A metabolomics-based laboratory developed test to better predict and monitor progression of type 1 diabetes

Abstract Type I diabetes (T1D) is an autoimmune disorder in which the host’s T cells attack the insulin-producing β-cells in the pancreatic islet of Langerhans. The onset of symptomatic disease is preceded by two asymptomatic stages in which islet autoantibodies develop and glucose metabolism becomes disturbed. If the unwanted immune response is dampened during this critical asymptomatic period clinical T1D can be delayed by several years and, in some cases, prevented altogether. Currently, teplizumab is the sole therapeutic available for treating pre-clinical T1D and only a fraction of patients respond to it. This necessitates the development of novel alternative treatments. Unfortunately, this endeavor is hampered by a paucity of biomarkers that can accurately report a patient’s response to treatments that are designed to stop T1D from progressing. Advancing the development of novel therapies for T1D starts by identifying biomarkers that can accurately inform and predict the progression of pre-clinical disease. In this Fast Track, Metabolon will address the need for better predictive biomarkers by testing the hypothesis that using metabolomics data in conjunction with traditional risk factors of disease progression can predict the advancement of pre-clinical T1D more accurately than traditional risk factors alone. Metabolites are the small molecule intermediates and products of metabolism upon which inputs from the genome, environment, and lifestyle factors converge. Given their unique position in the central dogma of biology they are considered to be the closest reflection of an individual’s real-time health status. Metabolites reflect disease activity through changes in their abundance, which can be quantified using ultra-high performance liquid chromatography and tandem mass spectrometry (UHPLC-MS/MS). When used in an untargeted manner, UHPLC-MS/MS can measure a wide collection of metabolites in a given biological sample, enabling the identification of disease-causing metabolic perturbations (i.e., metabolic signatures of disease). We and others have shown that metabolic signatures associated with T1D can provide deep phenotypic insight into the activity that both precedes and aligns with T1D progression. In collaboration with Dr. Marian Rewers at the University of Colorado School of Medicine, Metabolon will leverage its proprietary UHPLC-MS/MS platform, NGPTM, to interrogate metabolic signatures unique to stage 1 and stage 2 of T1D and utilize high level statistical analyses to determine whether metabolomics data, used with or without traditional risk factors, can predict the progression of T1D more accurately than traditional risk factors alone. The ultimate outcome of a successful Fast Track will be the development of a tool that targets these metabolic biomarkers. Predicting the likelihood that a patient will progress to clinical disease with higher accuracy represents a step towards improving our ability to assess a patient’s response to novel T1D therapies to thereby improve patient outcomes.