Current Alzheimer Research - Volume 17, Issue 5, 2020

Alzheimer’s Disease (AD) incidence is increasing and with no disease modifying agents available, preventative measures through lifestyle factors are being investigated. Combined with the prevention of AD risk factors such as heart disease, diabetes, and with more recent evidence, microbiome dysfunction, there is a substantial foundation for diet as a modifiable risk factor and preventative measure for AD. Recent evidence suggests AD associated pathologies, such as oxidative stress and inflammation, can be modulated by the lipids, vitamins, and polyphenols obtained through nutritional intake. Furthermore, epidemiological and preclinical evidence has uncovered certain compounds within foods that may have beneficial effects in the prevention of AD, including omega-3 fatty acids, vitamin E, and resveratrol among others. However, clinical data examining specific compounds are often inconsistent and fail to replicate the preclinical data. On the other hand, dietary patterns such as the Mediterranean or MIND diet have shown promise in terms of clinical outcomes for patients, indicating a reductionist approach to diet is not as effective as a holistic dietary pattern. In this review, we summarize some of the biological mechanisms of key compounds in their relation to AD and how they fit into a dietary pattern that supports the role of diet as a risk reducing factor for AD.

Background: Because Alzheimer’s Disease (AD) has very complicated pattern changes, it is difficult to evaluate it with a specific factor. Recently, novel machine learning methods have been applied to solve limitations. Objective: The objective of this study was to investigate the approach of classification and prediction methods using the Machine Learning (ML)-based Optimized Combination-Feature (OCF) set on Gray Matter Volume (GMV) and Quantitative Susceptibility Mapping (QSM) in the subjects of Cognitive Normal (CN) elderly, Amnestic Mild Cognitive Impairment (aMCI), and mild and moderate AD. Materials and Methods: 57 subjects were included: 19 CN, 19 aMCI, and 19 AD with GMV and QSM. Regions-of-Interest (ROIs) were defined at the well-known regions for rich iron contents and amyloid accumulation areas in the AD brain. To differentiate the three subject groups, the Support Vector Machine (SVM) with the three different kernels and with the OCF set was conducted with GMV and QSM values. To predict the aMCI stage, regression-based ML models were performed with the OCF set. The result of prediction was compared with the accuracy of clinical data. Results: In the group classification between CN and aMCI, the highest accuracy was shown using the combination of GMVs (hippocampus and entorhinal cortex) and QSMs (hippocampus and pulvinar) data using the 2nd SVM classifier (AUC = 0.94). In the group classification between aMCI and AD, the highest accuracy was shown using the combination of GMVs (amygdala, entorhinal cortex, and posterior cingulate cortex) and QSMs (hippocampus and pulvinar) data using the 2nd SVM classifier (AUC = 0.93). In the group classification between CN and AD, the highest accuracy was shown using the combination of GMVs (amygdala, entorhinal cortex, and posterior cingulate cortex) and QSMs (hippocampus and pulvinar) data using the 2nd SVM classifier (AUC = 0.99). To predict aMCI from CN, the exponential Gaussian process regression model with the OCF set using GMV and QSM data was shown the most similar result (RMSE = 0.371) to clinical data (RMSE = 0.319). Conclusion: The proposed OCF based ML approach with GMV and QSM was shown the effective performance of the subject group classification and prediction for aMCI stage. Therefore, it can be used as personalized analysis or diagnostic aid program for diagnosis.

Background: Presenilin 1 (PSEN1) was suggested as the most common causative gene of early onset Alzheimer’s Disease (AD). Methods: Patient who presented progressive memory decline in her 40s was enrolled in this study. A broad battery of neuropsychological tests and neuroimaging was applied to make the diagnosis. Genetic tests were performed in the patient to evaluate possible mutations using whole exome sequencing. The pathogenic nature of missense mutation and its 3D protein structure prediction were performed by in silico prediction programs. Results: A pathogenic mutation in PSEN1 (NM_000021.3: c.1027T>C p.Ala285Val), which was found in a Korean EOAD patient. Magnetic resonance imaging scan showed mild left temporal lobe atrophy. Hypometabolism appeared through 18F-fludeoxyglucose Positron Emission Tomography (FDG-PET) scanning in bilateral temporal and parietal lobe, and 18F-Florbetaben-PET (FBB-PET) showed increased amyloid deposition in bilateral frontal, parietal, temporal lobe and hence presumed preclinical AD. Protein modeling showed that the p.Ala285Val is located in the random coil region and could result in extra stress in this region, resulting in the replacement of an alanine residue with a valine. This prediction was confirmed previous in vitro studies that the p.Trp165Cys resulted in an elevated Aβ42/Aβ40 ratio in both COS-1 and HEK293 cell lines compared that of wild-type control. Conclusion: Together, the clinical characteristics and the effect of the mutation would facilitate our understanding of PSEN1 in AD pathogenesis for the disease diagnosis and treatment. Future in vivo study is needed to evaluate the role of PSEN1 p.Ala285Val mutation in AD progression.

