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Underwood, M. A., German, J. B., Lebrilla, C. B. & Mills, D. A. Bifidobacterium longum subspecies infantis: champion colonizer of the infant gut. Pediatr. Res. 77, 229–235 (2015). TEDDY Study Group. The Environmental Determinants of Diabetes in the Young (TEDDY) Study. Ann. NY Acad. Sci. 1150, 1–13 (2008). Hober, D. & Sauter, P. Pathogenesis of type 1 diabetes mellitus: interplay between enterovirus and host. Nat. Rev. Endocrinol. 6, 279–289 (2010). Further information on research design is available in the Nature Research Reporting Summary linked to this paper. Code availability Lachin, J. M. Sample size evaluation for a multiply matched case–control study using the score test from a conditional logistic (discrete Cox PH) regression model. Statist. Med. 27, 2509–2534 (2012).

Pathways with the highest statistical significance in case–control comparisons were related to bacterial fermentation (Supplementary Table 4). The superpathway of fermentation (MetaCyc identifier PWY4LZ-257) was increased in controls in the T1D cohort ( q = 0.019) and Finnish IA cohort ( q = 0.049). SCFAs such as butyrate, acetate and propionate are common by-products of bacterial fermentation, and butyrate and acetate protected NOD mice against T1D 9. Consistently, we observed that several bacterial pathways that contribute to the biosynthesis of short-chain fatty acids were increased in healthy controls. Among pathways involved in butyrate production, the degradation of l-arginine, putrescine and 4-aminobutanoate (ARGDEG-PWY) superpathway was increased in T1D controls cohort-wide ( q = 0.043), whereas the fermentation of acetyl coenzyme A to butanoate (PWY-5676) was more abundant in the Finnish T1D controls ( q = 0.053). The degradation of acetylene (P161-PWY), which contributes to acetate production, was increased in T1D controls cohort-wide ( q = 0.14), and the degradation of l-1,2-propanediol (PWY-7013), which is involved in propionate biosynthesis, was higher in the German T1D controls ( q = 0.019). These findings support existing evidence for the protective effects of SCFAs in human T1D 7, 8 and T2D 19 cohorts and the NOD mouse model 9. Ziegler, A. G. et al. Seroconversion to multiple islet autoantibodies and risk of progression to diabetes in children. J. Am. Med. Assoc. 309, 2473–2479 (2013).Rewers, M. et al. Assessment and monitoring of glycemic control in children and adolescents with diabetes. Pediatr. Diabetes 8, 408–418 (2007).

Stewart, C. J. et al. Temporal development of the gut microbiome in early childhood from the TEDDY study. Nature https://doi.org/10.1038/s41586-018-0617-x (2018).

TEDDY Study Group The Environmental Determinants of Diabetes in the Young (TEDDY) study. Ann. NY Acad. Sci. 1150, 1–13 (2008).

Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, Turku, Finland Lernmark, Daniel Agardh, Carin Andrén Aronsson, Maria Ask, Rasmus Bennet, Corrado Cilio, Susanne Dahlberg, Helene Engqvist, Emelie Ericson-Hallström, Annika Björne Fors, Lina Fransson, Thomas Gard, Monika Hansen, Hanna Jisser, Fredrik Johansen, Berglind Jonsdottir, Helena Elding Larsson, Marielle Lindström, Markus Lundgren, Marlena Maziarz, Maria Månsson-Martinez, Jessica Melin, Zeliha Mestan, Caroline Nilsson, Karin Ottosson, Kobra Rahmati, Anita Ramelius, Falastin Salami, Anette Sjöberg, Birgitta Sjöberg, Carina Törn, Åsa Wimar, Carin Andrén Aronsson, Carina Törn, Corrado Cilio, Daniel Agardh, Corrado Cilio, Helena Elding Larsson, Carin Andrén Aronsson, Berglind Jonsdottir, Jessica Melin, Daniel Agardh, Katri Lindfors, Carin Andrén Aronsson, Helena Elding Larsson, Markus Lundgren & Åsa Wimar Mejía-León, M. E., Petrosino, J. F., Ajami, N. J., Domínguez-Bello, M. G. & de la Barca, A. M. Fecal microbiota imbalance in Mexican children with type 1 diabetes. Sci. Rep. 4, 3814 (2014).Desmond Schatz, Michael Haller, Laura Jacobsen, John Marks, P. D. Towe, Eric Triplett, Desmond Schatz, Desmond Schatz, Desmond Schatz, Desmond Schatz, John Marks, Michael Haller, Laura Jacobsen & John Marks Accompanying our taxonomic profiling, functional profiling of these metagenomes suggested the development of a consistent microbial functional core during infancy, with a smaller subject-specific variable functional pool (Extended Data Fig. 5a, b, Supplementary Note 3). As in most microbial community studies 33, microbial gene families of uncharacterized function made up a substantial fraction of these profiles, averaging roughly 50% based on Gene Ontology 34 annotations (Extended Data Fig. 5c) and more than 90% based on more functionally specific MetaCyc pathways (Extended Data Fig. 5d). We observed an increasing longitudinal trend in the proportion of unmapped reads (Extended Data Fig. 5e, Pearson’s r = 0.318, P< 2.2 × 10 −16). However, within the reads that mapped to either microbial pangenomes or known protein sequences (the proportion of which decreased with age), we saw an increase in the proportion of reads with MetaCyc annotation, mainly during the first year (Extended Data Fig. 5f, Pearson r = 0.391, P< 2.2 × 10 −16). This suggests that although the early life microbiome is relatively well-covered by current microbial reference genomes, less functional and biochemical characterization has been carried out on gene families within these microorganisms, which will thus particularly benefit from future work. Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA Krischer, J. P. et al. The 6 year incidence of diabetes-associated autoantibodies in genetically at-risk children: the TEDDY study. Diabetologia 58, 980–987 (2015).

Yassour, M. et al. Natural history of the infant gut microbiome and impact of antibiotic treatment on bacterial strain diversity and stability. Sci. Transl. Med. 8, 343ra81 (2016). Polly Bingley, Alistair Williams, Kyla Chandler, Ilana Kelland, Yassin Ben Khoud, Huma Zahid, Matthew Randell & Polly Bingley The UniProt Consortium. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 45, D158–D169 (2017). LaGasse, J. M. et al. Successful prospective prediction of type 1 diabetes in schoolchildren through multiple defined autoantibodies: an 8-year follow-up of the Washington State Diabetes Prediction Study. Diabetes Care 25, 505–511 (2002). Hippich, M. et al. Genetic contribution to the divergence in type 1 diabetes risk between children from the general population and children from affected families. Diabetes 68, 847–857 (2019).Paun, A., Yau, C. & Danska, J. S. The influence of the microbiome on type 1 diabetes. J. Immunol. 198, 590–595 (2017).