AUTHOR=Lozano-Rodríguez Roberto , Hierro Loreto , Quiles María José , Pascual-Iglesias Alejandro , Terrón-Arcos Verónica , Muñoz-Bartolo Gema , Frauca Esteban , Cueto Francisco J. , Calvo Cristina , Córdoba-García Laura , Fernández-Felipe Jesús , Hurtado-Navarro Laura , del Prado-Montero Julia , Sáenz de Santa María-Diez Gonzalo , Arvelo-Rosario Daniel , Jara Paloma , del Fresno Carlos , López-Collazo Eduardo 

TITLE=Comprehensive immune profiling and predictive modelling of paediatric acute hepatitis of unknown aetiology from a Spanish cohort

JOURNAL=Frontiers in Immunology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1599982

DOI=10.3389/fimmu.2025.1599982

ISSN=1664-3224

ABSTRACT=IntroductionPaediatric acute hepatitis of unknown aetiology (PAHUA) has emerged as a global health concern, yet its cause remains unidentified. This study characterises the clinical and immunological profiles of PAHUA to identify reliable immune biomarkers for accurate diagnosis.MethodsSamples from 24 PAHUA patients, 6 children with autoimmune hepatitis (AIH), and 13 healthy paediatric volunteers (HVs) were analysed. Immunophenotyping, soluble immune checkpoints (ICs) and cytokine profiling, and ex vivo immune responses were assessed using spectral flow cytometry. Clustering and logistic regression modelling were used to identify predictive biomarkers.ResultsPAHUA cases frequently presented with gastrointestinal symptoms and liver damage preceding jaundice, with 59% progressing to paediatric acute liver failure (pALF). Adenovirus was detected in only 17.6% of PAHUA patients, suggesting it is unlikely to be the primary causative agent. Antibodies against the SARS-CoV-2 Spike protein were identified in 88.2% of PAHUA patients, as well as in AIH and HV groups, indicating prior exposure. Immunophenotyping, ICs and cytokine profiling, and ex vivo immune revealed distinct immune profiles between PAHUA and non-PAHUA individuals. Furthermore, clustering and logistic regression modelling identified potential predictive biomarkers, including the plasmatic ICs Gal-9 and sTim-3, alongside specific immune cell populations. Notably, a combined Gal-9 and sTim-3 model achieved an AUC of 1.000 in differentiating PAHUA patients from non-PAHUA individuals.ConclusionsDespite the limited cohort analysed, owing to the rarity of cases worldwide, our data provide valuable insights for an accurate, early, and minimally invasive diagnosis of PAHUA. These patients exhibit a distinct immunological profile, with ICs, particularly Gal-9 and sTim-3, showing strong potential as reliable biomarkers.