You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Introduction {#s1}
============

Chronic obstructive pulmonary disease (COPD) causes chronic morbidity and mortality, and is expected to be the third leading cause of death in 2020, with around 8 million deaths annually [@pone.0061783-Mathers1]; [@pone.0061783-Murray1]. COPD is characterized by progressive airflow limitation due to parenchymal destruction (i.e. emphysema) and/or small airways remodeling, and is primarily caused by exposure to tobacco smoke [@pone.0061783-Rabe1]. It has been reported that not all smokers are susceptible to the harmful effects of tobacco, and only a subgroup has a decline in lung function large enough to develop COPD [@pone.0061783-Fletcher1]. Since smoking cessation is crucial in managing this disease [@pone.0061783-Fiore1], it would be advantageous to estimate the rate of decline in heavily smoking subjects without or with early stage COPD. Given the high expectations of lung cancer screening [@pone.0061783-Aberle1], CT may gain a role in early identification of such subjects [@pone.0061783-Mets1].

Both pulmonary emphysema and air trapping can nowadays be quantified in vivo using computed tomography (CT), but the relationship between quantitative CT measurements and lung function decline over time received little attention. It has been reported that visual [@pone.0061783-RemyJardin1] and quantitative CT measures of emphysema [@pone.0061783-MohamedHoesein1]--[@pone.0061783-Nishimura1] and hyperinflation [@pone.0061783-Yuan1] are associated with loss of lung function over time, and may thus be used to identify subjects at a higher risk to develop COPD. Given that airflow obstruction in COPD starts in the small airways before the onset of emphysematous destruction [@pone.0061783-McDonough1], air trapping -which is thought to reflect small airways disease- might show a strong and more important association with lung function decline in early stages of the disease, independent of emphysema. However, to date no studies have investigated the relationship between lung function decline and expiratory CT data. Therefore, the objective of this study is to assess the association between lung function decline and quantitative CT measures of air trapping in a cohort of male heavy smokers in a lung cancer screening setting.

Materials and Methods {#s2}
=====================

Ethics statement {#s2a}
----------------

This study was performed as part of the population-based Dutch Belgian Lung Cancer Screening Trial (NELSON-trial) [@pone.0061783-vanIersel1], which was approved by the Dutch Ministry of Health and by the ethical review board of the University Medical Center Utrecht. In our center, expiratory CT was added to the screening protocol in July 2007 to study COPD. This addition was separately approved by the ethical review board of the University Medical Center Utrecht. Written informed consent was obtained from each individual participating in the screening trial.

Study subjects {#s2b}
--------------

Participants in the screening trial are current or former heavy smokers who have smoked at least 16.5 packyears and were physically fit enough to undergo potential surgery [@pone.0061783-vanIersel1]. For this study, we included all subjects from our center with a lung function test in the first round of the screening and a paired inspiratory and expiratory CT, processed for quantitative CT estimates of emphysema and air trapping (N = 985). Multiple pulmonary function tests were available for 442 of these 985 subjects, spanning an observation time of around three years.

Pulmonary Function Testing {#s2c}
--------------------------

Prebronchodilator spirometry was performed using ZAN equipment (ZAN Messgeräte GmbH, Oberthulba, Germany), according to American Thoracic Society and European Respiratory Society guidelines [@pone.0061783-Miller1]. Spirometry was obtained between 2004 and 2008, and provided forced expiratory volume in one second (FEV~1~), forced vital capacity (FVC) and the FEV~1~/FCV ratio.

