Association of the 2019 Canada’s food guide food choices assessment score with 10-year cardiovascular disease risk and heart age in Canadian adults
Bibliographic record
Abstract
Healthy diet plays an important role in the prevention of cardiovascular disease (CVD), which is the second leading cause of death in Canada. In 2019, Health Canada released an updated Canada's Food Guide (CFG) which is accompanied with supportive evidence of Canada's Dietary Guidelines (CDG) to reflect the latest evidence of the relationship between diet and prevention of chronic diseases including CVD. The Canadian Cardiovascular Society recommends the use of the Framingham risk score (FRS) to estimate the 10-year CVD risk and heart age in individuals aged 30 and older, aiding in CVD prevention interventions such as lifestyle modifications. However, the relationship between the intake of dietary choices aligned with 2019 CFG/CDG and CVD risk among Canadians was not studied. This study aims to examine the association between dietary choices assessed by a Food Choices Assessment Score (FCAS) according to 2019 CFG/CDG and 10-year CVD risk and heart age among Canadian adults. This cross-sectional study was conducted using the national Canadian Health Measures Survey (CHMS) (2016-2019) and included Canadian adults (≥ 30 years) without heart disease (n = 5,111). The 2019 CFG/CDG FCAS was calculated using the CHMS food frequency questionnaire. Canadians in the highest quintile (healthiest) of the FCAS had 55% lower odds (OR) of having high risk (≥ 20%) of estimated 10-year CVD risk (OR: 0.45; 95%CI: 0.23, 0.90) (P trend = 0.011), and 47% lower odds of having unhealthy heart age difference (heart age > chronological age) (OR: 0.53; 95%CI: 0.30, 0.92) (P trend = 0.02), compared to those in the first quintile (unhealthy) of the FCAS. This study indicates a strong inverse association of dietary choices as measured by the 2019 CFG/CDG FCAS with high 10-year CVD risk (≥ 20%) and unhealthy heart age (older than chronological age), estimated with the FRS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".