Angiopoietin-Like Proteins: Cardiovascular Biology and Therapeutic Targeting for the Prevention of Cardiovascular Diseases
Bibliographic record
Abstract
Despite the best pharmacologic tools available, cardiovascular diseases (CVDs) remain a major cause of morbidity and mortality in developed countries. After 2 decades of research, new therapeutic targets, such as angiopoietin-like proteins (ANGPTLs), are emerging. ANGPTLs belong to a family of 8 members, from ANGPTL1 to ANGPTL8; they have structural homology with angiopoietins and are secreted in the circulation. ANGPTLs display a multitude of physiological and pathologic functions; they contribute to inflammation, angiogenesis, cell death, senescence, hematopoiesis, and play a role in repair, maintenance, and tissue homeostasis. ANGPTLs-particularly the triad ANGPTL3, 4, and 8-have an established role in lipid metabolism through the regulation of triacylglycerol trafficking according to the nutritional status. Some ANGPTLs also contribute to glucose metabolism. Therefore, dysregulation in ANGPTL expression associated with abnormal circulating levels are linked to a plethora of CVD and metabolic disorders including atherosclerosis, heart diseases, diabetes, but also obesity and cancers. Because ANGPTLs bind to different receptors according to the cell type, antagonists are therapeutically inadequate. Recently, direct inhibitors of ANGPTLs, mainly ANGPTL3, have been developed, and specific monoclonal antibodies and antisense oligonucleotides are currently being tested in clinical trials. The aim of the current review is to provide an up-to-date preclinical and clinical overview on the function of the 8 members of the ANGPTL family in the cardiovascular system, their contribution to CVD, and the therapeutic potential of manipulating some of them.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".