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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.

Journal article
Authors Daniel Stamate
Min Kim
Petroula Proitsi
Sarah Westwood
Alison Baird
Alejo Nevado-Holgado
Abdul Hye
Isabelle Bos
Stephanie J B Vos
Rik Vandenberghe
Charlotte E Teunissen
Mara Ten Kate
Philip Scheltens
Silvy Gabel
Karen Meersmans
Olivier Blin
Jill Richardson
Ellen De Roeck
Sebastiaan Engelborghs
Kristel Sleegers
Régis Bordet
Lorena Ramit
Petronella Kettunen
Magda Tsolaki
Frans Verhey
Daniel Alcolea
Alberto Lléo
Gwendoline Peyratout
Mikel Tainta
Peter Johannsen
Yvonne Freund-Levi
Lutz Frölich
Valerija Dobricic
Giovanni B Frisoni
José L Molinuevo
Anders Wallin
Julius Popp
Pablo Martinez-Lage
Lars Bertram
Kaj Blennow
Henrik Zetterberg
Johannes Streffer
Pieter J Visser
Simon Lovestone
Cristina Legido-Quigley
Published in Alzheimer's & dementia (New York, N. Y.)
Volume 5
Pages 933-938
ISSN 2352-8737
Publication year 2019
Published at Institute of Neuroscience and Physiology
Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry
Pages 933-938
Language en
Links dx.doi.org/10.1016/j.trci.2019.11.0...
www.ncbi.nlm.nih.gov/entrez/query.f...
Subject categories Clinical Medicine

Abstract

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.

Page Manager: Webmaster|Last update: 9/11/2012
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