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Alzheimers Dement (N Y). 2019 Dec 18;5:933-938. doi: 10.1016/j.trci.2019.11.001. eCollection 2019.

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.

Author information

1
Division of Population Health, Health Services Research and Primary Care, University of Manchester, Manchester, UK.
2
Data Science & Soft Computing Lab, London, UK.
3
Computing Department, Goldsmiths College, University of London, London, UK.
4
Steno Diabetes Center Copenhagen, Gentofte, Denmark.
5
Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King's College London, London, UK.
6
Department of Psychiatry, University of Oxford, Oxford, UK.
7
Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands.
8
Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands.
9
Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, the Netherlands.
10
Department of Clinical Chemistry, Neurochemistry Laboratory, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, the Netherlands.
11
University Hospital Leuven, Leuven, Belgium.
12
Department of Neurosciences, Laboratory for Cognitive Neurology, KU Leuven, Belgium.
13
AIX Marseille University, INS, Ap-hm, Marseille, France.
14
Neurosciences Therapeutic Area, GlaxoSmithKline R&D, Stevenage, UK.
15
Faculty of Psychology & Educational Sciences Vrije Universiteit Brussel (VUB), Brussels, Belgium.
16
Reference Center for Biological Markers of Dementia (BIODEM), University of Antwerp, Antwerp, Belgium.
17
Institute Born-Bunge, University of Antwerp, Antwerp, Belgium.
18
Department of Neurology, UZ Brussel and Center for Neurosciences, Vrije Universiteit Brussel (VUB), Brussels, Belgium.
19
Neurodegenerative Brain Diseases Group, Center for Molecular Neurology, VIB, Belgium.
20
University of Lille, Inserm, CHU Lille, Lille, France.
21
Alzheimer's Disease & Other Cognitive Disorders Unit, Hospital Clínic-IDIBAPS, Barcelona, Spain.
22
Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden.
23
1st Department of Neurology, AHEPA University Hospital, Makedonia, Thessaloniki, Greece.
24
Memory Unit, Neurology Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain.
25
University Hospital of Lausanne, Lausanne, Switzerland.
26
Center for Research and Advanced Therapies, Fundacion CITA-alzheimer Fundazioa, Donostia/San Sebastian, Spain.
27
Danish Dementia Research Centre, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.
28
Department of Neurobiology, Caring Sciences and Society (NVS), Division of Clinical Geriatrics, Karolinska Institute, and Department of Geriatric Medicine, Karolinska University Hospital Huddinge, Stockholm, Sweden.
29
Department of Geriatric Psychiatry, Zentralinstitut für Seelische Gesundheit, University of Heidelberg, Mannheim, Germany.
30
Lübeck Interdisciplinary Platform for Genome Analytics, Institutes of Neurogenetics and Cardiogenetics, University of Lübeck, Lübeck, Germany.
31
University of Geneva, Geneva, Switzerland.
32
IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
33
Barcelona Beta Brain Research Center, Unversitat Pompeu Fabra, Barcelona, Spain.
34
Institute of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden.
35
Department of Mental Health and Psychiatry, Geriatric Psychiatry, Geneva University Hospitals, Geneva, Switzerland.
36
Department of Psychology, University of Oslo, Oslo, Norway.
37
Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden.
38
Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.
39
UK Dementia Research Institute at UCL, London, UK.
40
Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK.
41
Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, Antwerp, Belgium.
42
Janssen-Cilag UK Ltd, Oxford, UK.
43
Institute of Pharmaceutical Science, King's College London, London, UK.

Abstract

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.

KEYWORDS:

Alzheimer's disease; Biomarkers; EMIF-AD; Machine-Learning; Metabolomics

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