Targeted proteomics improves cardiovascular risk prediction in secondary prevention
© The Author(s) 2022. Published by Oxford University Press on behalf of European Society of Cardiology..
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.
| Errataetall: |
CommentIn: Eur Heart J. 2022 Apr 19;43(16):1578-1581. - PMID 35165698 |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:43 |
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| Enthalten in: |
European heart journal - 43(2022), 16 vom: 19. Apr., Seite 1569-1577 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Nurmohamed, Nick S [VerfasserIn] |
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| Links: |
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| Themen: |
9007-41-4 |
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| Anmerkungen: |
Date Completed 22.04.2022 Date Revised 16.06.2022 published: Print CommentIn: Eur Heart J. 2022 Apr 19;43(16):1578-1581. - PMID 35165698 Citation Status MEDLINE |
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| doi: |
10.1093/eurheartj/ehac055 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM336718160 |
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| 245 | 1 | 0 | |a Targeted proteomics improves cardiovascular risk prediction in secondary prevention |
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| 500 | |a Date Completed 22.04.2022 | ||
| 500 | |a Date Revised 16.06.2022 | ||
| 500 | |a published: Print | ||
| 500 | |a CommentIn: Eur Heart J. 2022 Apr 19;43(16):1578-1581. - PMID 35165698 | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © The Author(s) 2022. Published by Oxford University Press on behalf of European Society of Cardiology. | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a ASCVD | |
| 650 | 4 | |a C-reactive protein | |
| 650 | 4 | |a Machine learning | |
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| 650 | 4 | |a Proteomics | |
| 650 | 4 | |a Risk score | |
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| 700 | 1 | |a Belo Pereira, João P |e verfasserin |4 aut | |
| 700 | 1 | |a Hoogeveen, Renate M |e verfasserin |4 aut | |
| 700 | 1 | |a Kroon, Jeffrey |e verfasserin |4 aut | |
| 700 | 1 | |a Kraaijenhof, Jordan M |e verfasserin |4 aut | |
| 700 | 1 | |a Waissi, Farahnaz |e verfasserin |4 aut | |
| 700 | 1 | |a Timmerman, Nathalie |e verfasserin |4 aut | |
| 700 | 1 | |a Bom, Michiel J |e verfasserin |4 aut | |
| 700 | 1 | |a Hoefer, Imo E |e verfasserin |4 aut | |
| 700 | 1 | |a Knaapen, Paul |e verfasserin |4 aut | |
| 700 | 1 | |a Catapano, Alberico L |e verfasserin |4 aut | |
| 700 | 1 | |a Koenig, Wolfgang |e verfasserin |4 aut | |
| 700 | 1 | |a de Kleijn, Dominique |e verfasserin |4 aut | |
| 700 | 1 | |a Visseren, Frank L J |e verfasserin |4 aut | |
| 700 | 1 | |a Levin, Evgeni |e verfasserin |4 aut | |
| 700 | 1 | |a Stroes, Erik S G |e verfasserin |4 aut | |
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