Evidence map›Paper›PMID 41809454›Full record

ReviewWorld journal of gastroenterology2026

Decoding liver injury in cystic fibrosis: How to tell drug-induced liver injury from cystic fibrosis liver disease.

Junseo Lee, Anuroop Yekula, Ava Wexler, William Zhuang, Ashwath Elangovan, Joshua Rosario, Philomena Burger, Gopal Ramaraju, Benyam Addissie, Nicholas Lim and 2 more

Abstract readReview
In one paragraph

Review in World journal of gastroenterology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Junseo LeeDepartment of Gastroenterology and Hepatology, University of Rochester Medical Center, Rochester, NY 14682, United States.
Anuroop YekulaDepartment of Gastroenterology and Hepatology, University of Rochester Medical Center, Rochester, NY 14682, United States.
Ava WexlerDepartment of Internal Medicine, University of Rochester Medical Center, Rochester, NY 14682, United States.
William ZhuangDepartment of Medicine, University of Rochester Medical Center, Rochester, NY 14682, United States.
Ashwath ElangovanDepartment of Medicine, University of Rochester Medical Center, Rochester, NY 14682, United States.
Joshua RosarioDepartment of Medicine, University of Rochester Medical Center, Rochester, NY 14682, United States.
Philomena BurgerDepartment of Medicine, University of Rochester Medical Center, Rochester, NY 14682, United States.
Gopal RamarajuDepartment of Transplant Hepatology, University of Rochester Medical Center, Rochester, NY 14682, United States.
Benyam AddissieDepartment of Transplant Hepatology, University of Rochester Medical Center, Rochester, NY 14682, United States.
Nicholas LimDepartment of Transplant Hepatology, University of Rochester Medical Center, Rochester, NY 14682, United States.
Michael R NarkewiczDepartment of Pediatrics, University of Colorado School of Medicine, Aurora, CO 80045, United States.
Patrick TwohigDepartment of Gastroenterology and Hepatology, University of Rochester Medical Center, Rochester, NY 14682, United States. patrick_twohig@urmc.rochester.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCystic fibrosis liver disease (CFLD) is a significant comorbidity in individuals with cystic fibrosis (CF), marked by biliary fibrosis and progressive cholestasis. The advent of CF transmembrane conductance regulator (CFTR) modulators has revolutionized care for lung disease, but their impact on liver-specific disease and outcomes remain unclear. Additionally, the risk of comorbid cholestatic liver injury from medications and progression of CFLD complicates the diagnostic landscape.

aimTo provide a clinical framework for differentiating CFLD and drug induced liver injury (DILI), including from CFTR modulators.

methodsA comprehensive literature review was conducted using PubMed, EMBASE, and Cochrane Library databases through March 2025. Studies evaluating pathogenesis, clinical features, diagnostic strategies, and management of CFLD and CFTR modulator-related DILI were included. Data were synthesized to highlight distinguishing clinical and histopathologic features and to guide evidence-based management.

resultsCFLD typically presents with insidious progression, portal hypertension, and biliary cirrhosis, whereas CFTR modulator-induced DILI often manifests acutely with jaundice, elevated liver enzymes, and a temporal association with therapy initiation. Key differentiators include biochemical patterns, imaging findings, response to drug withdrawal, and, when necessary, liver histology. Management strategies range from dose modification and supportive care in DILI to ursodeoxycholic acid, nutritional optimization, and portal hypertension management in CFLD.

conclusionEarly recognition and differentiation between DILI and underlying CFLD are essential for optimizing therapy, preserving liver function, and guiding long-term management in patients with CF. As CFTR modulators become the cornerstone of CF management, vigilance for hepatotoxicity is critical. A multidisciplinary approach involving hepatology and CF care teams is recommended.

Indexed as

Chemical and Drug Induced Liver InjuryCystic FibrosisLiver DiseasesCholestasisCystic Fibrosis Transmembrane Conductance RegulatorDiagnosis, DifferentialDisease ProgressionHumansLiverCFTR protein, humanCystic Fibrosis Transmembrane Conductance RegulatorCholestasisCystic fibrosisCystic fibrosis transmembrane conductance regulatorDrug-induced liver injuryLiver diseases

Identifiers

PMID41809454
PMCPMC12968613

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.