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Diagnostic approaches for diabetic cardiomyopathy

Overview of attention for article published in Cardiovascular Diabetology, February 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 (83rd percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

Mentioned by

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17 X users
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2 Wikipedia pages

Citations

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183 Dimensions

Readers on

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230 Mendeley
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Title
Diagnostic approaches for diabetic cardiomyopathy
Published in
Cardiovascular Diabetology, February 2017
DOI 10.1186/s12933-017-0506-x
Pubmed ID
Authors

A. Lorenzo-Almorós, J. Tuñón, M. Orejas, M. Cortés, J. Egido, Ó. Lorenzo

Abstract

Diabetic cardiomyopathy (DCM) is a cardiac dysfunction which affects approximately 12% of diabetic patients, leading to overt heart failure and death. However, there is not an efficient and specific methodology for DCM diagnosis, possibly because molecular mechanisms are not fully elucidated, and it remains asymptomatic for many years. Also, DCM frequently coexists with other comorbidities such as hypertension, obesity, dyslipidemia, and vasculopathies. Thus, human DCM is not specifically identified after heart failure is established. In this sense, echocardiography has been traditionally considered the gold standard imaging test to evaluate the presence of cardiac dysfunction, although other techniques may cover earlier DCM detection by quantification of altered myocardial metabolism and strain. In this sense, Phase-Magnetic Resonance Imaging and 2D/3D-Speckle Tracking Echocardiography may potentially diagnose and stratify diabetic patients. Additionally, this information could be completed with a quantification of specific plasma biomarkers related to related to initial stages of the disease. Cardiotrophin-1, activin A, insulin-like growth factor binding protein-7 (IGFBP-7) and Heart fatty-acid binding protein have demonstrated a stable positive correlation with cardiac hypertrophy, contractibility and steatosis responses. Thus, we suggest a combination of minimally-invasive diagnosis tools for human DCM recognition based on imaging techniques and measurements of related plasma biomarkers.

X Demographics

X Demographics

The data shown below were collected from the profiles of 17 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 230 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 230 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 28 12%
Researcher 27 12%
Student > Ph. D. Student 26 11%
Student > Master 24 10%
Student > Doctoral Student 17 7%
Other 37 16%
Unknown 71 31%
Readers by discipline Count As %
Medicine and Dentistry 78 34%
Biochemistry, Genetics and Molecular Biology 25 11%
Pharmacology, Toxicology and Pharmaceutical Science 13 6%
Agricultural and Biological Sciences 11 5%
Nursing and Health Professions 5 2%
Other 20 9%
Unknown 78 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 November 2023.
All research outputs
#2,913,353
of 25,732,188 outputs
Outputs from Cardiovascular Diabetology
#233
of 1,688 outputs
Outputs of similar age
#52,291
of 325,337 outputs
Outputs of similar age from Cardiovascular Diabetology
#6
of 29 outputs
Altmetric has tracked 25,732,188 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,688 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.6. This one has done well, scoring higher than 86% 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 325,337 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 83% of its contemporaries.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.