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Human gut microbiome composition and

tryptophan metabolites were changed differently byfast food and Mediterranean diet in 4 days: a pilotstudy

Chenghao Zhu, Lisa Sawrey-Kubicek, Elizabeth Beals, Chris H. Rhodes, Hannah Eve Houts,Romina Sacchi, Angela M. Zivkovic⁎

Department of Nutrition, University of California, Davis, Davis, CA, USA 95616

A R T I C L E I N F O

Abbreviations: ASV, amplicon sequencecholesterol; IAA, indole-3-acetic acid; IDO, iliquid chromatography–mass spectrometrpolymerase chain reaction; PUFA, polyunsa⁎ Corresponding author.

E-mail addresses: chhzhu@ucdavis.edu (Cchrhodes@ucdavis.edu (C.H. Rhodes), hehou(A.M. Zivkovic).

A B S T R A C T

Article history:Received 8 August 2019Revised 17 March 2020Accepted 24 March 2020

Diets rich in animal source foods vs plant-based diets have different macronutrientcomposition, and they have been shown to have differential effects on the gut microbiome.In this study, we hypothesized that diets with very different nutrient composition are ableto change gut microbiome composition and metabolites in a very short period. Wecompared a fast food (FF) diet (ie, burgers and fries) with a Mediterranean (Med) diet, whichis rich in vegetables, whole grains, olive oil, nuts, and fish. Ten healthy subjects participatedin a controlled crossover study in which they consumed a Med diet and FF diet inrandomized order for 4 days each, with a 4-day washout between treatments. Fecal DNAwas extracted and the 16S V4 region amplified using polymerase chain reaction followed bysequencing on an Illumina MiSeq. Plasma metabolites and bile acids were analyzed usingliquid chromatography–mass spectrometry. Certain bile-tolerant microbial genera andspecies including Collinsella, Parabacteroides, and Bilophila wadsworthia significantly increasedafter the FF diet. Some fiber-fermenting bacteria, including Lachnospiraceae andButyricicoccus, increased significantly after the Med diet and decreased after the FF diet.Bacterially produced metabolites indole-3-lactic acid and indole-3-propionic acid, whichhave been shown to confer beneficial effects on neuronal cells, increased after the Med dietand decreased after the FF diet. Interindividual variability in response to the treatmentsmay be related to differences in background diet, for example as shown by differences inBilophila response in relationship to the saturated fat content of the baseline diet. Inconclusion, an animal fat–rich, low-fiber FF diet v. a high-fiber Med diet altered human gutmicrobiome composition and its metabolites after just 4 days.© 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC

BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Keywords:Fast food dietMediterranean dietGut microbiomeBiogenic aminesBile acids

variant; CVD, cardiovascular disease; FF, fast food; HDL-C, high-density lipoproteinndoleamine 2,3-dioxygenase; ILA, indole-3-lactic acid; IPA, indole-3-propionic acid; LC-MS,y; LCA, lithocholic acid; Med, Mediterranean; MUFA, monounsaturated fatty acid; PCR,turated fatty acid; TMA, trimethylamine; TMAO, trimethylamine-N-oxide.

. Zhu), lsawreykubicek@ucdavis.edu (L. Sawrey-Kubicek), egbeals@ucdavis.edu (E. Beals),ts@ucdavis.edu (H.E. Houts), rsacchi@ucdavis.edu (R. Sacchi), amzivkovic@ucdavis.edu

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1. Introduction

It is well known that dietary pattern has a significant effecton diseases and conditions such as cardiovascular disease(CVD), type 2 diabetes, and obesity [1-3]. The Mediterranean(Med) and Western diets are 2 common dietary patterns thathave different effects on disease risk in both animal modelsand population-based studies. The Med diet is rich in freshfruits, vegetables, fiber, and monounsaturated (MUFAs) andpolyunsaturated fatty acids (PUFAs) [4-8]. It has beendemonstrated in epidemiological studies and interventionstudies that the Med diet improves cardiometabolic healthand reduces CVD risk [5,9,10]. Traditional American “fastfood” consisting of burgers, fries, and soda exemplifies theWestern diet, which is enriched in red meat, saturated fat,simple sugars, and cholesterol but low in fresh fruits,vegetables, and fiber [11-15], and its consumption is associ-ated with an increased risk for coronary heart diseases andCVD-related markers [1,16].

The gut microbiome plays an important role in diseaserisk, and its composition has been shown to be modified byboth disease state and diet. Gut microbiome composition isaltered drastically in metabolic syndrome, diabetes, CVD,and heart failure patients compared to healthy subjects [17-22]. Long-term dietary patterns have a significant impact ongut microbiome composition [23,24]. Short-term dietarychange has also been shown to alter gut microbial composi-tion and function, and this can occur in as short as 24 hours [25].

Gut microbes produce many bioactive compounds, some ofwhich contribute to normal physiological functions, whereasothersmay cause detrimental effects. Trimethylamine-N-oxide(TMAO) is ametabolite produced in the liver,modified from thegut bacterial product trimethylamine (TMA). Gut bacteriaproduce TMA from dietary choline and carnitine. TMAO hasbeen demonstrated to be proatherogenic, and its circulatingconcentration is associated with the prevalence of CVD [26,27].Bile acids are produced from cholesterol in the liver but can bemodified by gut bacteria to form secondary bile acids. Anincreased plasma ratio of secondary to primary bile acids isassociated with increased CVD risk [28].

Gastrointestinal bacteria also play important roles intryptophan metabolism, which further impact the gut andhost immunity [29]. Approximately 95% of absorbed trypto-phan is degraded through the kynurenine pathway [30], inwhich tryptophan is first catabolized to kynurenine by thekey enzyme indoleamine 2,3-dioxygenase (IDO) and ulti-mately is converted to niacin, nicotinic acid, and NAD(nicotinamide adenine dinucleotide) [31]. IDO is induced byinflammatory cytokines [32]. Tryptophan can also be catab-olized by gut bacteria, such as Lactobacillus and Clostridium,producing various types of indole derivatives, includingindole-3-acetic acid (IAA), indole-3-propionic acid (IPA), andindole-3-lactic acid (ILA), from tryptophan catabolism [29,33].Many bacterial tryptophan metabolites are ligands for thepregnane X receptor or aryl hydrocarbon receptor, thusplaying roles in maintaining intestinal barrier integrity andmodulating immune function [34-36].

We conducted a pilot study in which 10 healthy humansubjects were given a fast food (FF) and a Med diet for 4 days

in randomized order. The FF and Med diets were chosenbecause of their differences in dietary components andnutritional composition and their well-known effects onhealth. Some of the results of this study that were previouslyreported showed that the high-density lipoprotein lipidomebut not glycoproteome was widely remodeled in 4 days inresponse to FF vs Med diet [37,38]. In this secondary analysisstudy, we hypothesized that the 4-day dietary interventionsalter gut microbiome composition and that these changes arecorrelated with changes in plasma concentrations of bacteri-ally produced metabolites. To test the hypothesis,microbiome composition was analyzed from fecal samples,and host, microbial, and host-microbe cometabolites includ-ing biogenic amines and bile acids were analyzed fromplasma.

