| Title: | Large-scale plasma proteomics analysis in the UK Biobank for improved prediction of cardiovascular disease risk in a population without prior cardiovascular events |
| Lead Institution: | Tongji University School of Medicine |
| Principal investigator: |
Application 656613
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About
Cardiovascular disease (CVD) is a leading cause of global morbidity and mortality. Current prediction models mainly rely on traditional risk factors such as age, blood pressure, and cholesterol. However, these models often lack precision, particularly for asymptomatic individuals with no prior cardiovascular events. Plasma proteomics offers an opportunity to uncover novel biomarkers that reflect biological processes contributing to CVD. By utilizing large-scale plasma proteomics data from the UK Biobank, this study aims to improve CVD risk prediction in individuals without a history of cardiovascular events. The goal of this research is to identify plasma proteins associated with the onset of CVD, particularly in individuals without prior cardiovascular events. Integrating proteomics data with traditional risk factors could improve the accuracy of risk prediction models, allowing for better stratification. Findings may provide insights to support early intervention strategies and advance precision medicine. This research addresses the need for more accurate CVD risk prediction tools, particularly for asymptomatic individuals. Traditional models often fail to predict the onset of CVD in these individuals. By identifying new protein biomarkers, this study aims to improve risk assessment and prevention strategies. These findings could also have clinical applications, including developing predictive models for early detection and targeted interventions, ultimately improving patient outcomes. The findings will be published in peer-reviewed open-access journals for broad accessibility. Presentations at major international cardiovascular conferences will facilitate further discussion and feedback. Workshops, webinars, and collaborations with healthcare professionals will help communicate the implications of the findings for clinical practice and precision medicine.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 21033 | Incident chronic kidney disease risk and the predictive value of the uric acid-to-HDL ratio in lean and non-lean MASLD: a prospective cohort and multi-omics study | Yuting Zhang (+18) | 2026 | International Urology and Nephrology |
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