Dr Liang LiLiang Li, PhD, BSc
New research has identified a trio of biomarkers for the detection of both mild cognitive impairment (MCI) and Alzheimer disease in saliva samples, with the results suggesting a promising potential for clinical application.1

Ultimately, the research group from the University of Alberta used mass spectrometry to examine more than 6000 metabolites from biomarker panels tailored to the discrimination of 3 groups—those who were cognitively normal (n = 35), those with MCI (n = 25), and those with Alzheimer (n = 22)—seeking distinct signatures between groups.

Led by Liang Li, PhD, BSc, a professor in the Department of Chemistry, and Roger Dixon, MD, a professor in the Department of Psychology, the group compared the prediction importance of the panels with 5 other sets of modifiable and non-modifiable risk factors for Alzheimer, ranging from genetic, lifestyle, cognitive, functional health, and bio-demographic.

"In this analysis, we found three metabolites that can be used to differentiate between these three groups," Li said in a statement.2 "This is preliminary work because we've used a very small sample size. But the results are very promising. If we can use a larger set of samples, we can validate our findings and develop a saliva test of Alzheimer's disease."

"So far, no disease-altering interventions for Alzheimer's disease have been successful. For this reason, researchers are aiming to discover the earliest signals of the disease so that prevention protocols can be implemented,” Dixon said.

Li, Dixon, and colleagues used random forest analyses (RFA) for the 3 pairwise comparisons the Alzheimer and cognitively normal groups; the Alzheimer and MCI groups; and the MCI and cognitively normal groups. The salivary metabolite panels utilized were those identified in previous research, which laid the foundation for this assessment. In that research, pairwise analysis determined that a 3-metabolite panel distinguished Alzheimer from the cognitively normal 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 (Discovery Phase Area Under the Curve [DP-AUC], 0.779; Validation Phase AUC [VP-AUC], 0.889). Additionally, they were able to distinguish Alzheimer from the cognitively normal and MCI with good diagnostic performance (AUC, >0.8).3 

The authors noted that speed, memory, and the Alzheimer metabolite panel were the top predictors, in that order, for the Alzheimer-cognitively normal comparison. Specifically, poorer memory performance, slower speed performance, and higher levels of the metabolite panel discriminated Alzheimer from MCI and the cognitively normal.

For the Alzheimer-MCI analysis, the same 2 cognitive predictors, as well as the AD/MCI metabolite panel, were identified as important predictors (C-statistic, 0.99), although different order of importance was observed—slower speed performance, then poorer memory performance and higher levels of the AD/MCI metabolite panel.

In the MCI-cognitively normal comparison, 7 of 19 predictors were identified as important in discriminating the groups (C-statistic, 0.94). They were: higher pulse pressure; higher levels of the MCI metabolite panel; poorer memory performance; lower frequency of novel cognitive activity; elevated APOE risk; decreased social activity; and lower Mini-Mental State Examination (MMSE) score.

“Given the dynamic, insidious and multi-factorial nature of AD, it is likely that multiple modalities of risk biomarkers may contribute to the diagnosis of the disease,” Li, Dixon, and colleagues wrote. “A corresponding emerging interest is in determining viable combinations of predictors for use in timely (early) detection and targeted (precise) intervention. Our results supported both the multi-modal predictor expectation and the potential valuable role that salivary-based biomarkers discovered through metabolomics analyses may play in identifying important components of AD biomarker batteries.”

An additional benefit of identifying these biomarkers is the capability to conduct efficacy testing for therapies. "Using the biomarkers, we can also do testing to see what types of treatments are most effective in treating Alzheimer's disease—from diet to physical activity to pharmaceuticals," Li said.
1. Sapkota A, Huan T, Tran T, et al. Alzheimer’s biomarkers from multiple Modalities selectively discriminate clinical status: relative importance of salivary metabolomics panels, genetic, lifestyle, cognitive, functional health
and demographic risk markers. Front Aging Neurosci. Published online October 2018. doi: 10.3389/fnagi.2018.00296
2. Scientists pave the way for saliva test for Alzheimer's disease [press release]. Alberta, Canada: University of Alberta; Published December 12, 2018. sciencedaily.com/releases/2018/12/181212121904.htm. Accessed January 8, 2019.
3. Haun T, Tran T, Jaimin Z, et al. Metabolomics analyses of saliva detect novel biomarkers of Alzheimer’s disease. J Alzheimers Dis. 2018;65(4): 1401-1416.
doi: 10.3233/JAD-180711