Evidence map›Paper›PMID 42344002›Full record

ArticleFrontiers in neurology2026

Discovery of a preliminary urinary metabolite panel for Parkinson's disease: a pilot study using paired patient-spouse samples and machine learning consensus.

Qian-Qian Chen, De-Hai Gou, Jin-Yu Huang, Zhen-Hua Mo, Xiao-Yong Guan, Jia-Ning Xu

Abstract read
In one paragraph

Article in Frontiers in neurology, 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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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Qian-Qian ChenFaculty of Medicine, Guangxi University of Science and Technology, Liuzhou, Guangxi, China.
De-Hai GouGuangzhou National Laboratory, Guangzhou International Bio Island, Guangzhou, Guangdong, China.
Jin-Yu HuangThe First Affiliated Hospital of Guangxi University of Science and Technology, Guangxi University of Science and Technology, Liuzhou, China.
Zhen-Hua MoThe First Affiliated Hospital of Guangxi University of Science and Technology, Guangxi University of Science and Technology, Liuzhou, China.
Xiao-Yong GuanThe First Affiliated Hospital of Guangxi University of Science and Technology, Guangxi University of Science and Technology, Liuzhou, China.
Jia-Ning XuSchool of Electronic Engineering, Guangxi University of Science and Technology, Liuzhou, Guangxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Parkinson's disease (PD) lacks reliable non-invasive diagnostic biomarkers. Urine is a promising biofluid for biomarker discovery, but the profound influence of shared environment and lifestyle represents a major confounder. Methods: To rigorously address this, we designed a pilot study using a unique matched-pair cohort: PD patients together with their healthy spouses. Untargeted LC-MS metabolomics was performed on urine samples from 15 carefully matched pairs. Differential features were identified using VIP > 1.0 and Results: A preliminary five-metabolite panel (Cyanuric acid, Benzeneacetonitrile, 3-Formylsalicylic Acid, dADP, and ent-cassa-12,15-dien-2beta-ol) was defined. Despite the inherently small sample size of this niche cohort, the panel demonstrated promising internal discriminative performance (AUC > 0.95). Discussion: We emphasize that these results are exploratory. The primary contribution of this pilot study is not a validated diagnostic tool, but the demonstration of a carefully controlled design to isolate PD-specific metabolic signatures and the proposal of specific candidate biomarkers. This work establishes a critical proof-of-concept and prioritizes targets for essential future validation in larger, independent cohorts.

Indexed as

machine learningnon-invasive diagnostic biomarkersParkinson’s diseasepartial least squares discriminant analysisrandom forestsupport vector machineurine

Identifiers

PMID42344002
PMCPMC13286780

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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.