2. Methods and materials

2.1. Subjects and study design

The clinical study protocol was described previously [37,38].Briefly, 10 healthy human subjects (18-25 years old, non-smokers, body mass index 21.2-32.9 kg/m2) were phonescreened, consented, and enrolled in this isoenergetic,randomized-order, crossover study. Each subject was givenboth the 4-day FF diet and the Med diet treatment inrandomized order, with a 4-day washout in between. Subjectswere assigned to diet order using a randomized block design.All study food was provided by the study team. On the FF arm,subjects were given gift cards with exact dollar amounts topurchase food at a specific fast food restaurant, and on theMed arm, subjects were given all of the food they needed for2 days. All subjects brought back empty food containers, andany remaining food was weighed and recorded. The studymeal plans were adjusted to each individual's daily energyrequirement. The study diet was reported previously [37]. Asample study diet menu is listed in Supplemental Table S1.

Blood and fecal samples were collected in the morningafter a 12-hour overnight fast, and stool samples werecollected within the 24-hour period preceding each blooddraw. Stool was collected using the Fisherbrand commodespecimen collection system (catalog no. 02-544-208, FisherScientific Coompany LLC, Waltham, MA, USA) no more than24 hours prior to study visit. The sample collector was storedwith cooling gel packs in a container before the study visit.The samples were then stored at −80°C until analysis. Thestudy was approved by the University of California DavisInstitutional Review Board, and the study followed all of theethical standards of the Helsinki declaration. The study isregistered at clinicaltrials.gov under identifier NCT03205254.

2.2. Microbiome composition analysis

DNA was extracted using the Zymo Research Fecal DNAMiniPrep kit (ZYMO Research, Irvine, CA, USA) according tothemanufacturer's instructions, as previously described, witha few modifications [39]. Fifty to 200 mg of frozen stool wasaliquoted by chiseling from frozen stool and lysed by bead-

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beating using a bead-beater (MP FastPrep 24; MP Biomedicals,Santa Ana, CA, USA). The V4 region of the 16S rRNA gene wasamplified from extracted DNA by polymerase chain reaction(PCR) as previously described [40]. Briefly, the V4 region ofthe16S rRNA gene was amplified with barcoded primers F515and R806. PCR amplification of each sample was carried out intriplicate with initial denaturation of 2 minutes at 94°C;followed by 30 cycles of 95°C for 45 seconds, 50°C for60 seconds, and 72°C for 90 seconds; and a final extensionstep at 72°C for 10 minutes. Amplicons were evaluated by gelelectrophoresis, combined, and purified with the QiagenQIAquick PCR Purification Kit (Qiagen, Valencia, CA, USA).The purified DNA library was submitted to the UC DavisGenome Center DNA Technologies Core for sequencing on anIllumina MiSeq instrument (Illumina, San Diego, CA, USA).

2.3. Biogenic amines

The biogenic amines were measured using liquidchromatography–mass spectrometry (LC-MS) at the UC DavisWest Coast Metabolomics Center [41]. Samples were extractedusing a method modified from a previously publishedprotocol [42]. Each 20-μL plasma aliquot was thawed at roomtemperature and placed on ice once thawed, and 225 μLmethanol and 750 μL MTBE (methyl tert-butyl ether) wereadded to each sample in sequence, with a vortex for10 seconds in between. After shaking for 6 minutes at 4°C,188 μL LC-MS grad water was added. After vortexing for20 seconds, samples were centrifuged for 2 minutes at12 300 rpm. Half of the bottom layer was dried down andresuspended in 108.6 μL 90:10 (vol:vol) methanol:toluenesolvent. The resuspended extract was injected into a 1290 AInfinity UPLC system (Waters Technologies Corporation,Milford, MA, USA) with a Waters Acquity UPLC BEH Amidecolumn (1.7 μm, 2.1 mm × 150 mm) and analyzed with anAgilent 6530 QTOF mass spectrometry (Agilent Technologies,Santa Clara, CA, USA). Raw mass spectrometry data were firstprocessed in mzMine 2.0. Selected peaks were collated andconstrained into MassHunter for identification, according tothe precursor ion accurate mass, MS/MS fragmentation, aswell as the NIST14/Metlin/MassBank libraries.

2.4. Bile acids

Plasma bile acids were measured using a targeted LC-MSmethod at the UC DavisWest Coast Metabolomics Center [43].Each 50 μL plasma was mixed with 25 μL antioxidationsolution, 25 μL surrogate standards (1000 nmol/L), 25 μLCUDA/PHAU (1000 μmol/L in 50:50 [vol:vol] acetonitrile:meth-anol), and 125 μL 50:50 (vol:vol) acetonitrile:methanol. Twohundred microliters of supernatant was removed and trans-ferred into a filter plate after centrifugation for 5 minutes. Thefilter plate was centrifuged again to collect the bile acidcontaining fraction for LC-MS analysis. Bile acid compoundswere separated using a Waters Acquity BEH C18 column (2.1 ×100 mm 130 Å, 1.7 μm) and analyzed on a Sciex 4000 QTRAP(AB Sciex LLC, Framingham, MA, USA). Each bile acidcompound was quantified using an individual standardcalibration curve. The standards include cholic acid,chenodeoxycholic acid, deoxycholic acid, lithocholic acid

(LCA), urodeoxycholic acid, taurodehydrocholic acid,tauroursodeoxycholic acid, taurocholic acid, taurolithocholicacid, taurochenodeoxycholic acid, taurodeoxycholic acid,glycoursodeoxycholic acid, glycohyodeoxycholic acid,glycocholic acid, glycochenodeoxycholic acid,glycodeoxycholic acid, and glycolithocholic acid.

2.5. Statistical analyses

The command line software fastq-multx was used todemultiplex sequence data, with the additional help of acustomized python script to remove PhiX [44]. Fastx-toolkitwas used to trim off primers, and FastQC was used toevaluate the sequencing quality [45,46]. The R packageDADA2 (v 1.6) was used for amplicon sequence variant(ASV) clustering [47,48]. Taxonomy assignment was alsodone in DADA2, with the SILVA ribosomal RNA gene database(v128) [49]. The command line software qiime2 was used tocalculate the phylogenetic tree [50]. The R package phyloseqwas used to calculate the microbiome diversities [51]. TheASV table was converted into relative abundance andsummarized into each phylogenetic level for statisticaltesting.

Differential abundance analyses of bacterial genera, bio-genic amines, and bile acids were done using a linear mixedmodel with the R package limma [52]. Multiple test adjust-ments were performed on P values whenever more than 10variables were tested using the Benjamini-Hochberg method.The Shapiro-Wilk tests were performed on each variable totest normality, and log-2 transformations were performed ifneeded. Pearson correlation tests were performed to test thecorrelation between microbiome composition variables, bio-genic amines, and bile acids. Given the exploratory nature ofthis pilot study, no power calculations were performed priorto the study.

