Metformin Is Associated With Higher Relative Abundance of Mucin-Degrading Akkermansia muciniphila and Several Short-Chain Fatty Acid–Producing Microbiota in the Gut

Microbial communities (microbiota) and their associated genes (microbiome) constitute the interface of our environs and our cells, and their composition is believed to play a deterministic role in human health and disease. In particular, the development of type 2 diabetes, a disease rising in prevalence around the globe, has been linked in nonhuman (1) and human (2–5) studies to imbalances in microbiota of the intestinal tract (gut). However, the most recent human study on this topic found that the association was modulated in a potentially beneficial manner by metformin treatment (6).

Metformin (1,1-dimethylbiguanide hydrochloride) is the most frequent medication used to treat type 2 diabetes (7), and findings from recent studies suggest it may also prevent cancer (7) and cardiovascular events (8). Metformin has pleiotropic effects, yet the majority of mechanistic studies have focused on changes in liver function (7,9,10). Although metformin certainly alters hepatic glucose production via effects on AMPK, there is growing evidence that the genesis of its action is in the gut (11–15).

Metformin is ∼50% bioavailable, allowing for near-equal intestinal and plasma exposure, but intestinal accumulation of metformin is 300 times that of the plasma (16), making the gut the primary reservoir for metformin in humans. Unlike oral administration, intravenous administration of metformin in humans does not improve glycemia (14). Moreover, in mice, oral administration of a broad-spectrum antibiotic cocktail with oral metformin abrogates metformin’s glucose-lowering effect (12). Providing yet further evidence that the glucose-lowering effect of metformin may originate in the lower bowel, a delayed-release oral metformin, which targets the ileum, had a similar or greater glucose-lowering effect than immediate-release or extended-release metformin, despite the delayed-release metformin having lower systemic exposure than the other metformin formulations (15).

Recent studies in animals (11,12,13) and humans (6,17) provide evidence that metformin may partially restore gut dysbiosis associated with type 2 diabetes. In mice fed a high-fat diet, metformin treatment increased the relative abundance of Akkermansia muciniphila (11–13), a mucin-degrading bacteria that has been shown to reverse metabolic disorders (1,12). In humans, participants with diabetes taking metformin had similar abundance of Subdoligranulum and, to some extent, Akkermansia compared with control subjects without diabetes, suggesting that metformin may help ameliorate a type 2 diabetes–associated gut microbiome (6). It has also been shown that people with diabetes taking metformin had a higher relative abundance of Adlercreutzia (17), and metagenomic functional analyses demonstrated significantly enhanced butyrate and propionate production in people with diabetes using metformin (6). In contrast, people with diabetes who were not treated with metformin had a higher abundance of Eubacterium and Clostridiaceae SMB53 (17) and lower levels of short-chain fatty acid (SCFA) producers, such as Roseburia, Subdoligranulum, and a cluster of butyrate-producing Clostridiales (6). These findings provide evidence that gut microbes may contribute to the antidiabetes effects of metformin through pathways that include mucin degradation and SCFA production.

In this study we aimed to test the generalizability of previous observations concerning the influence of metformin on the association of type 2 diabetes and gut dysbiosis in a Colombian adult population. Given the considerable variation in the microbiota associated with type 2 diabetes and that the gut microbiota of Colombians is different to that of other populations (18), we hypothesized that the microbial taxa involved in the type 2 diabetes dysbiosis of Colombians are different to those observed in Chinese and European populations (2,3) but that the effect of metformin is similar, i.e., through enrichment of mucin-degrading and SCFA-producing microbiota.

Between July and November 2014, we enrolled 459 men and women 18–62 years old, with BMI ≥18.5 kg/m², living in the Colombian cities of Medellin, Bogota, Barranquilla, Bucaramanga, and Cali. All participants enrolled in the study were insured by the health insurance provider EPS y Medicina Prepagada Suramericana S.A. (EPS SURA). We excluded pregnant women, individuals who consumed antibiotics or antiparasitics <3 months prior to enrollment, and individuals diagnosed with Alzheimer disease, Parkinson disease, or any other neurodegenerative diseases; current or recent cancer (<1 year); and gastrointestinal diseases (Crohn disease, ulcerative colitis, short bowel syndrome, diverticulosis, or celiac disease).

