Mendelian randomization reveals interactions of the blood proteome and immunome in mitral valve prolapse
Bibliographic record
Abstract
BACKGROUND: Mitral valve prolapse (MVP) is a common heart disorder characterized by an excessive production of proteoglycans and extracellular matrix in mitral valve leaflets. Large-scale genome-wide association study (GWAS) underlined that MVP is heritable. The molecular underpinnings of the disease remain largely unknown. METHODS: We interrogated cross-modality data totaling more than 500,000 subjects including GWAS, 4809 molecules of the blood proteome, and genome-wide expression of mitral valves to identify candidate drivers of MVP. Data were investigated through Mendelian randomization, network analysis, ligand-receptor inference and digital cell quantification. RESULTS: In this study, Mendelian randomization identify that 33 blood proteins, enriched in networks for immunity, are associated with the risk of MVP. MVP- associated blood proteins are enriched in ligands for which their cognate receptors are differentially expressed in mitral valve leaflets during MVP and enriched in cardiac endothelial cells and macrophages. MVP-associated blood proteins are involved in the renewal-polarization of macrophages and regulation of adaptive immune response. Cytokine activity profiling and digital cell quantification show in MVP a shift toward cytokine signature promoting M2 macrophage polarization. Assessment of druggability identify CSF1R, CX3CR1, CCR6, IL33, MMP8, ENPEP and angiotensin receptors as actionable targets in MVP. CONCLUSIONS: Hence, integrative analysis identifies networks of candidate molecules and cells involved in immune control and remodeling of the extracellular matrix, which drive the risk of MVP.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".