The gut microbiome plays an important role in human health and disease. Understanding what constitutes a health-promoting (eubiotic) or disease-promoting (dysbiotic) microbial community has become the focus of significant research. In 2011, Arumugam et al. [1] postulated that all human gut microbiomes could be classified as one of three distinct bacterial communities or “enterotypes”¹. The researchers used ordination analysis on three datasets: a set of 33 European, Japanese, and American subjects, a set of 85 Danish, and a set of 154 Americans. Each dataset independently displayed a similar clustering pattern with subjects dominated by one of three different taxa: Bacteroides, Provetella, or members of the order Clostridiales. The authors speculated that if this clear separation of clusters were evident across all human gut samples, people could be classified by their enterotype, which could in turn be used to guide diagnostics and treatment options. Only one of datasets showed clear separation between the Bacteroides- and Clostridiales-dominated samples, while two showed overlapping points consistent with a gradient.

Since the introduction of the enterotypes hypothesis, several studies have evaluated their presence in other human gut communities. The first such study by Wu et al. [2] sequenced 98 Americans and used multiple clustering techniques but found only two of the three original clusters: Bacteroides and Prevotella, laying at either end of a Prevotella-Bacteroides gradient. The third cluster was not distinct in their data, with Ruminococcus (a Clostridiales genus) equally abundant in both of the first two enterotypes. They compared their findings with a previously published study on Italian and rural African children wherein Italian children had Bacteroides-dominated gut microbiomes and African children Prevotella-dominated gut microbiomes. As part of our own analysis of over 200 healthy American subjects enrolled in the Human Microbiome Project (HMP) [3], we confirmed the results of Wu et al. that two of the three clusters were not distinct. Samples high in Bacteroides or members of the order Clostridiales, including Ruminococcus, form a continuum, rather than discrete clusters. While the Prevotella-dominated samples did cluster separately with the HMP data, we speculated that as more samples and studies were evaluated, the separation between clusters would diminish, and gut communities would appear as a continuum of abundance gradients between Bacteroides, Prevotella, and various members of the order Clostridiales. In a review article published soon after, Jeffrey et al. [4] reached the same conclusion that a gradient of microbial populations was a more accurate interpretation of the data.

Several studies thereafter sought to re-examine enterotypes using more careful clustering analyses [5–7]. Overall, these additional studies demonstrated that with increased statistical rigor, support remained for only two community types: one dominated by Prevotella and one dominated by Bacteroides or members of the Firmicutes [5]. Additionally, the utility of enterotypes to classify patients was shown to be limited given that individuals can shift between Bacteroides/Firmicutes- and Prevotella-dominated communities over time, with as many as 30 % of samples in one dataset shifting between enterotypes [6, 7]. Knights et al. [6] further cautioned that supervised ordination methods can lead to false clustering when the number of features in the data (here microbial genera) is much higher than the number of samples, as is the case in most human gut microbiome studies. Overall, these studies found that the weight of evidence did not support the classification of human gut microbiomes into discrete and stable enterotypes.

Despite the ongoing refutation of the appropriateness and stability of enterotypes, researchers continue to use them to interpret ordination analyses that separate Prevotella-dominated communities from those dominated by Bacteroides, Clostridiales, and a fourth taxon, Bifidobacterium. These communities recur across various study populations and diets. For instance, in a study that predated the enterotypes hypothesis, De Filippo et al. [8] studied 29 children, 15 from Italy and 14 from Burkino Faso. The gut microbiomes of the Burkino Faso children were mostly dominated by Prevotella, while the Italian children were mostly dominated by Bacteroides and Clostridiales. Both populations included a few subjects dominated by Actinobacteria, mainly Bifidobacterium, a beneficial microbe associated with milk-based diets such as breast-feeding [9]. The Burkino Faso diet was rich in vegetables and fiber, and children were breast-fed up to 2 years of age. The diet of the Italian children was a “Western diet” low in fiber, incorporating more animal protein, fat, and sugar, and children tended to be breast-fed for only 1 year. It was postulated that diet could be a driving factor in shaping the gut microbiome.

