Mathematical models are able to reflect biological processes and to capture epidemiological data. Thus, they may help elucidating roles of risk factors in disease progression. We propose to account for smoking, hypertension and dyslipidemia in a previously published process-oriented model that de- scribes the development of atherosclerotic lesions resulting in myocardial infarction (MI). The model is sex-specific and incorporates individual heterogeneity. It was applied to population-based individual risk factors and MI rates (KORA study) together with subclinical atherosclerotic le-sion data (PDAY Study). Different model variants were evaluated testing the association of risk factors with different disease processes. Best fits were obtained for smoking affecting a late stage disease process, suggesting a thrombo-genic role. Hypertension was mainly related to complicated, vulnerable lesions. Dyslipidemia was con-sistent with increasing the number of initial lesions. By accounting for heterogeneity, individual hazard ratios differ from the population average. The mean individual hazard ratio for smoking was twice the population based hazard ratio for men, and even more for women. Atherosclerotic lesion progression and MI incidence data can be related in a mathematical model to illuminate how risk factors affect different phases of this pathological process.