{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T19:57:19Z","timestamp":1792267039148,"version":"4.4.0"},"reference-count":49,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2020,8,16]],"date-time":"2020-08-16T00:00:00Z","timestamp":1597536000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","award":["UC4 DK100238"],"award-info":[{"award-number":["UC4 DK100238"]}],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","award":["UL1 TR000064"],"award-info":[{"award-number":["UL1 TR000064"]}],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","award":["UL1 TR001082"],"award-info":[{"award-number":["UL1 TR001082"]}],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Diabetes"],"published-print":{"date-parts":[[2021,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>The Environmental Determinants of the Diabetes in the Young (TEDDY) study has prospectively followed, from birth, children at increased genetic risk of type 1 diabetes. TEDDY has collected heterogenous data longitudinally to gain insights into the environmental and biological mechanisms driving the progression to persistent islet autoantibodies.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We developed a machine learning model to predict imminent transition to the development of persistent islet autoantibodies based on time\u2010varying metabolomics data integrated with time\u2010invariant risk factors (eg, gestational age). The machine learning was initiated with 221 potential features (85 genetic, 5 environmental, 131 metabolomic) and an ensemble\u2010based feature evaluation was utilized to identify a small set of predictive features that can be interrogated to better understand the pathogenesis leading up to persistent islet autoimmunity.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The final integrative machine learning model included 42 disparate features, returning a cross\u2010validated receiver operating characteristic area under the curve (AUC) of 0.74 and an AUC of ~0.65 on an independent validation dataset. The model identified a principal set of 20 time\u2010invariant markers, including 18 genetic markers (16 single nucleotide polymorphisms [SNPs] and two HLA\u2010DR genotypes) and two demographic markers (gestational age and exposure to a prebiotic formula). Integration with the metabolome identified 22 supplemental metabolites and lipids, including adipic acid and ceramide d42:0, that predicted development of islet autoantibodies.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The majority (86%) of metabolites that predicted development of islet autoantibodies belonged to three pathways: lipid oxidation, phospholipase A2 signaling, and pentose phosphate, suggesting that these metabolic processes may play a role in triggering islet autoimmunity.<\/jats:p><\/jats:sec>","DOI":"10.1111\/1753-0407.13093","type":"journal-article","created":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T06:05:20Z","timestamp":1595484320000},"page":"143-153","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Prediction of the development of islet autoantibodies through integration of environmental, genetic, and metabolic markers"],"prefix":"10.1111","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4744-2397","authenticated-orcid":false,"given":"Bobbie\u2010Jo M.","family":"Webb\u2010Robertson","sequence":"first","affiliation":[{"name":"Biological Sciences Division, Pacific Northwest National Laboratory  Richland Washington USA"},{"name":"Colorado School of Public Health University of Colorado Anschutz Medical Campus  Aurora California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8384-1926","authenticated-orcid":false,"given":"Lisa M.","family":"Bramer","sequence":"additional","affiliation":[{"name":"Computing and Analytics Division Pacific Northwest National Laboratory  Richland Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0612-5333","authenticated-orcid":false,"given":"Bryan A.","family":"Stanfill","sequence":"additional","affiliation":[{"name":"Computing and 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