Attempting to predict the onset and progression of disease is one of the cornerstones of epidemiology. Accurate risk prediction can enable targeted preventative treatments, such as fitness regimens for patients at risk of cardiovascular disease, or increased mammogram frequency for patients with high breast cancer risk. Traditional epidemiological risk prediction incorporates a small number of environmental and clinical factors known to be associated with disease, such as body mass index and lipid levels for type 2 diabetes (1) or the various Framingham risk scores for predicting cardiovascular outcomes (2). Some of these predictions (e.g. type 2 diabetes) are accurate enough to be clinically useful, but for many diseases (e.g. Crohn's) the prediction is barely better than chance. Evaluating the accuracy of environmental prediction is further complicated by recall-bias and the potential for reverse causality when data are retrospectively collected.