Ou et al. [10] compared twelve African Americans and twelve native Africans as part of a study examining the higher prevalence of colon cancer in Americans. The populations clustered distinctly, with the African Americans dominated by Bacteroides and the native Africans by Prevotella. The native African diet was high in complex fiber, while the African American diet was a Western diet high in protein. Yatsunenko et al. [11] compared gut microbiome samples of healthy adults and children from Malawi, Venezuela, and the USA. They found a gradient of Bacteroides and Prevotella in both adults and children over 6 months. They also found a discrete group of samples dominated by Bifidobacterium, in children under 6 months of age, which is consistent with its association with breast-feeding. The US subjects clustered separately from the Malawian and Venezuelans, who had relatively more Prevotella.

In a comparison of 50 metropolitan and 46 rural Russians, Tyakht et al. [12, 13] found between two and three community types. Prevotella accounted for one of the clusters and was prevalent in samples from one of the rural villages, but Firmicutes (rather than the combination of Bacteroides and Firmicutes) and Bifidobacterium dominated the remaining samples. The ingredients of the diet eaten by the metropolitan subjects were similar to those in a Western diet, but the sources of their food were very different. The authors speculated that the lack of Bacteroides-dominated samples could be due to the metropolitan Russian food sources, which were homegrown rather than industrially processed. Overall, the results from these various studies are consistent. The gut microbiomes of Americans and Europeans having a Western diet tend to be dominated by Bacteroides and Clostridiales, while rural populations with a high fiber, low-protein diet tend to be dominated by Prevotella.

Despite the frequent use of the term “enterotype” to describe clustering of the gut microbiomes of research subjects from different cultures and with different dietary habits, the membership of the microbial communities associated with the enterotypes has not been clearly defined. Prevotella, Bacteroides, Clostridiales, and Bifidobacterium are recurring taxa, but a deeper understanding of the community structure of samples dominated by these taxa across continents and cultures has not been undertaken. Whether distinct community types or gradients, stable over time or not, we do not have a functional description of what other bacteria comprise the Prevotella- or Bacteroides/Clostridiales-dominated clusters beyond the dominant taxon. Here we present an analysis of the combined data from these published studies to characterize the broader community membership of the two primary enterotypes, so that we might better understand what these communities can tell us about the human gut microbiome.

Our analysis compiled 747 samples from five studies encompassing 484 genera. We included 126 samples from the Arumugam et al. [14]: 41 mixed Europe and Asia, 85 Europeans, 24 Native African and African American samples [10], 96 urban and rural Russians samples [12], 210 samples of healthy American adults from the Human Microbiome Project [15], and 291 adult samples from Malawi, Venezuela, and America [11].

Multivariate analysis

The “enterotype” hypothesis was initially proposed based on clustering results seen in a principal coordinates analysis. In our initial ordination analysis, we found that all three populations from the Yatsunenko study clustered separately from the remaining studies (Additional file 1: Figure S1). We therefore performed our primary multivariate analyses without these samples but then confirmed our findings with an independent analysis of them. Combined samples from the four remaining datasets do not cluster discretely with either metric or non-metric multidimensional scaling (PCoA² or NMDS³), using Bray-Curtis or Morisita-Horn distances, but instead show a gradient across both dominant taxa and study populations (Fig. 1, Additional file 2: Figure S10). Coloring by dominant taxa (Fig. 1a) and the Prevotella ratio (the ratio of Prevotella to the sum of Prevotella and Bacteroides) (Fig. 1b) illustrate the relationship of axes 1 and 2 to Bacteroides (toward the upper right) and Prevotella (toward the lower left) in the samples. When colored by population, we see that the second axis separates the samples within a population, as does the first axis but to a lesser extent (Fig. 1c). In PCoA analysis of the Malawi, Venezuela, and US populations from the Yatsunenko study (Additional file 3: Figure S7), the first axis appears to correspond primarily to the Prevotella ratio. The three countries are spread along the second axis with the US samples separating from the other two, but the Malawian and Venezuelan samples are intermingled. In general, these data do segregate along the first axis, with fewer intermediate samples than in the combined studies, but distinct clustering is unclear. These ordination results show that the primary axes continue to correlate with Bacteroides and Prevotella when the studies are combined.

