Abstract

   The human microbiome has been associated with health status, and risk
   of disease development. While the etiology of microbiome-mediated
   disease remains to be fully elucidated, one mechanism may be through
   microbial metabolism. Metabolites produced by commensal organisms,
   including in response to host diet, may affect host metabolic
   processes, with potentially protective or pathogenic consequences. We
   conducted multi-omic phenotyping of healthy subjects (N = 136), in
   order to investigate the interaction between diet, the microbiome, and
   the metabolome in a cross-sectional sample. We analyzed the nutrient
   composition of self-reported diet (3-day food records and food
   frequency questionnaires). We profiled the gut and oral microbiome (16S
   rRNA) from stool and saliva, and applied metabolomic profiling to
   plasma and stool samples in a subset of individuals (N = 75). We
   analyzed these multi-omic data to investigate the relationship between
   diet, the microbiome, and the gut and circulating metabolome. On a
   global level, we observed significant relationships, particularly
   between long-term diet, the gut microbiome and the metabolome. Intake
   of plant-derived nutrients as well as consumption of artificial
   sweeteners were associated with significant differences in circulating
   metabolites, particularly bile acids, which were dependent on gut
   enterotype, indicating that microbiome composition mediates the effect
   of diet on host physiology. Our analysis identifies dietary compounds
   and phytochemicals that may modulate bacterial abundance within the gut
   and interact with microbiome composition to alter host metabolism.

   Keywords: microbiome, diet, metabolome, multi-omics analysis,
   mediation, interaction

Introduction

   The human microbiome is a complex ecosystem of bacteria, viruses,
   fungi, and bacteriophages, which interact with each other and their
   host ([37]Sears, 2005; [38]Goodman and Gordon, 2010; [39]Minot et al.,
   2011). Microbiome composition is unique to an individual, is
   established early in life, and plays a crucial role in lifelong health
   ([40]Kau et al., 2011; [41]Minot et al., 2011; [42]Maynard et al.,
   2012; [43]Koren et al., 2013; [44]Mohammadkhah et al., 2018). Recent
   discoveries implicating the microbiome in disease have been
   paradigm-shifting. However, we do not yet understand the molecular
   mechanisms linking microbiota to health status.

   There is considerable site-specificity in microbiome composition, with
   distinct populations residing within each body site of an individual
   ([45]Faust et al., 2012; [46]Ding and Schloss, 2014). The relative
   contributions of the microbiota at each body site to overall host
   health are not yet clearly defined, but are likely be depend on both
   the nature of the disease, and the overall health of the host
   ([47]Zhang et al., 2015). The microbiome composition of the gut is of
   particular interest, given its location at the crucial interface
   between exogenous dietary intake and internal nutrient metabolism.
   Translocation of microbes and microbial metabolites from the intestine
   to the bloodstream may occur in the absence of intestinal disease, for
   example during diet-induced post-prandial metabolic endotoxemia
   ([48]Moreira et al., 2012; [49]Pendyala et al., 2012; [50]Piya et al.,
   2013). The gut microbiome, in combination with habitual diet, is likely
   to play a major role in determining gut mucosal membrane permeability
   and influencing systemic inflammation ([51]Moreira et al., 2012;
   [52]Pendyala et al., 2012).

   Numerous factors determine the specific population of microbiota in
   humans, with diet being a key contributor ([53]Zeevi et al., 2015;
   [54]Ferguson et al., 2016). Specific dietary components act as
   substrates for microbial metabolism, shaping microbiome composition and
   function. Multiple macronutrient-microbiome associations have been
   reported, including carbohydrate intake and Prevotella abundance
   ([55]Wu et al., 2011), saturated fat intake and Bacteroides and
   Faecalibacterium prausnitzii, and animal protein intake and Bacteroides
   and Alistipes ([56]De Filippo et al., 2010; [57]Cotillard et al., 2013;
   [58]David et al., 2014). Microbiome composition has been linked to
   disease through modulation of specific metabolites and signaling
   pathways ([59]Wang et al., 2011; [60]Koeth et al., 2013; [61]Marcobal
   et al., 2013; [62]Tang et al., 2013). Gut microbial metabolism of
   animal-product-derived carnitine to the pro-atherogenic metabolite
   trimethylamine N-Oxide (TMAO) has been found to associate with
   increased atherosclerotic risk ([63]Wang et al., 2011; [64]Koeth et
   al., 2013). Many other dietary components may modulate disease risk
   through parallel mechanisms.

   We hypothesized that habitual diet is associated with microbiome
   composition in healthy humans, and that microbiome composition is
   associated with gut and plasma metabolites. Using multi-omic sample
   analysis in up to 150 healthy subjects we profiled the microbiome (16S
   rRNA; stool and saliva) and the metabolome (stool and plasma) to
   examine the interaction between diet, the microbiome, and systemic
   metabolism. Our results identify global relationships and highlight
   novel associations between specific dietary components and circulating
   metabolites, that are modulated by gut bacteria, and may have
   consequences on health status and future disease risk.

Materials and Methods

Study Population

   The ABO Glycoproteomics in Platelets and Endothelial Cells (ABO) Study
   recruited healthy volunteers (N = 150; men and non-pregnant/lactating
   women age 18–50) to a protocol at the University of Pennsylvania from
   2012–2014. Exclusion criteria included known illnesses, history of
   organ transplant, tobacco, and prescription medication use (except oral
   contraceptives). Participants were instructed to avoid over-the-counter
   medications, supplements, and vitamins for the 2-week period prior to
   the scheduled visit. Subjects provided a fasting blood sample
   (following a 12-h overnight fast). As part of a diet and
   microbiome-focused sub-study, reported here, subjects provided a stool
   and saliva sample for microbiome analysis (N = 136 with stool samples).
   All subjects completed validated 3-day food records prior to the study
   visit ([65]Trabulsi and Schoeller, 2001), including on the day directly
   before the visit, and a weekend day. Nutrient composition was analyzed
   using Food Processor 8.1 (ESHA Research, Salem, OR). In addition, all
   subjects completed food frequency questionnaires (FFQ) to assess
   habitual dietary intake, including serving size, of 134 food items over
   the previous year [the National Cancer Institute’s Diet History
   Questionnaire (DHQ I)] ([66]Subar et al., 2001, [67]2010). Completed
   subject responses were analyzed using Diet^∗Calc version 1.5.1. Diet
   data were converted to nutrient intake values of 191 long-term dietary
   variables and 139 short-term dietary variables. All subjects provided
   written informed consent. The study was approved by the Institutional
   Review Boards of the University of Pennsylvania and Vanderbilt
   University.

