Machine learning insights into thrombo-ischemic risks and bleeding events through platelet lysophospholipids and acylcarnitine species

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.

Figure 1

Machine learning of cardiovascular risk factors including the platelet lipidome facilitates sub-phenotyping and prediction of adverse events in patients with CAD. Workflow of this large-scale (n = 595) prospective study investigating the significance of the platelet lipidome to predict adverse thrombo-ischemic and bleeding events in patients with CAD by machine learning. The platelet lipidome in this study was assessed though an untargeted UHPLC-MS/MS assay. Alongside reliable risk parameters including platelet functional data, platelet lipids significantly contributed to risk prediction of adverse thrombo-ischemic and major bleeding events during the three-year clinical follow-up. CAR, acylcarnitines; LPE, lysophosphatidylethanolamines; UHPLC-MS/MS, Ultra-high performance liquid chromatography tandem mass spectrometry.

Figure 2

Machine learning of cardiovascular risk factors including the platelet lipidome facilitates sub-phenotyping of CAD patients. (A) Medoid clustering with the corresponding standardized level (z scores) of the feature risk variables (LVEF, left ventricular ejection fraction; HDL, high-density lipoprotein; LDL, low-density lipoprotein; triglycerides; HbA1c; LPE, lysophosphatidylethanolamines; CAR, acylcarnitines; platelet aggregation). Remarkably, patients summarized in cluster 5 mainly showed aberrant platelet function and enhanced platelet CAR concentrations, whereas cluster 6 was exclusively characterized by increased LPE concentrations. (B) Number of patients with CAD by cluster according to conventional risk parameter with color indicating cut-off values of individual measurements. In addition, alongside median platelet LPE and CAR concentrations, median area under the curve (AUC) from merged collagen-, arachidonic acid-, adenosine diphosphate-, and thrombin-induced platelet aggregation was depicted by cluster to identify patients with platelet hyperreactivity and aberrant platelet lipid signatures. Error bars were constructed based on interquartile range (IQR).

Figure 3

Patients with coronary artery disease and aberrant platelet lipid signatures are at increased risk to develop adverse cardiovascular events. Kaplan–Meier curves showing cluster-specific probability to develop adverse ischemic (A; ischemic stroke, myocardial infarction, CV death) or major bleeding events (B), respectively. Failure curves were significantly (p < 0.05) divergent between cluster groups. N = 595, mean follow-up 36 months, number of adverse ischemic events n = 25 and number of bleeding events n = 16.

Figure 4

Machine learning of cardiovascular risk factors including the platelet lipidome in patients with CAD enhances the diagnostic accuracy of CV risk prediction. Comparison machine-learning algorithms on platelet lipidomics data showing mean absolute error (MAE) of predicting adverse ischemic (A) and major bleeding (B) events in patients with CAD. Least absolute shrinkage and selection operator (LASSO) showed a superior MAE among regression models and was implemented for further analyses. (C) Receiver operator characteristic (ROC) plot of the final LASSO model including platelet lipid subspecies (lysophosphatidylethanolamines (LPE) and acylcarnitines (CAR)) to predict adverse ischemic events. (D) ROC plot of the final LASSO model including platelet LPE/CAR to predict major bleeding events.

Figure 5

Platelet lipid species correlate with clinical parameters in patients with coronary artery disease. (A) Comprehensive correlation matrix of clinical parameters alongside platelet lipidomics data. Spearman's ρ is color accordingly and significant values (*p < 0.05, **p < 0.01, ***p < 0.001) are labeled. Platelet lipids belonging to the class of lysophosphatidylethanolamines (LPE) or acylcarnitines (CAR) integrated into LASSO models are colored accordingly. (B-E) Correlation analyses of platelet LPE and CAR with concentrations of LDL, HDL, triglycerides and HbA1c. Pearson correlation coefficients (ρ) and their 95% CI for each sex are shown for lipid subspecies. Significant correlations (p < 0.05) are highlighted.

Figure 6

Prediction models of adverse cardiovascular events including platelet lipidomics risk scores outperformed conventional risk parameters. (A) Patients with CAD were partitioned into deciles according to predictive LASSO models and for each decile the fractional incidence of future CV events during the three-year follow-up is shown. The risk scores were calculated according to the included predictor variables: age/gender, CV risk factors (LVEF, LDL, HDL, triglycerides, HbA1c, platelet aggregation), platelet lipidome (mean concentrations of lysophosphatidylethanolamines (LPE) and acylcarnitines (CAR), and individual lipid LPE and CAR concentrations. The estimated mean incidence rate across the full cohort is indicated by the dotted line. (B) Predictive modeling of major bleeding events in patients with CAD employing different LASSO risk scores. Likewise, platelet lipids (LPE and CAR) were compared to baseline risk models (age/gender, CV risk factors) to assess the future case rates of incident bleeding.