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Review
. 2019 Oct 16;11(1):6-10.
doi: 10.1007/s13340-019-00414-9. eCollection 2020 Jan.

Islet autoantibodies in disease prediction and pathogenesis

Affiliations
Review

Islet autoantibodies in disease prediction and pathogenesis

Xiaofan Jia et al. Diabetol Int. .

Abstract

Type 1 diabetes (T1D) is now predictable by measuring specific islet autoantibodies (IAbs). Almost all children who developed multiple IAbs will progress to T1D with time, while individuals with single IAb have a very low risk although it is an important earlier biomarker. The poor prediction of single IAb has been found to be associated with IAb affinity. Majority of single IAb generated in current standard IAb radio-binding assay (RBA) are of low affinity, which have been demonstrated low risk in T1D development. New generation of nonradioactive IAb assay with electrochemiluminescence (ECL) technology has been shown to discriminate high-affinity from low-affinity IAbs and greatly improve sensitivity and disease specificity. With a high-affinity IAb assay, like ECL assay, single IAb will be expected to be a reliable biomarker for T1D early prediction. Although appearance of IAbs is most reliable biomarkers for T1D, there are no direct evidences that IAbs contribute to β-cell damage. With recent studies on ZnT8, a merging protein on β-cell surface membrane associated with insulin secretion, a subclass of ZnT8 autoantibodies directed to extra-cellular epitopes of ZnT8 on β-cell surface has recently been identified in T1D patients and these cell surface autoantibodies have been found to appear very early, before other IAbs. These findings lead us to a hypothesis that the immunogenic epitopes on β-cell surface might be early targets for autoimmune disease and IAbs to cell surface epitopes might be involved in β-cell destruction, which will change the paradigm of IAbs in T1D pathogenesis.

Keywords: Autoantibodies; ECL assay; Pathogenesis; Prediction; Type 1 diabetes.

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Conflict of interest statement

Conflict of interestAuthor Xiaofan Jia, Author Yong Gu, Author Hilary High, and Author Liping Yu declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Comparison of predictive values between ECL and RBA on 2,944 subjects for their very first initial screening samples in TrialNet Pathway to Prevention Study. a Comparison of positive predictive values between RBA-IAA and ECL-IAA (p < 0.0001), and between RBA-GADA and ECL-GADA (p < 0.0001). b Comparison of negative predictive values between RBA-IAA and ECL-IAA (p < 0.05), and between RBA-GADA and ECL-GADA (p = 0.007)

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