Association of blood lipids with Alzheimer's disease: A comprehensive lipidomics analysis.
The aim of this study was to (1) replicate previous associations between six blood lipids and Alzheimer's disease (AD) (Proitsi et al 2015) and (2) identify novel associations between lipids, clinical AD diagnosis, disease progression and brain atrophy (left/right hippocampus/entorhinal cortex).



We performed untargeted lipidomic analysis on 148 AD and 152 elderly control plasma samples and used univariate and multivariate analysis methods.



We replicated our previous lipids associations and reported novel associations between lipids molecules and all phenotypes. A combination of 24 molecules classified AD patients with >70% accuracy in a test and a validation data set, and we identified lipid signatures that predicted disease progression (R2= 0.10, test data set) and brain atrophy (R2≥ 0.14, all test data sets except left entorhinal cortex). We putatively identified a number of metabolic features including cholesteryl esters/triglycerides and phosphatidylcholines.



Blood lipids are promising AD biomarkers that may lead to new treatment strategies.
King's College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK. Electronic address: petroula.proitsi@kcl.ac.uk.
Petroula
P
Proitsi
King's College London, Institute of Pharmaceutical Science, London, UK.
Min
M
Kim
King's College London, Institute of Pharmaceutical Science, London, UK.
Luke
L
Whiley
King's College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK; NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation Trust, London UK.
Andrew
A
Simmons
King's College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK; NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation Trust, London UK.
Martina
M
Sattlecker
King's College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK.
Latha
L
Velayudhan
QIMR Berghofer Medical Research Institute, Brisbane, Australia.
Michelle K
MK
Lupton
Department of Neurology, Kuopio University Hospital and University of Eastern Finland, Kuopio, Finland.
Hillka
H
Soininen
Department of Old Age Psychiatry & Psychotic Disorders, Medical University of Lodz, Lodz, Poland.
Iwona
I
Kloszewska
Section of Gerontology and Geriatrics, Department of Medicine, University of Perugia, Perugia, Italy.
Patrizia
P
Mecocci
Memory and Dementia Centre, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Magda
M
Tsolaki
Department of Internal and Geriatrics Medicine, INSERM U 1027, Gerontopole, Hôpitaux de Toulouse, Toulouse, France.
Bruno
B
Vellas
Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK.
Simon
S
Lovestone
King's College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK.
John F
JF
Powell
King's College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK; NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation Trust, London UK; The Farr Institute of Health Informatics Research, UCL Institute of Health Informatics, UCL, UK.
Richard J B
RJ
Dobson
King's College London, Institute of Pharmaceutical Science, London, UK.
Cristina
C
Legido-Quigley
D000368:Aged; D000544:Alzheimer Disease / Q000097:blood* / Q000000981:diagnostic imaging; D001284:Atrophy; D015415:Biomarkers / Q000097:blood; D015331:Cohort Studies; D018450:Disease Progression; D018728:Entorhinal Cortex / Q000000981:diagnostic imaging; D005260:Female; D006624:Hippocampus / Q000000981:diagnostic imaging; D006801:Humans; D008055:Lipids / Q000097:blood*; D008279:Magnetic Resonance Imaging; D008297:Male; D015999:Multivariate Analysis; D012044:Regression Analysis
D015415:Biomarkers; D008055:Lipids
Alzheimer's disease; Biomarkers; Brain atrophy; Classification; Dementia; Lipidomics; Machine learning; Metabolomics; Multivariate; Random forest; Rate of cognitive decline; sMRI