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Record W4406980504 · doi:10.1016/j.jhepr.2025.101338

Serum proteomics of adults with acute liver failure provides mechanistic insights and attractive prognostic biomarkers

2025· article· en· W4406980504 on OpenAlexaff
Katharina Remih, Franziska-Maria Hufnagel, Anna Sophie Karl, Valerie Durkalski‐Mauldin, William M. Lee, Constantine Karvellas, Zemin Su, Jody A. Rule, Petra Tomanová, Laura Krieg, Isabel Karkossa, Kristin Schubert, Martin von Bergen�, Frank Tacke, Sonja Luckhardt, Nicole Ziegler, Aimo Kannt, Bastian Engel, Richard Taubert, Robert J. Fontana, Pavel Strnad

Bibliographic record

VenueJHEP Reports · 2025
Typearticle
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsUniversity of Alberta
FundersDeutsche Forschungsgemeinschaft
KeywordsProteomicsComputational biologyMedicineLiver failureBioinformaticsIntensive care medicineBiologyInternal medicineGenetics

Abstract

fetched live from OpenAlex

Background & Aims: Acute liver failure (ALF) is defined as rapid onset coagulopathy and encephalopathy in patients without a prior history of liver disease. We performed untargeted and targeted serum proteomics to delineate processes occurring in adult patients with ALF and to identify potential biomarkers. Methods: Sera of 319 adult patients with ALF (∼50% acetaminophen [APAP]-related cases) were randomly selected from admission samples of the multicenter USA Acute Liver Failure Study Group consortium and subdivided into discovery/validation cohorts. They were analyzed using untargeted proteomics with mass spectroscopy and a serum cytokine profiling and compared with 30 healthy controls. The primary clinical outcome was 21-day transplant-free survival. Single-cell RNAseq data mapped biomarkers to cells of origin; functional enrichment analysis provided mechanistic insights. Novel prognostic scores were compared with the model for end-stage liver disease and ALFSG prognostic index scores. Results: In the discovery cohort, 117 proteins differed between patients with ALF and healthy controls. There were 167 proteins associated with APAP-related ALF, with the majority being hepatocyte-derived. Three hepatocellular proteins (ALDOB, CAT, and PIGR) robustly and reproducibly discriminated APAP from non-APAP cases (AUROCs ∼0.9). In the discovery cohort, 37 proteins were related to 21-day outcome. The key processes associated with survival were acute-phase response and hepatocyte nuclear factor 1α signaling. SERPINA1 and LRG1 were the best individual discriminators of 21-day transplant-free survival in both cohorts. Two models of blood-based proteomic biomarkers outperformed the model for end-stage liver disease and ALFSG prognostic index and were reproduced in the validation cohort (AUROCs 0.83-0.86) for 21-day transplant-free survival. Conclusions: Proteomics and cytokine profiling identified new, reproducible biomarkers associated with APAP etiology and 21-day outcome. These biomarkers may improve prognostication and understanding of the etiopathogenesis of ALF but need to be independently validated. Impact and implications: Acute liver failure (ALF) is a sudden, and severe condition associated with high fatality. More sensitive and specific prognostic scores are urgently needed to facilitate decision-making regarding liver transplantation in patients with ALF. Our proteomic analysis uncovered marked differences between acetaminophen and non-acetaminophen-related ALF. The identification of routinely measurable biomarkers that are associated with 21-day transplant-free survival and the derivation of novel prognostic scores may facilitate clinical management as well as decisions for/against liver transplantation. Further studies are needed to quantify less abundant proteins. Although we used two cohorts, our findings still need to be independently and prospectively validated.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.218
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2025
Admission routes1
Has abstractyes

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