Obese Individuals with and without Type 2 Diabetes Show Different Gut Microbial Functional Capacity and Composition

•Obesity, but not type 2 diabetes, associated with gut microbiome variation•Medications and dietary supplements associated with gut microbiome variation•High iron intake affected microbiome composition in mice•Microbiome variation was also reflected in serum metabolite profiles Obesity and type 2 diabetes (T2D) are metabolic disorders that are linked to microbiome alterations. However, their co-occurrence poses challenges in disentangling microbial features unique to each condition. We analyzed gut microbiomes of lean non-diabetic (n = 633), obese non-diabetic (n = 494), and obese individuals with T2D (n = 153) from German population and metabolic disease cohorts. Microbial taxonomic and functional profiles were analyzed along with medical histories, serum metabolomics, biometrics, and dietary data. Obesity was associated with alterations in microbiome composition, individual taxa, and functions with notable changes in Akkermansia, Faecalibacterium, Oscillibacter, and Alistipes, as well as in serum metabolites that correlated with gut microbial patterns. However, microbiome associations were modest for T2D, with nominal increases in Escherichia/Shigella. Medications, including antihypertensives and antidiabetics, along with dietary supplements including iron, were significantly associated with microbiome variation. These results differentiate microbial components of these interrelated metabolic diseases and identify dietary and medication exposures to consider in future studies. Obesity and type 2 diabetes (T2D) are metabolic disorders that are linked to microbiome alterations. However, their co-occurrence poses challenges in disentangling microbial features unique to each condition. We analyzed gut microbiomes of lean non-diabetic (n = 633), obese non-diabetic (n = 494), and obese individuals with T2D (n = 153) from German population and metabolic disease cohorts. Microbial taxonomic and functional profiles were analyzed along with medical histories, serum metabolomics, biometrics, and dietary data. Obesity was associated with alterations in microbiome composition, individual taxa, and functions with notable changes in Akkermansia, Faecalibacterium, Oscillibacter, and Alistipes, as well as in serum metabolites that correlated with gut microbial patterns. However, microbiome associations were modest for T2D, with nominal increases in Escherichia/Shigella. Medications, including antihypertensives and antidiabetics, along with dietary supplements including iron, were significantly associated with microbiome variation. These results differentiate microbial components of these interrelated metabolic diseases and identify dietary and medication exposures to consider in future studies. The incidences of both obesity and type 2 diabetes are increasing worldwide, and their comorbidities and medical requirements incur high and rising healthcare costs. Obesity is a risk factor for T2D, but while 86% of individuals with T2D are overweight or obese, not all obese individuals develop T2D (Daousi et al., 2006Daousi C. Casson I.F. Gill G.V. MacFarlane I.A. Wilding J.P.H.H. Pinkney J.H. Prevalence of obesity in type 2 diabetes in secondary care: association with cardiovascular risk factors.Postgrad. Med. J. 2006; 82: 280-284Crossref PubMed Scopus (205) Google Scholar, Narayan et al., 2007Narayan K.M.V. Boyle J.P. Thompson T.J. Gregg E.W. Williamson D.F. Effect of BMI on lifetime risk for diabetes in the U.S.Diabetes Care. 2007; 30: 1562-1566Crossref PubMed Scopus (324) Google Scholar). Multiple factors play a role in the development of these diseases, including