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 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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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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| 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. |
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| AbstractList | 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. 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. 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.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. |
| ArticleNumber | 21745 |
| Author | Gómez-Romero, María Jiménez, Beatriz D’Hondt, Ellie Snowden, Stuart Legido-Quigley, Cristina Brazma, Alvis Whiley, Luke Hye, Abdul Vellas, Bruno Chekmeneva, Elena Tsolaki, Magda Proitsi, Petroula Soininen, Hilkka Liu, Benjamine Aarsland, Dag Holmes, Elaine Kłoszewska, Iwona Kimhofer, Torben Kurbatova, Natalja Nicholson, Jeremy K. Garg, Manik Mecocci, Patrizia Nevado-Holgado, Alejo Lewis, Matthew R. Pearce, Jake Ashton, Nicholas J. Lovestone, Simon |
| Author_xml | – sequence: 1 givenname: Natalja surname: Kurbatova fullname: Kurbatova, Natalja email: natalja@ebi.ac.uk organization: European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI – sequence: 2 givenname: Manik surname: Garg fullname: Garg, Manik organization: European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI – sequence: 3 givenname: Luke surname: Whiley fullname: Whiley, Luke organization: Department of Metabolism, Digestion and Reproduction, National Phenome Centre, Imperial College London, UK Dementia Research Institute, Hammersmith Hospital, Imperial College London – sequence: 4 givenname: Elena surname: Chekmeneva fullname: Chekmeneva, Elena organization: Department of Metabolism, Digestion and Reproduction, National Phenome Centre, Imperial College London – sequence: 5 givenname: Beatriz surname: Jiménez fullname: Jiménez, Beatriz organization: Department of Metabolism, Digestion and Reproduction, National Phenome Centre, Imperial College London – sequence: 6 givenname: María surname: Gómez-Romero fullname: Gómez-Romero, María organization: Department of Metabolism, Digestion and Reproduction, National Phenome Centre, Imperial College London – sequence: 7 givenname: Jake surname: Pearce fullname: Pearce, Jake organization: Department of Metabolism, Digestion and Reproduction, National Phenome Centre, Imperial College London – sequence: 8 givenname: Torben surname: Kimhofer fullname: Kimhofer, Torben organization: Division of Systems Medicine, Imperial College London – sequence: 9 givenname: Ellie surname: D’Hondt fullname: D’Hondt, Ellie organization: IMEC – sequence: 10 givenname: Hilkka surname: Soininen fullname: Soininen, Hilkka organization: Department of Neurology, University of Eastern Finland and Kuopio University Hospital – sequence: 11 givenname: Iwona surname: Kłoszewska fullname: Kłoszewska, Iwona organization: Medical University of Lodz – sequence: 12 givenname: Patrizia surname: Mecocci fullname: Mecocci, Patrizia organization: Institute of Gerontology and Geriatrics, University of Perugia – sequence: 13 givenname: Magda surname: Tsolaki fullname: Tsolaki, Magda organization: 3rd Department of Neurology, Aristotle University – sequence: 14 givenname: Bruno surname: Vellas fullname: Vellas, Bruno organization: INSERM U 558, University of Toulouse – sequence: 15 givenname: Dag surname: Aarsland fullname: Aarsland, Dag organization: King’s College London, Institute of Psychiatry, Psychology and Neuroscience – sequence: 16 givenname: Alejo surname: Nevado-Holgado fullname: Nevado-Holgado, Alejo organization: Department of Psychiatry, Warneford Hospital, University of Oxford – sequence: 17 givenname: Benjamine surname: Liu fullname: Liu, Benjamine organization: Department of Psychiatry, Warneford Hospital, University of Oxford – sequence: 18 givenname: Stuart surname: Snowden fullname: Snowden, Stuart organization: King’s College London, Institute of Psychiatry, Psychology and Neuroscience – sequence: 19 givenname: Petroula surname: Proitsi fullname: Proitsi, Petroula organization: King’s College London, Institute of Psychiatry, Psychology and Neuroscience – sequence: 20 givenname: Nicholas J. surname: Ashton fullname: Ashton, Nicholas J. organization: King’s College London, Institute of Psychiatry, Psychology and Neuroscience, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation – sequence: 21 givenname: Abdul surname: Hye fullname: Hye, Abdul organization: King’s College London, Institute of Psychiatry, Psychology and Neuroscience – sequence: 22 givenname: Cristina surname: Legido-Quigley fullname: Legido-Quigley, Cristina organization: King’s College London, Institute of Psychiatry, Psychology and Neuroscience – sequence: 23 givenname: Matthew R. surname: Lewis fullname: Lewis, Matthew R. organization: Department of Metabolism, Digestion and Reproduction, National Phenome Centre, Imperial College London – sequence: 24 givenname: Jeremy K. surname: Nicholson fullname: Nicholson, Jeremy K. organization: Department of Metabolism, Digestion and Reproduction, National Phenome Centre, Imperial College London, Health Futures Institute, Murdoch University – sequence: 25 givenname: Elaine surname: Holmes fullname: Holmes, Elaine organization: UK Dementia Research Institute, Hammersmith Hospital, Imperial College London, Health Futures Institute, Murdoch University, The Perron Institute for Neurological and Translational Science – sequence: 26 givenname: Alvis surname: Brazma fullname: Brazma, Alvis organization: European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI – sequence: 27 givenname: Simon surname: Lovestone fullname: Lovestone, Simon organization: Department of Psychiatry, Warneford Hospital, University of Oxford, Janssen-Cilag Ltd |
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| Keywords | Biomarkers Metabolomics Alzheimer's disease Genomics Dementia |
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| Snippet | Finding early disease markers using non-invasive and widely available methods is essential to develop a successful therapy for Alzheimer’s Disease. Few studies... Finding early disease markers using non-invasive and widely available methods is essential to develop a successful therapy for Alzheimer's Disease. Few studies... Abstract Finding early disease markers using non-invasive and widely available methods is essential to develop a successful therapy for Alzheimer’s Disease.... |
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| Title | Urinary metabolic phenotyping for Alzheimer’s disease |
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