Objectives: To re-evaluate a sample of older adults enrolled in a randomized controlled trial of lithium for amnestic mild cognitive impairment (MCI) after 11 to 15 years, re-assessing their current (or last available) global cognitive and functional state. Methods: We recalled all former participants of the Lithium-MCI trial conducted by our group between 2009 and 2012 to perform a single-blinded, cross-sectional evaluation of their global clinical state to compare the long-term outcome of those who received lithium vs. those who received placebo. Results: Of the original sample (n=61), we were able to reach 36 participants (59% of retention), of whom 22 had previously received lithium (61% of the recall sample) and 14 (39%) had received placebo. Since 30.5% of the recalled sample was deceased, psychometric data were collected only for 69.5% of the participants. We found statistically significant differences in current mean Mini Mental State Examination score according to previous treatment group (25.5 [SD, 5.3] vs. 18.3 [SD, 10.9], p = 0.04). The lithium group also had better performance in the phonemic Verbal Fluency Test than the control group (34.4 [SD, 14.4] vs. 11.6 [SD, 10.10], p < 0.001). Differences in these measures also had large effect sizes, as shown by Cohen’s d values of 0.92 and 1.78, respectively. Conclusion: This data set suggests that older adults with amnestic MCI who had been treated with lithium during a previous randomized controlled trial had a better long-term global cognitive outcome than those from a matched sample who did not receive the intervention.

Objective: Cerebrospinal fluid (CSF) biomarkers add accuracy to the diagnostic workup of cognitive impairment by illustrating Alzheimer’s disease (AD) pathology. However, there are no universally accepted cutoff values for the interpretation of AD biomarkers. The aim of this study is to determine the viability of a decision-tree method to analyse CSF biomarkers of AD as a support for clinical diagnosis. Methods: A decision-tree method (automated classification analysis) was applied to concentrations of AD biomarkers in CSF as a support for clinical diagnosis in older adults with or without cognitive impairment in a Brazilian cohort. In brief, 272 older adults (68 with AD, 122 with mild cognitive impairment [MCI], and 82 healthy controls) were assessed for CSF concentrations of Aβ1-42, total-tau, and phosphorylated-tau using multiplexed Luminex assays; biomarker values were used to generate decision-tree algorithms (classification and regression tree) in the R statistical software environment. Results: The best decision tree model had an accuracy of 74.65% to differentiate the three groups. Cluster analysis supported the combination of CSF biomarkers to differentiate AD and MCI vs. controls, suggesting the best cutoff values for each clinical condition. Conclusion: Automated analyses of AD biomarkers provide valuable information to support the clinical diagnosis of MCI and AD in research settings.

The prevalence of Alzheimer’s disease (AD), a progressive neurodegenerative disorder, is expected to more than double by 2050. Studies on the pathophysiology of AD have been changing our understanding of this disorder and setting a new scenario for drug development and other therapies. Concepts like the “amyloid cascade” and the “continuum of AD,” discussed in this article, are now well established. From updated classifications and recommendations to advances in biomarkers of AD, we aim to critically assess the literature on AD, addressing new definitions and challenges that emerged from recent studies on the subject. Updates on the status of major clinical trials are also given, and future perspectives are discussed.