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, NOVELTY study investigators

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Background
Asthma and chronic obstructive pulmonary disease (COPD) are complex diseases whose definitions overlap.
Objective
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).
Methods
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.
Results
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.
Conclusion
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
JournalThe Journal of Allergy and Clinical Immunology: In Practice
Early online date23 May 2023
DOIs
Publication statusE-pub ahead of print - 23 May 2023

Keywords

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

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