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Verge, C. et al. Prediction of type I diabetes in first-degree relatives using a combination of insulin, GAD, and ICA512bdc/IA-2 autoantibodies. Diabetes 45, 926–933 (1996). Bonifacio, E. et al. Effects of high-dose oral insulin on immune responses in children at high risk for type 1 diabetes. J. Am. Med. Assoc. 313, 1541–1549 (2015). Kostic, A. D. et al. The dynamics of the human infant gut microbiome in development and in progression toward type 1 diabetes. Cell Host Microbe 17, 260–273 (2015). Needell, J. C. & Zipris, D. The role of the intestinal microbiome in type 1 diabetes pathogenesis. Curr. Diab. Rep. 16, 89 (2016). Soyucen, E. et al. Differences in the gut microbiota of healthy children and those with type 1 diabetes. Pediatr. Int. 56, 336–343 (2014).

Personalised Grey Pink or Blue Bunny Plush Soft Toy, Customised New Baby Gift, Boys and Girls Birth Teddy Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA We next investigated the effects of antibiotics on the early life microbiome. Courses of oral antibiotics disrupted microbial stability, with a larger effect in the earliest comparisons (Fig. 2c, Extended Data Fig. 4a–f, Extended Data Table 2, Supplementary Note 2). Previous studies have found Bifidobacterium species to be especially vulnerable to antibiotics 31, 32, leading us to investigate how antibiotic perturbations influenced these common dominant members of the early gut. Comparing microbial relative abundances before and after antibiotics (assuming that the given species was present in the preceding sample), we saw a decrease in the abundances of the Bifidobacterium members B. bifidum, B. pseudocatenulatum, B. adolescentis, B. dentium and B. catenulatum, whereas B. longum and B. breve did not systematically decline owing to antibiotics (Fig. 2d), suggesting that certain Bifidobacterium species are particularly susceptible to out-competition by other community members after depletion by antibiotics. Given their dominance in the typical developing gut microbiota and finely tuned balance of metabolic interactions with breast milk, this finding underscores the importance of approaching antibiotic prescriptions in early childhood with care, especially during breastfeeding. 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).TEDDY Study Group The Environmental Determinants of Diabetes in the Young (TEDDY) study. Ann. NY Acad. Sci. 1150, 1–13 (2008). Krischer, J. P. et al. Genetic and environmental interactions modify the risk of diabetes-related autoimmunity by 6 years of age: the TEDDY study. Diabetes Care 40, 1194–1202 (2017). Endesfelder, D. et al. Towards a functional hypothesis relating anti-islet cell autoimmunity to the dietary impact on microbial communities and butyrate production. Microbiome 4, 17 (2016).

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Suzek, B. E., Wang, Y., Huang, H., McGarvey, P. B. & Wu, C. H. UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics 31, 926–932 (2015). Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA Endesfelder, D. et al. Compromised gut microbiota networks in children with anti-islet cell autoimmunity. Diabetes 63, 2006–2014 (2014). Taxonomic profiling of the metagenomic samples was performed using MetaPhlAn2 46 v2.6.0, which uses a library of clade-specific markers to provide pan-microbial (bacterial, archaeal, viral and eukaryotic) quantification at the species level. MetaPhlAn2 was run using default settings. Ferrat, L.A., Vehik, K., Sharp, S.A. et al. A combined risk score enhances prediction of type 1 diabetes among susceptible children.