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Interventions and public health nutrition

Pretreatment Prevotella-to-Bacteroides ratio and markers of glucose metabolism as prognostic markers for dietary weight loss maintenance

Abstract

Background/Objectives

Pre-treatment gut microbial Prevotella-to-Bacteroides (P/B) ratio and markers of glucose metabolism (i.e., fasting glucose and insulin) have been suggested as biomarkers for optimal weight management. However, both biomarkers need further validation, and the interactions between them for optimal weight management are largely unknown. To investigate differences in weight loss maintenance between subjects with low and high P/B ratio and the potential interactions with markers of glucose metabolism and dietary fiber intake.

Subjects/Methods

Following an 8-week weight loss period using meal replacement products, subjects losing ≥ 8% of their initial body weight were randomized to one of three protein supplements or maltodextrin for a 24-week weight maintenance period. Habitual diet was consumed along with the supplements expected to constitute 10–15% of total energy. For this analysis we stratified the participants into low and high strata based on median values of pre-intervention P/B ratio, pre-weight loss Homeostatic model assessment of insulin resistance (HOMA-IR) (<2.33 or > 2.33), and dietary fiber intake during the intervention (< 28.5 or > 28.5 g/10 MJ).

Results

Regardless of weight maintenance regimen, subjects with high P/B ratio (n = 63) regained 1.5 (95% CI 0.4, 2.7) kg body weight (P = 0.007) more than subjects with low P/B ratio (n = 63). The regain among subjects with high P/B ratio was particular evident if HOMA-IR was high and dietary fiber intake was low. Consequently, in the high P/B strata, subjects with high HOMA-IR and low fiber intake (n = 17) regained 5.3 (95% CI 3.3, 7.3) kg (P < 0.001) more body weight compared with participants with low HOMA-IR and high fiber intake (n = 16).

Conclusions

Subjects with high P/B ratio were more susceptible to regain body weight compared with subjects with low P/B ratio, especially when dietary fiber intake was low and glucose metabolism was impaired. These observations underline that both the P/B ratio and markers of glucose metabolism should be considered as important biomarkers within personalized nutrition for optimal weight management.

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References

  1. Astrup A, Grunwald G, Melanson E, Saris W, Hill J. The role of low-fat diets in body weight control: a meta-analysis of ad libitum dietary intervention studies. Int J Obes. 2000;24:1545–52.

    Article  CAS  Google Scholar 

  2. Astrup A, Larsen TM, Harper A. Atkins and other low-carbohydrate diets: hoax or an effective tool for weight loss? Lancet. 2004;364:897–9.

    Article  Google Scholar 

  3. Hall KD, Bemis T, Brychta R, Chen KY, Courville A, Crayner EJ, et al. Calorie for calorie, dietary fat restriction results in more body fat loss than carbohydrate restriction in people with obesity. Cell Metab. 2015;22:427–36.

    Article  CAS  Google Scholar 

  4. Larsen TM, Dalskov S-M, van Baak M, Jebb SA, Papadaki A, Pfeiffer AFH, et al. Diets with high or low protein content and glycemic index for weight-loss maintenance. N Engl J Med. 2010;363:2102–13.

    Article  CAS  Google Scholar 

  5. Sacks FM, Bray GA, Carey VJ, Smith SR, Ryan DH, Anton SD, et al. Comparison of weight-loss diets with different compositions of fat, protein, and carbohydrates. N Engl J Med. 2009;360:859–73.

    Article  CAS  Google Scholar 

  6. Foster GD, Wyatt HR, Hill JO, Makris AP, Rosenbaum DL, Brill C, et al. Weight and metabolic outcomes after 2 years on a low-carbohydrate versus low-fat diet: a randomized trial. Ann Intern Med. 2010;153:147–57.

    Article  Google Scholar 

  7. Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015;163:1079–94.

    Article  CAS  Google Scholar 

  8. Zmora N, Zeevi D, Korem T, Segal E, Elinav E. Taking it personally: personalized utilization of the human microbiome in health and disease. Cell Host Microbe. 2016;19:12–20.

    Article  CAS  Google Scholar 

  9. Korem T, Zeevi D, Zmora N, Weissbrod O, Bar N, Lotan-Pompan M. et al. Bread affects clinical parameters and induces gut microbiome-associated personal glycemic responses. Cell Metab. 2017;25:1243–53.

