Macrophagic Sclerostin Loop2‐ApoER2 Interaction Required by Sclerostin for Cardiovascular Protective Action
Bibliographic record
Abstract
Abstract Therapeutic antibody against sclerostin loop2 promoted bone formation in postmenopausal osteoporosis but caused severe cardiovascular events in clinical applications. The studies of atherosclerosis and aortic aneurysm in SOST ki .ApoE −/− mice and sost −/− . ApoE −/− mice collectively indicated the cardiovascular protective action of sclerostin. However, how sclerostin exerts cardiovascular protective action remains unclear. In this study, ApoER2 (LRP8) is notably identified as a novel transmembrane receptor for sclerostin in macrophages. Mechanistically, blockade of macrophagic sclerostin loop2‐ApoER2 interaction attenuates the suppressive effects of sclerostin on NF‐κB nuclear translocation, phosphorylation, and mRNA expression in macrophages, reduces the promotive effects of sclerostin on macrophage conversion to anti‐inflammatory phenotypes, and inhibits the preventive effects of sclerostin on atherosclerosis and aortic aneurysm in ApoE −/− mice. Together, macrophagic sclerostin loop2‐ApoER2 interaction is required by sclerostin to suppress inflammatory responses, atherosclerosis, and aortic aneurysm in ApoE −/− mice. Sclerostin plays a compensatory protective role in the cardiovascular system when ApoE is absent or mutated. Translationally, it provided critical pre‐clinical evidence regarding the prediction of cardiovascular risk populations (e.g. , APOE variants) for the marketed antibody against sclerostin loop2. Importantly, targeting sclerostin while preserving macrophagic sclerostin loop2‐ApoER2 interaction would offer the next generation of precise sclerostin inhibition strategy without cardiovascular safety concern, while promoting bone formation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".