Objective
Majority of our DNA is made up of genes that do not produce any proteins. This ‘non-protein-coding’ compartment was previously thought to be junk and not contributing to any cellular functions. However, in recent years, small non-coding RNAs (produced from non-coding DNA) are demonstrated to be important in several processes (including insulin production) and in disease onset/progression. These small non-coding RNAs are also released in circulation in response to body’s response to a change environment, enabling them to be dynamic (changing) biomarkers capable of capturing and prognosing disease progression early when measured in circulation (blood). Our recent Nature Medicine 2025 publication presents the world’s first blood-based small non-coding RNA-based Dynamic Risk Score (DRS) for type 1 diabetes. In our study, we have also showed that the risk score in our sub-analysis could potentially identify misdiagnosis of T1D/T2D and enhance the prediction of responders to drug therapy in T1D. There are other dynamic biomarkers (e.g. cytokines, lipids, metabolites, proteins) which are also known to be released in circulation, reflecting the body’s response to change.
The proposed study aims to i) improve the T1D risk prediction by adding other biomarkers (cytokines, lipids, metabolites, proteins) to our already identified non-coding RNAs for T1D stratification; ii) and also improve the classification of T1D and T2D (as present diagnostics still misdiagnose T1D, particular in adults estimated to have ~38% misdiagnosis). The stratifications in this proposed study will be tested across a wide range of multi-ethnic populations around the world. T1D is classically considered an autoimmune disorder, the proposed study will also iii) identify the specificity of these dynamic biomarkers for T1D compared to other autoimmune disorders.
Background Rationale
Currently genetic (genetic risk scoring) and biochemical (antibody) tests are used to screen for Type 1 diabetes (T1D) progression. Although, there are strong genetic links, 90% of individuals who develop T1D do not have any family history of T1D. Also, autoantibodies tests clinical presentation of T1D progression can take from months to years; and antibodies themselves are consequences of the underlying pre-existent pathology (a reminder of the “streetlight effect”). Current genetic and biochemical tests do not entirely explain the progression of T1D.
MicroRNA are small RNA molecules that do not code for protein, known to be important biomarkers and regulators of cellular processes. They are also released in circulation, therefore can be measured in blood. MicroRNAs are established prognostic biomarkers of cancer progression, although not yet in T1D progression. Our recent study in Nature Medicine 2025, shows how a blood-based microRNA-based dynamic risk score (DRS) can measure changes in the risk of T1D. This project builds on our innovative DRS and further validates their applicability in other cohorts from across multi-ethnic populations around the world.
Description of Project
Identifying biomarkers of functional beta cell loss is an important step in the risk stratification of Type 1 Diabetes (T1D). Currently genetic (genetic risk scoring (GRS)) and biochemical (antibody) tests are used to screen for T1D progression. However, many individuals with T1D have no family history of T1D. GRS also represents a “static” biomarker which does not change over a lifetime. While, autoantibodies are the consequences of underlying pathological mechanisms leading to T1D, and are therefore, not the best biomarkers for T1D progression.
Functional loss of beta cells in the pancreas is a hallmark of T1D and Type 2 Diabetes (T2D). Present diagnostics still can misdiagnose T1D for T2D, particularly in adults- with ~38% presented with T1D misclassification. In addition, features which are associated with T1D including body mass index, ketoacidosis at presentation and islet autoantibodies, can also be present in T2D diagnosed individuals; therefore, emphasizing the need to identify biomarkers which can more accurately classify T1D and T2D.
This proposed study focuses on identifying and further developing a dynamic blood-based biomarker signature for islet beta cell loss and T1D and understand the applicability of these biomarkers for i) T1D risk stratification, ii) classification of T1D and T2D, iii) and their specificity for T1D autoimmunity in comparison to other autoimmune disorders.
This study will build on from our recently identified regulator molecule (small non-coding RNA: microRNA) biomarkers from blood (published in Nature Medicine 2025) for T1D. These microRNA biomarkers are known to change (i.e. are dynamic) overtime (due to factors such as the environment). This proposed study will identify and validate of other dynamic regulatory biomarkers (which include proteins, lipids and metabolites) for beta cell functional loss in T1D. These biomarkers are then validated along with microRNAs for stratifying individuals without T1D, at risk of T1D, with T1D, and will be used to improve T1D misdiagnosis (as forementioned many adults particularly are misdiagnosed with T2D) in multi-ethnic populations, and will also be further investigated to understand their specificity in T1D compared other autoimmune diseases (which includes Systemic Lupus Erythematosus, Multiple sclerosis, Rheumatoid Arthritis, Celiac/Coeliac disease).
This study will identify dynamic risk scores (DRS) through these dynamic blood-based biomarkers in multi-ethnic populations in the world for T1D risk stratification, and also improve T1D misdiagnosis. This is important, as identification of T1D progression early will help determine the initiation of early immune-modulatory therapies in high-risk populations when little to no beta-cell damage has occurred can further delay or even prevent the requirement for future exogenous insulin. This would be particularly lifechanging for children where the diagnosis of T1D before 10 years of age is associated with a 16-year reduction in life expectancy. Accurate diagnosis of T1D will help facilitate the correct clinical line of treatment for T1D and T2D. This study also paves way to enhancing the prediction of the effectiveness of therapies known to delay/prevent clinical progression to T1D, shifting the paradigms from a static, one-size-fits-all approach to a personalised and precision-guided model of care.
Anticipated Outcome
It is anticipated the dynamic risk score represents a highly accurate and responsive set of biomarkers measured in blood that precisely capture changes in T1D risk. Since some of these dynamic biomarkers (such as microRNAs) are also regulators of gene expression and this proposed research develops biomarkers from islet beta-cell loss and T1D, the specificity of these biomarker is applicable for T1D risk stratification.
Relevance to T1D
With the availability of immune-modulatory therapies that can delay the clinical onset of T1D, the need for accurate predictive biomarkers, such as ours, is imminent. A dynamic biomarker-based screening test would be particularly life-changing for children where the diagnosis of T1D before 10 years of age is associated with a 16-year reduction in life expectancy. As the initiation of early immune-modulatory therapies in high-risk populations when little to no beta-cell damage has occurred can further delay or even prevent the requirement for future exogenous insulin. In addition, with present misdiagnose of T1D, particularly in adults estimated to be ~38%, the use of these biomarkers to accurately diagnose T1D and T2D, will help facilitate the correct clinical line of treatment for T1D and T2D.