to account for peak shift (15). Peak fitting can address peak shifting and overlap, but it is time consuming (16). Batch normalization, scaling, and outlier removal are also important parts of data preparation; normalization is used to account for uncontrolled metabolome-wide effects like dilution. Normalization is aimed to remove any unwanted variation in the spectrometric signal that cannot be controlled for or removed in any other way (17). Annotation of metabolites is usually the last step before applying statistical analysis. Comparing peaks with standards, databases, and commercial software is an important part of the process to avoid "putative annotation" because each feature could correspond to multiple metabolites. Although these methods are generally applied to targeted and untargeted data, identification of individual metabolite from untargeted peaks is challenging. Although online databases of annotated metabolites are growing, many unknown compounds still need to be identified.

Missing values in metabolomics data sets occur widely and can arise from different sources, including technical and biological reasons. Several methods to deal with missing data in metabolomics analysis have been examined (18-21). In mass spectrometry data analysis, a common approach is to remove from the analysis individual metabolites when a large proportion of participants have missing values or to remove a participant with many missing metabolites (21). Other standard methods of missing value imputation include the replacement of missing values with a nonzero value while maintaining data structure (i.e., minimum value, mean, or median value between 0 and the detection limit). Importantly, results and interpretation of the data can vary depending on the imputation method used (18). Other algorithms, including K-nearest neighbors, Bayesian principal component analysis (PCA), local least-squares regression, singular value decomposition, and random forest, can also be used to impute missing data in metabolomics analysis (19, 22). In a study comparing 5 different imputation methods on unsupervised and supervised analyses, random forest provided better classification rates for PCA and partial least-squares discriminant analysis (PLS-DA) (19). However, in a recent study comparing 7 imputation methods, no imputation method was perfect, but the simple substitution methods (half minimum and mean) consistently performed poorly (22).

Statistical methods to derive metabolomics fingerprints of diet. To maximize the recovery of information and to help in the interpretation of high-dimensional data sets, advanced statistical and bioinformatics tools are applied (11). Clustering methods are often used to reduce and divide the data into groups with a high degree of similarity (23). The most popular clustering techniques in nutritional metabolomics include multivariate analysis such as simple unsupervised clustering algorithms, PCA, and supervised techniques like PLS-DA and its variant combining a data-filtering step such as orthogonal signal correction and orthogonal partial least-squares discriminant analysis (O-PLS-DA) (24). These techniques create subgroups of metabolites without a priori hypotheses of metabolic pathways or the association with dietary information. Unsupervised PCA derives a linear transformation that preserves as much of the variance in the original data as possible while maximizing intergroup variation and minimizing within-group variation. PLS-DA, on the other hand, maximizes the covariance between scores in x (predictor) and y (outcome) spaces, and it accounts for both systematic variations in the metabolic profiles and correlations between metabolomics data and the outcome. Partial least-squares analysis is often used for discriminant analysis to classify metabolic profiles according to categories (i.e., dietary patterns) (13).

PCA is usually the starting point for an exploratory analysis, as it allows visualization of biological sets based on the resemblance of samples with respect to their biochemical composition, as well as allowing the extraction of information on factors contributing to the difference among samples (25). PCA creates a reduction summary of the data, which can be analyzed graphically using scores plot and loading plots (11). PCA can facilitate the comparison of many complex data such as biofluid spectra and can provide information on metabolite changes. This technique is particularly efficient for the identification of outliers. Although PCA can help to reduce the dimension of the data set, it does not give an insight of the association between metabolite signatures and dietary components. Other techniques such as correlations and multivariate regression analyses are then applied to test the associations between the factors extracted from PCA with dietary patterns and food groups.

Supervised multivariate analyses are commonly used in nutritional metabolomics. PLS-DA provides a way to filter out metabolic information that is not correlated to the predefined classes, whereas the PLS-DA loadings, similarly to PCA loadings, yield information on which spectral signals are associated with the observed clustering (11). The O-PLS-DA method is similar to PLS-DA, but the interpretation of the models is improved because the structured noise is modeled separately from the variation common to the matrices. O-PLS-DA enables a more straightforward and accurate interpretation of metabolomics data (24). O-PLS-DA models the metabolic profile that is linearly predictive of the dietary component of an individual but also captures external factors not linearly related to the outcome, such as age, sex, and batch effects. These techniques are useful in exploring the relation of any features in the metabolomics profiling data set with an external variable (i.e., intake of a specific food based on a questionnaire or any biological outcome marker).

All these statistical methods require rigorous model validation using cross-validation and permutation testing [i.e., using a proportion of the data as the test set (usually 10%) and building the model in the remaining training set] and ideally external validation in independent studies to avoid false-positive discoveries and to ensure model robustness (13). Moreover, as a comprehensive way of interpreting the data, it is highly recommended to report Bonferroni adjustment and false-positive discovery rate procedures to account for multiple comparison testing, as well as P values and CIs, effect size, and adjustment for covariates (26).

Fig. 1 depicts an example of scores (A) and loading plots (B) obtained from O-PLS-DA. Data in Fig. 1 are hypothetical and used only for the purpose of illustrating scores and loading plots. Fig. 1A could represent O-PLS-DA of [sup.1]H NMR of urine data and dietary interventions. Blue dots in the figure represent control diet, and red triangles represent dietary pattern intervention. Component 1 and component 2 are extracted from O-PLS-DA analysis. Loading scatterplot (Fig. 1B) shows the individual compounds.

Pathway analyses and other systems biology approaches. Because of the efforts to analyze complex genomic data and their integration with bioinformatics in the past decades, several metabolic databases, such as KEGG, MetaCyc, and BiGG families, have been developed (27, 28). These databases can help to elucidate underlying metabolic pathways and integrate targeted and untargeted metabolomics data. Semisupervised pathway analysis, such as the web-based tool Metabolic Set Enrichment Analysis (29) (which includes >1000 predefined metabolite sets covering various metabolic pathways, biofluids, and tissue locations), follows the principle of gene enrichment analysis to derive metabolic sets. These techniques, often used for the prediction of health outcomes, can also be applied to nutritional metabolomics to evaluate the associations between these enriched metabolite sets and dietary patterns. Agnostic network models are commonly used to combine significant metabolites identified from the targeted approach with the untargeted method to discover and validate metabolomics signatures. Correlation networks, a systems biology tool that enables visualization of the complex correlation structure between metabolites and clinical parameters (30), can be combined with algorithms for detecting active subnetworks to integrate data into metabolic pathways. These software tools, although not yet widely used in nutrition metabolomics, can help in the understanding and visualization of metabolic networks, as well as the description and prediction of metabolic pathways, and also constitute an important resource for nutrition research (25). Most of the statistical approaches that are currently used for genomics and metabolomics data treatment can serve as a foundation for nutritional studies, for which high-throughput metabolomics data are becoming extensively available. A schematic summary work flow of nutritional metabolomics approaches is presented in Fig. 2.

