Bacterial clusters are associated with the risk of severe disease progression in inflammatory bowel disease irrespective of conventional disease categories

Abstract

Background: Inflammatory bowel diseases (IBDs) are complex conditions marked by chronic inflammation in the gastrointestinal tract. Traditional classification separates IBD into Crohn’s disease and ulcerative colitis, but this division may not fully capture disease heterogeneity. Here, we examine whether microbiome-driven subtyping can describe novel clinical IBD phenotypes. To achieve this, we applied unsupervised clustering to fecal microbiota profiles from the population-based Inflammatory Bowel Disease in South-Eastern Norway III (IBSEN III) cohort.

Methods: A Gaussian Mixture Model (GMM) was used to cluster participants with IBD based on microbiome composition and examine associations between clusters and clinical outcomes, including inflammatory markers and disease severity during the first year after inclusion.

Results: Three microbiome-based clusters were identified: CLO (dominated by Clostridia UCG-014), ALF (Agathobacter, Lachnoclostridium, and Faecalibacterium), and RUM (Ruminococcus gnavus). Participants in the RUM cluster had a higher risk of future severe disease than those in the CLO cluster, even among participants with remission-to-mild disease at inclusion (21% vs. 6%, P < 0.00001). This association could not be explained by antibiotic use or baseline disease severity. Cluster membership alone performed comparably to fecal calprotectin in distinguishing severe disease, and a combined model significantly improved accuracy (P < 0.0001).

Conclusion: Our findings demonstrate a connection between microbiome composition and the risk of severe disease development, which is partly independent of inflammation levels at the time of sampling. Microbiome-informed subgrouping could lead to more personalized treatment strategies. Further validation is needed to determine the clinical utility of these clusters.

Keywords

Microbiome / clustering / IBD / prognosis / GMM

References

[1] Lee M,Chang EB. Inflammatory bowel diseases (IBD) and the microbiome-searching the crime scene for clues Gastroenterology. 2021 160 524 37 PMC8098834

[2] Mentella MC,Scaldaferri F,Pizzoferrato M,Gasbarrini A,Miggiano GAD. Nutrition, IBD and gut microbiota: a review Nutrients. 2020 12 944 PMC7230231

[3] Rigottier-Gois L. Dysbiosis in inflammatory bowel diseases: the oxygen hypothesis ISME J. 2013 7 1256 61 PMC3695303

[4] Lee J-Y, Bays DJ, Savage HP, Bäumler AJ. The human gut microbiome in health and disease: time for a new chapter? Infect Immun. 2024;92:e0030224 PMC11556149

[5] Verstockt B,Bressler B,Martinez-Lozano H,McGovern D,Silverberg MS. Time to revisit disease classification in inflammatory bowel disease: is the current classification of inflammatory bowel disease good enough for optimal clinical management? Gastroenterology. 2022;162:1370-82

[6] Cleynen I,Boucher G,Jostins L.et al. ; Interna tional Inflammatory Bowel Disease Genetics Consortium. Inherited determinants of Crohn’s disease and ulcerative colitis phenotypes: a genetic association study Lancet. 2016 387 156 67 PMC4714968

[7] Vestergaard MV,Allin KH,Eriksen C.et al. Gut microbiota signatures in inflammatory bowel disease United European Gastroenterol J. 2024 12 22 33 PMC10859715

[8] Zheng J,Sun Q,Zhang J,Ng SC. The role of gut microbiome in inflammatory bowel disease diagnosis and prognosis United European Gastroenterol J. 2022 10 1091 102 PMC9752296

[9] Hansen SH,Maseng MG,Grännö O.et al. Fecal microbiome reflects disease state and prognosis in inflammatory bowel disease in an adult population-based inception cohort Inflamm Bowel Dis. 2025 31 2066 80 PMC12491950

[10] Kugathasan S,Denson LA,Walters TD.et al. Prediction of complicated disease course for children newly diagnosed with Crohn’s disease: a multicentre inception cohort study Lancet. 2017 389 1710 8 PMC5719489

[11] Kristensen VA,Opheim R,Perminow G.et al. Inflammatory bowel disease in South-Eastern Norway III (IBSEN III): a new population-based inception cohort study from South-Eastern Norway Scand J Gastroenterol. 2021 56 899 905

