Association of lipid profile biomarkers with breast cancer by molecular subtype: analysis of the MEND study
Bibliographic record
Abstract
There is conflicting evidence on the role of lipid biomarkers in breast cancer (BC), and no study to our knowledge has examined this association among African women. We estimated odds ratios (ORs) and 95% confidence intervals (95% CI) for the association of lipid biomarkers-total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), and triglycerides-with odds of BC overall and by subtype (Luminal A, Luminal B, HER2-enriched and triple-negative or TNBC) for 296 newly diagnosed BC cases and 116 healthy controls in Nigeria. Each unit standard deviation (SD) increase in triglycerides was associated with 39% increased odds of BC in fully adjusted models (aOR: 1.39; 95% CI: 1.03, 1.86). Among post-menopausal women, higher total cholesterol (aOR: 1.65; 95% CI: 1.06, 2.57), LDL cholesterol (aOR: 1.59; 95% CI: 1.04, 2.41), and triglycerides (aOR: 1.91; 95% CI: 1.21, 3.01) were associated with increased odds of BC. Additionally, each unit SD increase in LDL was associated with 64% increased odds of Luminal B BC (aOR 1.64; 95% CI: 1.06, 2.55). Clinically low HDL was associated with 2.7 times increased odds of TNBC (aOR 2.67; 95% CI: 1.10, 6.49). Among post-menopausal women, higher LDL cholesterol and triglycerides were significantly associated with increased odds of Luminal B BC and HER2 BC, respectively. In conclusion, low HDL and high LDL are associated with increased odds of TN and Luminal B BC, respectively, among African women. Future prospective studies can definitively characterize this association and inform clinical approaches targeting HDL as a BC prevention strategy.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".