Evidence map›Paper›PMID 40700400›Full record

ArticlePloS one2025

Magnetic resonance evaluation of three-dimensional liver fat fraction by hepatitis C status and associations with inflammatory cytokines.

Jessie Torgersen, Craig W Newcomb, Dean M Carbonari, Shanae M Smith, Katherine L Brecker, Chamith S Rajapakse, Brandon C Jones, Christiana Cottrell, Rasleen Grewal, Jennifer C Price and 7 more

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

17 authors.

Jessie TorgersenDepartment of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.ORCID https://orcid.org/0000-0002-7861-1421
Craig W NewcombDepartment of Biostatistics, Epidemiology, and Informatics, Center for Clinical Epidemiology and Biostatistics, Center for Real-world Effectiveness and Safety of Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Dean M CarbonariDepartment of Biostatistics, Epidemiology, and Informatics, Center for Clinical Epidemiology and Biostatistics, Center for Real-world Effectiveness and Safety of Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Shanae M SmithDepartment of Biostatistics, Epidemiology, and Informatics, Center for Clinical Epidemiology and Biostatistics, Center for Real-world Effectiveness and Safety of Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Katherine L BreckerDepartment of Biostatistics, Epidemiology, and Informatics, Center for Clinical Epidemiology and Biostatistics, Center for Real-world Effectiveness and Safety of Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Chamith S RajapakseDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Brandon C JonesDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Christiana CottrellDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Rasleen GrewalDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Jennifer C PriceDepartment of Medicine, University of California, San Francisco School of Medicine, San Francisco, California, United States of America.
Joshua F BakerDepartment of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Jay R KostmanPhiladelphia FIGHT, Philadelphia, Pennsylvania, United States of America.
Stacey TrooskinMazzoni Center, Philadelphia, Pennsylvania, United States of America.
Rebecca A HubbardDepartment of Biostatistics, Brown University School of Public Health, Providence, Rhode Island, United States of America.
Babette S ZemelDepartment of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America.
Mary B LeonardDepartment of Pediatrics, Stanford University School of Medicine, Palo Alto, California, United States of America.
Vincent Lo Re IiiDepartment of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.

Funding

Use of Deep Learning Algorithms to Enable Evaluation of the Determinants and Outcomes of Hepatic Steatosis, by HIV StatusK08DK132977 · NIDDK · UNIVERSITY OF PENNSYLVANIA · PI Jessie Torgersen · 2022 to 2026
$840k
NIDDK NIH HHS K08 DK132977
6 · The paper itself

Abstract

backgroundChronic hepatitis C virus (HCV) infection may influence cytokine and insulin-like growth factor (IGF-1) levels, which could contribute to increased hepatic steatosis. We utilized MRI to compare three-dimensional volumetric liver fat fraction by chronic HCV status and evaluated associations between liver fat fraction and inflammatory cytokines and IGF-1.

methodsParticipants with untreated, non-genotype 3 chronic HCV and participants without HCV were enrolled between 2019-2022 and underwent MRI to quantify three-dimensional volumetric liver fat fraction. Interleukin (IL)-6, IL-18, tumor necrosis factor (TNF)-α, and IGF-1 were also measured. Multivariable linear regression was used to determine associations between liver fat fraction, chronic HCV, and cytokine and IGF-1 levels.

resultsAmong 54 participants with HCV and 54 without HCV, median volumetric liver fat fraction was 12.4% (IQR: 9.3, 18.0%) and 10.9% (IQR: 8.7, 13.3%), respectively. After adjustment for age, sex, and body mass index, mean liver fat fraction was 2.28% (95% CI: 0.55, 4.02%) higher in participants with HCV. HCV was associated with higher mean log TNF-α (0.11 [95% CI: 0.06, 0.16]) and IL-18 (0.14 [95% CI: 0.05, 0.24]), but lower mean log IGF-1 (-0.18 [95% CI: -0.26, -0.11]) when compared to those without HCV. IL-6, IL-18, TNF-α, and IGF-1 were not associated with liver fat fraction.

conclusionChronic HCV is associated with higher volumetric liver fat fraction by MRI. TNF-α and IL-18 levels were higher with chronic HCV but were not associated with liver fat fraction. Further research is needed to identify alternative mechanisms that potentiate liver fat deposition in chronic HCV.

Indexed as

CytokinesFatty LiverHepatitis C, ChronicLiverMagnetic Resonance ImagingAdultFemaleHepacivirusHumansInsulin-Like Growth Factor IInterleukin-18MaleMiddle AgedTumor Necrosis Factor-alphaCytokinesIGF1 protein, humanInsulin-Like Growth Factor IInterleukin-18Tumor Necrosis Factor-alpha

Identifiers

PMID40700400
PMCPMC12286359

What Socratic holds

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LicenceCC BY
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Registered trials

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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 Socratic graph.