Differential Association of COPD Subtypes With Cardiovascular Events and COPD Exacerbations

Han-Mo Yang; Min Hyung Ryu; Vincent J Carey; Kendra Young; Gregory L Kinney; Mark T Dransfield; Raymond C Wade; James M Wells; Matthew J Budoff; Peter J Castaldi; Craig P Hersh; Edwin K Silverman

doi:10.1016/j.chest.2024.07.148

. 2024 Jul 31;166(6):1360–1370. doi: 10.1016/j.chest.2024.07.148

Differential Association of COPD Subtypes With Cardiovascular Events and COPD Exacerbations

Han-Mo Yang ^a,^b,^c, Min Hyung Ryu ^a,^b, Vincent J Carey ^a,^b, Kendra Young ^d, Gregory L Kinney ^d, Mark T Dransfield ^e, Raymond C Wade ^e, James M Wells ^e, Matthew J Budoff ^f, Peter J Castaldi ^a,^b, Craig P Hersh ^a,^b, Edwin K Silverman ^a,^b,^∗

PMCID: PMC11638542 PMID: 39094733

Abstract

Background

The coronary artery calcium score (CACS) and ratio of the pulmonary artery to aorta diameters (PA:A ratio) measured from chest CT scans have been established as predictors of cardiovascular events and COPD exacerbations, respectively. However, little is known about the reciprocal relationship between these predictors and outcomes. Furthermore, the prognostic implications of COPD subtypes on clinical outcomes remain insufficiently characterized.

Research Question

How can these two chest CT scan-derived parameters predict subsequent cardiovascular events and COPD exacerbations in different COPD subtypes?

Study Design and Methods

Using COPDGene study data, we assessed prospective cardiovascular disease (CVD) and COPD exacerbation risk in participants with COPD (Global Initiative for Chronic Obstructive Lung Disease spirometric grades 2-4), focusing on CACS and PA:A ratio at study enrollment, with logistic regression models. These outcomes were analyzed in three COPD subtypes: 1,042 participants with non-emphysema-predominant COPD (NEPD; low attenuation area at −950 Hounsfield units [LAA-950] < 5%), 1,324 participants with emphysema-predominant COPD (EPD; LAA-950 ≥ 10%), and 465 participants with intermediate emphysema COPD (IE; 5% ≤ LAA-950 < 10%).

Results

Our study indicated significantly higher overall risk for cardiovascular events in participants with higher CACS (≥ median; OR, 1.61; 95% CI, 1.30-2.00) and increased COPD exacerbations in those with higher PA:A ratios (≥ 1; OR, 1.80; 95% CI, 1.46-2.23). Notably, participants with NEPD showed a stronger association between these indicators and clinical events compared to EPD (with CACS/CVD, NEPD vs EPD: OR, 2.02 vs 1.41; with PA:A ratio/COPD exacerbation, NEPD vs EPD: OR, 2.50 vs 1.65); the difference in ORs between COPD subtypes was statistically significant for CACS/CVD.

Interpretation

Two chest CT scan parameters, CACS and PA:A ratio, hold distinct predictive values for cardiovascular events and COPD exacerbations that are influenced by specific COPD subtypes.

Trial Registration

ClinicalTrials.gov; No.: NCT00608764; URL: www.clinicaltrials.gov

Key Words: cardiovascular events, COPD, COPD exacerbations, COPD subtypes, coronary artery calcification

Take-home Points.

Study Question: How can chest CT scan-derived coronary artery calcium score and pulmonary artery to aorta diameter ratio predict subsequent cardiovascular events and COPD exacerbations in different COPD subtypes?

Results: The non-emphysema-predominant COPD subtype showed a stronger association between coronary artery calcium score and subsequent cardiovascular events compared with the emphysema-predominant COPD subtype. Pulmonary artery to aorta diameter ratio was associated with increased COPD exacerbation risk in both emphysema-predominant and non-emphysema-predominant COPD subtypes.

Interpretation: These two imaging biomarkers could be used to guide risk stratification and treatment decisions in patients with COPD.

As a leading cause of morbidity and mortality worldwide, COPD imposes a substantial burden on both individual health and global health care systems.¹ The complexity of COPD is further accentuated by its association with various comorbidities, particularly cardiovascular diseases (CVDs), which have been recognized as a major contributor to the overall disease burden and mortality in patients with COPD.²^,³^,⁴

The intricate relationship between COPD and cardiovascular risk highlights the necessity of comprehensively understanding the predictors and mechanisms underlying these associations. Advancements in imaging techniques, particularly chest CT scan, have provided valuable insights into this domain.⁵^,⁶ The coronary artery calcium score (CACS) and the ratio of the diameter of the pulmonary artery to the diameter of the aorta (PA:A ratio) are two such parameters obtained from chest CT scans, which have shown promise in predicting cardiovascular events and COPD exacerbations, respectively.⁷^,⁸^,⁹^,¹⁰ Besides its relationship with exacerbations, a larger PA:A ratio has been associated with pulmonary fibrosis, atrial fibrillation, and all-cause death/rehospitalization in patients with heart failure.¹¹^,¹²^,¹³ Additionally, a higher CACS in patients with COPD is also related to reduced physical activity and increased mortality.¹⁴^,¹⁵ However, little is known about the reciprocal relationships between these predictors (CACS/PA:A ratio) and outcomes (CVD/COPD exacerbations) in patients with COPD. Furthermore, the prognostic implications of these parameters within the context of different COPD subtypes have not been extensively studied.

COPD is a heterogeneous disease encompassing a spectrum of clinical characteristics, including differences in physiology, CT imaging, and respiratory symptoms.¹⁶^,¹⁷^,¹⁸^,¹⁹^,²⁰ This heterogeneity poses a significant challenge in the clinical management of COPD, necessitating a tailored approach based on individual patient characteristics.¹⁶^,¹⁷^,¹⁸^,¹⁹^,²⁰ A variety of clinical, epidemiologic, and machine learning approaches have been used to identify COPD subtypes.¹⁶^,¹⁷^,¹⁸ Many of the approaches to COPD subtyping have been shown to be at least partially captured by a straightforward approach based on a combination of airflow obstruction either with significant emphysema on chest CT scan (emphysema-predominant COPD [EPD]) or without significant emphysema (non-emphysema-predominant COPD [NEPD]).¹⁶^,¹⁷^,¹⁸^,²⁰ The distinction between EPD and NEPD is particularly notable, given their differential impact on pulmonary function, and comorbid conditions, including CVD.¹⁶^,¹⁸^,²⁰

In this context, our study aims to elucidate the relationships between CACS and PA:A ratio and cardiovascular events and COPD exacerbations, with a focus on the influence of COPD subtypes.

Study Design and Methods

Study Population

The COPDGene study, a multicenter, longitudinal cohort study, investigates the epidemiology, genetics, and progression of COPD across 21 clinical centers in the United States.²¹^,²² COPDGene enrolled non-Hispanic White and African American participants 45 to 80 years of age with a history of at least 10 pack-years of smoking. All participants provided written informed consent, and each study center received institutional review board approval. Participants were classified according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) spirometric grading system.²³

We included data from the COPDGene initial visit and subsequent longitudinal follow-up. At baseline, participants underwent comprehensive assessments, including spirometry (before and after bronchodilator medication) and chest CT scans, performed at full inspiration and relaxed expiration following a standardized protocol.²¹ Enrolled participants were contacted biannually through the COPDGene longitudinal follow-up program via phone or online surveys to collect data on incident COPD-related events, comorbidities, and mortality.

COPD exacerbations were defined as a self-reported acute worsening of respiratory symptoms requiring systemic steroids and/or antibiotic treatment. CVD events were defined as a composite end point of self-reported events, including coronary artery disease, stroke, heart attack indicating myocardial infarction, coronary artery angioplasty, coronary artery bypass graft surgery, and peripheral artery disease.