This study was conducted in accordance with the principles of the Declaration of Helsinki, as revised in 2008, and had minimal risk according to the Colombian Ministry of Health (Resolution 008430 of 1993). All of the participants were thoroughly informed about the study and procedures. Participants were assured of anonymity and confidentiality. Written informed consent was obtained from all the participants before beginning the study. The Bioethics Committee of Sede de Investigación Universitaria—University of Antioquia reviewed the protocol and the consent forms and approved the procedures described here (approbation act 14–24–588 dated 28 May 2014).

We calculated BMI as weight (kg)/height squared (m²) to classify participants as lean (18.5 ≤ BMI < 25.0 kg/m²), overweight (25.0 ≤ BMI < 30.0 kg/m²), or obese (BMI ≥30 kg/m²). In addition, values of HDL, LDL, VLDL, total cholesterol, triglycerides, apolipoprotein B, fasting glucose, glycated hemoglobin (HbA_1c), fasting insulin, adiponectin, and hs-CRP were obtained (collection and measurement explained in Supplementary Data). Dietary intake was evaluated through 24-h dietary recalls (see Supplementary Data).

Each participant collected their own fecal sample in a hermetically sealed, sterile receptacle provided by the research team. Samples were immediately refrigerated in household freezers and brought to an EPS SURA facility in each city within 12 h; receipt of samples occurred exclusively in the morning (6 a.m.–12 p.m.). As such, stools were collected between the evening of the day before and the morning of the day of sample receipt. Fecal samples were stored on dry ice and sent to a central laboratory via next-day delivery. Before DNA extraction, stool consistency was evaluated by trained laboratory technicians.

Total microbial DNA was extracted using the QIAamp DNA Stool Mini Kit (Qiagen, Hilden, Germany) following the manufacturer’s instructions, with a slight modification consisting in a bead-beating step with the lysis buffer (20 s at 15 Hz using a stainless steel bead with a 5-mm diameter). After extraction, we quantified DNA concentration using a NanoDrop spectrophotometer (Nyxor Biotech, Paris, France) and sent it to the Microbial Systems Laboratory, University of Michigan Medical School (Ann Arbor, MI). The V4 hypervariable region of the 16S rRNA gene was amplified using the F515 (5′-CACGGTCGKCGGCGCCATT-3′) and R806 (5′-GGACTACHVGGGTWTCTAAT-3′) primers and sequenced using the Illumina MiSeq sequencing platform with V2 chemistry and the dual-index sequencing strategy (19). In addition to DNA from fecal samples, we sequenced negative controls (ultrapure water and the QIAamp DNA Stool Mini Kit’s EB elution buffer), a DNA extraction blank, and a mock community (HM-782D, BEI Resources, Manassas, VA) in each instrument’s run. Sequences were curated following the MiSeq standard operating procedure implemented by Mothur v.1.36 (20) (see Supplementary Data). Raw sequences were deposited at the NCBI and can be accessed through the BioProject (accession number PRJNA325931).

We identified 28 participants in our study that had type 2 diabetes; 26 self-reported physician-diagnosed diabetes prior to the beginning of the study and 2 were diagnosed through laboratory testing (fasting blood glucose ≥126 mg/dL and HbA_1c ≥6.5%). Of the 28 participants with type 2 diabetes, 14 were under metformin treatment, 14 were not (1 was treated with insulin alone, 2 with glibenclamide, and 11 were under no pharmacological treatment for type 2 diabetes, including the 2 participants unaware of their diabetes status) (Supplementary Table 1).

We matched each participant with diabetes with three participants without diabetes in our study based on sex, age (to the closest possible age; maximum difference between case and control subjects 6 years; mean 1.5 years; median 1 year), and BMI category (lean, overweight, or obese). This left us with a total analytic sample of 112 study participants comprising 14 with type 2 diabetes using metformin (T2D-met⁺), 14 with type 2 diabetes not using metformin (T2D-met⁻), and 84 without diabetes (ND).

Anthropometric and clinical variables were compared across study groups using ANOVA and t tests after checking for homoscedasticity and normal distribution of residuals (using Fligner-Killeen tests of homogeneity of variances and Shapiro-Wilk normality tests). When necessary, variables were appropriately transformed (natural log for unconstrained variables or arcsin square root for proportions). Sex ratio and stool consistency were compared using χ² tests. Statistical analyses were performed with R v.3.2.2 (21).

