Current Alzheimer Research - Volume 19, Issue 1, 2022

Astrocytes are fast climbing the ladder of importance in neurodegenerative disorders, particularly in Alzheimer’s Disease (AD), with the prominent presence of reactive astrocytes surrounding amyloid-β plaques, together with activated microglia. Reactive astrogliosis, implying morphological and molecular transformations in astrocytes, seems to precede neurodegeneration, suggesting a role in the development of the disease. Single-cell transcriptomics has recently demonstrated that astrocytes from AD brains are different from “normal” healthy astrocytes, showing dysregulations in areas such as neurotransmitter recycling, including glutamate and GABA, and impaired homeostatic functions. However, recent data suggest that the ablation of astrocytes in mouse models of amyloidosis results in an increase in amyloid pathology, worsening of the inflammatory profile, and reduced synaptic density, indicating that astrocytes mediate neuroprotective effects. The idea that interventions targeting astrocytes may have great potential for AD has therefore emerged, supported by a range of drugs and stem cell transplantation studies that have successfully shown a therapeutic effect in mouse models of AD. In this article, we review the latest reports on the role and profile of astrocytes in AD brains and how manipulation of astrocytes in animal models has paved the way for the use of treatments enhancing astrocytic function as future therapeutic avenues for AD.

Alzheimer’s disease is a progressive and deadly neurodegenerative disorder and one of the most common causes of dementia globally. Current, insufficiently sensitive and specific methods of early diagnosing and monitoring this disease prompt a search for new tools. Numerous literature data have indicated that the pathogenesis of Alzheimer’s disease (AD) is not limited to the neuronal compartment but involves various immunological mechanisms. Neuroinflammation has been recognized as a very important process in AD pathology. It seems to play pleiotropic roles, both neuroprotective and neurodegenerative, in the development of cognitive impairment depending on the stage of the disease. Mounting evidence demonstrates that inflammatory proteins could be considered biomarkers of disease progression. Therefore, the present review summarizes the role of some inflammatory molecules and their potential utility in detecting and monitoring dementia severity. This paper also provides a valuable insight into new mechanisms leading to the development of dementia, which might be useful in discovering possible anti-inflammatory treatment.

Objective: Recently, neuron specific enolase (NSE), Visinin-like protein-1 (VLP-1), neurogranin (Ng), and YKL-40 have been identified as candidates for neuronal degeneration and glial activation biomarkers. Therefore, we perform a comprehensive meta-analysis to assess the diagnostic value of CSF NSE, VLP-1, Ng and YKL-40 in Alzheimer’s disease (AD). Methods: We searched Pubmed, MEDLINE, EMBASE databases for research about the levels of CSF NSE, VLP-1, Ng and YKL-40 in AD patients compared with controls or other dementia diseases until Dec 2020. Results: The present meta-analysis contained a total of 51 studies comprising 6248 patients with dementia disorders and 3861 controls. Among them, there were 3262 patients with AD, 2456 patients with mild cognitive impairment (MCI), 173 patients with vascular dementia (VaD), 221 patients with frontotemporal dementia (FTD), and 136 with Lewy bodies dementia (DLB). Our study demonstrated that CSF NSE, VLP-1, Ng and YKL-40 levels were increased in AD as compared to healthy controls. We also observed that the CSF NSE level was higher in AD than VaD, suggesting CSF NSE might act as a key role in distinguishing between AD and VaD. Interestingly, there was a higher VLP-1 expression in AD, and a lower expression in DLB patients. Moreover, we found the CSF Ng level was increased in AD than MCI, implying CSF Ng might be a biomarker for identifying the progression of AD. Additionally, a significantly higher CSF YKL-40 level was detected not only in AD, but also in FTD, DLB, VaD, signifying YKL-40 was not sensitive in the diagnosis of AD. Conclusion: Our study confirmed that CSF levels of NSE, VLP-1, and Ng could be valuable biomarkers for identifying patients who are more susceptible to AD and distinguishing AD from other neurodegenerative dementia disorders.

