Journal article
Urinary metabolic phenotyping for Alzheimer’s disease
Scientific Reports, Vol.10(1), Art. 21745
2020
Abstract
Finding early disease markers using non-invasive and widely available methods is essential to develop a successful therapy for Alzheimer’s Disease. Few studies to date have examined urine, the most readily available biofluid. Here we report the largest study to date using comprehensive metabolic phenotyping platforms (NMR spectroscopy and UHPLC-MS) to probe the urinary metabolome in-depth in people with Alzheimer’s Disease and Mild Cognitive Impairment. Feature reduction was performed using metabolomic Quantitative Trait Loci, resulting in the list of metabolites associated with the genetic variants. This approach helps accuracy in identification of disease states and provides a route to a plausible mechanistic link to pathological processes. Using these mQTLs we built a Random Forests model, which not only correctly discriminates between people with Alzheimer’s Disease and age-matched controls, but also between individuals with Mild Cognitive Impairment who were later diagnosed with Alzheimer’s Disease and those who were not. Further annotation of top-ranking metabolic features nominated by the trained model revealed the involvement of cholesterol-derived metabolites and small-molecules that were linked to Alzheimer’s pathology in previous studies.
Details
- Title
- Urinary metabolic phenotyping for Alzheimer’s disease
- Authors/Creators
- N. Kurbatova (Author/Creator) - European Bioinformatics InstituteM. Garg (Author/Creator) - European Bioinformatics InstituteL. Whiley (Author/Creator) - Hammersmith HospitalE. Chekmeneva (Author/Creator) - Imperial College LondonB. Jiménez (Author/Creator) - Imperial College LondonM. Gómez-Romero (Author/Creator) - Imperial College LondonJ. Pearce (Author/Creator)T. Kimhofer (Author/Creator) - Imperial College LondonE. D’Hondt (Author/Creator) - IMECH. Soininen (Author/Creator) - University of Eastern FinlandI. Kłoszewska (Author/Creator) - Medical University of LodzP. Mecocci (Author/Creator) - University of PerugiaM. Tsolaki (Author/Creator) - Aristotle University of ThessalonikiB. Vellas (Author/Creator) - Université Fédérale de Toulouse Midi-PyrénéesD. Aarsland (Author/Creator) - King's College LondonA. Nevado-Holgado (Author/Creator) - Warneford HospitalB. Liu (Author/Creator) - Warneford HospitalS. Snowden (Author/Creator) - King's College LondonP. Proitsi (Author/Creator) - King's College LondonN.J. Ashton (Author/Creator) - King's College LondonA. Hye (Author/Creator) - King's College LondonC. Legido-Quigley (Author/Creator) - King's College LondonM.R. Lewis (Author/Creator) - Imperial College LondonJ.K. Nicholson (Author/Creator) - Imperial College LondonE. Holmes (Author/Creator) - Murdoch UniversityA. Brazma (Author/Creator) - European Bioinformatics InstituteS. Lovestone (Author/Creator) - Warneford Hospital
- Publication Details
- Scientific Reports, Vol.10(1), Art. 21745
- Publisher
- Springer Nature
- Identifiers
- 991005543829307891
- Copyright
- © 2020 The Authors.
- Murdoch Affiliation
- Health Futures Institute
- Language
- English
- Resource Type
- Journal article
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Metrics
106 File views/ downloads
124 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Industry collaboration
- Domestic collaboration
- International collaboration
- Citation topics
- 2 Chemistry
- 2.211 Mass Spectrometry
- 2.211.990 Metabolomics
- Web Of Science research areas
- Neurosciences
- ESI research areas
- Neuroscience & Behavior