Polyphenol intake and differentiated thyroid cancer risk in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort
Why this work is in the frame
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Bibliographic record
Abstract
Polyphenols are bioactive compounds with several anticarcinogenic activities; however, human data regarding associations with thyroid cancer (TC) is still negligible. Our aim was to evaluate the association between intakes of total, classes and subclasses of polyphenols and risk of differentiated TC and its main subtypes, papillary and follicular, in a European population. The European Prospective Investigation into Cancer and Nutrition cohort included 476,108 men and women from 10 European countries. During a mean follow‐up of 14 years, there were 748 incident differentiated TC cases, including 601 papillary and 109 follicular tumors. Polyphenol intake was estimated at baseline using validated center/country‐specific dietary questionnaires and the Phenol‐Explorer database. In multivariable‐adjusted Cox regression models, no association between total polyphenol and the risks of overall differentiated TC (HR Q4 vs . Q1 = 0.99, 95% confidence interval [CI] 0.77–1.29), papillary (HR Q4 vs . Q1 = 1.06, 95% CI 0.80–1.41) or follicular TC (HR Q4 vs . Q1 = 1.10, 95% CI 0.55–2.22) were found. No associations were observed either for flavonoids, phenolic acids or the rest of classes and subclasses of polyphenols. After stratification by body mass index (BMI), an inverse association between the intake of polyphenols ( p ‐trend = 0.019) and phenolic acids ( p ‐trend = 0.007) and differentiated TC risk in subjects with BMI ≥ 25 was observed. In conclusion, our study showed no associations between dietary polyphenol intake and differentiated TC risk; although further studies are warranted to investigate the potential protective associations in overweight and obese individuals.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it