Evidence map›Paper›PMID 41421352›Full record

ArticleCell reports. Medicine2026

Blood metabolic panels for identifying significant fibrosis and inflammation in patients with MASLD.

Yan Huang, Jiaqi Li, Shuying Song, Bingying Du, Yutang Cao, Yangyang Wang, Haoshuang Fu, Tianhui Zhou, Shuwu Yu, Yameng Liu and 5 more

Abstract read
In one paragraph

Article in Cell reports. Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Article
  6. Frontiers in nutrition · 2026
    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

15 authors.

Yan HuangDepartment of Infectious Disease, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Jiaqi LiState Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Shuying SongDepartment of Infectious Disease, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Bingying DuDepartment of Infectious Disease, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Yutang CaoSchool of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing 210023, China.
Yangyang WangSchool of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China.
Haoshuang FuDepartment of Infectious Disease, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Tianhui ZhouDepartment of Infectious Disease, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Shuwu YuState Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Yameng LiuState Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
Kanglong WangState Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
Zhujun CaoDepartment of Infectious Disease, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China. Electronic address: estherlucifer@163.com.
Xiaozhen GuoState Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China. Electronic address: guoxz@simm.ac.cn.
Cen XieDepartment of Infectious Disease, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; University of Chinese Academy of Sciences, Beijing 100049, China; School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing 210023, China. Electronic address: xiecen@simm.ac.cn.
Qing XieDepartment of Infectious Disease, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China. Electronic address: xieqingrjh@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metabolic dysfunction-associated steatotic liver disease (MASLD) remains a prevalent condition with limited diagnostic and therapeutic options. This study aims to identify metabolic signatures of disease progression and develop non-invasive diagnostic models through three independent cohorts (including two cohorts confirmed by biopsy and one cohort confirmed by ultrasound) involving 293 participants for detecting significant fibrosis (≥F2) and mild to severe inflammatory activity (≥I2) using multiple machine learning techniques. The fibrosis panel shows area under the receiver operating characteristic curve (AUROC) of 0.928 (95% confidence interval [CI]: 0.835-0.978), 0.829 (0.732-0.902), and 0.806 (0.724-0.872) in the discovery cohort, validation cohort 1, and validation cohort 2, respectively, outperforming the fibrosis-4 index (FIB-4), aspartate aminotransferase-to-platelet ratio index (APRI), non-alcoholic fatty liver disease fibrosis score (NFS), liver stiffness measurement (LSM), and combination of hoMa, Ast and CK18 (MACK-3). The inflammation panel achieves AUROCs of 0.894 (0.791-0.957) and 0.776 (0.673-0.859) in the discovery cohort and validation cohort 1, respectively. The key metabolites guanidinoacetic acid (GAA) and sebacic acid (SA) demonstrate therapeutic efficacy in mice. These validated panels provide accurate stratification of MASLD severity, and GAA/SA offer therapeutic potential, advancing both diagnosis and treatment strategies.

Indexed as

InflammationLiver CirrhosisNon-alcoholic Fatty Liver DiseaseAdultAgedAnimalsAspartate AminotransferasesBiomarkersCohort StudiesFemaleHumansLiverMaleMiceMiddle AgedROC CurveAspartate AminotransferasesBiomarkersbiomarkerdiagnosis panelmachine learningMASHmetabolic dysfunction-associated steatotic liver diseasemild to severe inflammatory activityserum metabolomicssignificant fibrosistherapeutic metabolites

Identifiers

PMID41421352
PMCPMC12866143

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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 OpenQuestion graph.