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years.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>Newborns with human leukocyte antigen (HLA) typing were enrolled in the prospective birth cohort of The Environmental Determinants of Diabetes in the Young (TEDDY). TEDDY ascertained children in Finland, Germany, Sweden, and the United States. TEDDY children were either from the general population or from families with T1D with an HLA genotype associated with T1D specific to TEDDY eligibility criteria. From the TEDDY cohort there were 702 children will all data sources measured at ages 3, 6, and 9 months, 11.4% of whom progressed to T1D by age 6 years. The main outcome measure was a diagnosis of T1D as diagnosed by American Diabetes Association criteria.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Machine learning\u2013based feature selection yielded classifiers based on disparate demographic, immunologic, genetic, and metabolite features. The accuracy of the model using all available data evaluated by the area under a receiver operating characteristic curve is 0.84. Reducing to only 3- and 9-month measurements did not reduce the area under the curve significantly. Metabolomics had the largest value when evaluating the accuracy at a low false-positive rate.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>The metabolite features identified as important for progression to T1D by age 6 years point to altered sugar metabolism in infancy. Integrating this information with classic risk factors improves prediction of the progression to T1D in early childhood.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1210\/clinem\/dgac225","type":"journal-article","created":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T18:05:52Z","timestamp":1650909952000},"page":"2329-2338","source":"Crossref","is-referenced-by-count":19,"title":["Integration of Infant Metabolite, Genetic, and Islet Autoimmunity Signatures to Predict Type 1 Diabetes by Age 6 Years"],"prefix":"10.1210","volume":"107","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4744-2397","authenticated-orcid":false,"given":"Bobbie-Jo M","family":"Webb-Robertson","sequence":"first","affiliation":[{"name":"Biological Sciences Division, Pacific Northwest National Laboratory , Richland, Washington 99352, USA"},{"name":"Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado 80045 , 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Washington 99352, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3727-5801","authenticated-orcid":false,"given":"Sarah M","family":"Akers","sequence":"additional","affiliation":[{"name":"Computing & Analytics Division, Pacific Northwest National Laboratory , Richland, Washington 99352 , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8674-2598","authenticated-orcid":false,"given":"Jill M","family":"Norris","sequence":"additional","affiliation":[{"name":"Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado 80045 , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6243-6772","authenticated-orcid":false,"given":"Kendra","family":"Vehik","sequence":"additional","affiliation":[{"name":"Health Informatics Institute, Morsani College of Medicine, University of South Florida , Tampa, Florida 33612 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