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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 x 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.