Current Alzheimer research最新文献

Background: Alzheimer's disease (AD) is a neurodegenerative disorder characterized by extracellular β-amyloid (Aβ) plaques and cognitive impairments. D-Serine, produced by the enzyme serine racemase (SR) in the brain, functions as an endogenous co-agonist at the glycine-binding site of N-methyl-D-aspartate receptor (NMDAR), has been implicated in the pathophysiological progression of AD.

Objectives: Evidence regarding the understanding of the role and dynamic modulation of D-serine during AD progression remains controversial. This literature review aims to offer novel research directions for studying the functions and metabolisms of D-serine in AD brains.

Methods: We searched PubMed, using D-serine/SR and AD as keywords. Studies related to NMDAR dysfunction, neuronal excitotoxicity, D-serine dynamic changes and inflammatory response were included.

Results: This review primarily discusses: (i) Aβ oligomers' role in NMDAR dysregulation, and the subsequent synaptic dysfunction and neuronal damage in AD, (ii) D-serine's role in NMDAR-elicited excitotoxicity, and (iii) the involvement of D-serine and SR in AD-related inflammatory pathological progression.

Conclusion: We also presented supposed metabolism and dynamic changes of D-serine during AD progression and hypothesized that: (i) the possible modulation of D-serine levels or SR expression as an effective method of alleviating neurotoxicity during AD pathophysiological progression, and (ii) the dynamic changes of D-serine levels in AD brains possibly resulting from complex processes.

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.

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: Vascular lesions may be a common finding also in Alzheimer's dementia, but their role on cognitive status is uncertain.

Objective: The study aims to investigate their distribution in patients with Alzheimer's, vascular or mixed dementia and detect any distinctive neuroradiological profiles.

Methods: Seventy-six subjects received a diagnosis of Alzheimer's (AD=32), vascular (VD=26) and mixed (MD=18) dementia. Three independent raters assessed the brain images acquired with an optimized 3T MRI protocol (including (3D FLAIR, T1, SWI, and 2D coronal T2 sequences) using semiquantitative scales for vascular lesions (periventricular lesions (PVL), deep white matter lesions (DWML), deep grey matter lesions (DGML), enlarged perivascular spaces (PVS), and microbleeds (MB)) and brain atrophy (medial temporal atrophy (MTA), posterior atrophy (PA), global cortical atrophy- frontal (GCA-F) and Evans' index).

Results: Raters reached a good-to-excellent agreement for all scales (ICC ranging from 0.78-0.96). A greater number of PVL (p<0.001), DWML (p<0.001), DGML (p=0.010), and PVS (p=0.001) was observed in VD compared to AD, while MD showed a significant greater number of PVL (p=0.001), DWML (p=0.002), DGML (p=0.018), and deep and juxtacortical MB (p=0.006 and p<0.001, respectively). Comparing VD and MD, VD showed a higher number of PVS in basal ganglia and centrum semiovale (p=0.040), while MD showed more deep and juxtacortical MB (p=0.042 and p=0.022, respectively). No significant difference was observed in scores of cortical atrophy scales and Evans' index among the three groups.

Conclusion: The proposed MRI protocol represents a useful advancement in the diagnostic assessment of patients with cognitive impairment by more accurately detecting vascular lesions, mainly microbleeds, without a significant increase in time and resource expenditure. Our findings confirm that white and grey matter lesions predominate in vascular and mixed dementia, whereas deep and juxtacortical microbleeds predominate in mixed dementia, suggesting that cerebral amyloid angiopathy could be the main underlying pathology.

Alzheimer's disease (AD) is an age-related neurodegenerative disease seriously influencing the quality of life and is a global health problem. Many factors affect the onset and development of AD, but specific mechanisms underlying the disease are unclear. Most studies investigating AD have focused on neurons and the gray matter in the central nervous system (CNS) but have not led to effective treatments. Recently, an increasing number of studies have focused on white matter (WM). Magnetic resonance imaging and pathology studies have shown different degrees of WM abnormality during the progression of AD. Myelin sheaths, the main component of WM in the CNS, wrap and insulate axons to ensure conduction of the rapid action potential and axonal integrity. WM damage is characterized by progressive degeneration of axons, oligodendrocytes (OLs), and myelin in one or more areas of the CNS. The contributions of OLs to AD progression have, until recently, been largely overlooked. OLs are integral to myelin production, and the proliferation and differentiation of OLs, an early characteristic of AD, provide a promising target for preclinical diagnosis and treatment. However, despite some progress, the key mechanisms underlying the contributions of OLs to AD remain unclear. Given the heavy burden of medical treatment, a better understanding of the pathophysiological mechanisms underlying AD is vital. This review comprehensively summarizes the results on WM abnormalities in AD and explores the relationship between OL progenitor cells and the pathogenesis of AD. Finally, the underlying molecular mechanisms and potential future research directions are discussed.

Background: Neurodegenerative diseases, such as Alzheimer's disease patients (AD), Huntington's disease (HD) and Parkinson's disease (PD), are common causes of morbidity, mortality, and cognitive impairment in older adults.

Objective: We aimed to understand the transcriptome characteristics of the cortex of neurodegenerative diseases and to provide an insight into the target genes of differently expressed microRNAs in the occurrence and development of neurodegenerative diseases.

Methods: The Limma package of R software was used to analyze GSE33000, GSE157239, GSE64977 and GSE72962 datasets to identify the differentially expressed genes (DEGs) and microRNAs in the cortex of neurodegenerative diseases. Bioinformatics methods, such as GO enrichment analysis, KEGG enrichment analysis and gene interaction network analysis, were used to explore the biological functions of DEGs. Weighted gene co-expression network analysis (WGCNA) was used to cluster DEGs into modules. RNA22, miRDB, miRNet 2.0 and TargetScan7 databases were performed to predict the target genes of microRNAs.

