Current Alzheimer research最新文献

Introduction: Alzheimer's disease (AD) represents the most common neurodegenerative disorder, characterized by progressive cognitive decline and memory loss. Despite the recognition of mitochondrial dysfunction as a critical factor in the pathogenesis of AD, the specific molecular mechanisms remain largely undefined.

Methods: This study aimed to identify novel biomarkers and therapeutic strategies associated with mitochondrial dysfunction in AD by employing bioinformatics combined with machine learning methodologies. We performed Weighted Gene Co-expression Network Analysis (WGCNA) utilizing gene expression data from the NCBI Gene Expression Omnibus (GEO) database and isolated mitochondria-related genes through the MitoCarta3.0 database. By intersecting WGCNA-derived module genes with identified mitochondrial genes, we compiled a list of 60 mitochondrial dysfunction- related genes (MRGs) significantly enriched in pathways pertinent to mitochondrial function, such as the citrate cycle and oxidative phosphorylation.

Results: Employing machine learning techniques, including random forest and LASSO, along with the CytoHubba algorithm, we identified key genes with strong diagnostic potential, such as ACO2, CS, MRPS27, SDHA, SLC25A20, and SYNJ2BP, verified through ROC analysis. Furthermore, an interaction network involving miRNA-MRGs-transcription factors and a protein-drug interaction network revealed potential therapeutic compounds such as Congo red and kynurenic acid that target MRGs.

Conclusion: These findings delineate the intricate role of mitochondrial dysfunction in AD and highlight promising avenues for further exploration of biomarkers and therapeutic interventions in this devastating disease.

Alzheimer's disease (AD) is a degenerative neurological disease characterized by a loss of memory and cognitive ability. One of the main factors influencing the development of AD is the accumulation of amyloid β (Aβ) plaque in the brain. The sequential production of Aβ is mediated by two enzymes: gamma-secretase and β-secretase (BACE1). The goal of beta-secretase inhibitors is to prevent the initial cleavage of amyloid precursor protein (APP), which reduces the production of (Aβ) peptides by limiting the substrate available for gamma-secretase. Simultaneously, gamma-secretase modulators are engineered to specifically modify enzyme performance, reducing the synthesis of the harmful Aβ42 isoform while maintaining vital physiological processes. Targeting both secretases reduces amyloidogenic processing synergistically. Selective inhibitors, which have been recently developed, have also shown good clinical development. They can reduce Aβ levels effectively with minimal side effects. The therapeutic strategy also underlines the importance of early therapy intervention in the preclinical AD phase for an optimum effect. Although there are some problems in the optimization of drug delivery and the alleviation of side effects, targeting beta and gamma secretases remains a promising direction. However, all these strategies still need more research and clinical testing to improve existing treatments and develop new, efficient Alzheimer's disease therapies. This review seeks to examine the therapeutic promise of β- and γ-secretase inhibition in Alzheimer's disease and review recent progress, challenges, and new dual-inhibition approaches.

Alzheimer's disease (AD) is a complex neurodegenerative disorder and a growing global health challenge, driven by increasing life expectancy and an aging population. This review provides a comprehensive exploration of AD pathophysiology, integrating current hypotheses such as the amyloid cascade, tau protein pathology, cholinergic dysfunction, neuroinflammation, vascular contributions, and potential infection-related mechanisms. The multifactorial etiology of AD, encompassing genetic predispositions and environmental factors, underscores its intricate nature. This study delves into the diagnostic advancements, including the identification and utilization of biomarkers for early detection and disease monitoring. Therapeutic approaches are critically evaluated, highlighting anti-amyloid and anti-tau strategies, alongside emerging innovations in stem cell therapy and nanobiotechnology. A detailed examination of clinical trials offers insights into the achievements and setbacks of translating research into effective treatments. By synthesizing epidemiological trends, molecular mechanisms, and therapeutic developments, this review aims to advance our understanding of AD and foster collaborative efforts to develop transformative solutions. It emphasizes the urgency of addressing this multifaceted disease, presenting a nuanced perspective on its complexity while illuminating future directions for research and clinical practice.

Introduction: Alzheimer's disease is expressed as chronic neuroinflammation in the brain, which results in neuronal dysfunction, aberrant protein folding, and declining cognitive abilities. miR-146a-5p is a potent anti-inflammatory agent that can attenuate several inflammatory diseases and promote wound healing. Our research aimed to utilize network pharmacology to elucidate the therapeutic potential of miR-146a-5p in treating Alzheimer's disease using a biocomputational approach.

Method: Alzheimer's disease genes were extracted from DisGeNET, OMIM, and GeneCards databases. At the same time, miR-146a-5p candidate genes were sourced from four prediction databases: miRDB, miRWalk, miRNet, and TargetScan.

Results: The overlap between miR-146a-5p and Alzheimer's disease genes was established using STRING, with a score greater than 0.9, revealing a total of 157 nodes in the compound-target disease network.

