Evidence map›Paper›PMID 42825228›Full record

ArticlePeerJ2026

Exploring metabolic candidates and inflammatory mediation in Parkinson's disease: a pilot integrative metabolomics and Mendelian randomization study.

Yue Lang, Hui Zhang, Rui Feng, Yongzhong Lin

Abstract read
In one paragraph

Article in PeerJ, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Yue Lang *Department of Neurology, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
Hui Zhang *Department of Neurology, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
Rui FengDepartment of Neurology, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
Yongzhong LinDepartment of Neurology, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Parkinson's disease (PD) is a complex neurodegenerative disorder characterized by multifaceted molecular dysregulation. Integrating genetic approaches with metabolomics may help to systematically investigate potential links between metabolites, inflammatory proteins, and PD risk. Methods: In this pilot discovery phase, untargeted Liquid Chromatography-Mass Spectrometry (LC-MS) was conducted in a small cohort (15 PD patients and 10 healthy controls). Differentially expressed metabolites (DEMs) were identified using variable importance in projection (VIP) > 1, |log Results: A total of 3,537 metabolites were identified, of which 570 were classified as DEMs, representing the features remaining significant after multiple-testing correction. Integration of Random Forest and LASSO identified 3-phenylpropionylglycine as a key candidate metabolite. KEGG analysis highlighted caffeine metabolism as the top enriched pathway. After FDR correction, MR analyses suggested inverse associations between genetically predicted levels of 3-phenylpropionylglycine ( Conclusion: This exploratory study integrates pilot-scale metabolomics with genetic analyses to identify candidate metabolic signals and inflammatory pathways linked to PD. However, given the small metabolomics sample size, cross-cohort data integration, and potential biological mismatch between plasma-derived metabolites and genetically predicted metabolite levels, these findings should be considered hypothesis-generating and interpreted with caution.

Indexed as

InflammationMetabolomicsParkinson DiseaseAgedCase-Control StudiesChromatography, LiquidFemaleGenome-Wide Association StudyHumansMaleMass SpectrometryMendelian Randomization AnalysisMiddle AgedPilot ProjectsCaffeine metabolismInflammatory cytokinesMediation analysisMendelian randomizationMetabolomicsParkinson’s disease

Identifiers

PMID42825228
PMCPMC13629568

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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.