Evaluation of Antioxidant Intakes in Relation to Inflammatory Markers Expression Within the Normal Breast Tissue of Breast Cancer Patients
Bibliographic record
Abstract
Chronic inflammation may be a causative factor in breast cancer. One possible underlying mechanism is the generation of oxidative stress, which may favor tumorigenic processes. Antioxidant consumption may, therefore, help reduce tissue inflammation levels. However, few studies have explored this relation in breast tissue. We aimed to evaluate correlations between antioxidant (vitamin A/retinol, vitamin C, vitamin E, β-carotene, α-carotene, lycopene, lutein/zeaxanthin, β-cryptoxanthin, selenium, and zinc) intakes and protein expression levels of interleukin (IL)-6, tumor necrosis factor-α, C-reactive protein, cyclooxygenase-2, leptin, serum amyloid A1, signal transducer and activator of transcription 3, IL-8, IL-10, lactoferrin, and transforming growth factor-β measured in the normal breast tissue of 160 women diagnosed with breast cancer. Antioxidant intakes were collected using a self-administered food frequency questionnaire. Inflammation marker expression was assessed by immunohistochemistry. Correlations between antioxidant intakes and inflammatory marker expression were evaluated using Spearman's partial correlation coefficients ( r) for all women and for premenopausal and postmenopausal women separately. After Bonferroni correction, negative correlations were observed between dietary β-tocopherol and IL-10 expression in all women combined ( r = -0.26, P = .003) and among postmenopausal women ( r = -0.39, P = .003). For all women, a negative correlation was found between total zinc intakes and IL-10 ( r = -0.26, P = .002). Among postmenopausal women, dietary selenium intake was negatively correlated with the expression of lactoferrin ( r = -0.39, P = .003). No associations were observed in premenopausal women. Our findings suggest that consumption of specific antioxidants, including β-tocopherol, zinc, and selenium, may act on the breast tissue through mechanisms affecting the expression of some inflammation markers, particularly among postmenopausal women.
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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 | 0.001 | 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".