Association between overall fruit and vegetable intake, and fruit and vegetable sub-types and blood pressure: the PRIME study (Prospective Epidemiological Study of Myocardial Infarction)
Bibliographic record
Abstract
Increased fruit and vegetable (FV) intake is associated with reduced blood pressure (BP). However, it is not clear whether the effect of FV on BP depends on the type of FV consumed. Furthermore, there is limited research regarding the comparative effect of juices or whole FV on BP. Baseline data from a prospective cohort study of 10 660 men aged 50-59 years examined not only the cross-sectional association between total FV intake but also specific types of FV and BP in France and Northern Ireland. BP was measured, and dietary intake assessed using FFQ. After adjusting for confounders, both systolic BP (SBP) and diastolic BP (DBP) were significantly inversely associated with total fruit, vegetable and fruit juice intake; however, when examined according to fruit or vegetable sub-type (citrus fruit, other fruit, fruit juices, cooked vegetables and raw vegetables), only the other fruit and raw vegetable categories were consistently associated with reduced SBP and DBP. In relation to the risk of hypertension based on SBP >140 mmHg, the OR for total fruit, vegetable and fruit juice intake (per fourth) was 0·95 (95 % CI 0·91, 1·00), with the same estimates being 0·98 (95 % CI 0·94, 1·02) for citrus fruit (per fourth), 1·02 (95 % CI 0·98, 1·06) for fruit juice (per fourth), 0·93 (95 % CI 0·89, 0·98) for other fruit (per fourth), 1·05 (95 % CI 0·99, 1·10) for cooked vegetable (per fourth) and 0·86 (95 % CI 0·80, 0·91) for raw vegetable intakes (per fourth). Similar results were obtained for DBP. In conclusion, a high overall intake of fruit, vegetables and fruit juice was inversely associated with SBP, DBP and risk of hypertension, but this differed by FV sub-type, suggesting that the strength of the association between FV sub-types and BP might be related to the type consumed, or to processing or cooking-related factors.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".