Obesity and Breast Cancer: Molecular and Epidemiological Evidence
Bibliographic record
Abstract
Carcinoma of the breast is a leading cause of cancer deaths among women world-wide. Obesity is recognized as a well-established risk factor for epithelial tumors including the mammary epithelium. Adipose tissue is considered to be metabolically active organ with the ability to secrete a wide range of biologically active adipokines. Multiple studies have evaluated the potential mechanisms correlating obesity to increased risk of breast cancer. Altered circulating levels of adipokines or changed adipokine signaling pathways are now increasingly recognized to be associated with breast cancer development and progression. Leptin and adiponectin were the main adipokines that have been investigated in the context of breast cancer in both preclinical and epidemiological studies. Obesity is also believed to promote inflammatory response and induce activity of key enzymes like aromatase, leading to higher risk of breast cancer development. The goal of this review is to provide recent insights into the potential molecular mechanisms linking adipokines to the etiopathogenesis of breast cancer including recently identified adipokines and trying to correlate these molecular mechanisms to more established metabolic and hormonal dysregulations of obesity. A better understanding of the interplay between adipokines and other deregulated mechanisms in obesity is important for the development of preventive strategies with therapeutic potential against breast cancer in obese patients.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".