Discussions: Pathway enrichment analysis further revealed key candidate genes associated with Alzheimer's, including those involved in neuronal death, leukocyte migration, and axon development. EGFR, IL6, NFKB1, TLR4, CXCL8, FN1, CXCR4, and BCL2 were pinpointed as the top 8 key candidate genes of miR-146a-5p. Between these key candidate genes, the miR-146a-5p Regulatory Network also demonstrated that miR-146a-5p downregulates EGFR and CXCR4. Furthermore, this research revealed the regulatory network of miR-146a-5p, which modulates the transcriptional activities of IL6, NFKB1, TLR4, CXCL8, FN1, and BCL2.

Conclusion: Therefore, the current network pharmacology study explored the principal mechanism behind the anti-inflammatory effects of miR-146a-5p in treating Alzheimer's disease, and potentially to be applied to other neurodegenerative diseases.

Alzheimer's disease (AD) is an age-related, progressive neurodegenerative disorder of cognition with clinical features and anatomical hallmarks of amyloid-β plaques and/or neurofibrillary tangles. New studies revealed that microglia, the native immune cells in the brain, are crucial in the development of AD. The present review aims at outlining various roles of microglia in AD especially targeting their role in neuroinflammation. These indicate that microglial dysfunction contributes to AD pathology by affecting both amyloid-β phagocytosis and tau hyperphosphorylation. Other investigative molecular perpetrators, including TREM2, also influence the microglial relevance to amyloid and tau, as well as the overall disease phase. The functional microglia can protect neurons, while the dysfunctional one has the capability of derailing neuronal potentials and aggravating neurodegeneration. We have also discussed therapeutic strategies that start with targeting microglia to reduce neuroinflammation and reinstate balance. However, certain problems, including the side effects of microglial modulation, cost constraint, and accessibility, are areas of concern. In this review, the author presents the current state of knowledge on the potential of microglia-targeted treatments, their risks, and benefits. Thus, this article emphasizes the importance of the expansion of research to decipher the exact manipulation of microglia in AD with the goal of applying these findings given therapeutic approaches.

Introduction: Nowadays, the large increase in environmental pollutants has led to the occurrence and development of an increasing number of diseases. Studies have shown that exposure to environmental pollutants, such as methyl-4-hydroxybenzoate (MEP) may lead to Alzheimer's disease (AD). Therefore, the purpose of this study was to elucidate the complex effects and potential molecular mechanisms of environmental pollutants MEP on AD.

Methods: Through exhaustive exploration of databases, such as ChEMBL, STITCH, SwissTarget- Prediction, and Gene Expression Omnibus DataSets (GEO DataSets), we have identified a comprehensive list of 46 potential targets closely related to MEP and AD. After rigorous screening using the STRING platform and Cytoscape software, we narrowed the list to nine candidate targets and ultimately identified six hub targets using three proven machine learning methods (LASSO, RF, and SVM): CREBBP, BCL6, CXCR4, GRIN1, GOT2, and ITGA5. The "clusterProfiler" R package was used to conduct GO and KEGG enrichment analysis. At the same time, we also constructed disease prediction models for core genes. At last, six hub targets were executed molecular docking.

Results: We derived 46 key target genes related to MEP and AD and conducted gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. MEP might play a role in AD by affecting the pathways of neuroactive ligand-receptor interaction. Nine genes were screened as pivotal targets, followed by machine learning methods to identify six hub targets. Molecular docking analysis showed a good binding ability between MEP and CREBBP, BCL6, CXCR4, GRIN1, GOT2 and ITGA5. In addition, changes in the immune microenvironment revealed a significant impact of immune status on AD.

Discussions: This study revealed that MEP may induce AD through multiple mechanisms, such as oxidative stress, neurotoxicity, and immune regulation, and identified six core targets (CREBBP, BCL6, etc.) and found that they are related to changes in the immune microenvironment, such as T cells and B cells, providing new molecular targets for AD intervention.

Conclusion: Overall, CREBBP, BCL6, CXCR4, GRIN1, GOT2, and ITGA5 have been identified as the crucial targets correlating with AD. Our findings provide a theoretical framework for understanding the complex molecular mechanisms underlying the effects of MEP on AD and provide insights for the development of prevention and treatment of AD caused by exposure to MEP.

Introduction: The complement receptor 1 (CR1) gene is identified as the one closely associated with Alzheimer's disease (AD). However, there has been no exploration of the imaging alterations associated with the CR1 gene in AD patients of the Han population. The purpose of this study is to investigate the association between the rs6656401 mutation and neuroimaging variations in Han AD patients.

