Saliva is a biofluid that is sensitive to metabolic changes and is straightforward to collect in a non-invasive manner, but it is seldom used for metabolite analysis when studying neurodegenerative disorders. We present a procedure for both an untargeted and targeted analysis of the saliva metabolome in which nuclear magnetic resonance (NMR) spectroscopy is used in combination with multivariate data analysis. The applicability of this approach is demonstrated on saliva samples selected from the 25 year prospective Betula study, including samples from dementia subjects with either Alzheimer's disease (AD) or vascular dementia at the time of sampling or who developed it by the next sampling/assessment occasion five years later, and age-, gender-, and education-matched control individuals without dementia. Statistically significant multivariate models were obtained that separated patients with dementia from controls and revealed seven discriminatory metabolites. Dementia patients showed significantly increased concentrations of acetic acid (fold change (fc) = 1.25, p = 2 × 10⁻⁵), histamine (fc = 1.26, p = 0.019), and propionate (fc = 1.35, p = 0.002), while significantly decreased levels were observed for dimethyl sulfone (fc = 0.81, p = 0.005), glycerol (fc = 0.79, p = 0.04), taurine (fc = 0.70, p = 0.007), and succinate (fc = 0.62, p = 0.008). Histamine, succinate, and taurine are known to be important in AD, and acetic acid and glycerol are involved in related pathways. Dimethyl sulfone and propionate originate from the diet and bacterial flora and might reflect poorer periodontal status in the dementia patients. For these seven metabolites, a weak but statistically significant pre-diagnostic value was observed. Taken together, we present a robust and general NMR analysis approach for studying the saliva metabolome that has potential use for screening and early detection of dementia.

Differentiating between Parkinson’s disease (PD) and the atypical Parkinsonian disorders of multiple system atrophy (MSA) and progressive supranuclear palsy (PSP) is difficult clinically due to overlapping symptomatology, especially at early disease stages. Consequently, there is a need to identify metabolic markers for these diseases and to develop them into viable biomarkers. In the present investigation, solution nuclear magnetic resonance and mass spectrometry metabolomics were used to quantitatively characterize the plasma metabolomes (a total of 167 metabolites) of a cohort of 94 individuals comprising 34 PD, 12 MSA, and 17 PSP patients, as well as 31 control subjects. The distinct and statistically significant differences observed in the metabolite concentrations of the different disease and control groups enabled the identification of potential plasma metabolite markers of each disorder and enabled the differentiation between the disorders. These group-specific differences further implicate disturbances in specific metabolic pathways. The two metabolites, formic acid and succinate, were altered similarly in all three disease groups when compared to the control group, where a reduced level of formic acid suggested an effect on pyruvate metabolism, methane metabolism, and/or the kynurenine pathway, and an increased succinate level suggested an effect on the citric acid cycle and mitochondrial dysfunction.

There is a great need for diagnostic biomarkers of impending dementia. Metabolite markers in blood have been investigated in several studies, but inconclusive findings encourage further investigation, particularly in the pre-diagnostic phase. In the present study, the serum metabolomes of 110 dementia or pre-diagnostic dementia individuals and 201 healthy individuals matched for age, gender, and education were analyzed by nuclear magnetic resonance spectroscopy in combination with multivariate data analysis. 58 metabolites were quantified in each of the 311 samples. Individuals with dementia were discriminated from controls using a panel of seven metabolites, while the pre-diagnostic dementia subjects were distinguished from controls using a separate set of seven metabolites, where threonine was a common significant metabolite in both panels. Metabolite and pathway alterations specific for dementia and pre-diagnostic dementia were identified, in particular a disturbed threonine catabolism at the pre-diagnostic stage that extends to several threonine-linked pathways at the dementia stage.