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. 2023 Jan 19;24(1):20.
doi: 10.1186/s12931-023-02318-4.

Principal component analysis of flow-volume curves in COPDGene to link spirometry with phenotypes of COPD

Kenneth Verstraete  1   2 , Nilakash Das  1 , Iwein Gyselinck  1 , Marko Topalovic  3 , Thierry Troosters  4 , James D Crapo  5 , Edwin K Silverman  6 , Barry J Make  5 , Elizabeth A Regan  5 , Robert Jensen  7 , Maarten De Vos  2   8 , Wim Janssens  9
Affiliations

Principal component analysis of flow-volume curves in COPDGene to link spirometry with phenotypes of COPD

Kenneth Verstraete et al. Respir Res. .

Abstract

Background: Parameters from maximal expiratory flow-volume curves (MEFVC) have been linked to CT-based parameters of COPD. However, the association between MEFVC shape and phenotypes like emphysema, small airways disease (SAD) and bronchial wall thickening (BWT) has not been investigated.

Research question: We analyzed if the shape of MEFVC can be linked to CT-determined emphysema, SAD and BWT in a large cohort of COPDGene participants.

Study design and methods: In the COPDGene cohort, we used principal component analysis (PCA) to extract patterns from MEFVC shape and performed multiple linear regression to assess the association of these patterns with CT parameters over the COPD spectrum, in mild and moderate-severe COPD.

Results: Over the entire spectrum, in mild and moderate-severe COPD, principal components of MEFVC were important predictors for the continuous CT parameters. Their contribution to the prediction of emphysema diminished when classical pulmonary function test parameters were added. For SAD, the components remained very strong predictors. The adjusted R2 was higher in moderate-severe COPD, while in mild COPD, the adjusted R2 for all CT outcomes was low; 0.28 for emphysema, 0.21 for SAD and 0.19 for BWT.

Interpretation: The shape of the maximal expiratory flow-volume curve as analyzed with PCA is not an appropriate screening tool for early disease phenotypes identified by CT scan. However, it contributes to assessing emphysema and SAD in moderate-severe COPD.

Keywords: COPD; Computed tomography; Maximal expiratory flow-volume curve; Principal component analysis.

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Conflict of interest statement

KV has nothing to disclose. ND has nothing to disclose. IG receives personal funding from Research Foundation Flanders (FWO). MT is CEO and co-founder of ArtiQ but received no payments related to the manuscript. TT has nothing to disclose. JDC received funding from NIH Grant Support R01-089897 and is chairman of the COPD Foundation Board of Directors. EKS received funding from NIH Grant Support and institutional grant support from GlaxoSmithKline and Bayer. BJM received funding from NHLBI, grants from AstraZeneca, GlaxoSmithKline and Pearl Research and fees from AstraZeneca, GlaxoSmithKline, Sunovion, Verona, Boehringer Ingelheim, Takeda, Third Pole, Phillips and Circasia. He is member of the NHLBI and Spiration Data Safety Monitoring Boards and chairman of Mt. Sinai SOM Data Safety Monitoring Board. ER received a COPDGene grant from NHBLI. RJ received consulting fees from National Jewish Health for an LLC he owns. MDV received funding from the AI in Flanders project. WJ received grants from AstraZeneca and Chiesi and obtained fees from AstraZeneca, Chiesi and GlaxoSmithKline. He is chairman of Board of Flemish Society for TBC prevention and board member of ArtiQ.

Figures

Fig. 1
Fig. 1
The mean MEFVC shapes per GOLD stage in panel A, the mean MEFVC shapes per CT-based phenotype in panel B and the mean MEFVC shapes per number of abnormalities on CT in panel C. B, bronchial wall thickening; E, emphysema; GOLD, Global Initiative for Chronic Obstructive Lung Disease; MEFVC, maximal expiratory flow-volume curve; S, small airways disease
Fig. 2
Fig. 2
A Variance explained by the principal components on the left, cumulative variance explained by the principal components on the right. B influence of the first four principal components visualized. The blue curve is the overall mean MEFVC shape. The green curve illustrates the influence of each principal component when the coefficient is increased to the 95th percentile, the red curve when the coefficient is decreased to the 5th percentile. Component 1 influences PEF and the descending limb without altering the angle of collapse or concavity. Component 2 pivots the descending limb around a fixed point, thereby also influencing PEF. Component 3 and 4 mainly model concavity in MEFVC
Fig. 3
Fig. 3
Percentage emphysema (PRMemph), percentage gas trapping (PRMfSAD) and bronchial wall thickening (Pi10) per group (never-smoked normal control subjects; mild COPD (GOLD I); moderate-severe COPD (GOLD II-III-IV)). The cut-offs (dashed lines) are determined by using the 95th percentile (upper limit of normal) on the control subjects
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