Metabolomic Fingerprinting of Diet

Metabolomic techniques have been applied to several clinical and population settings to characterize metabolic effects of nutrients, foods, and dietary patterns. Biomarkers identified with the use of high-throughput metabolomics (usually mass spectrometry and NMR) have been measured in urine, plasma, and serum, and fall into different subclasses, including polar metabolites (amino acids, acylcarnitines, carbohydrates, bile acids, metabolites from purine and pyrimidine) and apolar metabolites (lipid classes). Briefly, most studies focused on the identification of dietary biomarkers of specific food and food groups, whereas fewer identified biomarkers of single nutrients (6). The most extensively studied food groups include fruits, vegetables, meat, fish, bread, whole-grain cereals, nuts, wine, coffee, tea, cocoa, and chocolate (6). The number of participants in these studies ranged from 4 to 500 participants, and a majority used a cross-sectional study design to identify specific metabolites of a food or food group differing between consumers and nonconsumers (6).

BIOMARKERS AND METABOLITES OF SINGLE NUTRIENTS

Several biomarkers of single nutrients including fiber and protein intake have been identified. In a randomized intervention study, concentrations of metabolite markers 2,6-dihydroxybenzoic acid and 2-aminophenol sulfate increased after participants followed a high-fiber diet (mean intake 48 g compared with the low-fiber group at 30.2 g) for 5 weeks (31). In another controlled intervention study in which participants were randomized to a high or low glycemic index diet for 6 months, hippuric acid was correlated with dietary fiber intake (32). However, none of the biomarkers identified in the first study were confirmed in the second. The lack of consistency across studies may be explained because of differences in study duration (5 weeks vs 6 months), interventions (high-fiber diet in the first study consisted of oat bran, rye bran, and sugar beet fiber incorporated into test food products, whereas the intervention diets in the second study were designed to achieve a difference of 15 glycemic index points), or the age of participants (37-45 years vs 30-70 years). This highlights the need to validate and replicate biomarkers in different populations to identify consistent biomarkers for the same nutrient.

In another crossover feeding trial of two 28-day diet periods of high and low glycemic load diet, concentrations of kynurenate and trimethylamine-A-oxide (TMAO) were found to be significantly higher after consumption of a low glycemic load diet (33). A cross-sectional analysis of 1003 participants from the Twins UK cohort showed that protein intake was positively associated with several plasma amino acids, including valine, phenylalanine, and tyrosine, and inversely associated with glutamine (34). In a randomized, crossover feeding study including 11 obese nondiabetic participants, plasma concentrations of tryptophan, phenylalanine, and kynurenine were increased after a high-fat meal with whey protein isolate (35).

METABOLOMIC FINGERPRINTS OF SPECIFIC FOOD GROUPS

Several studies have identified biomarkers of total polyphenol intake and polyphenols from specific food groups, including polyphenols from cocoa powder (36-38), red wine and grape juice (39, 40), almond skin (41), nuts (42), and orange juice (43). In the PREDIMED study, a randomized, parallel, clinical trial for primary prevention of cardiovascular disease, using an untargeted metabolomics analysis of urine from 32 consumers of cocoa-derived products and 32 controls, the discriminant biomarkers of cocoa consumption were related to the metabolic pathways of theobromine and polyphenols (38). In another study of 57 volunteers at high cardiovascular risk, tartrate was associated with wine polyphenol consumption (40). Hydroxyphenylvaleric, hydroxyphenylpropionic, and hydroxyphenylacetic acids were identified in human urine samples after the consumption of flavan-3-ols from almond skin in an acute feeding study (41). Betonicine, stachydrine, methyl glucopyranoside (a + (5), dihydroferulic acid, and galactonate were proposed as new metabolic signatures to distinguish the intake of orange juice with different polyphenol content in the BIONAOS study, which compared a normal-polyphenol orange juice with a high-polyphenol orange juice for 12 weeks in a randomized parallel study (43). Moreover, a recent study evaluating metabolic profiles in urine of 481 subjects from the European Prospective Investigation into Cancer (EPIC) and Nutrition cohort identified >80 polyphenol metabolites associated with the consumption of 6 polyphenol-rich foods (coffee, tea, red wine, citrus fruit, apples and pears, and chocolate products) (44).

As extensively reviewed by Scalbert et al. (6), many biomarkers identified using metabolomics were associated with the consumption of fruits, vegetables, and specific markers of citrus fruit and cruciferous vegetables. As an example, proline and betaine were identified as biomarkers of citrus intake in an acute feeding study of 8 participants and then validated with 499 participants from INTERMAP (7) and another cross-sectional study (45). At the same time, urine concentrations of S-methyl-L-cysteine sulfoxide were found to be related to the intake of cruciferous vegetables in a short-term intervention study that included 20 healthy men who consumed 250 g/day of cruciferous vegetables (broccoli and Brussels sprouts) for 14 days (46). Metabolite biomarkers of tomato sauce consumption have also been characterized (47). Specifically, serum concentrations of creatine, creatinine, leucine, choline, methionine, and acetate were found to be increased after a 4-week intervention with 160 g/day of high-lycopene tomato sauce, and ascorbic acid, lactate, pyruvate, isoleucine, and alanine were increased after the intervention with 160 g/day of normal-lycopene content tomato sauce (47).

Potential markers of nut intake identified in intervention studies ranging from 12 weeks to 6 months (42, 48, 49) include conjugated fatty acids, serotonin metabolites, and microbial-derived phenolic metabolites. In the PREDIMED study, walnut consumption was characterized by the presence of 18 urinary metabolites, including markers of fatty acid metabolism, ellagitannin-derived microbial compounds, and intermediate metabolites of the tryptophan/serotonin pathway (38).

