Improved prediction of symptomatic type 1 diabetes using a luciferase-based assay to measure (pro)insulin autoantibodies

Rebecca Wyatt, Cristina Brigatti, Sian L Grace, Claire L Williams, Ilaria Marzinotto, Benjamin T Gillard, Elena Bazzigaluppi, Deborah K Shoemark, M A Chandler, Peter Achenbach, Lorenzo Piemonti, Kathleen M Gillespie, Vito Lampasona, Alistair J K Williams, Anna E Long

Research output: Contribution to journalArticle (Academic Journal)peer-review

Abstract

Introduction:
Insulin autoantibodies (IAA) are key predictors of type 1 diabetes, particularly in young children. Micro-radiobinding assays (RBA) are the gold-standard for IAA measurement but have limitations. We assessed whether a luciferase immunoprecipitation system (LIPS) assay improved diabetes risk assessment.

Methods:
To validate LIPS compared with RBA, samples from people with new-onset type 1 diabetes (n=150) and first-degree relatives (FDRs) (n=619), of whom 91 had developed diabetes during follow-up, were used. This cross-sectional observational data was analysed using area under the receiver operator characteristic curve and cox-proportional hazard models.

Results:
In new-onset diabetes, RBA and LIPS showed 88% agreement in IAA status. Positive IAA LIPS was more common in 89 FDRs with high-moderate affinity IAA (61%) compared with 22 FDRs with low-affinity IAA (18%) (p<0.001). In FDRs positive for multiple other islet autoantibodies, 20-year diabetes risk was 80% for those positive compared with 30% for those negative for IAA by LIPS (p=0.013). IAA LIPS added to diabetes risk independently of status/level of IAA by RBA, other autoantibodies, and sampling age (p<0.001).

Conclusion:
The IAA LIPS low-blood volume, high-throughput technique identifies more individuals with the highest risk of diabetes. The ability to identify high-affinity IAA makes LIPS an ideal method for future clinical trials and population screening strategies to predict risk of diabetes.
Original languageEnglish
JournalDiabetic Medicine
Publication statusAccepted/In press - 22 Jan 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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