genetics, lifestyle, and the gut microbiome, with an increasing body of evidence supporting the microbiome’s role in obesity and in T2D (Boulangé et al., 2016Boulangé C.L. Neves A.L. Chilloux J. Nicholson J.K. Dumas M.E. Impact of the gut microbiota on inflammation, obesity, and metabolic disease.Genome Med. 2016; 8: 42Crossref PubMed Scopus (751) Google Scholar, Chobot et al., 2018Chobot A. Górowska-Kowolik K. Sokołowska M. Jarosz-Chobot P. Obesity and diabetes-not only a simple link between two epidemics.Diabetes Metab. Res. Rev. 2018; 34: e3042Crossref PubMed Scopus (106) Google Scholar, Peters et al., 2018Peters B.A. Shapiro J.A. Church T.R. Miller G. Trinh-Shevrin C. Yuen E. Friedlander C. Hayes R.B. Ahn J. A taxonomic signature of obesity in a large study of American adults.Sci. Rep. 2018; 8Crossref Scopus (140) Google Scholar, Trøseid et al., 2013Trøseid M. Nestvold T.K. Rudi K. Thoresen H. Nielsen E.W. Lappegård K.T. Plasma lipopolysaccharide is closely associated with glycemic control and abdominal obesity: evidence from bariatric surgery.Diabetes Care. 2013; 36: 3627-3632Crossref PubMed Scopus (125) Google Scholar, Turnbaugh et al., 2006Turnbaugh P.J. Ley R.E. Mahowald M.A. Magrini V. Mardis E.R. Gordon J.I. An obesity-associated gut microbiome with increased capacity for energy harvest.Nature. 2006; 444: 1027-1031Crossref PubMed Scopus (8182) Google Scholar, Vrieze et al., 2012Vrieze A. Van Nood E. Holleman F. Salojärvi J. Kootte R.S. Bartelsman J.F.W.M. Dallinga-Thie G.M. Ackermans M.T. Serlie M.J. Oozeer R. et al.Transfer of intestinal microbiota from lean donors increases insulin sensitivity in individuals with metabolic syndrome.Gastroenterology. 2012; 143: 913-916.e7Abstract Full Text Full Text PDF PubMed Scopus (1912) Google Scholar). However, it remains difficult to disentangle the exact microbial features associated with obesity and T2D while also ruling out external confounders. Studies to identify distinguishing features of the microbiome that uniquely characterize each of the two conditions are needed in order to understand whether the progression from obesity to T2D is, in part, mediated by the gut microbiome. Obesity and diabetes are both metabolic conditions associated with a range of physiological functions closely related to the gut and gut microbiota. Intestinal dysbiosis is a common observation in obesity, while the observation is less consistent in T2D (Aron-Wisnewsky et al., 2019Aron-Wisnewsky J. Prifti E. Belda E. Ichou F. Kayser B.D. Dao M.C. Verger E.O. Hedjazi L. Bouillot J.L. Chevallier J.M. et al.Major microbiota dysbiosis in severe obesity: fate after bariatric surgery.Gut. 2019; 68: 70-82Crossref PubMed Scopus (214) Google Scholar, Le Chatelier et al., 2013Le Chatelier E. Nielsen T. Qin J. Prifti E. Hildebrand F. Falony G. Almeida M. Arumugam M. Batto J.M. Kennedy S. et al.Richness of human gut microbiome correlates with metabolic markers.Nature. 2013; 500: 541-546Crossref PubMed Scopus (2772) Google Scholar, Forslund et al., 2015Forslund K. Hildebrand F. Nielsen T. Falony G. Le Chatelier E. Sunagawa S. Prifti E. Vieira-silva S. Gudmundsdottir V. Pedersen H.K. et al.Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota.Nature. 2015; 528: 262-266Crossref PubMed Scopus (1217) Google Scholar, Karlsson et al., 2013Karlsson F.H. Tremaroli V. Nookaew I. Bergström G. Behre C.J. Fagerberg B. Nielsen J. Bäckhed F. Gut metagenome in European women with normal, impaired and diabetic glucose control.Nature. 2013; 498: 99-103Crossref PubMed Scopus (1843) Google Scholar, Qin et al., 2012Qin J. Li Y. Cai Z. Li S.S. Zhu J. Zhang F. Liang S. Zhang W. Guan Y. Shen D. et al.A metagenome-wide association study of gut microbiota in type 2 diabetes.Nature. 