Prediction of the development of islet autoantibodies through integration of environmental, genetic, and metabolic markers

Webb‐Robertson, Bobbie‐Jo M.; Bramer, Lisa M.; Stanfill, Bryan A.; Reehl, Sarah M.; Nakayasu, Ernesto S.; Metz, Thomas O.; Frohnert, Brigitte I.; Norris, Jill M.; Johnson, Randi K.; Rich, Stephen S.; Rewers, Marian J.

doi:10.1111/1753-0407.13093

Prediction of the development of islet autoantibodies through integration of environmental, genetic, and metabolic markers

Journal Article · Wed Jul 22 00:00:00 EDT 2020 · Journal of Diabetes (Online)

DOI:https://doi.org/10.1111/1753-0407.13093· OSTI ID:1756070

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Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Univ. of Colorado, Aurora, CO (United States). Anschutz Medical Campus
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Univ. of Colorado, Aurora, CO (United States). Anschutz Medical Campus
Univ. of Virginia, Charlottesville, VA (United States)

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. We evaluated the potential of machine learning to identify new biomarkers that predict imminent (within 6 months) development of persistent islet autoantibodies to insulin, GAD or IA-2 in TEDDY participants through integration of time-invariant risk factors with time-varying metabolomics. The predictive modeling was initiated with over 220 potential biomarkers; through ensemble-based feature evaluation, the optimal model included 42 biomarkers, returning a cross-validated receiver operating characteristic area under the curve of 0.74. The model identified a principal set of 20 time-invariant markers, including 16 single nucleotide polymorphisms and two HLA-DR genotypes, gestational age, and exposure to a prebiotic formula. Integration of the metabolome identified 22 high-priority metabolites and lipids, including adipic acid and ceramide d42:0, that predicted development of islet autoantibodies, dependent upon the time horizon. The majority (86%) of metabolites that predicted development of islet autoantibodies belonged to 3 pathways: lipid oxidation, phospholipase A2 signaling, and pentose phosphate pathway. TEDDY data suggest that these metabolic processes may play a role in triggering islet autoimmunity.

View Accepted Manuscript (DOE)

Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: National Institutes of Health (NIH); USDOE

Grant/Contract Number:: AC05-76RL01830

OSTI ID:: 1756070

Report Number(s):: PNNL-SA--146734

Journal Information:: Journal of Diabetes (Online), Journal Name: Journal of Diabetes (Online) Journal Issue: 2 Vol. 13; ISSN 1753-0407

Publisher:: Wiley and Ruijin Hospital, Shanghai Jiaotong University School of MedicineCopyright Statement

Country of Publication:: United States

Language:: English

References (47)

