Evidence map›Paper›PMID 42021356›Full record

ArticleLipids in health and disease2026

Untargeted plasma lipidomic profiling associated with pediatric metabolic dysfunction-associated steatohepatitis (MASH).

Gahyun Lim, Helaina E Huneault, Chih-Yu Chen, Grey Won, Cristian Sanchez-Torres, Kristal Maner-Smith, Miriam B Vos

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Article in Lipids in health and disease, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Gahyun Lim *Nutrition & Health Sciences Program, Laney Graduate School, Emory University, Atlanta, GA, USA.
Helaina E Huneault *Nutrition & Health Sciences Program, Laney Graduate School, Emory University, Atlanta, GA, USA.
Chih-Yu Chen *Emory Integrated Metabolomics and Lipidomics Core, Emory University, Atlanta, GA, USA.
Grey WonEmory Integrated Metabolomics and Lipidomics Core, Emory University, Atlanta, GA, USA.
Cristian Sanchez-TorresDepartment of Pediatrics, Emory University, Atlanta, GA, USA.
Kristal Maner-SmithEmory Integrated Metabolomics and Lipidomics Core, Emory University, Atlanta, GA, USA.
Miriam B VosNutrition & Health Sciences Program, Laney Graduate School, Emory University, Atlanta, GA, USA. miriam.vos@msu.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAltered lipid metabolism is central to the pathogenesis of MASH, yet lipidomic data in pediatric populations are limited. We conducted a cross-sectional analysis using high-resolution lipidomics to identify lipid alterations associated with pediatric MASH in youth enrolled in NASH CRN studies.

methodsClinical assessments included anthropometrics, lipid panels, liver enzymes, and markers of glucose metabolism. MASLD status was determined by liver biopsy or MRI-PDFF. Plasma untargeted lipidomics was performed using UHPLC-MS/MS. Linear models identified differentially abundant lipids. Network analysis characterized lipid-clinical associations and identified network-informed central lipids to evaluate MASH classification performance.

resultsThe cohort included 102 participants with MASH (29% Zone 1, 19% Zone 3, 52% Definite) and 58 non-MASH participants (29 MASL and 29 controls). Participants were aged 7–18 years; 77% were male and 58% Hispanic, with a mean BMI Z-score of 2.01 ± 0.77. Compared with non-MASH participants, those with MASH had higher BMI Z-scores, waist circumference, liver enzymes, insulin resistance markers, and triglycerides, and lower HDL cholesterol (p < 0.05). Lipidomics analysis revealed higher levels of saturated TG species and lower levels of Cer and SM species in participants with MASH (p < 0.05). Network analysis showed greater centrality of insulin resistance markers (fasting insulin, HOMA-IR) among participants with MASH, whereas non-MASH participant networks exhibited broader lipid-clinical connectivities. Adding central lipids [TG(12:0_16:0_18:2), SM(d32:3), TG(50:5)] with a high Eigenvector Centrality Score (ECS) significantly improved MASH classification performance.

conclusionsPediatric MASH was associated with plasma lipid remodeling, and network-derived central lipids improved MASH classification performance. These findings highlight the potential of lipid signatures for the noninvasive diagnosis of pediatric MASH and warrant validation in independent, longitudinal cohorts.

Indexed as

LipidomicsLipidsNon-alcoholic Fatty Liver DiseaseAdolescentBiomarkersChildCholesterol, HDLCross-Sectional StudiesFemaleHumansInsulin ResistanceLipid MetabolismLiverMaleTandem Mass SpectrometryTriglyceridesBiomarkersCholesterol, HDLLipidsTriglyceridesInsulin ResistanceLipidomicsPediatric MASHTG

Identifiers

PMID42021356
PMCPMC13235174

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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 OpenQuestion graph.