
==== Front
J Appl Physiol (1985)
J Appl Physiol (1985)
J Appl Physiol (1985)
JAPPL
Journal of Applied Physiology
8750-7587
1522-1601
American Physiological Society Rockville, MD

38420676
JAPPL-00694-2022
JAPPL-00694-2022
10.1152/japplphysiol.00694.2022
Research Article
Airway tree caliber heterogeneity and airflow obstruction among older adults
AIRWAY TREE CALIBER HETEROGENEITY AND AIRFLOW OBSTRUCTION
https://orcid.org/0000-0003-0651-6537
Vameghestahbanati Motahareh 1
Kingdom Leina 1
https://orcid.org/0000-0001-8456-9437
Hoffman Eric A. 2
Kirby Miranda 3
Allen Norrina B. 4
Angelini Elsa 5 6
Bertoni Alain 7
Hamid Qutayba 1 8
Hogg James C. 9
Jacobs David R. Jr. 10
https://orcid.org/0000-0003-3797-0628
Laine Andrew 6
Maltais Francois 11
Michos Erin D. 12
Sack Coralynn 13
https://orcid.org/0000-0002-0756-6643
Sin Don 9
Watson Karol E. 14
Wysoczanksi Artur 6
Couper David 15
https://orcid.org/0000-0002-6314-0903
Cooper Christopher 14
Han Meilan 16
Woodruff Prescott 17
Tan Wan C. 9
Bourbeau Jean 1
Barr R. Graham 6
https://orcid.org/0000-0002-0619-5799
Smith Benjamin M. 1 6
on behalf of investigators from the Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study; the Canadian Cohort of Obstructive Lung Disease (CanCOLD); and the Subpopulations and Intermediate Outcome Measures in Chronic Obstructive Pulmonary Disease Study (SPIROMICS)
1Department of Medicine, McGill University , Montreal, Quebec, Canada
2Department of Radiology, University of Iowa , Iowa City, Iowa, United States
3Department of Physics, Ryerson University , Toronto, Ontario, Canada
4Center for Translational Metabolism and Health, Institute for Public Health and Medicine, Northwestern University , Chicago, Illinois, United States
5Faculty of Medicine, Imperial College London , London, United Kingdom
6Department of Medicine, Columbia University , New York, New York, United States
7Department of Public Health Sciences, Wake Forest University , Winston-Salem, North Carolina, United States
8Faculty of Medicine, University of Sharjah , Sharjah, United Arab Emirates
9Centre for Heart Lung Innovation, University of British Columbia , Vancouver, British Columbia, Canada
10School of Public Health, University of Minnesota , Minneapolis, Minnesota, United States
11Faculty of Medicine , University of Laval , Laval, Quebec, Canada
12Faculty of Medicine, Johns Hopkins University , Baltimore, Maryland, United States
13Department of Medicine, University of Washington , Seattle, Washington, United States
14Department of Medicine, University of California , Los Angeles, California, United States
15Department of Biostatistics, University of North Carolina , North Carolina, United States
16Division of Pulmonary and Critical Care Medicine, University of Michigan , Ann Arbor, Michigan, United States
17Division of Pulmonary and Critical Care Medicine, University of California , San Francisco, California, United States
Correspondence: B. M. Smith (benjamin.m.smith@mcgill.ca).
1 5 2024
29 2 2024
29 2 2024
136 5 11441156
15 11 2022
7 2 2024
22 2 2024
Copyright © 2024 The Authors.
2024
The Authors.
https://creativecommons.org/licenses/by/4.0/ Licensed under Creative Commons Attribution CC-BY 4.0. Published by the American Physiological Society.

Smaller mean airway tree caliber is associated with airflow obstruction and chronic obstructive pulmonary disease (COPD). We investigated whether airway tree caliber heterogeneity was associated with airflow obstruction and COPD. Two community-based cohorts (MESA Lung, CanCOLD) and a longitudinal case-control study of COPD (SPIROMICS) performed spirometry and computed tomography measurements of airway lumen diameters at standard anatomical locations (trachea-to-subsegments) and total lung volume. Percent-predicted airway lumen diameters were calculated using sex-specific reference equations accounting for age, height, and lung volume. The association of airway tree caliber heterogeneity, quantified as the standard deviation (SD) of percent-predicted airway lumen diameters, with baseline forced expired volume in 1-second (FEV1), FEV1/forced vital capacity (FEV1/FVC) and COPD, as well as longitudinal spirometry, were assessed using regression models adjusted for age, sex, height, race-ethnicity, and mean airway tree caliber. Among 2,505 MESA Lung participants (means ± SD age: 69 ± 9 yr; 53% female, mean airway tree caliber: 99 ± 10% predicted, airway tree caliber heterogeneity: 14 ± 5%; median follow-up: 6.1 yr), participants in the highest quartile of airway tree caliber heterogeneity exhibited lower FEV1 (adjusted mean difference: −125 mL, 95%CI: −171,−79), lower FEV1/FVC (adjusted mean difference: −0.01, 95%CI: −0.02,−0.01), and higher odds of COPD (adjusted odds ratio: 1.42, 95%CI: 1.01–2.02) when compared with the lowest quartile, whereas longitudinal changes in FEV1 and FEV1/FVC did not differ significantly. Observations in CanCOLD and SPIROMICS were consistent. Among older adults, airway tree caliber heterogeneity was associated with airflow obstruction and COPD at baseline but was not associated with longitudinal changes in spirometry.

NEW & NOTEWORTHY In this study, by leveraging two community-based samples and a case-control study of heavy smokers, we show that among older adults, airway tree caliber heterogeneity quantified by CT is associated with airflow obstruction and COPD independent of age, sex, height, race-ethnicity, and dysanapsis. These observations suggest that airway tree caliber heterogeneity is a structural trait associated with low baseline lung function and normal decline trajectory that is relevant to COPD.

airflow obstruction
; airway tree caliber heterogeneity
; chronic obstructive pulmonary disease
; computed tomography
; Vanier Canada Graduate Scholarship Motahareh VameghestahbanatiGouvernement du Canada | Canadian Institutes of Health Research (CIHR) 10.13039/501100000024 K23ES030725 Coralynn SackHHS | NIH | National Heart, Lung, and Blood Institute (NHLBI) 10.13039/100000050 R01-HL077612, R01-HL093081 R. Graham BarrHHS | NIH | National Heart, Lung, and Blood Institute (NHLBI) 10.13039/100000050 R01-HL130506 Benjamin M. SmithGouvernement du Canada | Canadian Institutes of Health Research (CIHR) 10.13039/501100000024 PJT-162335 Benjamin M. Smith
==== Body
pmcINTRODUCTION

Chronic obstructive pulmonary disease (COPD) is a leading cause of death and disability worldwide (1). Tobacco smoking is a major COPD risk factor (2), but despite decades of declining smoking rates in many countries (3–5), the corresponding decreases in disease burden have been modest (6, 7). Although other factors have been linked to COPD (e.g., secondhand smoke, air pollutants, asthma), emerging evidence suggests that host factors, such as lung development may play a central role (2, 8, 9).

The airway tree forms early in life and variation in airway tree structure is common among adults in the general population (9). Structural properties of the airway tree influence airway resistance and flow (10–13), and smaller mean airway tree caliber quantified by computed tomography (CT) is associated with COPD independent of tobacco smoking and other risk factors, but not with accelerated lung function decline (9). This observation is consistent with the trajectory of low early-life lung function followed by normal lung function decline that accounts for up to 50% of COPD encountered later in life (14), but the relevance of other aspects of the native airway tree structure to COPD remain poorly understood.

Both computational studies of airway tree fluid dynamics and in vivo inert gas washout studies of ventilation maldistribution suggest that heterogeneity of conducting airway tree caliber contribute to obstructive lung disease pathophysiology (15–23). We note, however, that computational modeling study inputs are often informed by airway tree measurements obtained from just a handful of donor lungs with limited clinical/phenotypic characterization (24). Moreover, in vivo inert gas washout studies have made inferences about underlying mechanisms, such as conduction-dependent inhomogeneity in the central conducting airways, but rarely quantify conducting airway tree structure in vivo. Indeed, a consensus statement for inert gas washout measurement summarizing the mechanism of convection-dependent inhomogeneity in airways proximal to terminal bronchioles references a single study that quantified acinar airway dimensions rather than conducting airway dimensions from two human cadavers for which subject age, sex, and lung disease status were not reported (25, 26). A small but growing number of hyperpolarized gas imaging studies have overcome some of these limitations and demonstrated that regional gas distribution deficits tend to correlate with corresponding conducting airway lumen caliber (27–31) but the empiric distribution of conducting airway tree caliber heterogeneity in the general population and its potential clinical relevance to spirometry-assessed airflow obstruction independent of mean airway tree caliber remains uncertain.

This study sought to characterize the extent of airway tree caliber heterogeneity among nonsmoking adults free of standard COPD risk factors using CT. Next, we tested the hypothesis that airway tree caliber heterogeneity would be associated with baseline airflow obstruction and COPD independent of mean airway tree caliber but would not be associated with prospective lung function decline.

METHODS

Study Participants

Data from two community-based cohorts [the Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study and the Canadian Cohort of Obstructive Lung Disease (CanCOLD)] were used to characterize airway tree caliber heterogeneity. Data from these cohorts and from a longitudinal case-control study of smokers with and without COPD [the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS)] were then used to evaluate the association of airway tree caliber heterogeneity with airflow obstruction and COPD. Institutional review board approval was obtained at each study site. All participants provided written informed consent.

