Impact of serum lipids on prognosis in breast cancer patients: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Whether serum lipids have an impact on breast cancer(BC) prognosis remains controversial and unstudied. We conducted this systematic review (SR) and meta-analysis (MA) to explore the impact of levels of various components of the lipid profile on multiple survival outcomes (OSs) of BC. METHODS: We searched Pubmed, Embase, Cochrane Library, and Web of Science for relevant cohort studies to assess the impact of multiple lipids on the prognosis of BC patients. Included studies were subjected to quality assessment using the Newcastle-Ottawa scale (NOS). MA of extracted data was performed using StataSE 15.1. RESULTS: 17 studies in total were included, involving a sample size of 24,026. MA showed that high levels of low-density lipoprotein cholesterol (LDL-C ) (HR (hazard ratios) = 1.96, 95% confidence interva (CI): 1.03-3.73), apolipoprotein E (ApoE) (HR = 3.68, 95% CI: 1.71-7.94), and apolipoprotein B (ApoB) (HR = 1.93, 95% CI: 1.44-2.59) were associated with poorer OS, while high levels of low-density lipoprotein (LDL) (HR = 0.81, 95% CI. 0.74-0.88) and apolipoprotein D (ApoD) (HR = 0.44, 95% CI: 0.24-0.81) were associated with better OS. Both a high level of total cholesterol (TC) (HR = 1.60, 95% CI:1.08-2.37) and dyslipidemia (HR = 1.71, 95% CI:1.12-2.62) had a negative impact on disease-free survival (DFS) in BC patients. CONCLUSION: This MA showed that the levels of LDL-C, ApoE, and ApoB in serum were associated with OS, and the TC level in serum and dyslipidemia were associated with DFS. However, the levels of blood lipids were less associated with other prognostic outcomes. Other high-quality studies are needed to further elucidate this issue. REGISTRATION: PROSPERO CRD42024541755.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".