Associations of <b>ω</b>-3 Fatty Acids With Interstitial Lung Disease and Lung Imaging Abnormalities Among Adults
Bibliographic record
Abstract
Docosahexaenoic acid (DHA), an ω-3 polyunsaturated fatty acid, attenuates interstitial lung disease (ILD) in experimental models, but human studies are lacking. We examined associations of circulating levels of DHA and other polyunsaturated fatty acids with hospitalization and death due to ILD over 12 years in the Multi-Ethnic Study of Atherosclerosis (MESA; n = 6,573). We examined cross-sectional associations with CT lung abnormalities in MESA (2000-2012; n = 6,541), the Framingham Heart Study (2005-2011; n = 3,917), and the Age, Gene/Environment Susceptibility-Reykjavik Study (AGES-Reykjavik) (2002-2006; n = 1,106). Polyunsaturated fatty acid levels were determined from fasting blood samples and extracted from plasma phospholipids (MESA and AGES-Reykjavik) or red blood cell membranes (Framingham Heart Study). Higher DHA levels were associated with a lower risk of hospitalization due to ILD (per standard-deviation increment, adjusted rate ratio = 0.69, 95% confidence interval (CI): 0.48, 0.99) and a lower rate of death due to ILD (per standard-deviation increment, adjusted hazard ratio = 0.68, 95% CI: 0.47, 0.98). Higher DHA was associated with fewer interstitial lung abnormalities on computed tomography (per natural log increment, pooled adjusted odds ratio = 0.65, 95% CI: 0.46, 0.91). Higher DHA levels were associated with a lower risk of hospitalization and death due to ILD and fewer lung abnormalities on computed tomography in a meta-analysis of data from population-based cohort studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".