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A plasma lipid signature predicts incident coronary artery disease

Ottosson, Filip LU ; Emami Khoonsari, Payam ; Gerl, Mathias J. ; Simons, Kai ; Melander, Olle LU orcid and Fernandez, Céline LU (2021) In International Journal of Cardiology 331.
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... (More)

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.

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author
; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Coronary artery disease, Lipidome, Lipidomics
in
International Journal of Cardiology
volume
331
publisher
Elsevier
external identifiers
  • pmid:33545264
  • scopus:85100653442
ISSN
0167-5273
DOI
10.1016/j.ijcard.2021.01.059
project
AIR Lund - Artificially Intelligent use of Registers
language
English
LU publication?
yes
id
727b4afb-b815-4357-9e07-f04624329ebf
date added to LUP
2021-02-24 09:35:53
date last changed
2024-06-13 07:35:26
@article{727b4afb-b815-4357-9e07-f04624329ebf,
  abstract     = {{<p>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 &lt; 0.001). The risk increase for being in the highest quartile of PC2 (HR = 2.33, P &lt; 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.</p>}},
  author       = {{Ottosson, Filip and Emami Khoonsari, Payam and Gerl, Mathias J. and Simons, Kai and Melander, Olle and Fernandez, Céline}},
  issn         = {{0167-5273}},
  keywords     = {{Coronary artery disease; Lipidome; Lipidomics}},
  language     = {{eng}},
  month        = {{02}},
  publisher    = {{Elsevier}},
  series       = {{International Journal of Cardiology}},
  title        = {{A plasma lipid signature predicts incident coronary artery disease}},
  url          = {{http://dx.doi.org/10.1016/j.ijcard.2021.01.059}},
  doi          = {{10.1016/j.ijcard.2021.01.059}},
  volume       = {{331}},
  year         = {{2021}},
}