Key result
An 87-protein plasma panel substantially improves long-term ASCVD prediction over classical risk factors alone.
Why the study?
Non-invasive carotid imaging faces challenges from inter-operator variability and organizational barriers, prompting the investigation of plasma proteomics as an alternative to associate with carotid plaques and predict incident ASCVD in apparently healthy subjects.
Does plasma proteomics improve the prediction of carotid plaques and incident ASCVD compared to classical risk factors in apparently healthy subjects?
Observational (n=664)
No
Does plasma proteomics improve the prediction of carotid plaques and incident ASCVD compared to classical risk factors in apparently healthy subjects?
Effect estimate: AUC 0.739 (95% CI 0.704-0.773)
Absolute Event Rate: 0.739% vs 0.559%
p-value: p=<0.001
Plasma proteomics combined with artificial intelligence significantly improves the prediction of carotid plaque burden and incident ASCVD beyond classical risk factors in apparently healthy individuals.
Plasma proteins associated with carotid plaques and incident ASCVD; hypothesis-generating for refined primary prevention risk models.
PURPOSE: Performing non-invasive carotid imaging is challenging, owing inter-operator variability and organizational barriers, but plasma proteomics can offer an alternative. We sought plasma proteins that associate with the presence of carotid plaques, their number and predict the incidence of clinically overt atherosclerotic cardiovascular events (ASCVD) above currently recognized risk factors in "apparently healthy" subjects. METHODS: We studied the plasma levels of 368 proteins in 664 subjects from the PLIC study, who underwent an ultrasound imaging screening of the carotids to check for the presence of plaques. We clustered, by artificial intelligence (A.I.), the proteins that associate with the presence, the number of plaques and that predict incident ASCVDs over 22 years (198 events were registered). FINDINGS: 299/664 subjects had at least 1 carotid plaque (1+) (77 with only one plaque, 101 with 2 plaques, 121 with ≥3 plaques (3+)). The remaining 365 subjects with no plaques acted as controls. 106 proteins were associated with 1+ plaques, but 97 proteins significantly predicted 3+ plaques only (AUC = 0.683 (0.601-0.785), p < 0.001), when considered alone. A.I. underscored 87 proteins that improved the performance of the classical risk factors both in detecting 3+ plaques (AUC = 0.918 (0.887-0.943) versus risk factors alone, AUC = 0.760 (0.716-0.801), p < 0.001) and in predicting the incident ASCVD (AUC = 0.739 (0.704-0.773) vs risk factors alone AUC = 0.559 (0.521-0.598), p < 0.001). The chemotaxis/migration of leukocytes and interleukins/cytokines signaling were biological pathways mostly represented by these proteins. DISCUSSION AND CONCLUSIONS: Plasma proteomics marks the number of carotid plaques and improve the prediction of incidence ASCVDs in apparently healthy subjects.
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Baragetti et al. (2024) conducted an observational in Subclinical atherosclerosis (n=664). Plasma protein panel (87 proteins) vs. Classical ASCVD risk factors alone was evaluated on Prediction of incident atherosclerotic cardiovascular events (ASCVD) (AUC 0.739, 95% CI 0.704-0.773, p=<0.001). A panel of 87 plasma proteins significantly improved the prediction of incident atherosclerotic cardiovascular events over 22 years compared to classical risk factors alone (AUC 0.739 vs 0.559).
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