Evidence map›Paper›PMID 30209538›Full record

ReviewDiabetologia2018

Immunological biomarkers for the development and progression of type 1 diabetes.

Chantal Mathieu, Riitta Lahesmaa, Ezio Bonifacio, Peter Achenbach, Timothy Tree

Abstract readReview
PubMed Publisher
In one paragraph

Review in Diabetologia, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing 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

38 citing papers in PubMed.

  1. Observational
  2. Article
  3. Article
  4. Review
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  6. Article
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  8. Review
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  11. Review
  12. Article
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  14. Review
  15. Article
  16. Type 1Micromachines · 2023
    Review
  17. Review
  18. Article
  19. Review
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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

5 authors.

Chantal MathieuDepartment of Endocrinology, University Hospital Gasthuisberg, KU Leuven, Herestraat, 49 3000, Leuven, Belgium. chantal.mathieu@uzleuven.be.
Riitta LahesmaaTurku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland.
Ezio BonifacioDFG Center for Regenerative Therapies Dresden, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany.
Peter AchenbachGerman Center for Diabetes Research (DZD), Neuherberg, Germany.
Timothy TreeDepartment of Immunobiology, School of Immunology & Microbial Sciences, King's College London, Borough Wing Guy's Hospital, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Immune biomarkers of type 1 diabetes are many and diverse. Some of these, such as the autoantibodies, are well established but not discriminative enough to deal with the heterogeneity inherent to type 1 diabetes progression. As an alternative, high hopes are placed on T cell assays, which give insight into the cells that actually target the beta cell or play a crucial role in maintaining tolerance. These assays are approaching a level of robustness that may allow for solid conclusions on both disease progression and therapeutic efficacy of immune interventions. In addition, 'omics' approaches to biomarker discovery are rapidly progressing. The potential emergence of novel biomarkers creates a need for the introduction of bioinformatics and 'big data' analysis systems for the integration of the multitude of biomarker data that will be available, to translate these data into clinical tools. It is worth noting that it is unlikely that the same markers will apply to all individuals. Instead, individualised signatures of biomarkers, combining autoantibodies, T cell profiles and other biomarkers, will need to be used to classify at-risk patients into various categories, thus enabling personalised prediction, prevention and treatment approaches. To achieve this goal, the standardisation of assays for biomarker discovery, the integration of analyses and data from biomarker studies and, most importantly, the careful clinical characterisation of individuals providing samples for these studies are critical. Longitudinal sample-collection initiatives, like INNODIA, should lead to novel biomarker discovery, not only providing a better understanding of type 1 diabetes onset and progression, but also yielding biomarkers of therapeutic efficacy of interventions to prevent or arrest type 1 diabetes.

Indexed as

AnimalsAutoantibodiesBiomarkersComputational BiologyDiabetes Mellitus, Type 1HumansAutoantibodiesBiomarkersAutoantibodiesBioinformaticsBiomarkerImmuneReviewT cell assaysType 1 diabetes

Identifiers

What OpenQuestion holds

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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.