Disease-modifying management aims to prevent deterioration and progression of the disease, and not just to relieve symptoms. We present a solution for the management by a methodology that allows the prediction of progression risk and morphology in individuals using a latent extrapolation approach. To this end, we combined a regularized generative adversarial network and a latent nearest neighbour algorithm for joint optimization to generate plausible images of future time points. We evaluated our method on osteoarthritis data from a multicenter longitudinal study (the Osteoarthritis Initiative). With presymptomatic baseline data, our model is generative and considerably outperforms the end-to-end learning model in discriminating the progressive cohort. Two experiments were performed with seven radiologists. When no synthetic follow-up radiographs were provided, our model performed better than all seven radiologists. In cases in which the synthetic follow-ups generated by our model were made available to the radiologist for diagnosis support, the specificity and sensitivity of all readers in discriminating progressors increased from 72.3% to 88.6% and from 42.1% to 51.6%, respectively. Our results open up a new possibility of using model-based morphology and risk prediction to make predictions about disease occurrence, as demonstrated by the example of osteoarthritis.