Body Shape Phenotypes And Breast Cancer Risk: A Mendelian Randomization Analysis.
Bibliographic record
Abstract
Abstract Background: Observational and genetic studies have linked different anthropometric traits to breast cancer risk, with inconsistent results. We aimed to investigate the association between body shape defined by principal component (PC) analysis of anthropometric traits (body mass index (BMI), height, weight, waist-to-hip ratio (WHR), waist and hip circumference) and overall breast cancer risk and by sub-type (luminal A, luminal B, HER2+, triple negative and luminal B/HER2 negative). Methods: We performed two-sample Mendelian randomization analyses to assess the association between 188 genetic variants robustly linked to the first three PCs and breast cancer (133,384 combined pre- and post-menopausal cases/113,789 controls from the Breast Cancer Association Consortium (BCAC)). Results: PC1 (general adiposity) was inversely associated with overall breast cancer risk (odds ratio (OR) 0.89 [95% confidence interval (CI) 0.81-0.98]; p-value = 0.016). PC2 (tall with low WHR) was weakly positively associated with overall breast cancer risk (OR = 1.05 [95% CI: 0.98-1.12]; p-value = 0.135), but with a confidence interval including the null. PC3 (tall with large WHR) was not associated with overall breast cancer risk. Some of these associations differed by breast cancer sub-types. For instance, PC2 was positively associated with risk of luminal A breast cancer sub-type (OR = 1.09 [95% CI: 1.01-1.18]; p-value = 0.02).Conclusions: Our study provides evidence for potential causal associations between body shape and breast cancer risk and breast cancer sub-types highlighting the importance to also assess body morphology holistically.
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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 | 0.028 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".