CT scanning and quantitative analysis {#s2d}
-------------------------------------

All subjects received low-dose volumetric CT in inspiration and at end-expiration after standardized breathing instructions. CT imaging was obtained between July 2007 and September 2008. The images were acquired with 16×0.75 mm collimation (Brilliance 16P; Philips Medical Systems, Cleveland OH, USA). Settings were adjusted to body weight of the patient: 120 kVp (≤80 kg) or 140 kVp (\>80 kg) both at 30 mAs for inspiratory CT, and 90 kVp (≤80 kg) or 120 kVp (\>80 kg) both at 20 mAs for expiratory CT. A scan pair yielded an estimated effective dose of 1.2--2.0 millisievert (mSv), of which 0.3--0.65 mSv is accounted for by the expiration scan. Images with slice thickness of 1.0 mm at 0.7 mm increment were reconstructed from lung bases to lung apices using a smooth reconstruction kernel (B-filter; Philips).

The lungs were automatically segmented using dedicated software [@pone.0061783-vanRikxoort1], and a noise reduction filter was applied to decrease the influence of noise on the quantitative measurements [@pone.0061783-Schilham1]. The density of each voxel in the segmented lung volume was assessed and distributed in an attenuation histogram. From these histograms the quantitative CT measures were calculated. CT air trapping was defined as the expiratory to inspiratory ratio of mean lung density; E/I-ratio~MLD~ [@pone.0061783-ODonnell1]; [@pone.0061783-Mets2]. CT emphysema was defined as the percentage of voxels in inspiratory CT with an attenuation below −950 Hounsfield Unit (HU); IN~−950~ [@pone.0061783-Gevenois1], [@pone.0061783-Gevenois2].

Statistical analysis {#s2e}
--------------------

It has been shown that lung function decline is linear over a three year period [@pone.0061783-Tashkin1]. FEV~1~ and FEV~1~/FVC were therefore analyzed with a random slope, random intercept linear mixed model. Observation time was chosen as a random parameter, while all other parameters were fixed. We used an unstructured covariance matrix. Quantitative CT air trapping, CT emphysema, age, height, smoking status, packyears smoked and observation time were inserted into the model. We inserted the interaction between smoking status and observation time to test for differences in decline between current- and former smokers. We also inserted the interactions between CT air trapping and observation time to test whether differences in decline were dependent on the extent of CT air trapping.

Observation time and quantitative CT measures are expressed as median with interquartile range (IQR), all other data are presented as mean ± standard deviation (SD). All analyses were performed with SPSS Version 19.0 for Windows (SPSS, Chicago, Illinois, USA). A p-value below 0.05 was considered significant.

Results {#s3}
=======

Study population {#s3a}
----------------

The total study population consisted of 985 subjects (99.1% males) with an age of 61.3±5.5 years. Current and former smokers were about equally present. Average FEV~1~ at baseline was 3.28±0.71 L (96.5±18% of predicted value), and average FEV~1~/FVC at baseline was 71.6±9.2%. Study population characteristics are summarized in [Table 1](#pone-0061783-t001){ref-type="table"}.

10.1371/journal.pone.0061783.t001

###### Characteristics of the study population.

![](pone.0061783.t001){#pone-0061783-t001-1}

                                                              N = 985
  ----------------------------------------------------- -------------------
  Male, n (%)                                               976 (99.1)
  Age \[year\], mean ± SD                                    61.3±5.5
  Length \[cm\], mean ± SD                                     178±7
  Packyears \[year\], mean ± SD                              40.6±17.5
  Smoking status                                        
  Current smoker, n (%)                                     528 (53.6)
  Former smoker, n (%)                                      457 (46.4)
  FEV~1~ \[L\], mean ± SD                                    3.28±0.71
  FEV~1~ \[%predicted\], mean ± SD                           96.5±18.0
  FEV~1~/FVC \[%\], mean ± SD                                71.6±9.2
  Airflow obstruction[a](#nt101){ref-type="table-fn"}   
  No COPD                                                   624 (63.4)
  GOLD 1                                                    235 (23.9)
  GOLD 2                                                    107 (10.9)
  GOLD 3                                                     19 (1.9)
  CT Emphysema, IN~−950~ \[%\], median (IQR)             0.66 (0.32--1.38)
  CT Air trapping, E/I-ratio~MLD~, median (IQR)          0.84 (0.80--0.88)
  Number of PFT                                         
  One PFT, n(%)                                             543 (55.1)
  Two PFT, n(%)                                             369 (37.5)
  Three PFT, n(%)                                            68 (6.9)
  Four PFT, n(%)                                              5 (0.5)
  Observation time \[year\], median (IQR)^b^              2.9 (2.9--3.0)

