Current Alzheimer Research - Volume 18, Issue 12, 2021

Age and comorbidities are key indicators of hospital admission, serious illness, and mortality in COVID-19 patients. Patients with age-related comorbidities, such as cardiovascular disease, hypertension, diabetes, chronic kidney disease, NAFLD, obesity, and metabolic syndrome, are more likely to require hospitalization and suffer severe sickness of COVID-19. Patients with Alzheimer’s disease and risk factors associated with dementia may also be more vulnerable to serious COVID-19 infection. Peripheral inflammation, including in patients who recover from illness, may promote the course of neurodegenerative disorders through neuroinflammatory pathways. The aim of this study is to examine the impact of COVID-19 on immunity in patients with age-related diseases such as metabolic syndrome and Alzheimer’s disease and also to hypothesize the possible correlation between metabolic syndrome, Alzheimer’s disease, and COVID-19. Identifying the mechanisms that explain the complicated interaction between metabolic syndrome, Alzheimer’s disease, COVID-19, inflammation, and immunity could be crucial to designing effective pharmacological therapies and procedures. This study adds to our basic information about the new coronavirus by synthesizing current knowledge of these linkages. To reduce inflammation and enhance immunity, patients should acquire good lifestyle practices. Walking, breathing exercises, and a nutritious diet all help in improving lung capacity and immunity. Future research into novel therapeutics for patients with metabolic syndrome, Alzheimer’s disease, and COVID-19 inflammation and immunology is encouraged by this paper.

Astrocytes contribute to brain development and homeostasis and support diverse functions of neurons. These cells also respond to the pathological processes in Alzheimer’s disease (AD). There is still considerable debate concerning the overall contribution of astrocytes to AD pathogenesis since both the protective and harmful effects of these cells on neuronal survival have been documented. This review focuses exclusively on the neurotoxic potential of astrocytes while acknowledging that these cells can contribute to neurodegeneration through other mechanisms, for example, by lowered neurotrophic support. We identify reactive oxygen and nitrogen species, tumor necrosis factor α (TNF-α), glutamate, and matrix metalloproteinase (MMP)-9 as molecules that can be directly toxic to neurons and are released by reactive astrocytes. There is also considerable evidence suggesting their involvement in AD pathogenesis. We further discuss the signaling molecules that trigger the neurotoxic response of astrocytes with a focus on human cells. We also highlight microglia, the immune cells of the brain, as critical regulators of astrocyte neurotoxicity. Nuclear imaging and magnetic resonance spectroscopy (MRS) could be used to confirm the contribution of astrocyte neurotoxicity to AD progression. The molecular mechanisms discussed in this review could be targeted in the development of novel therapies for AD.

Background: β-Amyloid precursor protein-cleaving enzyme-1 (BACE1) initiates the production of Aβ-peptides that form Aβ-plaque in Alzheimer’s disease. Methods: Reportedly, acute insulin treatment in normal mice, and hyperinsulinemia in high-fat-fed (HFF) obese/diabetic mice, increase BACE1 activity and levels of Aβ-peptides and phospho- -thr-231-tau in the brain; moreover, these effects are blocked by PKC-λ/ι inhibitors. However, as chemical inhibitors may affect unsuspected targets, we presently used knockout methodology to further examine PKC-λ/ι requirements. We found that total-body heterozygous PKC-λ knockout reduced acute stimulatory effects of insulin and chronic effects of hyperinsulinemia in HFF/obese/diabetic mice, on brain PKC-λ activity and production of Aβ1-40/42 and phospho-thr-231-tau. This protection in HFF mice may reflect that hepatic PKC-λ haploinsufficiency prevents the development of glucose intolerance and hyperinsulinemia. Results: On the other hand, heterozygous knockout of PKC-λ markedly reduced brain levels of BACE1 protein and mRNA, and this may reflect diminished activation of nuclear factor kappa-B (NFΚB), which is activated by PKC-λ and increases BACE1 and proinflammatory cytokine transcription. Accordingly, whereas intravenous administration of aPKC inhibitor diminished aPKC activity and BACE1 levels by 50% in the brain and 90% in the liver, nasally-administered inhibitor reduced aPKC activity and BACE1 mRNA and protein levels by 50-70% in the brain while sparing the liver. Additionally, 24-hour insulin treatment in cultured human-derived neurons increased NFΚB activity and BACE1 levels, and these effects were blocked by various PKC-λ/ι inhibitors. Conclusion: PKC-λ/ι controls NFΚB activity and BACE1 expression; PKC-λ/ι inhibitors may be used nasally to target brain PKC-λ/ι or systemically to block both liver and brain PKC-λ/ι, to regulate NFΚB-dependent BACE1 and proinflammatory cytokine expression.

