Abstract

Rates of colon cancer are much higher in African Americans (65:100,000) than in rural South Africans (<5:100,000). The higher rates are associated with higher animal protein and fat, and lower fibre consumption, higher colonic secondary bile acids, lower colonic short-chain fatty acid quantities and higher mucosal proliferative biomarkers of cancer risk in otherwise healthy middle-aged volunteers. Here we investigate further the role of fat and fibre in this association. We performed 2-week food exchanges in subjects from the same populations, where African Americans were fed a high-fibre, low-fat African-style diet and rural Africans a high-fat, low-fibre western-style diet, under close supervision. In comparison with their usual diets, the food changes resulted in remarkable reciprocal changes in mucosal biomarkers of cancer risk and in aspects of the microbiota and metabolome known to affect cancer risk, best illustrated by increased saccharolytic fermentation and butyrogenesis, and suppressed secondary bile acid synthesis in the African Americans.

Additional access options:

Already a subscriber?  Log in  now or  Register  for online access.

References

  1. 1.

    et al. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int. J. Cancer 127, 2893–2917 (2010).

  2. 2.

    Cancer among Japanese migrants to Hawaii: gene-environment interactions. Rev. Epidemiol. Sant. Publiq. 40, 425–430 (1992).

  3. 3.

    , , & Fruits and vegetables: updating the epidemiologic evidence for the WCRF/AICR lifestyle recommendations for cancer prevention. Cancer Treat. Res. 159, 35–50 (2014).

  4. 4.

    et al. Why do African Americans get more colon cancer than Native Africans? J. Nutr. 137, (1 Suppl): 175S–182S (2007).

  5. 5.

    et al. Diet, microbiota, and microbial metabolites in colon cancer risk in rural Africans and African Americans. Am. J. Clin. Nutr. 98, 111–120 (2013).

  6. 6.

    et al. Pattern of epithelial cell proliferation in colorectal mucosa of normal subjects and of patients with adenomatous polyps or cancer of the large bowel. Cancer Res. 48, 4121–4126 (1988).

  7. 7.

    & Microbial induction of immunity, inflammation, and cancer. Front. Physiol. 1, 168 (2011).

  8. 8.

    & The microbiota and its metabolites in colonic mucosal health and cancer risk. Nutr. Clin. Prac. 27, 624–635 (2012).

  9. 9.

    et al. The microbial metabolites, short-chain fatty acids, regulate colonic Treg cell homeostasis. Science 341, 569–573 (2013).

  10. 10.

    et al. Carcinogenicity of deoxycholate, a secondary bile acid. Arch. Toxicol. 85, 863–871 (2011).

  11. 11.

    et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).

  12. 12.

    et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105–108 (2011).

  13. 13.

    , , & Diversity of human colonic butyrate-producing bacteria revealed by analysis of the butyryl-CoA:acetate CoA-transferase gene. Environ. Microbiol. 12, 304–314 (2010).

  14. 14.

    , & Sulphate reducing bacteria and hydrogen metabolism in the human large intestine. Gut 34, 437–439 (1993).

  15. 15.

    et al. Phylogenetic relationships of butyrate-producing bacteria from the human gut. Appl. Environ. Microbiol. 66, 1654–1661 (2000).

  16. 16.

    et al. Development and application of the human intestinal tract chip, a phylogenetic microarray: analysis of universally conserved phylotypes in the abundant microbiota of young and elderly adults. Environ. Microbiol. 11, 1736–1751 (2009).

  17. 17.

    Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).

  18. 18.

    & Systems biology: metabonomics. Nature 455, 1054–1056 (2008).

  19. 19.

    , , & MetaboNetworks, an interactive Matlab-based toolbox for creating, customizing and exploring sub-networks from KEGG. Bioinformatics 30, 893–895 (2014).

  20. 20.

    & Microbial conversion of choline to trimethylamine requires a glycyl radical enzyme. Proc. Natl Acad. Sci. USA 109, 21307–21312 (2012).

  21. 21.

    et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472, 57–63 (2011).

  22. 22.

    Dietary bioactive compounds and their health implications. J. Food Sci. 78, (Suppl 1): A18–A25 (2013).

  23. 23.

    & Identification and characterization of a bile acid 7α-dehydroxylation operon in Clostridium sp. strain TO-931, a highly active 7α-dehydroxylating strain isolated from human feces. Appl. Environ. Microbiol. 66, 1107–1113 (2000).

  24. 24.

    et al. Bile acid concentrations, cytotoxicity, and pH of fecal water from patients with colorectal adenomas. Digest. Dis. Sci. 44, 2218–2225 (1999).