Infant Exposures and Development of Type 1 Diabetes Mellitus: The Diabetes Autoimmunity Study in the Young (DAISY) Frederiksen, Brittni; Kroehl, Miranda; Lamb, Molly M. JAMA Pediatrics, Vol. 167, Issue 9 https://doi.org/10.1001/jamapediatrics.2013.317	journal	September 2013
Metabolomics by Gas Chromatography–Mass Spectrometry: Combined Targeted and Untargeted Profiling Fiehn, Oliver Current Protocols in Molecular Biology, Vol. 114, Issue 1 https://doi.org/10.1002/0471142727.mb3004s114	journal	April 2016
Comparison of insulin autoantibody: polyethylene glycol and micro-IAA 1-day and 7-day assays Babaya, Naru; Yu, Liping; Miao, Dongmei Diabetes/Metabolism Research and Reviews, Vol. 25, Issue 7 https://doi.org/10.1002/dmrr.1014	journal	October 2009
Biomarker discovery study design for type 1 diabetes in The Environmental Determinants of Diabetes in the Young (TEDDY) study: Biomarker Discovery Study Design Lee, Hye-Seung; Burkhardt, Brant R.; McLeod, Wendy Diabetes/Metabolism Research and Reviews, Vol. 30, Issue 5 https://doi.org/10.1002/dmrr.2510	journal	July 2014
Nested case‐control data analysis using weighted conditional logistic regression in The Environmental Determinants of Diabetes in the Young (TEDDY) study: A novel approach Lee, Hye‐Seung; Lynch, Kristian F.; Krischer, Jeffrey P. Diabetes/Metabolism Research and Reviews, Vol. 36, Issue 1 https://doi.org/10.1002/dmrr.3204	journal	July 2019
Precision diabetes: a realistic outlook on a promising approach Marshall, Sally M. Diabetologia, Vol. 60, Issue 5 https://doi.org/10.1007/s00125-017-4244-0	journal	March 2017
Late-onset islet autoimmunity in childhood: the Diabetes Autoimmunity Study in the Young (DAISY) Frohnert, Brigitte I.; Ide, Lisa; Dong, Fran Diabetologia, Vol. 60, Issue 6 https://doi.org/10.1007/s00125-017-4256-9	journal	March 2017
Early prediction of autoimmune (type 1) diabetes Regnell, Simon E.; Lernmark, Åke Diabetologia, Vol. 60, Issue 8 https://doi.org/10.1007/s00125-017-4308-1	journal	May 2017
Newborn screening for HLA markers associated with IDDM: Diabetes Autoimmunity Study in the Young (DAISY) Rewers, M.; Bugawan, T. L.; Norris, J. M. Diabetologia, Vol. 39, Issue 7 https://doi.org/10.1007/s001250050514	journal	July 1996
Clinical and Novel Biomarkers in the Management of Prostate Cancer Sanhueza, Cristóbal; Kohli, Manish Current Treatment Options in Oncology, Vol. 19, Issue 2 https://doi.org/10.1007/s11864-018-0527-z	journal	February 2018
The Environmental Determinants of Diabetes in the Young (TEDDY) Study: 2018 Update Rewers, Marian; Hyöty, Heikki; Lernmark, Åke Current Diabetes Reports, Vol. 18, Issue 12 https://doi.org/10.1007/s11892-018-1113-2	journal	October 2018
FiehnLib: Mass Spectral and Retention Index Libraries for Metabolomics Based on Quadrupole and Time-of-Flight Gas Chromatography/Mass Spectrometry Kind, Tobias; Wohlgemuth, Gert; Lee, Do Yup Analytical Chemistry, Vol. 81, Issue 24 https://doi.org/10.1021/ac9019522	journal	December 2009
Validating Quantitative Untargeted Lipidomics Across Nine Liquid Chromatography–High-Resolution Mass Spectrometry Platforms Cajka, Tomas; Smilowitz, Jennifer T.; Fiehn, Oliver Analytical Chemistry, Vol. 89, Issue 22 https://doi.org/10.1021/acs.analchem.7b03404	journal	November 2017
Systematic Error Removal Using Random Forest for Normalizing Large-Scale Untargeted Lipidomics Data Fan, Sili; Kind, Tobias; Cajka, Tomas Analytical Chemistry, Vol. 91, Issue 5 https://doi.org/10.1021/acs.analchem.8b05592	journal	February 2019
LipidBlast in silico tandem mass spectrometry database for lipid identification Kind, Tobias; Liu, Kwang-Hyeon; Lee, Do Yup Nature Methods, Vol. 10, Issue 8 https://doi.org/10.1038/nmeth.2551	journal	June 2013
Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry Dunn, Warwick B.; Broadhurst, David; Begley, Paul Nature Protocols, Vol. 6, Issue 7 https://doi.org/10.1038/nprot.2011.335	journal	June 2011
