Abundance of gut Prevotella at baseline and metabolic response to barley prebiotics

Jonna Sandberg, Petia Kovatcheva-Datchary, Inger Björck, Fredrik Bäckhed, Anne Nilsson, Jonna Sandberg, Petia Kovatcheva-Datchary, Inger Björck, Fredrik Bäckhed, Anne Nilsson

Abstract

Purpose: We previously showed that short-term intervention with barley kernel bread (BKB) improved glucose tolerance. However, glucose tolerance was not improved in a subset of individuals (non-responders) who were characterized by a low Prevotella/Bacteroides ratio. The purpose of the present study was to investigate if the baseline Prevotella/Bacteroides ratio can be used to stratify metabolic responders and non-responders to barley dietary fiber (DF).

Methods: Fecal samples were collected from 99 healthy humans with BMI < 28 kg/m2 between 50 and 70 years old. The abundance of fecal Prevotella and Bacteroides was quantified with 16S rRNA quantitative PCR. 33 subjects were grouped in three groups: subjects with highest Prevotella/Bacteroides ratios, "HP", n = 12; subjects with lowest Prevotella/Bacteroides ratios, "LP", n = 13; and subjects with high abundance of both measured bacteria, HPB, n = 8. A 3-day randomized crossover intervention with BKB and white wheat bread (control) was performed. Cardiometabolic test variables were analyzed the next day following a standardized breakfast.

Results: The BKB intervention lowered the blood glucose responses to the breakfast independently of Prevotella/Bacteroides ratios (P < 0.01). However, independently of intervention, the HP group displayed an overall lower insulin response and lower IL-6 concentrations compared with the LP group (P < 0.05). Furthermore, the groups HP and HPB showed lower hunger sensations compared to the LP group (P < 0.05).

Conclusions: Here we show that the abundance of gut Prevotella and Bacteroides at baseline did not stratify metabolic responders and non-responders to barley DF intervention. However, our results indicate the importance of gut microbiota in host metabolic regulation, further suggesting that higher Prevotella/Bacteroides ratio may be favorable. CLINICALTRIALS.

Gov id: NCT02427555.

Keywords: Bacteroides; Barley; Glucose regulation; Prevention; Prevotella; Stratification.

Conflict of interest statement

AN and IB are founders and shareholders of ProPrev AB.

Figures

Fig. 1
Fig. 1
Baseline levels of fecal bacteria and stratification of individuals. a Fecal levels of Prevotella and Bacteroides (determined by quantitative PCR and expressed as fraction of the total microbiota) in participants included and not included in the short-term barley prebiotic intervention. b Ratio of Prevotella vs Bacteroides in participants included and not included in the short-term barley prebiotic intervention. Included HP (High Prevotella), LP (Low Prevotella) and HPB (High Prevotella and Bacteroides). ANOVA followed by post hoc analysis—Tukey’s pairwise multiple comparison method was used to compare changes in the levels of Prevotella and Bacteroides and the ratio Prevotella/Bacteroides across the four groups. Data are mean ± SEM. ***P < 0.001, ****P < 0.0001
Fig. 2
Fig. 2
Incremental b-glucose (a) and s-insulin (b) responses post the standardized breakfast after BKB or WWB interventions, respectively, in subgroups varying in baseline gut microbiota composition. One-way ANOVA was used to compare the metabolic responses depending on intervention in each subgroup. The percentage corresponds to differences in concentration of test variables after BKB intervention compared to WWB intervention. BKB barley kernel bread, WWB white wheat bread, HP high Prevotella, LP low Prevotella and HPB high Prevotella and Bacteroides. Data are mean ± SEM. *P < 0.05
Fig. 3
Fig. 3
Mean concentrations of p-GLP-1 (a), p-GLP-2 (b) and p-PYY (c) after BKB or WWB intervention, respectively, in subgroups varying in gut microbiota composition. One-way ANOVA was used to compare the metabolic responses depending on intervention in each subgroup. The percentage corresponds to differences in concentration of test variables after BKB intervention compared to WWB intervention. GLP glucagon-like peptide, PYY peptide YY, BKB barley kernel bread, WWB white wheat bread, HP high Prevotella, LP low Prevotella and HPB high Prevotella and Bacteroides. Data are mean ± SEM. *P < 0.05, #P = 0.06
Fig. 4
Fig. 4
Plasma concentrations of IL-6 (a) after the standardized breakfast and fasting values of CRP (b) after BKB or WWB intervention, respectively, in subgroups varying in gut microbiota composition. One-way ANOVA was used to compare the metabolic responses depending on intervention in each subgroup. The percentage corresponds to differences in concentration of test variables after BKB intervention compared to WWB intervention. IL interleukin, CRP c-reactive protein, BKB barley kernel bread, WWB white wheat bread, HP high Prevotella, LP low Prevotella and HPB high Prevotella and Bacteroides. Data are mean ± SEM
Fig. 5
Fig. 5
Subjective appetite ratings [subjective hunger (a), desire to eat (b) and satiety (c)] post standardized breakfast after BKB or WWB intervention, respectively, in subgroups varying in gut microbiota composition. One-way ANOVA was used to compare the metabolic responses depending on intervention in each subgroup. The percentage corresponds to differences in concentration of test variables after BKB intervention compared to WWB intervention. BKB barley kernel bread, WWB white wheat bread, HP high Prevotella, LP low Prevotella and HPB high Prevotella and Bacteroides. Data are mean ± SEM. #P = 0.065

