Characterization of biomarker profiles in patients with coronary artery disease: A prospective coronary computed tomography angiography study.

BACKGROUND AND AIMS: Conventional biomarkers such as low-density lipoprotein (LDL) and high-density lipoprotein may fail to identify patients' risk for significant coronary artery disease (CAD). This study evaluates the associations between multiple biomarkers and different CAD phenotypes, exploring a machine-learning biomarker-based patient clustering. METHODS AND RESULTS: We included 787 patients on primary prevention from the prospective ACTION registry (January 2024 to June 2025). All patients underwent coronary CTA and simultaneous biomarker testing, including LDL, high-density lipoprotein, triglyceride, apolipoprotein A-1, apolipoprotein B, lipoprotein(a) [Lp(a)], glycated hemoglobin (HbA1c), and high-sensitivity C-reactive protein. Of 382 patients (48.5%) with coronary artery calcium = 0, 42 (11%) had coronary plaque. These patients showed higher Lp(a) vs those without plaque (16.5 vs 11.5, P = .030), despite comparable SCORE2 risk (3.5% vs 3.0%, P = .284). Three biomarker-defined groups were identified after a machine learning unsupervised clustering: Cluster 1 had a favorable lipid profile with the lowest prevalence of CAD-Reporting and Data System (RADS) ≥ 3 (9.9%). Cluster 2 and 3, despite their significant intercluster differences in terms of Lp(a), LDL, and HbA1c levels, both showed a significantly higher prevalence of CAD-RADS ≥ 3 compared to cluster 1 (respectively 21.8% and 17.9%; vs cluster 1, P = .001). High-risk biomarker signatures were significantly associated to the prevalence of CAD-RADS ≥ 3, independently from the baseline SCORE2 (adjusted odds ratio 2.25; 95% confidence interval 1.32-3.82). CONCLUSIONS: Distinct biomarker signatures associate with distinct CAD prevalence and severity that conventional lipid markers fail to distinguish. Lp(a) appears relevant for early plaque detection in coronary artery calcium = 0 patients. A comprehensive biomarker evaluation may help identifying high-risk subgroups overlooked by a conventional assessment.