The use of cannabinoids as therapeutic drugs has increased among aging populations recently. Age-related changes in the endogenous cannabinoid system could influence the effects of therapies that target the cannabinoid system. At the preclinical level, cannabidiol (CBD) induces anti-amyloidogenic, antioxidative, anti-apoptotic, anti-inflammatory, and neuroprotective effects. These findings suggest a potential therapeutic role of cannabinoids to neurodegenerative disorders such as Parkinson’s disease (PD) and Alzheimer. Emerging evidence suggests that CBD and tetrahydrocannabinol have neuroprotective therapeutic-like effects on dementias. In clinical practice, cannabinoids are being used off-label to relieve symptoms of PD and AD. In fact, patients are using cannabis compounds for the treatment of tremor, non-motor symptoms, anxiety, and sleep assistance in PD, and managing responsive behaviors of dementia such as agitation. However, strong evidence from clinical trials is scarce for most indications. Some clinicians consider cannabinoids an alternative for older adults bearing Parkinson’s disease and Alzheimer’s dementia with a poor response to first-line treatments. In our concept and experience, cannabinoids should never be considered a first-line treatment but could be regarded as an adjuvant therapy in specific situations commonly seen in clinical practice. To mitigate the risk of adverse events, the traditional dogma of geriatric medicine, starting with a low dose and proceeding with a slow titration regime, should also be employed with cannabinoids. In this review, we aimed to address preclinical evidence of cannabinoids in neurodegenerative disorders such as PD and AD and discuss potential off-label use of cannabinoids in clinical practice of these disorders.

In this study, we obtained a lipidomic profile of plasma samples from drug-naïve patients with schizophrenia (SZ) and bipolar disorder (BD) in comparison to healthy controls. The sample cohort consisted of 30 BD and 30 SZ patients and 30 control individuals. An untargeted lipidomics strategy using liquid chromatography coupled with high-resolution mass spectrometry was employed to obtain the lipid profiles. Data were preprocessed, then univariate (t-test) and multivariate (principal component analysis and orthogonal partial least squares discriminant analysis) statistical tools were applied to select differential lipids, which were putatively identified. Afterward, multivariate receiver operating characteristic tests were performed, and metabolic pathway networks were constructed, considering the differential lipids. Our results demonstrate alterations in distinct lipid pathways, especially in glycerophospholipids, sphingolipids and glycerolipids, between SZ and BD patients. The results obtained in this study may serve as a basis for differential diagnosis, which is crucial for effective treatment and improving the quality of life of patients with psychotic disorders.

Key words: Biomarkers; Alzheimer’s disease; mild cognitive impairment; polyamines; plasma

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. 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 Aβ_1-42, 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. 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. Automated analyses of AD biomarkers provide valuable information to support the clinical diagnosis of MCI and AD in research settings.

The identification of Ultra-High Risk (UHR) individuals is thought to be useful for early intervention to improve psychosis outcomes. However, transition rates vary widely, and there is an effort t...