Baseline physiologic, metabolic and microbiome characteristics of the study participants

All twenty participants who had signs of metabolic syndrome at baseline and submitted adequate stool samples at four data collection time points were included in the current investigation (Fig. 1), which allowed for comparison of the gut microbial and SCFA profiles before and after the interventions and also between the endpoints of the RS4 and CF (control) interventions. Potential adverse gastrointestinal side effects from the interventions were not evaluated in this cohort since none were observed in the parent cohort6. Baseline characteristics of 20 participants are summarized in supplementary Table S1. Taxonomic classification of a total of 55,079 sequences (present in at least one of the samples) were sorted into 5,949 OTUs, of which ~78% were associated with the phylum Firmicutes and ~9% with the phylum Bacteroidetes (Supplementary Fig. S1).

Washout was effective in restoring microbiome characteristics

Before switching the RS4 and CF diets in the cross-over study design, all the participants were supplied with CF during the 2-week washout period in order to avoid the potential carry-over effects of the RS4 intervention. For endpoint comparison between the RS4 and CF groups, it was necessary to check for a consistent baseline prior to each treatment period. Using permutational multivariate analysis of variance for distance matrices, no significant differences were observed among the starting microbiomes of the RS4 and CF groups (data not shown), which also confirmed that the two-week washout was effective and that any differences observed post-intervention are due to the intervention itself.

Macronutrient intake pattern did not vary during the study

Variation in macronutrient intake and total calories consumed can potentially influence the gut microbiota20, thereby confounding the effects of the intervention. Although a large number of food options are offered at each meal, Hutterites have relatively small interpersonal differences in diet due to common meal planning, kitchen and dining practices. No significant differences in overall macronutrients and caloric intake were observed between the baseline and post-intervention time periods, with the exception of dietary fibre (Table 1). Dietary fibre intake, analysed separately from total carbohydrate intake, was significantly higher in the RS4 group (p < 0.001), due to RS4 being classified as a prebiotic dietary fibre (Table 1). The average calories (~1,774 Kilocalories) consumed at baseline were estimated to come from carbohydrate (~49%), protein (~17%) and fat (~34%). These values fall within the Dietary Reference Intakes (DRI) for macronutrients, which are 45–65%, 10–35% and 20–35% for carbohydrate, protein and fat, respectively21. Of particular interest, saturated fat (12.6%, DRI < 10%) and cholesterol (415 mg, DRI < 300 mg) intakes were higher, while daily fibre intake was lower (18 g at baseline, DRI 20–30 g) than recommended in the participants studied.

Table 1 Estimated nutrients intake at baseline and at the end of intervention periodsa. Full size table

Differential post-intervention effects of the RS4 diet compared with the CF diet on the gut microbiota

The current understanding is that, in studies without a proper control group, inter-individual variation in gut microbial composition in adults frequently offsets the smaller changes induced by dietary interventions10. To address this problem, we compared microbial composition and abundance post RS4 compared with post CF intervention. Three taxa, all unclassified species of Firmicutes, differentially shifted between CF and RS4 treatments in eight male participants. Similarly, a differential effect of RS4 was observed in 16 Firmicutes taxa, with the most numerous genus being Enterococcus, which was significantly enriched after CF intake in 12 female participants (data not shown). No distinct trend for Firmicutes to Bacteroidetes ratio was observed in male or female participants (data not shown). The dominance of Firmicutes and Bacteroidetes was consistent with previous results, as reported in Hutterite22 and other populations23. The Shannon diversity index was not associated with the age of the participants (r = −0.2, p > 0.05, data not shown). Likewise, the total diversity of the microbiota assessed from the Shannon diversity index did not significantly change after either CF or RS4 interventions (data not shown).