  25. 25.

    , , , & Bile acids as carcinogens in human gastrointestinal cancers. Mutat. Res. 589, 47–65 (2005).

  26. 26.

    et al. Inhibition by resistant starch of red meat-induced promutagenic adducts in mouse colon. Cancer Prev. Res. 4, 1920–1928 (2011).

  27. 27.

    , , & Resistant starch prevents colonic DNA damage induced by high dietary cooked red meat or casein in rats. Cancer Biol. Ther. 5, 267–272 (2006).

  28. 28.

    , & Effect of resistant starch on colonic fermentation, bile acid metabolism, and mucosal proliferation. Dig. Dis. Sci. 39, 834–842 (1994).

  29. 29.

    et al. Effects of resistant starch on the colon in healthy volunteers: possible implications for cancer prevention. Am. J. Clin. Nutr. 67, 136–142 (1998).

  30. 30.

    , , , & Development and application of a polymerase chain reaction assay for the detection and enumeration of bile acid 7α-dehydroxylating bacteria in human feces. Clin. Chim. Acta 331, 127–134 (2003).

  31. 31.

    et al. Dietary-fat-induced taurocholic acid promotes pathobiont expansion and colitis in Il10-/- mice. Nature 487, 104–108 (2012).

  32. 32.

    , , & Evidence that hydrogen sulfide is a genotoxic agent. Mol. Cancer Res. 4, 9–14 (2006).

  33. 33.

    et al. Genomic analysis identifies association of Fusobacterium with colorectal carcinoma. Genome Res. 22, 292–298 (2012).

  34. 34.

    , , & Microbial pathways in colonic sulfur metabolism and links with health and disease. Front. Physiol. 3, 448 (2012).

  35. 35.

    , , & Abnormal pattern of cell proliferation in the entire colonic mucosa of patients with colon adenoma or cancer. Gastroenterology 92, 704–708 (1987).

  36. 36.

    et al. Classification and risk assessment of individuals with familial polyposis, Gardner's syndrome, and familial non-polyposis colon cancer from [3H]thymidine labeling patterns in colonic epithelial cells. Cancer Res. 44, 4201–4207 (1984).

  37. 37.

    & Update on proliferation-associated antibodies applicable to formalin-fixed paraffin-embedded tissue and their clinical applications. Histochem. J. 25, 843–853 (1993).

  38. 38.

    & Inflammation and cancer. Nature 420, 860–867 (2002).

  39. 39.

    in Cancer Prevention II Springer181, 223–229 (2009).

  40. 40.

    , & T-cell activation in the intestinal mucosa. Immunol. Rev. 215, 189–201 (2007).

  41. 41.

    , & Intraepithelial lymphocytes: their shared and divergent immunological behaviors in the small and large intestine. Immunol. Rev. 215, 136–153 (2007).

  42. 42.

    et al. Reduction of CD68+ macrophages and decreased IL-17 expression in intestinal mucosa of patients with inflammatory bowel disease strongly correlate with endoscopic response and mucosal healing following infliximab therapy. Inflamm. Bowel Dis. 19, 729–739 (2013).

  43. 43.

    , , , , & Up-regulation of macrophage wnt gene expression in adenoma-carcinoma progression of human colorectal cancer. Br. J. Cancer 81, 496–502 (1999).

  44. 44.

    et al. Monocyte chemoattractant protein 1 and macrophage cyclooxygenase 2 expression in colonic adenoma. Gut 55, 54–61 (2006).

  45. 45.

    , , & The microbial metabolite butyrate regulates intestinal macrophage function via histone deacetylase inhibition. Proc. Natl Acad. Sci. USA 111, 2247–2252 (2014).

  46. 46.

    , & Relevance of protein fermentation to gut health. Mol. Nutr. Food Res. 56, 184–196 (2012).

  47. 47.

    , & Intestinal protozoa are hypothesized to stimulate immunosurveillance against colon cancer. Med. Hypotheses 71, 104–110 (2008).

  48. 48.

    et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 313, 1960–1964 (2006).

  49. 49.

    , , , & Cancer immunoediting: from immunosurveillance to tumor escape. Nat. Immunol. 3, 991–998 (2002).

  50. 50.

    , , , & Inhibition of histone-deacetylase activity by short-chain fatty acids and some polyphenol metabolites formed in the colon. J. Nutr. Biochem. 19, 587–593 (2008).

  51. 51.

    Utilization of nutrients by isolated epithelial cells of the rat colon. Gastroenterology 83, 424–429 (1982).

  52. 52.