[12] Walmsley RS,Ayres RC,Pounder RE,Allan RN. A simple clinical colitis activity index Gut. 1998 43 29 32 PMC1727189

[13] Harvey RF,Bradshaw JM. A simple index of Crohn’s-disease activity Lancet. 1980 1 514

[14] Fadrosh DW,Ma B,Gajer P.et al. An improved dual-indexing approach for multiplexed 16S rRNA gene sequencing on the Illumina MiSeq platform Microbiome. 2014 2 6 PMC3940169

[15] Bushnell B. BBTools software package. Available from http://sourceforge. net/projects/bbmap. [accessed 17 March 2026]

[16] Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads EMBnet j. 2011 17 10

[17] Bushnell B,Rood J,Singer E. BBMerge - accurate paired shotgun read merging via overlap PLoS ONE. 2017 12 e0185056 PMC5657622

[18] Amir A,McDonald D,Navas-Molina JA.et al. Deblur rapidly resolves single-nucleotide community sequence patterns mSystems. 2017 2 e00191 16 5340863

[19] Bolyen E,Rideout JR,Dillon MR.et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2 Nat Biotechnol. 2019 37 852 7 PMC7015180

[20] Bokulich NA,Kaehler BD,Rideout JR.et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin Microbiome. 2018 6 90 PMC5956843

[21] Quast C,Pruesse E,Yilmaz P.et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools Nucleic Acids Res. 2013 41 D590 6 PMC3531112

[22] Van Rossum GaD, Fred L. Python 3 Reference Manual. Scotts Valley: CreateSpace; 2009

[23] Granat R,Donnellan A,Heflin M.et al. Clustering analysis methods for GNSS observations: a data-driven approach to identifying California’s major faults Earth Space Sci. 2021 8 e2021EA001680 PMC8596415

[24] R Core Team. R: A language and environment for statistical computing; 2021. Vienna: R Foundation for Statistical Computing; 2021. Available from https://cran.rstudio.com/manuals.html. [accessed 17 March 2026]

[25] Wickham H. ggplot2 WIREs Computational Stats. 2011 3 180 5

[26] Peyrin-Biroulet L,Panés J,Sandborn WJ.et al. Defining disease severity in inflammatory bowel diseases: current and future directions Clin Gastroenterol Hepatol. 2016 14 348 354.e17

[27] Walsh AJ,Ghosh A,Brain AO.et al. Comparing disease activity indices in ulcerative colitis J Crohns Colitis. 2014 8 318 25

[28] Ling Lundström M,Peterson C,Hedin CRH.et al. ; BIOIBD Consortium. Faecal biomarkers for diagnosis and prediction of disease course in treatment-naïve patients with IBD Aliment Pharmacol Ther. 2024 60 765 77

[29] Aitchison J. The Statistical Analysis of Compositional Data J R Stat Soc Ser B (Stat Methodol). 1982 44 139 60

[30] Gloor GB,Macklaim JM,Pawlowsky-Glahn V,Egozcue JJ. Microbiome datasets are compositional: and this is not optional Front Microbiol. 2017 8 2224 PMC5695134

[31] Henke MT,Kenny DJ,Cassilly CD,Vlamakis H,Xavier RJ,Clardy J. Ruminococcus gnavus, a member of the human gut microbiome associated with Crohn's disease, produces an inflammatory polysaccharide Proc Natl Acad Sci U S A. 2019 116 12672 7 PMC6601261

[32] Olbjørn C,Cvancarova Småstuen M,Thiis-Evensen E.et al. Fecal microbiota profiles in treatment-naïve pediatric inflammatory bowel disease - associations with disease phenotype, treatment, and outcome Clin Exp Gastroenterol. 2019 12 37 49 PMC6362922

[33] Nilsen M,Madelen Saunders C,Leena Angell I.et al. Butyrate levels in the transition from an infant- to an adult-like gut microbiota correlate with bacterial networks associated with eubacterium rectale and ruminococcus gnavus Genes (Basel). 2020 11;1245 PMC7690385

