Systemic Correlates of White Adipose Tissue Inflammation in Early-Stage Breast Cancer
Bibliographic record
Abstract
PURPOSE: Obesity, insulin resistance, and elevated levels of circulating proinflammatory mediators are associated with poorer prognosis in early-stage breast cancer. To investigate whether white adipose tissue (WAT) inflammation represents a potential unifying mechanism, we examined the relationship between breast WAT inflammation and the metabolic syndrome and its prognostic importance. EXPERIMENTAL DESIGN: WAT inflammation was defined by the presence of dead/dying adipocytes surrounded by macrophages forming crown-like structures (CLS) of the breast. Two independent groups were examined in cross-sectional (cohort 1) and retrospective (cohort 2) studies. Cohort 1 included 100 women undergoing mastectomy for breast cancer risk reduction (n = 10) or treatment (n = 90). Metabolic syndrome-associated circulating factors were compared by CLS-B status. The association between CLS of the breast and the metabolic syndrome was validated in cohort 2, which included 127 women who developed metastatic breast cancer. Distant recurrence-free survival (dRFS) was compared by CLS-B status. RESULTS: In cohorts 1 and 2, breast WAT inflammation was detected in 52 of 100 (52%) and 52 of 127 (41%) patients, respectively. Patients with breast WAT inflammation had elevated insulin, glucose, leptin, triglycerides, C-reactive protein, and IL6 and lower high-density lipoprotein cholesterol and adiponectin (P < 0.05) in cohort 1. In cohort 2, breast WAT inflammation was associated with hyperlipidemia, hypertension, and diabetes (P < 0.05). Compared with patients without breast WAT inflammation, the adjusted HR for dRFS was 1.83 (95% CI, 1.07-3.13) for patients with inflammation. CONCLUSIONS: WAT inflammation, a clinically occult process, helps to explain the relationship between metabolic syndrome and worse breast cancer prognosis. Clin Cancer Res; 22(9); 2283-9. ©2015 AACR.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".