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

Rod Hughes; Eleni Rapsomaniki; Aruna T Bansal; Jørgen Vestbo; David Price; Alvar Agustí; Richard Beasley; Malin Fageras; Marianna Alacqua; Alberto Papi; Hana Müllerová; Helen K Reddel; NOVELTY Scientific Community; Ulla Møller Weinreich

doi:10.1016/j.jaip.2023.05.013

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

Rod Hughes^*, Eleni Rapsomaniki, Aruna T Bansal, Jørgen Vestbo, David Price, Alvar Agustí, Richard Beasley, Malin Fageras, Marianna Alacqua, Alberto Papi, Hana Müllerová, Helen K Reddel^*, NOVELTY Scientific Community, Ulla Møller Weinreich (Member of study group)

^*Corresponding author for this work

Research output: Contribution to journal › Journal article › Research › peer-review

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.

Original language	English
Journal	The journal of allergy and clinical immunology. In practice
Volume	11
Issue number	9
Pages (from-to)	2803-2811
Number of pages	9
DOIs	https://doi.org/10.1016/j.jaip.2023.05.013
Publication status	Published - Sept 2023

Bibliographical note

Keywords

Female
Humans
Male
Middle Aged
Asthma/diagnosis
Cluster Analysis
Longitudinal Studies
Pulmonary Disease, Chronic Obstructive/diagnosis
Smoking

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Hughes, R., Rapsomaniki, E., Bansal, A. T., Vestbo, J., Price, D., Agustí, A., Beasley, R., Fageras, M., Alacqua, M., Papi, A., Müllerová, H., Reddel, H. K., NOVELTY Scientific Community, & Weinreich, U. M. (2023). Cluster Analyses From the Real-World NOVELTY Study: Six Clusters Across the Asthma-COPD Spectrum. The journal of allergy and clinical immunology. In practice, 11(9), 2803-2811. https://doi.org/10.1016/j.jaip.2023.05.013

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

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 = "Female, Humans, Male, Middle Aged, Asthma/diagnosis, Cluster Analysis, Longitudinal Studies, Pulmonary Disease, Chronic Obstructive/diagnosis, Smoking",

author = "Rod Hughes and Eleni Rapsomaniki and Bansal, {Aruna T} and J{\o}rgen Vestbo and David Price and Alvar Agust{\'i} and Richard Beasley and Malin Fageras and Marianna Alacqua and Alberto Papi and Hana M{\"u}llerov{\'a} and Reddel, {Helen K} and {NOVELTY Scientific Community} and Weinreich, {Ulla M{\o}ller}",

year = "2023",

month = sep,

doi = "10.1016/j.jaip.2023.05.013",

language = "English",

volume = "11",

pages = "2803--2811",

journal = "The journal of allergy and clinical immunology. In practice",

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Hughes, R, Rapsomaniki, E, Bansal, AT, Vestbo, J, Price, D, Agustí, A, Beasley, R, Fageras, M, Alacqua, M, Papi, A, Müllerová, H, Reddel, HK, NOVELTY Scientific Community & Weinreich, UM 2023, 'Cluster Analyses From the Real-World NOVELTY Study: Six Clusters Across the Asthma-COPD Spectrum', The journal of allergy and clinical immunology. In practice, vol. 11, no. 9, pp. 2803-2811. https://doi.org/10.1016/j.jaip.2023.05.013

TY - JOUR

T1 - Cluster Analyses From the Real-World NOVELTY Study

T2 - Six Clusters Across the Asthma-COPD Spectrum

AU - Hughes, Rod

AU - Rapsomaniki, Eleni

AU - Bansal, Aruna T

AU - Vestbo, Jørgen

AU - Price, David

AU - Agustí, Alvar

AU - Beasley, Richard

AU - Fageras, Malin

AU - Alacqua, Marianna

AU - Papi, Alberto

AU - Müllerová, Hana

AU - Reddel, Helen K

AU - NOVELTY Scientific Community

A2 - Weinreich, Ulla Møller

PY - 2023/9

Y1 - 2023/9

N2 - 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.

AB - 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.

KW - Female

KW - Humans

KW - Male

KW - Middle Aged

KW - Asthma/diagnosis

KW - Cluster Analysis

KW - Longitudinal Studies

KW - Pulmonary Disease, Chronic Obstructive/diagnosis

KW - Smoking

U2 - 10.1016/j.jaip.2023.05.013

DO - 10.1016/j.jaip.2023.05.013

M3 - Journal article

C2 - 37230383

SN - 2213-2201

VL - 11

SP - 2803

EP - 2811

JO - The journal of allergy and clinical immunology. In practice

JF - The journal of allergy and clinical immunology. In practice

IS - 9

ER -

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

Abstract

Bibliographical note

Keywords

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