Metabolomics fingerprints of beverage consumption have been extensively examined as well. Coffee has been reported to be positively associated with specific classes of sphingomyelins and negatively associated with long- and medium-chain acylcarnitines (50). In a case-cohort study of type 2 diabetes incidence including 1610 EPIC-Potsdam participants, in which 163 metabolites were targeted, coffee consumption was inversely associated with diacylphosphatidylcholine C32:1 in both men and women and phenylalanine in men, whereas in women, coffee intake was positively associated with acyl-alkyl-phosphatidylcholines C34:3, C40:6, and C42:5 (51). Other confirmed metabolite markers of coffee exposure included methylxanthines and reduced, sulfated, and methylated forms of hydroxycinnamates (52-54), which are coffee-derived metabolites. Recently, among participants of a 3-stage coffee trial in which 47 participants refrained from drinking coffee in the first month, consumed 4 cups of filtered coffee per day in the second month, and 8 cups of coffee per day in the third month, 115 plasma metabolites, evaluated with a nontargeted metabolomic profiling approach, were significantly associated with coffee intake (55). Five metabolic pathways were significantly enriched: (a) xanthine metabolism, which includes caffeine metabolites, (b) benzoate metabolism that reflects polyphenol metabolite products of gut-microbiota metabolism, (c) steroid, which is novel but may reflect sterol content of coffee, (d) fatty acid metabolism (acyl choline), a novel link to coffee; and (e) endocannabinoid, a novel link to coffee (55).

Metabolites linked with carnitine metabolism and sulfation of tyrosine are among the set of metabolites linked to cocoa intake (37, 38). Findings from the Atherosclerosis Risk in Communities study suggested that sugar-rich foods and beverages were inversely associated with 5 metabolites in the 2-hydroxybutyrate-related sub-pathway and 7 unsaturated long-chain fatty acids, and positively associated with 5-[gamma]-glutamyl dipeptides (56). Hippuric acid, previously identified as a marker of fiber intake (32), has also been identified as a marker of tea consumption, especially green and black tea, in several intervention and cross-sectional studies (48, 57, 58). The nonspecificity of the biomarkers to 1 specific food highlights the limitation of using nutritional metabolomics for the characterization of markers of food intake. As another illustration, hydroxytyrosol, which is a minor dopamine metabolite that derives from oleuropein, is a well-established biomarker of olive oil consumption (59). Plasma and urinary hydroxytyrosol levels have been shown to increase in a dose-dependent manner with the phenolic content of food (i.e., olive oil) (60). However, recently, findings from the PREDIMED study have revealed a direct association between red wine consumption and urinary hydroxytyrosol, independent of the amount of olive oil consumed (59). Thus, hydroxytyrosol cannot be considered a specific biomarker, as it cannot differentiate between olive oil and red wine consumption.

In a randomized, crossover, feeding study of 19 postmenopausal women comparing the intake of refined wheat, whole-meal rye, and refined rye breads providing 50 g of carbohydrates, and in which 189 metabolites were targeted, 8 amino acids (leucine, isoleucine, citrulline, ornithine, proline, asparagine, methionine, and lysine) were significantly influenced by intervention (P < 0.01). Branched-chain amino acids (BCAA) were higher after refined wheat bread consumption compared with the other breads at 45 and 60 min (61). Other metabolites, including 3-(3,5-dihydroxyphenyl)-1-propanoic acid sulfate, enterolactone glucuronide, azelaic acid, and 2-aminophenol sulfate, have also been detected as markers of whole-grain rye bread intake (62). Finally, a targeted metabolomics study of >300 lipid metabolites suggested that lysophosphatidylcholine, lyso-platelet-activating factor, and several phospholipid fatty acids were associated with consumption of full-fat dairy products (63). In addition, a recent 24-week energy-restricted intervention study with low [0-1 dairy products/day (<600 mg calcium/day)] or high [4-5 dairy products/ day (approximately 1200 mg calcium/day)] dairy intake showed that high dairy consumption increased urinary citrate and creatinine and decreased the urinary excretion of TMAO and hippurate (64).

METABOLOMICS SIGNATURES OF DIETARY PATTERNS

Several studies have evaluated metabolite profiles related to overall dietary patterns. We searched PubMed for studies published in English from database inception to April 2017 using the search terms [["metabolomics" (MeSH Terms) OR "metabolomics" (All Fields)] OR [metabolite (All Fields)] AND profiling (All Fields)]} AND {["diet" (MeSH Terms) OR "diet" (All Fields) OR "dietary" (All Fields)] AND [pattern (All Fields)] OR ["diet therapy" (MeSH Terms)] OR ["diet" (All Fields) AND "therapy" (All Fields)] OR "diet therapy" (All Fields) OR ["dietary" (All Fields) AND "intervention" (All Fields)] OR "dietary intervention" (All Fields)} NOT ["review" (Publication Type) OR "review literature as topic" (MeSH Terms) OR "review" (All Fields)] AND "humans" (MeSH Terms). We excluded nonhuman studies, studies in children, reviews, and commentary articles. We further excluded studies focusing on dietary supplementation and single foods because of a recent comprehensive review by Scalbert et al. (6) on metabolomics studies on food groups. After exclusions, the search identified 130 studies, of which 16 (summarized in Table 1) evaluated biomarkers of dietary patterns or dietary interventions using metabolomics profiling.

Most studies included healthy participants and applied mass spectrometry or NMR in blood and urine to extract metabolite profiles. Nine articles included cross-sectional analyses discriminating consumers from nonconsumers of specific dietary patterns. Nine studies were intervention studies that evaluated the effect of overall dietary patterns on metabolic profiles. Several dietary patterns have been studied in the context of nutrition metabolomics, including vegetarian and lactovegetarian diets, omnivorous diet, Western dietary patterns, prudent dietary patterns, Nordic diet, and Mediterranean diet.