3. Results

3.1. Baseline characteristics and dietary records

Baseline characteristics were reported previously [37]. Briefly,subjects were healthy and young with an average body massindex of 24.39 (±3.71) and age of 22.1 (±2.33). Blood pressure(systolic 122.07 ± 14.27 and diastolic 79.00 ± 13.01), choles-terol (163.60 ± 25.01 mg/dL), and triglycerides (80.30 ±31.82 mg/dL) were all in the reference range. The baselinetotal energy intake was on average 2526 (±951) kcal/day (10568(3979) kJ/day), and there was an equivalent energy intake onthe FF andMed diet treatment for all subjects. The baseline fatintake was 35.6% (±10.8%) of total energy intake. The fatintake was 44.6% (±4.0%) on the FF diet and 33.2% (±2.5%) onthe Med diet treatment. Subjects also had significantlydifferent dietary fat composition on the FF diet vs the Meddiet. The saturated fatty acid content was 44.7% (±6.7%) on theFF diet and 13.2% (±2.8%) on the Med diet, whereas the MUFAsand PUFAs were 1.9% (±0.6%) and 3.2% (±1.0%) on the FF dietand 41.3% (±11.7%) and 15.6% (±4.7%) on the Med diet,respectively. Fiber intake was also higher on the Med diet at58.0 (±8.6) g/d compared to the FF diet at 12.9 (±2.8) g/d. The

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baseline protein intake was 102.0 (±50.6) g/d. The proteinintake was 89.9 (±10.9) g/d on the FF diet and 105.0 (±11.3) g/don theMed diet. Neither diet had a significantly different dailyprotein intake compared to the baseline (P > .05). The dailyamino acid intake at baseline, and on the FF and Med diet, isshown in Supplemental Table S2.

3.2. Microbiome diversity

Using the DADA2 clustering algorithm, 775 ASVs weregenerated from the sequencing library. Most ASVs wereassigned all the way to the genus level, whereas only asmall portion was assigned to the species level (SupplementalFig. S1). Only 9 ASVs were observed in all subjects, theabundance of which constituted 13.8% of the whole commu-nity, whereas 21 ASVs were observed in 9 subjects and theirabundance constituted 29% of the community on average.There were 496 ASVs only observed in 1 subject whichaccounted for 7.3% of the community (Supplemental Fig. S2).Gut microbial diversity was not significantly altered by FF vsMed diet (Fig. 1A). The PCoA plot with weighted unifracdistance (Fig. 1B) also showed that sample points wereclustered together according to the individual subjects ratherthan study treatment. This indicates that the interindividualvariance in response was higher than the treatment effect.

The cladogram in Fig. 2 displays the phylogenetic structureof the microbial community, with each node representing ataxonomy unit at its corresponding taxonomy level. Thecladogram shows that the FF and Med diets had a significantdifferential impact on the microbial community. Compared tothe FF diet, the Med diet tended to have a more positive effecton Firmicutes and its belonging taxonomy units, whereas theFF diet had a more positive effect on Coriobacteria,Deltaproteobacteria, Porphyromonadaceae, and Rikenellaceae.

Fig. 1 – A, Alpha diversity of gut microbiome community before ancolor coded with individual subjects. B, Weighted unifrac distanceindividual subjects.

This suggests that, although the microbial diversity was notsignificantly altered differentially by diet and interindividualvariance dominated, there were some consistent changes inmicrobial characteristics across the study subjects in re-sponse to the 2 diets.

One hundred and fifty-two microbial genera were identi-fied in this population. The fold changes from baseline afterFF and Med diet are presented in Fig. 3A. Fifteen microbialgenera were altered differently by diet (P value < .05,unadjusted), whereas Butyricicoccus and Collinsella remainedstatistically significant after adjustment for multiple testing(adjusted P = .019 and .028, correspondingly). The 15 generaare presented as a heat map in Fig. 3B, where each column isthe change from baseline after either FF or Med diet (20columns in total). Among them, Butyricicoccus (Fig. 3C, P =.0001) and Lachnospiraceae_UCG-004 (Fig. 3D, P = .0103) de-creased after FF and increased after Med, whereas Collinsella (Fig. 3E, P = .0004), Parabacteroides (Fig. 3F, P = .0045), Escherichia/Shigella (Fig. 3G, P = .0316), and Bilophila (Fig. 3H, P = .0347) wereincreased after FF and decreased after Med. Microbiomecomposition for all microbes at the phylum and genus levelsand the linear mixed-model P values (unadjusted andadjusted for multiple test correction) are listed in Supple-mental Tables S3 and S4.

3.3. Biogenic amines

With the untargeted LC-MS method used in this study, 84amine molecules were identified in this population, includingcholesterol metabolites, fatty acid amides, indole derivatives,amino acids and metabolites, acylcarnitines, choline metab-olites, niacin metabolites, and xanthine metabolites. Thephylogenetic tree in Supplemental Fig. S3 shows all thebiogenic amine molecules, clustered by their structural

d after FF and Med diet (P = .576, n = 10). Lines and points areof the gut microbiome community. Points are color coded with

Fig. 2 – Cladogram of the phylogenetic structure of gut microbiome community. Each node represents a taxonomy unit at a giventaxonomic level. The taxonomic levels are kingdom, phylum, class, order, family, genus, and species from center to periphery.Nodes are colored if they are significant in the mixed linear model (P < .05, n = 10), with blue representing either increased afterMed or decreased after FF and red representing either decreased after Med or increased after FF.

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similarities and annotated with categories. Twenty-fourbiogenic amine molecules had a P value < .05 (SupplementalFig. S4), and 14 of them were significant after multiple testingadjustment (Fig. 4). Several indole derivatives including ILA(adjusted P = .003), IPA (adjusted P = .021), and IAA (adjustedP = .021) were significantly decreased after FF and increasedafter Med, whereas tryptophan (adjusted P = .014), indole-6-carboxaldehyde (adjusted P = .014) and 4-(1-piperazinyl)-1H-indole (adjusted P = .030) increased after FF and decreasedafter Med. There was not a significant change in either TMAOor its precursor choline after either FF or Med diet. However,several acylcarnitine molecules including oleoylcarnitine(adjusted P = .038) and acetylcarnitine (unadjusted P = .011,adjusted P = .052) were increased after Med but not FF.Betaine (adjusted P = .032) and hippuric acids (adjusted P =.04) significantly increased after Med but not FF. Severalindole metabolites were found to be strongly correlated witheach other, including indole-6-carboxaldehyde and trypto-phan (P < .0001, r = 0.989), 4-(1-piperazinyl)-1H-indole andtryptophan (P < .0001, r = 0.959), and IAA and IPA (P < .0001,r = 0.922) (Fig. 4F-H).

