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Possible Role of Interleukin-1β in Type 2 Diabetes Onset and Implications for Anti-inflammatory Therapy Strategies

Overview of attention for article published in PLoS Computational Biology, August 2014
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Title
Possible Role of Interleukin-1β in Type 2 Diabetes Onset and Implications for Anti-inflammatory Therapy Strategies
Published in
PLoS Computational Biology, August 2014
DOI 10.1371/journal.pcbi.1003798
Pubmed ID
Authors

Gang Zhao, Gitanjali Dharmadhikari, Kathrin Maedler, Michael Meyer-Hermann

Abstract

Increasing evidence of a role of chronic inflammation in type 2 diabetes progression has led to the development of therapies targeting the immune system. We develop a model of interleukin-1β dynamics in order to explain principles of disease onset. The parameters in the model are derived from in vitro experiments and patient data. In the framework of this model, an IL-1β switch is sufficient and necessary to account for type 2 diabetes onset. The model suggests that treatments targeting glucose bear the potential of stopping progression from pre-diabetes to overt type 2 diabetes. However, once in overt type 2 diabetes, these treatments have to be complemented by adjuvant anti-inflammatory therapies in order to stop or decelerate disease progression. Moreover, the model suggests that while glucose-lowering therapy needs to be continued all the way, dose and duration of the anti-inflammatory therapy needs to be specifically controlled. The model proposes a framework for the discussion of clinical trial outcomes.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 106 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 <1%
Unknown 105 99%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 21 20%
Student > Master 13 12%
Student > Ph. D. Student 9 8%
Researcher 6 6%
Student > Doctoral Student 3 3%
Other 11 10%
Unknown 43 41%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 16 15%
Medicine and Dentistry 15 14%
Agricultural and Biological Sciences 13 12%
Immunology and Microbiology 4 4%
Nursing and Health Professions 2 2%
Other 5 5%
Unknown 51 48%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 28 August 2014.
All research outputs
#22,756,649
of 25,371,288 outputs
Outputs from PLoS Computational Biology
#8,565
of 8,958 outputs
Outputs of similar age
#212,258
of 247,674 outputs
Outputs of similar age from PLoS Computational Biology
#151
of 161 outputs
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