Metabolic Profiles Help Discriminate Mild Cognitive Impairment from Dementia Stage in Alzheimer's Disease

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

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

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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.

Original languageEnglish
Pages (from-to)277-286
Number of pages10
JournalJournal of Alzheimer's Disease
Issue number1
Publication statusPublished - 10 Mar 2020


  • Alzheimer’s disease
  • cognitive dysfunction
  • dementia
  • machine learning
  • metabolomics


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