학술논문
Integration of Infant Metabolite, Genetic, and Islet Autoimmunity Signatures to Predict Type 1 Diabetes by Age 6 Years
Clinical Research Article
Clinical Research Article
Document Type
Academic Journal
Author
Source
Journal of Clinical Endocrinology & Metabolism. August 2022, Vol. 107 Issue 8, p2329, 10 p.
Subject
Language
English
ISSN
0021-972X
Abstract
The development of type 1 diabetes (T1D) is driven by an interaction between genetic and environmental factors. The relationships and roles of human leukocyte antigen (HLA) and other genes as [...]
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. Key Words: type 1 diabetes, prediction, integration, machine learning Abbreviations: AUC, area under the receiver operating characteristic curve; CV, cross-validation; FDR-T1D, first-degree relative with T1D; FPR, false-positive rate; GADA, glutamic acid decarboxylase antibody; GC-TOF MS, gas chromatography--time-of-flight mass spectrometry; GIA, general infant attributes; GRS, genetic risk scores; HLA, human leukocyte antigen; IA-2A, insulinoma-associated antigen 2 autoantibody; IAA, insulin autoantibody; IAAb, islet autoantibody; ROC, receiver operating characteristic curve; ROFI, Repeated Optimization for Feature Interpretation; SNV, single-nucleotide variation; T1D, type 1 diabetes; TEDDY, The Environmental Determinants of Diabetes in the Young; TPR, true-positive rate.
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. Key Words: type 1 diabetes, prediction, integration, machine learning Abbreviations: AUC, area under the receiver operating characteristic curve; CV, cross-validation; FDR-T1D, first-degree relative with T1D; FPR, false-positive rate; GADA, glutamic acid decarboxylase antibody; GC-TOF MS, gas chromatography--time-of-flight mass spectrometry; GIA, general infant attributes; GRS, genetic risk scores; HLA, human leukocyte antigen; IA-2A, insulinoma-associated antigen 2 autoantibody; IAA, insulin autoantibody; IAAb, islet autoantibody; ROC, receiver operating characteristic curve; ROFI, Repeated Optimization for Feature Interpretation; SNV, single-nucleotide variation; T1D, type 1 diabetes; TEDDY, The Environmental Determinants of Diabetes in the Young; TPR, true-positive rate.