To assess the persistent clustering of the microbial communities independent of the relative abundances of Bacteroides and Prevotella, we removed these two taxa from all the samples and reran the PCoA analysis (Fig. 1d, Additional file 3: Figure S7B). Without Prevotella and Bacteroides, the original microbial community classifications (“enterotypes”) no longer segregate the samples. The ellipses in Figs. 1a and 1d were constructed as confidence regions for each of the groups; these regions would represent 95 % of the points if the bivariate data were Gaussian. This demonstrates the effect on the clustering of removing the two dominant taxa. When the dominant taxa are included, the ellipses abut without overlapping. When the dominant taxa are removed, two of the ellipses are almost completely within the third, and all but three of the Prevotella-dominated samples are contained within the ellipse delineating the central Bacteroides samples. Separation of the original clusters is no longer present, and the remaining taxa now account for only 33 % of the variability along axes 1 and 2, as opposed to 54 %. The ordinations of the full samples appear to be driven primarily by the relative quantities of these two highly abundant taxa and not by the presence of distinct microbial communities associated with each group. Interestingly, there is still clustering structure and differentiation of samples visible in Fig. 1d, as one would expect when comparing samples from patients in different geographic regions having different lifestyles and diets. These data structures, however, no longer correlate clearly with Bacteroides and Prevotella.

Taxonomic components of microbial communities

To identify taxa integral to a broader definition of microbial community structures, we looked for taxa that correlated with the dominant genus (Prevotella or Bacteroides) across studies (Additional file 4: Table S1). Several researchers reported correlations within Bacteroides-dominated samples within their studies, including Acidminococcus, Roseburia, Faecalibacterium, Anaerostipes, Parabacteroides, and Clostridiales [14], Alistipes only [16], Escherichia (Enterobacteriaceae) and Acinetobacter [10], and Faecalibacterium and Enterobacteriaceae [8]. None of these associations, however, were apparent across all studies. In fact, only Faecalibacterium and Enterobacteriaceae appear in even two of the four studies that reported correlation analyses. No correlations were found within the Prevotella-dominated samples in more than one previous study. Prevotella was associated with Streptococcus, Enterococcus, Desulfovibrio, and Lachnospiraceae [14], Succinivibrio and Oscillospira [10], and Xylanibacter and Butyrivibrio [8]. The majority of taxa found in the Russian communities [12, 13] were not found in previously reported studies. Tyakht et al. used triplets of the three most abundant members of their various samples to compare with these other studies and found that 43 % of their samples were dominated by triplets not found in the non-Russian groups.

To confirm this finding of no consistent multi-study taxa associated with either dominant taxon, and to increase our statistical power of detection, we combined the samples from all of the studies and reanalyzed them. We used the Spearman coefficient (a nonparametric method used in most of the previous studies) as well as SparCC [17]. Using Spearman on all the data and the DESeq2 [18] negative binomial test on the HMP data, we found no taxa in the combined set of Prevotella-dominated samples that correlated with Prevotella above our threshold for statistical and biological significance (Additional file 4: Table S1). SparCC did find two Clostridiales genera that correlated with Prevotella in the combined dataset, Ruminococcus (R ² = 0.77) and Dialister (R ² = 0.73), but neither of these had a significant correlation in any single study. In the combined Bacteroides-dominated samples, we did not find any significant correlations using the Spearman correlation. A few Clostridiales taxa and one Bacteroidales genus in individual studies did seem correlated with Bacteroides, but none of these were found in more than two of the studies. Using SparCC, Subdoligranulum (R ² = 0.75) and Faecalibacterium (R ² = 0.73) correlated with Bacteroides but only in the combined set, not in any of the individual datasets. No taxa were significant in more than one of the studies when analyzed separately. These data do highlight that members of both Bacteroidales and Clostridiales are prominent members of the human gut microbiome of Americans and Western Europeans. To explore additional taxonomic differences between groups defined in the Yatsunenko and HMP studies as Prevotella-rich and Bacteroides-rich, we used the package DESeq2 and the tests based on variance transformed data using the negative binomial model (DESeq function with default arguments) as explained in [18]. This provided a ranking of the most differentially abundant taxa. The analysis of the Yatsunenko data showed Parabacteroides, Alistipes, and Subdoligranulum, to be more prevalent in Bacteroides-dominated samples, but these results were not reflected in the HMP data. The DESeq2 analysis confirmed the previous analyses, showing no taxa consistently associating with the Bacteroides- and Prevotella-dominated samples. Recent work by Lovell et al. [19] has emphasized the importance of proportionality analysis when working with relative abundance data. Applying their proposed algorithm on our compiled data, we still found no significant proportionalities across studies. The taxa that had proportionality statistics less than 0.01 were either very rare or even possibly artifacts of the sequencing process. Two of these taxa were found in only one sample, and all of the others were identified in only populations from one study (the European samples from the Arumugam study). They are not prevalent in either the Prevotella- or Bacteroides-dominated samples, and in fact, are absent from the majority of both. These taxa are clearly not important functional components or biomarkers of a human gut community. The analysis itself can be found in the Additional file 5 containing the Rmd commands, data and output. This file contains all the code the reader could require to follow the same workflow on a different data set.

