Previous studies have demonstrated altered metabolites in samples of Alzheimer's disease (AD) patients. However, the sample size from many of them is relatively small and the metabolites are relatively limited. Here we applied a comprehensive platform using ultraperformance liquid chromatography-time-of-flight mass spectrometry and gas chromatography-time-of-flight mass spectrometry to analyze plasma samples from AD patients, amnestic mild cognitive impairment (aMCI) patients, and normal controls. A biomarker panel consisting of six plasma metabolites (arachidonic acid, N,N-dimethylglycine, thymine, glutamine, glutamic acid, and cytidine) was identified to discriminate AD patients from normal control. Another panel of five plasma metabolites (thymine, arachidonic acid, 2-aminoadipic acid, N,N-dimethylglycine, and 5,8-tetradecadienoic acid) was able to differentiate aMCI patients from control subjects. Both biomarker panels had good agreements with clinical diagnosis. The 2 panels of metabolite markers were all involved in fatty acid metabolism, one-carbon metabolism, amino acid metabolism, and nucleic acid metabolism. Additionally, no altered metabolites were found among the patients at different stages, as well as among those on anticholinesterase medication and those without anticholinesterase medication. These findings provide a comprehensive global plasma metabolite profiling and may contribute to making early diagnosis as well as understanding the pathogenic mechanism of AD and aMCI.

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Background/Aims: As a suitable test to screen for Alzheimer's disease (AD) or mild cognitive impairment (MCI), studies to validate the Chinese version of Addenbrooke's Cognitive Examination-Revised (ACE-R) are rare. Methods: A total of 151 subjects were recruited and the neuropsychological assessments were employed. One-way analysis of variance and Bonferroni correction were used to compare scores of different psychometric scales. Intraclass correlation coefficient (ICC) and Cronbach's coefficient α were used to evaluate the reliability of psychometric scales. The validity of ACE-R to screen for mild AD and amnestic subtype of MCI (a-MCI) was assessed by receiver operating characteristic (ROC) curves. Results: The Chinese ACE-R had good reliability (inter-rater ICC = 0.994; test-retest ICC = 0.967) as well as reliable internal consistency (Cronbach's coefficient α = 0.859). With its cutoff of 67/68, the sensitivity (0.920) and specificity (0.857) were lower than for the Mini-Mental State Examination (MMSE) cutoff (sensitivity 1.000 and specificity 0.937) to screen for mild AD. However, the sensitivity of ACE-R to screen for a-MCI was superior to the MMSE with a cutoff of 85/86. The specificity of ACE-R was lower than that of the MMSE to screen for a-MCI. The area under the ROC curve of ACE-R was much larger than that of the MMSE (0.836 and 0.751) for detecting a-MCI rather than mild AD. Conclusion: The Chinese ACE-R is a reliable assessment tool for cognitive impairment. It is more sensitive and accurate in screening for a-MCI rather than for AD compared to the MMSE.

Background: Progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) dementia can be predicted by clinical features and a combination of biomarkers may increase the predictive power. In the present study, we investigated whether the combination of olfactory function and plasma neuronal-derived exosome (NDE) Aβ 1-42 can best predict progression to AD dementia. Methods: 87 MCI patients were enrolled and received the cognitive assessment at 2-year and 3-year follow-up to reevaluate cognition. In the meanwhile, 80 healthy controls and 88 AD dementia patients were enrolled at baseline as well to evaluate the diagnose value in cross-section. Olfactory function was evaluated with the sniffin sticks (SS-16) and Aβ 1-42 levels in NDEs were determined by ELISA. Logistic regression was performed to evaluate the risk factors for cognitive decline in MCI at 2-year and 3-year revisits. Results: In the cross cohort, lower SS-16 scores and higher Aβ 1-42 levels in NDEs were found in MCI and AD dementia compared to healthy controls. For the longitudinal set, 8 MCI individuals developed AD dementia within 2 years, and 16 MCI individuals developed AD dementia within 3 years. The two parameter-combination of SS-16 scores and Aβ 1-42 level in NDEs showed better prediction in the conversion of MCI to AD dementia at 2-year and 3-year revisit. Moreover, after a 3-year follow-up, SS-16 scores also significantly predicted the conversion to AD dementia, where lower scores were associated with a 10-fold increased risk of developing AD dementia (p = 0.006). Similarly, higher Aβ 1-42 levels in NDEs in patients with MCI increased the risk of developing AD dementia by 8.5fold (p = 0.002). Conclusion: A combination of two biomarkers of NDEs (Aβ 1-42) and SS-16 predicted the conversion of MCI to AD dementia more accurately in combination. These findings have critical implications for understanding the pathophysiology of AD dementia and for developing preventative treatments for cognitive decline.