Associations of Biomarkers of Inflammation and Breast Cancer in the Breast Adipose Tissue of Women with Combined Measures of Adiposity
Bibliographic record
Abstract
Background. Mechanisms underlying the obesity-breast cancer link involve inflammation but need to be elucidated. Determining obesity by combining body mass index (BMI) with the waist circumference (WC) may clarify the role of inflammatory and hormonally related markers in breast cancer. We examined the effect of combining adiposity indices (BMI/WC) with the gene expression of several biomarkers involved in breast cancer. Methods. Expression of cytochrome P450 family 19 subfamily A member 1 (CYP19A1), estrogen receptor-alpha (ER-α), allograft inflammatory factor 1 (AIF1), cyclooxygenase-2 (COX2), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), and leptin (LEP) in 141 adipose breast tissues was quantified using qPCR method. BMI and WC were measured by a trained nurse and categorized using the median split, BMILOWCLO, BMILOWCHI, BMIHIWCLO, and BMIHIWCHI. Results. Gene expression of IL-6 (3-fold), TNF-α (2-fold), and LEP (2-fold) was higher in the breast adipose tissue of women with high WC regardless of BMI, that is, BMILOWCHI and BMIHIWCHI women (all P < 0.01). Compared to BMILOWCLO women, gene expression of CYP19A1, COX2, and AIF1 was increased by two-fold in breast adipose tissue of BMIHIWCHI women ( P < 0.10). ER-α was not different across adiposity categories. Conclusions. The expression of some biomarkers, particularly those related to inflammation, is elevated in breast adipose tissue of women with a high WC independent of BMI. Obesity monitoring should also include women with normal or low BMI, but with central adiposity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".