2012; 490: 55-60Crossref PubMed Scopus (3988) Google Scholar, Turnbaugh et al., 2009Turnbaugh P.J. Hamady M. Yatsunenko T. Cantarel B.L. Duncan A. Ley R.E. Sogin M.L. Jones W.J. Roe B.A. Affourtit J.P. et al.A core gut microbiome in obese and lean twins.Nature. 2009; 457: 480-484Crossref PubMed Scopus (5567) Google Scholar). Low-grade inflammation and altered levels of lipopolysaccharides (LPS) and short-chain fatty acid (SCFA) have also been associated with metabolic disease (Forslund et al., 2015Forslund K. Hildebrand F. Nielsen T. Falony G. Le Chatelier E. Sunagawa S. Prifti E. Vieira-silva S. Gudmundsdottir V. Pedersen H.K. et al.Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota.Nature. 2015; 528: 262-266Crossref PubMed Scopus (1217) Google Scholar, Karlsson et al., 2013Karlsson F.H. Tremaroli V. Nookaew I. Bergström G. Behre C.J. Fagerberg B. Nielsen J. Bäckhed F. Gut metagenome in European women with normal, impaired and diabetic glucose control.Nature. 2013; 498: 99-103Crossref PubMed Scopus (1843) Google Scholar, Qin et al., 2012Qin J. Li Y. Cai Z. Li S.S. Zhu J. Zhang F. Liang S. Zhang W. Guan Y. Shen D. et al.A metagenome-wide association study of gut microbiota in type 2 diabetes.Nature. 2012; 490: 55-60Crossref PubMed Scopus (3988) Google Scholar). Together, these observations suggest that the development of obesity-associated T2D, characterized by dysregulated glucose metabolism and insulin resistance, could be related to progressive disruption of the gut microbiome after initiation by obesity. While there is general agreement that ecological diversity and taxonomic composition of the gut microbiome are altered in obesity and T2D, associations with single microbes or microbial products have been inconsistent between studies. These deviations may potentially be due to small sample sizes, differing study designs, geography, and clinical factors (Falony et al., 2016Falony G. Joossens M. Vieira-Silva S. Wang J. Darzi Y. Faust K. Kurilshikov A. Bonder M.J. Valles-Colomer M. Vandeputte D. et al.Population-level analysis of gut microbiome variation.Science. 2016; 352: 560-564Crossref PubMed Scopus (1203) Google Scholar, Finucane et al., 2014Finucane M.M. Sharpton T.J. Laurent T.J. Pollard K.S. A taxonomic signature of obesity in the microbiome? Getting to the guts of the matter.PLoS One. 2014; 9: e84689Crossref PubMed Scopus (227) Google Scholar, Sze and Schloss, 2016Sze M.A. Schloss P.D. Looking for a signal in the noise: revisiting obesity and the microbiome.MBio. 2016; 7: 1-9Crossref Scopus (337) Google Scholar, Yassour et al., 2016Yassour M. Lim M.Y. Yun H.S. Tickle T.L. Sung J. Song Y.M. Lee K. Franzosa E.A. Morgan X.C. Gevers D. et al.Sub-clinical detection of gut microbial biomarkers of obesity and type 2 diabetes.Genome Med. 2016; 8: 17Crossref PubMed Scopus (166) Google Scholar). The high inter-individual variability of the gut microbiome, and its sensitivity to environmental influences complicates population-scale microbial research in complex diseases generally, potentially explaining some of these inconsistencies (Falony et al., 2016Falony G. Joossens M. Vieira-Silva S. Wang J. Darzi Y. Faust K. Kurilshikov A. Bonder M.J. Valles-Colomer M. Vandeputte D. et al.Population-level analysis of gut microbiome variation.Science. 2016; 352: 560-564Crossref PubMed Scopus (1203) Google Scholar, Zhernakova et al., 2016Zhernakova A. Kurilshikov A. Bonder M.J. Tigchelaar E.F. Schirmer M. Vatanen T. Mujagic Z. Vila A.V. Falony G. Vieira-Silva S. et al.Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity.Science. 