Intake of dietary flavonoids and incidence of ischemic heart disease in the Danish Diet, Cancer, and Health cohort
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Few studies have investigated the association between dietary flavonoid intake, including all major subclasses, and the long-term risk of ischemic heart disease (IHD). We examined whether dietary flavonoid intake associated with IHD incidence, assessing the possible modifying role of sex and smoking, in participants from the Danish Diet, Cancer, and Health study. SUBJECTS/METHODS: In a cohort study design, 54,496 adults (46.8% male), aged 50-64 years, without a history of IHD, were followed for up to 23 years. Habitual dietary flavonoid intake was estimated from food frequency questionnaires using Phenol-Explorer. Incident cases of IHD were identified within Danish nationwide health registries. Restricted cubic splines in Cox proportional hazards models were used to examine associations between flavonoid intake and IHD risk. RESULTS: During follow-up, 5560 IHD events were recorded. No overall association was seen between total flavonoid intake, nor any subclass, and IHD, following adjustment for demographics, lifestyle, and dietary confounders. Stratified by sex and smoking status, higher intakes of specific subclasses associated with lower IHD risk among ever-smokers [Q5 vs. Q1 flavonols HR (95% CI): 0.90 (0.82, 0.99); flavanol oligo+polymers: 0.88 (0.80, 0.97)], but not among never-smokers, nor either sex specifically. CONCLUSIONS: While we did not find clear evidence that higher habitual dietary flavonoid intake was associated with lower IHD risk, these results do not exclude the possibility that certain subclasses may have a protective role in prevention of IHD among population sub-groups; this was evident among smokers, who are at a higher risk of atherosclerosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".