    , , , , & The Warburg effect dictates the mechanism of butyrate-mediated histone acetylation and cell proliferation. Mol. Cell 48, 612–626 (2012).

  53. 53.

    , & Butyrate and Wnt signaling: a possible solution to the puzzle of dietary fiber and colon cancer risk? Cell Cycle 7, 1178–1183 (2008).

  54. 54.

    , , , , & Genotoxic effect of bile acids on human normal and tumour colon cells and protection by dietary antioxidants and butyrate. Eur. J. Nutr. 47, 301–309 (2008).

  55. 55.

    et al. A human colonic commensal promotes colon tumorigenesis via activation of T helper type 17 T cell responses. Nat. Med. 15, 1016–1022 (2009).

  56. 56.

    & Dietary fibre for the prevention of colorectal adenomas and carcinomas (Review). The Cochrane Library, Issue 4 (2008).

  57. 57.

    Diseases of the alimentary tract and western diets. Pathol. Microbiol. 39, 177–186 (1971).

  58. 58.

    et al. Cancer burden in Africa and opportunities for prevention. Cancer 118, 4372–4384 (2012).

  59. 59.

    et al. Comparative analysis of pyrosequencing and a phylogenetic microarray for exploring microbial community structures in the human distal intestine. PLoS ONE 4, e6669 (2009).

  60. 60.

    et al. Richness of human gut microbiome correlates with metabolic markers. Nature 500, 541–546 (2013).

  61. 61.

    , , & Microarray analysis and barcoded pyrosequencing provide consistent microbial profiles depending on the source of human intestinal samples. Appl. Environ. Microbiol. 77, 2071–2080 (2010).

  62. 62.

    , , & Probabilistic analysis of probe reliability in differential gene expression studies with short oligonucleotide arrays. IEEE/ACM Trans. Comput. Biol. Bioinform. 8, 217–225 (2011).

  63. 63.

    , , , & A fully scalable online pre-processing algorithm for short oligonucleotide microarray atlases. Nucleic Acids Res. 41, e110 (2013).

  64. 64.

    In Bioinformatics and Computational Biology Solutions Using R and Bioconductor eds Gentleman R., Carey V., Dudoit S., R. Irizarry WH 397–420Springer (2005).

  65. 65.

    & Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440–9445 (2003).

  66. 66.

    , & in Proceedings of the 3rd International AAAI Conference on Weblogs and Social Media, 361–362 (San Jose, CA, USA, 2009).

  67. 67.

    et al. Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat. Protoc. 2, 2692–2703 (2007).

  68. 68.

    et al. Recursive segment-wise peak alignment of biological (1)H NMR spectra for improved metabolic biomarker recovery. Anal. Chem. 81, 56–66 (2009).

  69. 69.

    , , & Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Anal. Chem. 78, 4281–4290 (2006).

  70. 70.

    A direct approach to false discovery rates. J. R. Stat. Soc. B 64, 479–498 (2002).

Download references

Acknowledgements

We thank Chadd and Kate Bain, and the iZulu Lodge and Projects team in KwaZulu-Natal, for help with the recruitment and housing of the African volunteers, and Dr Iain Thirsk and his surgical endoscopy staff at Ngwelezana Hospital, Empangeni, KwaZulu-Natal, for providing the endoscopic facilities for the study, and Faye Brouard for help with the rural dietary assessments. We acknowledge the help of Dr Robert Branch and the staff of the University of Pittsburgh Clinical Translation and Research Center for help with the dietary switch studies on African Americans. We thank Ms Kayellen Umeakunne for help with the design of the intervention diets. Primary funding for the study was provided by a grant from the National Institutes of Health R01 CA135379 (O’Keefe) and CTRC support from UL1 RR024153 and UL1TR000005. The research was also supported by the National Institute for Health Research Biomedical Research Centre based at Imperial College Healthcare NHS Trust and Imperial College London. We acknowledge the Academy of Medical Sciences, which funded part of the metabolic profiling work. Additional funding for the microbiota studies was provided by the Spinoza Award of the Netherlands Organization (de Vos) for Scientific Research, the ERC Advanced Grant 250172 (Microbes Inside) of the European Research Council and the Academy of Finland (grant 141140 to W.M.dV. and grant 256950 to L.L.). The University of Pittsburgh Genomics and Proteomics Core Laboratory (GPCL) produced the mucosal gene expression microarray data.