Results: Among 310 Alzheimer's disease (AD) patients, 157 Huntington's disease (HD) patients and 157 non-demented control (Con) individuals, 214 co-DEGs were identified. Those co-DEGs were filtered into 2 different interaction network complexes, representing immune-related genes and synapserelated genes. The WGCNA results identified five modules: yellow, blue, green, turquoise, and brown. Most of the co-DEGs were clustered into the turquoise module and blue module, which respectively regulated synapse-related function and immune-related function. In addition, human microRNA-4433 (hsa-miR-4443), which targets 18 co-DEGs, was the only 1 co-up-regulated microRNA identified in the cortex of neurodegenerative diseases.

Conclusion: 214 DEGs and 5 modules regulate the immune-related and synapse-related function of the cortex in neurodegenerative diseases. Hsa-miR-4443 targets 18 co-DEGs and may be a potential molecular mechanism in neurodegenerative diseases' occurrence and development.

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 the most common neurodegenerative disease in the world, characterized by the progressive loss of neuronal structure and function, whose main histopathological landmark is the accumulation of β-amyloid in the brain.

Objective: It is well known that exercise is a neuroprotective factor and that muscles produce and release myokines that exert endocrine effects in inflammation and metabolic dysfunction. Thus, this work intends to establish the relationship between the benefits of exercise through the chronic training of HIIT on cognitive damage induced by the Alzheimer's model by the injection of β amyloid_1-42.

Methods: For this purpose, forty-eight male Wistar rats were divided into four groups: Sedentary Sham (SS), Trained Sham (ST), Sedentary Alzheimer's (AS), and Trained Alzheimer's (AT). Animals were submitted to stereotactic surgery and received a hippocampal injection of Aβ_1-42 or a saline solution. Seven days after surgery, twelve days of treadmill adaptation followed by five maximal running tests (MRT) and fifty-five days of HIIT, rats underwent the Morris water maze test. The animals were then euthanized, and their gastrocnemius muscle tissue was extracted to analyze the Fibronectin type III domain containing 5 (FNDC5), PPARG Coactivator 1 Alpha (PPARGC1A), and Integrin subunit beta 5 (ITGB5-R) expression by qRT-PCR in addition to cross-sectional areas.

Results: The HIIT prevents the cognitive deficit induced by the infusion of amyloid β_1-42 (p < 0.0001), causes adaptation of muscle fibers (p < 0.0001), modulates the gene expression of FNDC5 (p < 0.01), ITGB5 (p < 0.01) and PPARGC1A (p < 0.01), and induces an increase in peripheral protein expression of FNDC5 (p < 0.005).

Conclusion: Thus, we conclude that HIIT can prevent cognitive damage induced by the infusion of Aβ_1-42, constituting a non-pharmacological tool that modulates important genetic and protein pathways.

Background: Early-onset Semantic dementia (EOSD) and early-onset Alzheimer's disease (EOAD) are often difficult to clinically differentiate in the early stages of the diseases because of the overlaps of clinical symptoms such as language symptoms. We compared the degree of atrophy in medial temporal structures between the two types of dementia using the voxel-based specific regional analysis system for Alzheimer's disease (VSRAD).

Methods: The participants included 29 (age: 61.7±4.5 years) and 39 (age: 60.2±4.9 years) patients with EOSD and EOAD, respectively. The degree of atrophy in medial temporal structures was quantified using the VSRAD for magnetic resonance imaging data. Receiver operating characteristic (ROC) analysis was performed to distinguish patients with EOSD and EOAD using the mean Z score (Z-score) in bilateral medial temporal structures and the absolute value (laterality score) of the laterality of Z-score (| right-left |) for indicating the degree of asymmetrical atrophy in medial temporal structures.

Results: The EOSD group had significantly higher Z and laterality scores than the EOAD group (Zscores: mean ± standard deviation: 3.74±1.05 vs. 1.56±0.81, respectively; P<0.001; laterality score: mean ± standard deviation: 2.35±1.23 vs. 0.68±0.51, respectively; P<0.001). In ROC analysis, the sensitivity and specificity to differentiate EOSD from EOAD by a Z-score of 2.29 were 97% and 85%, respectively and by the laterality score of 1.05 were 93% and 85%, respectively.

Conclusion: EOSD leads to more severe and asymmetrical atrophy in medial temporal structures than EOAD. The VSRAD may be useful to distinguish between these dementias that have several clinically similar symptoms.

Background: Over a dozen studies have investigated the effect of the R219K variant in the ATP-binding cassette transporter A1 (ABCA1) gene on the risk of Alzheimer's disease (AD), but the results are conflicting.

Objective: We performed a systematic review and meta-analysis to comprehensively assess the association between the ABCA1 R219K variant and the risk of AD.

Methods: Studies included in the meta-analysis were obtained by searching PubMed, Web of Science and AlzGene. Review Manager 5.4 was used for meta-analysis. Both the odds ratio (OR) and its 95% confidence interval (CI) were used to evaluate the effect of ABCA1 R219K polymorphism on AD susceptibility. Heterogeneity between the included studies was assessed using I2 statistics and Cochran Qtest. Funnel plots were used to assess publication bias.

Results: A total of 14 eligible studies involving 10084 subjects were retrieved from PubMed, Web of Science and AlzGene. Meta-analysis results showed that R219K polymorphism was significantly associated with a decreased risk of AD in Chinese under a recessive model (OR = 0.67; 95% CI = 0.51- 0.88; P = 0.004).

Conclusion: The present meta-analysis indicated that the KK genotype of R219K polymorphism may act as a protective factor for AD in the Chinese population. Additional studies with larger sample sizes are needed to further confirm this association.