Body Mass Index and Metabolic Phenotypes in Breast Cancer Risk: A Meta-Analysis and Systematic Review
Bibliographic record
Abstract
Introduction: Numerous studies have established that obesity, often assessed through body mass index (BMI), is one of the most significant risk factors for the development of breast cancer (BC). However, not all individuals with obesity have the same risk of developing BC and vice versa. Objective: To determine the association between metabolic states and the risk of BC. Materials: AS systematic review (SR) with a meta-analysis of cohort studies was conducted. The search was performed in four databases: PubMed/Medline, SCOPUS, Web of Science, and EMBASE. Metabolic states were classified as Metabolically Healthy Normal Weight (MHNW), Metabolically Unhealthy Normal Weight (MUNW), Metabolically Healthy Obesity (MHO), and Metabolically Unhealthy Obesity (MUO). Association measures were presented as hazard ratios (HR) with their 95% confidence intervals (CI95%). Results: A total of four studies were evaluated. The meta-analysis found a statistically significant association between the development of BC and the MHO state (HR: 1.14; CI95% 1.02, 1.28) and MUO state (HR: 1.37; CI95% 1.16, 1.62) compared to individuals with MHNW. No association was found with the MUNW state. Conclusions: The findings suggest that obesity, as determined by BMI, is significantly associated with an increased risk of BC, regardless of metabolic state. Additionally, metabolically unhealthy states, especially in obese individuals, appear to increase the risk of BC. Proposed mechanisms include systemic inflammation, metabolic dysfunction, and altered hormone production. These results have important public health implications, emphasizing the need for prevention strategies focused on obesity management and awareness of its associated BC risks.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.028 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".