Research Projects
Deep Phenotyping of Gestational Diabetes Mellitus and Associated Risk Profiles for Short- and Long-Term Maternal and Neonatal Outcomes

Gestational diabetes mellitus (GDM)—hyperglycemia first identified during pregnancy without prior diabetes—has been recognized as a significant health concern for over fifty years. According to the 2021 International Diabetes Federation report, its global prevalence is approximately 16.7%, rising to 25.9% in Hong Kong. GDM poses serious health risks to both mothers and their offspring, including increased lifelong susceptibility to obesity, type 2 diabetes (T2D), cardiovascular disease (CVD), non-alcoholic fatty liver disease (NAFLD), and neurocognitive disorders.

Recent systematic reviews from my research team have shown that women with GDM face a 46% increased risk of cardiovascular diseases and a twofold increased risk of NAFLD, with offspring also experiencing elevated risks. Certain GDM subtypes are associated with even higher risks of specific conditions such as coronary artery disease and venous thromboembolism. Despite its clinical importance, GDM is primarily diagnosed based on glucose thresholds from the oral glucose tolerance test (OGTT), leading to a binary classification that masks underlying heterogeneity in metabolic profiles. Factors beyond glycemia—such as lipid levels, insulin resistance, and blood pressure—also influence maternal and neonatal outcomes independently and in combination.

Emerging research indicates that identifying distinct metabolic phenotypes within GDM could enable more targeted monitoring and interventions. Phenotyping approaches, including clustering, have begun to reveal subtypes associated with adverse outcomes and postpartum risks, such as progression to T2D or metabolic syndrome. However, there remains limited evidence on early postpartum cardiometabolic and neurocognitive changes stratified by these phenotypes. Addressing this gap could facilitate earlier risk stratification, personalize postpartum care, and ultimately improve health outcomes while reducing healthcare costs. This project aims to characterize GDM phenotypes comprehensively and explore their implications for maternal and neonatal health in the short and long term.

Professor L Li, Department of Obstetrics and Gynaecology

Adopting a strong lifecourse perspective, A/Prof Li’s research emphasizes population health with a particular focus on women’s health across pregnancy and into later life. Early in her career, she utilized advanced retinal imaging technology coupled with state-of-the-art computer-based image analysis to investigate pregnancy outcomes and early-life diseases. By examining retinal microvascular structures—a “window” into systemic microcirculation—her work explored their diagnostic and predictive value for maternal conditions such as gestational diabetes and hypertension, as well as child health outcomes including fetal growth restriction and childhood obesity.

Building on this foundation, A/Prof Li’s research program centers on the determinants and transgenerational health consequences of maternal obesity and diabetes, approaching these issues from a life course perspective. Currently, her work primarily focuses on the roles of comprehensive pre-conception and antenatal factors, utilizing biospecimens to elucidate the pathogenesis of gestational diabetes and its long-term impacts on maternal and child health.

In addition to her traditional epidemiological research, her interests now include leveraging digital health and artificial intelligence (AI) to enhance patient care. She investigates the development and implementation of feasible, effective intervention programs—including telemedicine approaches—that utilize digital tools to modify personal behaviors in pregnant women at risk of GDM and postpartum women with prediabetes or type 2 diabetes. These innovative strategies aim to facilitate early detection, personalized intervention, and continuous monitoring, ultimately improving health outcomes while promoting accessible, technology-driven healthcare.

A/Prof Li’s ongoing research strives to bridge the gap between etiological understanding and practical, scalable interventions through the integration of advanced digital health technologies and AI, fostering personalized medicine approaches in reproductive and maternal health.

Biography
ORCID
queenie.lingjun.li@hku.hk

For more information or to express interest for this project, please email the supervisor or the specified contact point in the project description.  Interested candidates are advised to enclose with your email:

1. your CV,
2. a brief description of your research interest and experience, and
3. two reference letters (not required for HKUMed UG students seeking MRes[Med]/URIS projects).

Information on the research programme, funding support and admission documentations could be referenced online at the Research Postgraduate Admissions website. General admission enquiries should be directed to rpgmed@hku.hk.

HKUMed MBBS students interested in the Master of Research in Medicine (MRes[Med]) programme may visit the programme website for more information.  

HKUMed UG students interested in the Undergraduate Research Internship Scheme (URIS) may visit the scheme’s website for more information.