The gut microbiome drives inter- and intra-individual differences in metabolism of bioactive small molecules

Abstract

The origin of inter-individual variability in the action of bioactive small molecules from the diet is poorly understood and poses a substantial obstacle to harnessing their potential for attenuating disease risk. Epidemiological studies show that coffee lowers the risk of developing type 2 diabetes, independently of caffeine, but since coffee is a complex matrix, consumption gives rise to different classes of metabolites in vivo which in turn can affect multiple related pathways in disease development. We quantified key urinary coffee phenolic acid metabolites repeated three times in 36 volunteers, and observed the highest inter- and intra-individual variation for metabolites produced by the colonic microbiome. Notably, a urinary phenolic metabolite not requiring the action of the microbiota was positively correlated with fasting plasma insulin. These data highlight the role of the gut microbiota as the main driver of both intra- and inter-individual variation in metabolism of dietary bioactive small molecules.

Figure 1

Sites of absorption and metabolism of ingested phenolic acids. Compounds indicated in boxes were measured in this study. 5-CQA is hydrolysed by pancreatic esterase(s) fivefold faster than 3 or 4-CQA, whereas 3-CQA is hydrolysed by brush border esterase(s) tenfold more rapidly than 5-CQA. FQAs are not substrates for pancreatic enzymes. Both human pancreatic and brush border enzymes have relatively low activities compared to the gut microbiota esterases, which act efficiently on all chlorogenic acids. CGAs are partially hydrolysed in the small intestine, and lead to metabolites such as FA-4′-sulfate in the blood after 1–2 h^,. The majority of CGAs pass along the small intestine unmodified and, following microbial hydrolysis in the colon, are converted to metabolites such as 3-(4′-hydroxy-3′-methoxyphenyl)propanoic acid (DHFA), 3-(3′,4′-dihydroxyphenyl)propanoic acid-3′-sulfate (DHCA-3′-sulfate) and 3-(3′-methoxyphenyl)propanoic acid-4′-sulfate (DHFA-4′-sulfate) which appear in the blood after > 4 h. These lower molecular weight compounds are ultimately excreted in the urine, together with glycine conjugates.

Figure 2

Relationship and overlap between metabolism of phenolic acids and aminothiols. Enzymes shown by circled numbers: 1. vitamin B6-dependent cystathionine β-synthase; 2. cystathionine γ –lyase; 3. γ-glutamylcysteine synthetase; 4. GSH synthetase; 5. γ-glutamyltransferase; 6. dipeptidase; 7. Met S-adenosyltransferase; 8. glycine N-methyltransferase; 9. S-adenosylhomocysteine hydrolase; 10. GSH peroxidase; 11. sulfotransferase SULT1A1; 12. sulfotransferase SULT1E1; 13. Catechol-O-methyl transferase, COMT; 14. enzymes from gut microbiota; 15. glycine N-acetyltransferase, GLYAT. Solid arrows show direct conversion, dash arrows show multi-step conversions. Met = methionine; Glu = glutamate; GSH = L-glutathione; GSSG = oxidized GSH. Compounds with names in boxes were measured in this study.

Figure 3

Design of the intervention study. HAQ, health assessment questionnaire; FFQ, food frequency questionnaire.

Figure 4

Inter-individual and temporal intra-individual variation in excretion of metabolites from coffee. (A) Excretion of six major urinary metabolites over 36 h after coffee consumption (n = 38) showing inter-individual variation. Values in grey boxes above columns indicate coefficient of variation (CV, %). (B) Excretion of six major urinary metabolites over 36 h after coffee consumption (n = 38) showing the CV of intra-individual metabolite concentrations, with error bars indicating standard deviation of CV values.* < 0.05; ** < 0.01; *** < 0.001, from student paired t-test.