Urinary metabolic phenotyping for Alzheimer’s disease
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 meta...
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Nature Portfolio
2020
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oai:doaj.org-article:1bd85f619d574055ba7c1fb49956bdea2021-12-02T15:11:50ZUrinary metabolic phenotyping for Alzheimer’s disease10.1038/s41598-020-78031-92045-2322https://doaj.org/article/1bd85f619d574055ba7c1fb49956bdea2020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-78031-9https://doaj.org/toc/2045-2322Abstract 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.Natalja KurbatovaManik GargLuke WhileyElena ChekmenevaBeatriz JiménezMaría Gómez-RomeroJake PearceTorben KimhoferEllie D’HondtHilkka SoininenIwona KłoszewskaPatrizia MecocciMagda TsolakiBruno VellasDag AarslandAlejo Nevado-HolgadoBenjamine LiuStuart SnowdenPetroula ProitsiNicholas J. AshtonAbdul HyeCristina Legido-QuigleyMatthew R. LewisJeremy K. NicholsonElaine HolmesAlvis BrazmaSimon LovestoneNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-17 (2020) |
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DOAJ |
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DOAJ |
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EN |
| topic |
Medicine R Science Q |
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Medicine R Science Q 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 Urinary metabolic phenotyping for Alzheimer’s disease |
| description |
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. |
| format |
article |
| author |
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 |
| author_facet |
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 |
| author_sort |
Natalja Kurbatova |
| title |
Urinary metabolic phenotyping for Alzheimer’s disease |
| title_short |
Urinary metabolic phenotyping for Alzheimer’s disease |
| title_full |
Urinary metabolic phenotyping for Alzheimer’s disease |
| title_fullStr |
Urinary metabolic phenotyping for Alzheimer’s disease |
| title_full_unstemmed |
Urinary metabolic phenotyping for Alzheimer’s disease |
| title_sort |
urinary metabolic phenotyping for alzheimer’s disease |
| publisher |
Nature Portfolio |
| publishDate |
2020 |
| url |
https://doaj.org/article/1bd85f619d574055ba7c1fb49956bdea |
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