Open Access通过整合环境、遗传和代谢标记物预测胰岛自身抗体的发展
Author(s) -
WebbRobertson BobbieJo 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.
Publication year - 2021
Publication title -
journal of diabetes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.949
H-Index - 43
eISSN - 1753-0407
pISSN - 1753-0393
DOI - 10.1111/1753-0407.13093
Subject(s) - autoantibody , single nucleotide polymorphism , metabolomics , islet , medicine , receiver operating characteristic , type 1 diabetes , diabetes mellitus , immunology , genotype , bioinformatics , biology , genetics , endocrinology , antibody , gene
Background 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. Methods We developed a machine learning model to predict imminent transition to the development of persistent islet autoantibodies based on time‐varying metabolomics data integrated with time‐invariant risk factors (eg, gestational age). The machine learning was initiated with 221 potential features (85 genetic, 5 environmental, 131 metabolomic) and an ensemble‐based 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. Results The final integrative machine learning model included 42 disparate features, returning a cross‐validated 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‐invariant markers, including 18 genetic markers (16 single nucleotide polymorphisms [SNPs] and two HLA‐DR 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. Conclusions 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.