Abstract P360: Dietary Saturated Fats From Different Food Sources Show Inconsistent Associations With Various Indices of Diet Quality in the Canadian Population
Bibliographic record
Abstract
Background: Canadian dietary guidelines include a recommendation to limit the consumption of foods high in saturated fats (SFA), regardless of their dietary source. The same guidelines also recommend consumption of lean red meat and low-fat dairy products. Yet, the association between the consumption of SFA from different food sources and diet quality is currently unknown. The objective of this study was to examine associations between SFA from various food sources and different indices of diet quality. Methods: Analyses are based on a sample of 11 106 respondents representative of Canadian adults (19-70 y) from the 2015 Canadian Community Health Survey (CCHS 2015). Dietary intakes and diet quality indices were calculated using a single interview-administered 24-hour recall. Food sources of SFA were classified according to the 2019 Canada’s Food Guide categories: 1) vegetables and whole fruits, 2) whole grain foods and 3) protein foods (including dairy and meat, among others). Foods not included in these three categories were grouped as All other foods . The 2010 alternative Healthy eating index (aHEI), the 2015 Healthy eating index (HEI-2015) and the 2007 Canadian Healthy eating index (C-HEI) were calculated. Due to the unreliability of data for trans-fat consumption in the CCHS 2015 database, the trans-fat subscore of the aHEI was removed from the original score. Results: While total SFA intake and SFA from All other foods were inversely correlated with all indices of diet quality (-0.55 Conclusion: Consumption of SFA from different food sources are inconsistently associated with different indices of overall diet quality. Unsurprisingly, SFA from All other foods , which include low nutritive value foods, showed the strongest negative correlation with all diet quality scores. These results provide further support to the notion that guidance on SFA in future health policies should focus on food sources rather than on total intake of SFA.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".