Lipid Profile, <scp>PCSK9</scp>, <scp>ANGPTL3</scp> and Lipoprotein (a) Levels in Men Diagnosed With Localized High‐Grade Prostate Cancer and Men At‐Risk of Prostate Cancer
Bibliographic record
Abstract
BACKGROUND: Some cancers have been found to require abundant supplies of lipids for their development. One example is prostate cancer (PCa). To date, lipid-modifying factors, such as proprotein convertase subtilisin/kexin type 9 (PCSK9), angiopoietin-like 3 protein (ANGPTL3), and lipoprotein(a) or Lp(a), have not been reported in men with PCa. The present study aimed to verify whether plasma levels of these lipid-related proteins vary in men with PCa compared to at-risk but cancer-free men. METHODS: Plasma samples from 35 men with locally advanced PCa Gleason 8 and 9 versus 35 men at risk of PCa were selected as cases and controls. Blood samples were paired according to age and BMI. Apolipoprotein B100 (Apo B), Lp(a), and lipid profiles were measured on an analytical platform (Roche Cobas). PCSK9 and ANGPTL3 levels were determined by ELISA. RESULTS: No significant change in lipids and related factors levels was observed between men with localized PCa Gleason 8 or 9 and matched controls. A correlation between ANGPTL3 and HDL levels was only confirmed in controls (ρ = 0.54, p = 0.0009). PCSK9 was inversely associated with PSA levels in the entire cohort (ρ = -0.31, p < 0.01), suggesting that factors influencing PCSK9 could also influence PSA levels. In controls only, PSA levels were correlated with LDL, Apo B, non-HDL, total cholesterol, and triglycerides (all ρ coefficients ≥ 0.35, all p-values < 0.05). PCSK9 was correlated to LDL in PCa men, but the relationship was unexpectedly found to be inverse. CONCLUSIONS: In this observational study, lipid profiles, PCSK9, ANGPTL3, and Lp(a) levels did not change in men diagnosed with locally advanced Gleason 8 or 9 PCa compared to at-risk but cancer-free men. The present data suggest a complex interplay between PCSK9, PSA, and the lipid profile in localized PCa.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".