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Low tissue levels of the major marine ω3 fatty acids (FAs) DHA and EPA are found in Alzheimer's disease (AD). We investigated if healthy proxies to AD patients have higher levels of these ω3 FAs. We observed lower levels of EPA and DHA in subcutaneous adipose tissue biopsies from 64 AD patients compared with 16 cognitively healthy proxies. No significant difference was observed when pairwise comparisons were made between a subset of 16 AD patients and their co-habiting proxies. Larger studies are needed to replicate these findings and to determine if they could depend on FA intake or differences in metabolism.

Background: Treatment for neuropsychiatric symptoms (NPS) in dementia is insufficient. Antipsychotics and acetylcholinesterase inhibitors are used generating symptomatic improvements in behavior and cognition, but few studies have investigated their effect on Alzheimer's disease (AD) biomarkers in cerebrospinal fluid (CSF)., Objective: This is a secondary analysis based on an earlier clinical trial comparing the treatment effects on NPS. The aim of this study was to examine whether treatment with risperidone and galantamine affect levels of the biomarkers T-Tau, P-Tau, Aβ1-42, and Aβ42/40-ratio in CSF. The secondary aim was to test if baseline levels of these biomarkers are associated with the clinical course of NPS., Methods: 83 patients (mean + SD 77.9.6±7.7 years) with dementia and NPS were randomized to galantamine (n = 44) or risperidone (n = 39) treatment. CSF samples were collected at baseline and after 12 weeks., Results: Changes in levels of biomarkers between the two treatment groups did not differ significantly. Low baseline levels of Aβ1 - 42 was significantly associated with reduction of irritability at follow up. Low baseline levels of Aβ1-42, Aβ42/40, and P-Tau were significant correlates of reduction in appetite and eating disorders. CSF Aβ1-42 levels in patients treated with risperidone were significantly decreased at follow up, showing an 8% (40 pg/mL) reduction as compared with baseline (p = 0.03)., Conclusions: Our results suggest that risperidone may affect the CSF profile of AD biomarkers indicating more amyloid pathology. Treatment with galantamine did not affect the CSF biomarkers in any direction. The AD CSF biomarkers displayed correlations with specific NPS suggesting potential research questions to be pursued.

Background: ω3 fatty acids (ω3 FAs) may slow the rate of decline in cognitive performance in mild forms of cognitive impairment and Alzheimer's disease (AD). However, the relationship between changes of plasma ω3 FA levels and cognitive performance, as well as effects of gender, are poorly known., Objective: To study the effect of 6-month administration of DHA-rich ω3 FA supplementation on plasma FA profiles in patients with mild to moderate AD in relation to cognitive performance and gender. This investigation is part of the OmegAD Study., Methods: 174 AD patients (74 ± 9 years) were randomized to a daily intake of 2.3 g ω3 FA or placebo for 6 months; subsequently all received the ω3 FA preparation for the next 6 months. Baseline as well as changes in plasma levels of the main ω3 FAs in 165 patients, while receiving ω3 FA supplementation for 6 months, were analyzed for association to cognitive performance (assessed by ADAS-cog and MMSE scores) as well as to gender., Results: Preservation of cognitive functioning, assessed by ADAS-cog or its sub-items (but not MMSE) scores, was significantly associated to increasing plasma ω3 FA levels over time. Thus, the higher ω3 FA plasma levels rose, the lower was the rate of cognitive deterioration. This effect was not related to gender; since although females displayed higher ω3 FA plasma levels than did males after 6 months of supplementation, this difference disappeared when adjusted for body weight., Conclusions: Since our study suggests dose-response relationships between plasma levels of ω3 FA and preservation of cognition, future ω3 FA trials in patients with mild AD should consider exploring graded (and body weight adjusted) doses of ω3 FA.

Background: Due to age of onset, Alzheimer's disease (AD) is divided into early onset (EOAD) or late onset (LOAD), but emerging data also suggests that the underlying pathology may be different. Whether differences in clinical care exist is less well investigated., Objectives: To evaluate whether there are differences in demographics, diagnostic work-up, and pharmacological treatment between EOAD and LOAD., Material and Methods: Data on patients with newly diagnosed EOAD (n = 453) and LOAD (n = 4599) was obtained from the Swedish dementia registry (SveDem). Logistic regression models were used to adjust the comparisons for the baseline confounders including gender, cognitive decline, and co-morbidity., Results: The majority of EOAD and LOAD were in the mild stage of the disease when diagnosed. The majority of patients with EOAD went through an extended diagnostic work-up including more technical investigations as well as assessments by neuropsychologists and speech therapists than patients with LOAD. EOAD patients were treated with overall fewer medications but obtained treatment with cholinesterase inhibitors to a higher extent than those with LOAD, while there were no differences between the groups in antidepressant and antipsychotics use., Conclusions: There are differences between EOAD and LOAD in demographics, diagnostic work-up, and pharmacological treatment. Based on our findings, an extensive work-up should be recommended when EOAD is suspected.

Machine learning algorithms and multivariate data analysis methods have been widely utilized in the field of Alzheimer's disease (AD) research in recent years. Advances in medical imaging and medical image analysis have provided a means to generate and extract valuable neuroimaging information. Automatic classification techniques provide tools to analyze this information and observe inherent disease-related patterns in the data. In particular, these classifiers have been used to discriminate AD patients from healthy control subjects and to predict conversion from mild cognitive impairment to AD. In this paper, recent studies are reviewed that have used machine learning and multivariate analysis in the field of AD research. The main focus is on studies that used structural magnetic resonance imaging (MRI), but studies that included positron emission tomography and cerebrospinal fluid biomarkers in addition to MRI are also considered. A wide variety of materials and methods has been employed in different studies, resulting in a range of different outcomes. Influential factors such as classifiers, feature extraction algorithms, feature selection methods, validation approaches, and cohort properties are reviewed, as well as key MRI-based and multi-modal based studies. Current and future trends are discussed.