Key result
Novel 7-protein inflammatory signature improves CAD risk prediction, boosting MACCE validation AUC to ~0.75.
Why the study?
Chronic inflammation plays a critical role in the pathogenesis of CAD, yet its representation in current clinical risk models remains limited. This study aimed to identify novel inflammatory biomarkers associated with long-term outcomes using Olink proteomics and evaluate their incremental prognostic value beyond traditional clinical risk factors.
Does incorporating Olink proteomic inflammatory biomarkers into a traditional clinical risk model improve risk prediction of major adverse cardiac and cerebrovascular events (MACCE) in patients with coronary artery disease?
Cohort (n=250)
Yes
Does incorporating Olink proteomic inflammatory biomarkers into a traditional clinical risk model improve risk prediction of major adverse cardiac and cerebrovascular events (MACCE) in patients with coronary artery disease?
A 7-protein inflammatory signature identified via Olink proteomics significantly improves the prediction of major adverse cardiac and cerebrovascular events in patients with coronary artery disease when added to traditional clinical risk factors.
Background Chronic inflammation plays a critical role in the pathogenesis of coronary artery disease (CAD), yet its representation in current clinical risk models remains limited. With the advancement of proteomics technologies, novel inflammatory biomarkers may be identified to enhance risk prediction in CAD patients. Purpose To identify novel inflammatory biomarkers associated with long-term outcomes in CAD using Olink proteomics and to evaluate their incremental prognostic value beyond traditional clinical risk factors. Methods This study included 250 patients with CAD derived from a prospective multicenter cohort, including 125 who experienced major adverse cardiac and cerebrovascular events (MACCE) and 125 without events. MACCE was defined as a composite of all-cause mortality, myocardial infarction, stroke, and in-stent thrombosis. The cohort was randomly divided into a training set (80%) and an internal validation set (20%). Proteomic profiling was performed using the Olink Target 96 Inflammation panel, which measures 92 inflammatory-related proteins. Feature selection was performed using LASSO, and the optimal risk prediction model was determined through recursive feature elimination. Results Among the 250 patients, the mean age was 63.5±11.0 years and 182 (72.8%) were male. Differential expression analysis identified 28 proteins associated with MACCE, among which 25 were upregulated and 3 were downregulated. Seven inflammatory proteins were ultimately selected into the final model:IL6, FGF_21, CDCP1, TNF, FGF_23, TNFB, and TRAIL. The proteomics-based risk model achieved a C-index of 0.68 in the training set and 0.73 in the validation set, with AUC values of 0.71 and 0.75, respectively. Incorporation of the proteomic signature into a clinical model (including sex, AMI status, hyperlipidemia, and left ventricular ejection fraction) significantly improved model discrimination and reclassification. In the validation cohort, the AUC increased from 0.7143 to 0.7537 (P < 0.001), with a net reclassification improvement (NRI) of 41.05% and an integrated discrimination improvement (IDI) of 8.14%. Conclusion Inflammatory proteins identified by Olink proteomics alone effectively predicted long-term risk in CAD patients and provided incremental predictive value when added to traditional clinical risk factors. These findings support the utility of protein-based biomarkers for precise assessment of residual inflammatory risk and optimization of patient management.Figure 1.Differentially expressed proteinsFigure 2.
No takes yet. Share an insight, caveat, or question.
Zhang et al. (2026) conducted a cohort in Coronary artery disease (CAD) (n=250). 7-protein Olink inflammatory signature (IL6, FGF_21, CDCP1, TNF, FGF_23, TNFB, TRAIL) plus clinical risk model vs. Traditional clinical risk model alone (sex, AMI status, hyperlipidemia, LVEF) was evaluated on Major adverse cardiac and cerebrovascular events (MACCE: composite of all-cause mortality, myocardial infarction, stroke, and in-stent thrombosis) (AUC 0.7537 vs 0.7143; NRI 41.05%; IDI 8.14%, p=<0.001). Adding a 7-protein Olink inflammatory signature to a clinical risk model improved MACCE prediction in CAD patients, increasing validation AUC from 0.7143 to 0.7537 (P<0.001; NRI 41.05%).
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: