Consumption of sweet foods and mammographic breast density: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: The increasing consumption of sugar worldwide seems to lead to several health problems, including some types of cancer. While some studies reported a positive association between sweet foods intake and breast cancer risk, little is known about their relation to mammographic density (MD), a strong breast cancer risk factor. This study examined the association of sweet foods and drinks intake with MD among 776 premenopausal and 779 postmenopausal women recruited at mammography. METHODS: A food-frequency questionnaire was used to assess intake of sweet foods, sugar-sweetened beverages and spoonsful of sugar added. Percent and absolute breast density were estimated using a computer-assisted method. Multivariate generalized linear models were used to evaluate associations. All models were adjusted for potential confounders, including age and body mass index. RESULTS: For increasing quartiles of sugar-sweetened beverages intake, adjusted-mean absolute density was respectively 32, 34, 32 and 36 cm2 among all women (P(trend) = 0.040) and 43, 46, 44 and 51 cm2 among premenopausal women (P(trend) = 0.007). For increasing quartiles of sweet foods intake, adjusted-mean percent density was respectively 16, 16, 17 and 19% among postmenopausal women (P(trend) = 0.036). No association was shown between intake of spoonsful of sugar added and MD. CONCLUSION: Our results suggest that an increase in sweet foods or sugar-sweetened beverage intake is associated with higher MD.
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How this classification was reachedexpand
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 | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".