To further understand groupings based on the abundance of Prevotella, we compared the Prevotella ratio (Prevotella/[Bacteroides + Prevotella]) for all samples in each study. Figure 2 illustrates the clear presence of samples across the full spectrum of relative abundances of Prevotella and Bacteroides, although different studies have varying numbers of samples in the intermediate ranges. Americans and Europeans, which make up the largest number of subjects, have fewer samples with intermediate Prevotella ratios, while studies containing more rural subjects have a greater number of intermediate samples. Bacteroides tends to be higher in Western populations, with a more even distribution of values across samples (Additional file 1: Figure S1), while Prevotella tends to have fewer intermediate values in the Western samples and more in Russian and rural samples (Additional file 6: Figure S2).

We compared the abundance distributions of Prevotella and Bacteroides with other common genera in the combined studies (Fig. 3 and Figure S11 in the Additional file 7 for the same boxplot without the Yatsunenko study). Bacteroides, Prevotella, Lachnospiraceae, Faecalibacterium, Roseburia, Ruminococcus, Alistipes, and Coprococcus are among the next most common genera across all studies. Bacteroides, Prevotella, and Alistipes are members of the order Bacteroidales, while the remaining taxa are all members of Clostridiales. In approximately 42 % of samples, Bacteroides has a relative abundance between 10 and 40 %, with more than 80 % of samples having a relative abundance greater than 5 %. Prevotella has a relative abundance between 10 and 40 % in 16 % of samples, but 75 % of samples have a relative abundance less than 5 %. While Bacteroides exhibits a higher proportion of intermediate abundances overall than does Prevotella, Prevotella shows a spectrum of abundances rather than a bimodal distribution as some have assumed, with more samples between 40 and 80 % than any taxon other than Bacteroides.

Although several studies show some discrete clusters, most other studies have shown a continuous gradient of communities [2–7] that can change within an individual over time [6, 7], and no one has evaluated the taxonomic composition of the clusters across studies. The consistent body of evidence accumulating about the structure and role of the microbiome in human health demonstrates that standard microbiome methods are sufficient to compare across studies of the human gut. Here we analyze the combined microbiome data of five earlier studies and show that the gut microbiomes labeled as Bacteroides and Prevotella “enterotypes” do not represent consistent or predictable communities. No other bacterial taxa correlate with either of the two primary biomarkers across studies other than the taxa for which they are named. While the original study included a correlation analysis, the results represented the differences in their particular study populations, rather than a set of universal correlations. As was pointed out as early as 1896 by Pearson [20], proportions give rise to spurious correlations and our approach here relies on the study of proportions that eliminate the common denominators as suggested in [19].

Despite ongoing re-evaluation of the evidence, researchers have continued to use the term “enterotype” to describe differences between microbiome communities in study populations. The continued use of enterotype classifications is primarily a result of the ordination algorithms used to differentiate sets of samples based on the most distinguishing taxa. Of all the taxa in the combined studies, Bacteroides and Prevotella are the only two whose relative abundances frequently exceed 40 % (Fig. 3). Additionally, it is often the case that when Bacteroides is high in a sample, Prevotella will be low, and vice versa. These two taxa, therefore, contribute more than any others to the pairwise dissimilarity between samples. The various ordination method (PCoA, NMDS) plot samples with larger pairwise distances farther apart, and samples with smaller pairwise distances closer together. Because Prevotella and Bacteroides have the greatest range of values and tend to be inversely related, they contribute the most to the magnitude of pairwise distances, and therefore contribute the most to separation in ordination plots, leading to Bacteroides- and Prevotella-dominated gradients in ordination plots. Bacteroides abundance correlates significantly with the first axis, having a correlation of 0.93 (p value <10^-5). Prevotella abundance correlates significantly with the second axis having a negative correlation of −0.8 (p value <10^-4).

