A metabolite-based machine learning approach to diagnose Alzheimer-type dementia in blood : Results from the European Medical Information Framework for Alzheimer disease biomarker discovery cohort
Stamate, Daniel (University of London)
Kim, Min (Steno Diabetes Center Copenhagen)
Proitsi, Petroula (King's College London)
Westwood, Sarah (University of Oxford)
Baird, Alison (University of Oxford)
Nevado-Holgado, Alejo (University of Oxford)
Hye, Abdul (King's College London)
Bos, Isabelle (Amsterdam UMC)
Vos, Stephanie J.B. (Maastricht University)
Vandenberghe, Rik (Amsterdam UMC)
Teunissen, Charlotte E. (Amsterdam UMC. University Medical Center)
Kate, Mara Ten (Amsterdam UMC. University Medical Center)
Scheltens, Philip (Vrije Universiteit Amsterdam)
Gabel, Silvy (Laboratory for Cognitive Neurology)
Meersmans, Karen (Laboratory for Cognitive Neurology)
Blin, Olivier (Aix-Marseille Université)
Richardson, Jill (GlaxoSmithKline R&D)
De Roeck, Ellen (University of Antwerp)
Engelborghs, Sebastiaan (Vrije Universiteit Brussel (VUB))
Sleegers, Kristel (Center for Molecular Neurology. VIB)
Bordet, Régis (University of Lille)
Ramit, Lorena (Hospital Clínic i Provincial de Barcelona)
Kettunen, Petronella (Sahlgrenska Academy at University of Gothenburg)
Tsolaki, Magda (AHEPA University Hospital (Grècia))
Verhey, Frans (Maastricht University)
Alcolea, Daniel (Institut d'Investigació Biomèdica Sant Pau)
Lleó, Alberto (Institut d'Investigació Biomèdica Sant Pau)
Peyratout, Gwendoline (Lausanne University Hospital)
Tainta, Mikel (Fundacion CITA-alzheimer Fundazioa)
Johannsen, Peter (Copenhagen University Hospital)
Freund-Levi, Yvonne (Karolinska Institutet (Estocolm, Suècia))
Frölich, Lutz (University of Heidelberg)
Dobricic, Valerija (University of Lübeck)
Frisoni, Giovanni B. (IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli)
Molinuevo, José Luis (Universitat Pompeu Fabra)
Wallin, Anders (Sahlgrenska Academy at the University of Gothenburg)
Popp, Julius (Geneva University Hospitals)
Martinez-Lage, Pablo (Fundación CITA-Alzhéimer Fundazioa)
Bertram, Lars (University of Oslo)
Blennow, Kaj (Sahlgrenska University Hospital (Suècia))
Zetterberg, Henrik (UCL Institute of Neurology (Regne Unit))
Streffer, Johannes (University of Antwerp)
Visser, Pieter J. (Amsterdam UMC)
Lovestone, Simon (Janssen-Cilag UK Ltd)
Legido-Quigley, Cristina (King's College London)
Universitat Autònoma de Barcelona

Data:	2019
Resum:	Machine learning (ML) may harbor the potential to capture the metabolic complexity in Alzheimer Disease (AD). Here we set out to test the performance of metabolites in blood to categorize AD when compared to CSF biomarkers. This study analyzed samples from 242 cognitively normal (CN) people and 115 with AD-type dementia utilizing plasma metabolites (n = 883). Deep Learning (DL), Extreme Gradient Boosting (XGBoost) and Random Forest (RF) were used to differentiate AD from CN. These models were internally validated using Nested Cross Validation (NCV). On the test data, DL produced the AUC of 0. 85 (0. 80-0. 89), XGBoost produced 0. 88 (0. 86-0. 89) and RF produced 0. 85 (0. 83-0. 87). By comparison, CSF measures of amyloid, p-tau and t-tau (together with age and gender) produced with XGBoost the AUC values of 0. 78, 0. 83 and 0. 87, respectively. This study showed that plasma metabolites have the potential to match the AUC of well-established AD CSF biomarkers in a relatively small cohort. Further studies in independent cohorts are needed to validate whether this specific panel of blood metabolites can separate AD from controls, and how specific it is for AD as compared with other neurodegenerative disorders.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Alzheimer's disease ; Biomarkers ; EMIF-AD ; Machine-Learning ; Metabolomics
Publicat a:	Alzheimer's & dementia, Vol. 5 (2019) , p. 933-938, ISSN 2352-8737

DOI: 10.1016/j.trci.2019.11.001
PMID: 31890857

6 p, 731.5 KB

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