COPD Subtypes

Participants in GOLD spirometric grades 2 to 4 (moderate to very severe COPD) were categorized into specific COPD subtypes based on the extent of emphysema observed in chest CT scans at study enrollment: NEPD (low attenuation area at −950 Hounsfield units [LAA-950] < 5%), EPD (LAA-950 ≥ 10%), and intermediate emphysema (IE) COPD (5% ≤ LAA-950 < 10%). The same CT emphysema thresholds for the EPD and NEPD groups were previously used in articles by Castaldi et al¹⁸ and Hersh et al.²⁴ These definitions were based on analysis of nonsmoking control participants in COPDGene.²⁵ The use of LAA-950 < 5% to define lack of significant emphysema in COPDGene was also used by Bodduluri et al²⁶ and Lowe et al.²⁷

Chest CT Scan-Derived Parameters

The CACS and PA:A ratio were determined from chest CT scans as previously reported.⁷^,⁸^,¹⁰ The CACS was calculated using the Agatston method, which multiplies the area of each calcified coronary lesion by a weighted attenuation score based on the lesion’s maximal Hounsfield unit. The PA:A ratio was measured by comparing the diameters of the pulmonary artery and the aorta at the pulmonary artery bifurcation level.

Statistical Analysis

We assessed the risk of composite CVD end points and COPD exacerbation in participants with COPD based on their baseline CACS or PA:A ratio, using logistic regression models and adjusting for potential confounders including age, sex, race, BMI, current smoking status, pack-years of cigarette smoking, postbronchodilator FEV₁ percent predicted, self-reported hypertension, diabetes mellitus, and hypercholesterolemia. The risk of CVD events or COPD exacerbations in relation to CACS or PA:A ratio magnitudes was also analyzed within specific COPD subtypes. Using longitudinal follow-up survey data, we calculated the annual rate of exacerbations from the initial visit until the first cardiovascular event because cardiovascular events may affect incidence of subsequent COPD exacerbations. Then, COPD exacerbation rates were categorized into one or more and less than one exacerbations per year. We computed baseline characteristics and comorbidity prevalence rates of participants, using χ² tests for categorical variables and Kruskal-Wallis rank sum test for continuous variables to examine differences among subgroups. In addition, in comparing the relative effects of CACS/PA:A ratio on cardiovascular events/COPD exacerbations between subtypes, we conducted further analysis incorporating an interaction term between subtypes (NEPD/EPD) and predictors (CACS/PA:A ratio), along with known covariates. Given the high mortality rate in the EPD subtype (n = 763/1,324), which could influence assessment of nonfatal cardiovascular event occurrences, we conducted a Cox proportional hazards model with a competing risks analysis (Fine and Gray model). This model assessed the subdistribution hazard for cardiovascular events in the presence of competing death events, examining the association between CT scan-derived parameters and cardiovascular events. The Cox models, adjusted for relevant demographic and clinical characteristics, adhered to the proportionality hazards assumption. In terms of the association between imaging predictors and COPD exacerbations, we did not perform Cox proportional hazard analysis because exacerbation frequency was calculated based on exacerbation events assessed over the entire observation period. Our statistical methods are summarized in e-Table 1. Statistical significance was set at P < .05. Data analysis was performed using R (version 4.1.0) in R Studio Pro Server.

Results

Study Participants and Baseline Characteristics

Figure 1 shows the Consolidated Standards of Reporting Trials flowchart for the study’s participants, who had a median monitoring period of 8.8 years. Of the 10,652 initially eligible participants in the COPDGene phase I study, those who had undergone lung transplantation or lung volume reduction surgery, lacked follow-up data, or did not have quality-controlled chest CT data were excluded. Additionally, participants with normal spirometry (FEV₁ ≥ 80% predicted, FEV₁/FVC ≥ 0.7), preserved ratio impaired spirometry (FEV₁ < 80% predicted, FEV₁/FVC ≥ 0.7), and GOLD spirometry grade 1 (FEV₁ ≥ 80% predicted, FEV₁/FVC < 0.7) were also excluded from the primary analysis. This resulted in a final cohort of 2,831 participants, further categorized based on CT scan-detected emphysema severity into NEPD (n = 1,042), EPD (n = 1,324), and IE (n = 465), as detailed in Table 1 and e-Table 2.

Study flow diagram. From the initial eligible 10,652 participants in the COPDGene phase I study, individuals who had received lung transplants/lung volume reduction surgery, did not have longitudinal follow-up, or lacked chest CT scan data were omitted. Additionally, after excluding those with GOLD spirometry grades 0 and 1 and preserved ratio impaired spirometry, we obtained a cohort of 2,831 participants, who were then classified according to the severity of emphysema detected via CT scans into NEPD (n = 1,042), EPD (n = 1,324), and IE (n = 465) groups. EPD = emphysema-predominant COPD; GOLD = Global Initiative for Chronic Obstructive Lung Disease; IE = intermediate emphysema COPD; NEPD = non-emphysema-predominant COPD; PA:A ratio = the ratio of the diameter of the pulmonary artery to the diameter of the aorta.

Table 1.

Baseline Chest CT Scan Parameters and Comorbidities of COPDGene Participants According to COPD Subtypes

Characteristic	NEPD (n = 1,042)^a	IE (n = 465)^a	EPD (n = 1,324)^a	P Value^b
Emphysema at −950 HU, %LAA-950	1.9 (0.8-3.3)	7.2 (6.0-8.7)	20.8 (15.1-29.4)	< .001
CACS	29 (0-233)	79 (0-349)	87 (0-336)	< .001
PA:A ratio	0.90 (0.81-0.99)	0.89 (0.78-0.97)	0.89 (0.79-0.99)	.12
Underlying CVD	248 (24)	113 (24)	313 (24)	> .9
Hypertension	542 (52)	243 (52)	639 (48)	.13
Hypercholesterolemia	449 (43)	217 (47)	548 (41)	.14
Diabetes	172 (17)	65 (14)	114 (8.6)	< .001

Open in a new tab

Values are median (interquartile range), No. (%), or as otherwise indicated. %LAA-950 = percentage of computed tomography low attenuation area < −950 Hounsfield units; CACS = coronary artery calcium score; CVD = cardiovascular disease; EPD = emphysema-predominant COPD; HU = Hounsfield units; IE = intermediate emphysema COPD; NEPD = non-emphysema-predominant COPD; PA:A ratio = ratio of the diameter of the pulmonary artery to the diameter of the aorta.

Median (interquartile range) for emphysema at −950 HU, %LAA-950, PA:A ratio, and CACS.

Pearson χ² for categorical variables; Kruskal-Wallis rank sum test for continuous variables.

e-Table 2 outlines the baseline demographic and physiologic characteristics of participants in each COPD subtype. Participants with EPD were generally older, had a higher percentage of non-Hispanic White individuals, and experienced more severe COPD than the NEPD group. The EPD group was also less likely to currently use cigarettes, possibly due to the tendency for individuals with more severe COPD to have successfully quit smoking. Table 1 also shows no significant differences in the prevalence of preexisting composite CVD and several cardiovascular risk factors (hypertension and hypercholesterolemia) across the COPD subtypes. However, the prevalence of diabetes in the NEPD group was higher than in the EPD group, which is consistent with a previous study.²⁴ Of interest, the EPD group had a higher median value of CACS than the NEPD group, whereas the PA:A median values were not significantly different (Table 1).

Associations Between CACS and Subsequent Cardiovascular Events/COPD Exacerbations

In the entire group of participants with COPD, individuals with higher CACS values (≥ median value of CACS) exhibited a substantially increased risk of subsequent cardiovascular events than those with lower CACS values (OR, 1.61; 95% CI, 1.30-2.00). Within COPD subtypes, participants with NEPD showed a notably stronger association between CACS group and cardiovascular events than the EPD and IE groups (NEPD: OR, 2.02; 95% CI, 1.43-2.87; EPD: OR, 1.41; 95% CI, 1.02-1.96; IE: OR, 1.39; 95% CI, 0.86-2.28) (Table 2). However, regarding the relationship between CACS and COPD exacerbations, there was no significant association either in the overall population (OR, 0.97; 95% CI, 0.79-1.18) or within COPD subtypes (Table 2).

Table 2.

