Associations of sleep duration and sleep–wake rhythm with lung parenchymal abnormalities on computed tomography: The MESA study
Bibliographic record
Abstract
Abstract Impairment of the circadian rhythm promotes lung inflammation and fibrosis in pre‐clinical models. We aimed to examine whether short and/or long sleep duration and other markers of sleep–wake patterns are associated with a greater burden of lung parenchymal abnormalities on computed tomography among adults. We cross‐sectionally examined associations of sleep duration captured by actigraphy with interstitial lung abnormalities ( n = 1111) and high attenuation areas ( n = 1416) on computed tomography scan in the Multi‐Ethnic Study of Atherosclerosis at Exam 5 (2010–2013). We adjusted for potential confounders in logistic and linear regression models for interstitial lung abnormalities and high attenuation area, respectively. High attenuation area models were also adjusted for study site, lung volume imaged, radiation dose and stratified by body mass index. Secondary exposures were self‐reported sleep duration, sleep fragmentation index, sleep midpoint and chronotype. The mean age of those with longer sleep duration (≥ 8 hr) was 70 years and the prevalence of interstitial lung abnormalities was 14%. Increasing actigraphy‐based sleep duration among participants with ≥ 8 hr of sleep was associated with a higher adjusted odds of interstitial lung abnormalities (odds ratio of 2.66 per 1‐hr increment, 95% confidence interval 1.42–4.99). Longer sleep duration and higher sleep fragmentation index were associated with greater high attenuation area on computed tomography among participants with a body mass index < 25 kg m −2 ( p ‐value for interaction < 0.02). Self‐reported sleep duration, later sleep midpoint and evening chronotype were not associated with outcomes. Actigraphy‐based longer sleep duration and sleep fragmentation were associated with a greater burden of lung abnormalities on computed tomography scan.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".