Scientists at NUI Galway and Harvard T.H. Chan School of Public Health, Boston, US, created biomedical avatars of type 1 diabetes patients to enable new opportunities for treatment and diagnosis. Research published in the international journal Nature Computational Sciences showed that simulating disease effects at an individual level explains how different people respond to insulin and how diet may improve treatment outcomes.
Type 1 diabetes is prevalent in children and impacts patients during their entire lives. The disease influences insulin production, with knock-on effects also leading to disturbed metabolism and coronary heart disease, associated with early mortality. The effectiveness of insulin administration, the standard treatment, varies widely between individuals, including severe side effects. It is therefore desirable to devise bespoke treatments for the individual patient.
Professor Ines Thiele, study leader and Professor in Systems Biomedicine in the School of Medicine and Discipline of Microbiology at NUI Galway, explains:
“Precision medicine aims to enable a personalised approach, as opposed to the current ‘one-size-fits-all’ method, by considering individual health and lifestyle data, such as, age, sex, or diet. Combining all available health information on a person enables a holistic analysis approach to make personalised health recommendations, including considerations of health risks, lifestyle, and prior clinical history.
“Digital approaches are particularly amenable to integrate and analyse the diverse and large amounts of data for precision medicine. We were able to create digital mirror images of the individual metabolic systems of type 1 diabetes patients and consequently investigated how insulin differentially impacts the metabolism of one person compared to another. Our results not only highlighted the key role of glucose in the diabetes context but also suggested new therapeutic avenues, such as, calcium regulation.”
The outcome of the study suggests opportunities for diet-based intervention in the treatment of type 1 diabetes.
Dr Marouen Ben Guebila, Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, US and lead author of the study, concludes: “Based on our computer models, we may simulate the effect of diets and medication on individual insulin responses and improve disease management in the future. Overall, the study exemplifies how computational modelling fuels precision medicine approaches, which could lead to improvements in type 1 diabetes treatments.”
The study was funded by the European Research Council under the European Union’s Horizon 2020 research and innovation programme and by the Luxembourg National Research Fund through the ATTRACT programme.
To read the full study in Nature Computational Sciences, visit https://www.nature.com/
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