Principal coordinate analysis showed 26% and 13% variations on axes 1 and 2, respectively, indicating a major shift between the two groups (p = 0.01, Fig. 2). The RS4 diet differentially modified 71 microbial OTUs (q < 0.05), including enrichment of four each of Ruminococcus and Blautia, two each of Bacteroides and Oscillospira and one Parabacteroides OTUs (Supplementary Table S2). Of the 71 OTUs, 65 belonged to the phylum Firmicutes. The three Bacteroidetes OTUs all increased in abundance with RS4 relative to the CF treatment, while OTUs belonging to Firmicutes had a mixed response (Supplementary Table S2). At the species level, some species were significantly enriched in the RS4 group, including three Bacteroides species (>121.2 fold, q < 0.05) along with Blautia glucerasea (2497.1 fold, q < 0.001), Christensenella minuta (2.4 × 106 fold, q < 0.001), Eubacterium oxidoreducens (7723.2 fold, q < 0.01), Oscillospira spp. (2528.4 fold, q < 0.01), Ruminococcus lactaris (1.2 × 105 fold, q < 0.001) and Parabacteroides distasonis (8642.2 fold, q < 0.001), while some were significantly decreased in abundance in this group, including pathogenic Enterococcus casseliflavus (−13603.2 fold, q < 0.001) and Streptococcus cristatus (−229.7 fold, q < 0.05) (Fig. 3a). Although the enrichment fold changes for some of the bacterial species appear very high, their relative abundance in the whole microbial community could be low. This is due to the commonly used sampling normalization approach based on per million sequences to remove any bias due to varying sequencing depth (details in Methods). Overall, trends showed that Bacteroidetes OTUs were increased in the RS4 group, leading to an overall lowering of the average Firmicutes-to-Bacteroidetes (F:B) ratio in the RS4 group from 14.6 at baseline to 12.9, but increasing to 19.2 post CF diet (Fig. 3b). The lower F:B ratio is frequently perceived as an indicator of a leaner phenotype, although the previously reported results are not always consistent24. Firmicutes and Bacteroidetes are two major phyla and the species composition within each may vary widely in a given subject. It is possible that both phyla include species that may be characteristic of a particular phenotype. Therefore, a species level composition may represent a body weight phenotype more precisely than a broad estimation of F:B ratio.

Figure 2 Separation of the microbiome post intervention in RS4 and CF groups. Two-dimensional principal coordinate analyses (PCoA) based on the weighted UniFrac distance between samples, given the abundance of 5,831 taxa present in at least one sample (n = 19). Axes 1 and 2 explain 26% and 13% of the variation, respectively (p = 0.01). Full size image

Figure 3 Differential gut microbial composition after RS4 intervention at the species level. (a) Relative abundance of bacterial species (log fold change) in the RS4 group compared with the CF group post intervention (n = 19). Significant compositional variation between the two groups before the intervention was previously ruled out. (b) The Firmicutes/Bacteroidetes ratio after intervention (n = 14). The dotted line represents this ratio at baseline. (c) Abundance of major bacterial species (log fold change) before and after RS4 treatment. #, the closest hit from the NCBI 16S rRNA database cross referenced with the OTU from the Greengenes database. *q ≤ 0.05, **q ≤ 0.01, ***q ≤ 0.001, §q ≤ 0.09 (trend/approaching significance), n = 19. Full size image

Impact of RS4 on gut microbiota composition compared before and after RS4 intervention

Firmicutes species from Clostridium cluster XIVa account for almost 60% of the mucin-adhered microbiota25. A general observation was that species from Clostridial cluster XIVa, but not cluster IV, were enriched by RS4 supplementation of the diet. At the species level (Fig. 3c), RS4 consumption increased the abundance of Bifidobacterium adolescentis (90.5 fold, q = 0.087) and Parabacteroides distasonis (1180.2 fold, q < 0.001) but not Ruminococcus bromii (−3.2 fold, q > 0.05), Faecalibacterium prausnutzii (−1.2 fold, q > 0.05), or Dorea formicigenerans (1.1 fold, q > 0.05), which confirmed the previous report8. Novel observations include an RS4-induced increase in Christensenella minuta abundance (119.7 fold, q = 0.038, 97% query coverage, 88% identity and E < 0.001 in NCBI-BLAST) as well as in several OTUs in the family Ruminococcaceae and genus Bacteroides. At the species level, Bacteroides ovatus (37.6 fold, q = 0.087), Ruminococcus lactaris (2866.7 fold, q < 0.001), Eubacterium oxidoreducens (3.3 × 105 fold, q < 0.001), Bacteroides xylanisolvens (47.8 fold, q = 0.037) and Bacteroides acidifaciens (92.4 fold, q = 0.038) were enriched after RS4 intervention.

