Aim:
We aim to provide individualized risk prediction for type 1 diabetes mellitus (T1D) combining both clinical measurements and genetic data.
Scientific rationale:
T1D is a complex disease caused by both genetic and environmental factors such as lifestyles. Genetic factors include single-nucleotide changes that could alter functionality of proteins that could further lead to transition from physiological state to disease state. Many of the nucleotide changes have been discovered. A multitude of these changes have been found to be enriched in T1D patients compared to unaffected population.
Leveraging the rich datasets from UKBB, we aim to develop a prediction algorithm that would calculate individualized risk for T1D by combining effects from the nucleotide changes associated with T1D disease throughout the whole genome.
Additionally, we would investigate the interplay between genetics and clinical presentation including age, sex, glucose level, family history and biomarkers to further understand how genetics interacts with each component to influence disease susceptibility.
Project duration: 09/2021-09/2024
Public health impact:
The goal of developing a prediction model for T1D could potentially identify at-risk population for T1D when they are at pre- or early clinical stage. Our proposed model will add to the clinical tool box for risk prediction for T1D. It may later be applied to clinical consultation, disease management or personalized treatment. Implementation of this predictive scheme would potentially help clinicians to fine-tune the approach of caring or treating T1D patients to mitigate long-term impact on their lives.