The association of healthy eating index score and n-3 fatty acid intake with cardiovascular diseases incidence and lipid biomarkers in Alberta’s tomorrow project cohort
Bibliographic record
Abstract
Introduction Unhealthy diet and dyslipidemia are major risk factors for cardiovascular disease (CVD). Studies have shown an inverse association between greater n-3 fatty acid (FA) intake and reduced dyslipidemia and CVD risk. We aimed to assess the association of the healthy eating index (HEI) score and n-3 FA intake with CVD incidence and non-fasting RC in the Alberta’s Tomorrow Project (ATP) cohort. Methods This is a prospective study on a subset of ATP study participants (n = 23,248), with the mean age of 50.2 (35-69) years, 36% male and 64% female, and no history of cancer or CVD in Alberta, Canada. Dietary intake was assessed using the Canadian Diet History Questionnaire (CDHQ), from which the Canadian HEI-2005 score and total n-3 FA intake were calculated. Lipid panel markers were measured from non-fasting blood samples, and CVD was defined using the International Statistical Classification of Diseases and Related Health Problems from linked administrative health records. The Cox proportional hazard model, linear regression, and logistic regression were used to assess the association of dietary intakes with CVD incidence, and lipid biomarkers. Results The mean follow-up was 13.9 years. For every 1 unit increase in the HEI score, the adjusted Hazard Ratio (HR) of developing CVD decreased [HR: 0.98 (95% confidence interval (CI) 0.97–0.98), 0.99 (95%CI 0.98–0.99), and 0.97 (95%CI 0.97–0.98) in females, males, and total cohort, respectively (p < 0.05)]. No significant association was found between absolute n-3 FA intake (g/d) with CVD incidence. However, higher relative intake (i.e., n-3 FA as proportion of energy) increased the risk of developing CVD [HR = 1.42 (95%CI 1.1–1.84), p = 0.006] in males. Adjusted multivariate regression in a subset (n = 8,458) showed no association between n-3 FA (g/d) intake and lipid biomarkers but a significant inverse association between HEI score and non-fasting RC [coefficient: −0.006 (95%CI −0.009–−0.003) for females and −0.01 (95%CI −0.018–−0.005) for males], and TG levels [−0.01 (95%CI −0.015–−0.006) for females and −0.01 (95%CI −0.02–−0.006) for males]. Discussion Higher overall diet quality but not n-3 FA intake was associated with a lower risk of CVD incidence and non-fasting RC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".