Prediction of progression to type 1 diabetes with dynamic biomarkers and risk scores
Date
2024
Authors
Joglekar, M.V.
Kaur, S.
Pociot, F.
Hardikar, A.A.
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Journal article
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The Lancet Diabetes and Endocrinology, 2024; 12(7):483-492
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Mugdha V. Joglekar, Simranjeet Kaur, Prof Flemming Pociot, Prof Anandwardhan A. Hardikar
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Abstract
Identifying biomarkers of functional β-cell loss is an important step in the risk stratification of type 1 diabetes. Genetic risk scores (GRS), generated by profiling an array of single nucleotide polymorphisms, are a widely used type 1 diabetes risk-prediction tool. Type 1 diabetes screening studies have relied on a combination of biochemical (autoantibody) and GRS screening methodologies for identifying individuals at high-risk of type 1 diabetes. A limitation of these screening tools is that the presence of autoantibodies marks the initiation of β-cell loss, and is therefore not the best biomarker of progression to early-stage type 1 diabetes. GRS, on the other hand, represents a static biomarker offering a single risk score over an individual's lifetime. In this Personal View, we explore the challenges and opportunities of static and dynamic biomarkers in the prediction of progression to type 1 diabetes. We discuss future directions wherein newer dynamic risk scores could be used to predict type 1 diabetes risk, assess the efficacy of new and emerging drugs to retard, or prevent type 1 diabetes, and possibly replace or further enhance the predictive ability offered by static biomarkers, such as GRS.
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© 2024 Elsevier Ltd. All rights reserved.