Relationship between dietary patterns and COPD: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Findings from previous studies reporting on the associations between chronic obstructive pulmonary disease (COPD) and various dietary patterns have been inconsistent. This review aims to summarise the evidence on the strength of the association between dietary patterns and the prevalence and incidence of COPD. Methods We conducted a comprehensive search of seven databases between 1 January 1980 and 30 November 2019. Two reviewers independently reviewed each manuscript through the screening, selection, data extraction and quality assessment stages. Data from eight observational studies that met the inclusion criteria were extracted and random-effects meta-analysis was subsequently conducted. Results Eight observational studies (all eight reporting on healthy dietary patterns and three on unhealthy dietary patterns) met the inclusion criteria and data were extracted to include in the meta-analysis. Consumption of a healthy dietary pattern was associated with a lower risk of COPD (pooled OR 0.88, 95% CI 0.82–0.94). Consumption of unhealthy dietary patterns was associated with a higher risk of COPD (OR 1.22, 95% CI 0.84–1.76); however, the results were not statistically significant and had high heterogeneity (I2=91%). Conclusion Our results suggests that healthy dietary patterns are associated with a lower prevalence of COPD, while unhealthy dietary patterns are not. More studies, particularly adequately powered longitudinal studies, are needed to further elucidate the effects of healthy and unhealthy dietary patterns on risk of COPD.
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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 | 0.019 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.027 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".