2016; 352: 565-569Crossref PubMed Scopus (1012) Google Scholar). Despite the complications of microbial studies, a number of consistent associations have been observed among them, including an altered abundance of butyrate producing bacteria (Le Chatelier et al., 2013Le Chatelier E. Nielsen T. Qin J. Prifti E. Hildebrand F. Falony G. Almeida M. Arumugam M. Batto J.M. Kennedy S. et al.Richness of human gut microbiome correlates with metabolic markers.Nature. 2013; 500: 541-546Crossref PubMed Scopus (2772) Google Scholar, Forslund et al., 2015Forslund K. Hildebrand F. Nielsen T. Falony G. Le Chatelier E. Sunagawa S. Prifti E. Vieira-silva S. Gudmundsdottir V. Pedersen H.K. et al.Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota.Nature. 2015; 528: 262-266Crossref PubMed Scopus (1217) Google Scholar, Karlsson et al., 2013Karlsson F.H. Tremaroli V. Nookaew I. Bergström G. Behre C.J. Fagerberg B. Nielsen J. Bäckhed F. Gut metagenome in European women with normal, impaired and diabetic glucose control.Nature. 2013; 498: 99-103Crossref PubMed Scopus (1843) Google Scholar, Qin et al., 2012Qin J. Li Y. Cai Z. Li S.S. Zhu J. Zhang F. Liang S. Zhang W. Guan Y. Shen D. et al.A metagenome-wide association study of gut microbiota in type 2 diabetes.Nature. 2012; 490: 55-60Crossref PubMed Scopus (3988) Google Scholar, Vrieze et al., 2012Vrieze A. Van Nood E. Holleman F. Salojärvi J. Kootte R.S. Bartelsman J.F.W.M. Dallinga-Thie G.M. Ackermans M.T. Serlie M.J. Oozeer R. et al.Transfer of intestinal microbiota from lean donors increases insulin sensitivity in individuals with metabolic syndrome.Gastroenterology. 2012; 143: 913-916.e7Abstract Full Text Full Text PDF PubMed Scopus (1912) Google Scholar). In the current study, we performed a detailed analysis of lean non-diabetic (“lean healthy”, LH), obese non-diabetic (ObH), and obese T2D (ObT2D) individuals to identify compositional and functional features of the gut microbiome that associate with obesity, as well as those which deviate between obese individuals with and without T2D. We included 16S rRNA gene (16S) sequencing data for 1,280 samples and shotgun metagenomic data for a subset of 201 samples, in addition to extensive covariates describing anthropometrics, nutritional behavior, and medications. All samples in the core analysis are from the northern German cohorts PopGen (Krawczak et al., 2006Krawczak M. Nikolaus S. Von Eberstein H. Croucher P.J.P. El Mokhtari N.E. Schreiber S. PopGen: population-based recruitment of patients and controls for the analysis of complex genotype-phenotype relationships.Community Genet. 2006; 9: 55-61Crossref PubMed Scopus (278) Google Scholar) and FoCus (Müller et al., 2015Müller N. Schulte D.M. Türk K. Freitag-Wolf S. Hampe J. Zeuner R. Schröder J.O. Gouni-Berthold I. Berthold H.K. Krone W. et al.IL-6 blockade by monoclonal antibodies inhibits apolipoprotein (a) expression and lipoprotein (a) synthesis in humans.J. Lipid Res. 2015; 56: 1034-1042Crossref PubMed Scopus (95) Google Scholar), while key findings were replicated in 880 additional individuals from the North-Eastern German SHIP (Study of Health in Pomerania) cohort (Völzke et al., 2011Völzke H. Alte D. Schmidt C.O. Radke D. Lorbeer R. Friedrich N. Aumann N. Lau K. Piontek M. Born G. et al.Cohort profile: the study of health in Pomerania.Int. J. Epidemiol. 