Longitudinal Modeling of Lung Function Trajectories in Smokers with and without Chronic Obstructive Pulmonary Disease

Abstract

Rationale: The relationship between longitudinal lung function trajectories, chest computed tomography (CT) imaging, and genetic predisposition to chronic obstructive pulmonary disease (COPD) has not been explored.

Objectives: 1) To model trajectories using a data-driven approach applied to longitudinal data spanning adulthood in the Normative Aging Study (NAS), and 2) to apply these models to demographically similar subjects in the COPDGene (Genetic Epidemiology of COPD) Study with detailed phenotypic characterization including chest CT.

Methods: We modeled lung function trajectories in 1,060 subjects in NAS with a median follow-up time of 29 years. We assigned 3,546 non-Hispanic white males in COPDGene to these trajectories for further analysis. We assessed phenotypic and genetic differences between trajectories and across age strata.

Measurements and main results: We identified four trajectories in NAS with differing levels of maximum lung function and rate of decline. In COPDGene, 617 subjects (17%) were assigned to the lowest trajectory and had the greatest radiologic burden of disease (P < 0.01); 1,283 subjects (36%) were assigned to a low trajectory with evidence of airway disease preceding emphysema on CT; 1,411 subjects (40%) and 237 subjects (7%) were assigned to the remaining two trajectories and tended to have preserved lung function and negligible emphysema. The genetic contribution to these trajectories was as high as 83% (P = 0.02), and membership in lower lung function trajectories was associated with greater parental histories of COPD, decreased exercise capacity, greater dyspnea, and more frequent COPD exacerbations.

Conclusions: Data-driven analysis identifies four lung function trajectories. Trajectory membership has a genetic basis and is associated with distinct lung structural abnormalities.

Keywords: chronic obstructive pulmonary disease; longitudinal analysis; lung function trajectories.

Figure 1.

Lung function trajectories identified by nonparametric Bayesian mixture modeling in the Normative Aging Study. Connected dots indicate sequences of subject visits. Thick lines indicate expected (population-averaged) FEV₁ values as a function of age (assuming average height and pack-years of smoke exposure). Shaded regions represent FEV₁ 95% prediction interval estimated by the mixture modeling approach (see Discussion of Prediction Intervals in the online supplement for additional details).

Figure 2.

Airway wall thickening (left) and emphysema (right) trends in COPDGene (Genetic Epidemiology of COPD Study) by trajectory assignment, stratified by age. Airway wall thickening is assessed as the square root of the wall area ($\sqrt{\text{WA}}$) of a theoretical airway with an internal lumen perimeter of 10 mm (14). Emphysema is assessed as the Hounsfield unit (HU) value representing the 15th percentile of the lung region HU histogram (Perc15) (13). Greater amounts of emphysema are seen in the trajectory 2 subgroup relative to trajectories 3 and 4 only within older age strata, and thicker airway walls are observed in trajectory 2 individuals throughout (P < 0.001 for all airway thickness comparisons).

Figure 3.

Age-stratified average relative composition of emphysema (Emph), functional small-airway disease (fSAD), normal tissue, and “other” (dark yellow portions) for each trajectory, as assessed by parametric response mapping analysis (12). Rows correspond to age strata, and columns correspond to trajectories; color-coded arrows clarify the direction of increasing age for each trajectory.