GENOMICS


Genetic risk scores A genetic risk score (GRS) aims to quantify an individual’s genetic predisposition to a trait or disease. The score is based on the cumulative effect of multiple genetic variants where each variant may only provide a modest effect on disease risk. A positive family history suggests an increased risk of disease, yet a lack of such history does not guarantee a reduced risk.12


By combining the risk


associated with various loci, a more accurate prediction of disease risk can be produced. First, relevant loci must be identified, commonly through genome wide association studies (GWAS). The most appropriate variants, both protective and risk alleles, are selected based on statistical significance, effect size, the amount of evidence supporting their association and, in some cases, their biological relevance to the disease process. The chosen variants are then assigned a weighting based on their effect size so that those with larger impacts on risk contribute more to the overall GRS.


Once the loci have been chosen and weights assigned, the GRS can be calculated. The simplest method can be described as the sum of the products of the number of risk alleles a person has for each variant. In other words, for each variant, the number of risk alleles (0, 1 or 2) is multiplied by the weight of the variant. The sum of these products across all variants provides the risk score.13 Other methods for calculating a GRS include gene-gene interaction modelling, genome wide statistical learning approaches14 modelling.15


and logistical regression However, environmental and


lifestyle factors also play a crucial role in most diseases and this risk is not captured by a GRS alone. For this reason, GRSs are often combined with other clinical factors to produce a combined risk score (CRS) to improve predictive ability.


Predicting T1D with GRS Many of those who will develop T1D do not have a relative with the disease. This makes identifying the individuals who will progress to clinical T1D problematic. A T1D GRS (GRS1) developed and validated by Oram et al (2016)13


age at AAb determination, and the count of positive islet AAbs improved the predictive ability.16


This model also


independently predicted progression from single to multiple islet AAb status in participants under 35 years of age.16 Further examination of the model


revealed that its predictive capability was primarily attributed to the top 10 SNPs, due to the significant weights of these variants.16


applying this prediction model in large- scale studies or population screenings, where the cost per individual is a significant consideration.


A similar GRS (GRS2) was also applied


to a large case control cohort from the T1D Genetics Consortium (T1DGC) to predict T1D in newborn screening studies. GRS2 included more SNPs (67) from both HLA and non-HLA regions delivering a receiver operating characteristic (ROC) area under curve (AUC) of 0.927 in the T1DGC cohort. When validated using data from UK Biobank, this GRS achieved similar predictive power.17 These GRSs provide examples of those which could be used in population screening to identify those most at risk of developing T1D. Unlike immunologic and metabolic markers of T1D, genetic risk does not change over time. Therefore, a GRS or CRS, like those described, could be included in newborn or population screening to identify those at the highest risk of developing clinical disease who could then receive increased monitoring and AAb testing.


has been


shown to be an independent predictor of progression to islet autoimmunity and the development of clinical T1D in 1,244 participants from the TrialNet Pathway to Prevention study who initially did not have a diabetes diagnosis.16 Employing a predictive model for the


progression to T1D that incorporates a GRS (30 SNPs), metabolic Diabetes Prevention Trial–Type 1 (DPT-1) risk score,


Disease-modifying therapy Teplizumab is the first disease-modifying therapy for T1D approved by the US Food and Drug Administration (FDA). This anti-CD3 monoclonal antibody has been shown to effectively delay the onset of clinical T1D through improved insulin production and β-cell function18


and is


approved for use in the US in adults and children over eight years of age. Teplizumab is based on another anti-CD3 monoclonal antibody which is used to reduce acute organ transplant rejections. These drugs share a CD3 binding region, however, the humanised Teplizumab includes two Leucine →


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Alanine substitutions in the Fc region which reduce Fc receptor binding to limit crosslinking18


and the initiation of the


cytokine storm.19 Teplizumab acts through the induction


This factor is likely crucial when


of regulatory T cells which prevents CD4+ and CD8+ T cell activation. CD4+Foxp3+ T cell induction results in the secretion of anti-inflammatory cytokines including tissue growth factor-β and interleukin-10, restoring the balance between pro- and anti-inflammatory stimuli.18 While the approval of Teplizumab is a huge step in the treatment of T1D, its approval is currently limited to the US and those with dysglycaemia who test positive for two or more islet AAbs but have not reached the clinical stage of T1D.18 However, there are no clinically accepted methods for identifying these individuals in the general population. Therefore, the implementation of effective screening programmes is necessary to identify individuals at high risk of developing T1D who can then be monitored for islet AAbs status.


Non-type 1 diabetes It is well known that diabetes is a collection of syndromes which extends beyond T1D. However, the differences between the subtypes of diabetes are not as commonly understood. T2D has been classically associated with adult onset and obesity. Given the similarity in symptoms and with over 1 billion obese individuals worldwide,20


including many


young people, diabetes misdiagnosis is becoming more common. It is estimated that up to 15% of young adults presenting with symptoms of diabetes are incorrectly diagnosed and treated.21


Furthermore, up


to 40% of adults over 30 years with T1D are estimated to be incorrectly diagnosed with T2D.22


The misdiagnosis of T2D as T1D leads to unnecessary insulin therapy, higher drug and monitoring expenses and, in some cases, an increase in number and severity of symptoms. On the other hand, T1D misdiagnosed as T2D results in poor glycaemic control, more frequent visits to healthcare services and inappropriate insulin regimes.13


Diabetic ketoacidosis (DKA) is a potentially life-threating 43


Early identification of those at risk of T1D is essential to facilitate their inclusion in monitoring and prevention trials, aid studies of the mechanisms involved in the preclinical stages and progression to clinical T1D


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