Dietary intakes of flavonoids and carotenoids and the risk of developing an unhealthy metabolic phenotype
Bibliographic record
Abstract
This study was designed to investigate prospective associations between dietary habitual intakes of flavonoids and carotenoids and the development of an unhealthy metabolic phenotype. In this study 1114 adults, who had a healthy metabolic phenotype in the third examination cycle (2005-2008) participated. Dietary intakes of flavonoids and carotenoids were estimated using valid food frequency questionnaires evaluated during the study. Joint Scientific Statement criteria were used to define a metabolic unhealthy phenotype as meeting at least two criteria. During a median follow-up of 5.4 years, one standard deviation increase in total flavonoids reduced the risk of developing an unhealthy phenotype by 50% (95% CIs: 0.42-0.59), and inverse associations were observed for individual classes except anthocyanins, being the strongest for flavan-3-ols. Among carotenoid classes, a significant inverse association was only observed between lutein + zeaxanthin (HRs: 0.87, 95% CIs: 0.77-0.98) and the occurrence of an unhealthy phenotype. When data were stratified by baseline BMI, total flavonoids and individual classes of flavan-3-ols, flavonols, and flavones among both normal weight and overweight/obese individuals, isoflavones in those with excess weight and anthocyanins in normal weight individuals were inversely associated with the development of an unhealthy phenotype. Total carotenoids, β-carotene and lutein + zeaxanthin were significantly associated with the lower likelihood of the occurrence of an unhealthy phenotype, only among normal weight individuals. Higher intakes of flavonoids and their individual classes may contribute to the lower risk of a metabolic unhealthy phenotype in both normal weight and overweight/obese adults. Flavonoids may have more favorable metabolic health effects than carotenoids.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".