Urinary metabolic phenotyping for Alzheimer’s disease

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 phe...

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Vydáno v:Scientific reports Ročník 10; číslo 1; s. 21745 - 17
Hlavní autoři: Kurbatova, Natalja, Garg, Manik, Whiley, Luke, Chekmeneva, Elena, Jiménez, Beatriz, Gómez-Romero, María, Pearce, Jake, Kimhofer, Torben, D’Hondt, Ellie, Soininen, Hilkka, Kłoszewska, Iwona, Mecocci, Patrizia, Tsolaki, Magda, Vellas, Bruno, Aarsland, Dag, Nevado-Holgado, Alejo, Liu, Benjamine, Snowden, Stuart, Proitsi, Petroula, Ashton, Nicholas J., Hye, Abdul, Legido-Quigley, Cristina, Lewis, Matthew R., Nicholson, Jeremy K., Holmes, Elaine, Brazma, Alvis, Lovestone, Simon
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 10.12.2020
Nature Publishing Group
Nature Portfolio
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ISSN:2045-2322, 2045-2322
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Shrnutí: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.
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PMCID: PMC7730184
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-020-78031-9