Researchers at the University of Hong Kong’s LKS Faculty of Medicine (HKUMed) have developed an artificial intelligence tool that can help predict serious cardiovascular problems many years before symptoms appear.
The system, known as CardiOmicScore, uses information from a single blood test to estimate a person’s future risk of six major cardiovascular diseases (CVDs): coronary artery disease, stroke, heart failure, atrial fibrillation, peripheral artery disease and venous thromboembolism. In people at increased risk, the model was able to detect warning signs 15 years before clinical onset.
The results were published in Nature communication.
A blood test that records your current health status
Cardiovascular disease remains the leading cause of death worldwide, causing approximately 19.8 million deaths in 2022 alone.
Doctors typically assess cardiovascular risk based on factors such as age, blood pressure, smoking history and other standard clinical measurements. These indicators are useful, but they may not reveal the earliest biological changes that occur in the body before disease occurs.
As a result, some people may not be considered at risk until the best options for prevention have already begun to diminish.
Genetic risk tests offer another way to estimate the likelihood of developing the disease. Polygenic risk scores, for example, combine the effects of many genetic variants into a single measure of inherited risk. However, a person’s genetic makeup is largely determined at birth.
This means that genetic scores cannot fully reflect more immediate changes caused by diet, exercise, aging, disease, environmental exposures, or other influences on health.
CardiOmicScore is designed to provide a more current picture of what is happening in the body.
AI combines thousands of biological signals
To develop the tool, the HKUMed team used deep learning to combine multiple layers of biological information. This approach is called multiomics because it brings together data from different areas of biology, including genomics, metabolomics and proteomics.
Genomics studies genetic information. Proteomics focuses on proteins that perform many essential functions in the body. Metabolomics studies small molecules called metabolites that are created when the body processes food, produces energy and responds to disease.
The researchers analyzed large-scale population data from the UK Biobank. Their model examined 2,920 circulating proteins and 168 metabolites measured in blood samples.
Together, these molecules can provide a detailed snapshot of a person’s current biological state. They may reflect subtle changes in immune activity, metabolism, and vascular health before noticeable symptoms develop.
Professor Zhang Qingpeng, Associate Professor in the Department of Pharmacology and Pharmacy at HKUMed, explained: “Genes determine where we start – they define our basic health risk. However, proteins and metabolites reflect our current physical health. Our AI tool is designed to decipher these complex molecular signals, allowing doctors and patients to identify risks much earlier, potentially altering the course of the disease through timely lifestyle changes and early prevention.”
Prediction of six cardiovascular diseases
The results showed that CardiOmicScore can convert complex molecular measurements into personalized estimates of cardiovascular risk.
The system performed significantly better than traditional polygenic risk scores. Accuracy further improved when researchers added clinical information such as age and gender.
The model was developed to estimate the risk of coronary artery disease, stroke, heart failure, atrial fibrillation, peripheral artery disease and venous thromboembolism.
Atrial fibrillation is an irregular heartbeat that can increase the risk of stroke and other complications. Peripheral artery disease occurs when narrowed blood vessels restrict blood flow to the limbs. Venous thromboembolism refers to dangerous blood clots that form in a vein and can travel to the lungs.
In high-risk individuals, CardiOmicScore could indicate increased cardiovascular risk up to 15 years before symptoms appear.
Transition from treatment to earlier prevention
The research reflects a broader shift in precision medicine.
Traditional genetic approaches provide a relatively fixed estimate of inherited risk. Multiomics tools potentially provide a more dynamic assessment by tracking biological signals that change over time.
In the future, it may be possible to create a detailed risk profile from a small blood sample that covers several cardiovascular diseases at the same time. This information could give patients and doctors more time to respond with lifestyle changes, closer monitoring or other preventive measures.
Professor Zhang added: “We want to use technologies to detect and prevent diseases before they arise. By shifting health management from reactive treatment to proactive prediction and intervention, we aim to have a lasting impact on both public health and individual patient care.”
About the research team
The study was led by Professor Zhang Qingpeng, Associate Professor in the Department of Pharmacology and Pharmacy, HKUMed, and the HKU Musketeers Foundation Institute of Data Science (IDS).
The first author is Luo Yan from HKU IDS.
https://www.sciencedaily.com/releases/2026/07/260716023603.htm
