Association between dietary flavonoid intake and obesity among adults in Korea
Bibliographic record
Abstract
This study aimed to investigate the association between dietary flavonoid intake and the prevalence of obesity using body mass index (BMI), waist circumference, and percent body fat (%BF) according to sex among Korean adults. Based on the Korean Health and Nutrition Examination Survey 2008–2011, 23 118 adults in Korea were included. Dietary intakes were obtained using 24-h dietary recall data. A higher total intake of flavonoid was associated with a lower prevalence of obesity in women, based on %BF (odds ratio [95% confidence interval] = 0.82 [0.71–0.94]), and abdominal obesity (0.81 [0.71–0.92]). The intake of flavonols (0.88 [0.78–0.99]), flavanones (0.81 [0.72–0.92]), flavanols (0.85 [0.74–0.97]), isoflavones (0.85 [0.75–0.96]), and proanthocyanidins (0.81 [0.71–0.92]) was inversely associated with abdominal obesity, and a higher intake of flavanones (0.87 [0.76–0.99]) and proanthocyanidins (0.85 [0.75–0.98]) was associated with a lower prevalence of obesity, with respect to %BF in women. In contrast, the intake of flavonols (1.16 [1.02–1.33]), flavanones (1.18 [1.04–1.35]), and anthocyanidins (1.27 [1.11–1.46]) was positively associated with obesity based on BMI in men. In conclusion, high intake of dietary flavonoids may be associated with a decreased prevalence of abdominal obesity and obesity, based on %BF, among women. Novelty Higher flavonoid intake was associated with decreased prevalence of abdominal obesity and obesity based on %BF in Korean women. However, in men, the intake of flavonols, flavanones, and anthocyanidins was positively associated with obesity as given by BMI.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".