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Observational Study
. 2023 Sep;11(9):2803-2811.
doi: 10.1016/j.jaip.2023.05.013. Epub 2023 May 23.

Cluster Analyses From the Real-World NOVELTY Study: Six Clusters Across the Asthma-COPD Spectrum

Rod Hughes  1 , Eleni Rapsomaniki  2 , Aruna T Bansal  3 , Jørgen Vestbo  4 , David Price  5 , Alvar Agustí  6 , Richard Beasley  7 , Malin Fageras  8 , Marianna Alacqua  2 , Alberto Papi  9 , Hana Müllerová  2 , Helen K Reddel  10 ; NOVELTY Scientific Community; NOVELTY study investigators
Collaborators, Affiliations
Free article
Observational Study

Cluster Analyses From the Real-World NOVELTY Study: Six Clusters Across the Asthma-COPD Spectrum

Rod Hughes et al. J Allergy Clin Immunol Pract. 2023 Sep.
Free article

Abstract

Background: Asthma and chronic obstructive pulmonary disease (COPD) are complex diseases, the definitions of which 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 the NOVEL observational longiTudinal studY (NOVELTY; NCT02760329).

Methods: Two approaches were taken to variable selection using baseline data: approach A was data-driven, hypothesis-free and used the Pearson 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 3796 individuals (mean age, 59.5 years; 54% female); approach B included 2934 patients (mean age, 60.7 years; 53% female). Each identified 6 mathematically stable clusters, which had overlapping characteristics. Overall, 67% to 75% of patients with asthma were in 3 clusters, and approximately 90% of patients with COPD were in 3 clusters. Although traditional features such as 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 the approach A cluster membership were age, weight, childhood onset, prebronchodilator FEV1, duration of dust/fume exposure, and number of daily medications.

Conclusions: Cluster analyses in patients from NOVELTY 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.

Keywords: Asthma; Biomarkers; Chronic obstructive pulmonary disease; Cluster analysis; Precision medicine.

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