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 Agusti; Richard Beasley; Marianna Alacqua; Alberto Papi; Hana  Müllerová; Helen K.  Reddel; NOVELTY Scientific Community

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^* (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 journal › Article › peer-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 language	English
Pages (from-to)	2803-2811
Number of pages	9
Journal	The Journal of Allergy and Clinical Immunology: In Practice
Volume	11
Issue number	9
Early online date	23 May 2023
DOIs	https://doi.org/10.1016/j.jaip.2023.05.013
Publication status	Published - Sept 2023

Bibliographical note

Funding
The NOVELTY study is funded by AstraZeneca.

ACKNOWLEDGMENTS
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

Keywords

Precision medicine
asthma
biomarkers
chronic obstructive pulmonary disease
cluster analysis

Access to Document

10.1016/j.jaip.2023.05.013Licence: CC BY

Hughes_etal_TJACIIP_Cluster_Analyses_From_VOR
2023 The Authors. Published by Elsevier Inc. on behalf of the American Academy of Allergy, Asthma & Immunology. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Final published version, 375 KBLicence: CC BY

Cite this

Hughes, R., Rapsomaniki, E., Bansal, A. T., Vestbo, J., Price, D., Agusti, A., Beasley, R., Alacqua, M., Papi, A., Müllerová, H., Reddel, H. K., & NOVELTY Scientific Community (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

Hughes, R, Rapsomaniki, E, Bansal, AT, Vestbo, J, Price, D, Agusti, A, Beasley, R, Alacqua, M, Papi, A, Müllerová, H, Reddel, HK & NOVELTY Scientific Community 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

@article{7153ce7b22e844d09c1a53cf3f6418dc,

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

abstract = "BackgroundAsthma and chronic obstructive pulmonary disease (COPD) are complex diseases whose definitions overlap.ObjectiveTo investigate clustering of clinical/physiological features and readily available biomarkers in patients with physician-assigned diagnoses of asthma and/or COPD in NOVELTY (NCT02760329).MethodsTwo approaches were taken to variable selection, using baseline data: approach A was data119 driven, hypothesis-free, using Pearson{\textquoteright}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.ResultsApproach 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.ConclusionCluster 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. ",

keywords = "Precision medicine, asthma, biomarkers, chronic obstructive pulmonary disease, cluster analysis",

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

note = "Funding The NOVELTY study is funded by AstraZeneca. ACKNOWLEDGMENTS 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",

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",

issn = "2213-2198",

publisher = "Elsevier",

number = "9",

}

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 - Agusti, Alvar

AU - Beasley, Richard

AU - Alacqua, Marianna

AU - Papi, Alberto

AU - Müllerová, Hana

AU - Reddel, Helen K.

AU - NOVELTY Scientific Community

N1 - Funding The NOVELTY study is funded by AstraZeneca. ACKNOWLEDGMENTS 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

PY - 2023/9

Y1 - 2023/9

N2 - BackgroundAsthma and chronic obstructive pulmonary disease (COPD) are complex diseases whose definitions overlap.ObjectiveTo investigate clustering of clinical/physiological features and readily available biomarkers in patients with physician-assigned diagnoses of asthma and/or COPD in NOVELTY (NCT02760329).MethodsTwo 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.ResultsApproach 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.ConclusionCluster 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.

AB - BackgroundAsthma and chronic obstructive pulmonary disease (COPD) are complex diseases whose definitions overlap.ObjectiveTo investigate clustering of clinical/physiological features and readily available biomarkers in patients with physician-assigned diagnoses of asthma and/or COPD in NOVELTY (NCT02760329).MethodsTwo 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.ResultsApproach 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.ConclusionCluster 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.

KW - Precision medicine

KW - asthma

KW - biomarkers

KW - chronic obstructive pulmonary disease

KW - cluster analysis

U2 - 10.1016/j.jaip.2023.05.013

DO - 10.1016/j.jaip.2023.05.013

M3 - Article

C2 - 37230383

SN - 2213-2198

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

Access to Document

Fingerprint

Cite this