Airflow obstruction defined as FEV~1~/FVC\<0.70, and classified as GOLD 1 (FEV~1~≥80%), GOLD 2 (50%≤FEV~1~\<80%) and GOLD 3 (FEV~1~\<50%); ^b^follow-up time in years between multiple visits;

*FEV~1~/FVC* ratio of FEV~1~ over forced vital capacity; *FEV~1~* forced expiratory volume in the first second; *IN~−950~* percentage of voxels in inspiratory CT with an attenuation below −950 Hounsfield Unit; *Perc~15~* 15^th^ percentile of the attenuation distribution curve; *E/I-ratio~MLD~* expiratory to inspiratory ratio of mean lung density; *PFT* pulmonary function test.

Association with lung function {#s3b}
------------------------------

More extensive CT air trapping was significantly associated with a reduction in FEV~1~ (p\<0.001). For each 1% increase in CT air trapping the FEV~1~ is lowered by 33 ml; roughly the annual decline in healthy male subjects. The estimated effect sizes of the variables on FEV~1~ are shown in [Table 2](#pone-0061783-t002){ref-type="table"}.

10.1371/journal.pone.0061783.t002

###### Results of linear mixed model analysis--change in lung function parameter per unit change in covariable.

![](pone.0061783.t002){#pone-0061783-t002-2}

  Estimated effects of covariables on FEV~1~ (mL)                                                             
  -------------------------------------------------- ------------------------------- -------- --------------- ---------
  log CT emphysema[a](#nt103){ref-type="table-fn"}             Plus 1 Unit            −31.1    −53.7--−8.53     0.007
  CT air trapping^b^                                             Plus 1%              −33.4    −39.1--−27.7    \<0.001
  Smoking status                                             Current smoker           −112.8   −183.7--−41.9    0.002
  Packyears                                                    Plus 1 year             −3.9     −5.8--−1.9     \<0.001
  Age in years                                                 Plus 1 year            −32.9    −39.6--−26.3    \<0.001
  Length in cm                                                  Plus 1 cm              38.9     33.7--44.2     \<0.001
  Observation time                                             Plus 1 year            −56.7    −70.0--−43.5    \<0.001
  Current smoker \* Observation time                  Plus current smoker \* 1 year   −26.7     −44.7--−8.7     0.004

CT emphysema defined as the log transformed percentage of voxels below −950 Hounsfield Units (IN~−950~); ^b^CT air trapping defined as the expiratory to inspiratory ratio of mean lung density (E/I-ratio~MLD~); *FEV~1~/FVC* ratio of FEV~1~ over forced vital capacity; *FEV~1~* forced expiratory volume in the first second;

The effect of the covariable on FEV~1~ can be determined by calculating the product of the coefficient (ie. FEV~1~ difference) times the increase in covariable (ie. Change). For example, the maximum loss of FEV~1~ due to CT emphysema in our population is about 45 mL (ie. log\[range~CT\ Emphysema~\]\*coefficient) compared to 1276 mL due to CT air trapping (ie. range~CT\ Air\ trapping~\*coefficient).

Increase in height predictably leads to a higher FEV~1~, while increases in CT air trapping, CT emphysema, age, packyears smoked, observation time and being a current smoker all reduce the FEV~1~. Moreover, current smokers show an accelerated decline over time, compared to non-smokers (p = 0.004). CT air trapping was not significantly associated with an additional accelerated decline in FEV~1~. The effect of increase in CT air trapping on FEV~1~ is further illustrated in [Figure 1](#pone-0061783-g001){ref-type="fig"}.

