Serum fatty acid profiles using GC-MS and multivariate statistical analysis: potential biomarkers of Alzheimer's disease

Abstract Previous studies showed the relationship between fatty acids and the risk of developing Alzheimer's disease (AD). However, they did not address potential differences in free fatty acid (FFA) profiles that could be used to distinguish between AD patients and healthy controls. In the pre...

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Bibliographic Details
Published in:Neurobiology of aging 2012-06, Vol.33 (6), p.1057-1066
Main Authors: Wang, De-Cai, Sun, Chang-Hao, Liu, Li-Yan, Sun, Xiao-Hong, Jin, Xin-Wen, Song, Wen-Lei, Liu, Xiu-Qin, Wan, Xue-Lian
Format: Article
Language:English
Subjects:
Aged
Aged, 80 and over
Aging
Alzheimer Disease - blood
Alzheimer Disease - diagnosis
Alzheimer's disease
Biomarker
biomarkers
Biomarkers - blood
Data processing
Docosahexaenoic acid
Docosahexaenoic Acids - blood
Fatty acids
Fatty Acids - blood
Female
Free fatty acid
Gas chromatography
Gas Chromatography-Mass Spectrometry - methods
GC-MS
Humans
Internal Medicine
Male
Mass spectroscopy
Middle Aged
Multivariate Analysis
Nervous system
Neurodegenerative diseases
Neurology
partial least squares-discriminant analysis (PLS-DA)
Scaling
Statistical analysis
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Summary:Abstract Previous studies showed the relationship between fatty acids and the risk of developing Alzheimer's disease (AD). However, they did not address potential differences in free fatty acid (FFA) profiles that could be used to distinguish between AD patients and healthy controls. In the present study we used gas chromatography-mass spectrometry (GC-MS) technology coupled with multivariate statistical analysis to study profiles of FFA in AD. The results indicated 2 saturated fatty acids (C14:0 and C16:0; p < 0.001 and p < 0.05, respectively), 3 unsaturated fatty acids (C18:1, C18:3, and C22:6; p < 0.05, p < 0.05, and p < 0.001, respectively), where mean levels in serum from AD patients were significantly lower than controls. Partial least squares discriminant analysis (PLS-DA) models with unit variance (UV) scaling and orthogonal signal correction (OSC) data preprocessing methods were employed to refine intergroup differences between FFA profiles. The results of the analysis have highlighted docosahexaenoic acid (DHA) as the FFA with the greatest potential as a biomarker of AD, and this study has demonstrated that FFA biomarkers have considerable potential in diagnosing and monitoring AD.
ISSN:0197-4580
1558-1497
DOI:10.1016/j.neurobiolaging.2010.09.013