Association of Predictors With Outcomes Across Different COPD Subtypes in Multivariate Analysis (GOLD 2-4)

Predictor	Outcome	NEPD			IE			EPD
Predictor	Outcome	OR	95% CI	P Value	OR	95% CI	P Value	OR	95% CI	P Value
High CACS group	Composite of CV events	2.02	1.43-2.87	< .001	1.39	0.86-2.28	.2	1.41	1.02-1.96	.041
High CACS group	COPD exacerbations (≥ 1 per year)	1.22	0.81-1.84	.3	0.83	0.49-1.40	.5	0.91	0.71-1.18	.5
High CACS group	COPD exacerbations (≥ 2 per year)	0.97	0.50-1.86	.9	1.09	0.50-2.34	.8	0.83	0.59-1.16	.3
PA:A ratio ≥ 1 group	Composite of CV events	0.93	0.61-1.38	.7	1.47	0.80-2.65	.2	0.67	0.45-0.98	.044
PA:A ratio ≥ 1 group	COPD exacerbations (≥ 1 per year)	2.50	1.66-3.75	< .001	1.62	0.87-2.96	.12	1.65	1.25-2.18	< .001
PA:A ratio ≥ 1 group	COPD exacerbations (≥ 2 per year)	3.09	1.70-5.63	< .001	2.26	1.00-5.11	.051	2.07	1.47-2.93	< .001

Open in a new tab

Covariates include age, sex, race, BMI, current smoking status, pack-years of cigarette smoking, self-reported diabetes mellitus, baseline postbronchodilator FEV₁ percent predicted, self-reported hypertension, and self-reported hypercholesterolemia. CACS = coronary artery calcium score; CV = cardiovascular; EPD = emphysema-predominant COPD; GOLD = Global Initiative for Chronic Obstructive Lung Disease; IE = intermediate emphysema COPD; NEPD = non-emphysema-predominant COPD; PA:A ratio = the ratio of the diameter of the pulmonary artery to the diameter of the aorta.

Associations Between PA:A Ratio and Subsequent COPD Exacerbations/Cardiovascular Events

Consistent with previous reports,⁸^,⁹ participants with a higher PA:A ratio (≥ 1) demonstrated a markedly increased risk of COPD exacerbations compared with those with lower PA:A ratios (OR, 1.80; 95% CI, 1.46-2.23), using either an exacerbation frequency of at least one exacerbation per year or at least two exacerbations per year to define frequent exacerbators. Within COPD subtypes, the NEPD group exhibited a relatively stronger link between the PA:A ratio and COPD exacerbations than the EPD and IE groups (NEPD: OR, 2.50; 95% CI, 1.66-3.75; EPD: OR, 1.65; 95% CI, 1.25-2.18; IE: OR, 1.62; 95% CI, 0.87-2.96, using the frequent exacerbator threshold as at least one exacerbation per year), as seen in Table 2. Results using a frequent exacerbator threshold of at least two exacerbations per year provided similar results; however, the effect sizes were slightly larger. Because more participants with COPD meet the frequent exacerbator threshold of one exacerbation per year (660 vs 284), and because we recently demonstrated a significant relationship between COPD exacerbations and future cardiovascular events using an exacerbation threshold of at least one exacerbation per year,²⁸ we used the one exacerbation per year definition for our additional analyses. Regarding the association between PA:A ratio and subsequent cardiovascular events, there was no significant association either in the overall cohort (overall OR, 0.88; 95% CI, 0.68-1.12) or among participants with NEPD, as shown in Table 2. Participants with EPD had a reduced risk for cardiovascular events with elevated PA:A ratio, which was of borderline statistical significance (P = .04).

Evaluation of Covariates Associated With COPD Exacerbations and Cardiovascular Events

The influence of each covariate in our multivariate models varied depending on its association with other covariates and clinical outcomes (e-Tables 3-6). For instance, among those covariates, lower FEV₁ percent predicted at baseline was significantly associated with increased COPD exacerbation risks (e-Tables 4, 5). Moreover, female participants exhibited an increased risk of COPD exacerbations; however, the statistical significance varied among the COPD subtypes (e-Tables 4, 5).

To assess the effect of airway wall thickening on the association between the PA:A ratio and COPD exacerbations, we performed further analyses with additional covariates including segmental airway wall thickness, the square root of wall area of a hypothetical airway with an internal perimeter of 10 mm, and segmental wall area percentage. These airway-wall-thickness-associated factors did not substantially affect the association between PA:A ratio and COPD exacerbations.

Interaction Between COPD Subtypes (NEPD/EPD) and CT Scan Predictors (CACS/PA:A Ratio) in Relation to Cardiovascular Events and COPD Exacerbations

We performed further analysis focusing on evaluating the relative impact of the CACS and PA:A ratio on cardiovascular events and COPD exacerbations, considering the interaction between disease subtypes (NEPD/EPD). This analysis incorporated known covariates as used in the previous analysis.

As demonstrated in Table 3, the interaction between COPD subtypes and CACS indicated that the NEPD subtype showed a significant association with an increased risk of cardiovascular events when compared with the EPD subtype (NEPD vs EPD; OR, 1.63; 95% CI, 1.05-2.52; P = .028). The effect of PA:A ratio on COPD exacerbations varying by COPD subtypes was not statistically significant (OR, 1.39; 95% CI, 0.87-2.22; P = .167).

Table 3.

Relative Risk for Outcomes With the Interaction Term Between Predictors and COPD Subtypes: NEPD vs EPD

Predictor	Outcomes	Interaction Term	NEPD vs EPD
Predictor	Outcomes	Interaction Term	OR	95% CI	P Value
High CACS group	CV events	CACS group × subtype	1.63	1.05-2.52	.028
PA:A ratio ≥ 1 group	CV events	PA:A ratio group × subtype	1.36	0.79-2.33	.269
High CACS group	COPD exacerbations	CACS group × subtype	1.24	0.81-1.91	.323
PA:A ratio ≥ 1 group	COPD exacerbations	PA:A ratio group × subtype	1.39	0.87-2.22	.167

Open in a new tab

Covariates include age, sex, race, BMI, current smoking status, pack-years of cigarette smoking, self-reported diabetes mellitus, baseline postbronchodilator FEV₁ percent predicted, self-reported hypertension, and self-reported hypercholesterolemia. CACS = coronary artery calcium score; CV = cardiovascular; EPD = emphysema-predominant COPD; NEPD = non-emphysema-predominant COPD; PA:A ratio = the ratio of the diameter of the pulmonary artery to the diameter of the aorta.

These results suggest that the differential impact of the CACS on cardiovascular events was influenced by the COPD subtype. The significant interaction in the high CACS group for cardiovascular events underscores the importance of considering COPD subtypes in the assessment of cardiovascular risk.

In addition, we conducted further analysis incorporating an interaction term between CACS and PA:A ratio along with known covariates for outcomes of cardiovascular events and COPD exacerbations. No significant interactions were detected between CACS and PA:A ratio.

Competing Risk Analysis of Cardiovascular Events

In the Kaplan-Meier plots for cumulative incidence of composite cardiovascular events in the overall study population, there was a significant difference between groups categorized by the CACS, but not by the PA:A ratio group (e-Fig 1). Given the high mortality rate in the EPD subtype and the occurrence of competing death events potentially influencing the occurrence of nonfatal cardiovascular events, we conducted a competing risks analysis as an additional sensitivity analysis. The subdistribution hazard ratios (HRs) were estimated using a Fine and Gray competing risks model, considering cardiovascular events as the primary event of interest and death as a competing event (Table 4). After adjustment for competing risks and other covariates, no significant difference in HRs was observed between Cox analysis and competing risk analysis, except for the association between the PA:A ratio and cardiovascular events in the EPD subtype. Specifically, in the EPD subtype, the association between CACS and cardiovascular events had an HR of 1.41 (95% CI, 1.05-1.90) in the Cox regression model and an HR of 1.38 (95% CI, 1.04-1.83) in the competing risk analysis. For the association between the PA:A ratio and cardiovascular events in participants with EPD, the Cox regression model showed an HR of 0.82 (95% CI, 0.57-1.12), whereas the competing risk analysis showed an HR of 0.70 (95% CI, 0.49-0.99). In this analysis, IE subtype showed the highest HR of 1.35, but it was not statistically significant (95% CI, 0.83-2.22).

Table 4.

