Hippurate as a metabolomic marker of gut microbiome diversity: Modulation by diet and relationship to metabolic syndrome

www.nature.com/scientificreports OPEN Received: 18 May 2017 Accepted: 25 September 2017 Published: xx xx xxxx Hippurate as a metabolomic marker of gut microbiome diversity: Modulation by diet and relationship to metabolic syndrome Tess Pallister1, Matthew A. Jackson 1, Tiphaine C. Martin1, Jonas Zierer 1,2, Amy Jennings3, Robert P. Mohney4, Alexander MacGregor3, Claire J. Steves1, Aedin Cassidy3, Tim D. Spector1 & Cristina Menni1 Reduced gut microbiome diversity is associated with multiple disorders including metabolic syndrome (MetS) features, though metabolomic markers have not been investigated. Our objective was to identify blood metabolite markers of gut microbiome diversity, and explore their relationship with dietary intake and MetS. We examined associations between Shannon diversity and 292 metabolites profiled by the untargeted metabolomics provider Metabolon Inc. in 1529 females from TwinsUK using linear regressions adjusting for confounders and multiple testing (Bonferroni: P < 1.71 × 10−4). We replicated the top results in an independent sample of 420 individuals as well as discordant identical twin pairs and explored associations with self-reported intakes of 20 food groups. Longitudinal changes in circulating levels of the top metabolite, were examined for their association with food intake at baseline and with MetS at endpoint. Five metabolites were associated with microbiome diversity and replicated in the independent sample. Higher intakes of fruit and whole grains were associated with higher levels of hippurate cross-sectionally and longitudinally. An increasing hippurate trend was associated with reduced odds of having MetS (OR: 0.795[0.082]; P = 0.026). These data add further weight to the key role of the microbiome as a potential mediator of the impact of dietary intake on metabolic status and health. The diversity of bacteria in the human gut, both in term of the number of different microbes and the comparative evenness of their abundances, is associated with higher abundance of beneficial bacteria and is emerging as an important indicator of health1–6. Lower alpha-diversity (intra-individual diversity) is suggestive of dysbiosis (microbial imbalance) and has been associated with metabolic syndrome features6. Microbes transform food- and host-derived metabolites, such as bile acids and fibre7, and polyphenols8. The profound contribution of the gut microbiome to metabolism has been shown in conventional versus germ-free mice, where conventional mice exhibited elevated blood levels of indole-containing compounds (e.g. indoxyl sulfate and indole-3-propionic acid), serotonin, sulfated compounds (e.g. phenyl and p-cresol sulfate), and glycine-conjugated compounds (hippuric acid, cinnamoylglycine and phenylpropionylglycine)9. Many of the above metabolites are food-derived; therefore, merging microbiome and metabolomics approaches with studies which capture habitual intake is the logical next step for improving our understanding of the complex interplay between diet, the microbiome and metabolic disease. To date there are relatively few short-term human dietary intervention studies incorporating the microbiome and metabolome. It has been shown that daily consumption of 40 g of dark chocolate for 2 weeks altered urinary output of gut microbial metabolites, increasing hippurate and methylamines, and reducing p-cresol sulfate10. Moreover, a recent randomized controlled pilot study showed feeding 30 g/d of heat-stabilized rice bran for 28 1 Department of Twin Research and Genetic Epidemiology, King’s College London, London, SE1 7EH, UK. 2Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany. 3Department of Nutrition & Preventive Medicine, Norwich Medical School, University of East Anglia, Norwich, UK. 4Metabolon Inc., Research Triangle Park, NC, 27709, USA. Correspondence and requests for materials should be addressed to C.M. (email: ) Scientific Reports | 7: 13670 | DOI:10.1038/s41598-017-13722-4 1 www.nature.com/scientificreports/ Discovery (n = 1529) Validation (n = 420)2 Metabolite Superpathway Sub-pathway beta (SE) P beta (SE) P Hippurate Xenobiotics Benzoate metabolism 0.230 (0.040) 3.72 × 10−8 0.238 (0.072) 0.001* p-cresol sulfate Amino acid Phenylalanine & tyrosine 0.200 (0.040) metabolism 9.90 × 10−8 0.179 (0.063) 0.005* phenol sulfate Amino acid Phenylalanine & tyrosine −0.200 (0.040) metabolism 5.82 × 10−7 −0.121 (0.063) 0.055 Phenylacetylglutamine Amino acid Phenylalanine & tyrosine 0.180 (0.040) metabolism 5.21 × 10−6 0.195 (0.062) 0.002* 3-phenylpropionate (hydrocinnamate) Amino acid Phenylalanine & tyrosine 0.160 (0.040) metabolism 3.43 × 10−5 0.185 (0.084) 0.028* 4-ethylphenylsulfate Xenobiotics Benzoate metabolism 0.190 (0.050) 5.12 × 10 0.062 (0.081) 0.441 Hyodeoxycholate Lipid Bile acid metabolism −0.190 (0.050) 8.66 × 10−5 −0.215 (0.089) 0.016* Indolepropionate Amino acid Tryptophan metabolism 0.140 (0.040) 9.20 × 10−5 0.093 (0.083) 0.262 −5 Table 1. Metabolites associated with Shannon diversity in the discovery sample (following backward stepwise linear regression) and in the validation sample1. *Statistically significant: P < 0.05. 1A linear regression was performed using Shannon diversity to predict levels of 292 metabolites adjusting for age, BMI, batch effects (and sex in the validation) and family relatedness. 2Statistically significant (P < 1.71 × 10−4) associations from the discovery group were validated in the validation group. days increased abundance of 11 operational taxonomic units (OTUs), and elevated faecal levels of secondary bile acids and metabolites derived from microbial modifications of plant-derived components11. To our knowledge, the role of a diverse gut microbiome in humans as a potential mediator of the impact of dietary intake on metabolic status and health has not been robustly addressed. Therefore, the aims of this study are: to (i) identify blood metabolites correlated with gut microbiome diversity, (ii) examine the impact of food intake on these metabolites and to, (iii) examine if longitudinal changes in these metabolites are predictive of future development of the metabolic syndrome. Results Supplementary Table S1 provides the study population characteristics and subject numbers. Microbiome diversity metabolomics associations. Eight metabolites significantly correlated with Shannon diversity in the discovery sample after adjusting for multiple testing (Table 1). These include hippurate, p-cresol sulfate, phenylacetylglutamine, 4-ethylphenolsulfate, indolepropionate and 3-phenylpropionate which were positively associated; and hyodeoxycholate and phenol sulphate which were negatively associated. Five metabolites were validated in the replication sample (Table 1). These include hippurate, p-cresol sulfate, phenylacetylglutamine, 3-phenylpropionate, and hyodeoxycholate. Higher circulating level (...truncated)