Recent advances in the understanding and care of familial hypercholesterolaemia: significance of the biology and therapeutic regulation of proprotein convertase subtilisin/kexin type 9
Bibliographic record
Abstract
Familial hypercholesterolaemia (FH) is an autosomal co-dominant disorder that markedly raises plasma low-density lipoprotein-cholesterol (LDL-C) concentration, causing premature atherosclerotic coronary artery disease (CAD). FH has recently come under intense focus and, although there is general consensus in recent international guidelines regarding diagnosis and treatment, there is debate about the value of genetic studies. Genetic testing can be cost-effective as part of cascade screening in dedicated centres, but the full mutation spectrum responsible for FH has not been established in many populations, and its use in primary care is not at present logistically feasible. Whether using genetic testing or not, cholesterol screening of family members of index patients with an abnormally raised LDL-C must be used to determine the need for early treatment to prevent the development of CAD. The metabolic defects in FH extend beyond LDL, and may affect triacylglycerol-rich and high-density lipoproteins, lipoprotein(a) and oxidative stress. Achievement of the recommended targets for LDL-C with current treatments is difficult, but this may be resolved by new drug therapies. Lipoprotein apheresis remains an effective treatment for severe FH and, although expensive, it costs less than the two recently introduced orphan drugs (lomitapide and mipomersen) for homozygous FH. Recent advances in understanding of the biology of proprotein convertase subtilisin/kexin type 9 (PCSK9) have further elucidated the regulation of lipoprotein metabolism and led to new drugs for effectively treating hypercholesterolaemia in FH and related conditions, as well as for treating many patients with statin intolerance. The mechanisms of action of PCSK9 inhibitors on lipoprotein metabolism and atherosclerosis, as well as their impact on cardiovascular outcomes and cost-effectiveness, remain to be established.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".