Understanding the ins and outs of lipoprotein (a) metabolism
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review summarizes our current understanding of the processes of apolipoprotein(a) secretion, assembly of the Lp(a) particle and removal of Lp(a) from the circulation. We also identify existing knowledge gaps that need to be addressed in future studies. RECENT FINDINGS: The Lp(a) particle is assembled in two steps: a noncovalent, lysine-dependent interaction of apo(a) with apoB-100 inside hepatocytes, followed by extracellular covalent association between these two molecules to form circulating apo(a).The production rate of Lp(a) is primarily responsible for the observed inverse correlation between apo(a) isoform size and Lp(a) levels, with a contribution of catabolism restricted to larger Lp(a) isoforms.Factors that affect apoB-100 secretion from hepatocytes also affect apo(a) secretion.The identification of key hepatic receptors involved in Lp(a) clearance in vivo remains unclear, with a role for the LDL receptor seemingly restricted to conditions wherein LDL concentrations are low, Lp(a) is highly elevated and LDL receptor number is maximally upregulated. SUMMARY: The key role for production rate of Lp(a) [including secretion and assembly of the Lp(a) particle] rather than its catabolic rate suggests that the most fruitful therapies for Lp(a) reduction should focus on approaches that inhibit production of the particle rather than its removal from circulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".