Imperial College London
Alternate

DrMariaGomez Romero

Faculty of Medicine, Department of Metabolism, Digestion and Reproduction

Mass Spectrometry and Chromatography Manager
 
 
 
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Contact

 

+44 (0)20 7594 3765m.gomez-romero Website

 
 
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Location

 

Institute of Reproductive and Developmental BiologyHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Kurbatova:2020:10.1038/s41598-020-78031-9,
author = {Kurbatova, N and Garg, M and Whiley, L and Chekmeneva, E and Jimenez, B and Gomez-Romero, M and Pearce, J and Kimhofer, T and D'Hondt, E and Soininen, H and Kloszewska, I and Mecocci, P and Tsolaki, M and Vellas, B and Aarsland, D and Nevado-Holgado, A and Liu, B and Snowden, S and Proitsi, P and Ashton, NJ and Hye, A and Legido-Quigley, C and Lewis, MR and Nicholson, JK and Holmes, E and Brazma, A and Lovestone, S},
doi = {10.1038/s41598-020-78031-9},
journal = {Scientific Reports},
title = {Urinary metabolic phenotyping for Alzheimer's disease},
url = {http://dx.doi.org/10.1038/s41598-020-78031-9},
volume = {10},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Kurbatova,N
AU  - Garg,M
AU  - Whiley,L
AU  - Chekmeneva,E
AU  - Jimenez,B
AU  - Gomez-Romero,M
AU  - Pearce,J
AU  - Kimhofer,T
AU  - D'Hondt,E
AU  - Soininen,H
AU  - Kloszewska,I
AU  - Mecocci,P
AU  - Tsolaki,M
AU  - Vellas,B
AU  - Aarsland,D
AU  - Nevado-Holgado,A
AU  - Liu,B
AU  - Snowden,S
AU  - Proitsi,P
AU  - Ashton,NJ
AU  - Hye,A
AU  - Legido-Quigley,C
AU  - Lewis,MR
AU  - Nicholson,JK
AU  - Holmes,E
AU  - Brazma,A
AU  - Lovestone,S
DO  - 10.1038/s41598-020-78031-9
PY  - 2020///
SN  - 2045-2322
TI  - Urinary metabolic phenotyping for Alzheimer's disease
T2  - Scientific Reports
UR  - http://dx.doi.org/10.1038/s41598-020-78031-9
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000609195000101&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/85856
VL  - 10
ER  -
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