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Human genetics as a model for target validation: finding new therapies for diabetes

Overview of attention for article published in Diabetologia, April 2017
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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 (91st percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

Mentioned by

twitter
48 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
10 Dimensions

Readers on

mendeley
46 Mendeley
Title
Human genetics as a model for target validation: finding new therapies for diabetes
Published in
Diabetologia, April 2017
DOI 10.1007/s00125-017-4270-y
Pubmed ID
Authors

Soren K. Thomsen, Anna L. Gloyn

Abstract

Type 2 diabetes is a global epidemic with major effects on healthcare expenditure and quality of life. Currently available treatments are inadequate for the prevention of comorbidities, yet progress towards new therapies remains slow. A major barrier is the insufficiency of traditional preclinical models for predicting drug efficacy and safety. Human genetics offers a complementary model to assess causal mechanisms for target validation. Genetic perturbations are 'experiments of nature' that provide a uniquely relevant window into the long-term effects of modulating specific targets. Here, we show that genetic discoveries over the past decades have accurately predicted (now known) therapeutic mechanisms for type 2 diabetes. These findings highlight the potential for use of human genetic variation for prospective target validation, and establish a framework for future applications. Studies into rare, monogenic forms of diabetes have also provided proof-of-principle for precision medicine, and the applicability of this paradigm to complex disease is discussed. Finally, we highlight some of the limitations that are relevant to the use of genome-wide association studies (GWAS) in the search for new therapies for diabetes. A key outstanding challenge is the translation of GWAS signals into disease biology and we outline possible solutions for tackling this experimental bottleneck.

Twitter Demographics

The data shown below were collected from the profiles of 48 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Russia 1 2%
Spain 1 2%
United Kingdom 1 2%
Unknown 43 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 26%
Unspecified 8 17%
Student > Ph. D. Student 8 17%
Student > Master 6 13%
Student > Bachelor 4 9%
Other 8 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 13 28%
Unspecified 11 24%
Medicine and Dentistry 10 22%
Agricultural and Biological Sciences 7 15%
Social Sciences 2 4%
Other 3 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 26. 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 07 June 2017.
All research outputs
#607,476
of 13,204,027 outputs
Outputs from Diabetologia
#389
of 3,814 outputs
Outputs of similar age
#22,238
of 263,126 outputs
Outputs of similar age from Diabetologia
#18
of 67 outputs
Altmetric has tracked 13,204,027 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 3,814 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 16.6. This one has done well, scoring higher than 89% 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 263,126 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 91% of its contemporaries.
We're also able to compare this research output to 67 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.