Association of Lung Computed Tomography High Attenuation Areas With Physiological Exercise Measures
Bibliographic record
Abstract
Abstract Rationale: Interstitial lung abnormalities (ILAs) are incidental imaging findings thought to be precursors of interstitial lung diseases (ILDs). Computed tomography (CT)-based assessments of high-attenuation areas (HAAs) in the lungs may help identify adults at higher risk of developing ILAs. ILAs and HAAs have been linked to lower forced vital capacity (FVC), diminished self-reported health status, and greater overall risk of mortality, but little is known about their association with exercise function. This study aims to investigate whether a heightened burden of HAA on CT is associated with impaired exercise capacity in a community-based cohort. Methods: This observational cross-sectional study used data from the Canadian Cohort of Obstructive Lung Disease (CanCOLD) registry, a community-based sample of participants. Lung function and cardiopulmonary exercise testing (CPET) were performed using American Thoracic Society standards. Participants underwent inspiratory full-lung CT on helical scanners, following a standardized protocol (100 kVp, 1.00-1.25 mm slice thickness, and 0.5 s rotation time). HAA was defined as percent of lung volume with Hounsfield units between -600 and -250. Logarithmic transformation was applied to HAA to enhance normalization. Exercise measures of interest included work rate (watts/min), metabolic rate of oxygen consumption (VO2, L/min), oxygen saturation (%), and dyspnea (Borg 0-10 scale). Linear regression was performed to evaluate the correlation between log HAA and peak exercise measures, and adjusted for confounders (age, sex, height, and weight). Results: There were 1,177 participants from the CanCOLD cohort included in this study. The mean age was 66.6 ± 9.8 years, 683 (58%) participants were male, and 1,119 (95.1%) were of White European ancestry. Higher percentages of HAA were associated with significantly lower work rate (β coefficient -16.96; CI -23.36 to -10.55) and VO2 (β coefficient = -0.26; CI -0.35 to -0.16) at peak exercise (Figure 1). These associations remained statistically significant after adjusting for confounders. Oxygen saturation and dyspnea measures were not associated with HAA burden (β = 0.30; CI -0.22 to 0.83 and β = -0.04; CI -0.53 to 0.46, respectively). Conclusions: Burden of HAA on CT was significantly associated with peak exercise work rate and metabolic rate of oxygen consumption, though it does not exhibit a correlation with oxygen saturation or dyspnea. Further research should assess key risk factors (e.g., smoking and COPD) to more accurately determine how a heightened burden of HAA correlates with impaired exercise capacity. Understanding this association could help better identify high-risk adults and facilitate ILA and ILD detection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".