References

    1. Grundy SM. Metabolic syndrome update. Trends Cardiovasc Med. 2016;26(4):364–373. doi: 10.1016/j.tcm.2015.10.004.
    1. Singh RK, et al. Influence of diet on the gut microbiome and implications for human health. Jo Transl Med. 2017;15(1):73. doi: 10.1186/s12967-017-1175-y.
    1. Conlon MA, Bird AR. The impact of diet and lifestyle on gut microbiota and human health. Nutrients. 2015;7(1):17–44. doi: 10.3390/nu7010017.
    1. Ye EQ, et al. Greater whole-grain intake is associated with lower risk of type 2 diabetes, cardiovascular disease, and weight gain. J Nutr. 2012;142(7):1304–1313. doi: 10.3945/jn.111.155325.
    1. Grooms KN, et al. Dietary fiber intake and cardiometabolic risks among US adults, NHANES 1999–2010. Am J Med. 2013;126(12):1059e1–e4. doi: 10.1016/j.amjmed.2013.07.023.
    1. Nilsson AC, Johansson-Boll EV, Bjorck IM. Increased gut hormones and insulin sensitivity index following a 3-d intervention with a barley kernel-based product: a randomised cross-over study in healthy middle-aged subjects. Br J Nutr. 2015;114:1–9. doi: 10.1017/S0007114515002524.
    1. Priebe MG, et al. Factors related to colonic fermentation of nondigestible carbohydrates of a previous evening meal increase tissue glucose uptake and moderate glucose-associated inflammation. Am J Clin Nutr. 2010;91(1):90–97. doi: 10.3945/ajcn.2009.28521.
    1. Ibrugger S, et al. Second meal effect on appetite and fermentation of wholegrain rye foods. Appetite. 2014;80:248–256. doi: 10.1016/j.appet.2014.05.026.
    1. Sandberg JC, Bjorck IM, Nilsson AC. Rye-based evening meals favorably affected glucose regulation and appetite variables at the following breakfast; a randomized controlled study in healthy subjects. PLoS One. 2016;11(3):e0151985. doi: 10.1371/journal.pone.0151985.
    1. Kovatcheva-Datchary P, et al. Dietary fiber-induced improvement in glucose metabolism is associated with increased abundance of prevotella. Cell Metab. 2015;22(6):971–982. doi: 10.1016/j.cmet.2015.10.001.
    1. Zeevi D, et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015;163(5):1079–1094. doi: 10.1016/j.cell.2015.11.001.
    1. Korem T, et al. Bread affects clinical parameters and induces gut microbiome-associated personal glycemic responses. Cell Metab. 2017;25(6):1243–1253.e5. doi: 10.1016/j.cmet.2017.05.002.
    1. Salonen A, et al. Comparative analysis of fecal DNA extraction methods with phylogenetic microarray: effective recovery of bacterial and archaeal DNA using mechanical cell lysis. J Microbiol Methods. 2010;81(2):127–134. doi: 10.1016/j.mimet.2010.02.007.
    1. Walker AW, et al. Dominant and diet-responsive groups of bacteria within the human colonic microbiota. ISME J. 2011;5(2):220–230. doi: 10.1038/ismej.2010.118.
    1. Matsuki T, et al. Development of 16S rRNA-gene-targeted group-specific primers for the detection and identification of predominant bacteria in human feces. Appl Environ Microbiol. 2002;68(11):5445–5451. doi: 10.1128/AEM.68.11.5445-5451.2002.
    1. World Health Organization (2006) Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia: report of a WHO/IDF Consultation
    1. Björck IME, Siljeström MA. -vivo and in-vitro digestibility of starch in autoclaved pea and potato products. J Sci Food Agric. 1992;58(4):541–553. doi: 10.1002/jsfa.2740580414.
    1. Akerberg AK, et al. An in vitro method, based on chewing, to predict resistant starch content in foods allows parallel determination of potentially available starch and dietary fiber. J Nutr. 1998;128(3):651–660. doi: 10.1093/jn/128.3.651.
    1. Asp NG, et al. Rapid enzymatic assay of insoluble and soluble dietary fiber. J Agric Food Chem. 1983;31(3):476–482. doi: 10.1021/jf00117a003.
    1. Matthews DR, et al. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28(7):412–419. doi: 10.1007/BF00280883.
    1. Matsuda M, DeFronzo RA. Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care. 1999;22(9):1462–1470. doi: 10.2337/diacare.22.9.1462.
    1. Venkataraman A, et al. Variable responses of human microbiomes to dietary supplementation with resistant starch. Microbiome. 2016;4(1):33. doi: 10.1186/s40168-016-0178-x.
    1. Gorham JB, et al. Addition of arabinoxylan and mixed linkage glucans in porcine diets affects the large intestinal bacterial populations. Eur J Nutr. 2016;56:2193–2206. doi: 10.1007/s00394-016-1263-4.
    1. De Filippo C, et al. Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. Proc Natl Acad Sci USA. 2010;107(33):14691–14696. doi: 10.1073/pnas.1005963107.
    1. Wu GD, et al. Linking long-term dietary patterns with gut microbial enterotypes. Science. 2011;334(6052):105–108. doi: 10.1126/science.1208344.
    1. Johansson EV, et al. Effects of indigestible carbohydrates in barley on glucose metabolism, appetite and voluntary food intake over 16 h in healthy adults. Nutr J. 2013;12:46. doi: 10.1186/1475-2891-12-46.
    1. Nilsson AC, et al. Including indigestible carbohydrates in the evening meal of healthy subjects improves glucose tolerance, lowers inflammatory markers, and increases satiety after a subsequent standardized breakfast. J Nutr. 2008;138(4):732–739. doi: 10.1093/jn/138.4.732.
    1. Arumugam M, et al. Enterotypes of the human gut microbiome. Nature. 2011;473(7346):174–180. doi: 10.1038/nature09944.

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