Use of Antipsychotics in Patients with Behavioral and Psychological Symptoms of Dementia: Results of a Spanish Delphi Consensus.

BACKGROUND: Behavioral and psychological symptoms of dementia (BPSD) are difficult to manage and associated with poor outcome. OBJECTIVES: The aim of this study was to reach consensus on the use of antipsychotics in patients with BPSD in Spain. METHODS: A qualitative, multicenter, two-round Delphi study was carried out, with the participation of specialists involved in the care of dementia patients throughout Spain. They completed a 76-item questionnaire related to the identification of BPSD, treatment with antipsychotics, follow-up of patients, barriers for the use of atypical antipsychotics, and effects of antipsychotics on quality of life. RESULTS: A total of 162 specialists in neurology, psychiatry, and geriatrics (61% men) with a mean (SD) age of 45.9 (10) years participated in the study. Almost all participants (96.9%) strongly agreed that atypical antipsychotics are safer and better tolerated than typical antipsychotics. There was agreement on the importance to review the indication and dose of the antipsychotic drug at least every 3 months. There was consistent high rate of agreement on the beneficial impact of atypical antipsychotics on the quality of life of patients with dementia and their caregivers. A consensus was also reached on the need of detecting BPSD in patients with dementia as it decreases the quality of life of both patients and caregivers, and the need to routinely screen for dementia in elderly patients with no previous psychiatric history in the presence of suggestive symptoms of BPSD. Finally, the participants in the study agreed that administrative barriers for the prescription of atypical antipsychotics in Spain hinder the access to this drug group and favor the prescription of typical antipsychotics. CONCLUSIONS: The participants in the study agreed that atypical antipsychotics should be preferred to typical antipsychotics in the management of BPSD. Wide consensus was reached about the importance of early identification of BPSD in persons with cognitive impairment, the use and management of atypical antipsychotic drugs and their favorable impact on patients and caregiver's quality of life.

A blood-based, 7-metabolite signature for the early diagnosis of Alzheimer's disease.

Accurate blood-based biomarkers of Alzheimer's disease (AD) could constitute simple, inexpensive, and non-invasive tools for the early diagnosis and treatment of this devastating neurodegenerative disease. We sought to develop a robust AD biomarker panel by identifying alterations in plasma metabolites that persist throughout the continuum of AD pathophysiology. Using a multicenter, cross-sectional study design, we based our analysis on metabolites whose levels were altered both in AD patients and in patients with amnestic mild cognitive impairment (aMCI), the earliest identifiable stage of AD. UPLC coupled to mass spectrometry was used to independently compare the levels of 495 plasma metabolites in aMCI (n = 58) and AD (n = 100) patients with those of normal cognition controls (NC, n = 93). Metabolite alterations common to both aMCI and AD patients were used to generate a logistic regression model that accurately distinguished AD from NC patients. The final panel consisted of seven metabolites: three amino acids (glutamic acid, alanine, and aspartic acid), one non-esterified fatty acid (22:6n-3, DHA), one bile acid (deoxycholic acid), one phosphatidylethanolamine [PE(36:4)], and one sphingomyelin [SM(39:1)]. Detailed analysis ruled out the influence of potential confounding variables, including comorbidities and treatments, on each of the seven biomarkers. The final model accurately distinguished AD from NC patients (AUC, 0.918). Importantly, the model also distinguished aMCI from NC patients (AUC, 0.826), indicating its potential diagnostic utility in early disease stages. These findings describe a sensitive biomarker panel that may facilitate the specific detection of early-stage AD through the analysis of plasma samples.