Journal of diabetes · 2020
Prediction of the development of islet autoantibodies through integration of environmental, genetic, and metabolic markers.
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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
Аннотация
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.
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.