Using a cross-sectional study design, Bouchard-Mercier et al. investigated the associations between dietary patterns and metabolic profiles (65). In a targeted mass spectrometry analysis of 14 amino acids and 41 acylcarnitines, a PCA-derived Western dietary pattern was associated with a metabolite signature characterized by high levels of BCAA (leucine), aromatic amino acids (phenylalanine), and short-chain acylcarnitines (65). In another cross-sectional analysis of 2380 EPIC-Potsdam participants (127 serum metabolites were analyzed using mass spectrometry), a dietary pattern characterized by high intakes of red meat and fish and low intakes of whole-grain bread and tea was directly related to high plasma levels of hexose and phosphatidylcholines (66). A pattern consisting of high intake of potatoes, dairy products, and cornflakes was associated with higher methionine and BCAA concentrations (66). Another cross-sectional study of women from the Twins UK cohort, characterized by metabolites associated with several nutritional scores, including fruit and vegetable intake, high alcohol intake, low meat intake pattern, hypocaloric dieting, and traditional English diet; phosphatidylcholine-dyacil C38:6 and acylcarnitine C9 were significantly and positively associated with hypocaloric dieting (34).

Another interesting study aimed to investigate the differences in concentrations of 118 circulating metabolites between male meat eaters, fish eaters, vegetarians, and vegans was conducted in the Oxford EPIC cohort. The study found that concentrations of 79% of metabolites differed significantly by diet groups. Concentrations of acylcarnitines, C-0, C-4, and C-5 were highest among meat eaters, followed by fish eaters, vegetarians, and vegans. At the same time, concentrations of acylcarnitines, C-3 and C-16, 61 glycerophospholipids, and 12 sphingolipids were highest among meat eaters and lowest among vegans. In contrast, fish eaters and vegetarians had the highest concentrations of amino acids (such as leucine, valine, lysine, methionine, tryptophan, and tyrosine) and biogenic amines (67).

Recently, Playdon et al. (58) identified metabolomic fingerprints of diet quality [evaluated using different dietary scores (Healthy Eating Index-2010, Alternate Mediterranean Diet Score, WHO Healthy Dietary Indicator, and Baltic Sea Diet)] in healthy male smokers from 5 nested case--control studies of the Alpha-Tocopherol, Beta-Carotene Cancer Prevention study. Healthy Eating Index-2010 was associated with 17 identifiable chemical structure metabolites: 3 amino acids, 2 cofactors or vitamins, 9 lipids, and 3 exogenous xenobiotics. Alternate Mediterranean Diet Score was associated with 21 identifiable metabolites: 4 amino acids, 1 carbohydrate, 2 cofactors or vitamins, 11 lipids, and 3 xenobiotics. WHO Healthy Dietary Indicator was associated with 11 metabolites: 3 amino acids, 2 cofactors or vitamins, 4 lipids, and 2 xenobiotics. Baltic Sea Diet associated with 10 metabolites: 2 amino acids, 1 carbohydrate, 3 cofactors or vitamins, and 4 lipids. The lysolipid pathway contained the largest number of metabolites associated with diet quality (68).

Several randomized intervention trials evaluated the effect of dietary interventions on metabolic profiles; however, most of these studies had small samples sizes and short follow-up periods (Table 1). The effect of a Mediterranean diet intervention on the urinary metabolome was assessed in the PREDIMED trial by comparing a subsample of nondiabetic subjects at 1 and 3 years of follow-up (69). Findings from this study showed that the most relevant metabolic signatures related to a Mediterranean diet intervention were metabolites of carbohydrate pathways (3-hydroxybutyrate, citrate, and cisaconitate), creatine, creatinine, amino acids (proline, N-acetylglutamine, glycine, BCAA, and derived metabolites), lipids (oleic and suberic acids), and microbial cometabolites (phenylacetylglutamine and p-cresol) (69).

The RESMENA study was an intervention study that included 72 subjects with metabolic syndrome features who consumed either an energy-restricted Mediterranean diet or an energy-restricted control diet (low fat) for 6 months (70). Metabolomics in plasma samples showed that the Mediterranean diet intervention resulted in significant changes in the metabolic profile at 2 months (mainly phospholipids and lysophospholipids), but differences were attenuated at 6 months (70).

Garcia-Perez et al. published the findings of a controlled, crossover, feeding study of 19 healthy participants who followed 4 different diets for 72 h separated by 5 days (71). The 4 diets differed in compliance to the WHO healthy eating guidelines: decreased sugar, salt, and total fat consumption and increased intake of whole grains, fruits, vegetables, and dietary fiber. Diet 1 was the most concordant with the guidelines and diet 4, the least concordant. The authors noted that 19 urinary metabolites were significantly increased after consumption of diet 1 compared with diet 4. Specifically, diet 1 resulted in higher concentration of urinary biomarkers from individual healthy foods like hippurate (marker of fruit and vegetable consumption), (N-acetyl-)S'-methyl-L-cysteine-sulfoxide (cruciferous vegetables), dimethylamine and TMAO (fish), and 1-methylhistidine and 3-methylhistidine (oily fish and chicken). On the contrary, diet 4 was associated with higher concentration of9 urinary metabolites, related to higher amounts of red meat (O-acetylcarnitine, carnitine, and creatine) and sugars (glucose) (71).

DIETARY BIOMARKERS DERIVED FROM GUT MICROBIOTA METABOLISM OF DIETARY COMPONENTS

Microbial species in human gut can directly deliver compounds from their metabolome, which are absorbed and contribute to human metabolism (such as amino acids, bile acids, short-chain fatty acids, vitamins, and energy substrates) (3). On the other hand, the gut microbiota can change constituents in food and make them available to themselves or the host for additional metabolism (3).

A number of metabolites that are related to diet and can be metabolized by gut microbiota have been identified. For example, microbial enzymes can hydrolyze soy isoflavones and release several metabolites, including aglycons, daidzein, genistein, and glycetin (3). The gut microbiota can also transform polyphenols to phenolic breakdown products, including benzoate and various derivatives of hydroxyphenylacetic and hydropropionic acids (3). In a randomized, parallel, controlled study designed to compare a high-soy diet [104 (24) mg total isoflavones/day] with a low-soy diet [0.54 (0.58) mg total isoflavones/day] in 76 healthy young adults followed for 10 weeks, concentrations of isoflavones and their gut flora metabolites in plasma, urine, and feces were significantly increased in participants who consumed the high-soy diet. Fecal [beta]-glucosidase activity was significantly higher in the subjects who consumed the high-soy diet than in those who consumed the low-soy diet (72).

