Active Diabetes, Hormones & Metabolism Psychology & Behaviour

Leveraging the clinical, genetic, and molecular heterogeneity of type 2 diabetes to improve screening and treatment strategies

Funder Wellcome Charity-funded
Recipient organisation Queen Mary University of London
Funding Not disclosed
Period Oct 2025 — Sept 2028

In plain English

AI plain-English summary

Half a billion people have type 2 diabetes, but South Asian patients develop the disease younger, at lower body weight, and with worse complications—yet they are largely absent from the genetic studies that guide treatment. This matters because most diabetes research lumps all patients into one category, even though the disease behaves differently across populations. The researcher will use "soft" clustering methods on clinical, genetic, and molecular data from South and Southeast Asian cohorts, allowing individuals to belong to multiple disease subtypes at once. This better reflects real-world complexity than the rigid categories used today. She will then test how these subtypes predict progression, complications, and response to metformin. If successful, the work could give doctors a practical tool to match South Asian patients to the most effective treatment from the start, rather than cycling through drugs by trial and error. That would make diabetes care more personalised and equitable for a population that currently suffers worse outcomes with less evidence to guide their care. The research is applied and patient-focused, with no fundamental science component.

View original technical description
Type 2 diabetes (T2D) affects over half a billion people globally, but its causes, symptoms, and outcomes vary widely between individuals and populations. South Asian populations in particular develop T2D at a younger age and lower body weight, and have higher risks of complications – yet they remain underrepresented in research. I aim to better understand variations in T2D diagnoses in South and Southeast Asian (SSEA) populations by applying ‘soft’ clustering methods. These methods describe how individuals can belong to a mixture of disease subtypes, rather than assigning them to single disease categories – better reflecting the real-world complexity of T2D. I will use clinical, genetic, and molecular data from SSEA cohorts to explore how these subtypes relate to disease progression, complications, and response to treatments like metformin. I will also investigate how genetic risk scores and molecular markers can help improve our understanding of disease causes and progression. Ultimately, I aim to develop evidence that will help doctors choose the most appropriate treatments for SSEA patients with T2D. By reflecting the real-world complexity of T2D more accurately, my research aims to make diabetes care more personalised, effective, and equitable – in a disadvantaged community that is not well represented in medical research.

View the original record at the funder ↗

Researchers

Borbála Bánfalvi (EPMC Awardee)

Related Research

Grants with similar aims, by meaning.

Deciphering the heterogeneity of early-onset type 2 diabetes using epidemiological and genetic approaches
Type 2 Diabetes Mellitus and its Microvascular Complications in South Asians in the UK: the Impact of Genes, Sleep and Metabolic Factors
Outcomes-based data science to optimise individual therapy choice in type 2 diabetes
Investigating ethnic differences in the effectiveness of type 2 diabetes medications and opportunities for individualised diabetes treatment
NIHR Global Health Research Unit and Network for Diabetes and Cardiovascular disease in South Asia

Original classification

PhD Studentship (Basic)

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.