Sample Processing, DNA Extraction and Sequencing

   Subjects collected a stool sample within the 24 h prior to the study
   visit, using a stool collection kit (Commode Specimen Collection
   System, Fisher Scientific, Pittsburgh, PA, United States) provided to
   them. Samples were stored at 4°C and aliquots made within 36 h of
   sample collection. Processed samples were stored at −80°C prior to
   nucleic acid extraction. Subjects were instructed to brush their teeth
   and floss if desired, but not to use mouthwash, following their final
   meal on the day before the visit (>12 h before visit). Subjects were
   further instructed not to brush their teeth or use floss or mouthwash
   on the morning of their visit. Saliva samples were collected using the
   OMNIGene Discover OM505 DNA/RNA collection kit (DNA Genotek). Following
   collection, samples were divided into aliquots, and stored at −80°C
   prior to nucleic acid extraction. DNA was isolated from stool and
   saliva samples using the PSP Spin Stool DNA Plus Kit (Stratec,
   Germany). The 16S rRNA gene region was amplified using barcoded primers
   ([68]Caporaso et al., 2012) (Eurofins Genomics, Louisville, KY, United
   States) and DNA libraries were cleaned (MinElute PCR Purification kit,
   Qiagen, Germantown, MD, United States) prior to quantification and
   pooling. Pooled DNA libraries were sequenced on the MiSeq platform, 300
   bp paired-end reads, at an average depth of 158,000 reads/sample
   (Illumina Inc., San Diego, CA, United States). Stool samples were
   sequenced in two batches, at the University of Pennsylvania
   Next-Generation Sequencing Center (UPenn NGSC, N = 107) and the
   Vanderbilt University Technologies for Advanced Genomics (VANTAGE) Core
   (N = 29). All saliva samples (N = 85) were sequenced in one batch at
   VANTAGE. DNA sequences in Fastq files were de-multiplexed, assembled,
   clustered, and phylogenetically classified using the Mothur pipeline
   ([69]Schloss et al., 2009). Phylogenetic classification was performed
   against the Silva V123 16S database. Mothur was run using standard
   cutoffs, creating OTU clusters at 97% identity.

Metabolomics

   Samples for a subset of individuals (N = 75 plasma and N = 75 stool,
   matched subjects) were profiled at Metabolon (Metabolon Inc.,
   Morrisville, NC, United States) using their global metabolomics
   platform, which can identify and quantitate >1,000 metabolites through
   multiple mass spectrometry methods. In our study, 812 metabolites were
   detected in plasma, and 770 in stool samples. For each metabolite, the
   raw peak intensity was rescaled to set the median across all samples
   equal to 1, and values below the limit of detection were imputed with
   the lowest observed value in the dataset. Metabolite pathway enrichment
   analysis was conducted using MetaboAnalyst ([70]Xia and Wishart, 2011).

Data Processing for Microbiome, Dietary and Metabolite Variables

   Data processing and statistical analysis was performed in R. For the
   stool microbiome dataset, the OTUs were classified into 11 phyla, 20
   classes, 21 orders, 32 families, and 130 genera. For the saliva
   microbiome dataset, the OTUs were classified into 13 phyla, 21 classes,
   32 orders, 52 families, and 103 genera. We obtained two independent
   measures of dietary intake: 3-day food diaries (for short-term recent
   diet) and a food frequency questionnaire (FFQ, for long-term habitual
   diet). Dietary and metabolite variables were normalized using inverse
   normal transformation (INT) and transformed variables that did not
   follow a normal distribution (Shapiro–Wilk test p < 0.05) were removed
   ([71]Maritz, 1995). These removed variables had very small variability
   and/or had many tied observations. The remaining dietary variables were
   further normalized using the residual method to adjust for total
   caloric intake and gender, and standardized to have mean of 0 and SD of
   1. Since some dietary variables were almost identical, we chose one
   representative for each highly correlated cluster (Spearman correlation
   > 0.9), resulting in 91 long-term dietary variables and 82 short-term
   dietary variables in the final dataset for the downstream analysis. The
   complete list mapping dietary variables to the selected representative
   variables are available in [72]Supplementary Tables S1, [73]S2. In
   order to group metabolites that were highly correlated, we defined
   metabolic modules using weighted correlation network analysis WGCNA
   ([74]Langfelder and Horvath, 2008). The WGCNA has been shown to be an
   efficient and robust method in grouping metabolomic data ([75]McHardy
   et al., 2013) and allows us to summarize each module by its module
   eigenvalue. Using WGCNA, the gut metabolites were organized into 8
   modules with 40 un-clustered metabolites, and plasma metabolites were
   organized into 16 modules with 169 un-clustered metabolites. The
   complete list of metabolites and their module organization are
   available in [76]Supplementary Tables S3, [77]S4. The abundance values
   of the un-clustered metabolites were combined with standardized module
   eigenvalues in the downstream analysis.

Distance Correlation Analysis

   To evaluate the global association between pairs of high-dimensional
   variables among diet, microbiome and metabolomics, we used the distance
   correlation t-test ([78]Székely and Rizzo, 2013) implemented in the R
   package “energy” to test the dependence among each pair of these three
   data types. Compared to Pearson correlation, the distance correlation
   ([79]Székely et al., 2007; [80]Székely and Rizzo, 2009) is a
   non-parametric approach (without distributional assumption) and has the
   power to detect general (non-linear) dependence between two sets of
   high- dimensional random variables. The distance correlation t-test
   allows the dimension of the random vectors to be larger than the sample
   size. The ability for detecting general dependence and handling
   high-dimensionality of data makes distance correlation t-test suitable
   for analyzing this dataset.

Microbial Enterotypes Analysis

   We conducted distance-based clustering using the Partitioning Around
   Medoids (PAM) method ([81]Kaufman and Rousseeuw, 1987) with the various
   distances including Euclidean, Bray–Curtis and Jaccard, and identified
   two enterotypes. To evaluate if diet-metabolite associations are
   modulated by microbial enterotype, we tested diet-enterotype
   interaction through linear regression for each pair of diet-metabolite
   variables, with the metabolite as the outcome, using the individual
   metabolites rather than metabolite modules.