2011; 40: 294-307Crossref PubMed Scopus (785) Google Scholar). We identified several associations between the gut microbiome, plasma metabolome, obesity and diabetes phenotypes, and environmental factors. These comprised associations with gut microbial taxa, including a decrease of Akkermansia, Oscillibacter, and Intestinimonas in obesity, and a nominal increase of Escherichia/Shigella specific to T2D, as well as circulating metabolite changes [including branched-chain amino acids (BCAA)]. Dietary supplements, including iron and magnesium, and medications also covaried with microbial composition and functional potential, such as the known strong association with metformin, and we identified further effects of antihypertensive and antiphlogistic medications on the gut microbiome. The effects of dietary iron on microbiome composition were confirmed in a mouse feeding experiment, in which the difference between high and sufficient iron intake recapitulated the diet-associated microbiome divergence observed in human populations. The current study included 1,280 individuals from the Northern German cohorts PopGen (Krawczak et al., 2006Krawczak M. Nikolaus S. Von Eberstein H. Croucher P.J.P. El Mokhtari N.E. Schreiber S. PopGen: population-based recruitment of patients and controls for the analysis of complex genotype-phenotype relationships.Community Genet. 2006; 9: 55-61Crossref PubMed Scopus (278) Google Scholar) and FoCus (Müller et al., 2015Müller N. Schulte D.M. Türk K. Freitag-Wolf S. Hampe J. Zeuner R. Schröder J.O. Gouni-Berthold I. Berthold H.K. Krone W. et al.IL-6 blockade by monoclonal antibodies inhibits apolipoprotein (a) expression and lipoprotein (a) synthesis in humans.J. Lipid Res. 2015; 56: 1034-1042Crossref PubMed Scopus (95) Google Scholar) (Figure 1; Table S1). The PopGen and Focus cohorts have recorded information on medication, diet, and dietary supplement usage, together with an extensive phenotypic characterization including values of age, gender, BMI, and fasting glucose levels (from the PopGen/P2N biobank; see STAR Methods, Table S1). Furthermore, untargeted serum metabolomic profiles were generated for 400 study participants using the Metabolon platform, comprising 390 identified metabolites (STAR Methods). Data and specimens from both cohorts were handled by the same biobank using the same study protocol. Compatibility of the 16S microbiome data between the two cohorts is good as described previously (Wang et al., 2016Wang J. Thingholm L.B. Skiecevičienė J. Rausch P. Kummen M. Hov J.R. Degenhardt F. Heinsen F.A. Rühlemann M.C. Szymczak S. et al.Genome-wide association analysis identifies variation in vitamin D receptor and other host factors influencing the gut microbiota.Nat. Genet. 2016; 48: 1396-1406Crossref PubMed Scopus (386) Google Scholar). All individuals were grouped into three phenotypes: (a) lean (BMI ≤ 25) without diabetes, inflammatory bowel disease (IBD), or irritable bowel syndrome (IBS), with fasting glucose level below 125 mg/dl (“lean healthy”, LH, n = 633); (b) obese (BMI > 30) with same criteria as LH except for BMI (“obese healthy,” ObH, n = 494); and (c) obese (BMI > 30) with diagnosed T2D or fasting glucose level above 125 mg/dl, and without IBD and IBS, respectively (ObT2D, n = 153; Table S1). A subset of 201 samples was selected for shotgun metagenomic sequencing. These were targeted to exclude several potential confounders during comparisons of the three populations (LH n = 95, ObH n = 55, and ObT2D n = 51), notably to achieve uniformity of cardiovascular measures (see STAR Methods, Table S1). The ObH group was selected to be generally healthy (despite being obese) as reflected by the uniformity of mean fasting glucose level and blood pressure between the lean and obese non-diabetic subjects. We identified both age and gender as significantly associated with microbiome composition (adonis PERMANOVA, p < 0.001, 16S data, STAR Methods and Supplemental Information), in agreement with previous findings (Falony et al., 2016Falony G. Joossens M. Vieira-Silva S. Wang J. Darzi Y. Faust K. Kurilshikov A. Bonder M.J. Valles-Colomer M. Vandeputte D. et al.Population-level analysis of gut microbiome variation.Science. 