Coronary artery disease (CAD) is a complex, multifactorial, and serious condition influenced by genetic, environmental, and lifestyle factors. Thus, it is crucial to develop strategies to predict the risk of CAD for individuals. Plasma proteomics provides a powerful framework for identifying novel biomarkers, discovering potential therapeutic targets, and further improving risk stratification. Here, we examined the association between 2,919 plasma proteins and incident CAD in the UK Biobank cohort (n=35,778). As a result, we identified 576 proteins significantly associated with CAD and found significant alterations in key biological pathways, including signal transduction, immune regulation, and chemotaxis, before CAD onset. Subsequently, we developed machine learning models to predict CAD onset at different time intervals (5 years, 10 years, over 10 years, and entire cohort), demonstrating superior performance over models based on polygenic risk scores (ΔAUC = 0.052), and Pooled Cohort Equations (ΔAUC = 0.049). Notably, the integration of PRS with proteomic data resulted in a marked enhancement in predictive accuracy (AUC = 0.779), comparable to the full model (AUC = 0.780). Key plasma protein predictors, including MMP12, GDF15, and EDA2R, showed sustained importance across models predicting CAD onset at multiple time points. Additionally, Mendelian randomization analysis provided robust evidence for a causal relationship between six plasma proteins and CAD, including MMP12, LPA and PLA2G7, highlighting their potential as therapeutic targets. In conclusion, our study elucidates the plasma proteome associated with CAD, reveals underlying pathogenic mechanisms, and provides valuable insights for identifying high-risk individuals and advancing precision medicine.

The authors have declared no competing interest.

This study was supported by the grants from the National Natural Science Foundation of China [62025102, 32301239, 82470373] and National High Level Hospital Clinical Research Funding (2023-GSP-ZD-2).

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This study utilized data from the UK Biobank Resource under approved application number 87841.

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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The data used in this study are available from the UK Biobank under specific restrictions. As the data were accessed under license, they are not publicly available. Access to UK Biobank data can be requested through the standard application process (https://www.ukbiobank.ac.uk/registerapply/) under application ID 87841. All data supporting the findings of this study are included in the article and supplementary materials and can be obtained from the corresponding author upon request. Source data are provided with this article.