Elevated Serum Bile Acids Predict Poor Liver Outcomes in Children With Alagille Syndrome: Results From the <scp>GALA</scp> Study Group
Bibliographic record
Abstract
BACKGROUND AND AIM: Alagille syndrome (ALGS) is a rare disorder characterised by cholestasis and extrahepatic manifestations. Given the current era of ileal bile acid transporter (IBAT) inhibitor therapies that reduce serum bile acid (SBA) levels, we evaluated whether SBA predicts liver disease outcomes in ALGS. METHODS: Patients were ascertained from the Global ALagille Alliance (GALA) cohort. A prognostic threshold of SBA 102 μmol/L was assessed as a time-dependent covariate in Cox regression analyses for native liver survival (NLS) and event-free survival (EFS), while adjusting for total bilirubin (TB) levels. RESULTS: 570 GALA patients were included (348 [61%] male). There was a moderate positive correlation between SBA and TB (Pearson correlation = 0.47, p < 0.001). SBA below 102 μmol/L was a significant predictor of outcomes (NLS: HR = 3.78, 95% CI 2.39-5.99, p < 0.001; EFS: HR = 3.44, 95% CI 2.35-5.04, p < 0.001). SBA remained a significant predictor for improved EFS after adjusting for TB clearance at 1 year (TB < 2 mg/dL; HR = 2.00, 95% CI 1.10-3.65, p = 0.02). Median SBA in the first year of life above 102 μmol/L, predicted lower NLS (67.2% vs. 83.5% at 7 years p = 0.05) and EFS (63.4% vs. 80.9% at 7 years, p = 0.02). CONCLUSION: Lower SBA in children with ALGS liver disease predicts improved NLS and EFS. SBA is also associated with NLS in children with ALGS who clear their bilirubin, that is, those with anicteric cholestasis. Although the patients studied here did not receive IBAT inhibition, these data suggest that lowering SBA may improve important clinical outcomes.
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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 | 0.000 |
| 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) | 0.000 | 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".