Emerging applications of metabolomics in drug discovery and precision medicine Wishart, David S. Nature Reviews Drug Discovery, Vol. 15, Issue 7 https://doi.org/10.1038/nrd.2016.32	journal	March 2016
A data-driven, knowledge-based approach to biomarker discovery: application to circulating microRNA markers of colorectal cancer prognosis Vafaee, Fatemeh; Diakos, Connie; Kirschner, Michaela B. npj Systems Biology and Applications, Vol. 4, Issue 1 https://doi.org/10.1038/s41540-018-0056-1	journal	June 2018
Metabolic gatekeepers to safeguard against autoimmunity and oncogenic B cell transformation Müschen, Markus Nature Reviews Immunology, Vol. 19, Issue 5 https://doi.org/10.1038/s41577-019-0154-3	journal	March 2019
Temporal development of the gut microbiome in early childhood from the TEDDY study Stewart, Christopher J.; Ajami, Nadim J.; O’Brien, Jacqueline L. Nature, Vol. 562, Issue 7728 https://doi.org/10.1038/s41586-018-0617-x	journal	October 2018
The human gut microbiome in early-onset type 1 diabetes from the TEDDY study Vatanen, Tommi; Franzosa, Eric A.; Schwager, Randall Nature, Vol. 562, Issue 7728 https://doi.org/10.1038/s41586-018-0620-2	journal	October 2018
Common patterns of gene regulation associated with Cesarean section and the development of islet autoimmunity – indications of immune cell activation Laimighofer, M.; Lickert, R.; Fuerst, R. Scientific Reports, Vol. 9, Issue 1 https://doi.org/10.1038/s41598-019-42750-5	journal	April 2019
Interferon γ Attenuates Insulin Signaling, Lipid Storage, and Differentiation in Human Adipocytes via Activation of the JAK/STAT Pathway McGillicuddy, Fiona C.; Chiquoine, Elise H.; Hinkle, Christine C. Journal of Biological Chemistry, Vol. 284, Issue 46 https://doi.org/10.1074/jbc.M109.061655	journal	September 2009
Computer-aided biomarker discovery for precision medicine: data resources, models and applications Lin, Yuxin; Qian, Fuliang; Shen, Li Briefings in Bioinformatics, Vol. 20, Issue 3 https://doi.org/10.1093/bib/bbx158	journal	November 2017
iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data Chen, Zhen; Zhao, Pei; Li, Fuyi Briefings in Bioinformatics, Vol. 21, Issue 3 https://doi.org/10.1093/bib/bbz041	journal	April 2019
VIBE 2.0: Visual Integration for Bayesian Evaluation Beagley, Nathaniel; Stratton, Kelly G.; Webb-Robertson, Bobbie-Jo M. Bioinformatics, Vol. 26, Issue 2 https://doi.org/10.1093/bioinformatics/btp639	journal	November 2009
MissForest--non-parametric missing value imputation for mixed-type data Stekhoven, D. J.; Buhlmann, P. Bioinformatics, Vol. 28, Issue 1 https://doi.org/10.1093/bioinformatics/btr597	journal	October 2011
SNPedia: a wiki supporting personal genome annotation, interpretation and analysis Cariaso, Michael; Lennon, Greg Nucleic Acids Research, Vol. 40, Issue D1 https://doi.org/10.1093/nar/gkr798	journal	December 2011
ROFI - The Use of Repeated Optimization for Feature Interpretation Webb-Robertson, Bobbie-Jo M.; Bramer, Lisa M.; Reehl, Sarah M. 2016 International Conference on Computational Science and Computational Intelligence (CSCI) https://doi.org/10.1109/CSCI.2016.0013	conference	December 2016
Predicting progression to type 1 diabetes from ages 3 to 6 in islet autoantibody positive TEDDY children Jacobsen, Laura M.; Larsson, Helena E.; Tamura, Roy N. Pediatric Diabetes, Vol. 20, Issue 3 https://doi.org/10.1111/pedi.12812	journal	January 2019
Role of calcium-independent phospholipase A ₂ β in human pancreatic islet β-cell apoptosis Lei, Xiaoyong; Zhang, Sheng; Bohrer, Alan American Journal of Physiology-Endocrinology and Metabolism, Vol. 303, Issue 11 https://doi.org/10.1152/ajpendo.00234.2012	journal	December 2012
Precision Diabetes Is Slowly Becoming a Reality Mohan, Viswanathan; Radha, Venkatesan Medical Principles and Practice, Vol. 28, Issue 1 https://doi.org/10.1159/000497241	journal	January 2019
