Integration of Infant Metabolite, Genetic, and Islet Autoimmunity Signatures to Predict Type 1 Diabetes by Age 6 Years
| العنوان: | Integration of Infant Metabolite, Genetic, and Islet Autoimmunity Signatures to Predict Type 1 Diabetes by Age 6 Years |
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| المؤلفون: | Bobbie-Jo M Webb-Robertson, Ernesto S Nakayasu, Brigitte I Frohnert, Lisa M Bramer, Sarah M Akers, Jill M Norris, Kendra Vehik, Anette-G Ziegler, Thomas O Metz, Stephen S Rich, Marian J Rewers |
| المصدر: | J Clin Endocrinol Metab J. Clin. Endocrinol. Metab. 107, 2329-2338 (2022) |
| بيانات النشر: | The Endocrine Society, 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Clinical Research Article, Endocrinology, Diabetes and Metabolism, Biochemistry (medical), Clinical Biochemistry, Infant, Newborn, Infant, Autoimmunity, Biochemistry, United States, Cohort Studies, Islets of Langerhans, Diabetes Mellitus, Type 1, Endocrinology, Child, Preschool, Metabolomics, Humans, Genetic Predisposition to Disease, Prospective Studies, Child, Biomarkers, Integration, Machine Learning, Prediction, Type 1 Diabetes, Autoantibodies |
| الوصف: | Context Biomarkers that can accurately predict risk of type 1 diabetes (T1D) in genetically predisposed children can facilitate interventions to delay or prevent the disease. Objective This work aimed to determine if a combination of genetic, immunologic, and metabolic features, measured at infancy, can be used to predict the likelihood that a child will develop T1D by age 6 years. Methods 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. Results Machine learning–based 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. Conclusion 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. |
| وصف الملف: | application/pdf |
| تدمد: | 1945-7197 0021-972X |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::77ea715f97480eb604e302b14615ae25 https://doi.org/10.1210/clinem/dgac225 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....77ea715f97480eb604e302b14615ae25 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 19457197 0021972X |
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