MESA is a prospective community-based study that recruited 6,814 non-Hispanic whites, African Americans, Hispanics, and Chinese Americans 45–84 yr of age in 2000–2002 (exam 1) from the general population in six US communities (32). MESA excluded individuals if they had a clinical diagnosis of cardiovascular disease, weight >300 pounds, or impediment to long-term follow-up at the baseline visit (2000–2002). The MESA Lung Study enrolled participants sampled from MESA who consented to genetic analyses and completed an examination in 2004–2006 (exam 3/4) (33) and all participants in the MESA Air Study, which enrolled additional participants of comparable age from the same study sites and who were free of clinical cardiovascular disease in 2005–2007 (34). The MESA Lung Study performed full-lung CT and spirometry in 2010–2012 (exam 5) and 2016–2018 (exam 6). The current study used measures obtained at MESA Visits 5 and 6 (2010–2018). Thus, participants may have developed clinical cardiovascular disease by the time of assessment.

The Canadian Chronic Obstructive Lung Disease (COLD) prevalence study used census data to recruit a random sample of noninstitutionalized adults 40 yr and older from nine Canadian communities in 2005–2009 (35). In 2010–2014, CanCOLD, a nested community-based case-control study enrolled COLD participants with COPD, in addition to representative random subsets of COLD nonsmoking participants and smoking participants without COPD matched on age and sex, performed full-lung CT and spirometry, with 18 and 36-mo follow-up assessments (2011–2017). CanCOLD excluded institutionalized individuals but did not exclude individuals based on the presence or absence of disease (35).

SPIROMICS is a longitudinal case-control study that recruited participants with and without COPD, 40–80 yr of age reporting 20+ pack-yr of smoking, in addition to nonsmoking participants, at 12 US medical centers in 2010–2015, and performed full-lung CT and spirometry with up to four follow-up assessments in 2011–2019 (36). SPIROMICS excluded individuals with chronic lung diseases other than COPD or asthma, body mass index greater than 40 kg/m2, or prior surgical lung resection.

Airway Tree Caliber Heterogeneity

All participants underwent full-lung CT scan at suspended maximum inspiration according to standardized protocols (9). In all studies, total lung volume and airway lumen diameters were measured using Apollo Software (VIDA Diagnostics, Coralville, Iowa) (8, 11, 37). Briefly, airway lumen diameters at 19 standard anatomical locations (trachea, right mainstem, left mainstem, bronchus intermedius, right upper lobe, right middle lobe, right lower lobe, left upper lobe, left lower lobe, RB1, RB4, RB10, LB1, and LB10 bronchi), as well as the average airway lumen diameters of the subsegments along each of the five pre-specified paths (sRB1, sRB4, sRB10, sLB1, and sLB10) and total lung volume were measured by trained technologists unaware of other participant information. The scan rescan reproducibility of these measures was excellent (9, 38). For each of the 19 standard anatomical airways, the percent-predicted airway caliber was calculated using externally validated sex-stratified airway-specific reference equations derived from a general population sample of adults free of COPD risk factors (i.e., people who never smoked cigarettes, pipes, or cigars, who never lived or worked with someone who smoked indoors, who never worked a job that exposed them to vapor-gas dust or fumes, and who never had a diagnosis of asthma) (39). The sex-stratified airway-specific reference equations included spline terms for total lung volume, age, and body height (9). (see Airway lumen diameter reference equations section in appendix- https://figshare.com/s/20cb38b57ba3af1078a9). For each participant, the distribution of percent-predicted airway lumen diameters was approximately Gaussian (see results), therefore airway tree caliber heterogeneity was quantified as the standard deviation (SD) of the 19 percent-predicted airway lumen diameters. Participant mean airway tree caliber was quantified as the mean of the 19 percent-predicted airway lumen diameters.

Airflow Obstruction and COPD

Spirometry was performed in all studies following American Thoracic Society Standards (40). COPD was defined based on a postbronchodilator forced expired volume in 1-s (FEV1)/forced vital capacity (FVC) <0.7 (2, 41) and lower limit of normal (42). In sensitivity analysis, COPD was defined as FEV1/FVC < 0.7 with respiratory symptoms (2) defined by COPD Assessment Test Score of 10 or more (range, 0–40) (43), presence of chronic bronchitis (yes/no) (44), or modified Medical Research Council dyspnea score higher than 0 (range, 0–4) (45).

Other Variables

Age, sex, tobacco smoking status (cigarette, pipe, cigar), second-hand smoke exposure, occupational exposure status to vapor-gas dust or fumes, and physician diagnosis of asthma were self-reported. Race/ethnicity was assessed by self-report using fixed-category questionnaire items. Where questionnaire items differed by study, a harmonized variable was defined (9). Height and weight were measured using standardized protocols in all studies. Pack-years of smoking were calculated by multiplying the number of years smoked by the mean number of daily cigarettes divided by 20.

Statistical Analysis

Participant characteristics were summarized by the study. CanCOLD participant characteristics and analyses were weighted by the inverse probability of selection from the COLD study to provide estimates representative of this population-based sample. Multiple imputation was used to account for missing postbronchodilator spirometry in MESA Lung (9). Given little missing data in the other studies, missing covariate data were assigned an indicator variable.

We first characterized airway tree caliber heterogeneity among the community-based nonsmoking participants who were free of secondhand or occupational exposures, or asthma. The distribution of the 19 percent-predicted airway lumen diameters in each participant was depicted using a kernel density function and, based on the Gaussian-like distribution (see results), participant airway tree caliber heterogeneity was quantified as the SD of percent-predicted airway lumen diameters. The Spearman correlation between airway tree caliber heterogeneity and mean airway tree caliber was computed.

Next, the associations of airway tree caliber heterogeneity with baseline FEV1 and FEV1/FVC were assessed using linear regression models. Airway tree caliber heterogeneity was analyzed by quartile (with the lowest airway tree caliber heterogeneity quartile as the reference group) and as a continuous variable. Models were adjusted for age, age2, sex, height, height2, race-ethnicity, and mean airway tree caliber (model 1; main model) and additionally adjusted for primary tobacco, secondhand smoke and occupational exposures, and asthma (model 2). The association between airway tree caliber heterogeneity and baseline COPD status was assessed using logistic regression with the same covariables described earlier.

Airway tree caliber heterogeneity associations with longitudinal change in FEV1 and FEV1/FVC were assessed using mixed model regression with random intercepts and autoregressive covariance structure and were adjusted as above with smoking status as a time-varying covariate. The product term between time and airway tree caliber heterogeneity was the variable of interest.

Sensitivity analyses included 1) the use of restricted cubic splines to assess for nonlinear associations, 2) analysis of the unweighted CanCOLD sample, and the unimputed MESA sample, 3) stratification by cigarette smoking status (never/ever) in community-based samples, 4) quantification of airway tree caliber heterogeneity using the coefficient of variation (SD of percent-predicted airway lumen diameters divided by the mean of percent-predicted airway lumen diameters), a measure that combines airway tree caliber heterogeneity and mean airway tree caliber into a single ratio measure, 5) a race-ethnicity-stratified analysis of MESA Lung, 6) analyses additionally adjusted for the study site, and 7) association of airway tree caliber heterogeneity with COPD defined by spirometry and respiratory symptoms (9).

All analyses were performed using SAS 9.4 (Raleigh, NC). A two-sided P value < 0.05 was considered statistically significant.

RESULTS

A flowchart of participant selection for the present analysis is depicted in Supplemental Fig. S1 (all Supplemental material is available at https://doi.org/10.6084/m9.figshare.23298158) and participant characteristics by study are summarized in Table 1.