Curated DNA sequences ranged from 69 to 102,660 sequences per sample (median 28,699). To limit the effects of uneven sampling, we rarefied the data set to 4,091 sequences per sample, resulting in the exclusion of one T2D-met⁻ participant with 69 reads. Although rarefaction may lead to missing low-abundance data, it is a powerful way to reduce the likelihood of detecting false positives, especially among those operational taxonomic units (OTUs) with very low abundance.

The gut microbiota structure and composition was assessed by quantifying and interpreting similarities based on intra- and intergroup diversity analyses (α and β diversity, respectively). For α diversity, we calculated Good’s coverage and the number of OTUs of each sample using Mothur and constructed rarefaction curves. We compared these indices among groups of participants using analysis of similarity (ANOSIM) with 1,000 permutations using the Vegan package of R (22).

β Diversity was assessed using phylogeny-based generalized UniFrac distances (with the α parameter controlling weight on abundant lineages = 0.5) calculated with the GUniFrac package of R (23). For this, we first reduced the alignment and the OTU table to one representative sequence per OTU, then obtained a distance matrix from uncorrected pairwise distances between aligned sequences, and finally constructed a relaxed neighbor-joining phylogenetic tree using Mothur and Clearcut. Comparisons among groups of participants were performed using the adonis function (ANOVA using distance matrices) of the permutational multivariate ANOVA (PERMANOVA) implemented in the Vegan package of R (22).

We next used linear discriminant analysis (LDA) effect size (LEfSe) (24) to agnostically identify microbial biomarkers. LEfSe uses the nonparametric factorial Kruskal-Wallis sum rank test to detect individual OTUs with significant differential abundance among groups of participants, then performs a set of pairwise tests among groups of participants using the unpaired Wilcoxon rank sum test, and finally uses LDA to estimate the effect size of each differentially abundant OTU (24). The strength of LEfSe compared with standard statistical approaches is that, in addition to providing P values, it provides an estimation of the magnitude of the association between each OTU and the grouping categories (e.g., metformin, type 2 diabetes) through the LDA score. For stringency, microbial biomarkers in our study were retained if they had a P < 0.05 and a (log10) LDA score ≥3, i.e., one order of magnitude greater than LEfSe’s default.

Finally, across groups, we tested for differences in relative abundance of the mucin-degrading A. muciniphila and major butyrate-producing microbial genera, including Butyrivibrio, Roseburia, Subdoligranulum, and Faecalibacterium. For this analysis, we pooled all OTUs classified in each of these phylotypes (4 for A. muciniphila, 11 for Butyrivibrio, 4 for Roseburia, 10 for Subdoligranulum, and 5 for Faecalibacterium) and tested for differences using ANOVA and t tests on arcsin square root transformed relative abundances. This pooling served to examine whether differences in relative abundance of these groups of bacteria occurred across all OTUs or only in specific OTUs.

Results from LEfSe and from the pooled analysis of phylotypes were corrected for multiple testing using the Bayesian approach implemented in the qvalue package of R (25). Tests were considered significant if they had a P value ≤ 0.05 and a q value ≤0.1.

In Table 1 we present the characteristics of T2D-met⁺, T2D-met⁻, and ND participants. There were no statistically significant differences (all P values > 0.10) in demographic, anthropometric, or clinical parameters between T2D-met⁺ and T2D-met⁻ participants. Compared with ND participants, T2D-met⁺ participants had higher fasting glucose, HbA_1c, and insulin resistance than ND participants and lower levels of the insulin-sensitizing hormone adiponectin (P < 0.05). No other demographic, anthropometric, or clinical parameters were statistically different.

Gut microbiota communities were specific to each participant with marked intersubject differences (Fig. 1A) (overall interindividual generalized UniFrac distance = 0.720 ± 0.009). We found high coverage across all groups of participants (mean Good’s coverage ± SD = 0.990 ± 0.001); 99% of OTUs were detected at least by two DNA reads, demonstrating thorough sampling of the gut microbiota. We next tested for differences in the number of observed OTUs across the groups of participants. We found no differences between participants with diabetes and ND participants (ANOSIM statistic R = 0.005, P value = 0.425) or between T2D-met⁺ and T2D-met⁻ participants (ANOSIM statistic R = −0.018, P = 0.557). The number of observed OTUs tended to be more similar between T2D-met⁺ and ND than between T2D-met⁻ and ND participants (Supplementary Fig. 1); however, these differences were not statistically significant (T2D-met⁺ vs. ND: ANOSIM statistic R = 0.018, P = 0.348; T2D-met⁻ vs. ND: ANOSIM statistic R = 0.009, P = 0.409).

