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 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.