PXD044751-1

PXD044751 is an original dataset announced via ProteomeXchange.

Title	A multiplexed urinary biomarker panel has potential for Alzheimer’s Disease Diagnosis using targeted proteomics and machine learning
Description	As disease-modifying therapies are now available for Alzheimer’s disease (AD), accessible, accurate and affordable biomarkers to support diagnosis are urgently needed. We sought to develop a mass spectrometry-based urine test as a high-throughput screening tool for diagnosing AD. We collected urine from a discovery cohort (n=11) of well characterised individuals with AD (n=6) and their asymptomatic, CSF biomarker negative study partners (n=5) and used untargeted proteomics for biomarker discovery. Protein biomarkers identified were taken forward to develop a high-throughput, multiplexed and targeted proteomic assay which was tested on an independent cohort (n=21). The panel of proteins identified are known to be involved in AD pathogenesis. In comparing AD and controls, a panel of proteins including MIEN1, TNFB, VCAM1, REG1B and ABCA7 had a classification accuracy of 86%. These proteins have been previously implicated in AD pathogenesis. This suggests that urine targeted mass spectrometry has potential utility as a diagnostic screening tool in AD.
HostingRepository	PanoramaPublic
AnnounceDate	2026-05-04
AnnouncementXML	Submission_2026-05-04_21:01:25.097.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Supported dataset by repository
PrimarySubmitter	Jenny Hallqvist
SpeciesList	scientific name: Homo sapiens; NCBI TaxID: 9606;
ModificationList	Carbamidomethyl; Label:13C(6)15N(2); Label:13C(6)15N(4)
Instrument	Xevo TQ-S; ACQUITY UPLC

Revision	Datetime	Status	ChangeLog Entry
0	2023-08-22 14:27:06	ID requested
⏵ 1	2026-05-04 21:01:26	announced

H, ä, llqvist J, Pinto RC, Heywood WE, Cordey J, Foulkes AJM, Slattery CF, Leckey CA, Murphy EC, Zetterberg H, Schott JM, Mills K, Paterson RW, A Multiplexed Urinary Biomarker Panel Has Potential for Alzheimer's Disease Diagnosis Using Targeted Proteomics and Machine Learning. Int J Mol Sci, 24(18):(2023) [pubmed]

submitter keyword: Alzheimer’s, urine, machine learning, biomarkers, proteomics, mass spectrometry, diagnosis

Kevin Mills
contact affiliation	Great Ormond Street Institute of Child Health, University College London
contact email	kevin.mills@ucl.ac.uk
lab head
Jenny Hallqvist
contact affiliation	Great Ormond Street Institute of Child Health, University College London
contact email	j.hallqvist@ucl.ac.uk
dataset submitter

Panorama Public dataset URI