TY - JOUR
T1 - Urinary metabolic phenotyping for Alzheimer’s disease
AU - Kurbatova, Natalja
AU - Garg, Manik
AU - Whiley, Luke
AU - Chekmeneva, Elena
AU - Jiménez, Beatriz
AU - Gómez-Romero, María
AU - Pearce, Jake
AU - Kimhofer, Torben
AU - D’Hondt, Ellie
AU - Soininen, Hilkka
AU - Kłoszewska, Iwona
AU - Mecocci, Patrizia
AU - Tsolaki, Magda
AU - Vellas, Bruno
AU - Aarsland, Dag
AU - Nevado-Holgado, Alejo
AU - Liu, Benjamine
AU - Snowden, Stuart
AU - Proitsi, Petroula
AU - Ashton, Nicholas J.
AU - Hye, Abdul
AU - Legido-Quigley, Cristina
AU - Lewis, Matthew R.
AU - Nicholson, Jeremy K.
AU - Holmes, Elaine
AU - Brazma, Alvis
AU - Lovestone, Simon
PY - 2020/12
Y1 - 2020/12
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=85097420123&partnerID=8YFLogxK
U2 - 10.1038/s41598-020-78031-9
DO - 10.1038/s41598-020-78031-9
M3 - Article
AN - SCOPUS:85097420123
SN - 2045-2322
VL - 10
JO - Scientific Reports
JF - Scientific Reports
IS - 1
M1 - 21745
ER -