Evidence map›Paper›PMID 42810803›Full record

ArticleBMJ open2026

Natural history study of type 1 diabetes risk in adults (T1DRA): protocol for screening and follow-up of UK adults.

Rachel J Aitken, Ilana Kelland, Stu Toms, Clare Megson, Sarah Stollery, Ben Gillard, Zahra Hashmi, Jack Whitfield, Lilianna Bakinowska, Olivia Pearce and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

16 authors.

Rachel J AitkenDiabetes and Metabolism, Bristol Medical School, University of Bristol, Bristol, UK.
Ilana KellandDiabetes and Metabolism, Bristol Medical School, University of Bristol, Bristol, UK.
Stu TomsBristol Dental School, University of Bristol, Bristol, UK.
Clare MegsonDiabetes and Metabolism, Bristol Medical School, University of Bristol, Bristol, UK.
Sarah StolleryDiabetes and Metabolism, Bristol Medical School, University of Bristol, Bristol, UK.
Ben GillardDiabetes and Metabolism, Bristol Medical School, University of Bristol, Bristol, UK.
Zahra HashmiDiabetes and Metabolism, Bristol Medical School, University of Bristol, Bristol, UK.
Jack WhitfieldDiabetes and Metabolism, Bristol Medical School, University of Bristol, Bristol, UK.
Lilianna BakinowskaDiabetes and Metabolism, Bristol Medical School, University of Bristol, Bristol, UK.
Olivia PearceDiabetes and Metabolism, Bristol Medical School, University of Bristol, Bristol, UK.
Vito LampasonaSan Raffaele Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy.
Claire L WilliamsDiabetes and Metabolism, Bristol Medical School, University of Bristol, Bristol, UK.
Sian L GraceDiabetes and Metabolism, Bristol Medical School, University of Bristol, Bristol, UK.
Georgina L MortimerDiabetes and Metabolism, Bristol Medical School, University of Bristol, Bristol, UK.
Anna E LongDiabetes and Metabolism, Bristol Medical School, University of Bristol, Bristol, UK.
Kathleen M GillespieDiabetes and Metabolism, Bristol Medical School, University of Bristol, Bristol, UK K.M.Gillespie@bristol.ac.uk.ORCID http://orcid.org/0000-0002-3009-8032

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionType 1 diabetes can be predicted through the presence of multiple circulating islet autoantibodies in the blood. However, although more than half of type 1 diabetes cases are diagnosed in adults, natural history and prediction studies to date have focused on children. This protocol describes the first type 1 diabetes natural history study in adults, both general population and relatives for screening with longitudinal monitoring. METHODS AND ANALYSIS: The study launched on 14 November 2023 and the planned end date is 31 May 2027. The recruitment target is 20 000 adults. Participants are consented online and posted capillary blood kits for remote self-sampling. Serum, isolated from samples returned to the laboratory by mail, is tested for islet autoantibodies in a two-step process. Samples are initially screened for autoantibodies to truncated (t)glutamate decarboxylase 65, insulinoma-associated antigen-2 and zinc transporter 8 using a multiplex luciferase immunoprecipitation system (LIPS) screening assay. Positive samples are retested in individual LIPS assays. Autoantibodies to insulin are measured by radiobinding assay. Individuals positive for one or more islet autoantibody are recontacted and a remote confirmation sample is requested.Once positivity is confirmed, positive participants are provided education about the symptoms of type 1 diabetes and invited to annual monitoring to allow risk of future type 1 diabetes to be accurately measured. The confirmation sample is also tested for autoantibody characteristics including affinity and positivity by a second platform to allow a tiered approach to future follow-up. ETHICS AND DISSEMINATION: The study protocol was submitted to Liverpool Central Research Ethics Committee and received a favourable opinion on 26 October 2023. Results are disseminated through conference presentations, peer-reviewed journals and will be shared openly.

Indexed as

AutoantibodiesDiabetes Mellitus, Type 1Mass ScreeningAdultFemaleFollow-Up StudiesGlutamate DecarboxylaseHumansIslets of LangerhansMaleReceptor-Like Protein Tyrosine Phosphatases, Class 8Research DesignUnited KingdomZinc Transporter 8AutoantibodiesGlutamate DecarboxylaseReceptor-Like Protein Tyrosine Phosphatases, Class 8Zinc Transporter 8Diabetes & endocrinologyGENETICSImmunologyMass Screening

Identifiers

PMID42810803
PMCPMC13630054

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Registered trials

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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.