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Transl Psychiatry. 2015 Jan 13;5:e494. doi: 10.1038/tp.2014.127.

Plasma lipidomics analysis finds long chain cholesteryl esters to be associated with Alzheimer's disease.

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Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Institute of Pharmaceutical Science, King's College London, London, UK.
Department of Neurology, Kuopio University Hospital and University of Eastern Finland, Kuopio, Finland.
Department of Old Age Psychiatry & Psychotic Disorders, Medical University of Łódź, Łódź, Poland.
Section of Gerontology and Geriatrics, Department of Medicine, University of Perugia, Perugia, Italy.
Memory and Dementia Center, 3rd Department of Neurology, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Department of Internal and Geriatrics Medicine, INSERM U 1027, Gerontopole, Hôpitaux de Toulouse, Toulouse, France.
Department of Psychiatry, State Key Laboratory of Brain and Cognitive Sciences, and Centre for Genomic Sciences, Li Ka Shing Faculty of Medicine, the University of Hong Kong, Hong Kong.
Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK.
1] Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK [2] National Institute for Health Research Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation, London, UK.


There is an urgent need for the identification of Alzheimer's disease (AD) biomarkers. Studies have now suggested the promising use of associations with blood metabolites as functional intermediate phenotypes in biomedical and pharmaceutical research. The aim of this study was to use lipidomics to identify a battery of plasma metabolite molecules that could predict AD patients from controls. We performed a comprehensive untargeted lipidomic analysis, using ultra-performance liquid chromatography/mass spectrometry on plasma samples from 35 AD patients, 40 elderly controls and 48 individuals with mild cognitive impairment (MCI) and used multivariate analysis methods to identify metabolites associated with AD status. A combination of 10 metabolites could discriminate AD patients from controls with 79.2% accuracy (81.8% sensitivity, 76.9% specificity and an area under curve of 0.792) in a novel test set. Six of the metabolites were identified as long chain cholesteryl esters (ChEs) and were reduced in AD (ChE 32:0, odds ratio (OR)=0.237, 95% confidence interval (CI)=0.10-0.48, P=4.19E-04; ChE 34:0, OR=0.152, 95% CI=0.05-0.37, P=2.90E-04; ChE 34:6, OR=0.126, 95% CI=0.03-0.35, P=5.40E-04; ChE 32:4, OR=0.056, 95% CI=0.01-0.24, P=6.56E-04 and ChE 33:6, OR=0.205, 95% CI=0.06-0.50, P=2.21E-03, per (log2) metabolite unit). The levels of these metabolites followed the trend control>MCI>AD. We, additionally, found no association between cholesterol, the precursor of ChE and AD. This study identified new ChE molecules, involved in cholesterol metabolism, implicated in AD, which may help identify new therapeutic targets; although, these findings need to be replicated in larger well-phenotyped cohorts.

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