Complexity of mechanisms among human proprotein convertase subtilisin–kexin type 9 variants
Bibliographic record
Abstract
PURPOSE OF REVIEW: There are many reports of human variants in proprotein convertase subtilisin-kexin type 9 (PCSK9) that are either gain-of-function (GOF) or loss-of-function (LOF), with downstream effects on LDL cholesterol and cardiovascular disease (CVD) risk. However, data on particular mechanisms have only been minimally curated. RECENT FINDINGS: GOF variants are individually ultrarare, affect all domains of the protein, act to reduce LDL receptor expression through several mechanisms, are a minor cause of familial hypercholesterolemia, have been reported mainly within families, have variable LDL cholesterol-raising effects, and are associated with increased CVD risk mainly through observational studies in families and small cohorts. In contrast, LOF variants can be either ultrarare mutations or relatively more common polymorphisms seen in populations, affect all domains of the protein, act to increase LDL receptor expression through several mechanisms, have variable LDL cholesterol-lowering effects, and have been associated with decreased CVD risk mainly through Mendelian randomization studies in epidemiologic populations. SUMMARY: There is considerable complexity underlying the clinical concept of both LOF and GOF variants of PCSK9. But despite the underlying mechanistic heterogeneity, altered PCSK9 secretion or function is ultimately correlated with plasma LDL cholesterol level, which is also the driver of CVD outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".