Results
COPDGene enrolled 10,300 subjects, including 108 non-smoker controls who were excluded from this analysis. Table 1 shows the characteristics of the 8517 current and former smokers with complete inspiratory and expiratory chest CT data. Study subjects encompassed the range of COPD severity across all GOLD stages. Approximately 12% of subjects were unclassified by GOLD, with reduced FEV1, but normal FEV1/FVC ratio; the GOLD-unclassified subjects in COPDGene have been described previously. Surprisingly, 4% of subjects with severe emphysema on chest CT scan had only mild airflow obstruction on spirometry (GOLD1). Conversely, over 4% of subjects without emphysema had severe or very severe airflow obstruction (GOLD 3–4).
Among the quantitative CT measures, Exp−856 was most highly correlated with percent emphysema (Table 2, Figure 2). E/I MLA and RVC856–950 were significantly but less strongly correlated, while the residuals are completely uncorrelated with Insp−950, by definition. The small airway parameters showed varying degrees of correlations among themselves. Correlations between the small airway measures and "medium-sized" airway disease indicated by SRWA Pi10 are weak, except for the correlation with RVC856–950. In 1188 subjects with severe emphysema, E/I MLA was less strongly correlated with percent emphysema than were Exp−856 and RVC856–950 (r = 0.40 for E/I MLA, r = 0.62 for Exp−856, r = 0.51 for RVC856–950; all p < 0.0001). E/I MLA was only weakly correlated with Insp−950 in subjects without emphysema, while Exp−856 and RVC856–950 were more highly correlated in this group (r = 0.12 for E/I MLA, r = 0.50 for Exp−856, r = −0.33 for RVC856–950; all p < 0.0001).
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Figure 2.
Scatterplots of four gas trapping measurements vs. percent emphysema (seeMethodsfor definitions).
Correlations between the small airway measures, lung function, and other COPD-related traits are shown in Table 3 and Table 4. In all subjects, Exp−856 showed the highest correlations with lung function measurements, including FEV1, FEV1/FVC and FEF25–75, a putative small airway disease marker from spirometry. E/I MLA showed a similar correlation as Exp−856 with FEF25–75 in all subjects. FRC/TLC ratio, a measure of hyperinflation based on chest CT scan-derived lung volumes was most highly correlated with E/I MLA. Exp−856, E/I MLA and RVC856–950 showed similar correlations with SGRQ and MMRC dyspnea score; RVC856–950 was most strongly correlated with 6-minute walk distance (6MWD). Residuals showed the weakest correlations with all tested traits.
In subjects with severe emphysema (Table 4), E/I MLA and RVC856–950 were most highly correlated with all tested traits, except for FEV1/FVC which showed a slightly higher correlation with Exp−856. Again, residuals showed the poorest correlations with lung function, 6-minute walk distance and symptoms. In subjects without significant emphysema (Additional file 1: Table S1), RVC856–950 or E/I MLA generally showed the strongest correlations. E/I MLA was most highly correlated with FEV1/FVC, FEF25–75 and FRC/TLC ratio. Residuals generally showed the weakest correlations with all tested outcomes.
Table 5 shows the joint effects of emphysema (Insp−950) and each of three small airway measures (Exp−856, E/I MLA, and RVC856–950) on the COPD-related traits FEV1, FEV1/FVC, FEF25–75, 6-minute walk distance and SGRQ. Residuals were not used in the regression analysis due to consistently weak correlations in the earlier analyses. Emphysema and gas trapping variables were standardized, so the regression coefficients reflect the effect of a one standard deviation change in each CT variable. Based on percent variation explained (R), all three small airway measures were equally predictive of FEV1. The effects of emphysema and gas trapping on FEV1 were equivalent when either E/I MLA or RVC856–950 were used as the gas trapping measure, whereas the gas trapping variable exerted more than three times the effect of emphysema (−0.45 L vs. -0.11 L) in the model using Exp−856. Models including either Exp−856 or E/I MLA explained more of the variation in FEV1/FVC and in FEF25–75 than the model including RVC856–950. The three gas trapping variables were similar in models predicting 6MWD and SGRQ score in all subjects.
In subjects with severe emphysema (Table 6), models with E/I MLA or RVC856–950 were better predictors of FEV1 than was Exp−856. Only the model using E/I MLA captured significant effects from both emphysema and gas trapping variables. All three small airway measures yielded similarly predictive models for FEV1/FVC, FEF25–75, 6MWD, and SGRQ. In the analysis of 6MWD, use of Exp−856 led to a significant positive effect of gas trapping, meaning an increase in the gas trapping variable corresponded to an increase in 6MWD among severe emphysema subjects. The 6MWD models with E/I MLA or RVC856–950 captured significant effects of emphysema only, with an expected direction of effect, namely reduction in 6MWD. Both E/I MLA and RVC856–950 identified statistically significant effects of gas trapping on SGRQ score in subjects with severe emphysema.
In subjects without significant emphysema, models including each of the small airway measures explained similar fractions of the variation in the COPD-related traits, with the exception of RVC856–950 predicting FEV1/FVC (Additional file 2: Table S2). In these subjects, quantitative emphysema measurements were not significantly associated with FEV1, FEV1/FVC or 6MWD when E/I MLA was used as the gas trapping indicator.
In the eighteen subjects with repeat quantitative CT data, the gas trapping measures from the two time points were significantly correlated within each subject: Exp−856 ρ = 0.79, p < 0.0001; E/I MLA ρ = 0.67, p = 0.002; RVC856–950 ρ = 0.54, p = 0.02.