Table 1. Participant characteristics by study

	MESA Lung	CanCOLD	Spiromics	
All Participants	Participants Free of Standard COPD Risk Factors1	All Participants	Participants Free of Standard COPD Risk Factors	Participants with 20+ pack-yr	
No. of participants	2,505	289	1,297	204	2,730	
Age, means ± SD yr	69 ± 9	65 ± 8	67 ± 10	67 ± 12	63 ± 9	
Sex, No. (%)						
 Female	1,317 (53)	169 (58)	579 (45)	92 (45)	1,253 (46)	
 Male	1,188 (47)	120 (42)	718 (55)	112 (55)	1,477 (54)	
Height, means ± SD, cm	165 ± 10	164 ± 9	168 ± 9	167 ± 11	170 ± 10	
Race-ethnicity, No. (%)						
 Non-Hispanic White	972 (39)	79 (27)	1,215 (94)	183 (90)	2,018 (74)	
 Non-Hispanic Black	649 (26)	55 (19)	15 (1)	7 (3)	493 (18)	
 Hispanic	549 (22)	58 (20)	6 (0.5)	2 (0.8)	127 (5)	
 Non-Hispanic Chinese	335 (13)	97 (34)	44 (3)	13 (6)	26 (1)	
 Other	0 (0)	0 (0)	17 (1)	0 (0)	66 (2)	
Smoking status, No. (%)						
 Never	1,216 (49)	289 (100)	614 (47)	204 (100)	0 (0)	
 Former	1,073 (43)	0 (0)	511 (39)	0 (0)	1,652 (61)	
 Current	216 (9)	0 (0)	172 (13)	0 (0)	1,078 (39)	
Pack-yr among ever-smoking participants, median (IQR)	19 (7, 37)	0 (0, 0)	19 (6, 35)	0 (0, 0)	43 (31, 60)	
Pipe/cigar smoking, ever, No. (%)	186 (7)	0 (0)	159 (12)	0 (0)	230 (10)	
Second-hand smoke exposure in adulthood, ever, No. (%)	1,244 (50)	0 (0)	510 (39)	0 (0)	1,136 (42)	
Occupational exposure to vape, dust, gas or fume, No. (%)	987 (39)	0 (0)	117 (9)	0 (0)	1,145 (42)	
Asthma diagnosis ever, No. (%)	208 (8)	0 (0)	234 (18)	0 (0)	548 (20)	
Baseline spirometry						
 Prebronchodilator FEV1, L, means ± SD	2.3 ± 0.7	2.3 ± 0.7	2.5 ± 0.9	2.6 ± 0.9	2.0 ± 0.9	
 Prebronchodilator FEV1/FVC, mean ± SD	0.74 ± 0.08	0.76 ± 0.09	0.71 ± 0.08	0.74 ± 0.08	0.58 ± 0.16	
 COPD2 prevalence, No. (%)	471 (19)	27 (9)	314 (25)	33 (16)	1,816 (67)	
Follow-up spirometry analysis after baseline airway tree caliber heterogeneity assessment						
 No. of participants	2,505	257	1,043	172	2,144	
 Total follow-up interval, median (25th, 75th percentile), y	6.1 (6.0, 6.4)	6.1 (5.8, 6.4)	3.0 (1.8, 3.3)	3.0 (1.8, 3.3)	5.0 (2.0, 6.0)	
 No. of follow-up spirometry assessments, median (IQR)	1 (1, 1)	1 (1,1)	2 (1, 3)	2 (1,3)	3 (2, 4)	
 Change in FEV1/FVC, means ± SD/yr	−0.00 ± 0.01	−0.00 ± 0.01	−0.00 ± 0.02	−0.00 ± 0.02	−0.01 ± 0.03	
 Change in FEV1, means ± SD, mL/yr	−33 ± 31	−31 ± 35	−36 ± 75	−30 ± 87	−40 ± 137	
Percent-predicted airway tree caliber,3 means ± SD	99 ± 10	99 ± 9	96 ± 9	98 ± 12	98 ± 11	
Airway tree caliber heterogeneity was quantified as the standard deviation (SD) of percent-predicted airway diameters measured at 19 standard anatomic locations. COPD, chronic obstructive lung disease; CanCOLD, Canadian cohort of obstructive lung disease; FEV1, forced expired volume in 1 second; FVC, forced vital capacity; MESA, Multi-Ethnic study of atherosclerosis; SPIROMICS, Subpopulations and Intermediate Outcome Measures in COPD Study.

1Standard COPD risk factors included tobacco smoking (cigarettes, pipes, or cigars), occupational exposure to vapor-gas dust or fumes, or asthma.

2COPD is defined by postbronchodilator FEV1/FVC < 0.7.

3The percent-predicted airway tree caliber for each participant was quantified as the geometric mean of percent-predicted airway lumen diameters measured at 19 standard anatomical locations defined by externally validated sex-stratified airway-specific lumen diameter reference equations with terms for total lung volume, age, and height.

Among the 2,505 included MESA Lung participants, the means ± SD age was 69 ± 9 yr, 53% were female, and race-ethnic proportions were 39% non-Hispanic white, 26% non-Hispanic black, 22% Hispanic, and 13% non-Hispanic Chinese. The mean FEV1/FVC was 0.74 ± 0.08 and mean airway tree caliber was 99 ± 10% predicted. Over a median of 6.1-yr follow-up interval, the change in FEV1 and FEV1/FVC was −33 ± 31 mL/yr and −0.002 ± 0.008 per yr, respectively. Compared with included participants, excluded MESA participants differed slightly by age and smoking history (Supplemental Table S1).

Among 1,297 included CanCOLD participants, the mean age was 67 ± 10 yr, 45% were female, and 94% were non-Hispanic white. The mean FEV1/FVC was 0.71 ± 0.08 and mean airway tree caliber was 96 ± 9% predicted. Over a median of 3.0-yr follow-up interval, the change in FEV1 and FEV1/FVC was −36 ± 75 mL/yr and −0.003 ± 0.020 per yr, respectively. Excluded CanCOLD participants were more likely to be female and have fewer pack-years of smoking (Supplemental Table S1).

Among 2,730 included SPIROMICS participants with 20+ pack-yr of smoking history, the mean age was 63 ± 9 yr, 46% were female, 74% were non-Hispanic white, and 18% non-Hispanic black. The mean FEV1/FVC was 0.58 ± 0.16 and mean airway tree caliber was 98 ± 11% predicted. Over a median of 5.0-yr follow-up interval, the change in FEV1 and FEV1/FVC was −40 ± 137 mL/yr and −0.009 ± 0.032 per year, respectively. Excluded SPIROMICS participants were more likely to be female and report fewer pack-years of smoking (Supplemental Table S1).

Airway Tree Caliber Heterogeneity in the Absence of Standard COPD Risk Factors

The community-based nonsmoking participants without secondhand/occupational exposures or asthma included 289 MESA Lung participants (mean age 65 ± 8 yr, 58% female, mean FEV1/FVC 0.76 ± 0.09, mean airway tree caliber were 99 ± 9% predicted) and 204 CanCOLD participants (mean age 67 ± 11 yr, 41% female, mean FEV1/FVC 0.74 ± 0.08 mean airway tree caliber 96 ± 10% predicted) (Table 1).

The distribution of percent-predicted airway lumen diameters of each participant free of COPD risk factors and participants with COPD appeared Gaussian (Fig. 1 and Supplemental Fig. S2). Thus, airway tree caliber heterogeneity for each participant was quantified as the SD of their percent-predicted airway lumen diameters (Fig. 2). The Spearman correlation coefficient between airway tree caliber heterogeneity and mean airway tree caliber was 0.17 (95%CI: 0.05, 0.28; P = 0.004) in MESA Lung and 0.30 (95%CI: 0.16, 0.42; P < 0.0001) in CanCOLD. Figure 3 depicts representative airway trees from participants with low and high airway tree caliber heterogeneity.

Figure 1. Airway tree caliber distributions among older adults free of standard COPD risk factors. Each colored curve depicts the distribution of percent-predicted airway lumen diameters within a nonsmoking participant free of secondhand smoke or occupational exposures or asthma diagnosis in MESA Lung (A) and CanCOLD (B). The SD metric quantifies the width of the curve, whereas the mean precent-predicted airway tree caliber quantifies the location of distribution along the x-axis. With increasing airflow obstruction and COPD prevalence, the mean percent-predicted airway tree caliber tends to be smaller (i.e., the central location of the Gaussian distribution tends to be less than 100%), whereas the SD of percent-predicted airway tree caliber tends to be larger (i.e., the width of Gaussian distribution tends to be wider). Airway lumen diameters were measured at 19 standard anatomical locations and percent-predicted values were calculated from externally validated airway-specific lumen diameter reference equations (9). Each participant’s airway tree caliber distribution is depicted as a kernel density estimate rather than a histogram to visualize the variations in distribution width. CanCOLD, Canadian Cohort of Obstructive Lung Disease; COPD, chronic obstructive pulmonary disease; MESA, Multi-Ethnic Study of Atherosclerosis.

Figure 2. Box and whisker plot of airway tree heterogeneity among older adults free of standard COPD risk factors. Airway tree caliber heterogeneity was quantified as the standard deviation (SD) of percent-predicted airway lumen diameters. CanCOLD, Canadian Cohort of Obstructive Lung Disease; COPD, chronic obstructive pulmonary disease; MESA, Multi-Ethnic Study of Atherosclerosis.

Figure 3. Depiction of airway tree lumen diameter heterogeneity assessment. Airway lumen diameters at 19 standard anatomical locations were measured from inspiratory chest CT images using Apollo Software. For each of the 19 standard anatomical airways, the percent-predicted airway caliber was calculated using externally validated sex-stratified airway-specific reference equations among nonsmoking adults free of clinical lung disease that included terms for total lung volume, age, and body height (A) (9). The distribution of percent-predicted airway lumen diameters was Gaussian (B) and could be summarized for each participant using the mean (an index of mean airway tree caliber) and standard deviation (an index of airway tree caliber heterogeneity). Subject A exhibits a mean airway tree caliber of 93.7% predicted with SD of 5.7%. Subject B also exhibits a mean airway tree caliber of 93.7% predicted but has an SD of 17.2%, representing comparatively higher airway tree caliber heterogeneity. See methods for additional details. CT, computed tomography.

Airway Tree Caliber Heterogeneity, Baseline Airflow Obstruction, and COPD Status

The association between FEV1, FEV1/FVC < 0.7, and airway tree heterogeneity are presented graphically in Fig. 4. Among all MESA Lung participants, those in the highest quartile of airway tree caliber heterogeneity exhibited lower FEV1 (adjusted mean difference: −125 mL, 95%CI: −171, −79; P < 0.0001), lower FEV1/FVC (adjusted mean difference: −0.01, 95%CI: −0.02, −0.01; P = 0.001), and higher odds of COPD (adjusted odds ratio: 1.42, 95%CI: 1.01, 2.02; P = 0.043) when compared with participants to the lowest quartile, independent of age, age2, sex, height, height2, race-ethnicity, mean airway tree caliber, tobacco exposures, occupational exposures, and asthma (Table 2).

Figure 4. The association between FEV1, FEV1/FVC < 0.7 and airway tree heterogeneity. A and B show box and whisker plot and bar chart illustrating the association between FEV1, FEV1/FVC < 0.7, and airway tree heterogeneity, respectively. The diamonds and lower and upper bounds of the boxes denote the median, 25th and 75th percentiles, respectively; the whiskers define the upper and lower 25th percentiles and circle datapoints outside of these bounds. FEV1, forced expired volume in 1 second; FVC, forced vital capacity.