[34] Crost EH,Coletto E,Bell A,Juge N. Ruminococcus gnavus: friend or foe for human health FEMS Microbiol Rev. 2023 47 fuad014 PMC10112845

[35] Larsson JM,Karlsson H,Crespo JG.et al. Altered O-glycosylation profile of MUC2 mucin occurs in active ulcerative colitis and is associated with increased inflammation Inflamm Bowel Dis. 2011 17 2299 307

[36] Bakir-Gungor B,Hacılar H,Jabeer A,Nalbantoglu OU,Aran O,Yousef M. Inflammatory bowel disease biomarkers of human gut microbiota selected via different feature selection methods PeerJ. 2022 10 e13205 PMC9048649

[37] Clooney AG,Eckenberger J,Laserna-Mendieta E.et al. Ranking microbiome variance in inflammatory bowel disease: a large longitudinal intercontinental study Gut. 2021 70 499 510 PMC7873428

[38] Vieira-Silva S,Falony G,Belda E.et al. ; MetaCardis Consortium. Statin therapy is associated with lower prevalence of gut microbiota dysbiosis Nature. 2020 581 310 5

[39] Knights D,Ward TL,McKinlay CE.et al. Rethinking “enterotypes” Cell Host Microbe. 2014 16 433 7 PMC5558460

[40] Caenepeel C,Falony G,Machiels K.et al. Dysbiosis and associated stool features improve prediction of response to biological therapy in inflammatory bowel disease Gastroenterology. 2024 166 483 95

[41] Vieira-Silva S,Sabino J,Valles-Colomer M.et al. Quantitative microbiome profiling disentangles inflammation- and bile duct obstruction-associated microbiota alterations across PSC/IBD diagnoses Nat Microbiol. 2019 4 1826 31

[42] Reynders T,Devolder L,Valles-Colomer M.et al. Gut microbiome variation is associated to Multiple Sclerosis phenotypic subtypes Ann Clin Transl Neurol. 2020 7 406 19 PMC7187717

[43] Alili R,Belda E,Fabre O.et al. Characterization of the gut microbiota in individuals with overweight or obesity during a real-world weight loss dietary program: a focus on the bacteroides 2 enterotype Biomedicines. 2021 10 16 PMC8772804

[44] Togo AH,Diop A,Bittar F.et al. Correction to: Description of Mediterraneibacter massiliensis, gen. nov., sp. nov., a new genus isolated from the gut microbiota of an obese patient and reclassification of Ruminococcus faecis, Ruminococcus lactaris, Ruminococcus torques, Ruminococcus gnavus and Clostridium glycyrrhizinilyticum as Mediterraneibacter faecis comb. nov., Mediterraneibacter lactaris comb. nov., Mediterraneibacter torques comb. nov., Mediterraneibacter gnavus comb. nov. and Mediterraneibacter glycyrrhizinilyticus comb. nov Antonie Van Leeuwenhoek. 2018 111 2129 30

[45] Shi Y,Zhang L,Peterson CB,Do KA,Jenq RR. Performance determinants of unsupervised clustering methods for microbiome data Microbiome. 2022 10 25 PMC8817542

[46] Koren O,Knights D,Gonzalez A.et al. A guide to enterotypes across the human body: meta-analysis of microbial community structures in human microbiome datasets PLoS Comput Biol. 2013 9 e1002863 PMC3542080

[47] Vandeputte D,Kathagen G,D’hoe K.et al. Quantitative microbiome profiling links gut community variation to microbial load Nature. 2017 551 507 11

[48] Arumugam M,Raes J,Pelletier E.et al. ; MetaHIT Consortium. Enterotypes of the human gut microbiome Nature. 2011 473 174 80 PMC3728647

[49] Popov IV,Koopmans B,Venema K. Modulation of human gut microbiota by linear and branched fructooligosaccharides in an in vitro colon model (TIM-2) J Appl Microbiol. 2024 135:lxae170.[PMID:38986506 DOI 10 1093/jambio/lxae170

[50] Yang J,Li Y,Wen Z,Liu W,Meng L,Huang H. Oscillospira - a candidate for the next-generation probiotics Gut Microbes. 2021 13 1987783 PMC8547878