GENOMICS


complication of diabetes which results from an accumulation of ketones due to insulin deficiency. DKA is more common in T1D patients, however, the prevalence in T2D populations is increasing.23 Levels of endogenous insulin can be measured by assessing C-peptide concentrations in serum or urine. Sustained non-fasting C-peptide levels of >600pmol/l is indicative of T2D, whereas low or non-existent C-peptide concentrations are suggestive of T1D7. This method can accurately distinguish between T1D and T2D but only after the honeymoon period (3-5 years post- diagnosis).7


Monogenic diabetes is another form of diabetes that is often misclassified. Monogenic diabetes is a collection of rare forms caused by mutations in a single gene24


diabetes cases.7


making up an estimated 5% of There are two main


forms: Neonatal diabetes mellitus (NDM) and Maturity-onset diabetes of the young (MODY). NDM occurs in infants under six months of age, up to 85% of whom have a monogenic aetiology.7


MODY is


associated with autosomal inheritance of 1 of 13 known genes characterised by impaired insulin secretion with limited interference in insulin action, in the absence of obesity.7


used to treat complications,27 many of


which may arise due to misdiagnosis. Correct classification is essential to ensure correct treatment strategies are implemented which will in turn will help to reduce the complications associated with diabetes. As discussed, differentiation of subtypes of diabetes through current clinical factors and metabolic biomarkers are not fit for this purpose. GRSs and CRSs may provide a potential novel method for the discrimination of diabetes subtypes.


GRS1 has been shown to be highly discriminatory between T1D and T2D in a large white European cohort, with an AUC of 0.8813. Oram et al, showed that a GRS cutoff of >0.280 was indicative of T1D with 95% specificity and 50% sensitivity, while a cutoff of <0.234 indicated T2D with 95% specificity and 53% sensitivity.13 A multiple logistic regression CRS which combined GRS1, islet AAbs status, BMI and age at diagnosis was shown to be an independent and additive predictor of severe insulin deficiency, with an AUC of 0.96.13


As was the case for predicting T1D onset, most of the discriminatory power was found in the top 10 SNPs.13


Misdiagnosis occurs


because the phenotypes of monogenic diabetes are not distinct enough to facilitate straightforward clinical distinction from the more common types of diabetes. For example, individuals with MODY are commonly young and slim, like those with T1D, but do not require insulin and are islet AAbs negative, like those with T2D.25 Like T1D vs T2D, differentiating monogenic diabetes from other forms through C-peptide measurement is not viable during the honeymoon period. Additionally, C-peptide measurement cannot discriminate between T1D and NDM as patients from both groups are generally C-peptide negative.26


GRS for the discrimination of diabetes subtypes


It is estimated that almost 80% of the £10 billion spent on diabetes by the NHS is


of GRS1 and GRS2 have also been shown to be predictive of aetiology in large cohorts from India28 youth.29


and of multiethnic This CRS provides a method


of discriminating T1D from T2D where traditional methods cannot.


This GRS showed an ROC AUC of 0.87 for the discrimination of T1D and MODY.26


GRS1 was shown to be discriminative of monogenic diabetes of any aetiology from T1D in large, white European cohorts.26


Using the same cutoffs as


described above, this GRS was indicative of T1D (cutoff >0.280) with 94% specificity and 50% sensitivity. A cutoff of <0.234 was indicative of MODY with 95% specificity and 50% sensitivity.26 GRS1 may also have utility in the identification of novel aetiologies of NDM. Patients with suspected NDM receive next-generation sequencing (NGS) for all genes known to be associated with NDM, a strategy that successfully identifies aetiology in 82% of cases.26


However, NGS does not


While these GRSs have been shown to be useful in various cohorts including multiethnic youth, the genetic heterogeneity between populations of different ancestry associated with T1D indicates the need for extensive validation of the chosen screening method across different groups


44 Variations


help identify novel genes associated with NDM. This GRS could be used to distinguish NDM from early onset T1D when known NDM aetiologies have been ruled out.26


Conclusions Increasing obesity rates in the young, rising numbers of adult onset T1D and novel disease-modifying therapies are reinforcing the need for effective screening methods to identify those at risk of developing clinical disease. GRSs and CRSs, like those described here, have be shown to provide strong predictive ability for the development of clinical T1D and for differentiating it from other forms of diabetes. The correct classification of diabetes is essential to help reduce the complications of diabetes and their burden on healthcare services. While these GRSs have been shown to be useful in various cohorts including multiethnic youth, the genetic heterogeneity between populations of different ancestry associated with T1D indicates the need for extensive validation of the chosen screening method across different groups. Teplizumab is currently only approved in the US, however, many healthcare services around the world are considering how to best implement this new therapy. Teplizumab is only effective if administered before the onset of clinical T1D. Therefore, the ability to predict the onset of autoimmunity is likely to be a key factor when selecting a successful screening approach.


References 1 Banting FG, Best CH, Collip JB, Campbell


WR, Fletcher AA. Pancreatic Extracts in the Treatment of Diabetes Mellitus. Can Med Assoc J. 1922;12(3):141-146


2 Zaharia OP, Lanzinger S, Rosenbauer J et al. Comorbidities in Recent-Onset Adult Type 1 Diabetes: A Comparison of German Cohorts. Front Endocrinol (Lausanne). 2022 Jun 3;13:760778. doi:10.3389/fendo.2022.760778


3 Martínez-Ortega AJ, Muñoz-Gómez C, Gros-Herguido N et al. Description of a Cohort of Type 1 Diabetes Patients: Analysis of Comorbidities, Prevalence of Complications and Risk of Hypoglycemia. J Clin Med. 2022;11(4):1039. doi:10.3390/ jcm11041039


4 Gregory GA, Robinson TIG, Linklater SE et al. Global incidence, prevalence, and mortality of type 1 diabetes in 2021 with projection to 2040: a modelling study. Lancet Diabetes Endocrinol. 2022;10(10):741-760. doi:10.1016/S2213- 8587(22)00218-2


5 Redondo MJ, Gignoux CR, Dabelea D et al. Type 1 diabetes in diverse ancestries and the use of genetic risk scores. Lancet


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