Causal plasma metabolites for breast cancer risk: a two-sample Mendelian randomization study with colocalization evidence
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Breast cancer pathogenesis involves complex metabolic dysregulation, yet causal biomarkers remain elusive. This study aimed to assess causal effects of 1,400 human plasma metabolites on breast cancer (BC) risk using a two-sample Mendelian randomization (MR) framework. METHODS: We employed a rigorous two-sample Mendelian randomization framework with tiered quality control (Bonferroni correction, sensitivity analyses, meta-analyses) to investigate causal metabolite-BC associations. Colocalization (PPH4 > 0.80) and phenome-wide MR (2,099 FinnGen phenotypes) validated mechanistic specificity and clinical safety profiles. RESULTS: Five genetically determined plasma metabolites were identified as the potential causal biomarkers for BC risk: 3,5-dichloro-2,6-dihydroxybenzoic acid (odds ratio [OR]: 0.90; 95% confidence interval [CI] 0.87-0.94; p < 0.001), carnitine C14 (OR: 0.72; 95% CI 0.64-0.83; p < 0.001) and epiandrosterone sulfate (OR: 1.04; 95% CI 1.01-1.06; p < 0.001), Glyco-beta-muricholate (OR: 0.95; 95% CI 0.93-0.97; p < 0.001), N4-acetylcytidine (OR: 0.93; 95% CI 0.91-0.96; p < 0.001). Colocalization analysis showed strong evidence for Glyco - beta - muricholate and Epiandrosterone sulfate with BC risk (PPH4 = 1). PheWAS-MR revealed metabolite-specific safety profiles, with carnitine C14 showing broadest phenotypic associations (96 outcomes). CONCLUSIONS: This study establishes carnitine C14 as a novel protective biomarker and epiandrosterone sulfate as a risk biomarker for breast cancer, with colocalization evidence supporting their therapeutic targeting. The metabolic risk profile provides a foundation for precision prevention strategies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 it