Serum Lipids, Lipoproteins, and Risk of Breast Cancer: A Nested Case-Control Study Using Multiple Time Points
Bibliographic record
Abstract
BACKGROUND: There is strong evidence that breast cancer risk is influenced by environmental factors. Blood lipid and lipoprotein levels are also influenced by environmental factors and are associated with some breast cancer risk factors. We examined whether serial measures of serum lipids and lipoproteins were associated with breast cancer risk. METHODS: We carried out a nested case-control study within a randomized long-term dietary intervention trial with 4690 women with extensive mammographic density followed for an average of 10 years for breast cancer incidence. We measured lipids in an average of 4.2 blood samples for 279 invasive breast cancer case subjects and 558 matched control subjects. We calculated subaverages of lipids for each subject based on menopausal status and use of hormone replacement therapy (HRT) at blood collection and analyzed their association with breast cancer using generalized estimating equations. All statistical tests were two-sided. RESULTS: High-density lipoprotein-cholesterol (HDL-C) (P = .05) and apoA1 (P = .02) levels were positively associated with breast cancer risk (75(th) vs 25(th) percentile: HDL-C, 23% higher; apoA1, 28% higher) and non-HDL-C (P = .03) and apoB (P = .01) levels were negatively associated (75(th) vs 25(th) percentile: non-HDL-C, 19% lower; apoB, 22% lower). These associations were observed only when lipids were measured when HRT was not used. Total cholesterol and triglyceride levels were not statistically significantly associated with breast cancer risk. CONCLUSIONS: These results demonstrate that serum lipids are associated with breast cancer risk in women with extensive mammographic density. The possibility that interventions for heart disease prevention, which aim to reduce non-HDL-C or raise HDL-C, may have effects on breast cancer risk merits examination.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".