A nationwide ChinaHEART cohort of 4,258,033 adults found that no single obesity measure provided consistently strong discrimination across all major chronic diseases. Relatively higher discrimination was observed for the Chinese visceral adiposity index (CVAI) in cardiovascular disease, the triglyceride-glucose index (TyG) in diabetes, and the a body shape index (ABSI) in COPD, while discrimination for cancer was generally limited. All measures showed limited discrimination for cancer, supporting disease- and population-specific risk assessment beyond BMI.

The LKS Faculty of Medicine of the University of Hong Kong (HKUMed) officially received the baton to host the World Health Summit (WHS) Regional Meeting 2027, Hong Kong, China, at the WHS Opening Night in Berlin, Germany, on 11 October 2026. The handover from Nairobi, the 2026 Regional Meeting host, set the stage for Hong Kong, China, to host this leading international platform for global health dialogue for the first time.

The Department of Family Medicine and Primary Care, School of Clinical Medicine of the LKS Faculty of Medicine of the University of Hong Kong (HKUMed), commissioned by the Health Bureau, the Government of the Hong Kong Special Administrative Region of the People’s Republic of China (HKSAR), is conducting a comprehensive evaluation of the Chronic Disease Co-Care (CDCC) Pilot Scheme, examining its quality of care, acceptability to participants, effectiveness and cost-effectiveness. Evaluation findings as of February 2026 indicate that the Scheme has shown strong participant engagement and high levels of satisfaction. Significant improvements have also been observed in cardiovascular and metabolic health indicators, highlighting the Scheme’s effectiveness in improving participants’ health and its potential to reduce the burden of chronic disease on the community.

A research team from the School of Public Health at the LKS Faculty of Medicine, the University of Hong Kong (HKUMed), has discovered that emerging Omicron sublineages of SARS-CoV-2 use distinct biological strategies to spread and cause disease. Specifically, the XBB.1.5 and EG.5.1 variants are better adapted for respiratory spread and may pose a heightened risk of severe respiratory disease, while BA.2.86 and JN.1 tend to replicate more effectively in the intestines, potentially opening alternative transmission routes. This research highlights the importance of ongoing monitoring and functional characterisation of emerging strains to support public health planning and preparedness for future outbreaks. The findings were published in Nature Communications.

The LKS Faculty of Medicine of the University of Hong Kong (HKUMed) officially launched the Centre for Health AI Research and Translation (CHART) on 29 September at an inauguration ceremony and symposium themed ‘Advancing Human-Centred Healthcare through AI’. The event brought together donors, academic leaders, clinicians, researchers, students, industry partners and stakeholders from the healthcare innovation sector to witness a major milestone in HKUMed’s efforts to create a meaningful healthcare impact through artificial intelligence (AI).

New research reveals that teenagers, their families and GPs are placed under increased pressure due to long waits for specialist mental healthcare, as researchers call on GPs to take part in a new service pilot designed to support young people while they wait.

An international collaborative research study led by investigators from the LKS Faculty of Medicine of the University of Hong Kong (HKUMed) and the University of California San Diego, has found that exposure to Helicobacter pylori (H. pylori), a bacterium known to cause stomach ulcers and gastric cancer, may be associated with approximately 22% of colorectal cancer cases globally. The findings highlight the potential public health benefits of H. pylori screening and eradication programmes, which would help prevent gastric cancer and reduce the risk of colorectal cancer. The study was published in eGastroenterology.

- Professor Uichin Lee’s team in the Department of AI Computing develops a technology that identifies daily stress from repeated behaviors within smartphone apps

- Analysis of data from 26 people in their 20s and 30s shows that personalized models achieve an average accuracy of about 75%

- Analyzing only a few behaviors, such as chatting and payment, still outperforms the conventional approach, showing potential for long-term monitoring