We observed no significant differences in β diversity estimates among the three groups of participants (PERMANOVA: R² = 0.019, P = 0.335) (Fig. 1A) or between participants with diabetes and ND participants (R² = 0.009, P = 0.416) (Fig. 1B). However, the comparison between metformin and nonmetformin users reached significance (R² = 0.013, P = 0.036) (Fig. 1C), demonstrating differences in the bacterial community structure associated with metformin use. The difference was also significant when comparing T2D-met⁺ and ND participants (R² = 0.015, P = 0.036) but not when comparing T2D-met⁻ and ND participants (R² = 0.008, P = 0.943). These results suggested the microbial communities of T2D-met⁺ versus T2D-met⁻ were modestly phylogenetically dissimilar.

We next used LEfSe to examine differences in the relative abundance of gut microbiota at the OTU level. Note that we were only interested in OTUs displaying strong associations in the LDA (represented by OTUs with [log10] LDA scores ≥3); such stringency resulted in fewer retained, but more likely biologically relevant, OTUs (19 displayed in Fig. 2 and Fig. 3 out of 273 statistically significant if not taking LDA scores into account). When comparing T2D-met⁻ and ND participants, we found that OTUs belonging to Clostridiaceae 02d06 (Firmicutes|Clostridiaceae|OTU074) and Prevotella (Bacteroidetes|Prevotellaceae|OTU236) were overrepresented in T2D-met⁻ participants, whereas Enterococcus casseliflavus (Firmicutes|Enterococcaceae|OTU048) was more abundant in ND participants (Figs. 2A and 3A and B). When comparing T2D-met⁺ participants to ND participants, we found that OTUs of Butyrivibrio (Firmicutes|Lachnospiraceae|OTU062), a different OTU of Prevotella (Bacteroidetes|Prevotellaceae|OTU028), Megasphaera (Firmicutes|Veillonellaceae|OTU069), Bifidobacterium bifidum (Actinobacteria|Bifidobacteriaceae|OTU176), two OTUs of Mollicutes RF39 (Tenericutes|OTU284 and OTU067), and Bulleidia p-1630-c5 (Firmicutes|Erysipelotrichaceae|OTU077) were more abundant in T2D-met⁺ than in ND participants. In contrast, four OTUs of Clostridiales including Clostridium celatum (Firmicutes|Clostridiaceae|OTU014), Clostridiaceae SMB53 (Firmicutes|Clostridiaceae|OTU026), Oscillospira (Firmicutes|Ruminococcaceae|OTU024), and Cellulosibacter alkalithermophilus (Firmicutes|Ruminococcaceae|OTU025) were more abundant in ND than in T2D-met⁺ participants (Figs. 2B and 3C and D). OTUs from Prevotella (Bacteroidetes|Prevotellaceae|OTU028) and Megasphaera (Firmicutes|Veillonellaceae|OTU069) were enriched in T2D-met⁺ compared with T2D-met⁻ participants, whereas OTUs from Oscillospira (Firmicutes|Ruminococcaceae|OTU024), Barnesiellaceae (Bacteroidetes|OTU102), and a different OTU of Clostridiaceae 02d06 (Firmicutes|Clostridiaceae|OTU080) were enriched in T2D-met⁻ compared with T2D-met⁺ participants (Figs. 2C and 3E and F).

Finally, when we pooled mucin-degrading and butyrate-producing microbes, we found that A. muciniphila and Butyrivibrio were more abundant (3.4 and 4.4 times, respectively) in T2D-met⁺ than in T2D-met⁻ participants; differences were statistically significant for A. muciniphila (F_{1, 109} = 9.46, P = 0.003, q value = 0.01) but not for Butyrivibrio (F_{1, 109} = 3.03, P = 0.08, q value = 0.21) (Fig. 3G and H). There were no significant differences in the other groups of butyrate producers between metformin and nonmetformin users (Roseburia: F_{1, 109} = 1.44, P = 0.23, q value = 0.39; Subdoligranulum: F_{1, 109} = 0.001, P = 0.97, q value = 0.97; Faecalibacterium: F_{1, 109} = 0.53, P = 0.47, q value = 0.59). There were no significant differences in any of these groups of bacteria between participants with diabetes and ND participants (all P > 0.1 and q values >0.2).