Background: Delirium and dementia are both disorders involving global cognitive impairment that can occur separately or at the same time in the elderly. Objective: The aim of this study was to examine the frequency, correlation, and relative risk between delirium and cognitive impairment in a prospective population study starting at the basal line (onset of delirium) over a period of five years. The secondary aim was to determine any possible correlation between the kind of delirium and a specific type of dementia. Materials and Methods: We studied 325 patients diagnosed according to the DSM-IV. The neuropsychological, moods and delirium disorders were evaluated with Hamilton Depression Rating Scale, Delirium Rating Scale-Revised-98, MMSE, Rey auditory-verbal learning test, Digit Span, Symbol Digit Modalities Test, Raven Progressive Matrices, ADL, and IADL. Results: The prevalence of delirium in our population was 89 cases (27.4%): 78 patients (48 women and 30 men) showed evolution toward dementia (mean age was 67.9 ± 6.1 years for men and 68.4 ± 9.1 for women), and 11 patients (5 men and 6 women) presented only isolated delirium without evolution toward cognitive impairment (mean age of men was 68.1 ± 5.1 years and of women 66.4 ± 7.1). The neuropsychological study of the patients with delirium with dementia evolution revealed statistically significant differences over time with a statistically significant intergroup difference and predisposition toward depression. Conclusion: The association between delirium and cognitive impairment and the possible role of delirium as an early marker of neurodegenerative diseases need to be investigated in the future.

Background: Vascular Cognitive Impairment, No Dementia (VCIND) is a key stage at which early intervention will delay or prevent dementia. The pathophysiology of VCIND posits that a lesion in a single location in the brain has the ability to disrupt brain networks, and the subsequent abnormal Functional Connectivity (FC) of brain networks leads to deficits in corresponding neurobehavioral domains. In this study, we tested the hypothesis that disrupted anterior cingulate cortex and striatal networks mediated the effects of Physical Activity (PA) on neurobehavioral function. Methods: In 27 patients with VCIND, FC within the brain networks and neurobehavioral dysfunction were assessed. The relationship between the cognitive scores, FC, and PA was studied. The Fitbit Charge 2 was used to measure step counts, distance, and calories burned. In patients with VCIND, a cross-sectional Spearman’s correlation to analyze the relationship among patient-level measures of PA, cognitive function scores, and FC strength within the brain networks. Results: Average step counts and average distance were associated with Trail Making Test B (TMB) time to completion (seconds) and Instrumental Activities of Daily Living (IADL) score (P < 0.05). The average calories burned were associated with IADL score (P = 0.009). The FC within the brain networks anchored by left caudal Anterior Cingulate Cortex (ACC) seeds (x= -5, y= 0, z= 36) and (x= -5, y= -10, z= 47) were positively correlated with average step counts and average distance, were negatively correlated with TMB time to completion (seconds), and were positively correlated with IADL score (P < 0.05). The FC within the brain networks anchored by left subgenual ACC seed (x= -5, y= 25, z= -10) were negatively correlated with average step counts and average distance were positively correlated with TMB time to completion (seconds), and were negatively correlated with IADL score (P < 0.05). The FC within the striatal networks was positively correlated with average calories burned and IADL score (P < 0.05). Conclusion: FC within the brain networks anchored by caudal ACC seeds was positively correlated with more average step counts/average distance and better IADL score; negatively correlated with longer TMB time to completion (seconds), whereas FC of subgenual ACC seed was negatively correlated with the same parameters. FC within the brain networks anchored by putamen rather than caudate or pallidum was positively correlated with average calories burned and IADL score.

Background: The effect that cytokines can exert on the progression from mild cognitive impairment (MCI) to ongoing dementia is a matter of debate and the results obtained so far are controversial. Objective: The aim of the study is to analyze the influence of markers of subclinical inflammation on the progression of MCI to dementia. Methods: A prospective study involving a cohort of patients ≥ 65 years of age diagnosed with MCI and followed for 3 years was conducted. 105 patients were enrolled, and serum concentrations of several subclinical inflammatory markers were determined. Results: After 3.09 (2 - 3.79) years of follow-up, 47 (44.76%) patients progressed to dementia. Alpha 1-antichymotrypsin (ACT) was found to be significantly higher in patients who progressed to dementia (486.45 ± 169.18 vs. 400.91 ± 163.03; p = 0.012), and observed to significantly increase the risk of developing dementia in patients with mild cognitive impairment (1.004, 1.001-1.007; p = 0.007). IL-10 levels were significantly higher in those who remained stable (6.69 ± 18.1 vs. 32.54 ± 89.6; p = 0.04). Regarding the type of dementia to which our patients progressed, we found that patients who developed mixed dementia had higher IL-4 levels than those who converted to AD (31.54 ± 63.6 vs. 4.43 ± 12.9; p = 0.03). No significant differences were observed between the groups with regard to the ESR and LPa, CRP, IL-1 and TNF-α levels. Conclusion: ACT levels have a significant predictive value in the conversion of MCI to dementia. IL-10 levels could be a protective factor. It is necessary to conduct studies with serial determinations of these and other inflammatory markers in order to determine their effect on the progression of MCI to dementia.