Author information

Author notes

    • Franck Carbonero

    Present address: Department of Food Science, University of Arkansas, Fayetteville, Arizona 72704, USA

Affiliations

  1. Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA

    • Stephen J. D. O’Keefe
    • , Junhai Ou
    • , Khaled Mohammed
    • , Elaine Wahl
    • , Kishore Vipperla
    •  & Hatem O. Kaseb
  2. Department of Surgery and Cancer and Centre for Digestive and Gut Health, Institution of Global Health Innovation, Imperial College, London SW7 2AZ, UK

    • Jia V. Li
    • , Joram M. Posma
    • , James Kinross
    •  & Jeremy K. Nicholson
  3. Laboratory of Microbiology, Wageningen University, Wageningen 6703 HB, The Netherlands

    • Leo Lahti
    • , Sebastian Tims
    • , Philippe G. B. Puylaert
    • , Willem M. de Vos
    •  & Erwin G. Zoetendal
  4. Department of Veterinary Bioscience, University of Helsinki, Helsinki, Finland

    • Leo Lahti
    •  & Willem M. de Vos
  5. University of Illinois at Urbana–Champaign, Champaign, Illinois 61801, USA

    • Franck Carbonero
    • , Ann C. Benefiel
    •  & H. Rex Gaskins
  6. Division of Sports Medicine and Nutrition, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA

    • Elizabeth Ruder
  7. University of KwaZulu-Natal, Durban, South Africa

    • Vasudevan Naidoo
    • , Lungile Mtshali
    •  & Keith Newton
  8. Division of Endocrinology, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA

    • James DeLany
  9. Division of Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA

    • Alyssa Krasinskas
  10. RPU Immunolbiology, Department of Bacteriology and Immunology, University of Helsinki, Helsinki 00014, Finland

    • Willem M. de Vos

Authors

  1. Search for Stephen J. D. O’Keefe in:

  2. Search for Jia V. Li in:

  3. Search for Leo Lahti in:

  4. Search for Junhai Ou in:

  5. Search for Franck Carbonero in:

  6. Search for Khaled Mohammed in:

  7. Search for Joram M. Posma in:

  8. Search for James Kinross in:

  9. Search for Elaine Wahl in:

  10. Search for Elizabeth Ruder in:

  11. Search for Kishore Vipperla in:

  12. Search for Vasudevan Naidoo in:

  13. Search for Lungile Mtshali in:

  14. Search for Sebastian Tims in:

  15. Search for Philippe G. B. Puylaert in:

  16. Search for James DeLany in:

  17. Search for Alyssa Krasinskas in:

  18. Search for Ann C. Benefiel in:

  19. Search for Hatem O. Kaseb in:

  20. Search for Keith Newton in:

  21. Search for Jeremy K. Nicholson in:

  22. Search for Willem M. de Vos in:

  23. Search for H. Rex Gaskins in:

  24. Search for Erwin G. Zoetendal in:

Contributions

S.J.D.O’K. designed and conducted the human studies, supervised the analysis and evaluation the results, prepared, edited and revised the manuscript. E.G.Z. assisted with study design, managed the global microbiota analysis and interpretation, and assisted with preparation of the manuscript. H.R.G. assisted with study design and execution of the human studies, managed the targeted microbiota analysis and interpretation, and assisted with preparation of the manuscript. J.K. was responsible for writing the text relating to the 1H NMR analysis and for interpreting the spectral data. J.N. provided strategic oversight for the metabonomic analytical strategy and was responsible for data interpretation. J.L. performed the 1H NMR analysis and the multivariate analysis. J.O. was responsible for targeted analysis of metabolites, data management, sample distribution and manuscript preparation. L.L. and S.T. conducted the microbiota data analysis and interpretation, and assisted with preparation of the manuscript. P.P. sample coordination and microbiota phylogenetic profiling. W.M.D.V. managed, interpreted and supported microbiota analysis. K.M. and H.K. performed the mucosal immunohistochemistry. E.W. (USA), E.R. (USA) and F.B. (S Africa) performed the dietary analysis, meal design and production. K.A.N. and V.N. helped organize the South African studies and endoscopies. K.V. was responsible for the screening, recruitment and management of African Americans dietary switch studies. F.C. performed the targeted microbiota analysis. L.M. supervised the recruitment of African subjects and helped with the dietary exchange studies. A.K. supervised the histological assessments of biopsy samples. A.C.B. coordinated the targeted microbiota analyses. J.D. supervised the targeted metabolite analysis and helped with manuscript preparation. J.P. carried out the metabolic reaction network analysis and interpretation.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Stephen J. D. O’Keefe.

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    Supplementary Figures 1-6, Supplementary Tables 1-11, Supplementary Discussion, Supplementary Methods, and Supplementary References

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.