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Immunological biomarkers for the development and progression of type 1 diabetes

Overview of attention for article published in Diabetologia, September 2018
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

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1 news outlet
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35 X users
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1 patent

Citations

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102 Mendeley
Title
Immunological biomarkers for the development and progression of type 1 diabetes
Published in
Diabetologia, September 2018
DOI 10.1007/s00125-018-4726-8
Pubmed ID
Authors

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

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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 35 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 102 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 102 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 15%
Student > Bachelor 13 13%
Student > Ph. D. Student 12 12%
Other 6 6%
Student > Master 5 5%
Other 14 14%
Unknown 37 36%
Readers by discipline Count As %
Medicine and Dentistry 21 21%
Biochemistry, Genetics and Molecular Biology 18 18%
Immunology and Microbiology 9 9%
Agricultural and Biological Sciences 5 5%
Computer Science 2 2%
Other 8 8%
Unknown 39 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 33. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 January 2024.
All research outputs
#1,225,168
of 25,959,914 outputs
Outputs from Diabetologia
#658
of 5,593 outputs
Outputs of similar age
#25,591
of 351,443 outputs
Outputs of similar age from Diabetologia
#17
of 63 outputs
Altmetric has tracked 25,959,914 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,593 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 24.2. This one has done well, scoring higher than 88% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 351,443 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 63 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.