Background: Mild cognitive impairment (MCI) is considered the early stage of Alzheimer's Disease (AD). The purpose of our study was to analyze the basic characteristics and serum and imaging biomarkers for the diagnosis of MCI patients as a more objective and accurate approach. Methods: The Montreal Cognitive Test was used to test 119 patients aged ≥65. Such serum biomarkers were detected as preprandial blood glucose, triglyceride, total cholesterol, Aβ1-40, Aβ1-42, and P-tau. All the subjects were scanned with 1.5T MRI (GE Healthcare, WI, USA) to obtain DWI, DTI, and ASL images. DTI was used to calculate the anisotropy fraction (FA), DWI was used to calculate the apparent diffusion coefficient (ADC), and ASL was used to calculate the cerebral blood flow (CBF). All the images were then registered to the SPACE of the Montreal Neurological Institute (MNI). In 116 brain regions, the medians of FA, ADC, and CBF were extracted by automatic anatomical labeling. The basic characteristics included gender, education level, and previous disease history of hypertension, diabetes, and coronary heart disease. The data were randomly divided into training sets and test ones. The recursive random forest algorithm was applied to the diagnosis of MCI patients, and the recursive feature elimination (RFE) method was used to screen the significant basic features and serum and imaging biomarkers. The overall accuracy, sensitivity, and specificity were calculated, respectively, and so were the ROC curve and the area under the curve (AUC) of the test set. Results: When the variable of the MCI diagnostic model was an imaging biomarker, the training accuracy of the random forest was 100%, the correct rate of the test was 86.23%, the sensitivity was 78.26%, and the specificity was 100%. When combining the basic characteristics, the serum and imaging biomarkers as variables of the MCI diagnostic model, the training accuracy of the random forest was found to be 100%; the test accuracy was 97.23%, the sensitivity was 94.44%, and the specificity was 100%. RFE analysis showed that age, Aβ1-40, and cerebellum_4_6 were the most important basic feature, serum biomarker, imaging biomarker, respectively. Conclusion: Imaging biomarkers can effectively diagnose MCI. The diagnostic capacity of the basic trait biomarkers or serum biomarkers for MCI is limited, but their combination with imaging biomarkers can improve the diagnostic capacity, as indicated by the sensitivity of 94.44% and the specificity of 100% in our model. As a machine learning method, a random forest can help diagnose MCI effectively while screening important influencing factors.

Background: Alzheimer's disease is a progressive neurodegenerative disorder characterized by memory loss and cognitive impairment. The diagnosis of Alzheimer's disease according to symptomatic events is still a puzzling task. Developing a biomarker-based, low-cost, and high-throughput test, readily applicable in clinical laboratories, dramatically impacts the rapid and reliable detection of the disease. Objective: This study aimed to develop an accurate, sensitive, and reliable screening tool for diagnosing Alzheimer's disease, which can significantly reduce the cost and time of existing methods. Methods: We have employed a MALDI-TOF-MS-based methodology combined with a microaffinity chromatography enrichment approach using affinity capture resins to determine serum kappa (Κ) and lambda (λ) light chain levels in control and patients with AD. Results: We observed a statistically significant difference in the kappa light chain over lambda light chain (ΚLC/λLC) ratios between patients with AD and controls (mean difference -0,409; % 95 CI:- 0.547 to -0.269; p<0.001). Our method demonstrated higher sensitivity (100.00%) and specificity (71.43%) for discrimination between AD and controls. Conclusion: We have developed a high-throughput screening test with a novel sample enrichment method for determining ΚLC/λLC ratios associated with AD diagnosis. Following further validation, we believe our test has the potential for clinical laboratories.