Prospective study on the role of glucose metabolism in breast cancer occurrence
Bibliographic record
Abstract
High circulating glucose, insulin resistance and obesity appear to be associated with increased risk of breast cancer (BC). We sought further insight into the relation of these variables to BC. We assessed associations of BC risk with serum fasting glucose, insulin, homeostasis model assessment-insulin resistance (HOMA-IR) index and sex-binding hormone globulin (SHBG) in women recruited to the ORDET cohort who gave blood samples in 1987-1992. After a median 13.5 years of follow-up, 356 women developed BC. Four matched controls per case were selected by incidence density sampling, and rate ratios (RR) were estimated by conditional logistic regression. Women in the highest glucose quartile had a significantly greater risk of BC than those in the lowest glucose quartile (RR 1.63; 95% CI: 1.14-2.32; p for trend of 0.003). The association was significant in pre and post menopausal women separately and in women diagnosed after 55 years. Women in the highest HOMA-IR quartile had higher BC risk than the lowest quartile (RR 1.44; 95% CI: 1.03-2.02). Significantly increased BC risk in women diagnosed after 55 years was also present in the highest HOMA-IR quartile; in the same group decreased BC risk was significantly associated with high SHBG. The results of this study add to the existing epidemiological evidence that hyperglycemia and insulin resistance increase BC risk.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".