Although kynurenine was unchanged by the diets, thekynurenine to tryptophan ratio significantly decreased afterFF and increased after Med (P = .005), in line with the increasein tryptophan after FF and decrease after Med. Kynureninewas found to be negatively correlated with high-density

lipoprotein cholesterol (HDL-C) level (P = 1.57 × 10−5, R = −0.632, Fig. 5A) and positively with the Bacteroidetes toFirmicutes ratio (P = .001, R = 0.496, Fig. 5B). The Bacteroidetesto Firmicutes ratio was negatively correlated with HDL-C (P =.004, R = −0.448, Fig. 5C). HDL-C and Bacteroideswere positivelycorrelated with each other (P = 4.54 × 10−4, R = 0.535, Fig. 5D).

3.4. Bile acids

In this study, 13 bile acid molecules within the 4 categorieswere detected and quantified. Their concentration (nmol/L)and the linear mixed-model P values were listed in Supple-mental Table S5. Conjugated primary bile acids (taurocholicacid, glycocholic acid, taurochenodeoxycholic acid, andglycochenodeoxycholic acid) constituted the largest portion,44.74%, of the plasma bile acid pool, followed by unconjugatedsecondary bile acids (deoxycholic acid, LCA, andurodeoxycholic acid). Conjugated secondary(taurodeoxycholic acid, glycodeoxycholic acid,glycoursodeoxycholic acid, and glycohyodeoxycholic acid)and unconjugated primary bile acid (cholic acid andchenodeoxycholic acid) constituted 19.26% and 14.81%, re-spectively (Fig. 5E). Although none of the bile acids detectedwere altered differentially by FF vs Med diet, LCA wasnegatively correlated with HDL-C (Fig. 5F). Moreover, theprimary to secondary bile acid ratio was correlated positively

Fig. 3 – A, Scatter plot of the fold change (post against pre) of microbial genera after FF vs Med. Each point represents a microbialgenus, and it is colored in blue if it is significant in the mixed linear model (P < .05, n = 10). B, Heat map of z-score transformedchange (post-pre) of genera relative proportions after FF and Med from baseline. The P values of the 12 selected genera are smallerthan .05 (unadjusted). C-H, Box plot of microbial genera Butyricicoccus (C, P = .0001, unadjusted), Lachnospiraceae_UCG-004 (D, P =.013, unadjusted), Collinsella (E, P = .0004, unadjusted), Parabacteroides (F, P = .0045, unadjusted), Escherichia/Shigella (G, P = .0316,unadjusted), and Bilophila (H, P = .0347, unadjusted) before and after the FF andMed treatment. The 6 genera presented in box plotshave the lowest P value.

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with Bifidobacterium (P < .001, r = 0.588, Fig. 5G) and negativelywith Roseburia (P = .003, r = −0.462, Fig. 5H).

4. Discussion

The goal of this study was to determine whether anisoenergetic dietary intervention that represents realisticdietary patterns representing what would be thought to be ahealthy diet (the Med diet) and an unhealthy diet (the FF diet)can have a measurable impact on gut microbiome composi-tion and human metabolism in as short a time frame as4 days. The Med diet has been studied extensively in bothobservational and large intervention trials and has beenshown to improve cardiometabolic health and decreasedisease risk [5,9,10]. The effects of the FF diet have beenstudied primarily in observational studies, which have foundincreased markers of inflammation and increased risk forheart disease [14]. The Med diet is characterized by high

amounts of plant-based foods (fruit, vegetable, nuts, beans,seeds, and grains), olive oil, and dairy products, and low tomoderate amounts of fish and poultry [5,53]. Although fastfood is not a strictly classified dietary category, we used theterm to refer to the common American FF meal including aburger, fries, and a soft drink. Many convenience foods,including the ones chosen in this study, are enriched in fat,particularly saturated fat in the form of red meat, and simplesugars in the form of sugar-sweetened soft drinks and are lowin dietary fiber.

Diet is well known as a factor in the variance of gutmicrobiome composition and has further effects on thehost's metabolism [54]. Although the 2 dietary patternstested in this study differed drastically across multiplecategories including fat content and composition (eg, satu-rated fat at 20% vs 4% of energy of FF and Med, respectively)and fiber content (on average 12 g/d fiber vs 60 g/d fiber for FFand Med, respectively), the interindividual differences inunderlying gut microbiome composition and responses to

Fig. 4 –A, Heatmap of z-score transformed change (post-pre) of biogenic amines abundance after FF andMed diet frombaseline. B-E, Box plot of N-methyl isoleucine (B, P < .001, adjusted, n = 10), indole-3-lactic acid (C, P = .003, adjusted), tryptophan (D, P = .014,adjusted), and indole-6-carboxaldehyde (E, P = .014, adjusted) abundance before and after FF and Med diet. Points and lines arecolor coded with individual subjects. F-H, Correlation between indole-6-carboxaldehyde and tryptophan (F, Pearson R = 0.989,P < .001), 4-(1-piperazinyl)-1H-indole and tryptophan (G, Pearson R = 0.959, P < .001), and indole-3-acetic acid and indole-3-propionic acid (H, Pearson R = 0.922, P < .001).

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the diets were still the dominant source of variance. Theweighted unifrac distance showed that the microbiomecommunity of each individual is still more similar tohimself/herself rather than moving toward a new dietaryintervention cluster. However, it is observed that some fiber-consuming bacteria in the Firmicutes phylum consistentlyincreased after Med (Lachnospira, Roseburia, Butyricicocus, etc),whereas some bile-tolerant bacteria increased after FF(Bilophila, Collinsella, Alistipes, etc). A similar result wasobserved in a short-term intervention study comparing aplant-based diet rich in grains, legumes, fruits, and vegeta-bles to an animal-based diet rich in meat, egg, and cheese ona 5-day time scale [55]. On the one hand, cross-sectionalstudies evaluating reported adherence to the Med dietassessed by food frequency questionnaires found thatadherence to the Med diet was associated with both higherand lower Lachnospiraceae, Bacteroides, and Firmicutes [56,57]. On the other hand, interventional studies investigatingthe effects of individual foods enriched in the Med diet haveshown increases in fiber-consuming microbes similar tothose observed in the current study. For example, in a recent

study, walnuts were found to increase Roseburia [58], andwhole grains were found to increase Lachnospira [59]. In aMacaca fascicularis monkey study, mammary glandCoprococcus, Ruminococcus, and Lachnospiraceae increasedafter 31 months of Western diet, whereas Lactobacillusincreased after Med diet [60]. Changes in gut bacterialcomposition are associated with a variety of clinical condi-tions. A study compared patients with systematic athero-sclerotic plaques with healthy controls and found thatCollinsella was significantly increased, whereas Eubacterium,Roseburia, and Bacteroides were decreased in patients [20].Another study reported that type 2 diabetes patients haddecreased Clostridium, Faecalibacteria, and Roseburia andelevated Bacteroides and Escherichia coli [19]. Moreover, it hasbeen reported that the Bacteroidetes to Firmicutes ratio wasreduced by fiber and plant polyphenols using animal and invitro model [61,62]. Although the dietary impact onBacteroidetes to Firmicutes ratio did not reach statisticalsignificance in this study, it was negatively correlated withHLD-C, suggesting a beneficial effect of lower Bacteroidetesto Firmicutes ratio on lipid profile.

