University of Oulu

Olli Jääskeläinen, Anette Hall, Mika Tiainen, Mark van Gils, Jyrki Lötjönen, Antti J. Kangas, Seppo Helisalmi, Maria Pikkarainen, Merja Hallikainen, Anne Koivisto, Päivi Hartikainen, Mikko Hiltunen, Mika Ala-Korpela, Pasi Soininen, Hilkka Soininen, & Sanna-Kaisa Herukka. (2020). Metabolic Profiles Help Discriminate Mild Cognitive Impairment from Dementia Stage in Alzheimer’s Disease. Journal of Alzheimer’s Disease, 74(1), 277–286.

Metabolic profiles help discriminate mild cognitive impairment from dementia stage in Alzheimer’s disease

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Author: Jääskeläinen, Olli1; Hall, Anette1; Tiainen, Mika2;
Organizations: 1Institute of Clinical Medicine – Neurology, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
2NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
3VTT Technical Research Centre of Finland Ltd, Tampere, Finland
4Combinostics Ltd, Tampere, Finland
5Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
6Neurocenter, Kuopio University Hospital, Kuopio, Finland
7Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
8Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
9Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
10Systems Epidemiology, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
11Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, The Alfred Hospital, Monash University, Melbourne, Victoria, Australia
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.2 MB)
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Language: English
Published: IOS Press, 2020
Publish Date: 2020-04-08


Accurate differentiation between neurodegenerative diseases is developing quickly and has reached an effective level in disease recognition. However, there has been less focus on effectively distinguishing the prodromal state from later dementia stages due to a lack of suitable biomarkers. We utilized the Disease State Index (DSI) machine learning classifier to see how well quantified metabolomics data compares to clinically used cerebrospinal fluid (CSF) biomarkers of Alzheimer’s disease (AD). The metabolic profiles were quantified for 498 serum and CSF samples using proton nuclear magnetic resonance spectroscopy. The patient cohorts in this study were dementia (with a clinical AD diagnosis) (N = 359), mild cognitive impairment (MCI) (N = 96), and control patients with subjective memory complaints (N = 43). DSI classification was conducted for MCI (N = 51) and dementia (N = 214) patients with low CSF amyloid-β levels indicating AD pathology and controls without such amyloid pathology (N = 36). We saw that the conventional CSF markers of AD were better at classifying controls from both dementia and MCI patients. However, quantified metabolic subclasses were more effective in classifying MCI from dementia. Our results show the consistent effectiveness of traditional CSF biomarkers in AD diagnostics. However, these markers are relatively ineffective in differentiating between MCI and the dementia stage, where the quantified metabolomics data provided significant benefit.

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Series: Journal of Alzheimer's disease
ISSN: 1387-2877
ISSN-E: 1875-8908
ISSN-L: 1387-2877
Volume: 74
Issue: 1
Pages: 277 - 286
DOI: 10.3233/JAD-191226
Type of Publication: A1 Journal article – refereed
Field of Science: 3124 Neurology and psychiatry
Funding: Olli Jääskeläinen has received a personal grant from Emil Aaltonen Foundation. Olli Jääskeläinen and Sanna-Kaisa Herukka have also attended SynaNet trainings and events within the framework of European Union’s Horizon 2020 research and innovation program (#692340). Mika Ala-Korpela is supported by a Senior Research Fellowship from the National Health and Medical Research Council (NHMRC) of Australia (APP1158958). He also works in a unit that is supported by the University of Bristol and UK Medical Research Council (MC_UU_12013/1). The Baker Institute is supported in part by the Victorian Government’s Operational Infrastructure Support Program.
Dataset Reference: Supplementary materials:
Copyright information: © 2020 – IOS Press and the authors. All rights reserved. This article is published online with Open Access and distributed under the terms of the Creative Commons Attribution Non-Commercial License (CC BY-NC 4.0).