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The association of dietary animal and plant protein with putative risk markers of colorectal cancer in overweight pre-diabetic individuals during a weight-reducing programme: a PREVIEW sub-study

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Abstract

Purpose

Diets with increased protein content are popular strategies for body weight regulation, but the effect of such diets for the colonic luminal environment is unclear. We aimed to investigate the associations between putative colorectal cancer-related markers and total protein intake, plant and animal proteins, and protein from red and processed meat in pre-diabetic adults (> 25 years).

Methods

Analyses were based on clinical and dietary assessments at baseline and after 1 year of intervention. Protein intake was assessed from 4-day dietary records. Putative colorectal cancer-related markers identified from 24-h faecal samples collected over three consecutive days were: concentration of short-chain fatty acids, phenols, ammonia, and pH.

Results

In total, 79 participants were included in the analyses. We found a positive association between change in total protein intake (slope: 74.72 ± 28.84 µmol per g faeces/E%, p = 0.01), including animal protein intake (slope: 87.63 ± 32.04 µmol per g faeces/E%, p = 0.009), and change in faecal ammonia concentration. For change in ammonia, there was a dose–response trend from the most negative (lowest tertile) to the most positive (highest tertile) association (p = 0.01): in the high tertile, a change in intake of red meat was positively associated with an increase in ammonia excretion (slope: 2.0 ± 0.5 µmol per g faeces/g/day, p < 0.001), whereas no such association was found in the low and medium tertile groups.

Conclusion

Increases in total and animal protein intakes were associated with higher excretion of ammonia in faeces after 1 year in overweight pre-diabetic adults undertaking a weight-loss intervention. An increase in total or relative protein intake, or in the ratio of animal to plant protein, was not associated with an increase in faeces of any of the other putative colorectal cancer risk markers.

Trial registration

ClinicalTrials.gov Identifier: NCT01777893.

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Abbreviations

SCFA:

Short-chain fatty acids

References

  1. Due A, Toubro S, Skov AR, Astrup A (2004) Effect of normal-fat diets, either medium or high in protein, on body weight in overweight subjects: a randomised 1-year trial. Int J Obes 28:1283–1290. https://doi.org/10.1038/sj.ijo.0802767

    Article  CAS  Google Scholar 

  2. Larsen TM, Dalskov S-M, van Baak M et al (2010) Diets with high or low protein content and glycemic index for weight-loss maintenance. N Engl J Med 363:2102–2113. https://doi.org/10.1056/NEJMoa1007137

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Windey K, de Preter V, Verbeke K (2012) Relevance of protein fermentation to gut health. Mol Nutr Food Res 56:184–196. https://doi.org/10.1002/mnfr.201100542

    Article  CAS  PubMed  Google Scholar 

  4. Corpet D, Yin Y, Zhang X et al (1995) Colonic protein fermentation and promotion of colon carcinogenesis by thermolyzed casein. Nutr Cancer 23:271–281

    Article  CAS  Google Scholar 

  5. Yao CK, Muir JG, Gibson PR (2016) Review article: insights into colonic protein fermentation, its modulation and potential health implications. Aliment Pharmacol Ther 43:181–196. https://doi.org/10.1111/apt.13456

    Article  CAS  PubMed  Google Scholar 

  6. Brahe LK, Astrup A, Larsen LH (2013) Is butyrate the link between diet, intestinal microbiota and obesity-related metabolic diseases? Obes Rev 14:950–959. https://doi.org/10.1111/obr.12068

    Article  CAS  PubMed  Google Scholar 

  7. World Cancer Research Fund International/American Institute for Cancer Research. Continous Update Project Report: Diet, Nutrition, Physical Activity and Colorectal Cancer. 2017. Available at: www.wcrf.org/colorectal-cancer-2017

  8. Russell WR, Gratz SW, Duncan SH et al (2011) High-protein, reduced-carbohydrate weight-loss diets promote metabolite profiles likely to be detrimental to colonic health. Am J Clin Nutr 93:1062–1072. https://doi.org/10.3945/ajcn.110.002188

    Article  CAS  PubMed  Google Scholar 

  9. Nowak A, Libudzisz Z (2006) Influence of phenol, p-cresol and indole on growth and survival of intestinal lactic acid bacteria. Anaerobe 12:80–84. https://doi.org/10.1016/j.anaerobe.2005.10.003