    Article  CAS  Google Scholar 

  10. Roager HM, Licht TR, Poulsen SK, Larsen TM, Bahl MI. Microbial enterotypes, inferred by the prevotella-to-bacteroides ratio, remained stable during a 6-month randomized controlled diet intervention with the new nordic diet. Appl Environ Microbiol. 2014;80:1142–9.

    Article  Google Scholar 

  11. Hjorth MF, Roager HM, Larsen TM, Poulsen SK, Licht TR, Bahl MI, et al. Pre-treatment microbial Prevotella-to-Bacteroides ratio, determines body fat loss success during a 6-month randomized controlled diet intervention. Int J Obes. 2018;42:580–3.

    Article  CAS  Google Scholar 

  12. Hjorth MF, Blædel T, Bendtsen LQ, Lorenzen JK, Holm JB, Kiilerich P, et al. Prevotella-to-Bacteroides ratio predicts body weight and fat loss success on 24-week diets varying in macronutrient composition and dietary fiber: results from a post-hoc analysis. Int J Obes. 2018. https://doi.org/10.1038/s41366-018-0093-2.

    Article  Google Scholar 

  13. Christensen L, Roager HM, Astrup A, Hjorth MF. Microbial enterotypes in personalized nutrition and obesity management. Am J Clin Nutr. 2018;108:645–51.

    Article  Google Scholar 

  14. Soty M, Gautier-Stein A, Rajas F, Mithieux G. Gut-brain glucose signaling in energy homeostasis. Cell Metab. 2017;25:1231–42.

    Article  CAS  Google Scholar 

  15. Tian Y, Nichols RG, Roy P, Gui W, Smith PB, Zhang J, et al. Prebiotic effects of white button mushroom (Agaricus bisporus) feeding on succinate and intestinal gluconeogenesis in C57BL/6 mice. J Funct Foods. 2018;45:223–32.

    Article  CAS  Google Scholar 

  16. Kovatcheva-Datchary P, Nilsson A, Akrami R, Lee YS, De Vadder F, Arora T, et al. Dietary fiber-induced improvement in glucose metabolism is associated with increased abundance of Prevotella. Cell Metab. 2015;22:971–82.

    Article  CAS  Google Scholar 

  17. Sandberg J, Kovatcheva-Datchary P, Björck I, Bäckhed F, Nilsson A. Abundance of gut Prevotella at baseline and metabolic response to barley prebiotics. Eur J Nutr. 2018. https://doi.org/10.1007/s00394-018-1788-9.

    Article  Google Scholar 

  18. Hjorth MF, Zohar Y, Hill JO, Astrup A. Personalized dietary management of overweight and obesity based on measures of insulin and glucose. Annu Rev Nutr. 2018;38:245–72.

    Article  CAS  Google Scholar 

  19. Hjorth MF, Due A, Larsen TM, Astrup A. Pretreatment fasting plasma glucose modifies dietary weight loss maintenance success: results from a stratified RCT. Obesity. 2017;25:2045–8.

    Article  CAS  Google Scholar 

  20. Hjorth MF, Ritz C, Blaak EE, Saris WH, Langin D, Poulsen SK, et al. Pretreatment fasting plasma glucose and insulin modify dietary weight loss success: results from 3 randomized clinical trials. Am J Clin Nutr. 2017;106:499–505.

    Article  CAS  Google Scholar 

  21. Kjølbæk L, Sørensen LB, Søndertoft NB, Rasmussen CK, Lorenzen JK, Serena A, et al. Protein supplements after weight loss do not improve weight maintenance compared with recommended dietary protein intake despite beneficial effects on appetite sensation and energy expenditure: a randomized, controlled, double-blinded trial. Am J Clin Nutr. 2017;106:684–97.

    Article  Google Scholar 

  22. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.

    Article  CAS  Google Scholar 

  23. Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl Environ Microbiol. 2013. https://doi.org/10.1128/aem.01043-13.

    Article  CAS  Google Scholar 

  24. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.

    Article  CAS  Google Scholar 

  25. DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72:5069–72.

    Article  CAS  Google Scholar 

  26. Wang Q, Garrity GM, Tiedje JM, Cole JR. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007. https://doi.org/10.1128/AEM.00062-07.

    Article  CAS  Google Scholar 

  27. Pedersen AN, Christensen T, Matthiessen J, Knudsen VK, Sørensen MR, Biltoft-Jensen A, et al. Danskernes kostvaner 2011–2013. Hovedresultater (Dietary Habits in Denmark 2011–2013. Main Results). Søborg Natl Food Institute, Tech Univ Denmark 2015.

