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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
Division of Population Health, Health Services Research and Primary Care, University of Manchester, Manchester, UK; Data Science & Soft Computing Lab, London, UK; Computing Department, Goldsmiths College, University of London, London, UK.
Steno Diabetes Center Copenhagen, Gentofte, Denmark.
Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King's College London, London, UK.
Department of Psychiatry, University of Oxford, Oxford, UK.
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2019 (English)In: Alzheimer’s & Dementia: Translational Research & Clinical Interventions, E-ISSN 2352-8737, Vol. 5, no C, p. 933-938Article in journal (Refereed) Published
Abstract [en]

Introduction

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.

Methods

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).

Results

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.

Discussion

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.

Place, publisher, year, edition, pages
John Wiley & Sons, 2019. Vol. 5, no C, p. 933-938
Keywords [en]
EMIF‐AD, Alzheimer's disease, Metabolomics, Biomarkers, Machine‐Learning
National Category
Medical and Health Sciences Geriatrics Neurosciences
Identifiers
URN: urn:nbn:se:oru:diva-79714DOI: 10.1016/j.trci.2019.11.001ISI: 000737692800097PubMedID: 31890857Scopus ID: 2-s2.0-85076456435OAI: oai:DiVA.org:oru-79714DiVA, id: diva2:1390904
Note

Funding: The present study was conducted as part of the EMIF‐AD project, which has received support from the Innovative Medicines Initiative Joint Undertaking under EMIF grant agreement no. 115372, resources of which are composed of financial contribution from the European Union's Seventh Framework Program (FP7/2007–2013) and EFPIA companies' in‐kind contribution. The DESCRIPA study was funded by the European Commission within the fifth framework program (QLRT‐2001‐2455). The EDAR study was funded by the European Commission within the fifth framework program (contract no. 37670). The San Sebastian GAP study is partially funded by the Department of Health of the Basque Government (allocation 17.0.1.08.12.0000.2.454.01. 41142.001.H). Kristel Sleegers is supported by the Research Fund of the University of Antwerp. Daniel Stamate is supported by the Alzheimer's Research UK (ARUK‐PRRF2017‐012).

Available from: 2020-02-03 Created: 2020-02-03 Last updated: 2023-03-13Bibliographically approved

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Freund-Levi, Yvonne

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