![The effect of increase in CT air trapping extent on FEV~1~.\
The effect of increasing extent of CT air trapping (25^th^ percentile, stars; 50^th^ percentile, squares; 75^th^ percentile, triangles) on FEV~1~ is shown in a current (left panel) and former smoker (right panel) with fixed values for age/length/packyears (mean of the study population) and CT emphysema (median of the study population). It is seen that more extensive CT air trapping leads to a reduction in FEV~1~.](pone.0061783.g001){#pone-0061783-g001}

CT air trapping was also significantly associated with a reduction in FEV~1~/FVC (p\<0.001). For each 1% increase in CT air trapping the FEV~1~/FVC is lowered by 0.58%, roughly three times the annual decline of 0.18% in healthy male subjects. The estimated effect sizes of the variables on FEV~1~/FVC are shown in [Table 3](#pone-0061783-t003){ref-type="table"}. Increase in CT air trapping, CT emphysema, packyears smoked, observation time and being a current smoker all reduce the FEV~1~/FVC. Additionally, CT air trapping not only lowers the FEV~1~/FVC, but also elicits an accelerated loss (p = 0.014). Smoking status was not significantly associated with an accelerated decline in FEV~1~/FVC. Our results show that when more extensive air trapping is present, the steeper this lung function parameter will decline. When CT air trapping worsens from the 25^th^ percentile (0.80) to the 75^th^ percentile (0.88) the FEV~1~/FVC decline increases with an extra 0.24% per year, which is substantial given the normal annual decline of 0.18%. The effect of increase in CT air trapping on FEV~1~/FVC is further illustrated in [Figure 2](#pone-0061783-g002){ref-type="fig"}.

![The effect of increase in CT air trapping extent on FEV~1~/FVC.\
The effect of increasing extent of CT air trapping (25^th^ percentile, star; 50^th^ percentile, square; 75^th^ percentile, triangle) on FEV~1~/FVC is shown in a current (left panel) and former smoker (right panel) with fixed values for age/length/packyears (mean of the study population) and CT emphysema (median of the study population). It is seen that more extensive CT air trapping leads to a reduction in FEV~1~/FVC, and the diverging course illustrates the association between CT air trapping and accelerated decline.](pone.0061783.g002){#pone-0061783-g002}

10.1371/journal.pone.0061783.t003

###### Results of linear mixed model analysis--change in lung function parameter per unit change in covariable.

![](pone.0061783.t003){#pone-0061783-t003-3}

  Estimated effects of covariables on FEV~1~/FVC (%)                                              
  ---------------------------------------------------- ------------------- ------- -------------- ---------
  log CT emphysema[a](#nt105){ref-type="table-fn"}         Plus 1 Unit      −2.68   −3.00--−2.36   \<0.001
  CT air trapping^b^                                         Plus 1%        −0.58   −0.65--−0.50   \<0.001
  Smoking status                                             Current        −1.82   −2.70--−0.94   \<0.001
  Packyears                                                Plus 1 year      −0.04   −0.06--−0.01    0.004
  Observation time                                         Plus 1 year      +2.50    0.35--4.65     0.02
  CT air trapping \* Observation time                   Plus 1% \* 1 year   −0.03   −0.06--−0.01    0.01

CT emphysema defined as the log transformed percentage of voxels below −950 Hounsfield Units; ^b^CT air trapping defined as the expiratory to inspiratory ratio of mean lung density; *FEV~1~/FVC* ratio of FEV~1~ over forced vital capacity; *FEV~1~* forced expiratory volume in the first second;

The effect of the covariable on FEV~1~/FVC can be determined by calculating the product of the coefficient (ie. FEV~1~/FVC difference) times the increase in covariable (ie. Change). For example, the maximum loss of FEV~1~/FVC due to CT emphysema in our population is about 3.9% (ie. log\[range~CT\ Emphysema~\]\*coefficient) compared to 22.2% due to CT air trapping (ie. range~CT\ Air\ trapping~\*coefficient).

