Greater adherence to the 2019 Canada’s Food Guide recommendations on healthy food choices reduces the risk of cardiovascular disease in adults: a prospective analysis of UK Biobank data
Bibliographic record
Abstract
BACKGROUND: Canada's Food Guide (CFG) was profoundly revised in 2019, but the extent to which adherence to recommendations on healthy food choices reduces the risk of cardiovascular disease (CVD) is unknown. OBJECTIVES: The aim of this study was to examine how greater adherence to the 2019 CFG's recommendations on healthy food choices influences the risk of incident CVD. METHODS: Participants were a sample of adults without history of CVD, diabetes, or cancer from the UK Biobank prospective cohort study. Usual dietary intakes were estimated by modeling data from repeated Web-based 24-h dietary recalls using the National Cancer Institute multivariate method. Adherence to key CFG recommendations on healthy food choices was assessed using the Healthy Eating Food Index (HEFI)-2019, which has a maximum of 80 points. The CVD outcome was a composite of fatal and nonfatal myocardial infarction and ischemic stroke. Cox regression models adjusted via inverse probability weighting were used to estimate CVD risks. Counterfactual models were used to interpret risks of hypothetical changes in the HEFI-2019 score. RESULTS: A total of 136,698 participants met the eligibility criteria (55% females; mean age: 57.2 y; range: 40-75 y). During the 11-y follow-up, there were 2843 cases of incident CVD. Compared with no change in the HEFI-2019 score, increasing the HEFI-2019 score of all participants to the 90th percentile of the score distribution (58.1 points) hypothetically reduced the risk of CVD by 24% (RR: 0.76; 95% CI: 0.58, 0.94; absolute risk difference: -0.58%). CONCLUSIONS: These results suggest that greater adherence to the 2019 CFG recommendations on healthy food choices reduces the 11-y risk of CVD in middle-aged and older adults.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".