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Validated and longitudinally stable asthma phenotypes based on cluster analysis of the ADEPT study

Overview of attention for article published in Respiratory Research, December 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

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6 X users
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Article details
Title
Validated and longitudinally stable asthma phenotypes based on cluster analysis of the ADEPT study
Published in
Respiratory Research, December 2016
DOI 10.1186/s12931-016-0482-9
Pubmed ID
Authors

Matthew J. Loza, Ratko Djukanovic, Kian Fan Chung, Daniel Horowitz, Keying Ma, Patrick Branigan, Elliot S. Barnathan, Vedrana S. Susulic, Philip E. Silkoff, Peter J. Sterk, Frédéric Baribaud, For the ADEPT (Airways Disease Endotyping for Personalized Therapeutics) and U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Disease Outcome Consortium) investigators

Abstract

Asthma is a disease of varying severity and differing disease mechanisms. To date, studies aimed at stratifying asthma into clinically useful phenotypes have produced a number of phenotypes that have yet to be assessed for stability and to be validated in independent cohorts. The aim of this study was to define and validate, for the first time ever, clinically driven asthma phenotypes using two independent, severe asthma cohorts: ADEPT and U-BIOPRED. Fuzzy partition-around-medoid clustering was performed on pre-specified data from the ADEPT participants (n = 156) and independently on data from a subset of U-BIOPRED asthma participants (n = 82) for whom the same variables were available. Models for cluster classification probabilities were derived and applied to the 12-month longitudinal ADEPT data and to a larger subset of the U-BIOPRED asthma dataset (n = 397). High and low type-2 inflammation phenotypes were defined as high or low Th2 activity, indicated by endobronchial biopsies gene expression changes downstream of IL-4 or IL-13. Four phenotypes were identified in the ADEPT (training) cohort, with distinct clinical and biomarker profiles. Phenotype 1 was "mild, good lung function, early onset", with a low-inflammatory, predominantly Type-2, phenotype. Phenotype 2 had a "moderate, hyper-responsive, eosinophilic" phenotype, with moderate asthma control, mild airflow obstruction and predominant Type-2 inflammation. Phenotype 3 had a "mixed severity, predominantly fixed obstructive, non-eosinophilic and neutrophilic" phenotype, with moderate asthma control and low Type-2 inflammation. Phenotype 4 had a "severe uncontrolled, severe reversible obstruction, mixed granulocytic" phenotype, with moderate Type-2 inflammation. These phenotypes had good longitudinal stability in the ADEPT cohort. They were reproduced and demonstrated high classification probability in two subsets of the U-BIOPRED asthma cohort. Focusing on the biology of the four clinical independently-validated easy-to-assess ADEPT asthma phenotypes will help understanding the unmet need and will aid in developing tailored therapies. NCT01274507 (ADEPT), registered October 28, 2010 and NCT01982162 (U-BIOPRED), registered October 30, 2013.

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 176 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Sweden 1 <1%
United Kingdom 1 <1%
Unknown 174 99%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 27 15%
Student > Master 16 9%
Researcher 16 9%
Other 10 6%
Professor 10 6%
Other 24 14%
Unknown 73 41%
Readers by discipline
Readers by discipline Count As %
Medicine and Dentistry 49 28%
Biochemistry, Genetics and Molecular Biology 10 6%
Agricultural and Biological Sciences 7 4%
Pharmacology, Toxicology and Pharmaceutical Science 7 4%
Immunology and Microbiology 5 3%
Other 22 13%
Unknown 76 43%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 24 September 2020.
All research outputs
#2,625,344
of 31,634,113 outputs
Outputs from Respiratory Research
#293
of 3,671 outputs
Outputs of similar age
#40,397
of 438,720 outputs
Outputs of similar age from Respiratory Research
#5
of 55 outputs
Altmetric has tracked 31,634,113 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,671 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.2. This one has done particularly well, scoring higher than 91% 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 438,720 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 55 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.