2016; 352: 560-564Crossref PubMed Scopus (1203) Google Scholar, Oksanen et al., 2015Oksanen J. Guillaume Blanchet F. Michael F. Roeland K. Pierre L. Dan M. Minchin P.R. O’Hara R.B. Simpson G.L. et al.Vegan: community ecology package.2015Google Scholar, Wang et al., 2016Wang J. Thingholm L.B. Skiecevičienė J. Rausch P. Kummen M. Hov J.R. Degenhardt F. Heinsen F.A. Rühlemann M.C. Szymczak S. et al.Genome-wide association analysis identifies variation in vitamin D receptor and other host factors influencing the gut microbiota.Nat. Genet. 2016; 48: 1396-1406Crossref PubMed Scopus (386) Google Scholar, Zhernakova et al., 2016Zhernakova A. Kurilshikov A. Bonder M.J. Tigchelaar E.F. Schirmer M. Vatanen T. Mujagic Z. Vila A.V. Falony G. Vieira-Silva S. et al.Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity.Science. 2016; 352: 565-569Crossref PubMed Scopus (1012) Google Scholar). Accordingly, we corrected for age and gender where possible in the following analyses. In initial comparisons of amplicon- and metagenome-based taxonomic profiles, abundances of genera within subject agreed well across taxa (mean Spearman ρ = 0.67, Figures 1 and S1). Exceptions to this included a small number of taxa (n = 5, of 31 total) with Spearman correlations below 0.5. Four of these five bacteria belonged to the clade Clostridiales, which has been subject to extensive reclassifications, potentially compromising consistent assignments between the two measurement types’ taxonomies (Yutin and Galperin, 2013Yutin N. Galperin M.Y. A genomic update on clostridial phylogeny: Gram-negative spore formers and other misplaced clostridia.Environ. Microbiol. 2013; 15: 2631-2641PubMed Google Scholar). In subsequent analyses, six microbiome feature types were studied (additional details in STAR Methods): taxonomic abundances from 16S rRNA gene sequencing [VSEARCH (Rognes et al., 2016Rognes T. Flouri T. Nichols B. Quince C. Mahé F. VSEARCH: a versatile open source tool for metagenomics.PeerJ. 2016; 4: e2584Crossref PubMed Scopus (4237) Google Scholar)]; species-level taxonomic profiles from metagenomes [MetaPhlAn2 (Truong et al., 2015Truong D.T. Franzosa E.A. Tickle T.L. Scholz M. Weingart G. Pasolli E. Tett A. Huttenhower C. Segata N. MetaPhlAn2 for enhanced metagenomic taxonomic profiling.Nat. Methods. 2015; 12: 902-903Crossref PubMed Scopus (1100) Google Scholar)]; functional profiles [HUMAnN2 (Franzosa et al., 2018Franzosa E.A. McIver L.J. Rahnavard G. Thompson L.R. Schirmer M. Weingart G. Lipson K.S. Knight R. Caporaso J.G. Segata N. et al.Species-level functional profiling of metagenomes and metatranscriptomes.Nat. Methods. 2018; 15: 962-968Crossref PubMed Scopus (686) Google Scholar)] summarized as MetaCyc (Caspi et al., 2014Caspi R. Altman T. Billington R. Dreher K. Foerster H. Fulcher C.A. Holland T.A. Keseler I.M. Kothari A. Kubo A. et al.The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases.Nucleic Acids Res. 2014; 42: D459-D471Crossref PubMed Scopus (758) Google Scholar) and informative Gene Ontology (iGO) pathways (STAR Methods) and as KEGG Ontology (KO) (Kanehisa et al., 2014Kanehisa M. Goto S. Sato Y. Kawashima M. Furumichi M. Tanabe M. Data, information, knowledge and principle: back to metabolism in KEGG.Nucleic Acids Res. 2014; 42: D199-D205Crossref PubMed Scopus (2298) Google Scholar) and Enzyme Commission (EC) (Bairoch, 2000Bairoch A. The ENZYME database in 2000.Nucleic Acids Res. 2000; 28: 304-305Crossref PubMed Scopus (749) Google Scholar) gene families. For each of these six feature types, only the subset of features with the greatest overall mean abundance and standard deviation were analyzed (see STAR Methods), and the