Evidence map›Paper›PMID 42026878›Full record

Observational studyDiabetic medicine : a journal of the British Diabetic Association2026

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

Rebecca C Wyatt, Cristina Brigatti, Sian L Grace, Claire L Williams, Ilaria Marzinotto, Ben T Gillard, Elena Bazzigaluppi, Deborah Shoemark, Michela A-M Chandler, Peter Achenbach and 7 more

Abstract readObservational StudyValidation Study
In one paragraph

Observational study in Diabetic medicine : a journal of the British Diabetic Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Observational
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Rebecca C WyattUniversity of Bristol Medical School, Bristol, UK.
Cristina BrigattiIRCCS Ospedale San Raffaele, Milan, Italy.
Sian L GraceUniversity of Bristol Medical School, Bristol, UK.
Claire L WilliamsUniversity of Bristol Medical School, Bristol, UK.ORCID https://orcid.org/0000-0002-6641-8018
Ilaria MarzinottoIRCCS Ospedale San Raffaele, Milan, Italy.
Ben T GillardUniversity of Bristol Medical School, Bristol, UK.
Elena BazzigaluppiIRCCS Ospedale San Raffaele, Milan, Italy.
Deborah ShoemarkUniversity of Bristol School of Biochemistry, Bristol, UK.
Michela A-M ChandlerUniversity of Bristol Medical School, Bristol, UK.
Peter AchenbachHelmholtz Zentrum Munchen Institute of Diabetes Research, Munich, Bavaria, Germany.
Lorenzo PiemontiIRCCS Ospedale San Raffaele, Milan, Italy.
BOX Study GroupUniversity of Bristol Medical School, Bristol, UK.
AWAFUniversity of Bristol Medical School, Bristol, UK.
Kathleen M GillespieUniversity of Bristol Medical School, Bristol, UK.ORCID https://orcid.org/0000-0002-3009-8032
Vito LampasonaIRCCS Ospedale San Raffaele, Milan, Italy.
Alistair J K WilliamsUniversity of Bristol Medical School, Bristol, UK.ORCID https://orcid.org/0000-0002-3615-3899
Anna E LongUniversity of Bristol Medical School, Bristol, UK.ORCID https://orcid.org/0000-0002-6847-2771

Funding

Diabetes UKJDRFLeona M. and Harry B. Helmsley Charitable TrustUniversity of Bristol
6 · The paper itself

Abstract

introductionInsulin 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.

methodsTo 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 the area under the receiver operator characteristic curve and cox-proportional hazard models.

resultsIn 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).

conclusionThe 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 the risk of diabetes.

Indexed as

AutoantibodiesDiabetes Mellitus, Type 1InsulinInsulin AntibodiesLuciferasesAdolescentAdultChildChild, PreschoolCross-Sectional StudiesFemaleHumansImmunoprecipitationMalePredictive Value of TestsRisk AssessmentAutoantibodiesInsulinInsulin AntibodiesLuciferasesantibody affinityautoimmunitydiabetes mellitusinsulin antibodiesluciferasepredictive value of teststype 1

Identifiers

PMID42026878
PMCPMC13380342

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.