Background: Early differentiation between Alzheimer’s disease (AD) and Dementia with Lewy Bodies (DLB) is important for accurate prognosis, as DLB patients typically show faster disease progression. Cortical neural networks, necessary for human cognitive function, may be disrupted differently in DLB and AD patients, allowing diagnostic differentiation between AD and DLB. Objective: This proof-of-concept study assessed whether the application of machine learning techniques to data derived from resting-state electroencephalographic (rsEEG) rhythms (discriminant sensor power, 19 electrodes) and source connectivity (between five cortical regions of interest) allowed differentiation between DLB and AD. Methods: Clinical, demographic, and rsEEG datasets from DLB patients (N=30), AD patients (N=30), and control seniors (NOld, N=30), matched for age, sex, and education, were taken from our international database. Individual (delta, theta, alpha) and fixed (beta) rsEEG frequency bands were included. The rsEEG features for the classification task were computed at both sensor and source levels. The source level was based on eLORETA freeware toolboxes for estimating cortical source activity and linear lagged connectivity. Fluctuations of rsEEG recordings (band-pass waveform envelopes of each EEG rhythm) were also computed at both sensor and source levels. After blind feature reduction, rsEEG features served as input to support vector machine (SVM) classifiers. Discrimination of individuals from the three groups was measured with standard performance metrics (accuracy, sensitivity, and specificity). Results: The trained SVM two-class classifiers showed classification accuracies of 97.6% for NOld vs. AD, 99.7% for NOld vs. DLB, and 97.8% for AD vs. DLB. Three-class classifiers (AD vs. DLB vs. NOld) showed classification accuracy of 94.79%. Conclusion: These promising preliminary results should encourage future prospective and longitudinal cross-validation studies using higher resolution EEG techniques and harmonized clinical procedures to enable the clinical application of these machine learning techniques.

Background: Neuropsychiatric symptoms (NPSs) are common in dementia. Their evaluation is based on Neuropsychiatric Inventory (NPI). Neuroimaging studies have tried to elucidate the underlying neural circuits either in isolated NPSs or in specific forms of dementia. Objective: The objective of this study is to evaluate the correlation of NPS in the NPI with Brodmann areas (BAs) perfusion, for revealing BAs involved in the pathogenesis of NPSs in dementia of various etiologies. Methods: We studied 201 patients (82 with Alzheimer's disease, 75 with Frontotemporal dementia, 27 with Corticobasal Syndrome, 17 with Parkinson Disease/Lewy Body Dementia). Exploratory factor analysis was carried out to evaluate underlying groups of BAs, and Principal Component analysis was chosen as extraction method using Varimax rotation. Partial correlation coefficients were computed to explore the association of factors obtained from analysis and NPI items controlling for age, educational yeas, and ACE-R. Results: We found 6 BAs Factors(F); F1 (BAs 8,9,10,11,24,32,44,45,46,47, bilaterally), F2 (BAs 4,5,6,7,23,31, bilaterally), F3 (BAs 19,21,22,37,39,40, bilaterally), F4 (BAs 20,28,36,38, bilaterally), F5 (BAs 25, bilaterally) and F6 (BAs 17,18, bilaterally). Significant and negative correlation was found between NPI1 (delusions) and F3,F6, NPI2 (hallucinations) and F6, NPI7 (apathy) and F1,F4,F5, NPI3 (agitation) - NPI10 (aberrant motor behavior) - NPI12 (eating disorders) and F1. We did not find any significant correlation for NPI4,5,6,8,9,11 (depression, anxiety, euphoria, disinhibition, irritability, sleep disorders, respectively). Conclusion: Several NPSs share the same BAs among different types of dementia, while the manifestation of the rest may be attributed to different neural networks. These findings may have an impact on patients’ treatment.

Objective: This study aimed to investigate the association between dietary prebiotic intake and risk for Alzheimer’s disease (AD). Methods: This longitudinal study includes 1,837 elderly (≥65 years) participants of a multi-ethnic community-based cohort study who were dementia-free at baseline and had provided dietary information from food frequency questionnaires. Total daily intake of fructan, one of the best-known prebiotics, was calculated based on consumption frequency and fructan content per serving of 8 food items. The associations of daily fructan intake with AD risk were examined using a Cox proportional hazards model, adjusted for cohort recruitment wave, age, gender, race/ethnicity, education, daily caloric intake, and APOE genotype. Effect modification by race/ethnicity, APOE genotype, and gender was tested by including an interaction term into the Cox models, as well as by stratified analyses. Results: Among 1,837 participants (1,263 women [69%]; mean [SD] age = 76 [6.3] years), there were 391 incident AD cases during a mean follow-up of 7.5 years (13736 person-years). Each additional gram of fructan intake was associated with 24% lower risk for AD ((95% CI)=0.60-0.97; P =0.03). Additional adjusting for smoking, alcohol consumption, and comorbidity index did not change results materially. The associations were not modified by race/ethnicity, gender, and APOE genotype, although stratified analyses showed that fructan intake was significantly associated with reduced AD risk in Hispanics but not in non-Hispanic Blacks or Whites. Conclusion: Higher dietary fructan intake is associated with a reduced risk of clinical Alzheimer’s disease among older adults.