Results of Competing Risks Analysis Using a Fine and Gray Model to Analyze the Impact of CACS or PA:A Ratio, With CV Events as the Event of Interest and Death as the Competing Event

Characteristic	NEPD			IE			EPD
Characteristic	HR	95% CI	P Value	HR	95% CI	P Value	HR	95% CI	P Value
Association between CACS and CV events
Cox regression model	2.01	1.47-2.73	< .001	1.53	0.99-2.36	.058	1.41	1.05-1.90	.022
Fine and Gray model	1.89	1.40-2.55	< .001	1.35	0.86-2.01	.19	1.38	1.04-1.83	.027
Association between PA:A ratio and CV events
Cox Regression model	1.06	0.74-1.53	.7	1.49	0.90-2.47	.13	0.82	0.57-1.12	.28
Fine and Gray model	0.94	0.67-1.34	.7	1.35	0.83-2.22	.23	0.70	0.49-0.99	.044

Open in a new tab

Covariates include age, sex, race, BMI, current smoking status, pack-years of cigarette smoking, self-reported diabetes mellitus, baseline postbronchodilator FEV₁ percent predicted, self-reported hypertension, and self-reported hypercholesterolemia. CACS = coronary artery calcium score; CV = cardiovascular; EPD = emphysema-predominant COPD; IE = intermediate emphysema COPD; NEPD = non-emphysema-predominant COPD; PA:A ratio = the ratio of the diameter of the pulmonary artery to the diameter of the aorta.

Sensitivity Analysis With Patients With GOLD 1 to 4

As a sensitivity analysis, we performed an additional analysis including participants with GOLD 1 within the COPD study population (GOLD 1-4). When we included participants with GOLD 1, mainly composed of the NEPD subtype (n = 620; NEPD/IE/EPD: 376/146/104), the relationships between CT imaging phenotypes, COPD exacerbations, and cardiovascular outcomes were not substantially different from the results with participants with GOLD 2-4, as shown in e-Table 7.

Discussion

The intersection of COPD and CVD represents a critical juncture in medicine, with significant implications for patient management and outcome prediction.²⁹^,³⁰^,³¹ Our study provides a comprehensive analysis of the relationships between CACS and PA:A ratio and the risk of cardiovascular events and COPD exacerbations in the context of COPD subtypes (Fig 2). Our findings, leveraging longitudinal data from the COPDGene study, confirmed that CACS and PA:A ratio are significant predictors of cardiovascular events and COPD exacerbations, respectively, and their prognostic value is influenced by the COPD subtype, which previous studies did not evaluate.⁸^,¹⁰ Moreover, PA:A ratio does not have an appreciable effect on CVD event risk, and CACS does not have a significant impact on COPD exacerbation risk.

Diagram showing the relative risks between CT scan-derived predictors (CACS and PA:A ratio) and clinical outcomes (CVD events and COPD exacerbations). CACS = coronary artery calcium score; CVD = cardiovascular disease; EPD = emphysema-predominant COPD; HR = hazard ratio; NEPD = non-emphysema-predominant COPD; PA:A ratio = the ratio of the diameter of the pulmonary artery to the diameter of the aorta. ∗P < .05. ∗∗Based on our data showing the association between COPD exacerbations and subsequent cardiovascular disease in COPDGene.²⁸

The CACS has long been recognized as a robust predictor of cardiovascular risk, with higher scores correlating with an elevated likelihood of cardiovascular events.⁷^,³²^,³³ In our study, participants with higher CACS values demonstrated a substantially increased risk of cardiovascular events than those with lower values. This finding aligns with previous research indicating that coronary artery calcification, as measured by CACS, is associated with an increased risk of cardiovascular events in the COPD population.⁷^,¹⁰^,¹⁵^,³⁴ Along with previous studies using the COPDGene study population,⁷^,¹⁰ our study extends these findings to participants with COPD with a more extensive follow-up period (median follow-up, 8.8 years), suggesting that CACS may be a useful tool for cardiovascular risk stratification in these patients.

The PA:A ratio, derived from chest CT scans, serves as an indicator of pulmonary vascular remodeling and right ventricular strain, which are hallmarks of COPD progression.³⁵^,³⁶ Similarly, the PA:A ratio has been shown to predict COPD exacerbations.⁸^,⁹ Our study confirmed this relationship, with participants exhibiting a higher PA:A ratio (≥ 1) demonstrating a higher rate of COPD exacerbations than their counterparts with lower ratios. Compared with prior research,⁸ our study demonstrated this trend over a more extended follow-up period (8.8 vs 2.1 years). This finding underscores the potential utility of the PA:A ratio as a predictive marker for COPD exacerbations, which could aid in the management and treatment of patients with COPD as the previous studies suggested.

However, no significant associations were observed between CACS groups and COPD exacerbations or PA:A ratio groups and cardiovascular events in the overall COPD population or within COPD subtypes. This finding suggests that although CACS and PA:A ratio may be useful predictors for their respective outcomes, their predictive value may not extend to other outcomes. This underscores the complexity of the relationships between these parameters and clinical outcomes in patients with COPD and highlights the need for further research to elucidate these relationships.

Our study revealed that the predictive value of these parameters is influenced by the specific COPD subtype. Among the COPD subgroups, the NEPD subtype demonstrated a stronger association between these parameters and events when compared with the EPD subtype group. Although the EPD subtype showed a higher median value of CACS at baseline than the NEPD subtype (potentially related to the greater average age and pack-years of smoking in the EPD group), the impact of having a high CACS was greater in the NEPD subtype than in the EPD subtype.

A key reason for identifying and studying COPD subtypes is that they may reflect different disease processes with differences in prognosis and treatment response. Further research will be required to determine whether this hypothesis is correct. Participants with EPD tend to have more severe airflow obstruction than participants with NEPD. However, the NEPD subtype may be associated with increased chronic systemic inflammation, which could facilitate the progression of atherosclerosis, resulting in increased cardiovascular events compared with the EPD subtype. Analyses of proteomics and/or transcriptomics data may provide biological insights into these potentially divergent disease processes.

These findings suggest that COPD subtypes may modulate the relationship between these chest CT scan-derived parameters and clinical outcomes, potentially reflecting differences in the underlying pathophysiology of these subtypes. This observation aligns with the growing recognition of COPD as a spectrum of disorders rather than a singular entity, necessitating a nuanced approach to diagnosis, risk stratification, and management. These findings underscore the heterogeneity within COPD and the necessity of considering these subtypes in clinical decision-making.

Furthermore, our analysis of various covariates, including baseline FEV₁ percent predicted, sex, and self-reported race, revealed distinct patterns of association with COPD exacerbations, adding another layer of complexity to the management and prognosis of this disease. An intriguing aspect of our analysis was the higher COPD exacerbation rate observed in female participants across different CACS and PA:A ratio categories. This finding, which is consistent with a previous study,³⁷ resonates with emerging evidence suggesting sex-based differences in COPD’s clinical course and outcomes. The mechanisms underlying these differences are multifaceted, potentially involving variations in airway anatomy, hormonal influences, and variable reporting of respiratory symptoms. In addition, lower values of FEV₁ % predicted at baseline were associated with higher COPD exacerbation rate, which is consistent with the known increased risk of COPD exacerbations as COPD severity increases. The influence of these covariates across different subgroups provides valuable insights for personalized treatment approaches.

Finally, the competing risk analysis, accounting for the high mortality rate in the EPD subtype, affirmed the robustness of our findings. This analysis is crucial because the competing risk of death can obscure the occurrence of cardiovascular events, and competing risk analysis may provide a more accurate assessment of the associations between the studied parameters and clinical outcomes. However, higher PA:A ratio in the EPD subtype was counterintuitively associated with lower risk of subsequent cardiovascular events, although this was of borderline statistical significance (P = .044). Because larger aortas have been associated with increased cardiovascular risk,³⁸ it is possible that in the EPD group, a smaller aortic diameter, and thus a higher PA:A ratio, might explain the observed lower cardiovascular risk. Further research will be required to determine whether the reduced cardiovascular risk in participants with EPD represents inadequate adjustment for the impact of mortality in the EPD subtype, an effect of aortic diameter, or a false-positive result.

Our study has several strengths, including the use of a well-characterized, multicenter cohort, and the longitudinal design, which allowed for the assessment of incident cardiovascular events and COPD exacerbations. Given that CACS, PA:A ratio, and EPD/NEPD COPD subtypes can be ascertained simultaneously from chest CT scans, our approach provides significant practical value in predicting cardiovascular events and COPD exacerbations, varying according to COPD subtypes. However, there are limitations to consider. The observational nature of the COPDGene study, despite its robust longitudinal design, precludes the establishment of causality. Moreover, the generalizability of our findings may be limited by the specific demographic characteristics of the COPDGene cohort, which primarily includes older adults with a significant smoking history in two ancestry groups. In addition, our analysis did not adjust for medication usage.³⁹ Finally, we demonstrated a statistically significant increase in CVD risk associated with CACS in NEPD compared to participants with EPD, but only a trend for greater COPD exacerbation risk related to elevated PA:A ratio in NEPD compared to participants with EPD. Future research should aim to validate these findings in more diverse populations and explore the underlying biological mechanisms linking COPD subtypes with cardiovascular risk and COPD exacerbations.

