Evidence map›Paper›PMID 42095709›Full record

ArticleCancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology2026

Prediagnostic Plasma Metabolite Profiles and Prediction of Hepatocellular Carcinoma Risk: The Multiethnic Cohort.

Sihao Han, Jesse A Goodrich, Hongxu Wang, Douglas I Walker, Robert O Wright, Lida Chatzi, David V Conti, Veronica Wendy Setiawan

Abstract read
In one paragraph

Article in Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2026. 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

8 authors.

Sihao HanDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California.ORCID 0000-0003-3433-3068
Jesse A GoodrichDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California.ORCID 0000-0001-6615-0472
Hongxu WangDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California.ORCID 0000-0002-4980-219X
Douglas I WalkerGangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia.ORCID 0000-0003-2912-398X
Robert O WrightDepartment of Environmental Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.ORCID 0000-0003-1180-6459
Lida ChatziDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California.ORCID 0000-0002-3319-2438
David V ContiDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California.ORCID 0000-0002-2941-7833
Veronica Wendy SetiawanDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California.ORCID 0000-0001-9239-5692

Funding

Translational Research Support CoreP30ES007048 · NIEHS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI ROB S MCCONNELL · 1996 to 2026
$46.4M
Statistical Services and Methods Development ResourceU2CES026555 · NIEHS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI KOVATCH, PATRICIA · 2015 to 2025
$26.4M
Statistical Methods for Integrative Genomics in CancerP01CA196569 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI David V Conti · 2016 to 2026
$25.5M
Untargeted Analysis ResourceU2CES026561 · NIEHS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI WRIGHT, ROBERT O · 2015 to 2025
$24.5M
Untargeted Analysis ResourceU2CES030859 · NIEHS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI ARORA, MANISH · 2019 to 2025
$8.2M
Mechanisms of Advanced NAFLD Disparities in Hispanics: A Multi-level AnalysisR01MD015971 · NIMHD · UNIVERSITY OF SOUTHERN CALIFORNIA · PI VERONICA WENDY SETIAWAN, Norah A. Terrault · 2021 to 2026
$3.4M
Effects of DDE exposure on adipose tissue function, weight loss and metabolic improvement after bariatric surgery: A new paradigm for study of lipophilic chemicalsR01ES030364 · NIEHS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI CHATZI, VAIA LIDA · 2020 to 2024
$3.2M
Environmental Chemical Exposures and Longitudinal Changes of Glucose Metabolism, Insulin Sensitivity and B Cell Function in YouthR01ES029944 · NIEHS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI CHATZI, VAIA LIDA · 2019 to 2023
$3.1M
Use of Circulating MicroRNAs for Early Detection and Risk Assessment for Pancreatic CancerR01CA227133 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI SETIAWAN, VERONICA WENDY, SHU, XIAO-OU · 2019 to 2023
$3.0M
Mapping the blood cancer exposome for environmental risk profiles of mature B-cell neoplasmsR01ES032831 · NIEHS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Douglas Ian Walker · 2022 to 2026
$2.3M
PFAS Exposure and Diabetic Kidney Disease in Youth with Type 2 Diabetes: A Multi-Omic Approach for Prevention and TreatmentR01DK140831 · NIDDK · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Jesse Allen Goodrich · 2024 to 2026
$2.1M
Hepatotoxic effects of perfluoroalkyl substances: a new epidemiological approach for studying environmental fatty liver diseaseR01ES030691 · NIEHS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI CHATZI, VAIA LIDA · 2020 to 2022
$1.9M
National Institutes of Health (NIH) K01ES036193National Institutes of Health (NIH) P01CA196569National Institutes of Health (NIH) P30ES007048National Institutes of Health (NIH) R01CA209798National Institutes of Health (NIH) R01CA227133National Institutes of Health (NIH) R01CA228589National Institutes of Health (NIH) R01CA300355National Institutes of Health (NIH) R01DK140831National Institutes of Health (NIH) R01ES029944National Institutes of Health (NIH) R01ES030364National Institutes of Health (NIH) R01ES030691National Institutes of Health (NIH) R01ES032831National Institutes of Health (NIH) R01MD015971National Institutes of Health (NIH) R21ES028903National Institutes of Health (NIH) R21ES029681National Institutes of Health (NIH) U2CES026555National Institutes of Health (NIH) U2CES026561National Institutes of Health (NIH) U2CES030859NCI NIH HHS P01 CA196569NCI NIH HHS R01 CA209798NCI NIH HHS R01 CA227133NCI NIH HHS R01 CA228589NCI NIH HHS R01 CA300355NIDDK NIH HHS R01 DK140831NIEHS NIH HHS K01 ES036193NIEHS NIH HHS P30 ES007048NIEHS NIH HHS R01 ES029944NIEHS NIH HHS R01 ES030364NIEHS NIH HHS R01 ES030691NIEHS NIH HHS R01 ES032831NIEHS NIH HHS R21 ES028903NIEHS NIH HHS R21 ES029681NIEHS NIH HHS U2C ES026555NIEHS NIH HHS U2C ES026561NIEHS NIH HHS U2C ES030859NIMHD NIH HHS R01 MD015971
6 · The paper itself

Abstract

backgroundHepatocellular carcinoma (HCC) is the most common primary liver cancer and often arises in cirrhosis cases. Current surveillance methods, including ultrasonography and α-fetoprotein, have limited sensitivity for early detection. Blood metabolomics may improve HCC risk prediction. We aimed to identify pre-diagnostic plasma metabolites associated with HCC risk and evaluate whether cirrhosis-related metabolites enhance prediction beyond established risk factors in a multiethnic population.

methodsWe analyzed data from a nested case-control study with pre-diagnostic blood samples in the Multiethnic Cohort, including 240 HCC cases, 151 cirrhosis cases, and individually matched controls. Metabolome-wide association studies and pathway enrichment analyses were performed, followed by feature selection in the cirrhosis samples to construct HCC prediction models.

resultsOf 294 metabolites analyzed, 53 were significantly associated with HCC after false discovery rate correction (odds ratios: 0.25-3.93). Pathway analyses highlighted perturbations in lipid and amino acid metabolism. Two cirrhosis-associated metabolites, glutamate and glycochenodeoxycholate, were consistently selected and improved HCC prediction. Adding these metabolites to known risk factors (age, sex, race/ethnicity, study area, BMI, smoking, alcohol consumption, and diabetes) increased the AUC from 0.64 to 0.73 (P < 0.001).

conclusionsPre-diagnostic metabolomic profiling revealed metabolic alterations linked to HCC risk, emphasizing dysregulated amino acid and bile acid pathways within the cirrhosis context. IMPACT: Glutamate and glycochenodeoxycholate improved HCC risk prediction beyond established factors, supporting biologically plausible links between hepatic metabolic dysfunction and hepatocarcinogenesis. These findings highlight the potential of metabolomic biomarkers to enhance surveillance and risk stratification among patients with cirrhosis.

Indexed as

Biomarkers, TumorCarcinoma, HepatocellularLiver NeoplasmsMetabolomeAgedCase-Control StudiesCohort StudiesFemaleHumansLiver CirrhosisMaleMetabolomicsMiddle AgedRisk FactorsBiomarkers, Tumor

Identifiers

PMID42095709
PMCPMC13341279

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