High-Fat Diet as a Risk Factor for Breast Cancer: A Meta-Analysis
Bibliographic record
Abstract
High-fat diets have been identified as a major cause of obesity and a potential risk factor for breast cancer. Fat tissue, also known as adipose tissue, produces an excess of estrogen, which has been linked to an increased risk of breast cancer. Determining the impact of HFDs in the development and progression of breast cancer is essential, as it will enable us to identify the role of dietary modification in preventing and managing the disease. The impact of a high-fat diet (HFD) on the development of breast cancer in humans has yet to be fully explained, as very few human studies are available to effectively analyze the effect fatty food has on breast cancer development. This meta-analysis, therefore, seeks to determine the strength of association, if any, between HFD and an increased risk of breast cancer development. This research will help inform good eating habits, potentially reducing the disease's incidence and outcome. This meta-analysis examined eight (8) papers from various nations examining the effect of a high-fat diet as a risk factor for breast cancer development between 2010 and 2020. The study employed the multivariable-adjusted hazard ratio (H.R.), odds ratio (OR), or relative risk (R.R.) from the studies. Breast cancer cases were histologically and radiologically confirmed in the studies evaluated, and validated food frequency questionnaires were used to assess their dietary patterns. This metanalysis study found a substantial link between a high-fat diet and an increased risk of breast cancer, with statistically significant results (I2 = 93.38%, p0.05). Changes in dietary fat consumption may thus help mitigate some of the unfavorable consequences of breast cancer and survival. Even if further research is needed to support this assertion, the findings are compelling enough to advocate for low-fat, healthy diets to avoid breast cancer.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.058 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".