Some of the most studied gut microbiota-dependent metabolites are those related to TMAO and its precursors choline and carnitine. Foods such as meat and meat products, egg yolks, and high-fat dairy products, which are high in phosphatidylcholine, choline, carnitine, and trimethylamine (TMA), serve as precursors to TMAO (73). TMAO has been found to be a potential biomarker of meat intake, but it has also been reported as a biomarker of fish and seafood intake, and more recently, it has been reported to be related to plant foods like soybeans (71, 74, 75). Although certain microbial metabolites can be putative food biomarkers, there is a complex relation between the food sources, gut microbiota, and the food metabolites derived; thus, these biomarkers should be interpreted with caution.

Application of Metabolomics in the Field of Nutritional Epidemiology

Widely used dietary assessment methods such as diet records and FFQ have been instrumental in their ability to measure diet in large populations and in examining the role of diet in human health (4). Because diet represents a complex set of exposures that are intercorrelated, and because foods are mixtures of known and unknown constituents, assessing dietary intakes in the context of relatively large within-individual variations is an ongoing challenge in nutritional epidemiology (76). The integration of metabolomics into traditional nutritional epidemiology can, to some extent, overcome the limitations of traditional dietary assessment methods and can provide important insights into mechanistic pathways. In Table 2, we briefly summarize and compare traditional dietary assessment methods with biomarkers of dietary intake. Traditional assessment methods are noninvasive and have the advantage of providing useful information on long-term dietary intake (FFQs and multiple-week diet records). FFQs are easy to administer with a low respondent burden; multiple-week records provide open-ended dietary data and do not rely on memory (4). Nevertheless, they are subject to some biases, such as recall errors, health consciousness bias, and errors in nutrient estimation from food composition diets. On the other hand, objective biomarkers, usually measured in serum, plasma, and urine, can objectively assess dietary intakes, represent the true "bioavailable" dose, and can serve as validation markers of other assessment methods. However, they may not be sensitive to intakes, can have low specificity, and are not available for many nutrients and foods. In addition, they are expensive, more invasive, and are subject to laboratory errors (4). Some examples of these biomarkers include urinary nitrogen for protein intake, 24-h urinary potassium and sodium, doubly labeled water for energy intake, plasma vitamin D, serum and plasma folate, essential fatty acids and other vitamins, trace minerals, and metabolite biomarkers.

Nutritional metabolomics holds considerable promise for the development of a more robust and unbiased strategy for measuring diet. However, there are important issues to consider. Certain metabolites have a short half-life and may, therefore, not represent usual intake, which is the most relevant exposure in nutritional epidemiology. Of note, a single measurement of metabolites is not sufficient to represent usual intake. For a metabolite to be a valid biomarker of dietary intake, it needs to be sensitive to intake and should be relatively easy to measure in biofluids.

So far, an extensive list of potential biomarkers related to the intake of nutrients, foods, and diets has been revealed by metabolomics. Compared with single biomarkers of food consumption, nutritional metabolomics is contributing to the discovery of biomarker patterns. Metabolomics can also generate biomarker patterns to evaluate the efficacy of nutritional interventions for maintaining and improving health at the individual level. However, it is unlikely that metabolomics biomarkers will replace traditional dietary assessments using self-reported methods because for most foods and nutrients, sensitive and specific biomarkers are not available or have not yet been identified. In addition, metabolomics assays are expensive, rendering them infeasible to assess dietary habits among hundreds and thousands of participants in large cohort studies. Therefore, biomarkers identified from metabolomics and traditional self-reported methods such as validated FFQs should be used in a complementary fashion. In the future, reproducible metabolomics biomarkers may be used to validate self-reported measurements of dietary intake, calibrate estimates of dietary intake, identify novel biomarkers of food consumption, and provide objective biomarkers of adherence to dietary interventions and dietary patterns. However, more efforts are needed to develop, validate, and fine-tune assessment methods that can capture the multidimensional nature of diet.

From the standpoint of public health, the incorporation of nutritional metabolomics into traditional nutritional epidemiology can help to identify subgroups that differ in their response to specific dietary components so that interventions can be tailored to those who will benefit the most, reducing the cost and side effects for those who will not (77). However, before nutritional metabolomics can have a real impact on public health, there is an urgent need to establish reference intervals based on absolute metabolite concentrations in defined human biofluids, improve the specificity of metabolite biomarkers of certain foods, and to conduct studies with adequate statistical power with independent replications in diverse cohorts while considering ethnic and regional differences.

Finally, because recent metabolomics efforts have focused on the analysis of known metabolites, current efforts to characterize unknowns may enable more comprehensive investigations and the discovery of novel metabolic pathways. The field of nutritional epidemiology will greatly benefit from the integration of other -omics technologies, such as genomics, proteomics, epigenomics, and metagenomics. Global initiatives are needed to standardized data collection and analytic methods for metabolomics in human nutrition (3) and to create consortia of metabolomics studies including well-assessed dietary data across diverse populations in the world.

Author Contributions: All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contribution to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article.

Authors' Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest:

Employment or Leadership: None declared.

Consultant or Advisory Role: None declared.

Stock Ownership: None declared.

Honoraria: None declared.

Research Funding: NIH grants HL60712, HL118264, and DK102896. M. Guasch-Ferre, postdoctoral fellowship granted by the Agency for Administration of University and Research Grants (AGAUR) of the Autonomous Government of Catalonia (2014-BPA 00017); S.N. Bhupathiraju, Career Development Grant from the National Institutes of Health (K01 DK107804).

Expert Testimony: None declared.

Patents: None declared.

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Marta Guasch-Ferre, [1] Shilpa N. Bhupathiraju, [1,2] and Frank B. Hu [1,2,3] *

[1] Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA; [2] Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA; [3] Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA.

* Address correspondence to this author at: Department of Nutrition, Harvard T.H. Chan School of Public Health, 655 Huntington Ave., Boston, MA 02115. Fax 617-432-2435; e-mail nhbfh@channing.harvard.edu.

Received June 13, 2017; accepted September 7, 2017.

Previously published online at DOI: 10.1373/clinchem.2017.272344 [c] 2017 American Association for Clinical Chemistry

[4] Nonstandard abbreviations: NMR, nuclear magnetic resonance; FFQ, food frequency questionnaire; PCA, principal component analysis; PLS-DA, partial least-square discriminant analysis; O-PLS-DA, orthogonal partial least-squares discriminant analysis; TMAO, trimethylamine-N-oxide; EPIC, European Prospective Study into Cancer; BCAA, branched-chain amino acids.