Interpretation

Our study demonstrates that CACS and PA:A ratio, derived from chest CT scans in patients with COPD, hold distinct predictive values for cardiovascular events and COPD exacerbations that are influenced by the specific COPD subtypes. These findings have important implications for the management of patients with COPD, suggesting that these imaging biomarkers could be used to guide risk stratification and treatment decisions. Further research is needed to validate these findings in other populations and to explore the mechanisms underlying these relationships.

Funding/Support

The COPDGene study (NCT00608764) is supported by grants from the NHLBI [Grants U01HL089897, U01HL089856], by NIH contract 75N92023D00011, and by the COPD Foundation through contributions made to an Industry Advisory Committee that has included AstraZeneca, Bayer Pharmaceuticals, Boehringer-Ingelheim, Genentech, GlaxoSmithKline, Novartis, Pfizer, and Sunovion. This study was also supported in part by the NHLBI [Grants R01HL133135, R01HL147148, P01HL114501, R01HL152728].

Financial/Nonfinancial Disclosures

The authors have reported to CHEST the following: In the past 3 years, E. K. S. has received grant support from Bayer and Northpond Laboratories. J. M. W. has received grant support from ARCUS-Med, Mereo BioPharma, Medscape, Verona Pharma, Grifols, Alpha-1 Foundation, and InhibrX. P. J. C. has received grant support from Bayer and Sanofi. C. P. H. has received grant support from Alpha-1 Foundation, Bayer, Boehringer-Ingelheim, and Vertex. None declared (H.-M. Y., M. H. R., V. J. C., K. Y., G. L. K., M. T. D., R. C. W., M. J. B.).

Acknowledgments

Author contributions: H.-M. Y. and E. K. S. contributed to concept and design, and manuscript writing; and take responsibility for the content of the manuscript, including the data and analysis. H.-M. Y., M. H. R., P. J. C., and C. P. H. contributed to acquisition or processing of the data. H.-M. Y., M. H. R., V. J. C., and E. K. S. contributed to statistical analysis. All authors contributed to data analysis; and reviewed, edited, and approved the final manuscript.

Role of sponsors: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of data; and preparation, review, or approval of this manuscript.

∗COPDGene Investigators: Core units: administrative center: James D. Crapo, MD (principal investigator); Edwin K. Silverman, MD, PhD (principal investigator); Barry J. Make, MD; and Elizabeth A. Regan, MD, PhD. Genetic analysis center: Terri H. Beaty, PhD; Peter J. Castaldi, MD, MSc; Michael H. Cho, MD, MPH; Dawn L. DeMeo, MD, MPH; Adel El Boueiz, MD, MMSc; Marilyn G. Foreman, MD, MS; Auyon Ghosh, MD; Lystra P. Hayden, MD, MMSc; Craig P. Hersh, MD, MPH; Jacqueline Hetmanski, MS; Brian D. Hobbs, MD, MMSc; John E. Hokanson, MPH, PhD; Wonji Kim, PhD; Nan Laird, PhD; Christoph Lange, PhD; Sharon M. Lutz, PhD; Merry-Lynn McDonald, PhD; Dmitry Prokopenko, PhD; Matthew Moll, MD, MPH; Jarrett Morrow, PhD; Dandi Qiao, PhD; Elizabeth A. Regan, MD, PhD; Aabida Saferali, PhD; Phuwanat Sakornsakolpat, MD; Edwin K. Silverman, MD, PhD; Emily S. Wan, MD; and Jeong Yun, MD, MPH. Imaging center: Juan Pablo Centeno; Jean-Paul Charbonnier, PhD; Harvey O. Coxson, PhD; Craig J. Galban, PhD; MeiLan K. Han, MD, MS; Eric A. Hoffman, PhD, Stephen Humphries, PhD; Francine L. Jacobson, MD, MPH; Philip F. Judy, PhD; Ella A. Kazerooni, MD; Alex Kluiber; David A. Lynch, MB; Pietro Nardelli, PhD; John D. Newell Jr, MD; Aleena Notary; Andrea Oh, MD; Elizabeth A. Regan, MD, PhD; James C. Ross, PhD; Raul San Jose Estepar, PhD; Joyce Schroeder, MD; Jered Sieren; Berend C. Stoel, PhD; Juerg Tschirren, PhD; Edwin Van Beek, MD, PhD; Bram van Ginneken, PhD; Eva van Rikxoort, PhD; Gonzalo Vegas Sanchez-Ferrero, PhD; Lucas Veitel; George R. Washko, MD; and Carla G. Wilson, MS. PFT QA Center, Salt Lake City, UT: Robert Jensen, PhD. Data coordinating center and biostatistics, National Jewish Health, Denver, CO: Douglas Everett, PhD; Jim Crooks, PhD; Katherine Pratte, PhD; Matt Strand, PhD; and Carla G. Wilson, MS. Epidemiology core, University of Colorado Anschutz Medical Campus, Aurora, CO: John E. Hokanson, MPH, PhD; Erin Austin, PhD; Gregory Kinney, MPH, PhD; Sharon M. Lutz, PhD; Kendra A. Young, PhD. Mortality adjudication core: Surya P. Bhatt, MD; Jessica Bon, MD; Alejandro A. Diaz, MD, MPH; MeiLan K. Han, MD, MS; Barry Make, MD; Susan Murray, ScD; Elizabeth Regan, MD; Xavier Soler, MD; and Carla G. Wilson, MS. Biomarker core: Russell P. Bowler, MD, PhD; Katerina Kechris, PhD; Farnoush Banaei-Kashani, PhD. COPDGene Investigators – clinical centers: Ann Arbor, VA: Jeffrey L. Curtis, MD; and Perry G. Pernicano, MD. Baylor College of Medicine, Houston, TX: Nicola Hanania, MD, MS; Mustafa Atik, MD; Aladin Boriek, PhD; Kalpatha Guntupalli, MD; Elizabeth Guy, MD; and Amit Parulekar, MD. Brigham and Women’s Hospital, Boston, MA: Dawn L. DeMeo, MD, MPH; Craig Hersh, MD, MPH; Francine L. Jacobson, MD, MPH; and George Washko, MD. Columbia University, New York, NY: R. Graham Barr, MD, DrPH; John Austin, MD; Belinda D’Souza, MD; and Byron Thomashow, MD. Duke University Medical Center, Durham, NC: Neil MacIntyre Jr, MD; H. Page McAdams, MD; and Lacey Washington, MD. HealthPartners Research Institute, Minneapolis, MN: Charlene McEvoy, MD, MPH; and Joseph Tashjian, MD. Johns Hopkins University, Baltimore, MD: Robert Wise, MD; Robert Brown, MD; Nadia N. Hansel, MD, MPH; Karen Horton, MD; Allison Lambert, MD, MHS; and Nirupama Putcha, MD, MHS. Lundquist Institute for Biomedical Innovationat Harbor UCLA Medical Center, Torrance, CA: Richard Casaburi, PhD, MD; Alessandra Adami, PhD; Matthew Budoff, MD; Hans Fischer, MD; Janos Porszasz, MD, PhD; Harry Rossiter, PhD; and William Stringer, MD. Michael E. DeBakey VAMC, Houston, TX: Amir Sharafkhaneh, MD, PhD; and Charlie Lan, DO. Minneapolis VA: Christine Wendt, MD; Brian Bell, MD; and Ken M. Kunisaki, MD, MS. Morehouse School of Medicine, Atlanta, GA: Eric L. Flenaugh, MD; Hirut Gebrekristos, PhD; Mario Ponce, MD; Silanath Terpenning, MD; and Gloria Westney, MD, MS. National Jewish Health, Denver, CO: Russell Bowler, MD, PhD; and David A. Lynch, MB. Reliant Medical Group, Worcester, MA: Richard Rosiello, MD; and David Pace, MD. Temple University, Philadelphia, PA: Gerard Criner, MD; David Ciccolella, MD; Francis Cordova, MD; Chandra Dass, MD; Gilbert D’Alonzo, DO; Parag Desai, MD; Michael Jacobs, PharmD; Steven Kelsen, MD, PhD; Victor Kim, MD; A. James Mamary, MD; Nathaniel Marchetti, DO; Aditi Satti, MD; Kartik Shenoy, MD; Robert M. Steiner, MD; Alex Swift, MD; Irene Swift, MD; and Maria Elena Vega-Sanchez, MD. University of Alabama, Birmingham, AL: Mark Dransfield, MD; William Bailey, MD; Surya P. Bhatt, MD; Anand Iyer, MD; Hrudaya Nath, MD; and J. Michael Wells, MD. University of California, San Diego, CA: Douglas Conrad, MD; Xavier Soler, MD, PhD; and Andrew Yen, MD. University of Iowa, Iowa City, IA: Alejandro P. Comellas, MD; Karin F. Hoth, PhD; John Newell Jr, MD; and Brad Thompson, MD. University of Michigan, Ann Arbor, MI: MeiLan K. Han, MD, MS; Ella Kazerooni, MD, MS; Wassim Labaki, MD, MS; Craig Galban, PhD; and Dharshan Vummidi, MD. University of Minnesota, Minneapolis, MN: Joanne Billings, MD; Abbie Begnaud, MD; and Tadashi Allen, MD. University of Pittsburgh, Pittsburgh, PA: Frank Sciurba, MD; Jessica Bon, MD; Divay Chandra, MD, MSc; and Joel Weissfeld, MD, MPH. University of Texas Health, San Antonio, San Antonio, TX: Antonio Anzueto, MD; Sandra Adams, MD; Diego Maselli-Caceres, MD; Mario E. Ruiz, MD; and Harjinder Singh.