Discussion {#s4}
==========

This study on CT air trapping and lung function decline in heavily smoking male lung cancer screening participants showed that more extensive CT air trapping is associated with a substantial accelerated decline of FEV~1~/FVC, but not of FEV~1~. CT air trapping is further associated with reduced FEV1/FVC and FEV1, independent from CT emphysema.

No previous studies assessed the association between expiratory CT measures and lung function over time, and only a few papers assessed this association for inspiratory CT measures. These studies mostly found that increasing emphysema was associated with accelerated decline in lung function. Remy-Jardin et al. [@pone.0061783-RemyJardin1], in a visual assessment study in 111 volunteers, found that persistent current smokers with emphysema showed more rapid lung function decline than subjects without CT abnormalities. This has been confirmed by several quantitative studies [@pone.0061783-MohamedHoesein1]--[@pone.0061783-Nishimura1] in which the extent of CT emphysema was related to a larger reduction in lung function over time. Contrarily, Yuan et al. [@pone.0061783-Yuan1] were unable to find an association between quantitative CT emphysema and lung function decline in 143 subjects, but they did report a weak association between hyperinflation on inspiratory CT (defined as the percentage of total lung volume that had an inflation value above the maximal predicted inflation value) and accelerated annual decline of FEV~1~.

The present study is compatible to the available literature. Our observation that CT air trapping, and not CT emphysema, elicits a steeper FEV~1~/FVC decline over time is in line with the recent evidence that COPD starts within the small airways and precedes emphysematous parenchymal destruction [@pone.0061783-McDonough1]. Also, as the subjects in the present study had mainly absent or mild obstruction, our findings are compatible with the idea that small airways disease leads to air trapping before emphysema develops. Our findings may thus suggest that small airways dysfunction is more important than emphysema for lung function decline in early disease. Nevertheless, it is important to realize that although our findings are compatible with literature our study does not proof that small airways disease develops earlier than emphysema. Also, although presumed, there is no definitive proof that CT air trapping measures pure small airways disease due to the lack of a true gold standard.

We further showed the significant relationship between a lower lung function and both CT emphysema and CT air trapping extent. However, the contribution of CT emphysema to the lung function reduction in our study population was limited compared to the effect of CT air trapping; this is illustrated by the calculated maximal achievable reduction due to these variables in our population. Since lung function parameters result from an expiratory maneuver, a greater effect should indeed be expected from an expiratory CT measure than from an inspiratory measure of emphysema extent.

Our study is of importance as it is the first study to report on the association between expiratory CT data and lung function over time. The study is strengthened by the fact that all scans were obtained according to the same protocol, which excludes interference of scanner differences with the quantitative CT values. Also, our study population was fairly large and population-based; it comprised heavily smoking subjects in a screening setting with mainly absent or mild airflow limitation, instead of severely affected subjects with end-stage disease. Our study is limited by the fact that the generalizability to other less exposed populations, to females subjects and to more severe stages of the disease may be limited.

In conclusion, we showed that expiratory CT air trapping in current and former male heavy smokers without or with mild COPD is independently associated with accelerated decline of FEV~1~/FVC and with reduction of FEV~1~ and FEV~1~/FVC.

[^1]: **Competing Interests:**The authors have declared that no competing interests exist.

[^2]: Critically revised manuscript and approved final version: OMM PADJ BVG CK MP MO JWJL PZ. Conceived and designed the experiments: OMM PADJ MP MO JWJL PZ. Performed the experiments: OMM PADJ BVG PZ. Analyzed the data: OMM PADJ CK PZ. Contributed reagents/materials/analysis tools: BVG MP. Wrote the paper: OMM PADJ PZ.