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 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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".