Caption: Fig. 1. Example of scores (A) and loading plots (B) of O-PLS-DA.

Data in this figure are hypothetical and used for the only purpose of illustrating scores and loading plots. (A) could represent O-PLS-DA of [sup.1]H NMR of urine data and dietary interventions. Blue dots represent control diet, and red triangles represent dietary pattern intervention. Component 1 and component 2 are extracted from O-PLS-DA analysis. Loading scatterplot(B) shows the individual compounds.

Caption: Fig. 2. Work flow of nutritional metabolomics approaches.
Table 1. Studies evaluating dietary patterns and dietary interventions
using metabolomics profiles.

Author, journal, year     Study population       Study design, n of
                                                  participants and
                                                      follow-up

Stella et al., J        White, nonsmoking       Randomized,
Proteome Res, 2006      men, of normal body     crossover, n = 12,
                        weight (ages 25-74      15-day diets with 7-
                        years)                  day washout

Xu et al., Anal         Healthy men and women   Cross-sectional,
Bioanal Chem, 2010      (ages 18-55 years)      habitual diet, n =
                                                161

O'Sullivan et al., Am   Healthy men and women   Participants from a
J Clin Nutr, 2011       (ages 18-63 years)      double-blind,
                                                randomized, placebo-
                                                controlled dietary
                                                intervention on
                                                vitamin D and
                                                probiotics, n = 160,
                                                results for
                                                metabolomics from a
                                                cross-sectional
                                                analysis of dietary
                                                records and biofluids

Altmaier et al., Eur    Healthy males (ages     Cross-sectional, n =
J Epidemiol, 2011       55-79 years) from       284
                        KORA

May et al., Br J        Healthy, nonsmoking     Randomized trial, n =
Nutr, 2013              men and women (ages     10, 2-week and cross-
                        21-45 years)            sectional, n = 93

Bouchard-Mercier et     Healthy participants    Cross-sectional, n =
al., Br J Nutr, 2013    (ages 18-50 years)      210

Floegel et al., Eur J   Healthy participants    Cross-sectional, n =
Clin Nutr, 2013         (ages 35-65 years)      2380
                        from EPIC-Potsdam

Menni et al.,           Healthy women (mean     Cross-sectional, n =
Metabolomics, 2013      age 59.5 years) from    1003
                        Twins UK

Andersen et al., Anal   Participants with       Randomized,
Bioanal Chem, 2014      increased waist         controlled, parallel,
                        circumference and       trial, n = 181, 6
                        [greater than or        months
                        equal to] 1 metabolic
                        syndrome risk factors
                        (ages 18-65 years)

Lankinen et al., PloS   Participants with       Randomized,
One, 2014               impaired glucose        controlled, parallel,
                        metabolism and          trial, n = 106, 12
                        features of metabolic   weeks
                        syndrome (ages 40-70
                        years)

Hanhineva et al., J     Participants with       Randomized,
Nutr, 2015              impaired glucose        controlled, parallel,
                        metabolism and          trial, n = 1 06, 12
                        features of metabolic   weeks
                        syndrome (ages 40-70
                        years)

Schmidt et al., Am J    Healthy men (ages >20   Cross-sectional, n =
Clin Nutr, 2015         years), from EPIC-      379
                        Oxford

Vazquez-Fresno R et     Nondiabetic             Randomized,
al., J Proteome Res,    participants at high    controlled, parallel
2015                    cardiovascular risk     trial, n = 98, 1 and
                        from the PREDIMED       3 years of follow-up
                        Study (ages 55-80
                        years)

Garcia-Perez I et       Healthy volunteers      Randomized,
al., Lancet Diabetes    (ages 21-65 years)      controlled, crossover
Endocrinol, 2015                                trial, n = 19, 4
                                                inpatient stays (72 h
                                                each, separated by at
                                                least 5 days)

Bondia-Pons et al.,     Participants with       Randomized,
Mol Nutr Food Res,      metabolic syndrome      controlled, trial, n
2015                    features from the       = 72, 2 and 6 months
                        RESMENA Study

Playdon et al., Am J    Healthy male smokers    Nested case-control
Clin Nutr, 2017         ([greater than or       studies in the ATBC
                        equal to] 5             study (randomized,
                        cigarettes-day) ages    double-blind,
                        50-69 years             placebo-controlled
                                                primary prevention
                                                trial of cancer)

Author, journal, year          Dietary                Biofluid
                           interventions/
                              patterns

Stella et al., J        a) Low meat diet (60    Urine
Proteome Res, 2006      g/day)
                        b) High red meat diet
                        (420 g/day),
                        c) Vegetarian diet
                        (420 g/day)

Xu et al., Anal         a) Lactovegetarian      Urine
Bioanal Chem, 2010      men
                        b) Lactovegetarian
                        women
                        c) Omnivorous men
                        d) Omnivorous women

O'Sullivan et al., Am   Cluster 1: favorable    Plasma
J Clin Nutr, 2011       food highest in         and urine
                        vegetables, whole-
                        qrain breads, and
                        fish
                        Cluster 2:
                        higher fruit intake
                        Cluster 3:
                        unhealthiest with
                        high intakes of meat
                        products, white
                        bread, butter, and
                        lower fruits and
                        vegetables, and
                        whole-meal bread

Altmaier et al., Eur    Dietary component 1:    Plasma
J Epidemiol, 2011       high poultry, fish,
                        rice, vegetables,
                        fruit, chocolate,
                        flaked oat, curds,
                        cheese, milk and low
                        meat and sausages
                        Component 4:
                        traditional Bavarian
                        with high meat,
                        poultry, fish, snacks
                        and eggs and low
                        potatoes, pasta,
                        whole-grain bread and
                        flaked oats
                        Component 6: high in
                        whole-grain bread,
                        eggs, milk, low
                        consumption offish,
                        fresh fruits, and
                        cakes

May et al., Br J        a) High phytochemical   Urine
Nutr, 2013              diet (rich in
                        cruciferous
                        vegetables, citrus,
                        and soya)
                        b) Diet free of
                        fruits and vegetables

Bouchard-Mercier et     a) Prudent dietary      Plasma
al., Br J Nutr, 2013    pattern (high in
                        vegetables, fruits,
                        whole-grain products)
                        b) Western dietary
                        pattern (high refined
                        grain products,
                        desserts, sweets,
                        processed meats)

