High-Fat Diet Exposure in Early Life Alters Mammary Metabolic and Inflammatory Microenvironment in Favor of Breast Tumorigenesis Later in Life in Mice
Bibliographic record
Abstract
Emerging evidence highlights the important impact of early-life exposures on cancer development later in life. The present study aimed to investigate the impacts of a high-fat diet in early life on the mammary microenvironment in relation to breast tumorigenesis. Forty-four female C57BL/6 mice were fed a low-fat diet (LF, 10 kcal% fat) or a high-fat diet (HF, 60 kcal% fat) for 8 weeks starting at ~4 weeks of age. Twenty-two mice were sacrificed immediately after an 8 week feeding, and the rest of mice were switched to a normal diet for maintenance (Lab Diet, #5P76) for additional 12 weeks. A panel of metabolic parameters, inflammatory cytokines, as well as tumorigenic Wnt-signaling target genes were analyzed. The HF diet increased body weight and exacerbated mammary metabolic and inflammatory status. The disrupted microenvironment remains significant to the later life equivalent to young adulthood (p < 0.05). Mammary Wnt-signaling was elevated right after the HF diet as indicated by the upregulated expression of its downstream genes, whereas it was surprisingly suppressed after switching diets (p < 0.05). In summary, HF-induced overweight/obesity in early life altered the mammary metabolic and inflammatory microenvironments in favor of breast tumorigenesis, although its overall impact to breast cancer later in life warrants further investigation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".