Flu activity has surged in many parts of the Northern Hemisphere, driven primarily by a newly emerged H3N2 strain known as 'subclade K'. A research team led by the Department of Microbiology and the Department of Medicine, both under the School of Clinical Medicine at the LKS Faculty of Medicine of the University of Hong Kong (HKUMed), has found that most hospital patients in Hong Kong have little to undetectable levels of neutralising antibodies against this mutated strain. HKUMed researchers strongly advise the public to get vaccinated as soon as possible.
HKUMed study reveals low immunity against H3N2 strain in Hong Kong and urges early vaccination.
Neutralising antibody levels are closely linked to protection against infection. The research team was led by Professor Kelvin To Kai-wang, Clinical Professor and Chairperson of the Department of Microbiology, and Professor Ivan Hung Fan-ngai, Chair Professor and Head of the Division of Infectious Diseases in the Department of Medicine of the School of Clinical Medicine at HKUMed. The team collected serum specimens from public hospitals to monitor viral genetics and antibody responses and assess the overall immunity level against H3N2 in Hong Kong.
Analysis of 277 serum samples collected in November 2025 revealed that 52% of individuals had detectable neutralising antibodies against the previously circulating subclade J.2.2, with 27% reaching a titer of 40 or above, indicating a relatively high level of immunity. In stark contrast, only 18% of individuals had detectable antibodies against the newly emerged subclade K, with 0.7% reaching a titer of 40 or above.
'Our findings suggest that the local community's immune barrier against the subclade K is insufficient and lower than that against the previously circulating subclade J.2.2,' explained Professor Ivan Hung.
Professor Ivan Hung Fan-ngai (left) said the findings suggest that the local community’s immune barrier against the subclade K is insufficient and lower than that against the previously circulating subclade J.2.2.
To reduce the risk of severe infection, the research team recommends the following measures:
• Get vaccinated against influenza. A study from England found that vaccine effectiveness against emergency department visits and hospital admissions is between 72% and 75% for children and adolescents, and 32% to 39% for adults.
• Seek early diagnosis using rapid antigen tests.
• Consult a doctor promptly. Antiviral medications are most effective when administered within 48 hours of symptom onset.
Professor Kelvin To emphasised, 'These data serve as a clear alert for Hong Kong. With influenza A cases rising and new variants emerging, we must reinforce the message that vaccination is the most effective way to prevent infection. For those who do become infected, seeking medical care promptly is essential to reduce the risk of severe complications.'
Professor Kelvin To Kai-wang (right) emphasised that for those who become infected, seeking medical care promptly is essential to reduce the risk of severe complications.
A research team from the Department of Pharmacology and Pharmacy at the LKS Faculty of Medicine of the University of Hong Kong (HKUMed) has developed an innovative AI-based cardiovascular risk prediction tool, called CardiOmicScore. With a single blood test, the system can accurately forecast the 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 [link to the publication].
HKUMed develops a cardiovascular risk prediction tool that can accurately predict the future risk of six major cardiovascular diseases with a single blood test. The system can provide early warning signals up to 15 years before clinical onset. The research is led by Professor Zhang Qingpeng (left).
AI-based multiomics integration reflects the body’s real-time health status
CVDs remain the leading cause of death worldwide, accounting approximately 19.8 million fatalities in 2022 alone. In routine health assessments, physicians typically evaluate cardiovascular risk based on age, blood pressure, smoking and other conventional clinical indicators. However, 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 in recent years, genetic predisposition is largely fixed at birth and does not change over time. Consequently, polygenic risk scores cannot reflect the immediate impact on health conditions resulting from lifestyle or environmental changes. This creates an urgent need for tools that can capture a person’s current biological state and provide accurate, early warnings for CVDs.
To address this problem, the 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 the UK Biobank, analysing 2,920 circulating proteins and 168 metabolites measured from blood samples. These molecular signals act as ‘real-time recorders’ of the body, sensitively reflecting subtle changes in the immune system, metabolism, and vascular health.
Professor Zhang Qingpeng, Associate Professor in the Department of Pharmacology and Pharmacy at HKUMed, explained, ‘Genes determine where we start—they define our baseline health risk. However, proteins and metabolites reflect our current physical health. Our AI tool is designed to decode these 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.’
Accurate prediction of six major cardiovascular diseases with 15-year advance warning in high-risk groups
The results showed that CardiOmicScore transforms complex multiomics measurements into personalised risk scores with substantially improved predictive performance compared with conventional polygenic risk scores. When combined with clinical information such as age and gender, the model significantly enhanced the risk prediction accuracy of six common CVDs and can even flag elevated risk up to 15 years before symptoms appear.
This study marks a shift in precision medicine from a static, gene-centric paradigm towards a more dynamic, multiomics-based 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 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 the HKU IDS.