In our community-based sample of Colombian adults, we provide evidence consistent with previous literature (6,11–13,17) that the association between gut microbiota and type 2 diabetes is modified by metformin use. T2D-met⁺ participants had higher relative abundance of purportedly beneficial mucin-degrading and SCFA-producing bacteria compared with T2D-met⁻ and ND participants matched on age, sex, and BMI.

Several studies have demonstrated that type 2 diabetes is associated with gut microbiota composition, but findings on the taxa involved have been inconsistent (2–5). Some of the variance in previous study findings may be explained by confounding factors, including demographics, body weight, and treatment with drugs, such as metformin. After matching on age, sex, and BMI and stratifying comparisons on metformin, we found only modest associations between type 2 diabetes and gut microbiota composition. One OTU related to Prevotella was higher among participants with diabetes, whereas another OTU related to E. casseliflavus was lower among participants with diabetes. Prevotella has been associated with carbohydrate-based diets and degradation of complex polysaccharides (26), whereas E. casseliflavus is an opportunistic pathogen that may cause serious infections in immunosuppressed individuals (27).

We found associations between metformin use and gut microbiota composition that were largely consistent whether we compared T2D-met⁺ participants to ND control subjects or to T2D-met⁻ participants, suggesting that metformin may have direct microbial effects. Our findings are congruent with multiple lines of evidence indicating the gut-mediated glycemic effect of metformin stem from alterations in the gut microbiota composition. Cabreiro et al. (28) first demonstrated that metformin impacts the metabolism of the microbiota hosted by Caenorhabditis elegans. Three mouse studies have shown that metformin treatment in mice on high-fat diet shifts the microbiota composition toward that of mice fed normal chow by increasing abundance of Akkermansia spp. (11–13). Moreover, a follow-up experiment by Shin et al. (12) found that mice fed high-fat diets treated with either cultured A. muciniphila or metformin had similar improvements in mucin-producing goblet cells, proinflammatory interleukin-6, and glucose tolerance, suggesting that A. muciniphila alone may explain the beneficial effects of metformin.

In human studies, a small nonrandomized clinical trial of 12 patients with type 2 diabetes demonstrated that stopping metformin treatment for 7 days led to alterations in the gut microbiota and glucagon-like peptide 1 (17). A three-country cross-sectional metagenomic study found that metformin use was positively associated with SCFA-producing bacteria (6). Metformin has also been shown to enhance active and total glucagon-like peptide 1 (17,29,30), which is consistent with the hypothesis that it increases SCFA production through modification of the gut microbiota composition. There is also the possibility that metformin alters glucose metabolism through effects on bile acid secretion (31).

As we hypothesized, on the basis of the aforementioned nonhuman (11–13) and human (6,17) studies, metformin use in our study was associated with greater relative abundance of the mucin-degrading A. muciniphila. Through LEfSe biomarker discovery, we also found metformin was positively associated with the mucolytic bacterium B. bifidum. The higher abundance of the mucin-degrading bacteria A. muciniphila and B. bifidum in the gut microbiota of metformin users suggests that metformin’s health benefits may derive from the strengthening of the intestinal mucosal barrier. A. muciniphila plays a crucial role in maintaining the integrity of the mucin layer, thereby reducing translocation of proinflammatory lipopolysaccharides and controlling fat storage, adipose tissue metabolism, and glucose homeostasis (1,12). Likewise, B. bifidum can grow on gastric mucin as a sole carbon source, and genome analysis revealed that this bacterium can use host mucins (32), potentially contributing to gastrointestinal health in the same way as A. muciniphila.

T2D-met⁺ participants in our study also had higher relative abundance of some SCFA-producing bacteria, but not others (e.g., Roseburia, Subdoligranulum, Faecalibacterium). Those positively associated with metformin use included B. bifidum, Prevotella (an OTU distinct from the OTU enriched in T2D-met⁻ participants), Megasphaera (a bacterium related to Megamonas), and Butyrivibrio (although this taxa was no longer significant after adjusting for multiple comparisons). These microbiota have been associated with production of SCFAs, including butyrate, propionate, and acetate (26,33–35). SCFAs may be beneficial for health. Butyrate, in particular, is one of the preferred energy sources of the colonic epithelium (36). Recent studies in mice showed that an increase in the colonic production of SCFAs, especially butyrate and propionate, triggers intestinal gluconeogenesis, benefiting glucose and energy homeostasis and reducing hepatic glucose production, appetite, and body weight (37). Acetate produced by bifidobacteria improves the intestinal defense mediated by epithelial cells and protects the host against lethal infection (34). Also, SCFAs, particularly butyrate, stimulate epithelial metabolism and deplete intracellular O₂, resulting in stabilization of the transcription factor HIF-1 and increasing epithelial barrier function (38). In humans, low levels of butyrate-producing bacteria have been associated with colonic disease (e.g., inflammatory bowel disease), highlighting the role of SCFAs in disease resistance (39,40).

