Fruit and Vegetable Consumption is Inversely Associated with Plasma Saturated Fatty Acids at Baseline in Predimed Plus Trial
Bibliographic record
Abstract
Scope Plasma fatty acids (FAs) are associated with the development of cardiovascular diseases and metabolic syndrome. The aim of our study is to assess the relationship between fruit and vegetable (F&V) consumption and plasma FAs and their subtypes. Methods and Results Plasma FAs are assessed in a cross‐sectional analysis of a subsample of 240 subjects from the PREDIMED‐Plus study. Participants are categorized into four groups of fruit, vegetable, and fat intake according to the food frequency questionnaire. Plasma FA analysis is performed using gas chromatography. Associations between FAs and F&V consumption are adjusted for age, sex, physical activity, body mass index (BMI), total energy intake, and alcohol consumption. Plasma saturated FAs are lower in groups with high F&V consumption (‐1.20 mg cL −1 [95% CI: [‐2.22, ‐0.18], p ‐value = 0.021), especially when fat intake is high (‐1.74 mg cL −1 [95% CI: [‐3.41, ‐0.06], p ‐value = 0.042). Total FAs and n‐6 polyunsaturated FAs tend to be lower in high consumers of F&V only in the high‐fat intake groups. Conclusions F&V consumption is associated with lower plasma saturated FAs when fat intake is high. These findings suggest that F&V consumption may have different associations with plasma FAs depending on their subtype and on the extent of fat intake.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".