Alternative type 2 diabetes screening tests may reduce the number of U.S. adults with undiagnosed diabetes.

AIM: To evaluate the U.S. population-level impact of two alternatives for initial type 2 diabetes screening [opportunistic random plasma glucose (RPG) > 6.7 mmol/l and a 1-h 50-g glucose challenge test (GCT) > 8.9 mmol/l] compared with American Diabetes Association (ADA)-recommended tests. METHODS: Using a sample (n = 1471) from the National Health and Nutrition Examination Survey (NHANES) 2013-2014 that represented 145 million U.S. adults at high risk for developing type 2 diabetes, we simulated a two-test screening process. We compared ADA-recommended screening tests [fasting plasma glucose (FPG), 2-h 75-g oral glucose tolerance test (OGTT), HbA1c ] vs. initial screening with opportunistic RPG or GCT (followed by FPG, OGTT or HbA1c ). After simulation, participants were entered into an individual-level Monte Carlo-based Markov lifetime outcomes model. Primary outcomes were representative number of U.S. adults correctly identified with type 2 diabetes, societal lifetime costs and quality-adjusted life years (QALYs). RESULTS: In NHANES 2013-2014, 100 individuals had undiagnosed diabetes [weighted estimate: 8.4 million, standard error (se): 1.1 million]. Among ADA-recommended screening tests, FPG followed by OGTT (FPG-OGTT) was most sensitive, identifying 35 individuals with undiagnosed diabetes (weighted estimate: 3.2 million, se: 0.9 million). Four alternative screening strategies performed superior to FPG-OGTT, with RPG followed by OGTT being the most sensitive overall, identifying 72 individuals with undiagnosed diabetes (weighted estimate: 6.1 million, se: 1.0 million). There was no increase in average lifetime costs and comparable QALYs. CONCLUSIONS: Initial screening using opportunistic RPG or a GCT may identify more U.S. adults with type 2 diabetes without increasing societal costs.

Statistically Driven Metabolite and Lipid Profiling of Patients from the Undiagnosed Diseases Network.

Advancements in molecular separations coupled with mass spectrometry have enabled metabolome analyses for clinical cohorts. A population of interest for metabolome profiling is patients with rare disease for which abnormal metabolic signatures may yield clues into the genetic basis, as well as mechanistic drivers of the disease and possible treatment options. We undertook the metabolome profiling of a large cohort of patients with mysterious conditions characterized through the Undiagnosed Diseases Network (UDN). Due to the size and enrollment procedures, collection of the metabolomes for UDN patients took place over 2 years. We describe the study designed to adjust for measurements collected over a long time scale and how this enabled statistical analyses to summarize the metabolome of individual patients. We demonstrate the removal of time-based batch effects, overall statistical characteristics of the UDN population, and two case studies of interest that demonstrate the utility of metabolome profiling for rare diseases.