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Alzheimers Dement. 2016 Jul;12(7):815-22. doi: 10.1016/j.jalz.2015.12.008. Epub 2016 Jan 21.

Blood metabolite markers of preclinical Alzheimer's disease in two longitudinally followed cohorts of older individuals.

Author information

1
Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA.
2
HiThru Analytics LLC, Laurel, MD, USA.
3
Clinical and Translational Neuroscience Unit, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA; School of Medicine, University of Mississippi Medical Center, Jackson, MS, USA.
4
Institute of Pharmaceutical Science, King's College, London, UK.
5
Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
6
School of Electrical Engineering and Computer Science, University of Newcastle, Callaghan, Australia.
7
Center of Biostatistics and Bioinformatics, University of Mississippi Medical Center, Jackson, MS, USA.
8
BIOCRATES Life Sciences AG, Innsbruck, Austria.
9
Faculty of Medicine, University of Iceland, Reykjavik, Iceland.
10
Icelandic Heart Association, Kopavogur, Iceland.
11
Faculty of Medicine, University of Iceland, Reykjavik, Iceland; Icelandic Heart Association, Kopavogur, Iceland.
12
Laboratory of Epidemiology, Demography and Biometry, National Institute on Aging, Bethesda, MD, USA.
13
Clinical and Translational Neuroscience Unit, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA. Electronic address: thambisettym@mail.nih.gov.

Abstract

INTRODUCTION:

Recently, quantitative metabolomics identified a panel of 10 plasma lipids that were highly predictive of conversion to Alzheimer's disease (AD) in cognitively normal older individuals (n = 28, area under the curve [AUC] = 0.92, sensitivity/specificity of 90%/90%).

METHODS:

Quantitative targeted metabolomics in serum using an identical method as in the index study.

RESULTS:

We failed to replicate these findings in a substantially larger study from two independent cohorts-the Baltimore Longitudinal Study of Aging ([BLSA], n = 93, AUC = 0.642, sensitivity/specificity of 51.6%/65.7%) and the Age, Gene/Environment Susceptibility-Reykjavik Study ([AGES-RS], n = 100, AUC = 0.395, sensitivity/specificity of 47.0%/36.0%). In analyses applying machine learning methods to all 187 metabolite concentrations assayed, we find a modest signal in the BLSA with distinct metabolites associated with the preclinical and symptomatic stages of AD, whereas the same methods gave poor classification accuracies in the AGES-RS samples.

DISCUSSION:

We believe that ours is the largest blood biomarker study of preclinical AD to date. These findings underscore the importance of large-scale independent validation of index findings from biomarker studies with relatively small sample sizes.

KEYWORDS:

Biomarker; Machine learning; Metabolomics; Phospholipids; Preclinical Alzheimer's disease

PMID:
26806385
PMCID:
PMC4947451
DOI:
10.1016/j.jalz.2015.12.008
[Indexed for MEDLINE]
Free PMC Article

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