dimensionality of functional features (pathways and gene families) was further reduced prior to testing by hierarchical clustering. This resulted in 39 MetaCyc pathway groups, 35 iGO term groups, 38-EC groups, and 78 KO groups used for all subsequent tests (Table S1). To identify individual microbial features (taxa and functions) associated specifically with obesity and/or with T2D, and not with other covariates (e.g., diet, medications), we assessed each feature’s abundances comparing (a) LH with ObH and (b) ObH with ObT2D using generalized linear models in MaAsLin (Multivariate Association with Linear Models; Morgan et al., 2012Morgan X.C. Tickle T.L. Sokol H. Gevers D. Devaney K.L. Ward D.V. Reyes J.A. Shah S.A. LeLeiko N. Snapper S.B. et al.Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment.Genome Biol. 2012; 13: R79Crossref PubMed Scopus (1726) Google Scholar), with automatic variable selection using boosting as a univariate prescreen (see STAR Methods). For the first analysis (LH versus ObH), covariates were selected from age, gender, fasting glucose levels, total iron intake (estimated from FFQ and dietary supplements), and medications summarized to the antihypertensive and analgesic classes. For the second analysis (ObH versus ObT2D), covariates were selected from age, gender and BMI, analgesics, antihypertensives, metformin and insulin medications, and the nutrients magnesium and iron intake. These analyses used transformed relative abundance data and implements corrections for sparse, compositional microbial feature data. Sparse (zero-inflated) models were used for taxonomic features with more than 30% zero elements. One of the simplest summary statistics analyzed as a microbiome feature was alpha-diversity, which was significantly reduced specifically in obesity (p = 3.20×10−11 by robust regression, ObH versus LH) and not for ObT2D (p = 0.92, ObH versus ObT2D; Figure 2). This was also the case for composition (beta-diversity) of taxonomic and functional profiles (genera, GO, EC, KO, and MetaCyc pathways; adonis q < 0.1) in obesity, and for taxonomic evaluation of dispersion (genera, betadisper q < 0.1), although not that of functional features (GO, EC, KO, and MetaCyc pathways; betadisper q > 0.1; Figure 2; Table S2). In contrast, when comparing obese subjects with and without T2D, composition was not significantly different across microbial taxonomic profiles or functional features (genera, GO, EC, KO, and MetaCyc pathways; all adonis q > 0.1) after adjusting for diabetic medications. To avoid confounding from metformin and insulin usage, we next evaluated association of compositional variation with fasting glucose levels across all subjects not using metformin or insulin (subsampling from 561 samples). Neither taxonomic profiles nor functional capacity associated significantly with fasting glucose levels (GO, EC, KO, and pathways; adonis q > 0.1). Thus in our cohort, obesity (with normal fasting glucose levels) had a striking association both with taxonomic and functional features of the microbiome, while T2D (in contrast to non-diabetic obesity) had a weak association with microbiome features once diabetic medications were properly considered (Table S2). When analyzing the association of individual microbial genera (from 16S rRNA gene sequencing) with obesity or T2D, a total of 17 genera were significant with respect specifically to obesity (q < 0.1), including decreased Akkermansia, Faecalibacterium, Oscillibacter, and Alistipes (Table S3). The abundant anti-inflammatory species Faecalibacterium prausnitzii was decreased in obese individuals (q = 5.29×10−3) while unassociated with T2D (q > 0.1). Faecalibacterium prausnitzii, together