Article type: Research Article

Authors: Huan, Taoa; 1 | Tran, Trana; 1 | Zheng, Jiamina | Sapkota, Shraddhab | MacDonald, Stuart W.c | Camicioli, Richardb; d | Dixon, Roger A.b; e; * | Li, Lianga; *

Affiliations: [a] Department of Chemistry, University of Alberta, Edmonton, Canada | [b] Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada | [c] Department of Psychology, University of Victoria, British Columbia, Canada | [d] Department of Medicine (Neurology), University of Alberta, Edmonton, Canada | [e] Department of Psychology, University of Alberta, Edmonton, Canada

Correspondence: [*] Correspondence to: Liang Li, Department of Chemistry, Chemistry Centre W3-39C, University of Alberta, Edmonton, Alberta, T6G 2G2, Canada. Tel.: +1 780 492 3250; E-mail: [email protected] and Roger A. Dixon, Department of Psychology, P-217 Biological Sciences Building, University of Alberta, Edmonton, Alberta, T6G 2E9, Canada. Tel.: +1 780 492 7602; E-mail: [email protected].

Note: [1] These authors contributed equally to this work.

Abstract: Using a non-invasive biofluid (saliva), we apply a powerful metabolomics workflow for unbiased biomarker discovery in Alzheimer’s disease (AD). We profile and differentiate Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and AD groups. The workflow involves differential chemical isotope labeling liquid chromatography mass spectrometry using dansylation derivatization for in-depth profiling of the amine/phenol submetabolome. The total sample (N = 109) was divided in to the Discovery Phase (DP) (n = 82; 35 CN, 25 MCI, 22 AD) and a provisional Validation Phase (VP) (n = 27; 10 CN, 10 MCI, 7 AD). In DP we detected 6,230 metabolites. Pairwise analyses confirmed biomarkers for AD versus CN (63), AD versus MCI (47), and MCI versus CN (2). We then determined the top discriminating biomarkers and diagnostic panels. A 3-metabolite panel distinguished AD from CN and MCI (DP and VP: Area Under the Curve [AUC] = 1.000). The MCI and CN groups were best discriminated with a 2-metabolite panel (DP: AUC = 0.779; VP: AUC = 0.889). In addition, using positively confirmed metabolites, we were able to distinguish AD from CN and MCI with good diagnostic performance (AUC > 0.8). Saliva is a promising biofluid for both unbiased and targeted AD biomarker discovery and mechanism detection. Given its wide availability and convenient accessibility, saliva is a biofluid that can promote diversification of global AD biomarker research.

Keywords: Alzheimer’s disease, biomarkers, liquid chromatography mass spectrometry, metabolomics, saliva, Victoria Longitudinal Study

DOI: 10.3233/JAD-180711

Journal: Journal of Alzheimer's Disease, vol. 65, no. 4, pp. 1401-1416, 2018