In this study, 73 men participated, including 46 (63.0%) in the patient group and 27 (37.0%) in the control group. The participants’ baseline characteristics are described in Table 1.

### Association of QCT measurements with spirometry results

Table 2 shows the association of all QCT measurements with spirometry parameters. There was a significant negative correlation between PRM^{Fsad} and Fev1/Fvc (*r* = -0.610, *P*-value < 0.001). Moreover, a significant negative correlation was found between ATI and Fev1/Fvc (*r* = -0.602, *P*-value < 0.001). The best correlation was observed between %LAA_{Exp} < -856 and Fev1/Fvc (*r* = -0.665, *P*-value < 0.001). Besides, PRM^{Fsad} + PRM^{Emph} demonstrated a strong negative correlation with Fev1/Fvc (*r* = -0.617, *P*-value < 0.001). The E/I ratio had a moderate negative correlation with Fev1/Fvc and MEEF (*r* = -0.429 and *r* = -0.323, respectively, with *P*-value < 0.01). The results revealed a significant negative correlation between PRM^{Emph} and Fev1/Fvc (*r* = -0.445, *P*-value < 0.001). In addition, there was a significant negative correlation between %LAA_{ins} < -950 and Fev1/Fvc (*r* = -0.506, *P*-value < 0.001; Table 2).

Univariate linear regression analysis confirmed %LAA_{Exp} < -856 as a significant predictor of Fev1/Fvc (β _{%LAAExp<-856} = -96.02, *P*-value = 0.002). Also, univariate linear regression analysis confirmed that ATI and %LAA_{Exp} < -856 were significant predictors of MMEF (β_{ATI} = 224.73, *P*-value = 0.033, β _{%LAAExp<-856} = -253.23, *P*-value = 0.007, respectively).

### Association of air trapping indices with emphysema

To illustrate the association between PRM^{Emph} and air trapping, a scatter plot and linear correlation coefficient were used. All the graphs showed a straight line with a positive slope. There was a positive and moderate relationship between PRM^{Emph} and ATI (*r* = 0.417, *P*-value < 0.001). Significant relationships were also observed between PRM^{Emph} and %LAA_{Exp} < -856 and between PRM^{Emph} and PRM^{Fsad} (*r* = 0.641, *P*-value < 0.001, *r* = 0.396, *P*-value = 0.001, respectively). All the mentioned correlations in any pair were statistically different from each other (*P*-value < 0.001; Fig. 2).

The scatter plot in Fig. 3 displays the significant positive relationship between PRM^{Emph} and E/I ratio (*r* = 0.373, *P*-value = 0.001).

Similarly, the Pearson correlation test was applied to assess the association between %LAA_{ins} < -950, as a measure of emphysema, and three classes of air trapping (PRM^{Fsad}, ATI, and %LAA_{Exp} < -856). The correlation between %LAA_{ins} < -950 and %LAA_{Exp} < -856 was significant and positive (*r* = 0.526, *P*-value < 0.001). In addition, %LAA_{ins} < -950 significantly correlated with PRM^{Fsad} and ATI (r = 0.373, *P*-value = 0.001, *r* = 0.462, *P*-value < 0.001, respectively).

### Comparison of three classes of air trapping measurement

Graphs and correlation tests were used to compare the correlation statistics with Fev1/Fvc across three classes of AT measurement (Fig. 4).

Table 2 presents the significant negative correlation between the three classes of AT measurement and Fev1/Fvc. There were significant differences in correlation coefficients for the pairs of %LAA_{Exp} < -856-Fev1/Fvc and PRM^{Fsad}-Fev1/Fvc (*r* = -0.665 and *r* = -0.610, respectively; *P*-value = 0.043) and for the pairs of %LAA_{Exp} < -856-Fev1/Fvc and ATI-Fev1/Fvc (*r* = -0.665, *r* = -0.602, respectively; *P*-value = 0.010). However, there was no significant difference in the correlation coefficient for the pairs of PRM^{Fsad}-Fev1/Fvc and ATI-Fev1/Fvc (*r* = -0.544, *r* = -0.606, respectively; *P*-value = 0.385).