Sparse Linear Log-Contrast Model

   To further narrow down the interplay between diet/metabolome and
   microbiome, we used the sparse linear log-contrast model ([82]Lin et
   al., 2014) to pinpoint important genera that are associated with
   dietary or metabolite variables. In this model, a dietary or metabolite
   variable is the response and the top 50 most abundant genera are
   compositional covariates. For the diet-microbiome analysis, it makes
   intuitive sense to analyze microbiome variables as the dependent
   variables since we hypothesize that diet perturbs microbial
   compositions. Nevertheless, we selected the log-contrast model for
   several reasons. It is very challenging to find a suitable
   probabilistic distribution for the microbial composition due to its
   unique features, such as zero-inflation, over-dispersion, and complex
   correlation structure ([83]Li, 2015; [84]Tang and Chen, 2018). Further,
   it has been demonstrated in genetic association studies that such
   inverse regression (treating dependent variables as covariates) is
   advantageous if there are multiple dependent variables and the
   distribution is difficult to specify ([85]Majumdar et al., 2016).
   Alternative methods that treat microbiome as dependent variables
   include sparse Dirichlet-Multinomial (DM) method ([86]Chen and Li,
   2013) and multivariate zero-inflated logistic-normal method ([87]Li et
   al., 2018), however, we determined that the log-contrast model was the
   most suitable currently available model for our study. For the taxa
   that are unclassified at the genus level, their identities at higher
   levels were used. Because of the unit-sum constraint of the microbial
   relative abundance, the components of a composition cannot vary freely.
   The sparse linear log-contrast model respects the compositional nature
   of the microbiome data, in which the unit-sum constraint on the
   compositional vector is translated into the zero-sum constraint on the
   association coefficients across taxa in log-ratio scale ([88]Lin et
   al., 2014). The zero-sum constraint is crucial for the resulting
   estimator to enjoy interpretive advantages over a standard lasso
   estimator ([89]Tibshirani, 1994). In our analysis, we used 10-fold
   cross validation to choose the tuning parameter. To obtain stable
   selection results, we generated 100 bootstrap samples and used the same
   cross-validation procedure to select the genera. The genera that were
   selected over 70 times out of 100 were considered associated with the
   dietary or metabolite variable.

Microbiome Mediation Analysis

   We considered how the effect of a dietary nutrient on a metabolite is
   transmitted through the microbial communities. Specifically, we were
   interested in identifying microbial taxa that mediate the
   diet-metabolite pathway. We focused on pairs of diet-metabolite
   variables linked to at least one common genus identified by the
   log-contrast model in section 2.7, and applied mediation analysis to
   the diet-gut microbiome-metabolite triplet. The top 50 most abundant
   genera were used as candidate microbiome mediators. To handle the
   compositional and high-dimensional nature of microbiome mediators, we
   utilized the state-of-the-art compositional mediation analysis for
   microbiome data (R Package ccmm) ([90]Sohn and Li, 2019). Certain
   assumptions are required to make casual interpretation of the mediation
   effects ([91]Imai et al., 2010; [92]Sohn and Li, 2019). In particular,
   the key assumption assumes that there is no unmeasured confounding
   variable after controlling covariates. The method enables us to
   estimate the total mediation effects of microbiome composition, as well
   as to select important microbial taxa mediating the diet-metabolite
   association and estimate taxon-specific mediation effects.

Results

   We conducted multi-omic phenotyping of up to 150 healthy subjects to
   probe diet, microbiome, and metabolome relationships in a
   cross-sectional sample. The overall study design, sample availability
   and subject characteristics are shown in [93]Figure 1. By design,
   participants were healthy with no overt disease, consuming diets
   broadly representative of a standard American diet. Dietary variables
   calculated from the short and long-term diet questionnaires were
   significantly correlated with each other, suggesting that subjects’
   diets immediately prior to microbiome sampling were broadly
   representative of their diets over the past year. Of 150 enrolled
   subjects who completed a dietary questionnaire, 136 subjects provided a
   stool sample for microbiome analysis. We conducted metabolomic
   profiling in matched stool and plasma samples in a subset of these
   individuals (N = 75) and collected saliva samples for microbiome
   analysis in a separate subset (N = 85). No global associations were
   detected between diet, the microbiome, or metabolome, and demographic
   variables (age, sex, race, and BMI; PERMANOVA p > 0.1). We observed a
   difference in gut microbiome composition by batch (p = 0.04, UPenn vs.
   VANTAGE, see section “Sample Processing, DNA Extraction and
   Sequencing”). There were no differences in metabolite or nutrient
   profiles between the batches (p > 0.1), or in enterotype distribution
   (chi-square test p = 0.86). To assess whether the batch effect had any
   effect on our results, we repeated all the relevant analyses using only
   batch 1 samples (N = 107) and confirmed the conclusions remained the
   same. As the overall results did not differ, we report here the results
   from the analyses of the entire sample.

FIGURE 1.

   [94]FIGURE 1
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   Overview of Study design, subject characteristics, and multi-omic
   sample availability.

The Gut Microbiome Is Related to Diet and Metabolites on a Global Level

   We ran a global analysis using distance correlation t-test to obtain an
   integrated view of the relationships and relative importance of dietary
   measures (short-term and long-term diet), microbiome body site samples
   (stool and saliva), and metabolites (stool and plasma). As shown in
   [96]Figure 2, there were considerable inter-relationships, with
   particularly strong associations between the gut microbiome and the gut
   metabolome (p = 2.2 × 10^−10), and between long-term diet and the gut
   microbiome (p = 7.8 × 10^−4). Short-term diet was significantly
   associated with the gut and plasma metabolome (p < 1 × 10^−3), but not
   the microbiome. We found no global associations between the
   saliva-derived oral microbiome and other data types. Within data types,
   there was very strong global correlation between short- and long-term
   diet (p < 1 × 10^−15), and between stool and plasma metabolites (p =
   2.1 × 10^−8), but not between the gut and oral microbiome (p = 0.7).
   Based on the evidence in the global analysis, we decided to focus our
   remaining analyses on the gut microbiome and long-term diet, and to
   evaluate their interplay with gut and circulating metabolites.

FIGURE 2.

   [97]FIGURE 2
   [98]Open in a new tab

   Overview of global relationships between microbiota, diet, and
   metabolites. Thick line: distance correlation t-test p-value < 10^−5;
   thin line: distance correlation t-test 10^−5 < p-value < 10^−1.

Dietary Nutrients Are Associated With Gut Microbes

   We hypothesized that gut microbiome composition would vary based on the
   intake of specific nutrients. From the sparse log-contrast model, we
   identified 61 (67%) long-term dietary nutrients associated with at
   least one bacterial genus ([99]Figure 3). Several nutrients associated
   with three or more genera, as shown in [100]Table 1. These dietary
   nutrients were predominately found in plant-derived foods and dairy
   products, suggesting that inclusion or exclusion of these food groups
   in the diet may be particularly important in the modulation of gut
   microbiome composition.

FIGURE 3.

   [101]FIGURE 3
   [102]Open in a new tab

   Associations between habitual dietary nutrient intake and gut
   microbiome. Color intensity reflects the magnitude of the association
   coefficients between dietary variables and taxa.

Table 1.

