Current Alzheimer research最新文献

Introduction: This study analyzed the current status, hotspots, and development trends of tau protein research in Alzheimer's disease (AD) and to provide a reference for future research in this field. CiteSpace software was used to scientifically measure and visualize the relevant articles in the field of tau protein in AD brain from the Web of Science Core Collection database from 1991 to 2022.

Methods: A total of 568 articles were included, with an exponential growth in the number of articles published from 1991 to 2022, with an average of 17.8 articles per year. The United States is the most productive country in this field, accounting for 46.83% of the total literature. The New York State Institute for Basic Research is the most productive organization, followed by MRC Laboratory Molecular Biology in the UK. The most influential are Kings College London, University of California, San Francisco, and others. Iqbal K is the most productive author.

Results: The most productive journal is the Journal of Biological Chemistry, and the journal with the highest impact factor is Acta Neuropathologica. The journal with the highest cumulative impact factor is Nature. The research hotspots mainly focus on the formation and degradation mechanisms of tau protein paired helical filaments and abnormal phosphorylation, AD neurofibrillary tangles and degenerative changes, and model research, mainly involving tau protein abnormal phosphorylation, phosphorylation sites, dephosphorylation, aggregate helical filaments, neurofibrillary tangles mouse models.

Conclusion: The research frontier trends mainly focus on the study of pathological changes in tau protein, intervention mechanisms, and the development and practice of clinical therapeutic drugs.

Background: Alzheimer's disease (AD) is a progressive neurodegenerative condition with rising prevalence due to the aging global population. Existing methods for diagnosing AD are struggling to detect the condition in its earliest and most treatable stages. One early indicator of AD is a substantial decrease in the brain's glucose metabolism. Metabolomics can detect metabolic disturbances in biofluids, which may be advantageous for early detection of some ADrelated changes. The study aims to predict brain hypometabolism in Alzheimer's disease using metabolomics findings and develop a predictive model based on metabolomic data.

Methods: The data used in this study were acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. We conducted a longitudinal cohort study with three assessment time points to investigate the predictive ability of baseline metabolomic data for modeling longitudinal fluorodeoxyglucose-positron emission tomography (FDG-PET) trajectory changes in AD patients. A total number of 44 participants with AD were included. The cognitive abilities of participants were evaluated using the Alzheimer's Disease Assessment Scale (ADAS) and the Mini-Mental State Examination (MMSE), while the overall severity of dementia was measured by the Clinical Dementia Rating-Sum of Boxes (CDR-SB). We employed the ADNI's FDG MetaROIs (Meta Regions of Interest) dataset to identify AD-associated hypometabolism in the brain. These MetaROIs were selected based on areas frequently mentioned in FDG-PET studies of AD and MCI subjects.

Results: Across models, we observed consistent positive relationships between specific cholesterol esters - CE (20:3) (p = 0.005) and CE (18:3) (p = 0.0039) - and FDG-PET metrics, indicating these baseline metabolites may be valuable indicators of future PET score changes. Selected triglycerides like DG-O (16:0-20:4) also showed time-specific positive associations (p = 0.017).

Conclusion: This research provides new insights into the disruptions in the metabolic network linked to AD pathology. These findings could pave the way for identifying novel biomarkers and potential treatment targets for AD.

Introduction: Muscarinic 1 acetylcholine receptor (M1AChR) is a member of the Gprotein- coupled receptor superfamily, with the dysfunction being linked to the onset of Alzheimer's Disease (AD).

Aims: Retromer complex with Vacuolar Protein Sorting-35 (VPS35) as the core plays an important role in the transport of biological proteins and has been confirmed to be closely related to the pathogenesis of AD. This study was designed to determine whether VPS35 could affect the trafficking mechanism of M1AChRs.

Method: The interaction between VPS35 and M1AChR was studied by co-immunoprecipitation method, and the recycling of M1AChR influence by VPS35 was analyzed using biotinylation technology.

Results: It was found that VPS35 affected the localization of M1AChR on the cell membrane by regulating intracellular M1AChR transport, thus controlling the M1AChR-mediated cholinergic signaling pathway.

Conclusion: The findings presented here provide a potential pathogenesis and pathway for the treatment of AD.

Extracellular vesicles (EVs) are nano-sized membranous particles that are secreted by various cell types and play a critical role in intercellular communication. Their unique properties and remarkable ability to deliver bioactive cargo to target cells have made them promising tools in the treatment of various diseases, including Alzheimer's disease (AD). AD is a devastating neurodegenerative disease characterized by progressive cognitive decline and neuropathological hallmarks, such as amyloid-beta plaques and neurofibrillary tangles. Despite extensive research, no disease-modifying therapy for AD is currently available. However, EVs have emerged as a potential therapeutic agent in AD due to their ability to cross the blood-brain barrier, deliver bioactive cargo, and modulate neuroinflammation. This review provides a comprehensive overview of the current knowledge on the role of EVs in AD and discusses their potential as a therapeutic approach. It covers the mechanisms of action, potential therapeutic targets, and challenges and limitations of EV-based therapies for AD.

Mitochondrial form and function are intricately linked through dynamic processes of fusion and fission, and disruptions in these processes are key drivers of neurodegenerative diseases, like Alzheimer's. The inability of mitochondria to transition between their dynamic forms is a critical factor in the development of pathological states. In this paper, we focus on the importance of different types of mitochondrial phenotypes in nervous tissue, discussing how mitochondria in Alzheimer's disease are "stuck" in certain patterns and how this pattern maintains itself. Understanding the specific roles and transitions between mitochondrial forms, including tiny, networked, and hyperfused, is crucial in developing new therapies aimed at restoring mitochondrial homeostasis. By targeting these dynamics, we may be able to intervene early in the disease process, offering novel avenues for preventing or treating neurodegeneration.