    Article  CAS  PubMed  Google Scholar 

  10. Russell WR, Scobbie L, Chesson A et al (2008) Anti-inflammatory implications of the microbial transformation of dietary phenolic compounds. Nutr Cancer 60:636–642. https://doi.org/10.1080/01635580801987498

    Article  CAS  PubMed  Google Scholar 

  11. Singleton V, Orthofer R, Lamuela-Raventós R (1999) Analysis of total phenols and other oxidation substrates and antioxidants by means of folin-ciocalteu reagent. Methods Enzymol 299:152–178

    Article  CAS  Google Scholar 

  12. Berryman CE, Agarwal S, Lieberman HR et al (2016) Diets higher in animal and plant protein are associated with lower adiposity and do not impair kidney function in US adults. Am J Clin Nutr 104:743–749. https://doi.org/10.3945/ajcn.116.133819

    Article  CAS  PubMed  Google Scholar 

  13. Larsson SC, Orsini N (2014) Red meat and processed meat consumption and all-cause mortality: a meta-analysis. Am J Epidemiol 179:282–289. https://doi.org/10.1093/aje/kwt261

    Article  PubMed  Google Scholar 

  14. Chan DSM, Lau R, Aune D et al (2011) Red and processed meat and colorectal cancer incidence: meta-analysis of prospective studies. PLoS One 6:e20456. https://doi.org/10.1371/journal.pone.0020456

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Aune D, Chan DSM, Vieira AR et al (2013) Red and processed meat intake and risk of colorectal adenomas: a systematic review and meta-analysis of epidemiological studies. Cancer Causes Control 24:611–627. https://doi.org/10.1007/s10552-012-0139-z

    Article  PubMed  Google Scholar 

  16. Alexander DD, Weed DL, Cushing CA, Lowe KA (2011) Meta-analysis of prospective studies of red meat consumption and colorectal cancer. Eur J Cancer Prev 20:293–307. https://doi.org/10.1097/CEJ.0b013e328345f985

    Article  CAS  PubMed  Google Scholar 

  17. Larsson SC, Wolk A (2006) Meat consumption and risk of colorectal cancer: a meta-analysis of prospective studies. Int J Cancer 119:2657–2664. https://doi.org/10.1002/ijc.22170

    Article  CAS  PubMed  Google Scholar 

  18. Pan A, Sun Q, Bernstein AM et al (2012) Red meat consumption and mortality: results from 2 prospective cohort studies. Arch Intern Med 172:555–563. https://doi.org/10.1001/archinternmed.2011.2287

    Article  PubMed  PubMed Central  Google Scholar 

  19. Kaluza J, Wolk A, Larsson SC (2012) Red meat consumption and risk of stroke: a meta-analysis of prospective studies. Stroke 43:2556–2560. https://doi.org/10.1161/STROKEAHA.112.663286

    Article  CAS  PubMed  Google Scholar 

  20. Micha R, Michas G, Mozaffarian D (2012) Unprocessed red and processed meats and risk of coronary artery disease and type 2 diabetes—an updated review of the evidence. Curr Atheroscler Rep 14:515–524. https://doi.org/10.1007/s11883-012-0282-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Shang X, Scott D, Hodge AM et al (2016) Dietary protein intake and risk of type 2 diabetes: results from the Melbourne Collaborative Cohort Study and a meta-analysis of prospective studies. Am J Clin Nutr AJCN. https://doi.org/10.3945/ajcn.116.140954

    Article  Google Scholar 

  22. Nothlings U, Schulze MB, Weikert C et al (2008) Intake of vegetables, legumes, and fruit, and risk for all-cause, cardiovascular, and cancer mortality in a European diabetic population. J Nutr 138:775–781

    Article  Google Scholar 

  23. Hermsdorff HHM, Zulet MÁ, Abete I, Martínez JA (2011) A legume-based hypocaloric diet reduces proinflammatory status and improves metabolic features in overweight/obese subjects. Eur J Nutr 50:61–69. https://doi.org/10.1007/s00394-010-0115-x

    Article  CAS  PubMed  Google Scholar 

  24. Shi Y, Yu P-W, Zeng D-Z (2015) Dose–response meta-analysis of poultry intake and colorectal cancer incidence and mortality. Eur J Nutr 54:243–250. https://doi.org/10.1007/s00394-014-0705-0