   Long term intake of dietary nutrients associated with at least three
   gut microbial taxa.
   Dietary nutrient Primary food source Bacterial taxon^∗
     __________________________________________________________________

   Positive association Negative association
   Alpha Carotene Plants Bacteroides, Coprococcus 2 Bilophila,
   Ruminiclostridium 5, Ruminiclostridium 6, Oscillibacter
   Beta Carotene Bacteroides, Butyricimonas Bilophila, Odoribacter,
   Ruminiclostridium 5, Oscillibacter
   Lutein and Zeaxanthin Bacteroides, Ruminococcaceae NK4A214,
   Butyricimonas Bilophila, Ruminiclostridium 5
   Vegetables Bacteroides, Lachnospira Prevotella 9, Bilophila,
   Ruminiclostridium 5
   Vitamin E Bacteroides, IncertaeSedis, Ruminococcaceae NK4A214,
   Butyricimonas Bilophila, Prevotella 2, Ruminiclostridium 5,
   Oscillibacter
   Vitamin C Subdoligranulum, Ruminococcaceae NK4A214 Bilophila
   Vitamin B12 Parabacteroides, Bilophila, Dialister, Bifidobacterium
   Ruminococcaceae NK4A214, Oscillibacter
   Folate Bacteroides, Incertae Sedis, Ruminococcaceae NK4A214 Bilophila,
   Ruminiclostridium 5, Megasphaera
   Dietary Fiber Bacteroides, Ruminococcaceae NK4A214 Parabacteroides,
   [Eubacterium]coprostanoligenes, Bilophila, Megasphaera
   Milk Dairy products Dialister, Ruminococcaceae UCG-013, f
   Prevotellaceae Bacteroides, Paraprevotella, Desulfovibrio
   Cheese Parasutterella, Erysipelotrichaceae UCG-003 Prevotella 7
   Calcium Dietary Metals Dialister Prevotella 7, Prevotella 2
   Zinc Faecalibacterium, Megasphaera, Oscillibacter
   Sodium Parasutterella, Lachnoclostridium Oscillibacter
   Magnesium Bacteroides Bilophila, Ruminiclostridium 5, Megasphaera
   Potassium Bacteroides, Faecalibacterium, Ruminococcaceae NK4A214
   Bilophila, Dialister, Megasphaera
   Aspartame Processed foods Prevotella 9, Parasutterella, Paraprevotella
   Mannitol Lachnospira, Lachnoclostridium Parabacteroides, Bilophila,
   Megasphaera
   Trans Fat Megasphaera Subdoligranulum, f Bacteroidales S24-7
   [103]Open in a new tab

   ^∗Bacterium reported as genus, unless otherwise specified (f, family;
   c, class; o, order; p, phylum; k, kingdom).

Circulating and Gut Metabolites Are Associated With Gut Microbes

   We hypothesized that gut microbiome composition would associate with
   specific metabolites in the gut and circulation, reflecting
   taxon-specific metabolism. We identified 123 (66%) circulating
   metabolite variables and modules and 34 (71%) gut metabolite variables
   and modules that associated with at least one bacterial genus
   ([104]Figure 4, [105]5). Several metabolites were associated with
   multiple genera, as shown in [106]Table 2. Of these highly
   bacterial-related metabolites, many have known functions in bile acid
   metabolism, lipid and amino acid metabolism, or metabolism of
   xenobiotics, highlighting the important role of microbes in modulating
   host metabolism in key pathways.

FIGURE 4.

   [107]FIGURE 4
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   Associations between gut microbiome and metabolites in plasma. Color
   intensity reflects the magnitude of the association coefficients
   between metabolites and taxa.

FIGURE 5.

   [109]FIGURE 5
   [110]Open in a new tab

   Associations between gut microbiome and metabolites in stool. Color
   intensity reflects the magnitude of the association coefficients
   between metabolites and taxa.

Table 2.

   Plasma and stool metabolites associated with three or more gut
   microbial taxa.
   Metabolite Metabolic function Bacterial Taxon^∗
     __________________________________________________________________

   Positive association Negative association
   Plasma metabolites
   Chenodeoxycholate Primary bile acid Parasutterella, Incertae Sedis
   Phascolarctobacterium, Faecalibacterium, Ruminococcus 1
   Glycolithocholate sulfate Secondary bile acid Alistipes,
   Parasutterella, Ruminococcaceae NK4A214 Acidaminococcus
   7-ketolithocholate Parasutterella, Ruminococcaceae UCG-013
   Alloprevotella
   Taurolithocholate 3-sulfate Ruminococcaceae NK4A214 Acidaminococcus,
   Ruminiclostridium 5
   Ursodeoxycholate Parasutterella, Dialister, Megasphaera
   4-cholesten-3-one Lipid Parabacteroides, Odoribacter Parasutterella
   5alpha-pregnan-3(alpha or beta), 20beta-diol disulfate Parasutterella,
   Dialister, Faecalibacterium, [Eubacterium]hallii Acidaminococcus, f
   Bacteroidales S24-7
   Cortisone Subdoligranulum, Lachnospira, [Eubacterium]hallii
   Ruminococcus 1, Ruminiclostridium 5
   4-hydroxy-2-oxoglutaric acid Paraprevotella, Odoribacter,
   Erysipelotrichaceae UCG-003 Prevotella 7, Butyricimonas
   Phenol sulfate Amino acid Butyricimonas, Lachnoclostridium
   Bifidobacterium, Megasphaera, f Prevotellaceae
   Asparagine Parabacteroides, [Eubacterium]coprostanoligenes, Dialister
   N-acetyl-aspartyl-glutamate (NAAG) Phascolarctobacterium,
   Subdoligranulum Ruminococcus 1
   3-methylglutaconate Paraprevotella, f Bacteroidales S24-7 f
   Lachnospiraceae
   Indolepropionate Sutterella f Prevotellaceae, Lachnoclostridium
   Phenylacetylglutamine k Bacteria Lachnospira, Lachnoclostridium
   N-acetylglucosaminylasparagine Odoribacter, Ruminococcaceae UCG-003
   Bifidobacterium, Faecalibacterium, f Bacteroidales S24-7
   Phosphate Energy k Bacteria, f Bacteroidales S24-7 Ruminiclostridium 5
   Fumarate Megasphaera, Lachnoclostridium o Bacteroidales
   N6-succinyladenosine Nucleotide Coprococcus 2, Oscillibacter,
   Lachnoclostridium Bifidobacterium, f Prevotellaceae
   3-ureidopropionate Sutterella Parabacteroides, Erysipelotrichaceae
   UCG-003
   4-ethylphenylsulfate Xenobiotic Acidaminococcus, Ruminococcaceae
   NK4A214 Parabacteroides, Faecalibacterium, Ruminococcus 1,
   Lachnoclostridium
   4-allylphenol sulfate Paraprevotella, Sutterella f Lachnospiraceae,
   Parabacteroides, Megasphaera
   4-hydroxychlorothalonil Parasutterella, f Bacteroidales S24-7
   Prevotella 7, Megasphaera
   Retinal Paraprevotella, Erysipelotrichaceae UCG-003 o Bacteroidales,
   Prevotella 7
   2,3-dihydroxyisovalerate Oscillibacter Bifidobacterium,
   Ruminiclostridium 5
   2-piperidinone Acidaminococcus Ruminiclostridium 6, Ruminococcaceae
   NK4A214
   Gamma-CEHC Ruminococcaceae UCG-013 Incertae Sedis, Sutterella
   Salmon module Parasutterella, Phascolarctobacterium, Paraprevotella f
   Bacteroidales S24-7, Coprococcus 2
   Brown module Bilophila, Acidaminococcus f Bacteroidales S24-7
   Pink module Alistipes, Phascolarctobacterium, Acidaminococcus
   Red module Odoribacter, Acidaminococcus Prevotella 2
   Stool metabolites
   Hexadecanedioate Lipid Lachnospira, [Eubacterium]hallii Ruminococcus 2,
   Ruminococcaceae NK4A214, Erysipelotrichaceae UCG-003
   Undecanedioate Barnesiella Ruminococcus 1, Lachnoclostridium
   3-hydroxyhexanoate k Bacteria Bacteroides, Lachnoclostridium
   1-(1-enyl-stearoyl)-GPE (P-18:0) Subdoligranulum Bilophila, Megasphaera
   Piperine Xenobiotic o Bacteroidales, Sutterella, Faecalibacterium
   Ruminococcaceae UCG-003
   Brown module Acidaminococcus, Lachnoclostridium Ruminococcaceae
   NK4A214, Butyricimonas
   Green module o Clostridiales Bacteroides, Lachnospira,
   Lachnoclostridium
   Red module Bacteroides, Lachnospira Megasphaera
   [111]Open in a new tab

