A plasma lipid signature predicts incident coronary artery disease. (15th May 2021)

• Record Type: Journal Article
• Authors: Ottosson, Filip; Emami Khoonsari, Payam; Gerl, Mathias J.; Simons, Kai; Melander, Olle; Fernandez, Céline
• Is Part Of: International journal of cardiology. Volume 331(2021)
• Page Start: 249
• Page End: 254
• Publication Date: 2021-05-15
• Subjects: Lipidome -- Lipidomics -- Coronary artery disease; Cardiology -- Periodicals; Electronic journals; 616.12
• DOI: 10.1016/j.ijcard.2021.01.059
• Languages: English
• ISSNs: 0167-5273

Abstract

Abstract: Background: Dyslipidemia is a hallmark of cardiovascular disease but is characterized by crude measurements of triglycerides, HDL- and LDL cholesterol. Lipidomics enables more detailed measurements of plasma lipids, which may help improve risk stratification and understand the pathophysiology of cardiovascular disease. Methods: Lipidomics was used to measure 184 lipids in plasma samples from the Malmö Diet and Cancer – Cardiovascular Cohort ( N = 3865), taken at baseline examination. During an average follow-up time of 20.3 years, 536 participants developed coronary artery disease (CAD). Least absolute shrinkage and selection operator (LASSO) were applied to Cox proportional hazards models in order to identify plasma lipids that predict CAD. Results: Eight plasma lipids improved prediction of future CAD on top of traditional cardiovascular risk factors. Principal component analysis of CAD-associated lipids revealed one principal component (PC2) that was associated with risk of future CAD (HR per SD increment =1.46, C· I = 1.35–1.48, P < 0.001). The risk increase for being in the highest quartile of PC2 (HR = 2.33, P < 0.001) was higher than being in the top quartile of systolic blood pressure. Addition of PC2 to traditional risk factors achieved an improvement (2%) in the area under the ROC-curve for CAD events occurring within 10 ( P = 0.03), 15 ( P = 0.003) and 20 ( P = 0.001) years of follow-up respectively. Conclusions: A lipid pattern improve CAD prediction above traditional risk factors, highlighting that conventional lipid-measures insufficiently describe dyslipidemia that is present years before CAD. Identifying this hidden dyslipidemia may help motivate lifestyle and pharmacological interventions early enough to reach a substantial reduction in absolute risk. Highlights: Lipidomics enables more detailed characterization of dyslipidemia. A lipid signature predicts cardiovascular disease up to 20 years prior to disease. The lipidomic prediction is independent of traditional cardiovascular risk factors. Identifying this hidden dyslipidemia could motivate early preventive interventions.