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Updated: Apr 11, 2026

Quantitative Analysis of Cellular Composition in Advanced Atherosclerotic Lesions of Smooth Muscle Cell Lineage-Tracing Mice
Published on: February 20, 2019
Novel methodologies for biomarker discovery in atherosclerosis
Imo E Hoefer1, Sabine Steffens2, Mika Ala-Korpela3
1Laboratory of Experimental Cardiology and Laboratory of Clinical Chemistry and Haematology, University Medical Centre Utrecht, Utrecht, Netherlands i.hoefer@umcutrecht.nl sabine.steffens@med.uni-muenchen.de.
Insights
Identifying individuals at high risk for cardiovascular events is crucial. Emerging -omics technologies and novel vascular biomarkers offer improved prediction beyond traditional risk factors.
Area of Science:
- Cardiovascular disease research
- Biomarker discovery
- Proteomics and metabolomics
Background:
- Traditional cardiovascular risk factors have limitations in predicting individual risk.
- Accurate identification of high-risk individuals is essential for effective cardiovascular disease prevention and management.
- Current biomarkers like hsCRP and NT-proBNP have limitations in precise individual risk assessment.
Purpose of the Study:
- To summarize current vascular biomarkers beyond traditional risk factors.
- To highlight the role of emerging -omics technologies in biomarker discovery.
- To discuss the limitations of existing biomarkers and the potential of novel approaches.
Main Methods:
- Review of current literature on vascular biomarkers.
- Focus on -omics technologies including micro-RNAs, proteomics, metabolomics, and lipidomics.
- Discussion of novel plasma-derived markers from microparticles, microvesicles, and exosomes.
Main Results:
- Shift in focus from HDL cholesterol to HDL composition and function.
- Identification of circulating micro-RNAs as promising biomarkers.
- Utilizing mass spectrometry and NMR spectroscopy for proteomic and metabolomic analysis, particularly pro-inflammatory lipid metabolites.
Conclusions:
- Novel -omics technologies and advanced analytical methods are key to discovering new cardiovascular biomarkers.
- Understanding HDL composition and function offers improved risk assessment.
- Future research should focus on integrating these novel biomarkers for enhanced individual cardiovascular risk prediction.
Abstract:
Identification of subjects at increased risk for cardiovascular events plays a central role in the worldwide efforts to improve prevention, prediction, diagnosis, and prognosis of cardiovascular disease and to decrease the related costs. Despite their high predictive value on population level, traditional risk factors fail to fully predict individual risk. This position paper provides a summary of current vascular biomarkers other than the traditional risk factors with a special focus on the emerging -omics technologies. The definition of biomarkers and the identification and use of classical biomarkers are introduced, and we discuss the limitations of current biomarkers such as high sensitivity C-reactive protein (hsCRP) or N-terminal pro-brain natriuretic peptide (NT-proBNP). This is complemented by circulating plasma biomarkers, including high-density lipoprotein (HDL), and the conceptual shift from HDL cholesterol levels to HDL composition/function for cardiovascular risk assessment. Novel sources for plasma-derived markers include microparticles, microvesicles, and exosomes and their use for current omics-based analytics. Measurement of circulating micro-RNAs, short RNA sequences regulating gene expression, has attracted major interest in the search for novel biomarkers. Also, mass spectrometry and nuclear magnetic resonance spectroscopy have become key complementary technologies in the search for new biomarkers, such as proteomic searches or identification and quantification of small metabolites including lipids (metabolomics and lipidomics). In particular, pro-inflammatory lipid metabolites have gained much interest in the cardiovascular field. Our consensus statement concludes on leads and needs in biomarker research for the near future to improve individual cardiovascular risk prediction.
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