   ^∗Bacterium reported as genus, unless otherwise specified (f, family;
   c, class; o, order; p, phylum; k, kingdom).

Gut Bacterial Taxa Mediate the Association Between Dietary Nutrients and
Metabolites

   We were interested in whether gut bacterial taxa mediate the
   relationship between diet and metabolites. Mediation analysis revealed
   multiple taxa influencing the association between dietary intake and
   metabolites in plasma or stool. Given the inter-relationships between
   metabolic variables, we were interested in which pathways were most
   affected by microbiome mediation. We identified metabolic pathways with
   evidence for strong diet-microbiome effects, defined as having 3 or
   more metabolites in a sub-pathway with significant diet associations
   mediated by the microbiome, or association with a metabolite module
   ([112]Table 3). These included amino acid metabolism (histidine,
   phenylalanine, and tyrosine), lipid metabolism (fatty acids, bile
   acids, and steroids), and xenobiotics (benzoate, and food components).
   Of the dietary variables, plant-derived nutrients (vitamins and
   phytochemicals) and metals were strongly represented. Our data suggest
   that metabolic flux through these pathways is particularly susceptible
   to interaction between dietary intake and microbiome composition.

Table 3.

   Diet and microbiome mediated metabolites.
   Dietary nutrient Metabolite Tissue Pathway Sub-pathway Bacterial
   taxon^∗
   Copper, lysine, vitamin E 1-methylimidazoleacetate Plasma Amino acid
   Histidine metabolism Oscillibacter
   Sodium, phytic acid Imidazole propionate Plasma Ruminiclostridium_6,
   Oscillibacter, Lachnoclostridium
   Cheese, sodium, vitamin E N-acetylhistidine Plasma Parasutterella,
   Prevotella_2
   Sugar 3-methoxytyrosine Plasma Phenylalanine and tyrosine metabolism
   f__Ruminococcaceae
   Vegetables, tomato 5-bromotryptophan Plasma Lachnospira
   Sugar N-formylphenylalanine Plasma Roseburia
   Lutein + zeaxanthin Phenol sulfate Plasma Butyricimonas
   Vegetables Thyroxine Plasma Parasutterella
   Vitamin E, B carotene, folate, lutein + zeaxanthin, copper
   3-carboxy-4-methyl-5-Propyl-2-furanpropanoate Plasma Lipid Fatty acid,
   dicarboxylate Butyricimonas, Oscillibacter
   Vitamin E, B carotene, lutein + zeaxanthin, grains, proline, vitamin
   B1, dairy, calcium, cheese 4-hydroxy-2-oxoglutaric acid Plasma
   Butyricimonas, Erysipelotrichaceae_UCG-003, Paraprevotella,
   Prevotella_7
   Sugar, mannitol, fiber, folate, glycitein, iron, magnesium, potassium,
   phosphorous, phytic acids, nuts, Dodecanedioate Stool Megasphaera
   Vitamin E, folate, lutein + zeaxanthin, cheese, tomatoes
   Hexadecanedioate Stool Bifidobacterium, Butyricimonas,
   Ruminococcaceae_UCG-002, Erysipelotrichaceae_UCG-003
   Sodium Undecanedioate Stool Prevotella_2
   Sugar, sodium, cheese, zinc Chenodeoxycholate Plasma Primary bile acid
   metabolism Parasutterella, Phascolarctobacterium, Incertae_Sedis,
   Faecalibacterium
   SFA (g) Cholate Plasma Phascolarctobacterium
   Cheese Glycochenodeoxycholate Glucuronide Plasma
   Erysipelotrichaceae_UCG-003
   Vegetables Glycochenodeoxycholate Sulfate Plasma Bacteroides
   Cheese, sodium 7-Ketolithocholate Plasma Secondary bile acid metabolism
   Parasutterella
   Cooking fats Glycocholenate sulfate Plasma Butyricimonas
   Potassium, folate, lutein +zeaxanthin, vitamin B12, cheese, sodium
   Glycolithocholate Sulfate Plasma Acidaminococcus, f__Prevotellaceae,
   Parasutterella, Ruminococcaceae_NK4A214_group
   B carotene, folate, vegetables Glycoursodeoxycholate Plasma Megasphaera
   Potassium, folate, lutein + zeaxanthin, vitamin B12, A carotene, B
   carotene Taurolithocholate 3-sulfate Plasma Acidaminococcus,
   Ruminiclostridium_5, Ruminococcaceae_NK4A214_group
   Sugar, fiber, folate, glycitein, magnesium, mannitol, nuts, potassium,
   vegetables, cheese, calcium, sodium Ursodeoxycholate Plasma
   Megasphaera, Parasutterella, k__Bacteria
   Zinc, calcium, lactose, dairy, cheese, sodium, sugar, fruit, potatoes,
   starch, trans fat, vitamin B12, vitamin D 5α-pregnan-3(α or β),20β-diol
   disulfate Plasma Steroid/sterol [Eubacterium]_coprostanoligenes_group,
   f__Bacteroidales_S24-7_group, Erysipelotrichaceae_UCG-003
   Zinc 5α-pregnan-3β,20β-diol monosulfate Plasma
   [Eubacterium]_coprostanoligenes_group
   Cooking fats Cortisol Plasma Butyricimonas
   Fruit, cheese, sodium, niacin 4-cholesten-3-One Plasma Parabacteroides
   Phytic acid, niacin, fruit, sugar 4-ethylphenylsulfate Plasma
   Xenobiotics Benzoate metabolism Lachnoclostridium, Parabacteroides,
   Ruminococcus_1
   Total Dairy 4-hydroxyhippurate Plasma Paraprevotella
   Lutein + zeaxanthin, folate, vitamin E, B carotene, copper
   Methyl-4-hydroxybenzoate sulfate Plasma Bilophila, Bifidobacterium
   B carotene, folate, lutein + zeaxanthin, sodium, copper, lysine,
   vitamin E 2,3-dihydroxyisovalerate Plasma Food component/plant
   Bifidobacterium
   Folate, potassium, vitamin B12 2-piperidinone Plasma Acidaminococcus,
   Ruminococcaceae_NK4A214_group
   Sugar, sorbitol, mannitol, glycein, iron, potassium, phosphorous, nuts,
   fiber, folate, magnesium, vegetables, niacin, fruit, dairy
   4-allylphenol sulfate Plasma Megasphaera, f__Prevotellaceae,
   Ruminiclostridium_6, Paraprevotella, Parabacteroides
   Lutein + zeaxanthin, vitamin B12, vitamin E, folate Methyl
   indole-3-acetate Plasma Ruminococcaceae_NK4A214_group, Butyricimonas
   Zinc, sucrose Piperine Stool Faecalibacterium, Parasutterella
   Proline, grains, vitamin B1, dairy, cheese, sorbitol Retinal Plasma
   Erysipelotrichaceae_UCG-003, Paraprevotella, o__Bacteroidales
   Sorbitol Stachydrine Plasma o__Bacteroidales
   Niacin, fruit Thymol sulfate Plasma Parabacteroides
   Sorbitol Black, blue module Plasma Module o__Bacteroidales
   Copper, folate, lutein + zeaxanthin, B carotene, vitamin E, potatoes,
   starch, trans fat, vitamin B12 Brown module Plasma Bilophila,
   Butyricimonas, f__Bacteroidales_S24-7_group
   Sugar, mannitol, fiber, phytic acid, potassium, vitamin B12, B
   carotene, folate Brown module Stool
   [Eubacterium]_coprostanoligenes_group, Acidaminococcus,
   Ruminococcaceae_NK4A214_group
   Calcium, fruit Cyan module Plasma Dialister, f__Ruminococcaceae
   Potatoes, starch, trans fat Green module Plasma
   f__Bacteroidales_S24-7_group
   Fiber, vegetables, tomatoes Green module Stool Lachnospira,
   Lachnoclostridium, f__Prevotellaceae
   Cheese, sodium Lightcyan module Plasma Parasutterella
   Starch, trans fat Purple module Plasma f__Bacteroidales_S24-7_group
   Vitamin E Red module Plasma Prevotella_2
   Tomatoes, vegetables Red module Stool Christensenellaceae_R-7_group,
   Lachnospira
   Dairy, sodium, cheese Salmon module Plasma Paraprevotella,
   Parasutterella, Phascolarctobacterium
   B carotene, lutein + zeaxanthin, vitamin B12, vitamin E, lysine,
   sodium, copper Tan module Plasma Ruminococcaceae_NK4A214_group,
   Oscillibacter
   Calcium, lactose, dairy, vitamin D, vitamin B12 Turquoise module Plasma
   Dialister
   Fiber Turquoise module Stool [Eubacterium]_coprostanoligenes_group
   [113]Open in a new tab