    Article  CAS  PubMed  Google Scholar 

  25. Wu S, Feng B, Li K et al (2012) Fish consumption and colorectal cancer risk in humans: a systematic review and meta-analysis. Am J Med 125:551–559. https://doi.org/10.1016/j.amjmed.2012.01.022

    Article  PubMed  Google Scholar 

  26. Ralston RA, Truby H, Palermo CE, Walker KZ (2014) Colorectal cancer and nonfermented milk, solid cheese, and fermented milk consumption: a systematic review and meta-analysis of prospective studies. Crit Rev Food Sci Nutr 54:1167–1179. https://doi.org/10.1080/10408398.2011.629353

    Article  CAS  PubMed  Google Scholar 

  27. Chen M, Sun Q, Giovannucci E et al (2014) Dairy consumption and risk of type 2 diabetes: 3 cohorts of US adults and an updated meta-analysis. BMC Med 12:215. https://doi.org/10.1186/s12916-014-0215-1

    Article  PubMed  PubMed Central  Google Scholar 

  28. Pala V, Sieri S, Berrino F et al (2011) Yogurt consumption and risk of colorectal cancer in the Italian European prospective investigation into cancer and nutrition cohort. Int J Cancer 129:2712–2719. https://doi.org/10.1002/ijc.26193

    Article  CAS  PubMed  Google Scholar 

  29. Duncan SH, Belenguer A, Holtrop G et al (2007) Reduced dietary intake of carbohydrates by obese subjects results in decreased concentrations of butyrate and butyrate-producing bacteria in feces. Appl Environ Microbiol 73:1073–1078. https://doi.org/10.1128/AEM.02340-06

    Article  CAS  PubMed  Google Scholar 

  30. Brinkworth GD, Noakes M, Clifton PM, Bird AR (2009) Comparative effects of very low-carbohydrate, high-fat and high-carbohydrate, low-fat weight-loss diets on bowel habit and faecal short-chain fatty acids and bacterial populations. Br J Nutr 101:1493. https://doi.org/10.1017/S0007114508094658

    Article  CAS  PubMed  Google Scholar 

  31. Fogelholm M, Larsen T, Westerterp-Plantenga M et al (2017) PREVIEW: prevention of diabetes through lifestyle intervention and population studies in europe and around the world. Design, methods, and baseline participant description of an adult cohort enrolled into a three-year randomised clinical trial. Nutrients 9:632. https://doi.org/10.3390/nu9060632

    Article  CAS  PubMed Central  Google Scholar 

  32. Souba WW, Wilmore DW (1968) Diet and Nutrition in the care of the patient with Surgery, Trauma and Sepsis. In: Shills ME, Olson JA, Shike M, Ross AC (eds) Modern nutrition in health and disease, 8th edn. Lippincott Williams & Wilkins, Baltimore, pp 1207–1240

    Google Scholar 

  33. Han J, Lin K, Sequeira C, Borchers CH (2015) An isotope-labeled chemical derivatization method for the quantitation of short-chain fatty acids in human feces by liquid chromatography–tandem mass spectrometry. Anal Chim Acta 854:86–94. https://doi.org/10.1016/j.aca.2014.11.015

    Article  CAS  PubMed  Google Scholar 

  34. Ainsworth EA, Gillespie KM (2007) Estimation of total phenolic content and other oxidation substrates in plant tissues using Folin–Ciocalteu reagent. Nat Protoc 2:875–877. https://doi.org/10.1038/nprot.2007.102

    Article  CAS  PubMed  Google Scholar 

  35. Wallace TM, Levy JC, Matthews DR (2004) Use and abuse of HOMA modeling. [Review] [42 refs]. Diabetes Care 27:1487–2004. https://doi.org/10.2337/diacare.27.6.1487

    Article  PubMed  Google Scholar 

  36. Azur MJ, Stuart EA, Frangakis C, Leaf PJ (2011) Multiple imputation by chained equations: what is it and how does it work? Int J Methods Psychiatr Res 20:40–49. https://doi.org/10.1002/mpr.329

    Article  PubMed  PubMed Central  Google Scholar 

  37. R Development Core Team (2013) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/. R Found. Stat. Comput. Vienna, Austria

