Type II diabetes is a preventable chronic disease. People with prediabetes have few symptoms, if any, and don’t discover their condition until complications develop. However, little is known about the progression of type II diabetes from prediabetes to overt diabetes due to a lack of data that can be used to track the natural history of the disease. In this study, we construct an unsupervised progression model for type II diabetes from a corpus of incomplete clinical data. By making use of the generative nature of our model, we introduce the notion of synthetic patients to simulate the entire progression path of type II diabetes. We demonstrate that modeling the full progression trajectory from a set of incomplete longitudinal medical records that only cover short segments of the progression enables prediction of the onset of complications from diabetes. Validation on a real-world patient cohort with type II diabetes associated with intensive care derived some interesting clinical insights like autoimmune diseases, one of the most infrequently reported complications corresponding to type II diabetes.