A new metabolomic workflow for early detection of Alzheimer's disease

• RP/UHPLC–MS and HILIC/UHPLC–MS methods developed for metabolic analysis of CSF samples.
• Altered metabolic fingerprints were related to AD progression in CSF samples.
• The accuracy of the prediction model from MCI to AD was 98.7%.
• Specificity and sensitivity of the prediction model from MCI to AD were above 95%.

Alzheimer's disease (AD) is the most prevalent cause of dementia among older people. Although AD probably starts 20–30 years before first clinical symptoms become noticeable, nowadays it cannot be diagnosed accurately in its early stages. In this work, we present a new MS-based metabolomic approach based on the use of ultra-high performance liquid chromatography–time-of-flight mass spectrometry (UHPLC–TOF MS) to investigate cerebrospinal fluid (CSF) samples from patients with different AD stages. With the aim to obtain wide metabolome coverage two different chromatographic separation modes, namely reversed phase (RP) and hydrophilic interaction chromatography (HILIC), were used. RP/UHPLC–MS and HILIC/UHPLC–MS methods were optimized and applied to analyze CSF samples from 75 patients related to AD progression. Significant metabolic differences in CSF samples from subjects with different cognitive status related to AD progression were detected using this methodology, obtaining a group of potential biomarkers together with a classification model by means of a multivariate statistical analysis. The proposed model predicted the development of AD with an accuracy of 98.7% and specificity and sensitivity values above of 95%.