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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 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

Mentioned by

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47 X users
facebook
1 Facebook page

Citations

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18 Dimensions

Readers on

mendeley
87 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 47 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 87 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
Spain 1 1%
Russia 1 1%
Unknown 84 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 21%
Researcher 17 20%
Student > Master 10 11%
Student > Bachelor 9 10%
Other 6 7%
Other 7 8%
Unknown 20 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 26 30%
Medicine and Dentistry 13 15%
Agricultural and Biological Sciences 10 11%
Pharmacology, Toxicology and Pharmaceutical Science 3 3%
Computer Science 3 3%
Other 9 10%
Unknown 23 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 25. 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
#1,550,333
of 25,732,188 outputs
Outputs from Diabetologia
#822
of 5,376 outputs
Outputs of similar age
#29,202
of 324,486 outputs
Outputs of similar age from Diabetologia
#22
of 77 outputs
Altmetric has tracked 25,732,188 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,376 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 24.7. This one has done well, scoring higher than 84% 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 324,486 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 90% of its contemporaries.
We're also able to compare this research output to 77 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 71% of its contemporaries.