154-LB: Population-Based Optimization of Metabolic Monitoring for Autoantibody-Positive Individuals at Risk for Type 1 Diabetes

Diabetic ketoacidosis (DKA) is often the sentinel event to a diagnosis of clinical type 1 diabetes. Research studies involving antibody positive (AAB+) individuals at risk for developing type 1 diabetes have demonstrated that metabolic monitoring every 6 months with oral glucose tolerance testing can dramatically reduce the incidence of DKA at diagnosis. It is unknown whether other schedules can reduce the population level burden of metabolic monitoring in AAB+ individuals while retaining the potential benefit of reducing DKA.

We reasoned that the shorter the time time-period during which clinical type 1 diabetes (T1D) remains undetected, the lower the likelihood of DKA. We aimed to develop models of optimized monitoring visit schedules to limit undiagnosed (UnDx) time for the population. Using OGTT data from 6193 islet AAB+ relatives identified in TrialNet’s Pathway to Prevention study, we developed models and an algorithm to optimize visit schedules to limit population-based UnDx time. The results of modeling were presented using estimated hazard rate as a function of age. An "optimal" visit schedule was determined for each model to achieve a minimum average level of UnDx time for the population. This was applied for risk patterns identified by each model thus producing optimal schedules for pediatric and adult populations with single or multiple antibodies.

Modeling an optimized visit schedule to keep UnDx time ≤ 6 months at a population level, we find that conducting half the number of metabolic monitoring visits usually done in research studies is likely to be effective in significantly impacting the population incidence of DKA at diagnosis of T1D. Our approach has implications for public health screening and monitoring for T1D risk.