2-Aminoadipic acid is a biomarker for diabetes risk Wang, Thomas J.; Ngo, Debby; Psychogios, Nikolaos Journal of Clinical Investigation, Vol. 123, Issue 10 https://doi.org/10.1172/JCI64801	journal	September 2013
Extending Classification Algorithms to Case-Control Studies Stanfill, Bryan; Reehl, Sarah; Bramer, Lisa Biomedical Engineering and Computational Biology, Vol. 10 https://doi.org/10.1177/1179597219858954	journal	January 2019
The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists Huang, Da; Sherman, Brad T.; Tan, Qina Genome Biology, Vol. 8, Issue 9 https://doi.org/10.1186/gb-2007-8-9-r183	journal	January 2007
Evidence of Contribution of iPLA2β-Mediated Events During Islet β-Cell Apoptosis Due to Proinflammatory Cytokines Suggests a Role for iPLA2β in T1D Development Lei, Xiaoyong; Bone, Robert N.; Ali, Tomader Endocrinology, Vol. 155, Issue 9 https://doi.org/10.1210/en.2013-2134	journal	September 2014
Association Between Vitamin D Metabolism Gene Polymorphisms and Risk of Islet Autoimmunity and Progression to Type 1 Diabetes: The Diabetes Autoimmunity Study in the Young (DAISY) Frederiksen, Brittni N.; Kroehl, Miranda; Fingerlin, Tasha E. The Journal of Clinical Endocrinology & Metabolism, Vol. 98, Issue 11 https://doi.org/10.1210/jc.2013-2256	journal	November 2013
Genetic scores to stratify risk of developing multiple islet autoantibodies and type 1 diabetes: A prospective study in children Bonifacio, Ezio; Beyerlein, Andreas; Hippich, Markus PLOS Medicine, Vol. 15, Issue 4 https://doi.org/10.1371/journal.pmed.1002548	journal	April 2018
Artificial intelligence and the analysis of multi-platform metabolomics data for the detection of intrauterine growth restriction Bahado-Singh, Ray Oliver; Yilmaz, Ali; Bisgin, Halil PLOS ONE, Vol. 14, Issue 4 https://doi.org/10.1371/journal.pone.0214121	journal	April 2019
Role of Type 1 Diabetes–Associated SNPs on Risk of Autoantibody Positivity in the TEDDY Study Törn, Carina; Hadley, David; Lee, Hye-Seung Diabetes, Vol. 64, Issue 5 https://doi.org/10.2337/db14-1497	journal	November 2014
The Influence of Type 1 Diabetes Genetic Susceptibility Regions, Age, Sex, and Family History on the Progression From Multiple Autoantibodies to Type 1 Diabetes: A TEDDY Study Report Krischer, Jeffrey P.; Liu, Xiang; Lernmark, Åke Diabetes, Vol. 66, Issue 12 https://doi.org/10.2337/db17-0261	journal	September 2017
Predictive Modeling of Type 1 Diabetes Stages Using Disparate Data Sources Frohnert, Brigitte I.; Webb-Robertson, Bobbie-Jo; Bramer, Lisa M. Diabetes, Vol. 69, Issue 2 https://doi.org/10.2337/db18-1263	journal	November 2019
Gestational Age and Birth Weight and the Risk of Childhood Type 1 Diabetes: A Population-Based Cohort and Sibling Design Study Khashan, Ali S.; Kenny, Louise C.; Lundholm, Cecilia Diabetes Care, Vol. 38, Issue 12 https://doi.org/10.2337/dc15-0897	journal	October 2015
Genetic and Environmental Interactions Modify the Risk of Diabetes-Related Autoimmunity by 6 Years of Age: The TEDDY Study Krischer, Jeffrey P.; Lynch, Kristian F.; Lernmark, Åke Diabetes Care, Vol. 40, Issue 9 https://doi.org/10.2337/dc17-0238	journal	June 2017
Early Infant Diet and Islet Autoimmunity in the TEDDY Study Uusitalo, Ulla; Lee, Hye-Seung; Andrén Aronsson, Carin Diabetes Care, Vol. 41, Issue 3 https://doi.org/10.2337/dc17-1983	journal	January 2018
Role of Type 1 Diabetes–Associated SNPs on Autoantibody Positivity in the Type 1 Diabetes Genetics Consortium: Overview Rich, Stephen S.; Concannon, Patrick Diabetes Care, Vol. 38, Issue Supplement 2 https://doi.org/10.2337/dcs15-2001	journal	September 2015
Probiotics and Prebiotics for the Amelioration of Type 1 Diabetes: Present and Future Perspectives Mishra, Sidharth; Wang, Shaohua; Nagpal, Ravinder Microorganisms, Vol. 7, Issue 3 https://doi.org/10.3390/microorganisms7030067	journal	March 2019

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