To illustrate how these Bacteroides and Prevotella abundances can drive ordination plots and that no other taxa were associated to the “enterotype” definitions, we compared ordination results with and without these two taxa (Fig. 1a, d, Additional file 8: Figure S4D, Additional file 9: Figure S5D, Additional file 3: Figure S7D, and Additional file 10: Figure S9D). We found that the remaining taxonomic-community members did not continue to separate these samples by microbial component classification. Ordination plots are not a means for defining underlying community characteristics and can only be part of the exploration of community structure rather than the endpoint of analysis. The dearth of any consistent features of these communities across independent studies is a reminder for us not to extrapolate from PCoA and NMDS plots to assumptions about the defining characteristics of the entire communities. In studies comparing only two very different populations (e.g., Western urban and non-Western rural), separation will likely remain even without Bacteroides and Prevotella, but we cannot extrapolate from any two populations to consistent clustering across a range of diverse populations when the two dominant taxa, Bacteroides and Prevotella, are removed. The combination of correlation results and ordinations with and without the dominant taxa can be used to compare across other studies to interpret clusters as driven by the most dominant taxa or by a deeper community structure.

We believe that using the term “enterotype” to describe gut communities is misleading because it implies a microbiome community type shared among samples with the same enterotype designation. We propose the use of the term “biomarker” to describe the dominant taxon of the community rather than “enterotypes” to describe gut microbiomes. Biomarkers serve as a measurable indicator of a biological state or environmental exposure [21]. In this regard, genera such as Prevotella, Bacteroides, and Bifidobacterium potentially serve as effective biomarkers of diet and lifestyle. Across the studies, Prevotella was associated with non-Western, rural communities, and a plant-based diet rich in polysaccharides and fiber [2, 8, 10, 11, 13, 21]. In each study sampling the gut microbiome of subjects from native or rural villages (Burkina Faso [8], South Africa [10], Venezuela and Malawi [11], and Russia [12]), Prevotella was commonly dominant in the adult samples. In these same studies, Bacteroides-dominated samples were more generally associated with samples from US and European subjects eating a Western diet, richer in protein and fat. Wu et al. [2] graphically illustrated correlations between Bacteroides and dietary amino acids and fats.

Beyond Prevotella and Bacteroides, additional taxa can also serve as biomarkers of diet or disease. Bifidobacterium was identified as a dominant bacterium in several studies [8, 11, 12], especially the gut microbiome of children [8, 11]. Bifidobacterium has specific adaptations that provide a competitive advantage for metabolizing the specific human milk oligosaccharides found in breast milk [9]. Various members of the phylum Firmicutes are associated with plant-derived fiber, amino acids, and fats. Firmicutes as a phylum, however, is unlikely to be an effective candidate for a biomarker, as it incorporates a diversity of genera. David et al. [22] report an increase in the bile-tolerant bacteria: Alistipes, Bilophila, and Bacteroides with an animal-based diet, while Roseburia, Eubacterium, and Ruminococcus were more abundant on a diet rich in plant polysaccharides. Some of these genera also have the potential to be biomarkers. Ou et al. [10] compared African Americans with a high risk of colon cancer and native Africans with only rare cases of colon cancer and suggested that Bacteroides, Escherichia, and Acinetobacter may be possible biomarkers for cancer. Baxter et al. [23] found correlations between Bacteroides, Parabacteroides, Alistipes, and Akkermansia with increased tumor burden and Clostridiales Cluster XIVa (which includes Roseburia and Faecalibacterium) with decreased tumor burden. Fusobacterium has also been associated with colon cancer [24]. The use of specific bacterial taxa to characterize the ecological roles of microbial community members was recently described by Trosvik and de Muinck [25]. They employed ecological concepts of keystone and foundation taxa, which complement their utility as biomarkers. Designation as keystone and foundation taxa requires knowledge of their causative role in a community, whereas biomarkers only need to demonstrate a correlation with diet, lifestyle, or disease state.

We have demonstrated that the “enterotypes” of the human gut microbiome do not represent recurrent microbial communities across the diversity of human populations, nor do they represent two distinguishable communities when Bacteroides and Prevotella are removed from the analysis. We propose that the dominant taxa associated with the original “enterotypes” hypothesis, Bacteroides and Prevotella, as well as other taxa such as Bifidobacterium, Fusobacterium, and various Clostridiales genera, are more accurately understood as biomarkers. The many correlative studies of the gut microbiome and associations with diet, environment, and disease can be made more useful by moving beyond basic ordination plots to rigorously assessing their sensitivity and specificity as biomarkers of lifestyle and disease in clinical subpopulations.

The data and the R script will be available at a permanent url for the html files associated to the article on publication (see the Rmd and html files have been deposited at the permanent url http://purl.stanford.edu/fs506ff9976).