Plasma proteome correlations with liver stiffness in pediatric cholestasis implicate epithelial to mesenchymal transition
Bibliographic record
Abstract
BACKGROUND: Pediatric cholestatic liver diseases can be characterized by rapidly progressive fibrosis. A multicenter cross-sectional analysis of vibration-controlled elastography in biliary atresia (BA), alpha-1 antitrypsin deficiency (A1AT), and Alagille syndrome (ALGS) was leveraged to interrogate the plasma proteome relative to liver stiffness measurements (LSM). METHODS: Slow off-rate modified aptamer scanning profiling of >7000 proteins in plasma from 187 children with BA (n=93), A1AT (n=31), ALGS (n=46), and healthy pediatric controls (n=17) was performed, and correlations with LSM were undertaken. RESULTS: There was an abundance of LSM correlated proteins (BA n=2720, A1AT n=694, ALGS n=5968). Interestingly, a distinct plasma proteome was found in ALGS relative to BA and A1AT. Weighted Correlation Network Analysis identified groups of proteins with strong LSM correlation (eg, in a BA module of interest, Pearson correlation coefficient 0.79, p=5´0-21). Machine learning developed models predicting LSM as a continuous variable (median R2=0.62 for BA). For BA, time to transplant could be predicted equally well by the proteome or clinical parameters (elastic net models achieved a C-index using proteome 0.91, clinical parameters 0.91, proteome and clinical parameters 0.90). Single-cell transcriptomics predicted the potential hepatic cell of origin for the most informative proteins, which included macrophage, mesenchymal, mesothelial, and endothelial cells. The epithelial-to-mesenchymal transition pathway was enriched in LSM correlated proteins in all 3 diseases. CONCLUSIONS: The plasma proteome is highly correlated in a disease-specific fashion with LSM in BA, A1AT, and ALGS. These correlations provide unique opportunities to identify biomarkers and focus attention on epithelial-to-mesenchymal transition in pediatric cholestasis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".