title: Lipoprotein metabolism indicators improve cardiovascular risk prediction.
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.
  
  
  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 (ΔAUC) 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.
authors.affiliation: Department of Microbiology and Systems Biology, TNO, Zeist, The Netherlands; Amsterdam University College, Amsterdam, The Netherlands.
authors.forename: Daniël B
authors.initials: DB
authors.lastname: van Schalkwijk
authors.affiliation: Department of Risk Analysis for Products in Development, TNO, Zeist, The Netherlands.
authors.forename: Albert A
authors.initials: AA
authors.lastname: de Graaf
authors.affiliation: Department of Microbiology and Systems Biology, TNO, Zeist, The Netherlands.
authors.forename: Evgeni
authors.initials: E
authors.lastname: Tsivtsivadze
authors.affiliation: The Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, Massachusetts, United States of America.
authors.forename: Laurence D
authors.initials: LD
authors.lastname: Parnell
authors.affiliation: Department of Risk Analysis for Products in Development, TNO, Zeist, The Netherlands.
authors.forename: Bianca J C
authors.initials: BJ
authors.lastname: van der Werff-van der Vat
authors.affiliation: Department of Microbiology and Systems Biology, TNO, Zeist, The Netherlands.
authors.forename: Ben
authors.initials: B
authors.lastname: van Ommen
authors.affiliation: Department of Microbiology and Systems Biology, TNO, Zeist, The Netherlands; Sino-Dutch Centre for Preventive and Personalized Medicine, Leiden Amsterdam Centre for Drug Research, Analytical Sciences Division, Leiden, The Netherlands.
authors.forename: Jan
authors.initials: J
authors.lastname: van der Greef
authors.affiliation: The Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, Massachusetts, United States of America.
authors.forename: José M
authors.initials: JM
authors.lastname: Ordovás
mesh_terms: D000328:Adult; D002318:Cardiovascular Diseases / Q000378:metabolism*; D005260:Female; D006801:Humans; D008066:Lipolysis; D008079:Lipoproteins, VLDL / Q000378:metabolism*; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D011237:Predictive Value of Tests; D012307:Risk Factors; D060388:Support Vector Machine
chemical_list: D008079:Lipoproteins, VLDL