High Body Mass Index Was Associated With Human Epidermal Growth Factor Receptor 2-Positivity, Histological Grade and Disease Progression Differently by Age
Bibliographic record
Abstract
Background: Breast cancer is the most commonly occurring cancer among women. The relationship between the obesity paradox and breast cancer is still unclear. The goal of this study is to elucidate the association between high body mass index (BMI) and pathological findings by age. Methods: We collected BMI information pertinent to breast cancer patients from the Gene Expression Omnibus (GEO) database. We use a BMI of 25 as a boundary, and those greater than 25 are defined as high BMI. Besides, we segregated the patients based on age into two age groups: < 55 years, and > 55 years. In this study, R × C Chi-square for trend and binary logistic regression was used to estimate the odds ratios (ORs) and corresponding 95% confidence intervals (CIs). Results: Higher BMI was associated with less breast cancer incidence in females younger than 55 years of age (OR = 0.313, CI: 0.240 - 0.407). High BMI was associated with human epidermal growth factor receptor 2 (HER2) positivity in breast cancer patients of less than 55 years (P < 0.001), but not in the older patients. High BMI was associated with histological grade lower than 2 in the breast cancer patients older than 55 years, but not in younger patients (OR = 0.288, CI: 0.152 - 0.544). Besides, high BMI was associated with worse progression-free survival in younger breast cancer patients, but not in older patients (P < 0.05). Conclusions: Our results described a significant relationship between breast cancer incidence and BMI at different ages and benefit breast cancer patients to implement strategies to control their BMI for reducing the recurrence and distant recurrence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".