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Proteomics and lipidomics in atherosclerotic cardiovascular disease risk prediction
Nick S Nurmohamed1,2, Jordan M Kraaijenhof1, Manuel Mayr3,4
1Department of Vascular Medicine, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands.
Insights
Novel risk prediction models using proteomics and lipidomics show promise for identifying atherosclerotic cardiovascular disease (ASCVD) risk. These advanced biomarker panels, combined with machine learning, offer improved accuracy over traditional scores.
Area of Science:
- Biomarkers
- Cardiovascular Disease Research
- Machine Learning in Healthcare
Background:
- Current clinical risk scores for atherosclerotic cardiovascular disease (ASCVD) have limited accuracy.
- There is an urgent need for novel risk algorithms to predict incident or recurrent ASCVD.
- Advancements in assay techniques enable high-throughput assessment of multimarker proteomic and lipidomic panels.
Purpose of the Study:
- To explore the potential of novel risk algorithms for ASCVD risk prediction.
- To evaluate the utility of proteomic and lipidomic panels in ASCVD risk stratification.
- To investigate the additive prognostic value of advanced biomarker panels over traditional clinical risk scores.
Main Methods:
- Utilizing machine learning approaches to analyze high-dimensional proteomic and lipidomic data.
- Conducting large-scale retrospective studies with diverse biomarker sets and statistical methods.
- Integrating genetic predisposition (polygenic risk scores) and coronary artery imaging for comprehensive risk assessment.
Main Results:
- Multiple retrospective studies demonstrate the additive prognostic value of proteomic and lipidomic panels.
- These advanced panels show potential for improving ASCVD risk prediction accuracy.
- Machine learning facilitates the use of complex, high-dimensional biomarker data.
Conclusions:
- Proteomics and lipidomics hold promise for reliable, high-throughput ASCVD risk assessment.
- Novel machine learning-based algorithms can leverage complex biomarker data for improved risk prediction.
- Prospective studies are necessary to confirm the clinical utility of biomarker panels in ASCVD risk stratification.
Abstract:
Given the limited accuracy of clinically used risk scores such as the Systematic COronary Risk Evaluation 2 system and the Second Manifestations of ARTerial disease 2 risk scores, novel risk algorithms determining an individual's susceptibility of future incident or recurrent atherosclerotic cardiovascular disease (ASCVD) risk are urgently needed. Due to major improvements in assay techniques, multimarker proteomic and lipidomic panels hold the promise to be reliably assessed in a high-throughput routine. Novel machine learning-based approaches have facilitated the use of this high-dimensional data resulting from these analyses for ASCVD risk prediction. More than a dozen of large-scale retrospective studies using different sets of biomarkers and different statistical methods have consistently demonstrated the additive prognostic value of these panels over traditionally used clinical risk scores. Prospective studies are needed to determine the clinical utility of a biomarker panel in clinical ASCVD risk stratification. When combined with the genetic predisposition captured with polygenic risk scores and the actual ASCVD phenotype observed with coronary artery imaging, proteomics and lipidomics can advance understanding of the complex multifactorial causes underlying an individual's ASCVD risk.
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