Floegel et al., Eur J   a) Habitual diet        Serum
Clin Nutr, 2013         b) Diet high in red
                        meat and fish, and
                        low in whole-grain
                        bread and tea
                        c) Diet high in
                        potatoes, dairy, and
                        cornflakes

Menni et al.,           Nutritional scores:     Serum
Metabolomics, 2013      a) Fruit and
                        vegetable intake
                        b) High alcohol
                        pattern intake
                        c) Low meat
                        intake pattern
                        d) Hypocaloric
                        dieting
                        e) Traditional
                        English diet

Andersen et al., Anal   a) New Nordic diet      Urine
Bioanal Chem, 2014      b) Average Danish
                        diet

Lankinen et al., PloS   a) Healthy diet,        Serum
One, 2014               whole grains, fatty
                        fish (3 times-week)
                        and bilberries (3
                        portions day)
                        b) Whole-grain
                        products
                        c) Control refined
                        wheat breads

Hanhineva et al., J     a) Healthy diet,        Plasma
Nutr, 2015              whole grains, fatty
                        fish (3 times-week)
                        and bilberries (3
                        portions day)
                        b) Whole-grain
                        products
                        c) Control refined
                        wheat breads

Schmidt et al., Am J    a) Meat eaters          Plasma
Clin Nutr, 2015         b) Fish eaters
                        c) Vegetarians
                        d) Vegans

Vazquez-Fresno R et     a) Mediterranean diet   Urine samples at
al., J Proteome Res,    supplemented with       baseline, 1  and 3
2015                    extra virgin olive      years
                        oil (n = 41)
                        b) Mediterranean diet
                        supplemented with
                        nuts (n = 27)
                        c) Control low-fat
                        diet (n = 30)

Garcia-Perez I et       Four diets differing    Urine samples during
al., Lancet Diabetes    in compliance to the    3 periods that were
Endocrinol, 2015        WHO healthy eating      then pooled to obtain
                        guidelines:             24-h urine
                        Decreased sugar,
                        salt, and total fat
                        consumption, and
                        increased intake of
                        whole grains, fruits,
                        vegetables, and
                        dietary fiber, with
                        diet 1 being the most
                        concordant with
                        guidelines and diet 4
                        the least concordant

Bondia-Pons et al.,     Two energy-             Plasma
Mol Nutr Food Res,      restricted diets
2015                    a) Mediterranean
                        dietary pattern (n =
                        47)
                        b) Control diet
                        (based on the
                        American Heart
                        Association
                        guidelines) (n = 45)

Playdon et al., Am J    Four dietary patterns   Serum
Clin Nutr, 2017         and their components:
                        a) HEl-2010
                        b) aMED
                        c) HDI
                        d) BSD

Author, journal, year    Analytical         Biomarkers identified
                          technique

Stella et al., J        [sup.1]H NMR    Taurine, carnitine, a cetyl
Proteome Res, 2006                      carnitine, 1-methylhistidine,
                                        3-methylhistidine,
                                        TMAO were elevated in high-
                                        meat period
                                        p-Hydroxyphenylacetate was
                                        higher in vegetarian dietthan
                                        meat diets

Xu et al., Anal         [sup.1]H NMR    TMAO, dimethylamine,
Bioanal Chem, 2010                      phenylalanine, methylhistidine
                                        were characteristic of
                                        omnivorous diet
                                        Citrate was characteristic of
                                        lactovegetarian diet

O'Sullivan et al., Am   [sup.1]H NMR    Phenylacetylglutamine marker
J Clin Nutr, 2011                       of vegetabfes intake, O-a
                                        cetyl carnitine marker of red
                                        meat
                                        Glycine,
                                        phenylacetylglutamine,
                                        acetoacetate higher in cluster
                                        1
                                        TMAO, O-a cetyl carnitine and
                                        N,N-dimethylglycine higher in
                                        cluster 3

Altmaier et al., Eur    MS/MS           Component 1 associated with
J Epidemiol, 2011                       diacyl-phosphatidylcholines
                                        with 4 and 5 bounds and acyl-
                                        alkyl-phosphatidylcholines
                                        with 1 or2 double bonds, and a
                                        decrease in saturation of
                                        fatty acids of glycerol-
                                        phosphatidylcholines
                                        Components 4 and 6 associated
                                        with several phospholipids

May et al., Br J        LS-MS/MS        Proline betaine marker of
Nutr, 2013                              citrus intake
                                        Phytochemical diet
                                        characteristic by higher
                                        sulforaphane, hippuric acid,
                                        genistein, daidzein, equol,
                                        glycitein, O-
                                        desmethylangolensin,
                                        enterolactone, trigonelline

Bouchard-Mercier et     MS              Western dietary pattern
al., Br J Nutr, 2013                    associated with short-chain
                                        acylcarnitines, leucine,
                                        phenylalanine
                                        Prudent pattern composed of
                                        medium-to long-chain
                                        acylcarnitines

Floegel et al., Eur J   (FIA)-MS/MS     Acylcarnitines, amino acids,
Clin Nutr, 2013                         hexoses, diacyl-, acyl-alkyl-
                                        lysophosphatidylcholines, and
                                        sphingomyelins were related to
                                        habitual diet
                                        Diet high in red meat and
                                        fish, and low in whole-grain
                                        bread and tea related to
                                        hexoses and
                                        phosphatidylcholines
                                        Diet high in potatoes, dairy,
                                        and cornflakes high in
                                        methionine and branched-chain
                                        amino acids

Menni et al.,           (FIA)-MS/MS     Fruits and vegetables related
Metabolomics, 2013                      to glycerophospholipid
                                        (phosphatidylchoIine-ayacil
                                        C38:6 and acylcarnitine C9
                                        associated with hypocaloric
                                        dieting)

Andersen et al., Anal   UPLC-qTOF- MS   TMAO was related to seafood
Bioanal Chem, 2014                      consumption; Nordic diet
                                        characterized by TMAO,
                                        hydroquinone-glucuronide,
                                        hippuric acid, 3,4,5,6-
                                        tetrahydrohippurate

Lankinen et al., PloS   NMR             Healthy diet increased
One, 2014                               polyunsaturation of plasma
                                        fatty acids, increase in n-3
                                        fatty, whereas n-6 and n-7
                                        fatty acids decreased
                                        Greater fish intake associated
                                        with large HDL lipid
                                        components