Fig. 5 – A-D, Scatter plots of kynurenine to tryptophan ratio to HDL-C (A, Pearson R = −0.681, P < .001, n = 10), Bacteroidetes toFirmicutes ratio to kynurenine (B, Pearson R = 0.496, P = .001), Bacteroidetes to Firmicutes ratio to HDL-C (C, Pearson R = −0.449, P =.004), and Bacteroides to HDL-C (D, Pearson R = 0.535, P < .001). E, The relative proportion of 4 bile acid categories (conjugated andunconjugated primary and secondary bile acids) in the plasma bile acid pool. F, Scatter plot of LCA and HDL-C (Pearson R = −0.649,P < .001). G andH, Scatter plot of Bifidobacterium (G, Pearson R = 0.588, P < .001) and Roseburia (H, Pearson R = −0.462, P = .003) vs theprimary to secondary bile acids ratio. Points are color coded with individual subjects.

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Of note, as has been observed in other studies investigat-ing the effects of diets on human gut microbiota, there wasinterindividual variability in response to the interventions.For example, although on average Bilophila was increasedafter FF and decreased after Med, there was interindividualvariability in this response. Supplemental Fig. S5A shows therelative proportion (%) of Bilophila before and after FF andMed (arranged by baseline Bilophila abundance). Subjects 6and 8 did not have any detectable levels of Bilophila at any ofthe 4 time points. Subjects 7, 4, and 2 had the lowest levels ofBilophila at baseline and the highest increase after the FF diet,possibly indicating that their Bilophila population is hyper-responsive to dietary change at this time scale. Subjects 5and 10 had the most stable levels of Bilophila, which mayindicate that their Bilophila population may not be asresponsive to changes in diet. Subject 3 had highly variablelevels of Bilophila at the 2 baseline time points (pre-FF andpre-Med), with an increase post-FF and a decrease post-Med,indicating this subject may be responsive to dietary changeand may have inconsistent dietary habits. Finally, subjects 1and 9 had the highest pre-FF Bilophila levels, whichdecreased after both the FF diet and the Med diet. Giventhat Bilophila wadsworthia was reported to bloom in responseto a diet high in saturated fat in particular [63], we examinedwhether there was a relationship between saturated fatintake and Bilophila in this group of subjects. Indeed, thescatter plot (Supplemental Fig. S5B) shows a statisticallysignificant positive correlation between baseline saturatedfat intake (kJ/d) and baseline Bilophila (r = 0.708, P = .022).Subject 9, with the highest baseline Bilophila abundance, alsohad the highest saturated fat intake at baseline, which washigher than the amount of saturated fat contained in thedaily FF meals (712.52 kcal/d at baseline vs 358.65 kcal/d onthe FF diet), and in this subject, Bilophila decreased after FF.This is an intriguing finding, which indicates the possibilitythat certain individuals may be more susceptible to blooms

of particular bacterial species in response to diet, whereasothers may not be responsive, while still others may notharbor a particular species at all, and therefore, diet has noeffect on that particular component of the microbiome. Alarger study powered to detect differences in responseacross the different responder types and different back-ground diets is needed to investigate the relationshipsbetween dietary saturated fat intake and changes in Bilophilain the gut and whether certain individuals are moresusceptible to the deleterious effects of blooms in thisparticular bacterium, which has been shown to induceinflammation and colitis in susceptible mice [63].

We did not find any statistically significant changes inTMAO following either the FF or Med diet. Whereas the FF dietwas enriched in carnitine from red meat, which is a precursorfor microbial TMA production, the Med diet contained fish(tuna on the lunch salad), which is a direct source of TMA. Ithas been reported previously that fish intake increasespostprandial plasma TMAO concentration up to 6 hours [64];however, it is not known whether 10- to 12-hour overnightfasted blood would reflect the transient postprandial increasedue to direct TMA absorption or not. Studies have shown thathigher adherence to the Mediterranean diet in the long termis associated with lower concentrations of TMAO, with sex-specific differences in both dietary adherence and TMAOlevels [65]. Chronic red meat consumption has been shown tobe associated with increased plasma TMAO concentrations [66,67]. However, none of these studies investigated the effectsof short-term diet on TMAO concentrations. Given the highinterindividual variability in normal healthy adult gutmicrobiome composition and the multiple bacterial generathat can contribute to TMAO production, it is possible thisstudy was not powered to detect changes in TMAO. It is alsopossible that changes in TMAO concentrations in the blood inresponse to dietary change are not evident at the 4-day timescale on the current study.

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Betaine was significantly increased after the Med diet. Thismay be because betaine is enriched inwhole grains and cereals[68], which were enriched on the Med diet arm. We observedthat several acylcarnitine species, including acetylcarnitine,oleoyl-L-carnitine, and hydroxybutyrylcarnitine but not freecarnitine, significantly increased after Med, whereas theydecreased or were not changed after FF. Both short-chain andlong-chain acylcarnitines increase in the fasting state, whereasfree carnitine decreases [69,70]. The elevation of acylcarnitinesduring fasting is attenuated in obese individuals compared tohealthy individuals [71]. There were no correlations betweengutmicrobiota and acylcarnitines in this study; thus, theymorelikely reflect changes in host metabolism in response todifferences in fat content and composition. We also observedan increase in hippuric acid after our fiber-rich Med diet.Hippuric acid is a polyphenolic compound related to fiberintake and gut fermentation, and its serum level increased afterresistant starch plus whole grain consumption in rats [72].

In our study, IAA, IPA, and ILA were significantlyincreased after Med and decreased after FF, whereastryptophan, indole-6-carboxaldehyde, and 4-(1-piperazinyl)-1H-indole increased after FF and decreasedafter Med. The latter 2 molecules were also highlycorrelated with tryptophan. The daily amino acid intakeson average were not statistically different between thedietary treatments; thus, the changes in these tryptophanmetabolites were likely caused by factors other than solelythe dietary tryptophan content. The absorption and bio-availability of different amino acids differed betweendifferent protein sources [73]; thus, it is possible that thetryptophan from red meat was more readily absorbed thantryptophan from sources on the Med diet. Both IAA and IPAincrease the anti-inflammatory cytokine interleukin-10after lipopolysaccharide stimulation, whereas IPA de-creases the proinflammatory cytokine tumor necrosisfactor-α [74]. IPA has protective effects on neuronal cellsagainst oxidative damage and the lethal effect of amyloidβ-protein [75]. The kynurenine to tryptophan ratio wassignificantly decreased after FF and increased after Med.This could be because beef, which was enriched on the FFdiet arm, is a rich source of tryptophan. The kynurenine totryptophan ratio was also negatively correlated with HDL-C, which suggests there may be a relationship betweentryptophan metabolism, the IDO enzyme, and lipoproteinmetabolism. A negative relationship between IDO activitymeasured as kynurenine to tryptophan ratio and HDL-Cwas observed previously [76].

