Serum biomarkers correlated with liver stiffness assessed in a multicenter study of pediatric cholestatic liver disease
Bibliographic record
Abstract
BACKGROUND AND AIMS: Detailed investigation of the biological pathways leading to hepatic fibrosis and identification of liver fibrosis biomarkers may facilitate early interventions for pediatric cholestasis. APPROACH AND RESULTS: A targeted enzyme-linked immunosorbent assay-based panel of nine biomarkers (lysyl oxidase, tissue inhibitor matrix metalloproteinase (MMP) 1, connective tissue growth factor [CTGF], IL-8, endoglin, periostin, Mac-2-binding protein, MMP-3, and MMP-7) was examined in children with biliary atresia (BA; n = 187), alpha-1 antitrypsin deficiency (A1AT; n = 78), and Alagille syndrome (ALGS; n = 65) and correlated with liver stiffness (LSM) and biochemical measures of liver disease. Median age and LSM were 9 years and 9.5 kPa. After adjusting for covariates, there were positive correlations among LSM and endoglin ( p = 0.04) and IL-8 ( p < 0.001) and MMP-7 ( p < 0.001) in participants with BA. The best prediction model for LSM in BA using clinical and lab measurements had an R2 = 0.437; adding IL-8 and MMP-7 improved R2 to 0.523 and 0.526 (both p < 0.0001). In participants with A1AT, CTGF and LSM were negatively correlated ( p = 0.004); adding CTGF to an LSM prediction model improved R2 from 0.524 to 0.577 ( p = 0.0033). Biomarkers did not correlate with LSM in ALGS. A significant number of biomarker/lab correlations were found in participants with BA but not those with A1AT or ALGS. CONCLUSIONS: Endoglin, IL-8, and MMP-7 significantly correlate with increased LSM in children with BA, whereas CTGF inversely correlates with LSM in participants with A1AT; these biomarkers appear to enhance prediction of LSM beyond clinical tests. Future disease-specific investigations of change in these biomarkers over time and as predictors of clinical outcomes will be important.
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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 | 0.000 | 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) | 0.001 | 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".