Donna Morelli: Personalized Cardiovascular Risk Assessment Through AI-Based Multiomics Modeling

Donna Morelli, Data Analyst of Adult Cardiac and Thoracic Surgery at Boston Medical Center Corporation, shared a post on LinkedIn about a recent article by Qingpeng Zhang et al, adding:

“University of Hong Kong (HKU Med) developed an innovative AI-based cardiovascular risk prediction tool, CardiOmicScore.

With a single blood test, the system can accurately forecast future risk of six major cardiovascular diseases (CVDs): coronary artery disease, stroke, heart failure, atrial fibrillation, peripheral artery disease and venous thromboembolism.

It can also provide early warning signals up to 15 years before clinical onset.

The findings were published in Nature Communications on February 2, 2026.

Cardiovascular Diseases (CVDs) remain the leading cause of death worldwide, accounting for approximately 19.8 million fatalities in 2022.

During health assessments, physicians typically evaluate cardiovascular risk based on age, blood pressure, smoking and other conventional clinical indicators.

These measures often fail to capture subtle and early biological changes before the disease becomes clinically apparent, leading to many patients missing the optimal window for preventive intervention.

Although polygenic risk scores have become popular, genetic predisposition is largely fixed at birth and does not change over time.

Polygenic risk scores cannot reflect the immediate impact on health conditions resulting from lifestyle or environmental changes.

An urgent need exists for tools that capture a person’s current biological state and provide accurate, early warnings for CVDs.

HKUMed research team applied deep learning techniques to integrate multiomics data, including genomics, metabolomics and proteomics, to develop the CardiOmicScore tool.

The study was based on large-scale population data from UK Biobank, analyzing 2,920 circulating proteins and 168 metabolites measured from blood samples.

These molecular signals act as ‘real-time recorders’ of the body, reflecting subtle changes in the immune system, metabolism, and vascular health.

Note: Professor Zhang Qingpeng, Associate Professor, Department of Pharmacology and Pharmacy at HKUMed, explained, ‘Genes determine where we start— defining baseline health risk. Proteins and metabolites reflect our current physical health.

Our AI tool is designed to decode complex molecular signals, enabling doctors and patients to identify risks much earlier, which can potentially change the trajectory of disease through timely lifestyle modifications and early prevention.’

This study marks a shift in precision medicine from a gene-centric paradigm towards a multi-omics approach.

In the future, a small-volume blood sample may be sufficient to generate a comprehensive cardiovascular risk profile for multiple diseases.

Professor Zhang added, ‘We aim to leverage technology to identify and prevent diseases before they develop.

By shifting health management from reactive treatment to proactive prediction and intervention, we aim to create a lasting impact for public health and individual patient care.'”

Title: AI-based multiomics profiling reveals complementary omics contributions to personalized prediction of cardiovascular disease

Authors: Yan Luo, Nan Zhang, Jiannan Yang, Mengyao Cui, Kelvin K. F. Tsoi, Gregory Y. H. Lip, Tong Liu, Qingpeng Zhang

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