It has been reported that the ratio of secondary bile acid toprimary bile acid is increased in CVD patients [28]. Secondarybile acids were increased after high-fat diet in mice after8 weeks [77] and in humans after 6 months [78]. We did notobserve a change in bile acids in this study. This suggests thatbile acids are not affected even by significant changes in fatcontent and composition at this time scale.

In conclusion, we accept our hypothesis that diets withvery different macronutrient composition are able to changegut microbiome composition and metabolites in a very shortperiod. The small sample size is a weakness of this study.However, because this study used a crossover study design,multiple observations were obtained from each subject,

which correct for the interindividual variation by using everypatient as their own control, thus enabling us to detectdifferences with small effect sizes [79]. Thus, we demon-strated that, even in as short a time frame as 4 days, a dietarypattern low in fiber and high in sugar and saturated fat shiftsthe microbiome toward a profile that has been foundrepeatedly to be associated with chronic metabolic diseases,whereas a dietary pattern enriched in fiber, MUFA, and PUFAshifts the microbiome and plasma microbial metabolitestoward a profile that has been associated with beneficialhealth effects.

Supplemental materials

Supplemental materials

Supplemental tables and figures to this article can be foundonline at https://doi.org/10.1016/j.nutres.2020.03.005.

Acknowledgment

The project described was supported by the UC Davis HellmanFellows Program; the West Coast Metabolomics Center pilotgrant through grant DK097154; the National Center forAdvancing Translational Sciences, National Institutes ofHealth, UC Davis Clinical and Translational Sciences CenterPilot Grant Program through grant UL1 TR001860; and theUSDA National Institute of Food and Agriculture, Hatchproject CA-D-NUT-2242-H. The content is solely the respon-sibility of the authors and does not necessarily represent theofficial views of the National Institutes of Health. We alsothank David Mills and Karen Kalanetra for technical assis-tance. None of the authors have any conflict of interest.

R E F E R E N C E S

[1] Fung TT, Rimm EB, Spiegelman D, Rifai N, Tofler GH, WillettWC, et al. Association between dietary patterns and plasmabiomarkers of obesity and cardiovascular disease risk. Am JClin Nutr 2001;73:61–7.

[2] Schulze MB, Hoffmann K, Manson JE, Willett WC, Meigs JB,Weikert C, et al. Dietary pattern, inflammation, and inci-dence of type 2 diabetes in women. Am J Clin Nutr 2005;82:675–84 [quiz 714–5].

[3] Panagiotakos DB, Pitsavos C, Arvaniti F, Stefanadis C.Adherence to the Mediterranean food pattern predicts theprevalence of hypertension, hypercholesterolemia, diabetesand obesity, among healthy adults; the accuracy of themeddietscore. Prev Med 2007;44:335–40.

[4] Estruch R. Anti-inflammatory effects of the Mediterraneandiet: the experience of the predimed study. Proc Nutr Soc2010;69:333–40.

[5] Estruch R, Ros E, Salas-Salvadó J, Covas M-I, Corella D, Arós F,et al. Primary prevention of cardiovascular disease with aMediterranean diet supplemented with extra-virgin olive oilor nuts. N Engl J Med 2018;378:e34.

71N U T R I T I O N R E S E A R C H 7 7 ( 2 0 2 0 ) 6 2 – 7 2

[6] Martínez-Lapiscina EH, Clavero P, Toledo E, Estruch R, Salas-Salvadó J, San Julián B, et al. Mediterranean diet improvescognition: the PREDIMED-NAVARRA randomised trial. JNeurol Neurosurg Psychiatry 2013;84:1318–25.

[7] Salas-Salvadó J, Bulló M, Babio N, Martínez-González MÁ,Ibarrola-Jurado N, Basora J, et al. Reduction in the incidenceof type 2 diabetes with the Mediterranean diet: results of thePREDIMED-Reus nutrition intervention randomized trial.Diabetes Care 2011;34:14–9.

[8] Serra-Majem L, Roman B, Estruch R. Scientific evidence ofinterventions using the Mediterranean diet: a systematicreview. Nutr Rev 2006;64:S27–47.

[9] de Lorgeril M, Salen P, Martin JL, Monjaud I, Delaye J, MamelleN. Mediterranean diet, traditional risk factors, and the rate ofcardiovascular complications after myocardial infarction:final report of the Lyon diet heart study. Circulation 1999;99:779–85.

[10] Esposito K, Marfella R, Ciotola M, Di Palo C, Giugliano F,Giugliano G, et al. Effect of a Mediterranean-style diet onendothelial dysfunction and markers of vascular inflamma-tion in the metabolic syndrome: a randomized trial. JAMA2004;292:1440–6.

[11] Bahadoran Z, Mirmiran P, Golzarand M, Hosseini-Esfahani F,Azizi F. Fast food consumption in iranian adults; dietaryintake and cardiovascular risk factors: Tehran lipid andglucose study. Arch Iran Med 2012;15:346–51.

[12] Deng FE, Shivappa N, Tang Y, Mann JR, Hebert JR. Associationbetween diet-related inflammation, all-cause, all-cancer, andcardiovascular disease mortality, with special focus on predi-abetics: findings from NHANES III. Eur J Nutr 2017;56:1085–93.

[13] Garcia-Arellano A, Ramallal R, Ruiz-Canela M, Salas-SalvadóJ, Corella D, Shivappa N, et al. Dietary inflammatory indexand incidence of cardiovascular disease in the PREDIMEDstudy. Nutrients 2015;7:4124–38.

[14] Nettleton JA, Steffen LM, Mayer-Davis EJ, Jenny NS, Jiang R,Herrington DM, et al. Dietary patterns are associated withbiochemical markers of inflammation and endothelial acti-vation in the Multi-Ethnic Study of Atherosclerosis (MESA). NEngl J Med 2006;83:1369–79.

[15] O’Neil A, Shivappa N, Jacka FN, Kotowicz MA, Kibbey K,Hebert JR, et al. Pro-inflammatory dietary intake as a riskfactor for CVD in men: a 5-year longitudinal study. Br J Nutr2015;114:2074–82.

[16] Fung TT, Willett WC, Stampfer MJ, Manson JE, Hu FB. Dietarypatterns and the risk of coronary heart disease in women.Arch Intern Med 2001;161:1857–62.

[17] Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL,Duncan A, Ley RE, et al. A core gut microbiome in obese andlean twins. Nature 2009;457:480–4.

[18] Ley RE, Turnbaugh PJ, Klein S, Gordon JI. Microbial ecology:human gut microbes associated with obesity. Nature 2006;444:1022–3.

[19] Qin J, Li Y, Cai Z, Li S, Zhu J, Zhang F, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes.Nature 2012;490:55–60.

[20] Karlsson FH, Fåk F, Nookaew I, Tremaroli V, Fagerberg B,Petranovic D, et al. Symptomatic atherosclerosis is associatedwith an altered gut metagenome. Nat Commun 2012;3:1245.