  38. van Buuren S, Groothuis-Oudshoorn K (2011) Mice: multivariate Imputation by Chained Equations in R. J Stat Softw 45:1–67. https://doi.org/10.18637/jss.v045.i03

    Article  Google Scholar 

  39. Cummings JH, Hill MJ, Bone ES et al (1979) The effect of meat protein and dietary fiber on colonic function and metabolism. II. Bacterial metabolites in feces and urine. Am J Clin Nutr 32:2094–2101. https://doi.org/10.1093/ajcn/32.10.2094

    Article  CAS  PubMed  Google Scholar 

  40. Bingham SA, Pignatelli B, Pollock JRA et al (1996) Does increased endogenous formation of N-nitroso compounds in the human colon explain the association between red meat and colon cancer? Carcinogenesis 17:515–523

    Article  CAS  Google Scholar 

  41. World Cancer Research Fund/American Institute for Cancer Research. Food, Nutrition, Physical Activity, and the Prevention of Cancer: a Global Perspective. Washington DC: AICR, 2007

  42. Alexander DD, Weed DL, Miller PE, Mohamed MA (2015) Red meat and colorectal cancer: a quantitative update on the state of the epidemiologic science. J Am Coll Nutr 34:521–543. https://doi.org/10.1080/07315724.2014.992553

    Article  PubMed  PubMed Central  Google Scholar 

  43. Verbeke KA, Boobis AR, Chiodini A et al (2015) Towards microbial fermentation metabolites as markers for health benefits of prebiotics. Nutr Res Rev 28:42–66. https://doi.org/10.1017/S0954422415000037

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Eckburg PB, Bik EM, Bernstein CN et al (2005) Diversity of the human intestinal microbial flora. Science 308:1635–1638. https://doi.org/10.1126/science.1110591

    Article  PubMed  PubMed Central  Google Scholar 

  45. Durbán A, Abellán JJ, Jiménez-Hernández N et al (2012) Daily follow-up of bacterial communities in the human gut reveals stable composition and host-specific patterns of interaction. FEMS Microbiol Ecol 81:427–437. https://doi.org/10.1111/j.1574-6941.2012.01368.x

    Article  CAS  PubMed  Google Scholar 

  46. Goris AH, Meijer EP, Westerterp KR (2001) Repeated measurement of habitual food intake increases under-reporting and induces selective under-reporting. Br J Nutr 85:629–634

    Article  CAS  Google Scholar 

  47. Livingstone MBE, Black AE (2003) Markers of the validity of reported energy intake. J Nutr 133:895S–920S. https://doi.org/10.1091/mbc.E05

    Article  CAS  PubMed  Google Scholar 

  48. Mattisson I, Wirfält E, Aronsson CA et al (2005) Misreporting of energy: prevalence, characteristics of misreporters and influence on observed risk estimates in the Malmö Diet and Cancer cohort. Br J Nutr 94:832–842

    Article  CAS  Google Scholar 

  49. Nordic Council of Ministers Nordic Nutrition Recommendations (2012) Integrating nutrition and physical activity. Nord Nutr Recomm. https://doi.org/10.6027/nord2014-002

    Article  Google Scholar 

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Acknowledgements

This study has received grants from the EU 7th Framework Programme (FP7-KBBE-2012), grant agreement No. 312057; the New Zealand Health Research Council, grant No. 14/191, the Danish Technological Institute and The Danish Agriculture & Food Council.

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Authors and Affiliations

Authors

Contributions

LOD designed the research. GM and CR performed the statistical analyses. GM wrote the paper. JRA, LOD, AR, SDP, MPS, CR. MF, EJ, TML, and JBM contributed to manuscript draft and revision. All authors read and approved the final manuscript.

Corresponding author

Correspondence to G. Møller.

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Conflict of interest

JBM is president of the Glycemic Index Foundation, a non-profit food endorsement programme, manager of a GI testing service at the University of Sydney and the co-author of books about the GI foods. SDP holds the Fonterra Chair in Human Nutrition at the University of Auckland. None of the other authors declare a conflict of interest.

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Møller, G., Andersen, J.R., Jalo, E. et al. The association of dietary animal and plant protein with putative risk markers of colorectal cancer in overweight pre-diabetic individuals during a weight-reducing programme: a PREVIEW sub-study. Eur J Nutr 59, 1517–1527 (2020). https://doi.org/10.1007/s00394-019-02008-2

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