Quantifying change in cardiometabolic risk

The probability of future cardiovascular disease can be predicted using the thoroughly validated Framingham risk score (FRS). The current FRS assigns weights to major CVD risk factors including age, sex, cholesterol, smoking, blood pressure and diabetes. Whereas the FRS includes non-modifiable risk factors such as age and gender, this study aims to evaluate a cumulative cardiometabolic risk score that is optimized on factors that can be modified (e.g. through longitudinal intervention studies) such as blood pressure cholesterol, smoking, body mass index, glycated hemoglobin and C-reactive protein to detect intervention or experimental effects. We describe methodology on how to optimally weight these factors to predict change in cardiometabolic risk. Because it is rare for any prediction model to maintain its predictive ability over time, we propose a dynamic prediction model that is continuously updated with new waves of Framingham data. The proposed prediction model will provide a similar Framingham risk score measure of cardiometabolic risk that is sensitive to modifiable known risk factors, while controlling for key non-modifiable factors of cardiometabolic risk through the final model where the risk score is the outcome. We use a gender-stratified cox proportional hazards model on the Framingham cohort data and use cross-validation for proper assessment of predictive ability of the proposed model.