Cluster analyses from the real-world NOVELTY study: six clusters across the asthma COPD spectrum

Rod Hughes* (Corresponding Author), Eleni Rapsomaniki, Aruna T Bansal, Jørgen Vestbo, David Price, Alvar Agusti, Richard Beasley, Marianna Alacqua, Alberto Papi, Hana Müllerová, Helen K. Reddel* (Corresponding Author), NOVELTY Scientific Community

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Asthma and chronic obstructive pulmonary disease (COPD) are complex diseases whose definitions overlap.
To investigate clustering of clinical/physiological features and readily available biomarkers in patients with physician-assigned diagnoses of asthma and/or COPD in NOVELTY (NCT02760329).
Two approaches were taken to variable selection, using baseline data: approach A was data119 driven, hypothesis-free, using Pearson’s dissimilarity matrix; approach B used an unsupervised Random Forest guided by clinical input. Cluster analyses were conducted across 100 random resamples using partitioning around medoids, followed by consensus clustering.
Approach A included 3,796 individuals (mean age 59.5 years, 54% female); approach B included 2,934 patients (mean age 60.7 years, 53% female). Each identified six mathematically stable clusters, which had overlapping characteristics. Overall, 67–75% of asthma patients were in three clusters, and ~90% of COPD patients in three clusters.
Although traditional features like allergies and current/ex-smoking (respectively) were higher in these clusters, there were differences between clusters and approaches in features such as sex, ethnicity, breathlessness, frequent productive cough and blood cell counts. The strongest predictors of approach A cluster membership were age, weight, childhood onset, prebronchodilator FEV1, duration of dust/fume exposure and number of daily medications.
Cluster analyses in NOVELTY patients with asthma and/or COPD yielded identifiable clusters, with several discriminatory features that differed from conventional diagnostic characteristics. The overlap between clusters suggests that they do not reflect discrete underlying mechanisms, and points to the need for identification of molecular endotypes and potential treatment targets across asthma and/or COPD.
Original languageEnglish
Pages (from-to)2803-2811
Number of pages9
JournalThe Journal of Allergy and Clinical Immunology: In Practice
Issue number9
Early online date23 May 2023
Publication statusPublished - Sept 2023

Bibliographical note

The NOVELTY study is funded by AstraZeneca.

The authors would like to thank the patients who participated in this study and the NOVELTY Scientific Community and the NOVELTY study investigators who are listed in full in Tables E7 and E8 in the Online Repository. Medical writing support, under the direction of the authors, was provided by Richard Knight, PhD, CMC Connect, a division of IPG Health Medical Communications, funded by AstraZeneca in accordance with Good Publication Practice (GPP 2022) guidelines (Ann Intern Med. 2022;175[9]:1298-1304). J. Vestbo is supported by the NIHR Manchester Biomedical Research Centre and the NIHR Manchester Clinical Research Facility


  • Precision medicine
  • asthma
  • biomarkers
  • chronic obstructive pulmonary disease
  • cluster analysis


Dive into the research topics of 'Cluster analyses from the real-world NOVELTY study: six clusters across the asthma COPD spectrum'. Together they form a unique fingerprint.

Cite this