Table 2. Airway tree caliber heterogeneity association with FEV1, FEV1/FVC, and COPD

	Airway Tree Caliber Heterogeneity	
Quartile 1	Quartile 2	Quartile 3	Quartile 4	Per 1-SD Increment	
MESA (n = 2,505)						
FEV1, mL, means ± SD	2,421 ± 696	2,315 ± 704	2,311 ± 733	2,220 ± 725		
 Mean difference (95% CI): Model 1	Reference	−54 (−99, −9) P = 0.020	−85 (−130, −39) P < 0.001	−149 (−196, −103) P < 0.0001	−64 (−80, −47) P < 0.0001	
 Mean difference (95% CI): Model 2	Reference	−53 (−97, −9) P = 0.018	−76 (−120, −32) P < 0.001	−125 (−171, −79) P < 0.0001	−56 (−72, −40) P < 0.0001	
FEV1/FVC, means ± SD	0.75 ± 0.07	0.75 ± 0.08	0.74 ± 0.09	0.74 ± 0.09		
 Mean difference (95% CI): Model 1	Reference	−0.00 (−0.01, 0.01) P = 0.410	−0.01 (−0.02, −0.00) P = 0.010	−0.02 (−0.03, −0.01) P < 0.0001	−0.01 (−0.01, −0.01) P < 0.0001	
 Mean difference (95% CI): Model 2	Reference	−0.00 (−0.01, 0.00) P = 0.376	−0.01 (−0.02, −0.00) P = 0.014	−0.01 (−0.02, −0.01) P = 0.001	−0.01 (−0.01, −0.00) P < 0.0001	
FEV1/FVC<LLN, no. (%)	34 (5.4)	49 (7.8)	60 (9.6)	51 (8.1)		
 Odds ratio (95% CI): Model 1	Reference	1.42 (0.88, 2.30) P = 0.631	1.95 (1.22, 3.10) P = 0.063	1.92 (1.18, 3.13) P = 0.095	1.31 (1.11, 1.54) P = 0.001	
 Odds ratio (95% CI): Model 2	Reference	1.39 (0.83, 2.31) P = 0.760	1.90 (1.16, 3.11) P = 0.062	1.70 (1.02, 2.84) P = 0.303	1.29 (1.09, 1.54) P = 0.004	
FEV1/FVC < 0.7, no. (%)	92 (14.7)	118 (18.9)	135 (21.6)	126 (20.1)		
 Odds ratio (95% CI): Model 1	Reference	1.32 (0.95, 1.83) P = 0.100	1.66 (1.20, 2.30) P = 0.002	1.71 (1.22, 2.39) P = 0.005	1.26 (1.12, 1.42) P < 0.0001	
 Odds ratio (95% CI): Model 2	Reference	1.19 (0.91, 1.81) P = 0.151	1.60 (1.14, 2.24) P = 0.006	1.42 (1.01, 2.02) P = 0.043	1.19 (1.05, 1.35) P = 0.006	
CanCOLD (n = 1,297)						
FEV1, mL, means ± SD	2,604 ± 852	2,537 ± 848	2,466 ± 830	2,465 ± 853		
 Mean difference (95% CI): Model 1	Reference	−58 (−151, 36) P = 0.226	−98 (−192, −3) P = 0.42	−223 (−321, −124) P < 0.0001	−102 (−136, −68) P < 0.0001	
 Mean difference (95% CI): Model 2	Reference	−62 (−151, 27) P = 0.172	−96 (−187, −6) P = 0.037	−206 (−301, −110) P < 0.0001	−91 (−124, −58) P < 0.0001	
FEV1/FVC, means ± SD	0.72 ± 0.08	0.71 ± 0.08	0.72 ± 0.09	0.71 ± 0.09		
 Mean difference (95% CI): Model 1	Reference	−0.01 (−0.02, 0.00) P = 0.185	−0.01 (−0.02, −0.00) P = 0.046	−0.02 (−0.03, −0.01) P < 0.001	−0.01 (−0.01, −0.00) P < 0.001	
 Mean difference (95% CI): Model 2	Reference	−0.01 (−0.02, 0.00) P = 0.159	−0.01 (−0.02, 0.00) P = 0.085	−0.02 (−0.03, −0.01) P = 0.003	−0.01 (−0.10, −0.00) P = 0.010	
FEV1/FVC<LLN, no. (%)	69 (21.5)	99 (31.1)	97 (30.6)	109 (34.4)		
 Odds ratio (95% CI): Model 1	Reference	1.88 (1.29, 2.74) P = 0.677	2.04 (1.40, 3.00) P = 0.254	2.70 (1.83, 3.98) P < 0.001	1.37 (1.20, 1.57) P < 0.0001	
 Odds ratio (95% CI): Model 2	Reference	1.98 (1.32, 2.96) P = 0.248	1.81 (1.20, 2.74) P = 0.663	2.43 (1.61, 3.68) P = 0.005	1.30 (1.13, 1.49) P < 0.001	
FEV1/FVC < 0.7, no. (%)	134 (41.7)	166 (52.2)	156 (49.2)	162 (51.1)		
 Odds ratio (95% CI): Model 1	Reference	1.72 (1.23, 2.39) P = 0.002	1.82 (1.30, 2.55) P < 0.001	1.95 (1.38, 2.76) P < 0.001	1.27 (1.11, 1.44) P < 0.001	
 Odds ratio (95% CI): Model 2	Reference	1.72 (1.21, 2.44) P = 0.003	1.61 (1.12, 2.31) P = 0.010	1.65 (1.14, 2.38) P = 0.003	1.17 (1.03, 1.34) P = 0.018	
SPIROMICS (n = 2,730)						
FEV1, mL, means ± SD	1,950 ± 855	1,950 ± 867	1,841 ± 923	1,861 ± 923		
 Mean difference (95% CI): Model 1	Reference	−51 (−117, 15) P = 0.131	−163 (−229, −97) P < 0.0001	−253 (−320, −187) P < 0.0001	−112 (−136, −88) P < 0.0001	
 Mean difference (95% CI): Model 2	Reference	−42 (−107, 22) P = 0.201	−150 (−214, −85) P < 0.0001	−238 (−303, −173) P < 0.0001	−106 (−130, −83) P < 0.0001	
FEV1/FVC, means ± SD	0.69 ± 0.15	0.58 ± 0.16	0.57 ± 0.17	0.56 ± 0.17		
 Mean difference (95% CI): Model 1	Reference	−0.02 (−0.03, −0.01) P = 0.003	−0.04 (−0.05, −0.03) P < 0.0001	−0.06 (−0.07, −0.04) P < 0.0001	−0.02 (−0.03, −0.02) P < 0.0001	
 Mean difference (95% CI): Model 2	Reference	−0.02 (−0.03, −0.01) P = 0.006	−0.04 (−0.05, −0.02) P < 0.0001	−0.05 (−0.07, −0.04) P < 0.0001	−0.02 (−0.03, −0.02) P < 0.0001	
 FEV1/FVC< LLN, no. (%)	358 (52.9)	371 (54.8)	403 (60.1)	406 (60.1)		
 Odds ratio (95% CI): Model 1	Reference	1.23 (0.96, 1.57) P = 0.022	1.72(1.33, 2.22) P = 0.050	2.20 (1.69, 2.85) P < 0.0001	1.39 (1.26, 1.53) P < 0.0001	
 Odds ratio (95% CI): Model 2	Reference	1.22 (0.95, 1.57) P = 0.025	1.71 (1.32, 2.22) P = 0.056	2.18 (1.67, 2.85) P < 0.0001	1.38 (1.26, 1.53) P < 0.0001	
FEV1/FVC < 0.7	429 (63.5)	453 (66.9)	460 (68.6)	457 (67.6)		
 Odds ratio (95% CI): Model 1	Reference	1.42 (1.09, 1.84) P = 0.003	1.64 (1.26, 2.14) P < 0.001	2.11 (1.60, 2.78) P < 0.0001	1.34 (1.21, 1.48) P < 0.0001	
 Odds ratio (95% CI): Model 2	Reference	1.41 (1.08, 1.83) P = 0.011	1.64 (1.25, 2.15) P < 0.001	2.07 (1.57, 2.74) P < 0.0001	1.32 (1.19, 1.47) P < 0.0001	
The mean differences and odds ratios were estimated by fitting linear and logistic regression models, respectively.

Model 1 covariables: age, age2, sex, height, height2, race-ethnicity, and mean airway tree caliber.

Model 2 covariables: model 1 variables + cigarette smoking status, pack-yr, pipe smoking status, pipe-years, cigar smoking status, cigar-years, secondhand smoke exposure, and occupational exposure to vapor-gas, dust, or fumes, and asthma diagnosis. Airway tree caliber heterogeneity was quantified as the standard deviation (SD) of percent-predicted airway diameters measured at 19 standard anatomical locations.

CanCOLD, Canadian cohort of obstructive lung disease; CI, confidence interval; COPD, chronic obstructive pulmonary disease; FEV1, forced expired volume in 1 second; FVC, forced vital capacity; LLN, lower limit of normal; MESA, Multi-Ethnic study of atherosclerosis; SPIROMICS, subpopulations and intermediate outcome measures in COPD study.

