Causal plasma metabolites for breast cancer risk: a two-sample Mendelian randomization study with colocalization evidence
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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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 | 0.089 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".