Low cholesterol states: clinical implications and management
Bibliographic record
Abstract
INTRODUCTION: Hypocholesterolemia results from genetic - both monogenic and polygenic - and non-genetic causes and can sometimes be a source of clinical concern. We review etiologies and sequelae of hypocholesterolemia and therapeutics inspired from genetic hypocholesterolemia. AREAS COVERED: Monogenic hypocholesterolemia disorders caused by the complete absence of apolipoprotein (apo) B-containing lipoproteins (abetalipoproteinemia and homozygous hypobetalipoproteinemia) or an isolated absence of apo B-48 lipoproteinemia (chylomicron retention disease) lead to clinical sequelae. These include gastrointestinal disturbances and severe vitamin deficiencies that affect multiple body systems, i.e. neurological, musculoskeletal, ophthalmological, and hematological. Monogenic hypocholesterolemia disorders with reduced but not absent levels of apo B lipoproteins have a milder clinical presentation and patients are protected against atherosclerotic cardiovascular disease. Patients with heterozygous hypobetalipoproteinemia have somewhat increased risk of hepatic disease, while patients with PCSK9 deficiency, ANGPTL3 deficiency, and polygenic hypocholesterolemia typically have anunremarkable clinical presentation. EXPERT OPINION: In patients with severe monogenic hypocholesterolemia, early initiation of high-dose vitamin therapy and a low-fat diet are essential for optimal prognosis. The molecular basis of monogenic hypocholesterolemia has inspired novel therapeutics to help patients with the opposite phenotype - i.e. elevated apo B-containing lipoproteins. In particular, inhibitors of PCSK9 and ANGPTL3 show important clinical impact.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".