[21] Luedde M, Winkler T, Heinsen F-A, Rühlemann MC,Spehlmann ME, Bajrovic A, et al. Heart failure is associatedwith depletion of core intestinal microbiota. ESC Heart Fail2017;4:282–90.

[22] Kummen M, Mayerhofer CCK, Vestad B, Broch K, Awoyemi A,Storm-Larsen C, et al. Gut microbiota signature in heartfailure defined from profiling of 2 independent cohorts. J AmColl Cardiol 2018;71:1184–6.

[23] Wu GD, Chen J, Hoffmann C, Bittinger K, Chen Y-Y, KeilbaughSA, et al. Linking long-term dietary patterns with gutmicrobial enterotypes. Science 2011;334:105–8.

[24] Duncan SH, Belenguer A, Holtrop G, Johnstone AM, Flint HJ,Lobley GE. Reduced dietary intake of carbohydrates by obesesubjects results in decreased concentrations of butyrate andbutyrate-producing bacteria in feces. Appl Environ Microbiol2007;73:1073–8.

[25] Turnbaugh PJ, Ridaura VK, Faith JJ, Rey FE, Knight R, GordonJI. The effect of diet on the human gut microbiome: ametagenomic analysis in humanized gnotobiotic mice. SciTransl Med 2009;1:6ra14.

[26] Wang Z, Klipfell E, Bennett BJ, Koeth R, Levison BS, Dugar B,et al. Gut flora metabolism of phosphatidylcholine promotescardiovascular disease. Nature 2011;472:57–63.

[27] Tang WHW, Wang Z, Levison BS, Koeth RA, Britt EB, Fu X,et al. Intestinal microbial metabolism of phosphatidylcholineand cardiovascular risk. N Engl J Med 2013;368:1575–84.

[28] Mayerhofer CCK, Ueland T, Broch K, Vincent RP, Cross GF,Dahl CP, et al. Increased secondary/primary bile acid ratio inchronic heart failure. J Card Fail 2017;23:666–71.

[29] Gao J, Xu K, Liu H, Liu G, Bai M, Peng C, et al. Impact of the gutmicrobiota on intestinal immunity mediated by tryptophanmetabolism. Front Cell Infect Microbiol 2018;8:13.

[30] Peters JC. Tryptophan nutrition and metabolism: an over-view. Adv Exp Med Biol 1991;294:345–58.

[31] Badawy AA-B. Tryptophan metabolism, disposition andutilization in pregnancy. Biosci Rep 2015;35:e00261.

[32] Campbell BM, Charych E, Lee AW, Möller T. Kynurenines incns disease: regulation by inflammatory cytokines. FrontNeurosci 2014;8:12.

[33] Zhang LS, Davies SS. Microbial metabolism of dietarycomponents to bioactive metabolites: opportunities for newtherapeutic interventions. Genome Med 2016;8:46.

[34] Zelante T, Iannitti RG, Cunha C, De Luca A, Giovannini G,Pieraccini G, et al. Tryptophan catabolites from microbiotaengage aryl hydrocarbon receptor and balance mucosalreactivity via interleukin-22. Immunity 2013;39:372–85.

[35] Venkatesh M, Mukherjee S, Wang H, Li H, Sun K, Benechet AP,et al. Symbiotic bacterial metabolites regulate gastrointesti-nal barrier function via the xenobiotic sensor PXR and Toll-like receptor 4. Immunity 2014;41:296–310.

[36] Lamas B, Richard ML, Leducq V, Pham H-P, Michel M-L, DaCosta G, et al. CARD9 impacts colitis by altering gutmicrobiota metabolism of tryptophan into aryl hydrocarbonreceptor ligands. Nat Med 2016;22:598–605.

[37] Zhu C, Sawrey-Kubicek L, Beals E, Hughes RL, Rhodes CH,Sacchi R, et al. The hdl lipidome is widely remodeled by fastfood versus Mediterranean diet in 4 days. Metabolomics 2019;15:114.

[38] Zhu C, Wong M, Li Q, Sawrey-Kubicek L, Beals E, Rhodes CH,et al. Site-specific glycoprofiles of HDL-associated apoe arecorrelated with HDL functional capacity and unaffected byshort-term diet. J Proteome Res 2019;18:3977–84.

[39] Frese SA, Parker K, Calvert CC, Mills DA. Diet shapes the gutmicrobiome of pigs during nursing and weaning. Microbiome2015;3:28.

[40] Sohn K, Kalanetra KM, Mills DA, Underwood MA. Buccaladministration of human colostrum: impact on the oralmicrobiota of premature infants. J Perinatol 2016;36:106–11.

[41] La Frano MR, Fahrmann JF, Grapov D, Fiehn O, Pedersen TL,Newman JW, et al. Metabolic perturbations of postnatalgrowth restriction and hyperoxia-induced pulmonary hy-pertension in a bronchopulmonary dysplasia model. Meta-bolomics 2017;13:32.

[42] Matyash V, Liebisch G, Kurzchalia TV, Shevchenko A,Schwudke D. Lipid extraction by methyl-tert-butyl ether forhigh-throughput lipidomics. J Lipid Res 2008;49:1137–46.

[43] Barupal DK, Zhang Y, Shen T, Fan S, Roberts BS, Fitzgerald P,et al. A comprehensive plasma metabolomics dataset for acohort of mouse knockouts within the international mousephenotyping consortium. Metabolites 2019;9:101.

72 N U T R I T I O N R E S E A R C H 7 7 ( 2 0 2 0 ) 6 2 – 7 2

[44] Aronesty E. Comparison of sequencing utility programs.Open Bioinforma J 2013;7:1–8.

[45] Hannon GJ. FASTX-toolkit. http://hannonlab.cshl.edu/fastx_toolkit; 2010.

[46] Andrews S, Krueger F, Segonds-Pichon A, Biggins L. KruegerC. FastQC: Wingett S; 2012. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/.

[47] Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA,Holmes SP. DADA2: high-resolution sample inference fromillumina amplicon data. Nat Methods 2016;13:581–3.

[48] Callahan BJ, McMurdie PJ, Holmes SP. Exact sequencevariants should replace operational taxonomic units inmarker-gene data analysis. ISME J 2017;11:2639–43.

[49] Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P,et al. The SILVA ribosomal RNA gene database project:improved data processing and web-based tools. NucleicAcids Res 2013;41:D590–6.

[50] Bolyen E, Rideout JR, R Dillon M, Bokulish N, Abnet C, Al-Ghalith G, et al.. QIIME 2: reproducible, interactive, scalable,and extensible microbiome data science; 2018.

[51] McMurdie PJ, Holmes S. Phyloseq: an R package for repro-ducible interactive analysis and graphics of microbiomecensus data. PloS One 2013;8:e61217.

[52] RitchieME, Phipson B,WuD,Hu Y, LawCW, ShiW, et al. Limmapowers differential expression analyses for RNA-sequencingand microarray studies. Nucleic Acids Res 2015;43:e47.

