Year: 2020
Type: article
Study design: Observational Study
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.
Source: Scientific Reports
Authors: Natalja Kurbatova, Manik Garg, Luke Whiley, Elena Chekmeneva, Beatriz Jiménez, María Gómez-Romero, Jake Pearce, Torben Kimhofer, Ellie D’Hondt, Hilkka Soininen, Iwona Kłoszewska, Patrizia Mecocci, Magda Tsolaki, Bruno Vellas, Dag Aarsland, Alejo Nevado-Holgado, Benjamine Liu, Stuart Snowden, Petroula Proitsi, Nicholas J. Ashton, Abdul Hye, Cristina Legido-Quigley, Matthew R. Lewis, Jeremy K. Nicholson, Elaine Holmes, Alvis Brazma, Simon Lovestone
Institutions: European Bioinformatics Institute, Wellcome Trust, Hammersmith Hospital, UK Dementia Research Institute, Imperial College London, IMEC, University of Eastern Finland, Kuopio University Hospital, Medical University of Lodz, University of Perugia, Aristotle University of Thessaloniki, Inserm, Laboratoire Epidémiologie et Analyses en Santé Publique : Risques, Maladies Chroniques et Handicaps, Université de Toulouse, King's College London, Warneford Hospital, University of Oxford, South London and Maudsley NHS Foundation Trust, Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Steno Diabetes Centers, Murdoch University, Perron Institute for Neurological and Translational Science, Janssen (United Kingdom)
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $1,870
Funders: Microsoft Research, European Federation of Pharmaceutical Industries and Associations, Alzheimer's Society, National Institute for Health and Care Research, European Commission, Innovative Medicines Initiative, UK Dementia Research Institute, Medical Research Council, NIHR Imperial Biomedical Research Centre
Awards: G1073258374, G1093531541, G1399919057, G160417252, G1732308444, G2473961061, Establishing MRC-NIHR Phenome Centre, a world leading high-throughput metabolic phenotyping centre for humanpopulation disease-risk biomarker discovery., G3207356259, G393177772, G5702163051, G6382720711, European Medical Information Framework
OpenAlex ID: W3111765626