Evidence mapPaperPMID 40988978Full record

ArticleDiabetes, metabolic syndrome and obesity : targets and therapy2025

Glucagon-Like Peptide-2 as a Potential Biomarker for Nonalcoholic Fatty Liver Disease in Children with Obesity: Preliminary Assessment of Metabolic Associations and Underlying Mechanisms.

Shu-Juan Zhang, Ke Xu, Feng Zhu, Yi-Qun Teng, Yan-Fei Tang, Hong-Wei Xu

Abstract read
In one paragraph

Article in Diabetes, metabolic syndrome and obesity : targets and therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. 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

6 authors.

Shu-Juan ZhangDepartment of Pediatrics, The Second Hospital of Jiaxing, Jiaxing, 314000, People's Republic of China.
Ke XuDepartment of Pediatrics, The Second Hospital of Jiaxing, Jiaxing, 314000, People's Republic of China.
Feng ZhuDepartment of Pediatrics, The Second Hospital of Jiaxing, Jiaxing, 314000, People's Republic of China.
Yi-Qun TengDepartment of Pediatrics, The Second Hospital of Jiaxing, Jiaxing, 314000, People's Republic of China.
Yan-Fei TangDepartment of Pediatrics, The Second Hospital of Jiaxing, Jiaxing, 314000, People's Republic of China.
Hong-Wei XuDepartment of Orthopedics, The Second Hospital of Jiaxing, Jiaxing, 314000, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aimed to investigate the effects of glucagon-like peptide-2 (GLP-2) on insulin resistance and lipid metabolism, as well as potential mechanisms contributing to the development of non-alcoholic fatty liver disease (NAFLD) in children with obesity. Methods: A cross-sectional study was conducted involving 107 children with obesity, aged between 5 and 15 years, including 55 with NAFLD and 52 without NAFLD. Anthropometric assessments and fasting blood samples were collected to evaluate GLP-2, plasma glucose, insulin (INS), lipids, leptin (LEP), and adiponectin (ADPN). Correlation and logistic regression analyses were performed to evaluate associations between GLP-2 and metabolic parameters. Results: Children with NAFLD exhibited significantly higher levels of GLP-2, LEP, total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), fasting blood glucose (FPG), INS, and the homeostasis model assessment of insulin resistance index (HOMA-IR) (all Conclusion: GLP-2 may contribute to the pathophysiology of insulin resistance and dyslipidemia in pediatric NAFLD, potentially via modulation of adipokine activity. These findings suggest GLP-2 as a candidate biomarker and possible therapeutic target in this population.

Indexed as

childrenglucagon-like peptide-2insulin resistancelipid metabolismnon-alcoholic fatty liver diseaseobesity

Identifiers

PMID40988978
PMCPMC12452961

What Socratic holds

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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 Socratic graph.