Among all CanCOLD participants, the highest quartile of airway tree caliber heterogeneity was also associated with lower FEV1 (adjusted mean difference: −206 mL, 95%CI: −301, −110; P < 0.0001), lower FEV1/FVC (adjusted mean difference: −0.02, 95%CI: −0.03, −0.01; P = 0.003), and higher odds of COPD (adjusted odds ratio: 1.65; 95%CI: 1.14, 2.38; P = 0.003) when compared with the lowest quartile in the main adjusted model (Table 2).

Among SPIROMICS participants with 20+ pack-yr of smoking, the highest quartile of airway tree caliber heterogeneity was associated with lower FEV1 (adjusted mean difference: −238 mL, 95%CI: −303, −173; P < 0.0001), lower FEV1/FVC (adjusted mean difference: −0.05, 95%CI: −0.07, −0.04; P < 0.0001), and higher COPD odds (adjusted odds ratio: 2.07, 95%CI: 1.57, 2.74; P < 0.0001) when compared with participants in the lowest quartile in the main adjusted model (Table 2).

Airway Tree Caliber Heterogeneity and Longitudinal Change in Spirometry

Among MESA Lung participants, there was no evidence that airway tree caliber heterogeneity was associated with longitudinal change in spirometry. Comparing participants in the highest quartile of airway tree caliber heterogeneity to those in the lowest quartile, there was no difference in the annualized change in FEV1 (adjusted mean difference: 10 mL/yr 95%CI: −34, 54; P = 0.717) or FEV1/FVC (adjusted mean difference: 0.007; 95%CI: −0.004, 0.017; P = 0.204). Consistent findings were observed in CanCOLD and SPIROMICS (Table 3).

Table 3. Airway tree caliber heterogeneity associations with longitudinal lung function

	Airway Tree Caliber Heterogeneity	
Quartile 1	Quartile 2	Quartile 3	Quartile 4	Per 1-SD Increment	
MESA (n = 2,505)						
 Annualized FEV1 change in mL, means ± SD	−34 ± 28	−32 ± 30	−34 ± 33	−33 ± 33		
 Mean difference (95%CI): Model 1	Reference	−58 (−101, −14) P = 0.009	−48 (−93, −4) P = 0.034	8 (−36, 52) P = 0.717	0.2 (−16, 16) P = 0.978	
 Mean difference (95%CI): Model 2	Reference	−56 (−100, −13) P = 0.011	−45 (−90, −1) P = 0.048	10 (−34, 54) P = 0.717	1 (−14, 17) P = 0.872	
 Annualized FEV1/FVC change, means ± SD	−0.00 ± 0.01	−0.00 ± 0.01	−0.00 ± 0.01	−0.00 ± 0.01		
 Mean difference (95%CI): Model 1	Reference	0.01 (−0.01, 0.02) P = 0.367	0.00 (−0.01, 0.01) P = 0.859	0.01 (−0.00, 0.02) P = 0.164	−0.00 (−0.00, 0.01) P = 0.309	
 Mean difference (95%CI): Model 2	Reference	0.00 (−0.01, 0.01) P = 0.446	0.00 (−0.01, 0.01) P = 0.908	0.01 (−0.00, 0.02) P = 0.204	0.00 (−0.00, 0.01) P = 0.327	
CanCOLD (n = 1,045)						
 Annualized FEV1 change in mL, means ± SD	−32 ± 78	−38 ± 76	−34 ± 84	−40 ± 77		
 Mean difference (95%CI): Model 1	Reference	6 (−18, 30) P = 0.645	−17 (−41, 7) P = 0.158	7 (−18, 32) P = 0.568	−2 (−11, 7) P = 0.721	
 Mean difference (95%CI): Model 2	Reference	3 (−21, 27) P = 0.802	−19 (−43, 5) P = 0.127	5 (−20, 30) P = 0.691	−2 (−11, 7) P = 0.668	
 Annualized FEV1/FVC change, means ± SD	−0.00 ± 0.02	−0.00 ± 0.02	−0.00 ± 0.02	−0.00 ± 0.02		
 Mean difference (95%CI): Model 1	Reference	−0.00 (−0.01, 0.00) P = 0.904	−0.00 (−0.01, 0.00) P = 0.595	0.01 (−0.00, 0.01) P = 0.619	0.00 (−0.00, 0.00) P = 0.169	
 Mean difference (95%CI): Model 2	Reference	−0.00 (−0.01, 0.00) P = 0.678	−0.00 (−0.01, 0.00) P = 0.483	0.00 (−0.00, 0.01) P = 0.116	0.00 (−0.00, 0.00) P = 0.254	
SPIROMICS (n = 2,144)						
 Annualized FEV1 change in mL, means ± SD	−50 ± 124	−28 ± 147	−41 ± 125	−43 ± 143		
 Mean difference (95%CI): Model 1	Reference	−0.4 (−7, 6) P = 0.894	1 (−6, 8) P = 0.759	−1 (−8, 6) P = 0.699	0.4 (−2, 3) P = 0.747	
 Mean difference (95%CI): Model 2	Reference	0.4 (−6, 7) P = 0.892	2 (−5, 8) P = 0.656	−0.1 (−7, 7) P = 0.968	1 (−1, 3) P = 0.433	
 Annualized FEV1/FVC change, means ± SD	−0.01 ± 0.03	−0.01 ± 0.03	−0.01 ± 0.03	−0.01 ± 0.03		
 Mean difference (95%CI): Model 1	Reference	−0.00 (−0.00, 0.00)P = 0.347	0.00 (−0.00, 0.00) P = 0.477	−0.00 (−0.00, 0.00) P = 0.425	−0.00 (−0.00, 0.00) P = 0.140	
 Mean difference (95%CI): Model 2	Reference	−0.00 (−0.00, 0.00) P = 0.371	0.00 (−0.00, 0.00) P = 0.620	−0.00 (−0.00, 0.00) P = 0.614	−0.00 (−0.00, 0.00) P = 0.285	
The mean differences were estimated by fitting linear regression models.

Model 1 covariables: age, age2, sex, height, height2, race-ethnicity, and mean airway tree caliber.

Model 2 covariables: model 1 variables + cigarette smoking status, pack-yr, pipe smoking status, pipe-years, cigar smoking status, cigar-years, secondhand smoke exposure, and occupational exposure to vapor-gas, dust, or fumes, and asthma diagnosis. Airway tree caliber heterogeneity was quantified as the standard deviation (SD) of percent-predicted airway diameters measured at 19 standard anatomical locations.

CanCOLD, Canadian cohort of obstructive lung disease; CI, confidence interval; FEV1, forced expired volume in 1 second; FVC, forced vital capacity; MESA, Multi-Ethnic study of atherosclerosis; SPIROMICS, subpopulations and intermediate outcome measures in COPD study.

Sensitivity Analyses

Restricted cubic splines did not improve model fit and associations were consistent when airway tree caliber heterogeneity was modeled per 1-SD increment (i.e., linear) and across quartiles (Tables 2 and 3). Observations were consistent in unweighted CanCOLD and unimputed MESA samples (Supplemental Table S2). Analyses stratified by ever and never smoking strata in MESA Lung and CanCOLD were consistent, except the association between airway tree caliber heterogeneity and COPD was attenuated among nonsmokers (Supplemental Table S3 and S4). There was no statistical evidence of modification of airway tree caliber heterogeneity associations by smoking status (P-interaction > 0.48) or mean airway tree caliber (P-interaction > 0.11). The coefficient of variation of percent-predicted airway lumen diameters, which combines airway tree heterogeneity and mean caliber into a single ratio measure, yielded consistent results, with even larger magnitude estimates of association with airflow obstruction and COPD (Supplemental Table S5). Race-ethnicity-stratified analyses in MESA Lung and analyses additionally adjusted for study sites in all cohorts were consistent (Supplemental Table S6–S7).

Defining COPD by spirometry and respiratory symptoms yielded similar associations (Supplemental Table S8).

DISCUSSION

Among older adults in two community-based samples and a case-control study of heavy smokers, conducting airway tree caliber heterogeneity quantified by CT was associated with baseline airflow obstruction and COPD independent of age, sex, height, race-ethnicity, and mean airway tree caliber, but was not associated with longitudinal lung function decline. These observations suggest that conducting airway tree caliber heterogeneity is a structural trait associated with low baseline lung function and normal decline trajectory that may contribute to prevalent COPD among older adults.

Initially inferred from maximum expiratory airflow variation among healthy adults, Green and colleagues (46) hypothesized that variation in airway tree caliber arising early in life was an important host susceptibility factor for COPD (“dysanapsis”). In support of this hypothesis, variation in mean airway tree caliber and its association with airflow obstruction is manifested by early adulthood and among older adults is independently associated with COPD prevalence but normal lung function decline (9, 39, 47). The present study adds to the emerging clinical relevance of native airway tree structure by showing that airway tree caliber heterogeneity is associated with airflow obstruction and COPD independent of mean airway tree caliber and other standard COPD risk factors. Whether airway tree caliber heterogeneity and mean airway tree caliber arise from the same underlying developmental mechanism postulated by Green and colleagues is uncertain, though we note that the correlation between these two structural traits was relatively weak.

