HKUMed reveals combining novel angiography FFR imaging technology with glucose lowering drug effectively protects the heart of diabetic patients

A recent study led by the Department of Medicine, School of Clinical Medicine, LKS Faculty of Medicine of the University of Hong Kong (HKUMed), in collaboration with the University of Hong Kong-Shenzhen Hospital, has demonstrated that combining an innovative coronary imaging technology known as the caFFR system, with diabetes drug SGLT2 inhibitors can significantly reduce the risks of major adverse cardiovascular events (MACE), heart failure and death among patients with type 2 diabetes mellitus (T2DM) and coronary artery disease. This dual-pronged strategy provides a precise measurement of coronary blood flow while lowering glucose levels, offering an effective approach to treating exceptionally high-risk patients. The findings were published in the Diabetes and Metabolism Journal link to the publication.

caFFR images access coronary blood flow

Patients with T2DM commonly develop more complex and severe forms of coronary artery disease, often involving multiple narrowed or blocked arteries. This complexity makes it challenging for cardiologists to achieve 'complete revascularisation', where all significantly blocked arteries are fully opened to restore blood flow. Without detailed functional assessment, some blockages that appear mild on imaging but are functionally important may be overlooked. If these high-risk blockages are not identified, patients may be at risk of 'incomplete revascularisation' and experience persistent ischaemia despite treatment, ultimately increasing their long-term cardiac risk.

To address this challenge, Professor Yiu Kai-hang, Clinical Professor in the Department of Medicine, School of Clinical Medicine, HKUMed, led a research team to evaluate the use of the caFFR system, an innovative imaging technology that allows accurate measurement of coronary blood flow from standard angiogram images. This technique enables cardiologists to identify which arterial blockages are truly responsible for ischaemia, thus supporting more precise decision-making and the development of more effective interventional treatment strategies for high-risk patients.

Professor Yiu Kai-hang explains that the functional assessment using the caFFR system is crucial for achieving optimal revascularisation in diabetic patients. SGLT2 inhibitors offer robust cardiovascular protection and significantly improve survival outcomes, even in cases of incomplete revascularisation.

SGLT2 inhibitors provide powerful cardiac protection

The study analysed data from 671 patients with both T2DM and coronary artery disease who underwent angiogram imaging in public hospitals between 2014 and 2016. While complete revascularisation was achieved in some patients, many still had residual stenosis after undergoing the revascularisation procedure due to diffuse and complex diabetic atherosclerosis. Remarkably, for those with incomplete revascularisation, the use of SGLT2 inhibitors provided powerful vascular protecton. The three-year incidence of MACE was markedly reduced from 17.8% to 8.3%, while all-cause mortality dropped sharply from 16.3% to 6.3% over the same period.

HKUMed reveals that combining novel caFFR imaging technology with glucose lowering drug effectively protects the heart of diabetic patients. In the photo are Professor Yiu Kai-hang (left), who led the research, and his team member Dr Xuan Haochen.

'Our findings show that functional assessment using the caFFR system allows clinicians to accurately identify blockages that truly cause ischemia, which is crucial for achieving optimal revascularisation in diabetic patients,' said Professor Yiu Kai-hang. 'Moreover, even when complete revascularisation cannot be achieved, SGLT2 inhibitors offer robust cardiovascular protection, significantly improving survival outcomes. This dual approach represents a major step forward in managing heart disease among individuals with diabetes.'

The study underscores the complementary roles of precision interventional imaging and pharmacological therapy in improving outcomes for diabetic patients with coronary artery disease. By integrating caFFR-guided vascular reconstruction procedures with SGLT2 inhibitor therapy, clinicians can better tailor treatment to each patient's needs, offering both anatomical and metabolic protection for the heart.

Professor Yiu added, 'This study has importance implications for clinical practice. It demonstrates that even when structural risks in the blood vessels may persist, SGLT2 inhibitors provide a vital safety net, reducing future cardiovascular risks. The findings reinforce HKUMed's ongoing commitment to translating clinical innovation into better patient care.'

The study was led by Professor Yiu Kai-hang, Clinical Professor, Department of Medicine, School of Clinical Medicine, HKUMed, and conducted in collaboration with the University of Hong Kong–Shenzhen Hospital.

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.