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. 2022 Apr 19;43(16):1569-1577.
doi: 10.1093/eurheartj/ehac055.

Targeted proteomics improves cardiovascular risk prediction in secondary prevention

Nick S Nurmohamed  1   2 , João P Belo Pereira  1 , Renate M Hoogeveen  1 , Jeffrey Kroon  1 , Jordan M Kraaijenhof  1 , Farahnaz Waissi  3 , Nathalie Timmerman  3 , Michiel J Bom  2 , Imo E Hoefer  4 , Paul Knaapen  2 , Alberico L Catapano  5   6 , Wolfgang Koenig  7   8   9 , Dominique de Kleijn  3 , Frank L J Visseren  10 , Evgeni Levin  1   11 , Erik S G Stroes  1
Affiliations

Targeted proteomics improves cardiovascular risk prediction in secondary prevention

Nick S Nurmohamed et al. Eur Heart J. .

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.

Keywords: ASCVD; C-reactive protein; Machine learning; NLRP3; Proteomics; Risk score.

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Figures

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.
Figure 1
Figure 1
Discriminatory value in the derivation and validation cohort. Receiver operating characteristic curve of protein, clinical, and combined model in the derivation cohort (A) and in the validation cohort (B). The 95% confidence interval is shown between brackets. AUC, area under the curve.
Figure 2
Figure 2
Importance plot of the protein model. Importance plot of the proteins in the protein model from the derivation cohort. The importance refers to the extent to which a model relies on a given protein. Shown is the relative importance of the 50 proteins in the model.
Figure 3.
Figure 3.
Calibration in the derivation and validation cohort. Calibration plots for the protein (A), clinical (B), and combined (C) model in the derivation cohort (SMART) and the protein (D), clinical (E), and combined (F) model in the validation cohort (Athero-Express). Predicted event risk vs. observed event rate per risk category quintiles.
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