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A proteomic model shows potential as a surrogate end point for CVD risk

Nature Reviews Cardiology volume 19, page 352 (2022)Cite this article

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A model generated using proteomics and machine learning that included 27 proteins was able to predict the 4-year risk of myocardial infarction, heart failure, stroke or all-cause death better than a clinical model and was sensitive to the adverse and beneficial changes in outcome, suggesting its potential as a ‘universal’ surrogate end point for cardiovascular outcomes.

The model included 27 proteins that were related to 10 biological processes reported to have prognostic value for cardiovascular outcomes. In addition, 12 of the proteins had at least one genetic causal association with cardiovascular disease (CVD) or CVD risk factors. The 27-protein model was superior to a clinical model in predicting the absolute likelihood of any component of a composite end point (myocardial infarction, stroke, hospitalization for heart failure, and all-cause death) in the 4 years after the blood sample acquisition. All standard metrics were better in the protein model, including the dynamic range of prognostic stratification, discrimination performance and risk reclassification. Adding the clinical model to the protein model improved discrimination performance only slightly.

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References

Original article

  • Williams, S. A. et al. A proteomic surrogate for cardiovascular outcomes that is sensitive to multiple mechanisms of change in risk. Sci. Transl. Med. 14, eabj9625 (2022)

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  1. Nature Reviews Cardiology http://www.nature.com/nrcardio/

    Irene Fernández-Ruiz

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  1. Irene Fernández-Ruiz
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Correspondence to Irene Fernández-Ruiz.

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Fernández-Ruiz, I. A proteomic model shows potential as a surrogate end point for CVD risk. Nat Rev Cardiol 19, 352 (2022). https://doi.org/10.1038/s41569-022-00716-7

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  • DOI: https://doi.org/10.1038/s41569-022-00716-7

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