Genetically predicted circulating concentrations of micronutrients and risk of breast cancer: A Mendelian randomization study
Bibliographic record
Abstract
Abstract The epidemiological literature reports inconsistent associations between consumption or circulating concentrations of micronutrients and breast cancer risk. We investigated associations between genetically predicted concentrations of 11 micronutrients (beta‐carotene, calcium, copper, folate, iron, magnesium, phosphorus, selenium, vitamin B 6 , vitamin B 12 and zinc) and breast cancer risk using Mendelian randomization (MR). A two‐sample MR study was conducted using 122 977 women with breast cancer and 105 974 controls from the Breast Cancer Association Consortium. MR analyses were conducted using the inverse variance‐weighted approach, and sensitivity analyses were conducted to assess the impact of potential violations of MR assumptions. A value of 1 SD (SD: 0.08 mmol/L) higher genetically predicted concentration of magnesium was associated with a 17% (odds ratio [OR]: 1.17, 95% confidence interval [CI]: 1.10‐1.25, P value = 9.1 × 10 −7 ) and 20% (OR: 1.20, 95% CI: 1.08‐1.34, P value = 3.2 × 10 −6 ) higher risk of overall and ER +ve breast cancer, respectively. An inverse association was observed for a SD (0.5 mg/dL) higher genetically predicted phosphorus concentration and ER −ve breast cancer (OR: 0.84, 95% CI: 0.72‐0.98, P value = .03). There was little evidence that any other nutrient was associated with breast cancer. The results for magnesium were robust under all sensitivity analyses and survived correction for multiple comparisons. Higher circulating concentrations of magnesium and potentially phosphorus may affect breast cancer risk. Further work is required to replicate these findings and investigate underlying mechanisms.
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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) | 0.000 | 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 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".