Additional information: The e-Figure and e-Tables are available online under "Supplementary Data."

Supplementary Data

e-Online Data

mmc1.docx^{(178KB, docx)}

e-Online Data

mmc2.docx^{(42.3KB, docx)}

References

1.Roth G.A., Mensah G.A., Johnson C.O., et al. Global burden of cardiovascular diseases and risk factors, 1990-2019: update from the GBD 2019 Study. J Am Coll Cardiol. 2020;76(25):2982–3021. doi: 10.1016/j.jacc.2020.11.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Divo M., Cote C., de Torres J.P., et al. Comorbidities and risk of mortality in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2012;186(2):155–161. doi: 10.1164/rccm.201201-0034OC. [DOI] [PubMed] [Google Scholar]
3.Seemungal T.A., Donaldson G.C., Paul E.A., Bestall J.C., Jeffries D.J., Wedzicha J.A. Effect of exacerbation on quality of life in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 1998;157(5 pt 1):1418–1422. doi: 10.1164/ajrccm.157.5.9709032. [DOI] [PubMed] [Google Scholar]
4.Curkendall S.M., DeLuise C., Jones J.K., et al. Cardiovascular disease in patients with chronic obstructive pulmonary disease, Saskatchewan Canada cardiovascular disease in COPD patients. Ann Epidemiol. 2006;16(1):63–70. doi: 10.1016/j.annepidem.2005.04.008. [DOI] [PubMed] [Google Scholar]
5.Jairam P.M., de Jong P.A., Mali W.P., Isgum I., van der Graaf Y., PROVIDI study-group Cardiovascular disease prediction: Do pulmonary disease-related chest CT features have added value? Eur Radiol. 2015;25(6):1646–1654. doi: 10.1007/s00330-014-3495-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Budoff M.J., Nasir K., Kinney G.L., et al. Coronary artery and thoracic calcium on noncontrast thoracic CT scans: comparison of ungated and gated examinations in patients from the COPD Gene cohort. J Cardiovasc Comput Tomogr. 2011;5(2):113–118. doi: 10.1016/j.jcct.2010.11.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Budoff M.J., Lutz S.M., Kinney G.L., et al. Coronary artery calcium on noncontrast thoracic computerized tomography scans and all-cause mortality. Circulation. 2018;138(21):2437–2438. doi: 10.1161/CIRCULATIONAHA.118.036835. [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Wells J.M., Washko G.R., Han M.K., et al. Pulmonary arterial enlargement and acute exacerbations of COPD. N Engl J Med. 2012;367(10):913–921. doi: 10.1056/NEJMoa1203830. [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Wells J.M., Dransfield M.T. Pathophysiology and clinical implications of pulmonary arterial enlargement in COPD. Int J Chron Obstruct Pulmon Dis. 2013;8:509–521. doi: 10.2147/COPD.S52204. [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Bhatt S.P., Kazerooni E.A., Newell J.D., Jr., et al. Visual estimate of coronary artery calcium predicts cardiovascular disease in COPD. Chest. 2018;154(3):579–587. doi: 10.1016/j.chest.2018.05.037. [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Wells J.M., Farris R.F., Gosdin T.A., et al. Pulmonary artery enlargement and cystic fibrosis pulmonary exacerbations: a cohort study. Lancet Respir Med. 2016;4(8):636–645. doi: 10.1016/S2213-2600(16)30105-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Kammerlander A.A., Aschauer S., Zotter-Tufaro C., et al. Diameter of the pulmonary artery in relation to the ascending aorta: association with cardiovascular outcome. Radiology. 2017;284(3):685–693. doi: 10.1148/radiol.2017161849. [DOI] [PubMed] [Google Scholar]
13.Ieki H., Nagatomo Y., Tsugu M., et al. Impact of pulmonary artery-to-aorta ratio by CT on the clinical outcome in heart failure. J Card Fail. 2019;25(11):886–893. doi: 10.1016/j.cardfail.2019.05.005. [DOI] [PubMed] [Google Scholar]
14.Tiller N.B., Kinninger A., Abbasi A., et al. Physical activity, muscle oxidative capacity, and coronary artery calcium in smokers with and without COPD. Int J Chron Obstruct Pulmon Dis. 2022;17:2811–2820. doi: 10.2147/COPD.S385000. [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Williams M.C., Murchison J.T., Edwards L.D., et al. Coronary artery calcification is increased in patients with COPD and associated with increased morbidity and mortality. Thorax. 2014;69(8):718–723. doi: 10.1136/thoraxjnl-2012-203151. [DOI] [PubMed] [Google Scholar]
16.Young K.A., Regan E.A., Han M.K., et al. Subtypes of COPD have unique distributions and differential risk of mortality. Chronic Obstr Pulm Dis. 2019;6(5):400–413. doi: 10.15326/jcopdf.6.5.2019.0150. [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Castaldi P.J., Boueiz A., Yun J., et al. Machine learning characterization of COPD subtypes: insights from the COPDGene Study. Chest. 2020;157(5):1147–1157. doi: 10.1016/j.chest.2019.11.039. [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Castaldi P.J., Xu Z., Young K.A., et al. Heterogeneity and progression of chronic obstructive pulmonary disease: emphysema-predominant and non-emphysema-predominant disease. Am J Epidemiol. 2023;192(10):1647–1658. doi: 10.1093/aje/kwad114. [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Kim W.D. Phenotype of chronic obstructive pulmonary disease based on computed tomography-defined underlying pathology. Tuberc Respir Dis (Seoul) 2022;85(4):302–312. doi: 10.4046/trd.2022.0029. [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Gregory A., Xu Z., Pratte K., et al. Clustering-based COPD subtypes have distinct longitudinal outcomes and multi-omics biomarkers. BMJ Open Respir Res. 2022;9(1) doi: 10.1136/bmjresp-2021-001182. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Regan E.A., Hokanson J.E., Murphy J.R., et al. Genetic epidemiology of COPD (COPDGene) study design. COPD. 2010;7(1):32–43. doi: 10.3109/15412550903499522. [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Washko G.R., Lynch D.A., Matsuoka S., et al. Identification of early interstitial lung disease in smokers from the COPDGene Study. Acad Radiol. 2010;17(1):48–53. doi: 10.1016/j.acra.2009.07.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Agusti A., Celli B.R., Criner G.J., et al. Global Initiative for Chronic Obstructive Lung Disease 2023 Report: GOLD executive summary. Eur Respir J. 2023;61(4) doi: 10.1183/13993003.00239-2023. [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Hersh C.P., Make B.J., Lynch D.A., et al. Non-emphysematous chronic obstructive pulmonary disease is associated with diabetes mellitus. BMC Pulm Med. 2014;14:164. doi: 10.1186/1471-2466-14-164. [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Zach J.A., Newell J.D., Jr., Schroeder J., et al. Quantitative computed tomography of the lungs and airways in healthy nonsmoking adults. Invest Radiol. 2012;47(10):596–602. doi: 10.1097/RLI.0b013e318262292e. [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Bodduluri S., Nakhmani A., Reinhardt J.M., et al. Deep neural network analyses of spirometry for structural phenotyping of chronic obstructive pulmonary disease. JCI Insight. 2020;5(13) doi: 10.1172/jci.insight.132781. [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Lowe K.E., Regan E.A., Anzueto A., et al. COPDGene® 2019: redefining the diagnosis of chronic obstructive pulmonary disease. Chronic Obstr Pulm Dis. 2019;6(5):384–399. doi: 10.15326/jcopdf.6.5.2019.0149. [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Yang H.-M., Ryu M.H., Carey V.J., et al. COPD exacerbations increase the risk of subsequent cardiovascular events: a longitudinal analysis of the COPDGene Study. J Am Heart Assoc. 2024;13(11) doi: 10.1161/JAHA.123.033882. [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Balbirsingh V., Mohammed A.S., Turner A.M., Newnham M. Cardiovascular disease in chronic obstructive pulmonary disease: a narrative review. Thorax. 2022;77:939–945. doi: 10.1136/thoraxjnl-2021-218333. [DOI] [PubMed] [Google Scholar]
30.Rabe K.F., Hurst J.R., Suissa S. Cardiovascular disease and COPD: Dangerous liaisons? Eur Respir Rev. 2018;27(149):180057. doi: 10.1183/16000617.0057-2018. [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Andre S., Conde B., Fragoso E., et al. COPD and cardiovascular disease. Pulmonology. 2019;25(3):168–176. doi: 10.1016/j.pulmoe.2018.09.006. [DOI] [PubMed] [Google Scholar]
32.Neves P.O., Andrade J., Moncao H. Coronary artery calcium score: current status. Radiol Bras. 2017;50(3):182–189. doi: 10.1590/0100-3984.2015.0235. [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Shreya D., Zamora D.I., Patel G.S., et al. Coronary artery calcium score - a reliable indicator of coronary artery disease? Cureus. 2021;13(12) doi: 10.7759/cureus.20149. [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Kahnert K., Jorres R.A., Jobst B., et al. Association of coronary artery calcification with clinical and physiological characteristics in patients with COPD: results from COSYCONET. Respir Med. 2022;204 doi: 10.1016/j.rmed.2022.107014. [DOI] [PubMed] [Google Scholar]
35.Iyer A.S., Wells J.M., Vishin S., Bhatt S.P., Wille K.M., Dransfield M.T. CT scan-measured pulmonary artery to aorta ratio and echocardiography for detecting pulmonary hypertension in severe COPD. Chest. 2014;145(4):824–832. doi: 10.1378/chest.13-1422. [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Terzikhan N., Bos D., Lahousse L., et al. Pulmonary artery to aorta ratio and risk of all-cause mortality in the general population: the Rotterdam Study. Eur Respir J. 2017;49(6) doi: 10.1183/13993003.02168-2016. [DOI] [PubMed] [Google Scholar]
37.Hurst J.R., Vestbo J., Anzueto A., et al. Susceptibility to exacerbation in chronic obstructive pulmonary disease. N Engl J Med. 2010;363(12):1128–1138. doi: 10.1056/NEJMoa0909883. [DOI] [PubMed] [Google Scholar]
38.Rueda-Ochoa O.L., Bons L.R., Zhu F., et al. Thoracic aortic diameter and cardiovascular events and mortality among women and men. Radiology. 2022;304(1):208–215. doi: 10.1148/radiol.210861. [DOI] [PubMed] [Google Scholar]
39.Bhatt S.P., Wells J.M., Kinney G.L., et al. β-blockers are associated with a reduction in COPD exacerbations. Thorax. 2016;71(1):8–14. doi: 10.1136/thoraxjnl-2015-207251. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

