Literature DB >> 24622385

Lipidomics profiling and risk of cardiovascular disease in the prospective population-based Bruneck study.

Christin Stegemann1, Raimund Pechlaner, Peter Willeit, Sarah R Langley, Massimo Mangino, Ursula Mayr, Cristina Menni, Alireza Moayyeri, Peter Santer, Gregor Rungger, Tim D Spector, Johann Willeit, Stefan Kiechl, Manuel Mayr.   

Abstract

BACKGROUND: The bulk of cardiovascular disease risk is not explained by traditional risk factors. Recent advances in mass spectrometry allow the identification and quantification of hundreds of lipid species. Molecular lipid profiling by mass spectrometry may improve cardiovascular risk prediction. METHODS AND
RESULTS: Lipids were extracted from 685 plasma samples of the prospective population-based Bruneck Study (baseline evaluation in 2000). One hundred thirty-five lipid species from 8 different lipid classes were profiled by shotgun lipidomics with the use of a triple-quadrupole mass spectrometer. Levels of individual species of cholesterol esters (CEs), lysophosphatidylcholines, phosphatidylcholines, phosphatidylethanolamines (PEs), sphingomyelins, and triacylglycerols (TAGs) were associated with cardiovascular disease over a 10-year observation period (2000-2010, 90 incident events). Among the lipid species with the strongest predictive value were TAGs and CEs with a low carbon number and double-bond content, including TAG(54:2) and CE(16:1), as well as PE(36:5) (P=5.1 × 10⁻⁷, 2.2 × 10⁻⁴, and 2.5 × 10⁻³, respectively). Consideration of these 3 lipid species on top of traditional risk factors resulted in improved risk discrimination and classification for cardiovascular disease (cross-validated ΔC index, 0.0210 [95% confidence interval, 0.0010-0.0422]; integrated discrimination improvement, 0.0212 [95% confidence interval, 0.0031-0.0406]; and continuous net reclassification index, 0.398 [95% confidence interval, 0.175-0.619]). A similar shift in the plasma fatty acid composition was associated with cardiovascular disease in the UK Twin Registry (n=1453, 45 cases).
CONCLUSIONS: This study applied mass spectrometry-based lipidomics profiling to population-based cohorts and identified molecular lipid signatures for cardiovascular disease. Molecular lipid species constitute promising new biomarkers that outperform the conventional biochemical measurements of lipid classes currently used in clinics.

Entities:  

Keywords:  biomarkers; cardiovascular diseases; lipids; mass spectrometry; metabolomics

Mesh:

Substances:

Year:  2014        PMID: 24622385     DOI: 10.1161/CIRCULATIONAHA.113.002500

Source DB:  PubMed          Journal:  Circulation        ISSN: 0009-7322            Impact factor:   29.690


  164 in total

1.  Human plasma lipidome is pleiotropically associated with cardiovascular risk factors and death.

Authors:  Claire Bellis; Hemant Kulkarni; Manju Mamtani; Jack W Kent; Gerard Wong; Jacquelyn M Weir; Christopher K Barlow; Vincent Diego; Marcio Almeida; Thomas D Dyer; Harald H H Göring; Laura Almasy; Michael C Mahaney; Anthony G Comuzzie; Sarah Williams-Blangero; Peter J Meikle; John Blangero; Joanne E Curran
Journal:  Circ Cardiovasc Genet       Date:  2014-11-02

2.  Plasma concentrations of molecular lipid species predict long-term clinical outcome in coronary artery disease patients.

Authors:  Sharda Anroedh; Mika Hilvo; K Martijn Akkerhuis; Dimple Kauhanen; Kaisa Koistinen; Rohit Oemrawsingh; Patrick Serruys; Robert-Jan van Geuns; Eric Boersma; Reijo Laaksonen; Isabella Kardys
Journal:  J Lipid Res       Date:  2018-06-01       Impact factor: 5.922

Review 3.  Taking Systems Medicine to Heart.

Authors:  Kalliopi Trachana; Rhishikesh Bargaje; Gustavo Glusman; Nathan D Price; Sui Huang; Leroy E Hood
Journal:  Circ Res       Date:  2018-04-27       Impact factor: 17.367

4.  Metabolome-Wide Association Study of the Relationship Between Habitual Physical Activity and Plasma Metabolite Levels.

Authors:  Ming Ding; Oana A Zeleznik; Marta Guasch-Ferre; Jie Hu; Jessica Lasky-Su; I-Min Lee; Rebecca D Jackson; Aladdin H Shadyab; Michael J LaMonte; Clary Clish; A Heather Eliassen; Frank Sacks; Walter C Willett; Frank B Hu; Kathryn M Rexrode; Peter Kraft
Journal:  Am J Epidemiol       Date:  2019-11-01       Impact factor: 4.897

5.  Plasma lipidome patterns associated with cardiovascular risk in the PREDIMED trial: A case-cohort study.

Authors:  Cristina Razquin; Liming Liang; Estefanía Toledo; Clary B Clish; Miguel Ruiz-Canela; Yan Zheng; Dong D Wang; Dolores Corella; Olga Castaner; Emilio Ros; Fernando Aros; Enrique Gomez-Gracia; Miquel Fiol; José Manuel Santos-Lozano; Marta Guasch-Ferre; Lluis Serra-Majem; Aleix Sala-Vila; Pilar Buil-Cosiales; Monica Bullo; Montserrat Fito; Olga Portoles; Ramon Estruch; Jordi Salas-Salvado; Frank B Hu; Miguel A Martinez-Gonzalez
Journal:  Int J Cardiol       Date:  2018-02-15       Impact factor: 4.164

Review 6.  Epidemiology of cardiovascular disease: recent novel outlooks on risk factors and clinical approaches.

Authors:  Teemu J Niiranen; Ramachandran S Vasan
Journal:  Expert Rev Cardiovasc Ther       Date:  2016-04-25

7.  Changes in plasma lipids predict pravastatin efficacy in secondary prevention.

Authors:  Kaushala S Jayawardana; Piyushkumar A Mundra; Corey Giles; Christopher K Barlow; Paul J Nestel; Elizabeth H Barnes; Adrienne Kirby; Peter Thompson; David R Sullivan; Zahir H Alshehry; Natalie A Mellett; Kevin Huynh; Malcolm J McConville; Sophia Zoungas; Graham S Hillis; John Chalmers; Mark Woodward; Ian C Marschner; Gerard Wong; Bronwyn A Kingwell; John Simes; Andrew M Tonkin; Peter J Meikle
Journal:  JCI Insight       Date:  2019-07-11

8.  Omics, big data and machine learning as tools to propel understanding of biological mechanisms and to discover novel diagnostics and therapeutics.

Authors:  Nikolaos Perakakis; Alireza Yazdani; George E Karniadakis; Christos Mantzoros
Journal:  Metabolism       Date:  2018-08-08       Impact factor: 8.694

9.  Profiling of lipidomics before and after antipsychotic treatment in first-episode psychosis.

Authors:  Liisa Leppik; Madis Parksepp; Sven Janno; Kati Koido; Liina Haring; Eero Vasar; Mihkel Zilmer
Journal:  Eur Arch Psychiatry Clin Neurosci       Date:  2019-01-02       Impact factor: 5.270

10.  Assessing hERG1 Blockade from Bayesian Machine-Learning-Optimized Site Identification by Ligand Competitive Saturation Simulations.

Authors:  Mahdi Mousaei; Meruyert Kudaibergenova; Alexander D MacKerell; Sergei Noskov
Journal:  J Chem Inf Model       Date:  2020-11-16       Impact factor: 4.956

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