Methods: We collected nuclear magnetic resonance images from 101 patients with AD and 98 healthy controls (HC). The subjects in this study, based on the different genotypes of rs6656401, were divided into three groups, with the number of AA, AG, and GG genotypes in the AD group being 1, 17, and 83, and 1, 8, and 89 in the HC group. Data were analyzed using the dominant model. Structural differences in the brain tissue between genotypes at the rs6656401 polymorphic locus were compared using voxel-based morphological analysis, cortical thickness, and graph-theoretic analysis to construct structural networks.

Results: Seven regions (namely, right precuneus, right caudal middle frontal cortical, right rostral middle frontal, right superior frontal, right bankssts, right superior parietal, and right paracentral) were significantly different across CR1 rs6656401 genotypes. The voxel-based morphometry analysis revealed that voxel cluster sizes in the left cerebellum, left superior temporal gyrus, right superior frontal gyrus orbital, right precuneus, and right superior parietal were significantly different in the AA, AG, and GG groups. The degree centrality (Dc) of the left inferior frontal gyrus was significantly greater in the GG group than in the AG group after false discovery rate correction in the structural network analysis.

Discussion: This study demonstrates that the rs6656401 AA genotype primarily induces structural alterations in the frontal, temporal, and parietal lobes of AD patients, with significant changes in the right middle frontal gyrus, precuneus, and superior parietal gyrus, along with Dc index alterations in the left inferior frontal gyrus affecting brain network function. Our findings confirm the association between the rs6656401 polymorphism and AD-related brain structural changes, providing the first evidence of these regional alterations in Han Chinese AD cohorts. Future studies will elucidate the locus's pathological mechanism to inform early diagnosis and targeted therapies.

Conclusion: Our study first indicated that CR1 rs6656401 genotypes significantly influenced the morphological and structural covariate networks in Han AD patients.

Introduction: The primary objective of this study was to examine changes in brain network architecture across multiple frequency bands using spectral analysis of both weighted and binarized functional connectivity networks. This cross-sectional observational study, conducted as a secondary analysis of a publicly available EEG dataset, analyzed spectral coherence measurements from 25 patients with Alzheimer's disease (AD) and 25 age- and sex-matched healthy controls (HC). Nevertheless, the modest sample size and cultural homogeneity of the dataset may limit the statistical power and generalizability of the results. A data-driven thresholding approach was employed to generate binary networks, allowing a robust comparison of connectivity disruptions associated with AD.

Method: Brain network features derived from the graph Laplacian, including weighted Fiedler value, spectral range, and Middle Eigenvalue, were analyzed across seven frequency layers: delta, theta, alpha1, alpha2, beta1, beta2, and gamma. For binary networks, the Fiedler value was calculated after thresholding. Statistical group comparisons between AD and HC were performed using t-tests (p < 0.05), and each feature was assessed based on the number of frequency bands showing significant differences.

Results: Among all features, the weighted Fiedler value was the most discriminative, showing significant reductions in AD patients within the alpha2 and beta1 bands. In binary networks, the Fiedler value remained significantly lower in AD within the alpha2 band, confirming topological degradation even without edge weight information. Other spectral features showed similar trends, but did not reach statistical significance in the binary networks.

Discussion: The consistent decline in Fiedler value across both weighted and binary networks indicates a global reduction in connectivity characteristic of AD. These spectral markers offer a quantitative and interpretable framework for understanding the progressive disconnection syndrome in AD.

Conclusion: This study demonstrates significant alterations in Laplacian spectral features of brain networks between the AD and HC groups across specific frequency bands. These exploratory findings indicate that the spectral features, particularly the Fiedler value, consistently differentiate AD patients from healthy controls across frequency bands, suggesting its potential as a biomarker. However, larger and longitudinal studies are needed to confirm its diagnostic and prognostic utility. The combined use of weighted and binarized connectivity matrices enhances analytical sensitivity and facilitates the application of spectral graph theory for the early detection and monitoring of AD.

Alzheimer's disease (AD) is a neurodegenerative disorder associated with age, marked by progressive memory loss linked to the decline of cholinergic neurons, accumulation of amyloid plaques, and the presence of Neurofibrillary Tangles (NFTs). Neuropil threads in the brain contribute to amyloidosis and dementia. Despite extensive research, AD's etiology remains unclear, and currently, no promising therapy exists. This review examines the role of natural, semi-synthetic, and synthetic drugs in AD treatment. Natural drugs demonstrate safety and efficacy with minimal adverse effects, while most agents, whether natural or synthetic, target multiple steps or directly counteract amyloidogenesis, tau protein pathology, oxidative stress, NMDA receptor activity, inflammation, acetylcholine (AChE) function, or α, β, γ secretase activity. In pursuit of improved treatment outcomes, we explore the effectiveness and challenges of various therapeutic interventions. Our hypothesis underscores the importance of an integrated approach combining these drug types for tailored symptom relief, suggesting combined therapies may offer greater therapeutic benefits compared to single-drug approaches. The drugs discussed show potential in regulating AD, thereby presenting viable options for its management. However, to obtain more favorable results, additional studies are needed by combining these drugs.