Location: Boston, MassachusettsTitle: Lipoprotein metabolism indicators improve cardiovascular risk prediction) Author
|Van schalkwijk, D.|
|De graaf, A.|
|Van der werff-van de, R vat b.|
|Van ommen, B.|
|Van der greef, J.|
Submitted to: PLoS One
Publication Type: Peer reviewed journal
Publication Acceptance Date: 2/26/2014
Publication Date: 3/25/2014
Citation: Van Schalkwijk, D., De Graaf, A., Tsivtsivadze, E., Parnell, L.D., Van Der Werff-Van De, R., Van Ommen, B., Van Der Greef, J., Ordovas, J.M. 2014. Lipoprotein metabolism indicators improve cardiovascular risk prediction. PLoS One. 9:e92840. Interpretive Summary: Currently, cardiovascular disease (CVD) risk is predicted based on a range of factors, such as age, gender, total cholesterol, HDL (“good”) cholesterol, smoking and blood pressure. From this information, the “Framingham risk score” allows the estimation of the 10-year risk for CVD. Although this approach identifies a sub-population at higher risk for CVD, there is need for improving risk prediction. This could be achieved by including in the predictive equations other important characteristics, particularly VLDL, or very low-density lipoprotein, the precursor to LDL (“bad cholesterol”). To this end, we have developed a computational model that can provide information about the production and break-down of various cholesterol particles as well as absorption of these particles by tissues, all based on a single measurement of the cholesterol particle profile in which the information gathered from a detailed blood lipid (or lipoprotein) profile is analyzed in a way that is useful in the clinic. We tested whether our approach can improve prediction of CVD risk in the well-known Framingham Heart Study. Using sophisticated approaches known as statistical learning algorithm with a support vector machine, we were able to select those conventional risk factors and new calculations of cholesterol particle production and break-down that contribute most to predicting 10-year risk for CVD. Adding these VLDL values to our model improved prediction of CVD over traditional risk prediction methods, especially the liver-turnover ratio we term VLDL(H). Specifically, we determined that VLDL(H) proves to be most important for classifying people with low and medium CVD risk. We believe that our results represent a significant step forward in cardiovascular risk prediction. We also believe that these results will increase ability to assess the therapeutic effect of drugs beyond the standard measures.
Technical Abstract: Background: Cardiovascular disease risk increases when lipoprotein metabolism is dysfunctional. We have developed a computational model able to derive indicators of lipoprotein production, lipolysis, and uptake processes from a single lipoprotein profile measurement. This is the first study to investigate whether lipoprotein metabolism indicators can improve cardiovascular risk prediction and therapy management. Methods and Results: We calculated lipoprotein metabolism indicators for 1981 subjects (145 cases, 1836 controls) from the Framingham Heart Study offspring cohort in which NMR lipoprotein profiles were measured. We applied a statistical learning algorithm using a support vector machine to select conventional risk factors and lipoprotein metabolism indicators that contributed to predicting risk for general cardiovascular disease. Risk prediction was quantified by the change in the Area-Under-the-ROC-Curve (delta AUC) and by risk reclassification (Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI)). Two VLDL lipoprotein metabolism indicators (VLDLE and VLDLH) improved cardiovascular risk prediction. We added these indicators to a multivariate model with the best performing conventional risk markers. Our method significantly improved both CVD prediction and risk reclassification. Conclusions: Two calculated VLDL metabolism indicators significantly improved cardiovascular risk prediction. These indicators may help to reduce prescription of unnecessary cholesterol-lowering medication, reducing costs and possible side-effects. For clinical application, further validation is required.