Evidence map›Paper›PMID 42369416›Full record

ArticleNAM journal2025

Exploring the Mechanisms of EDCs-Induced Metabolic Disorders in Humans Using Network Toxicology and Molecular Docking.

Min Zhao, Yong Niu, Qian Huang, Wenhua Li

Abstract read
In one paragraph

Article in NAM journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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

4 authors.

Min ZhaoSchool of Medicine, Lijiang University of Culture and Tourism, Lijiang, Yunnan, 674100, China.
Yong NiuSchool of Medicine, Lijiang University of Culture and Tourism, Lijiang, Yunnan, 674100, China.
Qian HuangSchool of Medicine, Lijiang University of Culture and Tourism, Lijiang, Yunnan, 674100, China.
Wenhua LiSchool of Medicine, Xizang Minzu University, Xianyang Shaanxi 712082, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aims to investigate the potential mechanisms by which EDCs, recognized as emerging pollutants, induce metabolic disorders leading to metabolic diseases in humans. Methods: Network toxicology and molecular docking techniques were employed to elucidate the molecular mechanisms underlying EDCs-induced pathogenesis of the six diseases. Potential targets associated with EDCs and these diseases were identified using databases such as PubChem, ChEMBL, Super-PRED, GeneCards, OMIM, and TTD. STRING analysis and Cytoscape software were further utilized to determine core targets most significantly linked to these metabolic disorders. GO and KEGG pathway enrichment analyses were performed on the core targets using the DAVID database. Finally, molecular docking was conducted to validate the binding affinities between EDCs and core target proteins. Results: EDCs may potentially induce metabolic disorders by modulating cellular expression, influencing apoptosis and proliferation, and regulating related signaling pathways. Notably, a close interrelationship was observed among lipid metabolism disorders and atherosclerosis, Alzheimer's disease, type 2 diabetes mellitus, osteoporosis, hyperuricemia, and non-alcoholic fatty liver disease. Conclusion: This study provides novel insights into the mechanisms through which EDCs induce metabolic diseases in humans and highlights correlations among distinct disorders, thereby establishing a theoretical foundation for disease prevention and therapeutic strategies.

Indexed as

Endocrine-disrupting chemicalsMetabolic diseasesMolecular dockingNetwork toxicologyNon-alcoholic fatty liver disease

Identifiers

PMID42369416
PMCPMC13289039

What OpenQuestion holds

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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.