Computer modeling of diabetes and its complications: a report on the fifth Mount Hood challenge meeting

OBJECTIVES: The Mount Hood Challenge meetings provide a forum for computer modelers of diabetes to discuss and compare models, to assess predictions against data from clinical trials and other studies, and to identify key future developments in the field. This article reports the proceedings of the Fifth Mount Hood Challenge in 2010. METHODS: Eight modeling groups participated. Each group was given four modeling challenges to perform (in type 2 diabetes): to simulate a trial of a lipid-lowering intervention (The Atorvastatin Study for Prevention of Coronary Heart Disease Endpoints in Non-Insulin-Dependent Diabetes Mellitus [ASPEN]), to simulate a trial of a blood glucose-lowering intervention (Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation [ADVANCE]), to simulate a trial of a blood pressure-lowering intervention (Cardiovascular Risk in Diabetes [ACCORD]), and (optional) to simulate a second trial of blood glucose-lowering therapy (ACCORD). Model outcomes for each challenge were compared with the published findings of the respective trials. RESULTS: The results of the models varied from each other and, in some cases, from the published trial data in important ways. In general, the models performed well in terms of predicting the relative benefit of interventions, but performed less well in terms of quantifying the absolute risk of complications in patients with type 2 diabetes. Methodological challenges were highlighted including matching trial end-point definitions, the importance of assumptions concerning the progression of risk factors over time, and accurately matching the patient characteristics from each trial. CONCLUSIONS: The Fifth Mount Hood Challenge allowed modelers, through systematic comparison and validation exercises, to identify important differences between models, address key methodological challenges, and discuss avenues of research to improve future diabetes models.