Table 2 indicates the significant negative correlation between three classes of AT measurement and MMEF, but there were no significant differences in correlation coefficients for the pairs of %LAA_{Exp} < -856-MMEF and PRM^{Fsad}-MMEF (*r* = -0.432, *r* = -0.388, respectively; *P*-value = 0.125) and the pairs of PRM^{Fsad}-MMEF and ATI-MMEF (*r* = -0.388, *r* = -0.351, respectively; *P*-value = 0.125). However, there was a significant difference in the correlation coefficient for the pairs of %LAA_{Exp} < -856-MMEF and ATI-MMEF (*r* = -0.432, ** r** = -0.351, respectively;

*P*-value = 0.006).

### Comparison of QCT measurements between the two groups

Table 3 compares QCT measurements between the two groups. The t-test results showed that for conventional methods, only the E/I ratio was statistically different between the two groups (*P*-value < 0.001). Moreover, PRM^{Emph} significantly differed between the patient and control groups (*P*-value < 0.001). All the AT measurements (PRM^{Fsad}, %LAA_{Exp} < -856, and ATI) significantly differed between the case and control groups (*P*-value < 0.001; Table 3).

A binary logistic regression model was applied to correlate PRM^{Fsad} with the likelihood of a participant being in the patient group. Age and sex were matched in both groups because all the participants were male, and the t-test showed no significant difference in age between the patient and control groups (*P*-value = 0.577). The logistic regression model demonstrated the significant effect of PRM^{Fsad} after adjusting for emphysema as a confounder (OR_{adj} = 1.30, *P*-value = 0.001), so PRM^{Fsad} was approved as a significant predictor of the outcome (being a patient). Then, to find an optimal cut-point to classify the participants into the case and control groups, the ROC curve analysis was applied using PRM^{Fsad} as an independent variable (Fig. 5a and Table 4). PRM^{Fsad} significantly identified patients with an area under the ROC curve of 0.80 (*P*-value < 0.001; Table 4). ROC analysis generated an optimal PRM^{Fsad} cut-point of 19% of the total lung volume. Values equal to or greater than 19% of PRM^{Fsad} identified patients with a sensitivity of 0.78 and specificity of 0.70.

The binary logistic regression model also showed the significant impact of %LAA_{Exp} < -856 on the outcome, adjusted for emphysema as a confounder (OR_{adj} = 1.18, *P*-value = 0.001). Next, ROC analysis using %LAA_{Exp} < -856 as a strong predictor was performed to find an optimal cut-point for classifying the participants into the two groups (Fig. 5b).

Table 4 (The second row) shows the significant and valuable accuracy of the logistic model based on %LAA_{Exp} < -856 as a significant predictor (AUC = 0.79, *P*-value < 0.001). ROC analysis identified the value of 0.23 as an %LAA_{Exp} < -856 optimal cut-point with a sensitivity of 0.72 and specificity of 0.70. The %LAA_{Exp} < -856 value > 0.23 of the total lung volume assigned the participants to the patient group.

Similarly, the binary logistic regression model expressed the significant impact of ATI on the outcome, adjusted for emphysema as a confounder (OR_{adj} = 1.16, *P*-value = 0.001). Subsequently, ROC analysis using ATI as a relatively strong predictor was performed to find an optimal cut-point to classify the participants into two groups (Fig. 5c).

Table 4 (The third row) lists the significant and valuable accuracy of the logistic model based on ATI as a significant predictor (AUC = 0.78, *P*-value < 0.001). ROC analysis identified the value of 0.27 as an ATI optimal cut-point with a sensitivity of 0.70 and specificity of 0.70. The ATI value > 0.27 of lung volume with radio-density of -856 to -950 HU in inhaled CT assigned the participants into the patient group.

The ability of PRM^{Fsad}, ATI, and %LAA_{Exp} < -856 to characterize AT is demonstrated in representative CT images in a patient and a control participant (Fig. 6).

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