Evidence map›Paper›PMID 38318437›Full record

ArticleComputational and structural biotechnology journal2024

Distinct changes in serum metabolites and lipid species in the onset and progression of NAFLD in Obese Chinese.

Jiarui Chen, Ronald Siyi Lu, Candela Diaz-Canestro, Erfei Song, Xi Jia, Yan Liu, Cunchuan Wang, Cynthia K Y Cheung, Gianni Panagiotou, Aimin Xu

Abstract read
In one paragraph

Article in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
–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

14 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

10 authors.

Jiarui ChenState Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong Special Administrative Region.
Ronald Siyi LuState Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong Special Administrative Region.
Candela Diaz-CanestroState Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong Special Administrative Region.
Erfei SongState Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong Special Administrative Region.
Xi JiaState Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong Special Administrative Region.
Yan LiuState Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong Special Administrative Region.
Cunchuan WangDepartment of Metabolic and Bariatric Surgery, The First Affiliated Hospital of Jinan University, Guangzhou, China.
Cynthia K Y CheungState Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong Special Administrative Region.
Gianni PanagiotouDepartment of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region.
Aimin XuState Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong Special Administrative Region.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Metabolic disturbances are major contributors to the onset and progression of non-alcoholic fatty liver disease (NAFLD), which includes a histological spectrum ranging from single steatosis (SS) to non-alcoholic steatohepatitis (NASH). This study aimed to identify serum metabolites and lipids enriched in different histological stages of NAFLD and to explore metabolites/lipids as non-invasive biomarkers in risk prediction of NAFLD and NASH in obese Chinese. Methods: Serum samples and liver biopsies were obtained from 250 NAFLD subjects. Untargeted metabolomic and lipidomic profiling were performed using Liquid Chromatography-Mass Spectrometry. Significantly altered metabolites and lipids were identified by MaAsLin2. Pathway enrichment was conducted with MetaboAnalyst and LIPEA. WGCNA was implemented to construct the co-expression network. Logistic regression models were developed to classify different histological stages of NAFLD. Results: A total of 263 metabolites and 550 lipid species were detected in serum samples. Differential analysis and pathway enrichment analysis revealed the progressive patterns in metabolic mechanisms during the transition from normal liver to SS and to NASH, including N-palmitoyltaurine, tridecylic acid, and branched-chain amino acid signaling pathways. The co-expression network showed a distinct correlation between different triglyceride and phosphatidylcholine species with disease severity. Multiple models classifying NAFLD versus normal liver and NASH versus SS identified important metabolic features associated with significant improvement in disease prediction compared to conventional clinical parameters. Conclusion: Different histological stages of NAFLD are enriched with distinct sets of metabolites, lipids, and metabolic pathways. Integrated algorithms highlight the important metabolic and lipidomic features for diagnosis and staging of NAFLD in obese individuals.

Indexed as

BiomarkersChineseLipidomicsMetabolomicsMulti-omicsNon-alcoholic fatty liver diseaseNonalcoholic steatohepatitisObesityPrediction modelSimple steatosis

Identifiers

PMID38318437
PMCPMC10839226

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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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.