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Diabetes at the crossroads: relevance of disease classification to pathophysiology and treatment

Overview of attention for article published in Diabetologia, October 2015
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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 (80th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

policy
1 policy source
twitter
7 X users
facebook
2 Facebook pages
googleplus
1 Google+ user

Citations

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155 Dimensions

Readers on

mendeley
326 Mendeley
Title
Diabetes at the crossroads: relevance of disease classification to pathophysiology and treatment
Published in
Diabetologia, October 2015
DOI 10.1007/s00125-015-3789-z
Pubmed ID
Authors

R. David Leslie, Jerry Palmer, Nanette C. Schloot, Ake Lernmark

Abstract

Diabetes is not a single homogeneous disease but composed of many diseases with hyperglycaemia as a common feature. Four factors have, historically, been used to identify this diversity: the age at onset; the severity of the disease, i.e. degree of loss of beta cell function; the degree of insulin resistance and the presence of diabetes-associated autoantibodies. Our broad understanding of the distinction between the two major types, type 1 diabetes mellitus and type 2 diabetes mellitus, are based on these factors, but it has become apparent that they do not precisely capture the different disease forms. Indeed, both major types of diabetes have common features, encapsulated by adult-onset autoimmune diabetes and maturity-onset diabetes of the young. As a result, there has been a repositioning of our understanding of diabetes. In this review, drawing on recent literature, we discuss the evidence that autoimmune type 1 diabetes has a broad clinical phenotype with diverse therapeutic options, while the term non-autoimmune type 2 diabetes obscures the optimal management strategy because it encompasses substantial heterogeneity. Underlying these developments is a general progression towards precision medicine with the need for precise patient characterisation, currently based on clinical phenotypes but in future augmented by laboratory-based tests. Key points • The need to clarify diabetes classification, which is currently imprecise in distinguishing major disease types, using laboratory tests • The importance of predictors of disease progression, including genetic, immune and metabolic features • The potential for predicting therapeutic responses to provide a more personalised approach to therapy.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 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 326 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 <1%
Netherlands 1 <1%
Brazil 1 <1%
Singapore 1 <1%
Mexico 1 <1%
Unknown 321 98%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 44 13%
Student > Master 42 13%
Student > Ph. D. Student 35 11%
Researcher 30 9%
Other 20 6%
Other 56 17%
Unknown 99 30%
Readers by discipline Count As %
Medicine and Dentistry 90 28%
Biochemistry, Genetics and Molecular Biology 33 10%
Nursing and Health Professions 23 7%
Agricultural and Biological Sciences 19 6%
Pharmacology, Toxicology and Pharmaceutical Science 11 3%
Other 39 12%
Unknown 111 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 June 2019.
All research outputs
#4,517,025
of 25,998,826 outputs
Outputs from Diabetologia
#2,017
of 5,646 outputs
Outputs of similar age
#56,832
of 298,537 outputs
Outputs of similar age from Diabetologia
#26
of 78 outputs
Altmetric has tracked 25,998,826 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,646 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 24.1. This one has gotten more attention than average, scoring higher than 63% 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 298,537 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 80% of its contemporaries.
We're also able to compare this research output to 78 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 66% of its contemporaries.