The mechanisms by which airway tree caliber heterogeneity relates to airflow obstruction and COPD were not assessed in this study. Several inert gas washout studies have suggested that ventilation distribution inhomogeneity is a feature of chronic obstructive lung diseases and, leveraging fundamental laws of gas flow and mixing (48, 49), these studies have articulated three principal mechanisms that include convection-dependent inhomogeneity arising from the conducting airways, diffusion-limitation inhomogeneity arising at the distal acinar level, and diffusion convection-interaction-dependent inhomogeneity arising at the diffusion-convection front, which is estimated to occur at the acinar entrance (17, 18, 20–22, 26). The present study complements these important works by 1) providing a new method for quantifying conducting airway tree caliber heterogeneity in vivo and 2) applying this method to quantify and replicate the presence of conducting airway tree caliber heterogeneity and its association with airflow obstruction in large and diverse samples. Combined with the in vivo quantification of mean conducting airway tree caliber, one can probe (i.e., partition) the independent contributions of both structural properties of the airway tree to obstructive pathophysiology. Application of these methods to cohorts with inert gas washout measurements may validate certain postulated mechanisms of ventilation inhomogeneity and, potentially, find some clinical utility in endo-phenotyping the seemingly heterogeneous cluster of chronic obstructive lung diseases.

Imaging studies have demonstrated heterogeneity of inhaled gas distribution in obstructive lung diseases and related these deficits to regional airway tree caliber narrowing in small-to-moderate samples (27–31, 50–52). The present study complements these works by demonstrating conducting airway tree caliber heterogeneity and its association with airflow obstruction across the spectrum of obstructive lung disease severity and, critically, even among individuals free of traditional obstructive lung disease risk factors (e.g., tobacco smoke exposures, asthma). These observations suggest developmental processes may contribute to airway tree caliber heterogeneity-associated airflow obstruction (51).

Airway tree caliber heterogeneity in the present study was not associated with lung function decline. Similar to prior observations on mean airway tree caliber, this finding suggests that airway tree caliber heterogeneity is another structural trait associated with the “low-baseline-normal-decline” lung function trajectory experienced by 50% of older adults with COPD (9, 14, 53). Indeed, the percent-predicted airway caliber coefficient of variation, which combines heterogeneity and mean airway tree caliber into a single quantitative measure exhibited the largest association estimates with baseline airflow obstruction and COPD prevalence. Together, these observations suggest that the dysanaptic pathway to COPD may not be limited to developmental differences in mean airway tree caliber, but rather may also include differences in airway tree caliber heterogeneity.

The etiology of airway tree caliber heterogeneity was not assessed in this study. We note, however, that variation in mean airway tree caliber has been demonstrated among young nonsmoking adults free of lung disease (39), suggesting that this structural trait arises earlier in life. We also note that the airway caliber reference equations did not include terms for race-ethnicity to avoid normalization of potential sources of disparities related to race-ethnicity (54), nevertheless associations were consistent within race-ethnic strata. These observations suggest that airway tree caliber associations with airflow obstruction and COPD are not related to race-ethnic differences. Investigating the etiology of airway tree caliber earlier in life will likely require radiation-free (or lower dose radiation) methods to quantify lung structure heterogeneity in children.

The findings from this study must be considered in the context of its limitations. First, unmeasured, imprecisely measured, or differential susceptibility to standard COPD risk factors associated with airway tree caliber heterogeneity may inflate association estimates. We think this is unlikely since there were detailed and standardized COPD risk factor assessments and analyses restricted to people who never smoked yielded similar results. Second, airway tree assessment was limited to 19 central airways. This approach minimized airway detection bias related to CT resolution or anatomical variation in airway tree branch patterns (8). Third, airway caliber inhomogeneity and airflow obstruction may be two aspects of obstructive lung disease that are not linked mechanistically. Establishing a causal mechanistic link between airway inhomogeneity and lung function decline requires experimental interruption of airway inhomogeneity while observing lung function over time and interruption of lung function decline while observing airway inhomogeneity. Fourth, the lack of association with lung function decline may be related to power (55). Fifth, inferences about the quality of inspiration at CT based upon a comparison between CT total lung volume (CT-TLV) and spirometry (e.g., FVC) are limited. Future studies assessing modifiable etiological factors, structure-clinical outcome relationships, and regional/peripheral airway tree heterogeneity are warranted (56).

Conclusion

Among community-dwelling older adults and heavy smokers with and without COPD, airway tree caliber heterogeneity was associated with baseline airflow obstruction and COPD independent of mean airway tree caliber but was not associated with prospective change in lung function. These findings suggest that native airway tree caliber heterogeneity may be a host structural factor relevant to COPD.

DATA AVAILABILITY

Data will be made available upon reasonable request.

GRANTS

This study was supported by NIH/National Heart, Lung, and Blood Institute: R01-HL130506 (to B. M. Smith), R01-HL077612 (to R. G. Barr), R01-HL093081 (to R. G. Barr), CIHR: PJT-162335 (to B. M. Smith), K23ES030725 (to C. Sack), and Vanier Canada Graduate Scholarship (to M. Vameghestahbanati). MESA was supported by contracts 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, R01-HL077612, R01-HL093081,75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, and N01-HC-95169 from the National Heart, Lung, and Blood Institute, and by grants UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420 from the National Center for Advancing Translational Sciences (NCATS). MESA Air was developed under the Science to Achieve Results (STAR) research assistance Agreement Nos. RD831697 (MESA Air) and RD-83830001 (MESA Air Next Stage), awarded by the US Environmental Protection Agency (EPA). CanCOLD was supported by the Canadian Respiratory Research Network; industry partners: Astra Zeneca Canada Ltd; Boehringer Ingelheim Canada Ltd; GlaxoSmithKline Canada Ltd; and Novartis. Previous funding partners are the CIHR (CIHR/Rx&D Collaborative Research Program Operating Grants 93326); the Respiratory Health Network of the Fonds de la recherche en santé du Québec (FRSQ); industry partners: Almirall; Merck Nycomed; Pfizer Canada Ltd; and Theratechnologies. SPIROMICS was supported by contracts from the NIH/NHLBI (HHSN268200900013C, HHSN268200900014C, HHSN268200900015C, HHSN268200900016C, HHSN268200900017C, HHSN268200900018C, HHSN268200900019C, and HHSN268200900020C), grants from the NIH/NHLBI (U01 HL137880, U24 HL141762, and R01-HL093081), and supplemented by contributions made through the Foundation for the NIH and the COPD Foundation from AstraZeneca/MedImmune; Bayer; Bellerophon Therapeutics; BoehringerIngelheim Pharmaceuticals, Inc.; Chiesi Farmaceutici S.p.A.; Forest Research Institute, Inc.; GlaxoSmithKline; Grifols Therapeutics, Inc.; Ikaria, Inc.; Novartis Pharmaceuticals Corporation; Nycomed GmbH; ProterixBio; Regeneron Pharmaceuticals, Inc.; Sanofi; Sunovion; Takeda Pharmaceutical Company; and Theravance Biopharma and Mylan.

DISCLAIMERS

The views expressed in this document have not been formally reviewed by the EPA and are solely those of the authors. The EPA does not endorse any products or commercial services mentioned in this publication.

DISCLOSURES

No conflicts of interest, financial or otherwise, are declared by the authors.

AUTHOR CONTRIBUTIONS

M.V. and B.M.S. conceived and designed research; M.V. analyzed data; M.V. and B.M.S. interpreted results of experiments; M.V. prepared figures; M.V. drafted manuscript; M.V. and B.M.S. edited and revised manuscript; M.V., L.K., E.A.H., M.K., N.B.A., E.A., A.B., Q.H., J.C.H., D.R.J., A.L., F.M., E.D.M., C.S., D.S., K.E.W., A.W., D.C., C.C., M.H., P.W., W.C.T., J.B., R.G.B., and B.M.S., approved final version of manuscript.

ACKNOWLEDGMENTS

The authors thank the other investigators, the staff, and the participants of the MESA study for their valuable contributions. A full list of participating MESA, CanCOLD, and SPIROMICS investigators and institutions can be found at www.mesa-nhlbi.org, www.cancold.ca, and www.spiromics.org/spiromics, respectively.
==== Refs
REFERENCES