with Bacteroides thetaiotaomicron (itself also obesity-associated), harbor most of the microbial functional features associated with obesity and may therefore be drivers of functional variation (Table S3). Of the 17 associations identified here, 15 were among the analyzed taxa in the independent SHIP cohort, and of these, 7 retained a significant association with obesity (q < 0.1) (Table S3).These obesity-associated taxa include replications of previous studies, thus lending further weight to a small but significant “core” of obesity-associated clades that are common among population contexts (in addition to more variable, population-specific associations). In contrast to obesity, no genera retained a significant robust association with T2D after incorporating the covariates above (i.e., remained differential between ObH and ObT2D individuals, q < 0.1, Figure S2; Table S3). Previous studies have found a positive association between Escherichia and both metformin usage and T2D, which we reproduced here in direction (i.e., enrichment) but without statistical significance (Pedersen et al., 2016Pedersen H.K. Gudmundsdottir V. Nielsen H.B. Hyotylainen T. Nielsen T. Jensen B.A.H. Forslund K. Hildebrand F. Prifti E. Falony G. et al.Human gut microbes impact host serum metabolome and insulin sensitivity.Nature. 2016; 535: 376-381Crossref PubMed Scopus (1080) Google Scholar, Qin et al., 2012Qin J. Li Y. Cai Z. Li S.S. Zhu J. Zhang F. Liang S. Zhang W. Guan Y. Shen D. et al.A metagenome-wide association study of gut microbiota in type 2 diabetes.Nature. 2012; 490: 55-60Crossref PubMed Scopus (3988) Google Scholar; Table S3). To understand how microbial functional capacities further relate to obesity and T2D, we first tested a group of pathways [from MetaCyc (Caspi et al., 2014Caspi R. Altman T. Billington R. Dreher K. Foerster H. Fulcher C.A. Holland T.A. Keseler I.M. Kothari A. Kubo A. et al.The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases.Nucleic Acids Res. 2014; 42: D459-D471Crossref PubMed Scopus (758) Google Scholar) and the informative Gene Ontology, iGO (STAR Methods)] and gene families [as summarized by KO (Kanehisa et al., 2014Kanehisa M. Goto S. Sato Y. Kawashima M. Furumichi M. Tanabe M. Data, information, knowledge and principle: back to metabolism in KEGG.Nucleic Acids Res. 2014; 42: D199-D205Crossref PubMed Scopus (2298) Google Scholar) and ECs (Bairoch, 2000Bairoch A. The ENZYME database in 2000.Nucleic Acids Res. 2000; 28: 304-305Crossref PubMed Scopus (749) Google Scholar)] for association specifically with obesity (LH versus ObH). Sixteen gene family clusters and six pathway clusters, together comprising 97 functional features, were associated with obesity (q < 0.1, Table S3). This included a decreased capacity for unidirectional conjugation (Gene Ontology: GO:0009291, q = 3.79×10−4) and superoxide reductase (KEGG Orthology: K05919, q = 1.84×10−2). Conjugation is an important mechanism for bacteria, and the mechanism has been shown to play an important role for the gut microbiome (Sitaraman, 2018Sitaraman R. Prokaryotic horizontal gene transfer within the human holobiont: ecological-evolutionary inferences, implications and possibilities.Microbiome. 2018; 6Crossref PubMed Scopus (34) Google Scholar). Superoxide reductase catalyzes the conversion of the reactive oxygen species superoxide into the less toxic hydrogen peroxide. A reduction in this capacity indicates a microbiome-induced increase in reactive oxygen species in the intestine of obese subjects. Induction of oxygen stress by microbial dysbiosis has previously been suggested as one of the mechanisms by which the microbiome can cause weight gain and insulin resistance (Qin et al.,