RS4 consumption altered faecal SCFAs linked to specific gut microbes

Acetate was the most abundant SCFA, accounting for over 60% of total SCFAs before and after the interventions in both RS4 and CF groups. The individual proportions of the SCFAs, butyric (69.5%, p = 0.03), propionic (50.2%), valeric (44.1%), isovaleric (20.3%) and hexanoic (19.2%) acids increased post intervention from baseline in the RS4 group (p < 0.05, Fig. 4a and Supplementary Fig. S2) but not in the CF group (data not shown). A 24.6% decrease in isobutyric acid in the RS4 group was observed. A Pearson correlation analysis showed a potential link between significant changes in the gut microbiota composition induced by RS4 and altered SCFA levels (Fig. 4b). Acetate and butyrate levels were correlated (p < 0.05) with Ruminococcus lactaris (r = 0.54) and Oscillospira spp. (r = 0.41). Total SCFAs were correlated with the abundance of two species: Methanobrevibacter spp. (r = 0.43) and Ruminococcus lactaris (r = 0.52). Propionate and isobutyrate levels were linked to Methanobrevibacter spp. (r = 0.65 and r = 0.79, respectively), Eubacterium dolichum (r = 0.42 and r = 0.43, respectively), Christensenella minuta (r = 0.39 and r = 0.59, respectively) and Ruminococcus lactaris (r = 0.59 and r = 0.40, respectively), of which the latter two were increased by RS4 (Figs 3a,c and 4b). Interestingly, these associations of SCFAs with specific gut microbiota were not observed after CF intervention (data not shown). To our knowledge, prior studies with RS4 have not reported significant SCFA changes in human faecal samples.

Figure 4 Effects of RS4 on faecal SCFAs. (a) SCFA abundance before and after RS4 intervention (*p ≤ 0.05, n = 19). (b) Positive correlation of six bacterial species with increased SCFA levels in an RS4-specific manner (all, p < 0.05). Pearson coefficients are shown on heat map. #, the closest hit from the NCBI 16S rRNA database cross referenced with the OTU from the Greengenes database. †, species either significantly enriched or approached significance in the RS4 group. Full size image

Impact of RS4 intervention on circulatory adipocytokines

In obesity, macrophages infiltrate adipose tissue and secrete proinflammatory cytokines such as IL6 and TNFα26. Also, adiponectin is released by adipocytes in the blood and has important roles in lipid and glucose metabolism27. Reduced adiponectin levels are associated with various aspects of metabolic dysfunction28. Compared with baseline, IL6 decreased by 38% (p = 0.04) and adiponectin levels increased by 20% (p = 0.002) in the RS4 group, while TNFα did not change significantly. Both TNFα and adiponectin concentrations were lower post RS4 diet compared with post CF diet (p = 0.08 and p = 0.02 respectively, Table 2). To our knowledge, this is the first report showing changes in adipocytokines, which help determine progression to cardiovascular aberrancies28,29, in response to RS4 intake in humans.

Table 2 Means of biological parameters at baseline and at the end of intervention periodsa. Full size table

Impact of RS4 consumption on body composition, lipids and glucose metabolism

Individuals had lower % body fat (p = 0.05) and lower non-high density lipoprotein (non-HDL, p = 0.003), HDL (p = 0.005) and total cholesterol (TC, p < 0.001) post RS4 consumption compared with post CF consumption (Table 1). A trend was observed for lower waist circumference (p = 0.06), glycosylated haemoglobin (HbA1C, p = 0.08) and fasting blood glucose (p = 0.09) following RS4 consumption compared with CF consumption. It is likely that response variation among participants to an RS4 diet contributed to these higher p-values. Varying responses to dietary interventions among individuals are frequently reported30. Changes in fasting glucose and HbA1C were more pronounced in this cohort (−8.6% and −1%, respectively) compared with the parent cohort (−4.2% and no decrease, respectively). Attenuation of % body fat combined with a smaller waist circumference indicates a potential reduction in central obesity in these individuals. Although significant, these changes were modest, as measures of body composition do not change rapidly in adults and can take several months to years to show a larger change. Waist circumference, TC, HDL and non-HDL were also reduced in the RS4 group compared with baseline (all, p < 0.05). No significant effects of RS4 were observed on blood pressure or triglyceride levels in either group (Table 2). The average lipid and glycaemic profiles were apparently within normal limits, likely due to prescribed medication usage for various metabolic dysfunctions (Table 2).