1. GBD 2015 Chronic Respiratory Disease Collaborators. Global, regional, and national deaths, prevalence, disability-adjusted life years, and years lived with disability for chronic obstructive pulmonary disease and asthma, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet Respir Med 5 : 691–706, 2017. doi:10.1016/s2213-2600(17)30293-x.28822787
2. Vogelmeier CF, Criner GJ, Martinez FJ, Anzueto A, Barnes PJ, Bourbeau J, Celli BR, Chen R, Decramer M, Fabbri LM, Frith P, Halpin DM, Lopez Varela MV, Nishimura M, Roche N, Rodriguez-Roisin R, Sin DD, Singh D, Stockley R, Vestbo J, Wedzicha JA, Agusti A. Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease 2017 Report. GOLD Executive Summary. Am J Respir Crit Care Med 195 : 557–582, 2017. doi:10.1164/rccm.201701-0218PP. 28128970
3. Ng M, Freeman MK, Fleming TD, Robinson M, Dwyer-Lindgren L, Thomson B, Wollum A, Sanman E, Wulf S, Lopez AD, Murray CJ, Gakidou E. Smoking prevalence and cigarette consumption in 187 countries, 1980-2012. JAMA 311 : 183–192, 2014. doi:10.1001/jama.2013.284692. 24399557
4. Thun MJ, Carter BD, Feskanich D, Freedman ND, Prentice R, Lopez AD, Hartge P, Gapstur SM. 50-year trends in smoking-related mortality in the United States. N Engl J Med 368 : 351–364, 2013. doi:10.1056/NEJMsa1211127. 23343064
5. Burns DM, Major JM, Shanks TG. Changes in number of cigarettes smoked per day: cross-sectional and birth cohort analysis using NHIS. In: Those Who Continue To Smoke: Is Achieving Abstinence Harder And Do We Need To Change Our Interventions? Smoking and Tobacco Control Monograph No 15. Bethesda, MD: National Cancer Institute, 2003, p. 83–99.
6. Adeloye D, Chua S, Lee C, Basquill C, Papana A, Theodoratou E, Nair H, Gasevic D, Sridhar D, Campbell H, Chan KY, Sheikh A, Rudan I; Global Health Epidemiology Reference Group (GHERG). Global and regional estimates of COPD prevalence: systematic review and meta-analysis. J Glob Health 5 : 020415, 2015. doi:10.7189/jogh.05.020415.26755942
7. US Burden of Disease Collaborators; Mokdad AH, Ballestros K, Echko M, Glenn S, Olsen HE, , et al The State of US Health, 1990-2016: burden of diseases, injuries, and risk factors among US States. JAMA 319 : 1444–1472, 2018. doi:10.1001/jama.2018.0158. 29634829
8. Smith BM, Traboulsi H, Austin JHM, Manichaikul A, Hoffman EA, Bleecker ER, Cardoso WV, Cooper C, Couper DJ, Dashnaw SM, Guo J, Han MK, Hansel NN, Hughes EW, Jacobs DR Jr, Kanner RE, Kaufman JD, Kleerup E, Lin CL, Liu K, Lo Cascio CM, Martinez FJ, Nguyen JN, Prince MR, Rennard S, Rich SS, Simon L, Sun Y, Watson KE, Woodruff PG, Baglole CJ, Barr RG; MESA Lung and SPIROMICS investigators. Human airway branch variation and chronic obstructive pulmonary disease. Proc Natl Acad Sci USA 115 : E974–E981, 2018. doi:10.1073/pnas.1715564115.29339516
9. Smith BM, Kirby M, Hoffman EA, Kronmal RA, Aaron SD, Allen NB, Bertoni A, Coxson HO, Cooper C, Couper DJ, Criner G, Dransfield MT, Han MK, Hansel NN, Jacobs DR Jr, Kaufman JD, Lin CL, Manichaikul A, Martinez FJ, Michos ED, Oelsner EC, Paine R 3rd, Watson KE, Benedetti A, Tan WC, Bourbeau J, Woodruff PG, Barr RG; Mesa Lung, CanCOLD, and SPIROMICS Investigators. Association of dysanapsis with chronic obstructive pulmonary disease among older adults. JAMA 323 : 2268–2280, 2020. doi:10.1001/jama.2020.6918. 32515814
10. Christou S, Chatziathanasiou T, Angeli S, Koullapis P, Stylianou F, Sznitman J, Guo HH, Kassinos SC. Anatomical variability in the upper tracheobronchial tree: sex-based differences and implications for personalized inhalation therapies. J Appl Physiol (1985) 130 : 678–707, 2021. doi:10.1152/japplphysiol.00144.2020. 33180641
11. Smith BM, Hoffman EA, Rabinowitz D, Bleecker E, Christenson S, Couper D, Donohue KM, Han MK, Hansel NN, Kanner RE, Kleerup E, Rennard S, Barr RG. Comparison of spatially matched airways reveals thinner airway walls in COPD. The Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study and the Subpopulations and Intermediate Outcomes in COPD Study (SPIROMICS). Thorax 69 : 987–996, 2014. doi:10.1136/thoraxjnl-2014-205160. 24928812
12. Bodduluri S, Puliyakote ASK, Gerard SE, Reinhardt JM, Hoffman EA, Newell JD Jr, Nath HP, Han MK, Washko GR, San Jose Estepar R, Dransfield MT, Bhatt SP; COPDGene Investigators. Airway fractal dimension predicts respiratory morbidity and mortality in COPD. J Clin Invest 128 : 5374–5382, 2018. doi:10.1172/JCI120693. 30256767
13. Mohamed Hoesein FA, de Jong PA, Lammers JW, Mali WP, Schmidt M, de Koning HJ, van der Aalst C, Oudkerk M, Vliegenthart R, Groen HJ, van Ginneken B, van Rikxoort EM, Zanen P. Airway wall thickness associated with forced expiratory volume in 1 second decline and development of airflow limitation. Eur Respir J 45 : 644–651, 2015. doi:10.1183/09031936.00020714. 25614166
14. Lange P, Celli B, Agusti A. Lung-function trajectories and chronic obstructive pulmonary disease. N Engl J Med 373 : 1575, 2015. doi:10.1056/NEJMc1510089. 26465997
15. Leary D, Bhatawadekar SA, Parraga G, Maksym GN. Modeling stochastic and spatial heterogeneity in a human airway tree to determine variation in respiratory system resistance. J Appl Physiol (1985) 112 : 167–175, 2012. doi:10.1152/japplphysiol.00633.2011. 21998266
16. Tawhai MH, Hunter P, Tschirren J, Reinhardt J, McLennan G, Hoffman EA. CT-based geometry analysis and finite element models of the human and ovine bronchial tree. J Appl Physiol (1985) 97 : 2310–2321, 2004. doi:10.1152/japplphysiol.00520.2004. 15322064
17. Saniie J, Saidel GM, Chester EH. Real-time moment analysis of pulmonary nitrogen washout. J Appl Physiol Respir Environ Exerc Physiol 46 : 1184–1190, 1979. doi:10.1152/jappl.1979.46.6.1184. 468643
18. Zaigham S, Wollmer P, Engstrom G. The association of lung clearance index with COPD and FEV(1) reduction in 'Men Born in 1914'. COPD 14 : 324–329, 2017. doi:10.1080/15412555.2017.1314455. 28453306
19. Downie SR, Salome CM, Verbanck S, Thompson B, Berend N, King GG. Ventilation heterogeneity is a major determinant of airway hyperresponsiveness in asthma, independent of airway inflammation. Thorax 62 : 684–689, 2007. doi:10.1136/thx.2006.069682. 17311839
20. Fowler WS. Lung function studies; uneven pulmonary ventilation in normal subjects and in patients with pulmonary disease. J Appl Physiol 2 : 283–299, 1949. doi:10.1152/jappl.1949.2.6.283. 15403653
21. Venegas JG, Winkler T, Musch G, Vidal Melo MF, Layfield D, Tgavalekos N, Fischman AJ, Callahan RJ, Bellani G, Harris RS. Self-organized patchiness in asthma as a prelude to catastrophic shifts. Nature 434 : 777–782, 2005. doi:10.1038/nature03490. 15772676
22. Verbanck S, Schuermans D, Paiva M, Vincken W. Nonreversible conductive airway ventilation heterogeneity in mild asthma. J Appl Physiol (1985) 94 : 1380–1386, 2003. doi:10.1152/japplphysiol.00588.2002. 12471044
23. Foy B, Kay D, Siddiqui S, Brightling C, Paiva M, Verbanck S. Increased ventilation heterogeneity in asthma can be attributed to proximal bronchioles. Eur Respir J 55 : 1901345, 2020. doi:10.1183/13993003.01345-2019.31806713
24. Weibel ER. Morphometry of the human lung: the state of the art after two decades. Bull Eur Physiopathol Respir 15 : 999–1013, 1979. 389332
25. Haefeli-Bleuer B, Weibel ER. Morphometry of the human pulmonary acinus. Anat Rec 220 : 401–414, 1988. doi:10.1002/ar.1092200410. 3382030
26. Robinson PD, Latzin P, Verbanck S, Hall GL, Horsley A, Gappa M, Thamrin C, Arets HG, Aurora P, Fuchs SI, King GG, Lum S, Macleod K, Paiva M, Pillow JJ, Ranganathan S, Ratjen F, Singer F, Sonnappa S, Stocks J, Subbarao P, Thompson BR, Gustafsson PM. Consensus statement for inert gas washout measurement using multiple- and single-breath tests. Eur Respir J 41 : 507–522, 2013 [Erratum in Eur Respir J 42: 1432, 2013]. doi:10.1183/09031936.00069712. 23397305
27. Pike D, Mohan S, Ma W, Lewis JF, Parraga G. Pulmonary imaging abnormalities in an adult case of congenital lobar emphysema. J Radiol Case Rep 9 : 9–15, 2015. doi:10.3941/jrcr.v9i2.2048. 25926923
28. Pike D, Kirby M, Guo F, McCormack DG, Parraga G. Ventilation heterogeneity in ex-smokers without airflow limitation. Acad Radiol 22 : 1068–1078, 2015. doi:10.1016/j.acra.2015.04.006. 26008133
