Filip Ottosson1, Payam Emami Khoonsari2, Mathias J Gerl3, Kai Simons4, Olle Melander5, Céline Fernandez5. 1. Department of Clinical Sciences, Lund University, Malmö, Sweden. Electronic address: Filip.ottosson@med.lu.se. 2. Department of Clinical Sciences, Lund University, Malmö, Sweden; Department of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Stockholm University, Box 1031, SE-17121 Solna, Sweden. 3. Lipotype GmbH, Dresden, Germany; Department of Clinical Sciences, Lund University, Malmö, Sweden. 4. Lipotype GmbH, Dresden, Germany. 5. Department of Clinical Sciences, Lund University, Malmö, Sweden.
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.
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.
Authors: Filip Ottosson; Einar Smith; Ulrika Ericson; Louise Brunkwall; Marju Orho-Melander; Salvatore Di Somma; Paola Antonini; Peter M Nilsson; Céline Fernandez; Olle Melander Journal: Diabetes Care Date: 2022-05-01 Impact factor: 17.152
Authors: Peter McGranaghan; Jennifer A Kirwan; Mariel A Garcia-Rivera; Burkert Pieske; Frank Edelmann; Florian Blaschke; Sandeep Appunni; Anshul Saxena; Muni Rubens; Emir Veledar; Tobias Daniel Trippel Journal: Metabolites Date: 2021-09-14
Authors: Loni Berkowitz; Fernanda Cabrera-Reyes; Cristian Salazar; Carol D Ryff; Christopher Coe; Attilio Rigotti Journal: Front Cardiovasc Med Date: 2022-01-14
Authors: Michał A Surma; Mathias J Gerl; Ronny Herzog; Jussi Helppi; Kai Simons; Christian Klose Journal: Sci Rep Date: 2021-09-29 Impact factor: 4.379