e-Online Data

mmc1.docx^{(178KB, docx)}

e-Online Data

mmc2.docx^{(42.3KB, docx)}

[bib1] 1.Roth G.A., Mensah G.A., Johnson C.O., et al. Global burden of cardiovascular diseases and risk factors, 1990-2019: update from the GBD 2019 Study. J Am Coll Cardiol. 2020;76(25):2982–3021. doi: 10.1016/j.jacc.2020.11.010. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib2] 2.Divo M., Cote C., de Torres J.P., et al. Comorbidities and risk of mortality in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2012;186(2):155–161. doi: 10.1164/rccm.201201-0034OC. [DOI] [PubMed] [Google Scholar]

[bib3] 3.Seemungal T.A., Donaldson G.C., Paul E.A., Bestall J.C., Jeffries D.J., Wedzicha J.A. Effect of exacerbation on quality of life in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 1998;157(5 pt 1):1418–1422. doi: 10.1164/ajrccm.157.5.9709032. [DOI] [PubMed] [Google Scholar]

[bib4] 4.Curkendall S.M., DeLuise C., Jones J.K., et al. Cardiovascular disease in patients with chronic obstructive pulmonary disease, Saskatchewan Canada cardiovascular disease in COPD patients. Ann Epidemiol. 2006;16(1):63–70. doi: 10.1016/j.annepidem.2005.04.008. [DOI] [PubMed] [Google Scholar]

[bib5] 5.Jairam P.M., de Jong P.A., Mali W.P., Isgum I., van der Graaf Y., PROVIDI study-group Cardiovascular disease prediction: Do pulmonary disease-related chest CT features have added value? Eur Radiol. 2015;25(6):1646–1654. doi: 10.1007/s00330-014-3495-0. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib6] 6.Budoff M.J., Nasir K., Kinney G.L., et al. Coronary artery and thoracic calcium on noncontrast thoracic CT scans: comparison of ungated and gated examinations in patients from the COPD Gene cohort. J Cardiovasc Comput Tomogr. 2011;5(2):113–118. doi: 10.1016/j.jcct.2010.11.002. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib7] 7.Budoff M.J., Lutz S.M., Kinney G.L., et al. Coronary artery calcium on noncontrast thoracic computerized tomography scans and all-cause mortality. Circulation. 2018;138(21):2437–2438. doi: 10.1161/CIRCULATIONAHA.118.036835. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib8] 8.Wells J.M., Washko G.R., Han M.K., et al. Pulmonary arterial enlargement and acute exacerbations of COPD. N Engl J Med. 2012;367(10):913–921. doi: 10.1056/NEJMoa1203830. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib9] 9.Wells J.M., Dransfield M.T. Pathophysiology and clinical implications of pulmonary arterial enlargement in COPD. Int J Chron Obstruct Pulmon Dis. 2013;8:509–521. doi: 10.2147/COPD.S52204. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib10] 10.Bhatt S.P., Kazerooni E.A., Newell J.D., Jr., et al. Visual estimate of coronary artery calcium predicts cardiovascular disease in COPD. Chest. 2018;154(3):579–587. doi: 10.1016/j.chest.2018.05.037. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib11] 11.Wells J.M., Farris R.F., Gosdin T.A., et al. Pulmonary artery enlargement and cystic fibrosis pulmonary exacerbations: a cohort study. Lancet Respir Med. 2016;4(8):636–645. doi: 10.1016/S2213-2600(16)30105-9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib12] 12.Kammerlander A.A., Aschauer S., Zotter-Tufaro C., et al. Diameter of the pulmonary artery in relation to the ascending aorta: association with cardiovascular outcome. Radiology. 2017;284(3):685–693. doi: 10.1148/radiol.2017161849. [DOI] [PubMed] [Google Scholar]

[bib13] 13.Ieki H., Nagatomo Y., Tsugu M., et al. Impact of pulmonary artery-to-aorta ratio by CT on the clinical outcome in heart failure. J Card Fail. 2019;25(11):886–893. doi: 10.1016/j.cardfail.2019.05.005. [DOI] [PubMed] [Google Scholar]

[bib14] 14.Tiller N.B., Kinninger A., Abbasi A., et al. Physical activity, muscle oxidative capacity, and coronary artery calcium in smokers with and without COPD. Int J Chron Obstruct Pulmon Dis. 2022;17:2811–2820. doi: 10.2147/COPD.S385000. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib15] 15.Williams M.C., Murchison J.T., Edwards L.D., et al. Coronary artery calcification is increased in patients with COPD and associated with increased morbidity and mortality. Thorax. 2014;69(8):718–723. doi: 10.1136/thoraxjnl-2012-203151. [DOI] [PubMed] [Google Scholar]