29. Svenningsen S, Kirby M, Starr D, Leary D, Wheatley A, Maksym GN, McCormack DG, Parraga G. Hyperpolarized (3) He and (129) Xe MRI: differences in asthma before bronchodilation. J Magn Reson Imaging 38 : 1521–1530, 2013. doi:10.1002/jmri.24111. 23589465
30. Kirby M, Svenningsen S, Kanhere N, Owrangi A, Wheatley A, Coxson HO, Santyr GE, Paterson NA, McCormack DG, Parraga G. Pulmonary ventilation visualized using hyperpolarized helium-3 and xenon-129 magnetic resonance imaging: differences in COPD and relationship to emphysema. J Appl Physiol (1985) 114 : 707–715, 2013. doi:10.1152/japplphysiol.01206.2012. 23239874
31. Svenningsen S, Kirby M, Starr D, Coxson HO, Paterson NA, McCormack DG, Parraga G. What are ventilation defects in asthma? Thorax 69 : 63–71, 2014. doi:10.1136/thoraxjnl-2013-203711. 23956019
32. Bild DE, Bluemke DA, Burke GL, Detrano R, Diez Roux AV, Folsom AR, Greenland P, Jacob DR Jr, Kronmal R, Liu K, Nelson JC, O'Leary D, Saad MF, Shea S, Szklo M, Tracy RP. Multi-ethnic study of atherosclerosis: objectives and design. Am J Epidemiol 156 : 871–881, 2002. doi:10.1093/aje/kwf113. 12397006
33. Rodriguez J, Jiang R, Johnson WC, MacKenzie BA, Smith LJ, Barr RG. The association of pipe and cigar use with cotinine levels, lung function, and airflow obstruction: a cross-sectional study. Ann Intern Med 152 : 201–210, 2010. doi:10.7326/0003-4819-152-4-201002160-00004. 20157134
34. Kaufman JD, Adar SD, Allen RW, Barr RG, Budoff MJ, Burke GL, Casillas AM, Cohen MA, Curl CL, Daviglus ML, Diez Roux AV, Jacobs DR Jr, Kronmal RA, Larson TV, Liu SL, Lumley T, Navas-Acien A, O'Leary DH, Rotter JI, Sampson PD, Sheppard L, Siscovick DS, Stein JH, Szpiro AA, Tracy RP. Prospective study of particulate air pollution exposures, subclinical atherosclerosis, and clinical cardiovascular disease: The Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Am J Epidemiol 176 : 825–837, 2012. doi:10.1093/aje/kws169. 23043127
35. Bourbeau J, Tan WC, Benedetti A, Aaron SD, Chapman KR, Coxson HO, Cowie R, Fitzgerald M, Goldstein R, Hernandez P, Leipsic J, Maltais F, Marciniuk D, O'Donnell D, Sin DD, CanCOLD study group. Canadian Cohort Obstructive Lung Disease (CanCOLD): fulfilling the need for longitudinal observational studies in COPD. COPD 11 : 125–132, 2014. doi:10.3109/15412555.2012.665520. 22433011
36. Couper D, LaVange LM, Han M, Barr RG, Bleecker E, Hoffman EA, Kanner R, Kleerup E, Martinez FJ, Woodruff PG, Rennard S; SPIROMICS Research Group. Design of the subpopulations and intermediate outcomes in COPD Study (SPIROMICS). Thorax 69 : 491–494, 2014. doi:10.1136/thoraxjnl-2013-203897. 24029743
37. Kirby M, Tanabe N, Tan WC, Zhou G, Obeidat M, Hague CJ, Leipsic J, Bourbeau J, Sin DD, Hogg JC, Coxson HO; CanCold Collaborative Research Group; Canadian Respiratory Research Network. Total airway count on computed tomography and the risk of chronic obstructive pulmonary disease progression. Findings from a population-based study. Am J Respir Crit Care Med 197 : 56–65, 2018. doi:10.1164/rccm.201704-0692OC. 28886252
38. Sieren JP, Newell JD Jr, Barr RG, Bleecker ER, Burnette N, Carretta EE, Couper D, Goldin J, Guo J, Han MK, Hansel NN, Kanner RE, Kazerooni EA, Martinez FJ, Rennard S, Woodruff PG, Hoffman EA; SPIROMICS Research Group. SPIROMICS Protocol for multicenter quantitative computed tomography to phenotype the lungs. Am J Respir Crit Care Med 194 : 794–806, 2016. doi:10.1164/rccm.201506-1208PP. 27482984
39. Vameghestahbanati M, Hiura GT, Barr RG, Sieren JC, Smith BM, Hoffman EA. CT-assessed dysanapsis and airflow obstruction in early and mid adulthood. Chest 161 : 389–391, 2022. doi:10.1016/j.chest.2021.08.038. 34391757
40. Miller MR, Hankinson J, Brusasco V, Burgos F, Casaburi R, Coates A, Crapo R, Enright P, van der Grinten CP, Gustafsson P, Jensen R, Johnson DC, MacIntyre N, McKay R, Navajas D, Pedersen OF, Pellegrino R, Viegi G, Wanger J; ATS/ERS Task Force. Standardisation of spirometry. Eur Respir J 26 : 319–338, 2005. doi:10.1183/09031936.05.00034805. 16055882
41. Bhatt SP, Balte PP, Schwartz JE, Cassano PA, Couper D, Jacobs DR Jr, Kalhan R, O'Connor GT, Yende S, Sanders JL, Umans JG, Dransfield MT, Chaves PH, White WB, Oelsner EC. Discriminative accuracy of FEV1:FVC thresholds for COPD-related hospitalization and mortality. JAMA 321 : 2438–2447, 2019. doi:10.1001/jama.2019.7233. 31237643
42. Quanjer PH, Stanojevic S, Cole TJ, Baur X, Hall GL, Culver BH, Enright PL, Hankinson JL, Ip MS, Zheng J, Stocks J; ERS Global Lung Function Initiative. Multi-ethnic reference values for spirometry for the 3–95-yr age range: the global lung function 2012 equations. Eur Respir J 40 : 1324–1343, 2012. doi:10.1183/09031936.00080312. 22743675
43. Jones PW, Harding G, Berry P, Wiklund I, Chen WH, Kline Leidy N. Development and first validation of the COPD Assessment Test. Eur Respir J 34 : 648–654, 2009. doi:10.1183/09031936.00102509. 19720809
44. Definition and classification of chronic bronchitis for clinical and epidemiological purposes. A report to the Medical Research Council by their Committee on the Aetiology of Chronic Bronchitis. Lancet 1 : 775–779, 1965. 4165081
45. Mahler DA, Rosiello RA, Harver A, Lentine T, McGovern JF, Daubenspeck JA. Comparison of clinical dyspnea ratings and psychophysical measurements of respiratory sensation in obstructive airway disease. Am Rev Respir Dis 135 : 1229–1233, 1987. doi:10.1164/arrd.1987.135.6.1229. 3592398
46. Green M, Mead J, Turner JM. Variability of maximum expiratory flow-volume curves. J Appl Physiol 37 : 67–74, 1974. doi:10.1152/jappl.1974.37.1.67. 4836570
47. Ripoll JG, Guo W, Andersen KJ, Baker SE, Wiggins CC, Shepherd JRA, Carter RE, Welch BT, Joyner MJ, Dominelli PB. Sex differences in paediatric airway anatomy. Exp Physiol 105 : 721–731, 2020. doi:10.1113/EP088370. 32003484
48. Crawford AB, Makowska M, Paiva M, Engel LA. Convection- and diffusion-dependent ventilation maldistribution in normal subjects. J Appl Physiol (1985) 59 : 838–846, 1985. doi:10.1152/jappl.1985.59.3.838. 4055573
49. Engel LA. Dynamic distribution of gas flow. In: Supplement 12. Handbook of Physiology, The Respiratory System, Mechanics of Breathing. Bethesda, MD: American Physiological Society, 1986.
50. Lui JK, Parameswaran H, Albert MS, Lutchen KR. Linking ventilation heterogeneity quantified via hyperpolarized 3He MRI to dynamic lung mechanics and airway hyperresponsiveness. PLoS One 10 : e0142738, 2015. doi:10.1371/journal.pone.0142738. 26569412
51. Sheikh K, Paulin GA, Svenningsen S, Kirby M, Paterson NA, McCormack DG, Parraga G. Pulmonary ventilation defects in older never-smokers. J Appl Physiol (1985) 117 : 297–306, 2014. doi:10.1152/japplphysiol.00046.2014. 24903918
52. Tgavalekos NT, Musch G, Harris RS, Vidal Melo MF, Winkler T, Schroeder T, Callahan R, Lutchen KR, Venegas JG. Relationship between airway narrowing, patchy ventilation and lung mechanics in asthmatics. Eur Respir J 29 : 1174–1181, 2007 [Erratum in Eur Respir J 30: 603, 2007]. doi:10.1183/09031936.00113606. 17360726
53. Bui DS, Lodge CJ, Burgess JA, Lowe AJ, Perret J, Bui MQ, Bowatte G, Gurrin L, Johns DP, Thompson BR, Hamilton GS, Frith PA, James AL, Thomas PS, Jarvis D, Svanes C, Russell M, Morrison SC, Feather I, Allen KJ, Wood-Baker R, Hopper J, Giles GG, Abramson MJ, Walters EH, Matheson MC, Dharmage SC. Childhood predictors of lung function trajectories and future COPD risk: a prospective cohort study from the first to the sixth decade of life. Lancet Respir Med 6 : 535–544, 2018. doi:10.1016/S2213-2600(18)30100-0. 29628376
54. Elmaleh-Sachs A, Balte P, Oelsner EC, Allen NB, Baugh A, Bertoni AG, Hankinson JL, Pankow J, Post WS, Schwartz JE, Smith BM, Watson K, Barr RG. Race/ethnicity, spirometry reference equations, and prediction of incident clinical events: the Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study. Am J Respir Crit Care Med 205 : 700–710, 2022. doi:10.1164/rccm.202107-1612OC. 34913853
55. Suissa S, Ernst P, Vandemheen KL, Aaron SD. Methodological issues in therapeutic trials of COPD. Eur Respir J 31 : 927–933, 2008. doi:10.1183/09031936.00098307. 18216056
56. Vameghestahbanati M, Kirby M, Tanabe N, Vasilescu DM, Janssens W, Everaerts S, Vanaudenaerde BM, Benedetti A, Hogg JC, Smith BM. Central airway tree dysanapsis extends to the peripheral airways. Am J Respir Crit Care Med 203 : 378–381, 2021. doi:10.1164/rccm.202007-3025LE. 33137261
