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Updated: Jul 1, 2025
Ferric Chloride-induced Murine Thrombosis Models
Published on: September 5, 2016
Machine learning insights into thrombo-ischemic risks and bleeding events through platelet lysophospholipids and
Tobias Harm1, Xiaoqing Fu2, Moritz Frey1
1Department of Cardiology and Angiology, University Hospital Tübingen, Eberhard Karls University Tübingen, Otfried-Müller-Straße 10, 72076, Tübingen, Germany.
Insights
Distinct platelet lipids help predict cardiovascular risk in coronary artery disease (CAD) patients. Analyzing platelet lipidomics with machine learning improves risk assessment beyond traditional factors.
Area of Science:
- Cardiovascular Medicine
- Lipidomics
- Machine Learning
Background:
- Coronary artery disease (CAD) poses a significant burden, with traditional risk factors insufficient for comprehensive cardiovascular (CV) risk assessment.
- Platelets play a role in atheroprogression, and their lipid profiles may indicate disease severity in CAD patients.
Purpose of the Study:
- To investigate the association between platelet lipid species and cardiovascular risk in CAD patients.
- To evaluate the utility of machine learning models integrating platelet lipidomics and clinical data for improved CV risk stratification.
Main Methods:
- Lipidomics data were obtained via mass spectrometry from a cohort of 595 CAD patients.
- Machine learning models were trained using CV risk measurements and clinical parameters to phenotype risk groups.
- Platelet lipids were analyzed in conjunction with conventional risk factors to assess diagnostic accuracy for adverse CV events.
Main Results:
- Specific platelet lipids were identified as independent predictors of increased CV or bleeding risk and adverse events.
- Integrating platelet lipid data with conventional risk factors significantly enhanced the diagnostic accuracy for adverse CV events.
- Machine learning models combining platelet lipidome and clinical data showed increased diagnostic value for risk discrimination in CAD.
Conclusions:
- Aberrant platelet lipid signatures and functions are linked to an elevated risk of adverse CV events in CAD patients.
- Machine learning approaches integrating platelet lipidomics offer improved early risk discrimination and classification for CV events in CAD.
- Platelet lipid analysis represents a promising biomarker for refining cardiovascular risk assessment in coronary artery disease.
Abstract:
Coronary artery disease (CAD) often leads to adverse events resulting in significant disease burdens. Underlying risk factors often remain inapparent prior to disease incidence and the cardiovascular (CV) risk is not exclusively explained by traditional risk factors. Platelets inherently promote atheroprogression and enhanced platelet functions and distinct platelet lipid species are associated with disease severity in patients with CAD. Lipidomics data were acquired using mass spectrometry and processed alongside clinical data applying machine learning to model estimates of an increased CV risk in a consecutive CAD cohort (n = 595). By training machine learning models on CV risk measurements, stratification of CAD patients resulted in a phenotyping of risk groups. We found that distinct platelet lipids are associated with an increased CV or bleeding risk and independently predict adverse events. Notably, the addition of platelet lipids to conventional risk factors resulted in an increased diagnostic accuracy of patients with adverse CV events. Thus, patients with aberrant platelet lipid signatures and platelet functions are at elevated risk to develop adverse CV events. Machine learning combining platelet lipidome data and common clinical parameters demonstrated an increased diagnostic value in patients with CAD and might improve early risk discrimination and classification for CV events.
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