Adherence to a priori dietary patterns in relation to obesity: results from two cycles of the Canadian National Nutrition Survey
Bibliographic record
Abstract
Abstract Objective: To test whether adherence to the Mediterranean diet, the Dietary Approaches to Stop Hypertension (DASH) or a dietary pattern in-line with the 2015–2020 Dietary Guidelines for Americans (DGA) was associated with obesity. Design: 24-h dietary recall data from the Canadian Community Health Survey (CCHS)-Nutrition, 2004 and 2015 cycles, were analysed. Diet quality index scores were computed for the Mediterranean-Style Dietary Pattern Score (MSDPS), a DASH index and the 2015 Dietary Guidelines for Americans Adherence Index (DGAI). Higher scores indicated greater adherence. Association between scores and obesity was examined using logistic regression, adjusting for age, sex, physical activity, smoking status, sequence of dietary recall and alcohol and energy intake. Setting: Canada (excluding territories and the institutionalised population). Participants: Canadian adults (≥ 18 years), non-pregnant and non-breast-feeding; 11 748 from CCHS 2004 and 12 110 from CCHS 2015. The percentage of females in each sample was 50 %. Results: Mean MSDPS, DASH and DGAI scores were marginally but significantly higher in CCHS 2015 than in CCHS 2004. Those affected by obesity obtained lower scores for all indexes in CCHS 2004 (OR 10th v. 90th percentile for DASH: 2·23 (95 % CI 1·50, 3·32), DGAI: 3·01 (95 % CI 1·98, 4·57), MSDPS: 2·02 (95 % CI 1·14, 3·58)). Similar results were observed in CCHS 2015; however, results for MSDPS were not significant (OR 10th v. 90th percentile for DASH: 2·45 (95 % CI 1·72, 3·49), DGAI: 2·73 (95 % CI 1·85, 4·03); MSDPS: 1·30 (95 % CI 0·82, 2·06)). Conclusion: Following DASH or the 2015–2020 DGA was associated with a lower likelihood of obesity. Findings do not indicate causation, as the data are cross-sectional.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".