   ^∗Bacterium reported as genus, unless otherwise specified (f, family;
   c, class; o, order; p, phylum; k, kingdom).

Differences in Abundance of Metabolites by Gut Microbial Enterotype

   We identified two gut microbiome enterotypes in our sample, with good
   separation of the sub-groups by Principal Coordinates Analysis (PCoA)
   using the Jaccard distance (see [114]Supplementary Figure S1). There
   were 54 individuals categorized as Enterotype 1, and 82 individuals
   categorized as Enterotype 2. There was no difference in age or race
   distribution across enterotypes, or in sequencing batch, although there
   was a trend toward a higher proportion of women in enterotype 2 (52%
   vs. 69% female, chi-square test p = 0.054). Individuals in enterotype 2
   had lower BMI (26.9 vs. 24.5, p = 0.01). The primary differentiating
   characteristic between the two gut enterotypes was in the abundance of
   family Ruminococcaceae, with significantly higher proportion of
   Ruminococcaceae in enterotype 2 ([115]Supplementary Figure S2).
   Analysis of metabolites by enterotype revealed striking differences
   between the groups: 112 plasma metabolites and 122 stool metabolites
   were significantly different by enterotype (unadjusted p < 0.05,
   [116]Supplementary Tables S5, [117]S6). Unadjusted p-values are
   reported in the enterotype analysis because the analysis used
   individual metabolites rather than metabolite modules and many
   metabolites are highly correlated. While the enterotype-associated
   metabolites spanned many biological pathways, they were enriched in
   certain categories. We selected all nominally associated metabolites
   for pathway enrichment analysis. Plasma metabolites that differed by
   enterotype were significantly enriched for amino acid metabolism (p <
   0.05), particularly the essential amino acids phenylalanine,
   tryptophan, and tyrosine, the essential branched-chain amino acids
   valine, leucine and isoleucine, as well as arginine and proline. Stool
   metabolites differing by enterotype were enriched in taurine and niacin
   (vitamin B3) metabolism (p < 0.05). Individuals in Enterotype 1 had
   slightly higher alcohol and cholesterol consumption than Enterotype 2
   (p < 0.05), but there were otherwise limited differences in dietary
   intake by enterotype, suggesting that the metabolite differences were
   not solely attributable to differences in diet.

Gut Microbial Enterotype Modulates the Relationship Between Diet and
Metabolites

   As observed in the mediation analysis for individual taxa, microbiome
   composition mediates the association between dietary nutrients and
   metabolites. We hypothesized that gut enterotype, as a composite
   measure of microbiome differences, would modify the relationship
   between dietary nutrient intake and downstream metabolism. We found
   evidence for significant interaction between habitual dietary intake
   and gut enterotype on plasma and stool metabolites across many classes
   of nutrients and metabolites. Of diet-metabolite pairs that were
   enterotype-dependent, the most frequent dietary components, which
   associated with >100 metabolites each, included plant-derived nutrients
   (fiber, carotenoids, and isoflavones) and artificial sweeteners
   (saccharin, mannitol, aspartame, and xylitol), as well as animal
   protein, trans fatty acids, caffeine, and alcohol. The diet- and
   enterotype-dependent metabolites spanned many pathways, but the
   metabolites with the most frequent associations with dietary variables
   (>30 dietary variables each) were predominately bile acids and
   xenobiotic metabolites in plasma, and xenobiotic and amino acid
   metabolites in stool.

