Dietary Intake of Lactose as a Strong Predictor for Secretor Status of Nipple Aspirate Fluid in Healthy Premenopausal Nonlactating Women
Bibliographic record
Abstract
PURPOSE: Nipple aspirate fluid (NAF) is considered a potential source for discovering breast cancer biomarkers. However, the success rate of obtaining NAF was reported to vary from 48% to 77%, and mechanisms for its secretion are not fully understood. The purpose of this study was to investigate dietary, demographic, reproductive, hormonal, and anthropometric factors that are associated with the ability to obtain NAF by aspiration (secretor status) from premenopausal women. STUDY DESIGN: NAF procedures were attempted for women who were 30 to 40 years old, not pregnant, not breast-feeding, and not taking contraceptive medications. RESULTS: Compared with nonsecretors, secretors of NAF consumed significantly more dietary lactose (mainly from milk), were more likely to be parous, were older at first and last childbirth, breast-fed their babies for a longer period of time, and had an earlier menarche and lower plasma concentrations of 17beta-estradiol (P < 0.05). Using multivariate logistic regression models, higher dietary intake of lactose [odds ratio (OR), 2.7; 95% confidence interval (95% CI), 1.5-4.8], earlier menarche (OR, 0.8; 95% CI, 0.7-1.0), being parous (OR, 2.3; 95% CI, 1.0-5.6), and being older at first childbirth (OR, 1.5; 95% CI, 1.0-2.1) were found to be independent and positive predictors for being a secretor of NAF. CONCLUSIONS: These findings suggest that dietary intake of lactose, a modifiable factor, may be used to change the NAF secretor status of women. This finding may facilitate the use of NAF as a diagnostic material for detecting breast diseases.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".