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

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.

Large-scale lipidomics profiling reveals characteristic lipid signatures associated with an increased cardiovascular risk.

BACKGROUND AND AIMS: Patients with cardiovascular disease (CVD) are at high risk to develop adverse events. The distinct risk of developing adverse cardiovascular (CV) events is not solely explained by traditional risk factors. Platelets are essentially involved in progression of CVD including coronary artery disease (CAD) and platelet hyperreactivity leads to development of adverse CV events. Alterations in the platelet lipidome lead to platelet hyperresponsiveness and thus might alter the individual risk profile. In this study, we investigate the platelet lipidome of CAD patients by untargeted lipidomics and elucidate alterations in the lipid composition of patients with adverse CV events. METHODS: We characterized the platelet lipidome in a large consecutive CAD cohort (n = 1057) by an untargeted lipidomics approach using liquid chromatography coupled to mass spectrometry. RESULTS: The platelet lipidome in this study identified 767 lipids and characteristic changes occurred in patients with adverse CV events. The most prominent upregulated lipids in patients with cardiovascular events primarily belong to the class of phospholipids and fatty acyls. Further, upregulated platelet lipids are associated with an increased cardiovascular or bleeding risk and independently associated with adverse events. In addition, alterations of the platelet lipidome are associated with modulation of in vitro platelet functions. CONCLUSIONS: Our results reveal that the composition of the platelet lipidome is altered in CVD patients with an increased cardiovascular risk and distinct platelet lipids may indicate adverse events. Results of this study may contribute to improved risk discrimination and classification for cardiovascular events in patients with CVD. Main findings of this study and hypothetical impact of altered platelet lipid signatures in patients with adverse cardiovascular events on platelet function and clinical outcome. LPE lysophosphatidylethanolamines, CAR acylcarnitines, FA fatty acids.

Statin Treatment Is Associated with Alterations in the Platelet Lipidome.

BACKGROUND: Platelets are key players in the pathophysiology of coronary artery disease (CAD) and platelet hyperreactivity leads to increased risk of developing adverse cardiovascular events. Further, significant changes in the platelet lipidome occur in patients with acute coronary syndrome (ACS) and critically regulated lipids lead to platelet hyperresponsiveness. Statin treatment is crucial in the treatment and prevention of patients with CAD by remodeling lipid metabolism. OBJECTIVE: In this study, we investigate the platelet lipidome of CAD patients by untargeted lipidomics, highlighting significant changes between statin-treated and naïve patients. METHODS: We characterized the platelet lipidome in a CAD cohort (n = 105) by an untargeted lipidomics approach using liquid chromatography coupled to mass spectrometry. RESULTS: Among the annotated lipids, 41 lipids were significantly upregulated in statin-treated patients, whereas 6 lipids were downregulated compared to naïve patients. The most prominent upregulated lipids in statin-treated patients belong to the class of triglycerides, cholesteryl esters, palmitic acid, and oxidized phospholipids, whereas mainly glycerophospholipids were downregulated compared to untreated patients. A more pronounced effect of statin treatment on the platelet lipidome was observed in ACS patients. We further highlight a dose-dependent influence on the platelet lipidome. CONCLUSION: Our results reveal that the platelet lipidome is altered in CAD patients with statin treatment and upregulated lipids embody mainly characteristic triglycerides, whereas downregulated lipids mostly compromise glycerophospholipids, which may play a role in the pathophysiology of CAD. Results of this study may contribute to the understanding of statin treatment softening the lipid phenotype.

Two-dimensional photon echoes reveal non-Markovian energy transfer in an excitonic dimer.

We show that strong non-Markovian effects can be revealed by the steady-state two-dimensional (2D) photon echo spectra at asymptotic waiting times. For this, we use a simple dimer toy model that is strongly coupled to a harmonic bath with parameters typical for photoactive biomolecules. We calculate the 2D photon echo spectra employing both the numerically exact hierarchy equation of motion and the quasiadiabatic path integral approach and compare these results with approximate results from a time-nonlocal quantum master equation approach. While the latter correctly reproduces the exact population dynamics at long times, it fails at the same time to correctly describe the 2D photon echo spectra at long waiting times. The differences show that non-Markovian effects are much more important for the steady-state 2D photon echoes than for the equilibrium populations. Thus, accurate theoretical descriptions of the energy transfer dynamics in biomolecular complexes have to be based on numerically exact simulations of the environmental fluctuations when nonlinear response functions are analyzed.