Evidence mapPaperPMID 39657668Full record

ReviewCell reports. Medicine2024

Metabolomics at the cutting edge of risk prediction of MASLD.

En Ying Tan, Mark D Muthiah, Arun J Sanyal

Abstract readReview
In one paragraph

Review in Cell reports. Medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

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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

3 authors.

En Ying TanDivision of Gastroenterology and Hepatology, Department of Medicine, National University Health System, Singapore, Singapore. Electronic address: enying.tan@mohh.com.sg.
Mark D MuthiahDivision of Gastroenterology and Hepatology, Department of Medicine, National University Health System, Singapore, Singapore; Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Arun J SanyalStravitz-Sanyal Institute for Liver Disease and Metabolic Health, Virginia Commonwealth University School of Medicine, Richmond, VA, USA. Electronic address: arun.sanyal@vcuhealth.org.

Funding

Predicting outcomes in nonalcoholic steatohepatitis with advanced fibrosisR01DK129564 · VIRGINIA COMMONWEALTH UNIVERSITY · 2025 to 2025
$534k
NIDDK NIH HHS R01 DK129564
6 · The paper itself

Abstract

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a major public health threat globally. Management of patients afflicted with MASLD and research in this domain are limited by the lack of robust well-established non-invasive biomarkers for diagnosis, prognostication, and monitoring. The circulating metabolome reflects both the systemic metabo-inflammatory milieu and changes in the liver in affected individuals. In this review we summarize the available literature on changes in the different components of the metabolome in MASLD with a focus on changes that are linked to the presence of underlying steatohepatitis, severity of disease activity, and fibrosis stage. We further summarize the existing literature around biomarker panels that are derived from interrogation of the metabolome. Their relevance to disease biology and utility in practice are also discussed. We further highlight potential direction for future studies particularly to ensure they are fit for purpose and suitable for widespread use.

Indexed as

BiomarkersFatty LiverMetabolomicsHumansMetabolomePrognosisBiomarkersamino acidsbile acidscholesteroleicosanoidslipidomeMASHmetabolomephospholipidssphingolipidstriglycerides

Identifiers

PMID39657668
PMCPMC11722125

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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 Socratic graph.