Association between expression of inflammatory markers in normal breast tissue and mammographic density among premenopausal and postmenopausal women
Bibliographic record
Abstract
OBJECTIVE: Inflammatory markers may be associated with breast cancer risk. We assessed the association between expression levels of proinflammatory (interleukin 6, tumor necrosis factor-α, C-reactive protein, cyclooxygenase 2, leptin, serum amyloid A1, interleukin 8, and signal transducer and activator of transcription 3) and anti-inflammatory markers (transforming growth factor-β, interleukin 10, and lactoferrin) in normal breast tissue with mammographic density, a strong breast cancer risk indicator, among 163 breast cancer patients. METHODS: The expression of inflammatory markers was visually evaluated on immunohistochemistry stained slides. The percent mammographic density (PMD) was estimated by a computer-assisted method in the contralateral cancer-free breast. We used generalized linear models to estimate means of PMD by median expression levels of the inflammatory markers while adjusting for age and waist circumference. RESULTS: Higher expression levels (above median) of the proinflammatory marker interleukin 6 were associated with higher PMD among all women (24.1% vs 18.5%, P = 0.007). Similarly, higher expression levels (above median) of the proinflammatory markers (interleukin 6, tumor necrosis factor-α, C-reactive protein, and interleukin 8) were associated with higher PMD among premenopausal women (absolute difference in the PMD of 8.8% [P = 0.006], 7.7% [P = 0.022], 6.7% [P = 0.037], and 16.5% [P = 0.032], respectively). Higher expression levels (above median) of the anti-inflammatory marker transforming growth factor-β were associated with lower PMD among all (18.8% vs 24.3%, P = 0.005) and postmenopausal women (14.5% vs 20.7%, P = 0.013). CONCLUSIONS: Our results provide support for the hypothesized role of inflammatory markers in breast carcinogenesis through their effects on mammographic density. Inflammatory markers could be targeted in future breast cancer prevention interventions.
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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) | 0.001 | 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".