Objective
My primary objective is to determine whether including additional information such as past viral infections, body's protein and autoantibody levels (against self-proteins) further improve our ability to predict and stratify an individual's risk of developing T1D. To do this, we will use advanced computing and statistical methods and ultimately, attempt to create a Combined Risk Score 2 (CRS2) that could be used in future programs to screen the general population for risk of developing T1D. Improving prediction of T1D risk can result in more advanced warning and follow up of individuals at a high risk, which could in turn, reduce their chances of presenting with life-threatening complications (DKA). Also, if we can predict T1D risk sooner, we can invite high-risk individuals to early intervention clinical trials, for a chance at preventing T1D.
Background Rationale
To date, no study has deeply evaluated the potential benefits of including early-life viral infection, protein expression profile and functional autoantibody (antibodies against our own proteins) information on the sensitivity and specificity of existing protocols/scores to predict an individual's T1D risk later in life. This is despite well known and intensely investigated observations by our group and others, that individuals who develop persistent T1D exhibit distinct differences in viral infections, proteins and immunological profiles (autoantibody signatures) preceding clinical T1D diagnoses (and some preceding detection of islet autoantibodies) compared to individuals who don't develop T1D. Furthermore, other than genetic risk, there remains a dearth of established/reliable biomarkers that can predict IA/T1D risk prior to the first appearance of islet autoantibodies in blood tests.
Description of Project
This Career Development Award project aims to add further to the current equations/formulae used by scientists to predict an individuals risk of developing T1D (which is based primarily on genetic information, islet autoantibody presences and family history of T1D), extra information such as past infection to viruses, changes in protein and autoantibody levels in the body. By doing so, we believe that this will further improve on the sensitivity and specificity of T1D risk prediction and classifying who is at a high, moderate or low risk (stratification). If this is the case, this could lead to the development of a new formula/equation ("CRS2") that could be used in future clinical studies and T1D risk screening programs. It is also possible, that some of these information can act as indicators of whether a preventative treatment is effective or not in a clinical trial. The sooner we can determine whether a treatment is beneficial/working for an individual the better. If a particular treatment appears to have no beneficial effect, they could be offered a place in a different clinical trial with an alternative therapy that may be more beneficial (i.e. offer people at high-risk of developing T1D multiple chances at early intervention).
Anticipated Outcome
One anticipated outcome of this project is the development and validation of a new and improved combined risk score (“CRS2”) for T1D, that combines existing components of a CRS with additional information based on the early-life exposure to viral infections, changes in protein and autoantibody levels. This is expected to then, further enhance our ability to accurately predict T1D risk of a large group of individuals. When broadly used in large general population screening settings, the hope is that it will ultimately improve prognosis and cost-effectiveness of identifying at-risk individuals eligible for clinical intervention. Earlier determination of individual’s risk of developing persistent IA/T1D will make enrolment into early intervention trials more efficient and sooner, providing greater access and broader window of opportunity for therapies to prevention of T1D. Also, it can significantly reduce the number of individuals presenting with DKA at diagnosis and with effective disease modifying treatments, significantly delay or prevent T1D diagnoses altogether.
Relevance to T1D
With increasing availability of proven disease-modifying therapies (e.g. teplizumab) and early intervention trials of other immunomodulatory drugs, there is an increasing need to identify individuals at a high risk of developing T1D. In the past, screening programs and cohort studies typically targeted individuals with inherently high risk for T1D, such as those with a family history, since relatives of diagnosed cases have ~15-fold higher risk of T1D compared to those without. However, ~90% of newly diagnosed cases do not have a positive family history and treatment effects of teplizumab and other immunomodulatory drugs appear to be comparable between individuals with and without family history, meaning that they are likely to be equally effective in the general population. Therefore, there is an urgent, unmet need to develop programs to better identify those at risk, with or without a family history, who can benefit from existing or soon to be available DMTs. Furthermore, there is a greater need to predict and stratify T1D risk sooner, well before the the appearance of islet autoantibodies. Earlier identification means high-risk individuals can be recruited into primary prevention trials sooner, and to reduce the likelihood of life-threatening diabetic ketoacidosis (DKA) presentation at diagnosis by starting islet autoantibody testing and follow-up sooner.