   Given the importance of bile acids in both gut metabolism and
   cardiometabolic disease risk, we were particularly interested in the
   observed microbiome-mediated effects of diet on bile acid signaling. As
   shown in [118]Figure 6, habitual intake of dietary fiber was associated
   with higher plasma ursodeoxycholate in individuals with enterotype 1,
   but there was no relationship between diet and ursodeoxycholate in
   enterotype 2. Conversely, high dietary fiber was associated with
   decreased plasma taurodeoxycholate in individuals with enterotype 1,
   and slightly increased levels in enterotype 2. Many of the circulating
   bile acids were highly correlated with each other, and as such the
   results for taurodeoxycholate represent similar significant
   associations for dietary fiber with taurocholate, taurolithocholate 3
   sulfate, glycolithocholate, glycolithocholate sulfate,
   taurochenodeoxychlate, glycodeoxycholate, glycocholate, and
   glycodeoxycholate sulfate, (Spearman correlation > 0.5 for metabolite
   pair, and p < 0.05 for enterotype-mediated association with diet). Of
   note, dietary choline was highly correlated with dietary fiber
   (Spearman correlation 0.7), reflecting some overlapping food sources
   and dietary patterns, and similar patterns of association with bile
   acids were also observed for choline. Interestingly, there was a modest
   positive relationship between plasma ursodeoxycholate (p < 0.05), but
   not plasma taurodeoxycholate, and plasma C-Reactive Protein (CRP) and
   BMI in individuals with enterotype 2, but not in enterotype 1
   ([119]Figure 7). These data suggest that individuals with enterotype 1
   have bile acid metabolism that is highly diet-responsive, whereas
   individuals with enterotype 2 have bile acid production which is less
   sensitive to differences in dietary intake, but may be more likely to
   relate to poor metabolic health.

FIGURE 6.

   [120]FIGURE 6
   [121]Open in a new tab

   Dietary Fiber has a gut enterotype-dependent association with plasma
   secondary bile acids including ursodeoxycholate and taurodeoxycholate.

FIGURE 7.

   [122]FIGURE 7
   [123]Open in a new tab

   Plasma Ursodeoxycholate has a gut enterotype-dependent relationship
   with plasma C-Reactive Protein and BMI, with a positive association in
   Enterotype 2, and no relationship in Enterotype 1.

Discussion

   The gut microbiome is recognized as a key intermediate between
   environmental inputs and host metabolism, however, the specific
   relationship between dietary nutrients, microbiome composition, and
   host metabolism remains poorly understood. We conducted multi-omic
   profiling to probe the relationship between diet, the microbiome, and
   metabolism in healthy adults. We identified associations between diet,
   the gut microbiome and the gut and plasma metabolome at a global level
   and identified specific microbiome-mediated associations between diet
   and metabolites. Our data suggest that gut microbiome composition, both
   at the taxon and the enterotype level, modulates how dietary nutrients
   are metabolized, impacting systemic host metabolism with potential
   downstream consequences on metabolic health.

   Diet, the microbiome, and the metabolome are complex, composed of
   multiple inter-dependent variables, which have independent and
   combinatorial effects. We first examined these multi-omic datasets on a
   global level, to understand the inter-relationships on a broad scale.
   Consistent with our hypothesis, diet, the gut microbiome, and the
   metabolome were all related to each other. We found minimal evidence of
   an association between the gut and oral microbiota in the same
   individuals, which is consistent with previous studies, which have also
   reported limited overlap between different body sites ([124]Caporaso et
   al., 2011; [125]Ding and Schloss, 2014). The salivary microbiome in our
   sample was also not strongly related to diet, or to metabolites. This
   may reflect both the smaller sample size for the oral microbiome, and
   distal relationships between the mouth and intestinal or whole-body
   metabolism.

   We assessed subjects’ diet using two independent methods, to identify
   the nutrients consumed shortly before microbiome sampling, and to
   identify habitual long-term food consumption. There was relatively high
   correlation between analogous dietary variables from short and
   long-term estimates within subjects, suggesting that participants’
   diets at the time of sampling were consistent with their longer-term
   dietary patterns. We were interested in the relative importance of
   day-to-day fluctuations in dietary intake compared with longer-term
   patterns. We found that long-term diet as assessed by FFQ was more
   strongly associated with the gut microbiome than the diet consumed
   immediately prior to sampling (generally the 3 days prior to stool
   elimination). This suggests a core gut microbial population, shaped by
   habitual diet, that remains relatively constant despite short-term
   dietary fluctuations. This is supported by findings from others, who
   have observed relative stability in gut microbiome profiles over time,
   particularly in adults ([126]Yatsunenko et al., 2012; [127]Ding and
   Schloss, 2014; [128]Dubois et al., 2017; [129]Ruggles et al., 2018).
   Although large shifts in diet acutely alter microbiome composition
   ([130]David et al., 2014), dietary habits over time appear to be more
   influential in shaping the gut microbial community. Short-term diet was
   more strongly associated with the gut and plasma metabolome than
   long-term diet, independent of the microbiome. This is consistent with
   a model where recently-consumed nutrients are rapidly metabolized by
   the host, influencing what is present in the gut and circulation at any
   given time. However, whether these short-term dynamic changes impact
   longer-term health outcomes is unknown. It is likely that repeated
   exposures to diet and microbiome derived metabolites over longer time
   frames have greater impact on lifelong health status.

   Of dietary variables associated with microbiome composition and
   exhibiting microbiome-mediated relationships with metabolites in our
   sample, a large proportion are derived from plant-based foods. This is
   consistent with our knowledge of microbiome-mediated digestion. Plants
   are complex food sources, and contain many diverse nutrients, some of
   which are already known to interact with the microbiome. Fiber is
   metabolized by bacteria for production of short-chain fatty acids,
   which not only provide energy and selective advantages to microbes, but
   can affect host metabolism and immunity ([131]Furusawa et al., 2013;
   [132]Vital et al., 2014; [133]Koh et al., 2016; [134]Maier et al.,
   2017). Individuals consuming diets high in plant-derived fiber have
   greater microbiome diversity ([135]Schnorr et al., 2014), while diets
   low in fiber lead to reduced bacterial diversity ([136]Sonnenburg et
   al., 2016). Many phytochemicals are selectively metabolized by gut
   microbiota including isoflavones ([137]Rowland et al., 2000;
   [138]Fernandez-Raudales et al., 2012), while plants are rich sources of
   many vitamins, including those with known microbial interaction such as
   Vitamin B3/Niacin ([139]Singh et al., 2014). Symbiotic relationships
   between the host and the microbiome, and optimal functioning of the
   holobiont, are dependent on environment, with diet being the archetypal
   environmental variable ([140]Postler and Ghosh, 2017). In addition to
   plant foods, which have long been consumed by humans, we observed
   inter-relationships with artificial sweeteners, which have entered the
   human diet in relatively recent time. Our data do not resolve whether
   these have positive or negative consequences on health, but indicate
   that shifts toward higher consumption of processed foods and lower
   consumption of complex plant-based foods, common to the Western diet,
   have potential consequences on the gut microbiota and metabolite
   production.

