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Defining Phenotypes in Asthma: A Step Towards Personalized Medicine

Overview of attention for article published in Drugs, May 2014
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Title
Defining Phenotypes in Asthma: A Step Towards Personalized Medicine
Published in
Drugs, May 2014
DOI 10.1007/s40265-014-0213-9
Pubmed ID
Authors

Kian Fan Chung

Abstract

Asthma is a common disease with a complex pathophysiology. It can present in various clinical forms and with different levels of severity. Unbiased cluster analytic methods have unravelled several phenotypes in cohorts representative of the whole spectrum of severity. Clusters of severe asthma include those on high-dose corticosteroid treatment, often with both inhaled and oral treatment, usually associated with severe airflow obstruction. Phenotypes with concordance between symptoms and sputum eosinophilia have been reported, including an eosinophilic inflammation-predominant group with few symptoms and late-onset disease who have a high prevalence of rhinosinusitis, aspirin sensitivity, and exacerbations. Sputum eosinophilia is also a biomarker that can predict therapeutic responses to antibody-based treatments to block the effects of the T-helper (Th)-2 cytokine, interleukin (IL)-5. Low Th2-expression has been predictive of poor therapeutic response to inhaled corticosteroid therapy. Current asthma schedules emphasise a step-up approach to treating asthma in relation to increasing severity, but, in more severe disease, phenotyping or endotyping of asthma will be necessary to determine new treatment strategies as severe asthma is recognized as being a particularly heterogeneous disease. Much less is known about 'non-eosinophilic' asthma. Phenotypic characterisation of corticosteroid insensitivity and chronic airflow obstruction of severe asthma is also needed. Phenotype-driven treatment of asthma will be further boosted by the advent of transcriptomic and proteomic technologies, with the application of systems biology or medicine approaches to defining phenotypes and biomarkers of disease and therapeutic response. This will pave the way towards personalized medicine and healthcare for asthma.

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The data shown below were collected from the profiles of 3 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 69 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%
Korea, Republic of 1 1%
United Kingdom 1 1%
Tunisia 1 1%
United States 1 1%
Unknown 63 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 19%
Other 6 9%
Student > Postgraduate 6 9%
Student > Master 6 9%
Student > Bachelor 5 7%
Other 16 23%
Unknown 17 25%
Readers by discipline Count As %
Medicine and Dentistry 31 45%
Agricultural and Biological Sciences 5 7%
Pharmacology, Toxicology and Pharmaceutical Science 3 4%
Biochemistry, Genetics and Molecular Biology 3 4%
Neuroscience 2 3%
Other 6 9%
Unknown 19 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 27 May 2015.
All research outputs
#13,915,028
of 22,755,127 outputs
Outputs from Drugs
#2,598
of 3,251 outputs
Outputs of similar age
#117,373
of 227,400 outputs
Outputs of similar age from Drugs
#27
of 38 outputs
Altmetric has tracked 22,755,127 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,251 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.8. This one is in the 19th percentile – i.e., 19% of its peers scored the same or lower than it.
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 227,400 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 38 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.