Hanhineva et al., J     LC-qTOF-MS      Glucuronidated
Nutr, 2015                              alk(en)ylresorcinols elevated
                                        in both intervention diets and
                                        correlated with whole-grain
                                        products
                                        Healthy diet increased furan
                                        fatty acids, hippuric acid,
                                        and various lipid species such
                                        as polyunsaturated fatty acids
                                        Furan fatty acids related with
                                        fish intake

Schmidt et al., Am J    LC-MS/MS        79% of metabolites differed
Clin Nutr, 2015                         significantly by diet group.
                                        Meat eaters had the highest
                                        concentrations of 6 acyl
                                        carnitines, 61
                                        glycerophospholipids, and 12
                                        sphingolipids, ana vegans had
                                        the lowest concentration of
                                        these metabolites. Fish eaters
                                        and vegetarians had the
                                        highest concentrations of
                                        amino acids and biogenic
                                        amines

Vazquez-Fresno R et     [sup.1]H NMR    Mediterranean diet groups:
al., J Proteome Res,                    carbohyd rates (3-nyd roxy
2015                                    butyrate, citrate, and
                                        cisaconitate), creatine,
                                        creatinine, amino acids
                                        (proline, N-acetylglutamine,
                                        glycine, branched-chain amino
                                        acids, and derived
                                        metabolites), lipids (oleic
                                        and suberic acids), and
                                        microbial cometabolites
                                        (phenylacetylglutamine and p-
                                        cresol) Control low-fat diet:
                                        hippurate, TMAO, histidine,
                                        and derivates
                                        (methylhistidines, carnosine,
                                        and anserine), and xanthosine

Garcia-Perez I et       [sup.1]H NMR    Diet 1: Higher concentration
al., Lancet Diabetes                    of urinary bio markers from
Endocrinol, 2015                        individual healthy foods,
                                        e.g., hippu rate (marker of
                                        fruit and vegetable
                                        consumption), N-a cety I-S-
                                        methyl-cysteine-sulfoxide
                                        (cruciferous vegetables),
                                        dimethylamine, and TMAO
                                        (fish), and 1-methylhistidine
                                        and 3-methylnistidine (oily
                                        fish and chicken)
                                        Diet 4: Higher concentration
                                        of nine urine metabolites,
                                        which had higher amounts of
                                        red meat (O-a cetyl carnitine,
                                        carnitine, and creatine) and
                                        sugars (glucose)

Bondia-Pons et al.,     LC-QTOF/MS      Mediterranean diet:
Mol Nutr Food Res,                      plasmalogen
2015                                    phosphatidylcholine (P-18:1/
                                        20:3) afterthe 2/month
                                        intervention, and palmitic
                                        acid afterthe 6/month
                                        intervention. PC (P/1 8:1/
                                        20:3) significantly increased
                                        in the RESMENA group and
                                        decreased in the Control group
                                        after 2 months

Playdon et al., Am J    UPLC-LC-MS/MS   HEI-2010 was associated with
Clin Nutr, 2017                         17 identifiable chemical
                                        structure metabolites: 3 amino
                                        acids, 2 cofactors or
                                        vitamins, 9 lipids, and 3
                                        exogenous xenobiotics
                                        aMED was associated with 21
                                        identifiable metabolites: 4
                                        amino acids, 1 carbohydrate, 2
                                        cofactors or vitamins, 11
                                        lipids, and 3 xenobiotics
                                        HDI was associated with 11
                                        metabolites: amino acids, 2
                                        were cofactors or vitamins, 4
                                        were lipids, and 2 were
                                        xenobiotics
                                        BSD associated with 10
                                        metabolites: 2 amino acids, 1
                                        carbohydrate, 3 cofactors or
                                        vitamins, and 4 lipids
                                        The lysolipid pathway
                                        contained the largest number
                                        of metabolites associated with
                                        diet quality

aMED, Alternate Mediterranean Diet Score; ATBC, Alpha-Tocopherol,
Beta-Carotene Cancer Prevention; BSD, Baltic Sea Diet; FIA, flow
injection analysis; HDI, Healthy Index Dietary Indicator; HEI, Healthy
Eating Index; 1H NMR, proton nuclear magnetic resonance; KORA,
Collaborative Health Research in the Region of Augsburg; LC-QTOF-MS,
liquid chromatography coupled to quadrupole time of flight-MS;
PREDIMED, Prevention con Dleta MEDiterranea; RESMENA, Metabolic
Syndrome Reduction in Navarra.

Table 2. Comparison of traditional dietary assessment methods and
biomarkers.

Self-reported measures of diet                   Objective measures
                                                       of diet

Validated FFQ            Multiple-week diet     Biomarkers identified
                               records          through metabolomics
                                                     approaches

Provides information    Detailed dietary data   Objective assessment
on usual long-term      that do not rely on     of intake and
intake                  memory                  represents
                                                bioavailable dose

Estimating and recall   Errors from incorrect   Subject to laboratory
errors                  estimate of portion     errors
                        size and omission of
                        foods

Easily administered     Participant burden is   Can be measured in
and low respondent      high                    stored biospecimens
burden

Least expensive         Noninvasive but         Expensive and more
method and              expensive               invasive
noninvasive

Can assess usual        Can assess usual        May not be time-
dietary intake          dietary intake if       integrated or
                        measured multiple       represent usual long-
                        times over the course   term intake; may not
                        of a year               be sensitive or
                                                specific to intakes

Health consciousness    Health consciousness    Not applicable
bias                    bias

Errors in nutrition     Errors in nutrition     Biomarkers are not
estimation from food    estimation from food    available for many
composition tables      composition tables      nutrients and most
                                                foods

Culture and             Needs literate and      Biomarker variations
population specific     motivated               may exist between
                        participants            cultures and
                                                populations

Association analyses    Validation of other     Can be used to assess
in large                methods and assess      associations in
epidemiologic studies   compliance              cohort or nested
                                                case-control studies

Adapted with permission from Satija et al. (4).
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Author:Guasch-Ferre, Marta; Bhupathiraju, Shilpa N.; Hu, Frank B.
Publication:Clinical Chemistry
Article Type:Report
Date:Jan 1, 2018
Words:11769
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