1449-P: Machine Learning Reveals Metabolic Signatures in Patients with Type 1 Diabetes

Background: Metabolic dysbiosis has been linked to the development of type 1 diabetes. However, there are few studies reflecting the metabolic signatures in patients with type 1 diabetes based on machine learning. Therefore, we aim to investigate the serum metabolic alterations and signatures in patients with type 1 diabetes. Methods: We recruited 29 type 1 diabetes patients and 29 healthy controls with matching age, sex and ethnicity. Serum metabolites were analyzed using liquid chromatograph-mass spectrometry (LC-MS). Four machine-learning approaches (Logistic Regression, Support Vector Machine, Gaussian Naive Bayes, and Random Forest) were used to screen potential T1D-related biomarkers. Results: Between the T1D group and the control group, 150 different metabolites were identified. These metabolites were significantly enriched in three metabolic pathways (P