Abstract Background The recent trends in sedentary life-styles and weight gain are likely to contribute to chronic conditions such as hypertension, diabetes, and cardiovascular diseases. The temporal sequence and pathways underlying these conditions can be modeled using the knowledge from the biomedical and social sciences. Methods The Framingham Offspring Study in the U.S. collected information on 5124 subjects at baseline, and 8, 12, 16, and 20 years after the baseline. Dynamic random effects models were estimated for the subjects' weight, LDL and HDL cholesterol, and blood pressure using 4 time observations. Logistic and probit models were estimated for the probability of diabetes and coronary heart disease (CHD) events. Results The subjects' age, physical activity, alcohol consumption, and cigarettes smoked were important predictors of the risk factors. Moreover, weight and height were found to differentially affect the probabilities of diabetes and CHD events; body weight was positively associated with the risk of diabetes while taller individuals had lower risk of CHD events. Conclusion The results showed the importance of joint modeling of body weight, LDL and HDL cholesterol, and blood pressure that are risk factors for diabetes and CHD events. Lower body weight and LDL concentrations and higher HDL levels achieved via physical exercise are likely to reduce diabetes and CHD events.