Comprehensive bile acid profiling in hereditary intrahepatic cholestasis: Genetic and clinical correlations
Bibliographic record
Abstract
BACKGROUND & AIMS: Genetic defects causing dysfunction in bile salt export pump (BSEP/ABCB11) lead to liver diseases. ABCB11 mutations alter the bile acid metabolome. We asked whether profiling plasma bile acids could reveal compensatory mechanisms and track genetic and clinical status. METHODS: We compared plasma bile acids in 17 ABCB11-mutated patients, 35 healthy controls and 12 genetically undiagnosed cholestasis patients by ultra-high-performance liquid chromatography/multiple-reaction monitoring-mass spectrometry (UPLC/MRM-MS). We developed an index to rank bile acid hydrophobicity, and thus toxicity, based on LC retention times. We recruited 42 genetically diagnosed hereditary cholestasis patients, of whom 12 were presumed to have impaired BSEP function but carried mutations in genes other than ABCB11, and 8 healthy controls, for further verification. RESULTS: The overall hydrophobicity indices of total bile acids in both the ABCB11-mutated group (11.89 ± 1.07 min) and the undiagnosed cholestasis group (11.46 ± 1.07 min) were lower than those of healthy controls (13.69 ± 0.77 min) (both p < 0.005). This was owing to increased bile acid modifications. Secondary bile acids were detected in patients without BSEP expression, suggesting biliary bile acid secretion through alternative routes. A diagnostic panel comprising lithocholic acid (LCA), tauro-LCA, glyco-LCA and hyocholic acid was identified that could differentiate the ABCB11-mutated cohort from healthy controls and undiagnosed cholestasis patients (AUC=0.946, p < 0.0001) and, in non-ABCB11-mutated cholestasis patients, could distinguish BSEP dysfunction from normal BSEP function (9/12 vs 0/38, p < 0.0000001). CONCLUSIONS: Profiling of plasma bile acids has provided insights into cholestasis alleviation and may be useful for the clinical management of cholestatic diseases.
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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".