Inter-associations between gut bacteria and metabolic functions

Multiple novel gut microbial associations with metabolic functions were observed post intervention in the RS4 group compared with the CF group (Fig. 5). We propose that the associations detected post RS4 diet, but not post CF diet, could be induced by RS4. However, several associations were common to both groups, lacking specific response to RS4 enrichment. RS4-specific inverse correlations were observed between TC and the abundances of Bacteroides plebeius (r = −0.46), Blautia producta (r = −0.49) and Prevotella stercorea (r = −0.45; all, p < 0.05). Although the abundances of Parabacteroides distasonis and Oscillospira spp. were enriched post RS4 compared with post CF intervention, their negative association with TC, low density lipoprotein (LDL) and non-HDL were not RS4-specific (all, p < 0.05). In another instance, while RS4 did not significantly alter the abundance of Faecalibacterium prausnitzii, an RS4-specific negative correlations between this species and body mass index (BMI, r = −0.45) and % body fat (r = −0.56) were observed (all, p < 0.05). An RS4-specific correlation between adiponectin and Bacteroides ovatus (r = 0.79, p < 0.01), Bacteroides uniformis (r = 0.56, p < 0.05) and Bacteroides acidifaciens (r = 0.82, p < 0.001) was observed (Fig. 5). RS4 intake did not significantly enrich Methanobrevibacter spp. and Eubacterium dolichum, but these bacteria were correlated with weight and BMI (Fig. 5) as well as with SCFA levels (Fig. 3b) in an RS4-specific manner. Methanobrevibacter spp. (r = −0.45), Ruminococcus gnavus (r = −0.56) and Prevotella stercorea (r = −0.45) were negatively correlated with LDL (p < 0.05), while Blautia producta (r = −0.44) and Prevotella stercorea (r = −0.50) were negatively associated with TC and non-HDL (all, p < 0.05) in an RS4-specific way.

Figure 5 Associations between gut microbiota and host biological parameters after RS4 and CF interventions. (a) Heat map showing Pearson’s r values (all, p < 0.05). Black rectangular borders indicate an association present only post RS4 intervention. #, the closest hit from the NCBI 16S rRNA database cross referenced with the OTU from the Greengenes database. †, species either significantly enriched or approached significance in the RS4 group, n = 15. Full size image

Intra-association within gut microbes

Little is known about how the relative abundance of one microbial species may influence the presence of another species within the gut ecosystem, particularly in response to RS4 consumption. To evaluate this question, we examined intra-association and clustering among those bacteria that showed RS4-specific association with SCFAs and metabolic features. Three Bacteroides species that showed a positive correlation with adiponectin and Prevotella stercorea, which associated with TC, LDL and non-HDL, were clustered together (Figs 5 and 6). In general, a higher association within Bacteroidetes species or within Firmicutes species was observed, although there were exceptions. One example is Bacteroides plebius, which correlated with the Firmicutes member Blautia producta (r = 0.98, p = <0.001) and both of these were negatively linked to RS4-induced changes in TC or non-HDL. Christensenella minuta tended to associate with Ruminococcus lactaris (r = 0.58, p = 0.02) and both were enriched after RS4 intervention. Both Christensenella minuta and Ruminococcus lactaris also clustered with Methanobrevibacter spp. and Eubacterium dolichum (both, p < 0.05), but not with Ruminococcus torques and Oscillospira spp., although all of them were associated with one or more SCFAs (Figs 4b and 6).

Figure 6 Intra-associations within bacterial species that were correlated with metabolic functions or SCFAs in an RS4-specific manner. Heat map showing Pearson’s r values, corresponding to the size of the circle (n = 19). The black border indicates clustering of species (*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, shown only in the upper triangle). #, the closest hit from the NCBI 16S rRNA database cross-referenced with the OTU from the Greengenes database. †, species either significantly enriched or approached significance in the RS4 group. Full size image

Association among metabolic features

Variation in response to RS4 among participants was observed and the pattern is similar for various host metabolism parameters (response variation data not shown). This pattern may be due to the known link between high levels of circulating lipids and glucose with lower blood adiponectin levels31 and the correlation of up-regulated IL6 and tissue necrotic factor-α with the pro-inflammatory state in obesity32. The clustering of metabolic dysfunctions and CVD risk factors in adults has been observed in epidemiological studies and in the clinical setting33. In line with that, we observed consistent intra-associations among parameters of metabolic dysfunction within our data set independent of dietary changes (Supplementary Fig. S3). TC and non-HDL, but not HDL, correlated more closely with each other (r = 0.95, p < 0.001). Similar correlations were observed among various anthropometric measures, such as weight, BMI and waist circumference, as well as fasting glucose (p < 0.05). Fasting glucose correlated with IL6 level (r = 0.51, p = 0.07), which in turn was associated with diastolic blood pressure (r = 0.78, p < 0.01), systolic blood pressure (r = 0.52, p = 0.07) and waist circumference (r = 0.60, p = 0.03). Triglyceride concentrations were positively associated with weight (r = 0.82, p < 0.001), waist circumference (r = 0.60, p = 0.03) and BMI (r = 0.64, p = 0.02), while negatively correlated with HDL (r = −0.65, p = 0.02) and adiponectin (r = −0.45, p = 0.12) but had little apparent link with TC and LDL.