  28. Chen T, Long W, Zhang C, Liu S, Zhao L, Hamaker BR. Fiber-utilizing capacity varies in Prevotella- versus Bacteroides-dominated gut microbiota. Sci Rep. 2017;7:2594.

    Article  Google Scholar 

  29. Fehlbaum S, Prudence K, Kieboom J, Heerikhuisen M, van den Broek T, Schuren F, et al. In vitro fermentation of selected prebiotics and their effects on the composition and activity of the adult gut microbiota. Int J Mol Sci. 2018;19:3097.

    Article  Google Scholar 

  30. de Vadder F, Mithieux G. Gut-brain signaling in energy homeostasis: the unexpected role of microbiota-derived succinate. J Endocrinol. 2018;236:R105–8.

    Article  Google Scholar 

  31. Chambers ES, Viardot A, Psichas A, Morrison DJ, Murphy KG, Zac-Varghese SEK, et al. Effects of targeted delivery of propionate to the human colon on appetite regulation, body weight maintenance and adiposity in overweight adults. Gut. 2015;64:1744–54.

    Article  CAS  Google Scholar 

  32. Astrup A, Hjorth MF. Classification of obesity targeted personalized dietary weight loss management based on carbohydrate tolerance. Eur J Clin Nutr. 2018;72:1300–4.

    Article  Google Scholar 

  33. Hwang JJ, Jiang L, Hamza M, Sanchez Rangel E, Dai F, Belfort-DeAguiar R, et al. Blunted rise in brain glucose levels during hyperglycemia in adults with obesity and T2DM. JCI Insight. 2017;2. https://doi.org/10.1172/jci.insight.95913.

  34. Ioannidis JPA. The challenge of reforming nutritional epidemiologic research. J Am Med Assoc. 2018;320:969.

    Article  Google Scholar 

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Acknowledgements

MFH and AA designed the overall research plan for this stratified analysis; MFH performed statistical analysis; MFH wrote paper; MFH have primary responsibility for final content; All authors have contributed to the discussion of analyses, reviewed the paper critically and approved the final paper. Arla Foods Ingredients Group P/S contributed to the overall study design but none of the funders had any role in the collection, management, analysis, interpretation of the data, preparation, review, or approval of the paper.

Funding

The original study was supported by Arla Foods, Viby J, Denmark, Arla Foods Ingredients Group P/S, Viby J, Denmark, and the Faculty of Science, University of Copenhagen, Denmark, all of which provided financial support. Arla Foods Ingredients Group P/S, Viby J, Denmark, and NUPO A/S, Taastrup, Denmark, donated intervention and low-calorie–diet products, respectively. The work reported in this paper was partly funded by grants from Gelesis Inc, Boston, USA.

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Correspondence to Mads F. Hjorth.

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Conflict of interest

MFH and AA are co-inventors on a pending provisional patent application for the use of biomarkers to predict responses to weight loss diets. AA is the co-inventor of other related patents and patent applications that are owned by UCPH, in accordance with Danish law. AA is a consultant for Gelesis Inc., providing scientific advice unrelated to the current paper. AA is a consultant or member of the advisory boards of Groupe Ethique et Sante, France; Weight Watchers, United States; BioCare, Copenhagen; Zaluvida, Switzerland; Novo Nordisk, Denmark; and Saniona, Denmark. MFH and AA are co-authors of the book Spis dig slank efter dit blodsukker (Eat healthily according to your blood sugar), published by Politikens Forlag, Denmark, and of other books about personalized nutrition for weight loss. AA is co-owner and member of the board of the consultancy company Dentacom ApS, Denmark, and cofounder and co-owner of the UCPH spin-off Mobile Fitness A/S and Flax-Slim ApS. MFH and AA are co-founders and co-owners of the UCPH spin-off Personalized Weight Management Research Consortium ApS (http://Gluco-diet.dk). Remaining authors reported no conflict of interest.

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Hjorth, M.F., Christensen, L., Kjølbæk, L. et al. Pretreatment Prevotella-to-Bacteroides ratio and markers of glucose metabolism as prognostic markers for dietary weight loss maintenance. Eur J Clin Nutr 74, 338–347 (2020). https://doi.org/10.1038/s41430-019-0466-1

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