Blood Iodine as a Potential Marker of the Risk of Cancer in BRCA1 Carriers
Bibliographic record
Abstract
Breast cancer and ovarian cancer pose a significant risk for BRCA1 carriers, with limited risk-reduction strategies. While improved screening helps in the early detection of breast cancer, preventive measures remain elusive. Emerging evidence suggests a potential link between iodine levels and modulation of cancer risk, but comprehensive studies are scarce. We conducted a prospective study among 989 BRCA1 carriers to assess the association between blood iodine levels and breast and ovarian cancer risk. Using inductively coupled plasma mass spectrometry, we measured blood iodine levels and observed a negative association with breast cancer risk, with a significantly lower risk observed in quartile 4 (iodine > 38.0 µg/L) compared with quartile 1 (iodine < 30 µg/L) (HR = 0.49; 95%CI: 0.27–0.87; p = 0.01). Conversely, a suggestive increase in ovarian cancer risk was observed at higher iodine levels (HR = 1.91; 95%CI: 0.64–5.67; p = 0.25). No significant association was found between iodine levels and overall cancer risk. Our results suggest the potential of iodine to reduce breast cancer risk in BRCA1 carriers after prophylactic oophorectomy but require further validation and investigation of its effect on ovarian cancer risk and overall mortality. These findings highlight the need for personalized strategies to manage cancer risk in BRCA1 carriers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".