Serum Matrix Metalloproteinase 7 Is a Diagnostic Biomarker of Biliary Injury and Fibrosis in Pediatric Autoimmune Liver Disease
Bibliographic record
Abstract
In autoimmune liver disease (AILD), including autoimmune hepatitis (AIH), primary sclerosing cholangitis (PSC), and overlap syndrome of AIH and PSC (ASC), the presence of biliary injury portends a worse prognosis. We studied serum matrix metalloproteinase 7 (sMMP7) as a biomarker for pediatric sclerosing cholangitis (SC). We prospectively enrolled 54 children (median age, 16 years) with AILD (AIH, n = 26; ASC, n = 16; and PSC, n = 12) at our center. The sMMP7 concentrations were higher in patients with SC compared to those without cholangiopathy ( P < 0.001). An sMMP7 concentration >23.7 ng/mL had a sensitivity and specificity of 79% and 96%, respectively, and outperformed alkaline phosphatase (ALP) and gamma‐glutamyltransferase (GGT) in segregating patients with SC. Serum concentrations correlated with liver gene expression levels for MMP7 ( r = 0.70; P < 0.001). Using immunofluorescence, MMP7 was localized primarily to the cholangiocytes of patients with SC. In 46 subjects with liver biopsy available for blinded review, elevation in sMMP7 concentrations segregated with the presence of lymphocytic and neutrophilic cholangitis and periductal fibrosis and correlated with Ishak, Ludwig, and Nakanuma scoring systems. Liver stiffness measured by magnetic resonance elastography also correlated with sMMP7 concentrations ( r = 0.56; P < 0.01). Using magnetic resonance cholangiopancreatography plus (MRCP+), sMMP7 in 34 patients correlated with the number of biliary dilatations ( r = 0.54; P < 0.01) and strictures ( r = 0.56; P < 0.01). MMP7 as a marker of biliary injury was validated in an independent cohort of children with ulcerative colitis. Higher sMMP7 concentrations also correlated with a history of SC‐related complication. Conclusion : MMP7 is a promising biomarker for pediatric SC that diagnostically outperforms ALP and GGT. sMMP7 may directly reflect biliary injury and fibrosis, the main drivers of disease progression in SC.
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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 | 0.001 |
| 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 | 0.001 |
| 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".