Total dietary antioxidant capacity is associated with lung function volumes in women
Bibliographic record
Abstract
Antioxidant-rich foods may favorably influence respiratory health. In the frame of the Genes Environment Interaction in Respiratory Diseases, a population-based case-control study, we studied the associations between total dietary antioxidant capacity (TAC) and lung function volumes in subjects with no respiratory diseases. TAC was measured in foods by 3 different methods: Trolox equivalent antioxidant capacity (TEAC), total radical-trapping antioxidant parameter (TRAP), ferric reducing-antioxidant power (FRAP). The European Investigation into Cancer and Nutrition Food Frequency Questionnaire was used for dietary assessment in controls (n=347). The associations between TAC and lung volumes (FEV1, FEV1%predicted, FVC and FEV1/FVC) were estimated by means of multiple linear regression models, in men (n=167) and women (n=180) separately. The estimates were adjusted for age, height (except for FEV1%predicted), weight, centre, smoking habits, physical activity, total energy intake and menopausal status (only women). In women, TEAC and FRAP were significantly associated with FEV1, FEV1%predicted and FVC, but not with FEV1/FVC, after adjustment for confounders. The estimated variations in FEV1, FEV1%predicted and FVC were 0.028 (95%CI: 0.004;0.053) l, 1.264 (0.240;2.288)% and 0.035 (0.003;0.068) l, respectively, per 1 unit increase in TEAC, 0.009 (0.001;0.017) l, 0.404 (0.070;0.739)% and 0.011 (0.001;0.022) l, respectively, per 1 unit increase in FRAP. TRAP was significantly associated with FEV1 and FEV1%predicted but not with FVC and FEV1/FVC. In men, there were no associations between TAC and lung volumes. In our study, we found that dietary TAC was associated with lung volumes in women, but not in men.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".