Background: Amyloid Precursor Protein (APP)-Binding Protein 1 (APP-BP1) is a crucial regulator of many key signaling pathways and functions mainly as a scaffold protein to enhance molecular interactions and facilitate catalytic reactions. The interaction of APP-BP1 with Amyloid Precursor Protein (APP) plays a role in cell cycle transit control, which determines the mechanism behind the loss of cell cycle regulation in Alzheimer’s Disease (AD). In contrast, neddylation, a posttranslational modification mediated by conjugation of ubiquitin-like protein neural precursor cell expressed developmentally downregulated protein 8 (NEDD8), is activated by a heterodimer composed of APP-BP1 and NEDD8-activating enzyme E1 catalytic subunit (Uba3). NEDD8 controls vital biological events, and along with APP-BP1, its levels are deregulated in AD. Objective: The present study investigated the role of melatonin in regulating the APP-BP1 pathway under both physiological and pathological conditions to develop an understanding of the underlying mechanisms. Methods: Therefore, human SH-SY5Y neuroblastoma cells were treated with various concentrations of Aβ42 to induce neurotoxic conditions comparable to AD. Results: The results are the first to demonstrate that melatonin prevents Aβ42-induced enhancement of APP-BP1 protein expression and alteration in the cellular localization of NEDD8. Moreover, using MLN4924 (APP-BP1 pathway blocker), we also verified the components of the downstream effector cascade of the APP-BP1 pathway, including tau, APP-cleaving secretases, β-catenin and p53. Conclusion: These findings indicate that melatonin regulates the interplay of molecular signaling associated with the APP-BP1 pathway and might preclude the pathogenic mechanisms occurring during disease development, thus providing a propitious therapeutic strategy for preventing AD.

Background: Telephone-based cognitive assessments may be preferable to in-person testing in terms of test burden, economic and opportunity cost. Objective: We sought to determine the accuracy of telephone-based screening for the identification of dementia or Mild Cognitive Impairment (MCI). Methods: Five multidisciplinary databases were searched. Two researchers independently screened articles and extracted data. Eligible studies compared any multi-domain telephone-based assessment of cognition to the face-to-face diagnostic evaluation. Where data allowed, we pooled test accuracy metrics using the bivariate approach. Results: From 11,732 titles, 34 papers were included, describing 15 different tests. There was variation in test scoring and quality of included studies. Pooled analyses of accuracy for dementia: Telephone Interview for Cognitive Status (TICS) (<31/41) sensitivity: 0.92, specificity: 0.66 (6 studies); TICSmodified (<28/50) sensitivity: 0.91, specificity: 0.91 (3 studies). For MCI: TICS-modified (<33/50) sensitivity: 0.82, specificity: 0.87 (3 studies); Telephone-Montreal Cognitive Assessment (<18/22) sensitivity: 0.98, specificity: 0.69 (2 studies). Conclusion: There is limited diagnostic accuracy evidence for the many telephonic cognitive screens that exist. The TICS and TICS-m have the greatest supporting evidence; their test accuracy profiles make them suitable as initial cognitive screens where face to face assessment is not possible.

Background: Cognitive Training (CT) has demonstrated some benefits to cognitive and psychosocial function in Mild Cognitive Impairment (MCI) and early dementia, but the certainty related to those findings remains unclear. Therefore, understanding the mechanisms by which CT improves cognitive functioning may help to understand the relationships between CT and cognitive function. The purpose of this review was to identify the evidence for neuroimaging outcomes in studies of CT in MCI and early Alzheimer’s Disease (AD). Methods: Medline, Embase, Web of Science, PsycINFO, CINAHL, and The Cochrane Library were searched with a predefined search strategy, which yielded 1778 articles. Studies were suitable for inclusion where a CT program was used in patients with MCI or AD, with a structural or functional Magnetic Resonance Imaging (MRI) outcome. Studies were assessed for quality using the Downs and Black criteria. Results: A total of 19 studies met the inclusion criteria. Quality of the included studies was variable and there was significant heterogeneity for studies included in this review. Task activation was generally increased post-training, but functional connectivity was both increased and decreased after training. Results varied by diagnosis, type of CT program, and brain networks examined. No effects were seen on hippocampal volumes post-training, but cortical thickening and increased grey matter volumes were demonstrated. Conclusions: CT resulted in variable functional and structural changes in dementia, and conclusions are limited by heterogeneity and study quality. Larger, more robust studies are required to correlate these findings with clinical benefits from CT.

Introduction: Social Cognition (SC) is a complex construct that reflects a wide variety of implicit and explicit cognitive processes. Many neurocognitive domains are associated with SC and the Executive Function (EF) is the most representative one. We conducted a systematic review aiming at clarifying whether SC impairments are associated with dysfunction on EF in people with Alzheimer Disease (AD). Methods: The search, based on the Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA), was undertaken between January 2007 and December 2019 using Pubmed, SciELO, BIREME and Thomson Reuters Web of Science electronic databases. The keywords were SC, AD, EF, Neuropsychological functioning and Executive Disorder. Results: One hundred thirty-six articles were identified and fifteen were included. These studies are not in agreement about the extent of SC deficits in AD, mainly in the mild stage of the disease. EF deficits, specifically inhibition and the ability to manipulate verbal information, are associated with the impairment in SC in AD. SC decreases with the disease progression, a relationship explained by global cognition impairment and SC specific symptoms. Conclusion: SC impairment is associated with disease progression, mainly because of the decline in EF. Studies on SC components are unequal, contributing to a frequent generalization of Theory of Mind results, and often hampering the investigation of other components, mainly empathy. More precise knowledge about SC functioning in AD may contribute to a better understanding of the behavioral changes and interpersonal interactions.