Machine learning integrated clinical-proteomics data identifies a 6-protein panel signature for atherosclerotic severity and enhanced patient stratification

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: ROC-AUC >0.9 across all machine learning algorithms

• Primary Outcome: 6-protein panel diagnostic accuracy for atherosclerotic severity

• Nnt Or Sensitivity: Sensitivity and specificity not reported, only ROC-AUC >0.9

• Confidence Interval: Not reported

Clinical Application

Requires specialized proteomic analysis and machine learning infrastructure, may be costly for routine implementation Would need TGA approval for diagnostic use, cost-effectiveness analysis for PBS consideration, integration with RACGP cardiovascular risk assessment guidelines Patients at risk for atherosclerotic disease, particularly those with carotid stenosis or dyslipidemia

Abstract

Atherosclerosis, a major cause of adverse cardiovascular events and mortality rates worldwide, stems from sustained lipid accumulation and subsequent chronic inflammation within the arterial walls. An early identification of patients at risk is crucial to prevent life-threatening thrombotic events and provide effective and personalized treatments. Leveraging the power of machine learning (ML) to enhance diagnostics and biomarker discovery, we applied a high-throughput approach using five ML classification algorithms (MLCA), integrating clinical and serum proteomic data from patients with carotid atherosclerotic stenosis (AT, n:60), dyslipidemic patients (DLP, n:55), and healthy controls (HC, n:66). As a result, a robust 6-protein panel (B2M, GPV, MMP9, PLF4, TSP1, and FB isoforms) was identified with a ROC-AUC value > 0.9 for all algorithms applied, highly discriminating AT patients compared to DLP or CTRL. The levels of these proteins were further validated in an independent external cohort, including patients presenting with acute atherothrombotic stroke, corroborating the potential of this panel as biomarker for atherosclerosis severity. In addition, the combined clinical-proteomic ML approach provided a more accurate patient stratification than the clinical or proteomic analysis alone. Mechanistically, the identified biomarkers highlight the importance of platelet activation, uncontrolled angiogenesis and intraplaque haemorrhage in the atherosclerotic process, underscoring the need for multipathway therapies to prevent unwanted thrombotic events.