   We identified many metabolites in plasma and stool that differed by
   microbiome composition; indeed the majority of metabolites appeared to
   be influenced by diet, the microbiome, or both. These spanned many
   biological pathways, but metabolites that were particularly
   microbiome-sensitive were pathways related to bile acid metabolism,
   amino acid metabolism, lipid and steroid metabolism, and metabolism of
   xenobiotics. While direct effects on diet or microbe-derived
   metabolites (e.g., xenobiotics) are to be expected, our data highlight
   that the microbiome also modulates key host metabolic pathways of
   importance not only for energy metabolism, but overall host health
   status including immune function. The consequences of alterations in
   these circulating metabolites are not fully known. Microbiome
   metabolites have been shown to affect inflammation and immune
   regulation ([141]Levy et al., 2017; [142]Haase et al., 2018), and we
   observed some association between enterotype-mediated metabolism and
   plasma CRP. However, further studies are needed to establish
   consequences of chronic alterations in metabolite signaling.

   Because different bacteria can have overlapping functionality, it can
   be helpful to collapse the taxonomic composition into related clusters,
   or enterotypes, to identify individuals within sub-groups of similar
   composition. We observed many enterotype-mediated associations, amongst
   them, a significant effect of gut enterotype on the relationship
   between dietary fiber and plasma bile acids. Bile acids are key
   regulators of hepatic and intestinal lipid metabolism, and have been
   linked to inflammation and metabolic disease ([143]Joyce and Gahan,
   2016; [144]Chávez-Talavera et al., 2017). Microbiota contribute to bile
   acid metabolism, transforming host-synthesized primary bile acids to
   secondary bile acids, while microbiome composition may itself be shaped
   by bile acids ([145]Wahlström et al., 2016; [146]Long et al., 2017).
   While we present data for dietary fiber, very similar results were
   found for dietary choline, with fiber and choline intake strong
   correlated in our sample. Thus, it is not clear whether the effect is
   specific to fiber, choline, or another phytonutrient common to the same
   food source. Both fiber and choline can act as substrates or inhibitors
   of bile acid metabolism ([147]Corbin and Zeisel, 2012; [148]Dziedzic et
   al., 2015; [149]Wang et al., 2017), and both have been linked to
   microbial metabolism ([150]LeBlanc et al., 1998; [151]Tang et al.,
   2013; [152]Mayengbam et al., 2018; [153]Tuncil et al., 2018),
   suggesting that either or both plausibly lie in a causal pathway
   linking diet to bile acid metabolism through microbiota.

   Our study had several key strengths, but also some limitations. We
   recruited healthy adults, and conducted deep multi-omic phenotyping,
   with the goal of identifying relationships between diet, the microbiome
   and the metabolome independent of a disease background. While this
   allowed for metabolic analysis independent of disease confounding or
   reverse causation, it did not allow us to directly assess relationships
   with cardiometabolic disease. However, at least half of the
   participants in our study are likely to develop cardiometabolic disease
   in later life ([154]Benjamin et al., 2018), suggesting that even mildly
   elevated risk factors may predict future disease. We measured plasma
   CRP, as a clinically-relevant marker of inflammation, which predicts
   future disease risk ([155]Ridker, 2003), and used BMI as a proxy for
   obesity and future metabolic risk ([156]Van Gaal et al., 2006). Despite
   our modest sample size, this is one of the largest studies of diet, the
   microbiome, and the metabolome conducted in humans. A pervasive
   limitation in nutritional studies is the difficulty in precise
   quantification of dietary intake in free-living humans. We used two
   independent validated dietary assessment methods, which were broadly
   consistent with each other, while allowing us to assess diet over
   different time frames. Because food is complex, and individual
   nutrients often co-occur in the same foods, in many cases we can not
   determine which food component is “causal” in a
   diet-microbiome-metabolite relationship. Future detailed studies to
   isolate individual nutrients will be required, while recognizing that
   nutrients exist within a complex food structure, and that an isolated
   nutrient (e.g., in a single supplement) may not behave the same way as
   a nutrient derived in conjunction with other nutrients in a food
   source. An important limitation of our study is the use of a single
   time point for data collection. While we were able to identify
   diet-microbiome-metabolite associations in our cross-sectional
   analysis, we are unable to infer causality. Future interventional
   studies with longitudinal sampling are required to assess relationships
   over time, and to determine whether changes in diet associate with
   microbiome-mediated changes in metabolism.

Conclusion

   Through multi-omic analysis in a deeply-phenotyped human sample, we
   identified microbiome-mediated relationships between diet and
   circulating metabolites. Both individual microbial taxa, and microbial
   enterotype may relate to how dietary precursors are metabolized within
   the gut, and in the circulation. The potential mechanisms involved, and
   any long-term consequences on health status remain to be determined.

Ethics Statement

   This study was carried out in accordance with the recommendations of
   the University of Pennsylvania’s clinical research standards that meet
   regulations relating to Good Clinical Practice (GCP). All subjects gave
   written informed consent in accordance with the Declaration of
   Helsinki. The protocol was approved by the Institutional Review Boards
   of the University of Pennsylvania and Vanderbilt University.

Author Contributions

   JF designed the study. HS and JF performed laboratory analysis. Z-ZT,
   GC, QH, SH, MS, and JF performed statistical analysis. Z-ZT, GC, RS,
   and JF contributed to writing the manuscript. All authors contributed
   to manuscript revision, read and approved the submitted version.

Conflict of Interest Statement

   The authors declare that the research was conducted in the absence of
   any commercial or financial relationships that could be construed as a
   potential conflict of interest. The reviewer AA declared a past
   co-authorship with several of the authors Z-ZT and GC.

Footnotes

   Funding. The ABO Study was supported by U01-HL108636 and K24-HL10763
   (PI: Reilly). The project was supported by an AHA Scientist Development
   Grant (15SDG24890015, PI: JF) and a P&F Award to JF from the Vanderbilt
   University Medical Center’s Digestive Disease Research Center supported
   by NIH grant P30DK058404. The project was also supported by the Data
   Science Initiative Award (PI: Z-ZT) provided by the University of
   Wisconsin–Madison Office of the Chancellor and the Vice Chancellor for
   Research and Graduate Education with funding from the Wisconsin Alumni
   Research Foundation.

Supplementary Material

   The Supplementary Material for this article can be found online at:
   [157]https://www.frontiersin.org/articles/10.3389/fgene.2019.00454/full
   #supplementary-material
   [158]Click here for additional data file.^ (295.3KB, XLSX)

References