Protein biomarkers GDF15 and FGF21 to differentiate mitochondrial hepatopathies from other pediatric liver diseases.
Bibliographic record
Abstract
BACKGROUND: Mitochondrial hepatopathies (MHs) are primary mitochondrial genetic disorders that can present as childhood liver disease. No recognized biomarkers discriminate MH from other childhood liver diseases. The protein biomarkers growth differentiation factor 15 (GDF15) and fibroblast growth factor 21 (FGF21) differentiate mitochondrial myopathies from other myopathies. We evaluated these biomarkers to determine if they discriminate MH from other liver diseases in children. METHODS: Serum biomarkers were measured in 36 children with MH (17 had a genetic diagnosis); 38 each with biliary atresia, α1-antitrypsin deficiency, and Alagille syndrome; 20 with NASH; and 186 controls. RESULTS: GDF15 levels compared to controls were mildly elevated in patients with α1-antitrypsin deficiency, Alagille syndrome, and biliary atresia-young subgroup, but markedly elevated in MH (p<0.001). FGF21 levels were mildly elevated in NASH and markedly elevated in MH (p<0.001). Both biomarkers were higher in patients with MH with a known genetic cause but were similar in acute and chronic presentations. Both markers had a strong performance to identify MH with a molecular diagnosis with the AUC for GDF15 0.93±0.04 and for FGF21 0.90±0.06. Simultaneous elevation of both markers >98th percentile of controls identified genetically confirmed MH with a sensitivity of 88% and specificity of 96%. In MH, independent predictors of survival without requiring liver transplantation were international normalized ratio and either GDF15 or FGF21 levels, with levels <2000 ng/L predicting survival without liver transplantation (p<0.01). CONCLUSIONS: GDF15 and FGF21 are significantly higher in children with MH compared to other childhood liver diseases and controls and, when combined, were predictive of MH and had prognostic implications.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".