[53] Willett WC, Sacks F, Trichopoulou A, Drescher G, Ferro-LuzziA, Helsing E, et al. Mediterranean diet pyramid: a culturalmodel for healthy eating. Am J Clin Nutr 1995;61:1402S–6S.

[54] Chassaing B, Vijay-Kumar M, Gewirtz AT. How diet canimpact gut microbiota to promote or endanger health. CurrOpin Gastroenterol 2017;33:417–21.

[55] David LA, Maurice CF, Carmody RN, Gootenberg DB, ButtonJE, Wolfe BE, et al. Diet rapidly and reproducibly alters thehuman gut microbiome. Nature 2014;505:559–63.

[56] De Filippis F, Pellegrini N, Vannini L, Jeffery IB, La Storia A,Laghi L, et al. High-level adherence to a Mediterranean dietbeneficially impacts the gut microbiota and associatedmetabolome. Gut 2016;65:1812–21.

[57] Gutiérrez-Díaz I, Fernández-Navarro T, Sánchez B, MargollesA, González S. Mediterranean diet and faecal microbiota: atransversal study. Food Funct 2016;7:2347–56.

[58] Holscher HD, Guetterman HM, Swanson KS, An R, MatthanNR, Lichtenstein AH, et al. Walnut consumption alters thegastrointestinal microbiota, microbially derived secondarybile acids, and health markers in healthy adults: a random-ized controlled trial. J Nutr 2018;148:861–7.

[59] Vanegas SM, Meydani M, Barnett JB, Goldin B, Kane A,Rasmussen H, et al. Substituting whole grains for refinedgrains in a 6-wk randomized trial has a modest effect on gutmicrobiota and immune and inflammatory markers ofhealthy adults. Am J Clin Nutr 2017;105:635–50.

[60] Shively CA, Register TC, Appt SE, Clarkson TB, Uberseder B,Clear KYJ, et al. Consumption of Mediterranean versusWestern diet leads to distinct mammary gland microbiomepopulations. Cell Rep 2018;25:47–56.e3.

[61] Molist F, Manzanilla EG, Pérez JF, Nyachoti CM. Coarse, but notfinely ground, dietary fibre increases intestinal Firmicutes:Bacteroidetes ratio and reduces diarrhoea induced by experi-mental infection in piglets. Br J Nutr 2012;108:9–15.

[62] Xue B, Xie J, Huang J, Chen L, Gao L, Ou S, et al. Plantpolyphenols alter a pathway of energy metabolism byinhibiting fecal bacteroidetes and firmicutes in vitro. FoodFunct 2016;7:1501–7.

[63] Devkota S, Wang Y, Musch MW, Leone V, Fehlner-Peach H,Nadimpalli A, et al. Dietary-fat-induced taurocholic acidpromotes pathobiont expansion and colitis in Il10−/− mice.Nature 2012;487:104–8.

[64] Cho CE, Taesuwan S, Malysheva OV, Bender E, Tulchinsky NF,Yan J, et al. Trimethylamine-n-oxide (TMAO) response toanimal source foods varies among healthy young men and isinfluenced by their gut microbiota composition: a random-ized controlled trial. Mol Nutr Food Res 2017;61:https://doi.org/10.1002/mnfr.201600324.

[65] Barrea L, Annunziata G, Muscogiuri G, Laudisio D, Di SommaC, Maisto M, et al. Trimethylamine n-oxide, Mediterraneandiet, and nutrition in healthy, normal-weight adults: also amatter of sex? Nutrition 2019;62:7–17.

[66] Koeth RA, Lam-Galvez BR, Kirsop J, Wang Z, Levison BS, Gu X,et al. L-carnitine in omnivorous diets induces an atherogenicgutmicrobial pathway in humans. J Clin Invest 2019;129:373–87.

[67] Wang Z, Bergeron N, Levison BS, Li XS, Chiu S, Jia X, et al. Impactof chronic dietary redmeat, whitemeat, or non-meat protein ontrimethylamine n-oxide metabolism and renal excretion inhealthy men and women. Eur Heart J 2019;40:583–94.

[68] Godos J, Federico A, Dallio M, Scazzina F. Mediterranean dietand nonalcoholic fatty liver disease: molecular mechanismsof protection. Int J Food Sci Nutr 2017;68:18–27.

[69] Frohlich J, Seccombe DW, Hahn P, Dodek P, Hynie I. Effect offasting on free and esterified carnitine levels in humanserum and urine: correlation with serum levels of free fattyacids and beta-hydroxybutyrate. Metabolism 1978;27:555–61.

[70] Hoppel CL, Genuth SM. Carnitine metabolism in normal-weight and obese human subjects during fasting. Am JPhysiol 1980;238:E409–15.

[71] Hoppel CL, Genuth SM. Urinary excretion of acetylcarnitineduring human diabetic and fasting ketosis. Am J Physiol 1982;243:E168–72.

[72] Vitaglione P, Mennella I, Ferracane R, Goldsmith F, Guice J,Page R, et al. Gut fermentation induced by a resistant starchrich whole grain diet explains serum concentration ofdihydroferulic acid and hippuric acid in a model of ZDF rats. JFunct Foods 2019;53:286–91.

[73] Gorissen SHM, Trommelen J, Kouw IWK, Holwerda AM,Pennings B, Groen BBL, et al. Protein type, protein dose, andage modulate dietary protein digestion and phenylalanineabsorption kinetics and plasma phenylalanine availability inhumans. J Nutr 2020:nxaa024.

[74] Wlodarska M, Luo C, Kolde R, d'Hennezel E, Annand JW, HeimCE, et al. Indoleacrylic acid produced by commensalpeptostreptococcus species suppresses inflammation. CellHost Microbe 2017;22:25–37.e6.

[75] Chyan YJ, Poeggeler B, Omar RA, Chain DG, Frangione B,Ghiso J, et al. Potent neuroprotective properties against thealzheimer beta-amyloid by an endogenous melatonin-re-lated indole structure, indole-3-propionic acid. J Biol Chem1999;274:21937–42.

[76] Pertovaara M, Raitala A, Juonala M, Lehtimäki T, Huhtala H,Oja SS, et al. Indoleamine 2,3-dioxygenase enzyme activitycorrelates with risk factors for atherosclerosis: the Cardio-vascular Risk in Young Finns Study. Clin Exp Immunol 2007;148:106–11.

[77] Murakami Y, Tanabe S, Suzuki T. High-fat diet-inducedintestinal hyperpermeability is associated with increased bileacids in the large intestine of mice. J Food Sci 2016;81:H216–22.

[78] Wan Y, Yuan J, Li J, Li H, Zhang J, Tang J, et al. Unconjugatedand secondary bile acid profiles in response to higher-fat,lower-carbohydrate diet and associated with related gutmicrobiota: a 6-month randomized controlled-feeding trial.Clin Nutr 2019;S0261–5614(19):30091–3.

[79] Senn S. Introduction. In:. Cross-over trials in clinical re-search, JohnWiley & Sons, Ltd; 2003, pp. 1–16. https://doi.org/10.1002/0470854596.ch1.