Serum High-Density Lipoprotein Cholesterol and Breast Cancer Risk by Menopausal Status, Body Mass Index, and Hormonal Receptor in Korea
Bibliographic record
Abstract
High-density lipoprotein cholesterol (HDL-C) has been suggested to be associated with breast cancer. However, the roles of HDL-C and hypertriglyceridemia on breast cancer still have been controversial. The goal of this study was to investigate the association between HDL-C with breast cancer risk, stratifying by menopausal status, and body mass index. The hormonal receptor status of breast has been proposed to modify the effect of HDL-C on breast cancer. Multicenter hospital-based case-control study was conducted from November 2004 to December 2005 in Korea. After one to two individual matchings by age (+/-5 years) and menopausal status, 690 cases and 1,380 controls were included in the analysis. Odds ratios (OR) and 95% confidence intervals (95% CI) were estimated by conditional, unconditional, and multinomial logistic regressions. Protective effect of HDL-C on breast cancer was only observed among premenopausal women with an OR (95% CI) of 0.49 (0.33-0.72) for HDL-C > or = 60 versus <50 mg/dL (P(trend) < 0.01). Only nonobese premenopausal women had a significant decreased risk (OR, 0.34; 95% CI, 0.22-0.53). OR (95% CI) of low HDL-C (<50 mg/dL) and high triglyceride (TG; > or = 150 mg/dL) category was 2.20 (1.32-3.67) on estrogen receptor-negative and progesterone receptor-negative breast cancer compared with high HDL-C (> or = 50 mg/dL) and low TG (<150 mg/dL) category. This study suggests that higher level of HDL-C may reduce breast cancer risk among premenopausal women. Estrogen receptor-negative and progesterone receptor-negative breast cancer was associated with dyslipidemia, which implicates that association among HDL-C, TG, and breast cancer may be modified by receptor status.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".