[bib16] 16.Young K.A., Regan E.A., Han M.K., et al. Subtypes of COPD have unique distributions and differential risk of mortality. Chronic Obstr Pulm Dis. 2019;6(5):400–413. doi: 10.15326/jcopdf.6.5.2019.0150. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib17] 17.Castaldi P.J., Boueiz A., Yun J., et al. Machine learning characterization of COPD subtypes: insights from the COPDGene Study. Chest. 2020;157(5):1147–1157. doi: 10.1016/j.chest.2019.11.039. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib18] 18.Castaldi P.J., Xu Z., Young K.A., et al. Heterogeneity and progression of chronic obstructive pulmonary disease: emphysema-predominant and non-emphysema-predominant disease. Am J Epidemiol. 2023;192(10):1647–1658. doi: 10.1093/aje/kwad114. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib19] 19.Kim W.D. Phenotype of chronic obstructive pulmonary disease based on computed tomography-defined underlying pathology. Tuberc Respir Dis (Seoul) 2022;85(4):302–312. doi: 10.4046/trd.2022.0029. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib20] 20.Gregory A., Xu Z., Pratte K., et al. Clustering-based COPD subtypes have distinct longitudinal outcomes and multi-omics biomarkers. BMJ Open Respir Res. 2022;9(1) doi: 10.1136/bmjresp-2021-001182. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib21] 21.Regan E.A., Hokanson J.E., Murphy J.R., et al. Genetic epidemiology of COPD (COPDGene) study design. COPD. 2010;7(1):32–43. doi: 10.3109/15412550903499522. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib22] 22.Washko G.R., Lynch D.A., Matsuoka S., et al. Identification of early interstitial lung disease in smokers from the COPDGene Study. Acad Radiol. 2010;17(1):48–53. doi: 10.1016/j.acra.2009.07.016. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib23] 23.Agusti A., Celli B.R., Criner G.J., et al. Global Initiative for Chronic Obstructive Lung Disease 2023 Report: GOLD executive summary. Eur Respir J. 2023;61(4) doi: 10.1183/13993003.00239-2023. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib24] 24.Hersh C.P., Make B.J., Lynch D.A., et al. Non-emphysematous chronic obstructive pulmonary disease is associated with diabetes mellitus. BMC Pulm Med. 2014;14:164. doi: 10.1186/1471-2466-14-164. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib25] 25.Zach J.A., Newell J.D., Jr., Schroeder J., et al. Quantitative computed tomography of the lungs and airways in healthy nonsmoking adults. Invest Radiol. 2012;47(10):596–602. doi: 10.1097/RLI.0b013e318262292e. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib26] 26.Bodduluri S., Nakhmani A., Reinhardt J.M., et al. Deep neural network analyses of spirometry for structural phenotyping of chronic obstructive pulmonary disease. JCI Insight. 2020;5(13) doi: 10.1172/jci.insight.132781. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib27] 27.Lowe K.E., Regan E.A., Anzueto A., et al. COPDGene® 2019: redefining the diagnosis of chronic obstructive pulmonary disease. Chronic Obstr Pulm Dis. 2019;6(5):384–399. doi: 10.15326/jcopdf.6.5.2019.0149. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib28] 28.Yang H.-M., Ryu M.H., Carey V.J., et al. COPD exacerbations increase the risk of subsequent cardiovascular events: a longitudinal analysis of the COPDGene Study. J Am Heart Assoc. 2024;13(11) doi: 10.1161/JAHA.123.033882. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib29] 29.Balbirsingh V., Mohammed A.S., Turner A.M., Newnham M. Cardiovascular disease in chronic obstructive pulmonary disease: a narrative review. Thorax. 2022;77:939–945. doi: 10.1136/thoraxjnl-2021-218333. [DOI] [PubMed] [Google Scholar]

[bib30] 30.Rabe K.F., Hurst J.R., Suissa S. Cardiovascular disease and COPD: Dangerous liaisons? Eur Respir Rev. 2018;27(149):180057. doi: 10.1183/16000617.0057-2018. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib31] 31.Andre S., Conde B., Fragoso E., et al. COPD and cardiovascular disease. Pulmonology. 2019;25(3):168–176. doi: 10.1016/j.pulmoe.2018.09.006. [DOI] [PubMed] [Google Scholar]

[bib32] 32.Neves P.O., Andrade J., Moncao H. Coronary artery calcium score: current status. Radiol Bras. 2017;50(3):182–189. doi: 10.1590/0100-3984.2015.0235. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib33] 33.Shreya D., Zamora D.I., Patel G.S., et al. Coronary artery calcium score - a reliable indicator of coronary artery disease? Cureus. 2021;13(12) doi: 10.7759/cureus.20149. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib34] 34.Kahnert K., Jorres R.A., Jobst B., et al. Association of coronary artery calcification with clinical and physiological characteristics in patients with COPD: results from COSYCONET. Respir Med. 2022;204 doi: 10.1016/j.rmed.2022.107014. [DOI] [PubMed] [Google Scholar]

[bib35] 35.Iyer A.S., Wells J.M., Vishin S., Bhatt S.P., Wille K.M., Dransfield M.T. CT scan-measured pulmonary artery to aorta ratio and echocardiography for detecting pulmonary hypertension in severe COPD. Chest. 2014;145(4):824–832. doi: 10.1378/chest.13-1422. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib36] 36.Terzikhan N., Bos D., Lahousse L., et al. Pulmonary artery to aorta ratio and risk of all-cause mortality in the general population: the Rotterdam Study. Eur Respir J. 2017;49(6) doi: 10.1183/13993003.02168-2016. [DOI] [PubMed] [Google Scholar]

[bib37] 37.Hurst J.R., Vestbo J., Anzueto A., et al. Susceptibility to exacerbation in chronic obstructive pulmonary disease. N Engl J Med. 2010;363(12):1128–1138. doi: 10.1056/NEJMoa0909883. [DOI] [PubMed] [Google Scholar]

[bib38] 38.Rueda-Ochoa O.L., Bons L.R., Zhu F., et al. Thoracic aortic diameter and cardiovascular events and mortality among women and men. Radiology. 2022;304(1):208–215. doi: 10.1148/radiol.210861. [DOI] [PubMed] [Google Scholar]

[bib39] 39.Bhatt S.P., Wells J.M., Kinney G.L., et al. β-blockers are associated with a reduction in COPD exacerbations. Thorax. 2016;71(1):8–14. doi: 10.1136/thoraxjnl-2015-207251. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Differential Association of COPD Subtypes With Cardiovascular Events and COPD Exacerbations

Han-Mo Yang, MD, PhD

Min Hyung Ryu, PhD

Vincent J Carey, PhD

Kendra Young, MSPH, PhD

Gregory L Kinney, PhD

Mark T Dransfield, MD

Raymond C Wade, MD

James M Wells, MD

Matthew J Budoff, MD

Peter J Castaldi, MD

Craig P Hersh, MD

Edwin K Silverman, MD, PhD

Abstract

Background

Research Question

Study Design and Methods

Results

Interpretation

Trial Registration

Take-home Points.

Study Design and Methods

Study Population

COPD Subtypes

Chest CT Scan-Derived Parameters

Statistical Analysis

Results

Study Participants and Baseline Characteristics

Figure 1.

Table 1.

Associations Between CACS and Subsequent Cardiovascular Events/COPD Exacerbations

Table 2.

Associations Between PA:A Ratio and Subsequent COPD Exacerbations/Cardiovascular Events

Evaluation of Covariates Associated With COPD Exacerbations and Cardiovascular Events

Interaction Between COPD Subtypes (NEPD/EPD) and CT Scan Predictors (CACS/PA:A Ratio) in Relation to Cardiovascular Events and COPD Exacerbations

Table 3.

Competing Risk Analysis of Cardiovascular Events

Table 4.

Sensitivity Analysis With Patients With GOLD 1 to 4

Discussion

Figure 2.

Interpretation

Funding/Support

Financial/Nonfinancial Disclosures

Acknowledgments

Supplementary Data

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases