Targeted proteomics improves cardiovascular risk prediction in secondary prevention. (9th February 2022)

• Record Type: Journal Article
• Authors: Nurmohamed, Nick S.; Belo Pereira, João P.; Hoogeveen, Renate M.; Kroon, Jeffrey; Kraaijenhof, Jordan M.; Waissi, Farahnaz; Timmerman, Nathalie; Bom, Michiel J.; Hoefer, Imo E.; Knaapen, Paul; Catapano, Alberico L.; Koenig, Wolfgang; de Kleijn, Dominique; Visseren, Frank L.J.; Levin, Evgeni; Stroes, Erik S.G.
• Is Part Of: European heart journal. Volume 43:Number 16(2022)
• Page Start: 1569
• Page End: 1577
• Publication Date: 2022-02-09
• Subjects: ASCVD -- Risk score -- Proteomics -- Machine learning -- NLRP3 -- C-reactive protein; Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; 616.12005
• DOI: 10.1093/eurheartj/ehac055
• Languages: English
• ISSNs: 0195-668X

Abstract

Abstract: Aims: Current risk scores do not accurately identify patients at highest risk of recurrent atherosclerotic cardiovascular disease (ASCVD) in need of more intensive therapeutic interventions. Advances in high-throughput plasma proteomics, analysed with machine learning techniques, may offer new opportunities to further improve risk stratification in these patients. Methods and results: Targeted plasma proteomics was performed in two secondary prevention cohorts: the Second Manifestations of ARTerial disease (SMART) cohort ( n = 870) and the Athero-Express cohort ( n = 700). The primary outcome was recurrent ASCVD (acute myocardial infarction, ischaemic stroke, and cardiovascular death). Machine learning techniques with extreme gradient boosting were used to construct a protein model in the derivation cohort (SMART), which was validated in the Athero-Express cohort and compared with a clinical risk model. Pathway analysis was performed to identify specific pathways in high and low C-reactive protein (CRP) patient subsets. The protein model outperformed the clinical model in both the derivation cohort [area under the curve (AUC): 0.810 vs. 0.750; P < 0.001] and validation cohort (AUC: 0.801 vs. 0.765; P < 0.001), provided significant net reclassification improvement (0.173 in validation cohort) and was well calibrated. In contrast to a clear interleukin-6 signal in high CRP patients, neutrophil-signalling-related proteins were associated with recurrent ASCVD in low CRP patients. Conclusion: A proteome-based risk model is superior to a clinical risk model in predicting recurrent ASCVD events. Neutrophil-related pathways were found in low CRP patients, implying the presence of a residual inflammatory risk beyond traditional NLRP3 pathways. The observed net reclassification improvement illustrates the potential of proteomics when incorporated in a tailored therapeutic approach in secondary prevention patients. Structured Graphical Abstract: Structured Graphical Abstract Targeted proteomics in two secondary prevention cohorts outperforms a clinical risk model in terms of discrimination and reclassification. The involvement of neutrophil-related pathways was found in the subset of low C-reactive protein patients. ASCVD, atherosclerotic cardiovascular disease; AUC, area under the curve; NRI, net reclassification improvement; IDI, integrated discrimination index.