A new method for measurement of total plasma PCSK9: clinical applications
Bibliographic record
Abstract
The proprotein convertase subtilisin kexin-9 (PCSK9) circulates in plasma as mature and furin-cleaved forms. A polyclonal antibody against human PCSK9 was used to develop an ELISA that measures total plasma PCSK9 rather than only the mature form. A cross-sectional study evaluated plasma levels in normal (n = 254) and hypercholesterolemic (n = 200) subjects treated or untreated with statins or statin plus ezetimibe. In controls, mean plasma PCSK9 (89.5 +/- 31.9 ng/ml) correlated positively with age, total cholesterol, LDL-cholesterol (LDL-C), triglycerides, and fasting glucose. Sequencing PCSK9 from individuals at the extremes of the normal PCSK9 distribution identified a new loss-of-function R434W variant associated with lower levels of circulating PCSK9 and LDL-C. In hypercholesterolemic subjects, PCSK9 levels were higher than in controls (99.3 +/- 31.7 ng/ml, P < 0.04) and increased in proportion to the statin dose, combined or not with ezetimibe. In treated patients (n = 139), those with familial hypercholesterolemia (FH; due to LDL receptor gene mutations) had higher PCSK9 values than non-FH (147.01 +/- 42.5 vs. 127.2 +/- 40.8 ng/ml, P < 0.005), but LDL-C reduction correlated positively with achieved plasma PCSK9 levels to a similar extent in both subsets (r = 0.316, P < 0.02 in FH and r = 0.275, P < 0.009 in non-FH). The detection of circulating PCSK9 in both FH and non-FH subjects means that this assay could be used to monitor response to therapy in a wide range of patients.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".