Our study is not without limitations. Our findings are based on observational data that cannot provide causal inference. We were able to reduce the potential for confounding by matching on age, sex, and BMI. Because our metformin–gut microbiota associations were largely consistent whether we used as a reference group the T2D-met⁻ or ND participants, we believe our findings were not due to confounding by indication. However, we cannot rule out unmeasured or residual confounding. Another limitation to our study was the lack of information on dose and duration of metformin treatment. This limitation could have resulted in a weaker, more conservative, association (attenuation toward the null) between metformin and gut microbiota composition and structure. Future studies are warranted to determine the dose-response of the metformin-microbiota relationship. We also had a small sample size relative to a previous observational study on this topic (Forslund et al. [6] analyzed 784 gut metagenomes of Danish, Swedish, and Chinese participants, of which, 199 had type 2 diabetes) and thus may have been underpowered to detect statistical significance for measures of α diversity and associations of microbial composition that were smaller in magnitude. Nevertheless, our ability to largely confirm hypotheses generated from previous studies with our modest sample size and in a different population suggests that the effect of metformin on the gut microbiota is robust and replicable across diverse populations.

In conclusion, our study of Colombian adults provides evidence congruent with the hypothesis that metformin has direct effects on gut microbiota composition through augmentation of mucin-degrading A. muciniphila as well as several SCFA-producing bacteria. Randomized controlled trials are needed to determine whether the antidiabetes and anti-inflammatory effects of metformin are mediated by the changes to gut microbiota composition.

Acknowledgments. Foremost, the authors are indebted to all the participants who agreed to take part in this study. The authors also thank Natalia Zuluaga (of Vidarium) for preselection of potential participants; Luz G. Betancur and Natalia E. Guarin (of Vidarium) for help in participant recruitment; Erika M. Loaiza, Natalia Pareja, D. Tatiana Garcia, and Yuliana M. Franco (of Vidarium) for their invaluable help during field work; Amalia Toro, Connie J. Arboleda, and Ana C. Ochoa (of EPS SURA) for their help authorizing and coordinating activities within EPS SURA; and the administrative and laboratory staff of EPS SURA and Dinámica I.P.S. The authors also acknowledge Paola A. Rios and Giovanni Torres from the Instituto Colombiano de Medicina Tropical for their help with sample handling and pretreatment and APOLO Scientific Computing Center of EAFIT University for hosting the bioinformatics resources for the study.

Funding. This work was funded by Grupo Empresarial Nutresa, EPS SURA, and Dinámica I.P.S.

The funders have not had any role in designing or conducting the study; in the collection, management, analysis, or interpretation of the data; in the preparation, review, or approval of the manuscript; or in the decision to submit the manuscript for publication.

Duality of Interest. J.d.l.C.-Z., V.C.-A., E.P.V.-M., and J.S.E. are employees of Grupo Empresarial Nutresa. J.A.C. is an employee of Dinámica I.P.S. J.M.A. is an employee of EPS SURA. No other potential conflicts of interest relevant to this article were reported.

Author Contributions. J.d.l.C.-Z. processed fecal samples and DNA sequences, performed analyses, and wrote the manuscript. N.T.M. conceived the study, put forward hypotheses to be tested, and wrote the manuscript. V.C.-A. designed the cohort study, recruited participants, coordinated field activities, collected fecal and blood samples, and measured anthropometric variables. E.P.V.-M. processed fecal samples and DNA sequences. J.A.C. coordinated field activities and transport and treatment of samples. J.M.A. coordinated participant recruitment and field activities. J.S.E. designed the cohort study, coordinated participant recruitment, supervised field activities and transport and treatment of samples, performed analyses, and wrote the manuscript. All authors read and approved the final version of the manuscript. J.S.E. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation. Parts of this study were presented at the XXIII Latin American Congress of Microbiology, Rosario, Argentina, 26–30 September 2016.