Metabolic Obesity, Adipose Inflammation and Elevated Breast Aromatase in Women with Normal Body Mass Index
Bibliographic record
Abstract
Abstract Obesity is associated with breast white adipose tissue (WAT) inflammation, elevated levels of the estrogen biosynthetic enzyme, aromatase, and systemic changes that have been linked to the pathogenesis of breast cancer. Here, we determined whether metabolic obesity, including changes in breast biology and systemic effects, occurs in a subset of women with normal body mass index (BMI). Breast WAT and fasting blood were collected from 72 women with normal BMI (<25 kg/m2) undergoing mastectomy for breast cancer risk reduction or treatment. WAT inflammation was defined by the presence of crown-like structures of the breast (CLS-B) which are composed of dead or dying adipocytes surrounded by macrophages. Severity of inflammation was measured as CLS-B/cm2. The primary objective was to determine whether breast WAT inflammation is associated with aromatase expression and activity. Secondary objectives included assessment of circulating factors and breast adipocyte size. Breast WAT inflammation was present in 39% of women. Median BMI was 23.0 kg/m2 (range, 18.4–24.9 kg/m2) in women with breast WAT inflammation versus 21.8 kg/m2 (range, 17.3–24.6 kg/m2) in those without inflammation (P = 0.04). Breast WAT inflammation was associated with elevated aromatase expression and activity, which increased with severity of inflammation (P < 0.05). Breast WAT inflammation correlated with larger adipocytes (P = 0.01) and higher circulating levels of C-reactive protein, leptin, insulin, and triglycerides (P ≤ 0.05). A subclinical inflammatory state associated with elevated aromatase in the breast, adipocyte hypertrophy, and systemic metabolic dysfunction occurs in some normal BMI women and may contribute to the pathogenesis of breast cancer. Cancer Prev Res; 10(4); 235–43. ©2017 AACR. See related article by Berger, p. 223–25.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".