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Record W4416910545 · doi:10.1016/j.jhepr.2025.101597

PD-L1 and the risk of bacterial infection in patients with chronic liver diseases: An international multicohort study

2025· article· en· W4416910545 on OpenAlexfundno aff
Adrià Juanola, Gabriel Mezzano, Elisa Pose, Maria José Moreta, Simone Incicco, Roberta Gagliardi, Stine Johansen, Nikolaj Torp, Mads Israelsen, Natalia Jiménez, Joaquin Castillo-Iturra, Jordi Ribera, Jordi Gratacós‐Ginès, Anna Sòria, Andrés Cárdenas, Martina Pérez, Marta Cervera, Ruth Nadal, Queralt Herms, Marta Tonon, Torben Hansen, Evelina Stankevič, Yun Huang, Giacomo Zaccherini, Carlo Alessandria, Frank Erhard Uschner, Ulrich Beuers, Claire Francoz, Rajeshwar P. Mookerjee, Wim Laleman, Cristina Solé, Rafael Bañares, Berta Cuyàs, Xavier Ariza, Mar Coll, Isabel Graupera, Núria Fabrellas, Manuel Morales‐Ruiz, Maja Thiele, Aleksander Krag, Paolo Angeli, Salvatore Piano, Elsa Solà, Pere Ginès

Bibliographic record

VenueJHEP Reports · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsnot available
FundersFaculty of Medicine and Health, University of SydneyKlinisk Institut, Syddansk UniversitetSchool of Medicine, Stanford UniversityEuropean Regional Development FundNorgineGrifolsSyddansk UniversitetUniversità di BolognaInstituto de Salud Carlos IIIUniversitat de BarcelonaNovo Nordisk Foundation Center for Basic Metabolic ResearchNovo NordiskAarhus UniversitetKU LeuvenUniversidad Complutense de MadridInstitut National de la Santé et de la Recherche MédicaleUniversità degli Studi di TorinoUniversity College LondonAmsterdam University Medical CentersNovo Nordisk FondenUniversiteit van AmsterdamInstituto de Investigación Sanitaria Gregorio MarañónCentres de Recerca de CatalunyaMallinckrodt PharmaceuticalsHospital Clínic de BarcelonaPfizerOdense UniversitetshospitalUniversitat Autònoma de BarcelonaBoston Scientific CorporationEuropean CommissionMinistero della Salute
KeywordsImmune systemBiomarkerChronic liver diseaseDiseaseRisk factorImmune DysfunctionLiver dysfunction

Abstract

fetched live from OpenAlex

Background & Aims: Impaired phagocytic capacity due to activation of the PD-1/PD-L1 pathway has been implicated in the development of bacterial infections in patients with cirrhosis. Soluble PD-L1 (sPD-L1) is easily measurable in plasma and has been proposed as a biomarker of sepsis. In the current study, we aim to evaluate the role of sPD-L1 as a biomarker of bacterial infection development in patients with cirrhosis. Methods: Plasma samples from 995 patients with chronic liver disease grouped in three cohorts were analyzed: an initial cohort of 268 hospitalized patients with acute decompensated cirrhosis, 327 out-patients with non-acute decompensated cirrhosis and finally 400 patients with high-risk alcohol consumption, including all stages of liver fibrosis, from mild/no fibrosis to cirrhosis (F0-F4). The main outcomes of the study were development of bacterial infection and mortality. Results: value <0.001; HR 1.066, 95% CI 1.043-1.089). These findings were observed in all cohorts. Conclusions: Plasma levels of sPD-L1 are associated with the risk of bacterial infection development irrespective of the stage of chronic liver disease. Furthermore, higher sPD-L1 levels are linked to increased mortality. Measurement of sPD-L1 levels may help identify patients at high risk of developing bacterial infections and guide the implementation of new preventive strategies. Impact and implications: This study explores the role of soluble PD-L1 as a biomarker of immune dysfunction and its association with clinical outcomes in patients with chronic liver disease. Our findings demonstrate that soluble PD-L1 levels increase with the progression of liver disease and they are independently associated with an increased risk of bacterial infection development and mortality. These results could help physicians identify high-risk individuals earlier and implement preventive strategies.

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 score0.001
Threshold uncertainty score0.164

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 head0.002
GPT teacher head0.237
Teacher spread0.235 · 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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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