Assessment of liver fibrosis progression and regression by a serological collagen turnover profile
Bibliographic record
Abstract
There is a need for noninvasive biomarkers that can identify patients with progressive liver fibrosis and monitor response to antifibrotic therapy. An equally important need is identification of patients with spontaneous fibrosis regression, since they may not need treatment nor be included in clinical studies with fibrosis as end point. Circulating biomarkers, originating from defined fragments of the scar tissue itself, may serve as valuable tools for this aspect of precision medicine. We investigated a panel of serological collagen formation and degradation markers to identify patients likely to regress or progress in absence of a therapeutic intervention. Plasma samples from patients with moderate-stage hepatitis C receiving placebo treatment in a phase II trial of the peroxisome proliferator-activated receptor agonist farglitazar were included. The patients had matched liver biopsies at baseline and 52 wk of follow-up. Serological biomarkers of collagen formation (PRO-C3, PRO-C4, PRO-C5) and collagen degradation (C3M, C4M, and C6M) were analyzed. Logistic regression analysis including PRO-C3 and C6M identified subjects with progressive liver fibrosis with an AUROC of 0.91 ( P < 0.0001) and positive and negative predictive values (PPV/NPV) of 75.0%/88.6%. Low levels of PRO-C5 predicted a spontaneous regression phenotype, with an odds ratio of 33.8 times higher compared with patients with high levels ( P < 0.0025) with an AUROC of 0.78 ( P < 0.0001) and PPV/NPV of 60.0%/95.7%. Two collagen fragments (PRO-C3 and C6M) identified liver fibrosis progressors, and one collagen fragment (PRO-C5) identified liver fibrosis regressors. These biomarkers may improve patient stratification and monitor treatment efficacy in studies with fibrosis as clinical end point. NEW & NOTEWORTHY In this study we report two biomarkers of collagen fragments (PRO-C3 and C6M) that are able to identify liver fibrosis progressors while one biomarker (PRO-C5) identified liver fibrosis regressors. In particular, we present three noninvasive biomarkers that can be used to identify patients with progressive liver fibrosis, monitor response to antifibrotic therapy, and also identify the spontaneous liver fibrosis regression phenotype.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| 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".