The seminal publication of the Diabetes Prevention Program (DPP) results in 2002 has provided insight into the impact of major therapies on the development of diabetes over a time span of a few years. In the present work, the publicly available DPP data set is used to calibrate and evaluate a recently developed mechanistic mathematical model for the long-term development of diabetes to assess the model's ability to predict the natural history of disease progression and the effectiveness of preventive interventions. A general population is generated from which virtual subject samples corresponding to the DPP enrollment criteria are selected. The model is able to reproduce with good fidelity the observed time courses of both diabetes incidence and average glycemia, under realistic hypotheses on evolution of disease and efficacy of the studied therapies, for all treatment arms. Model-based simulations of the long-term evolution of the disease are consistent with the transient benefits observed with conventional therapies and with promising effects of radical improvement of insulin sensitivity (as by metabolic surgery) or of β-cell protection. The mechanistic diabetes progression model provides a credible tool by which long-term implications of antidiabetic interventions can be evaluated.