Background: Disease progression in Alzheimer's Dementia (AD) is typically characterized by accelerated cognitive and functional decline, where heterogeneous trajectories can impact the observed treatment response.

Methods: We hypothesized that unobserved heterogeneity could obscure treatment benefits in AD. The effect of unobserved heterogeneity was empirically quantified within the Alzheimer's Management By Albumin Replacement (AMBAR) phase 2b trial data. The ADAS-Cog 12 cognition endpoint was reanalyzed in a 2-class latent growth mixture model initially fit to the treatment arm. The model with the best fit was then applied across both treatment arms to a larger (n=1000) simulated dataset that was representative of AMBAR trial cognitive data.

Results: Two classes of patients were observed: a stable cognitive trajectory class and a highly variable class. Removal of the latter (n=48, 22%) from the analysis and refitting efficacy models comparing the stable class to full placebo yielded significant treatment efficacy on cognition (p=0.007, Cohen's D=-0.4). Comparison of the stable class of each arm within the simulated dataset revealed a significant difference in treatment efficacy favoring the simulated stable treatment arm.

Conclusion: This post hoc exploratory analysis suggests that prespecified strategies for addressing unobserved heterogeneity may yield improved effect detection in AD trials. The generalizability of the analytic strategy is limited by latent stratification in only the treatment arm, a requirement given the small placebo arm in AMBAR. This limitation was partially addressed by the simulation modeling.

Alzheimer's disease (AD) is a neurodegenerative condition characterized by gradual onset and complex pathological mechanisms. Clinically, it presents with progressive cognitive decline and behavioral impairments, making it one of the most common causes of dementia. The intricacies of its pathogenesis are not fully understood, and current treatment options are limited, with diagnosis typically occurring at intermediate to advanced stages. The development of new biomarkers offers a crucial avenue for the early diagnosis of AD and improving patient outcomes. Several biomarkers with high specificity have been identified. This article reviews biomarkers related to tau protein, β-amyloid, and blood cells to deepen our understanding of AD and emphasize the advantages and disadvantages of various biomarkers in order to explore further and mine new biomarkers for AD diagnosis.

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.

Method: 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.

Background: Alzheimer's disease (AD) is a prevalent neurodegenerative disorder affecting the central nervous system (CNS), with its etiology still shrouded in uncertainty. The interplay of extracellular amyloid-β (Aβ) deposition, intracellular neurofibrillary tangles (NFTs) composed of tau protein, cholinergic neuronal impairment, and other pathogenic factors is implicated in the progression of AD.

Objective: The current study endeavors to delineate the proteomic landscape alterations in the hippocampus of an AD murine model, utilizing proteomic analysis to identify key physiological and pathological shifts induced by the disease. This endeavor aims to shed light on the underlying pathogenic mechanisms, which could facilitate early diagnosis and pave the way for novel therapeutic interventions for AD.

Methods: To dissect the proteomic perturbations induced by Aβ and Presenilin-1 (PS1) in the AD pathogenesis, we undertook a label-free quantitative (LFQ) proteomic analysis focusing on the hippocampal proteome of the APP/PS1 transgenic mouse model. Employing a multi-faceted approach that included differential protein functional enrichment, cluster analysis, and protein-protein interaction (PPI) network analysis, we conducted a comprehensive comparative proteomic study between APP/PS1 transgenic mice and their wild-type C57BL/6 counterparts.

Results: Mass spectrometry identified a total of 4817 proteins in the samples, with 2762 proteins being quantifiable. Comparative analysis revealed 396 proteins with differential expression between the APP/PS1 and control groups. Notably, 35 proteins exhibited consistent temporal regulation trends in the hippocampus, with concomitant alterations in biological pathways and PPI networks.

Conclusions: This study presents a comparative proteomic profile of transgenic (APP/PS1) and wild-type mice, highlighting the proteomic divergences. Furthermore, it charts the trajectory of proteomic changes in the AD mouse model across the developmental stages from 2 to 12 months, providing insights into the physiological and pathological implications of the disease-associated genetic mutations.

Alzheimer's disease (AD) and vascular dementia (VD) are the leading causes of dementia, presenting a significant challenge in differential diagnosis. While their clinical presentations can overlap, their underlying pathologies are distinct. AD is characterized by the accumulation of amyloid plaques and neurofibrillary tangles, leading to progressive neurodegeneration. VD, on the other hand, arises from cerebrovascular insults that disrupt blood flow to the brain, causing neuronal injury and cognitive decline. Despite distinct etiologies, AD and VD share common risk factors such as hypertension, diabetes, and hyperlipidemia. Recent research suggests a potential role for oral microbiota in both diseases, warranting further investigation. The diagnostic dilemma lies in the significant overlap of symptoms including memory loss, executive dysfunction, and personality changes. The absence of definitive biomarkers and limitations of current neuroimaging techniques necessitate a multi-modal approach integrating clinical history, cognitive assessment, and neuroimaging findings. Promising avenues for improved diagnosis include the exploration of novel biomarkers like inflammatory markers, MMPs, and circulating microRNAs. Additionally, advanced neuroimaging techniques hold promise in differentiating AD and VD by revealing characteristic cerebrovascular disease patterns and brain atrophy specific to each condition. By elucidating the complexities underlying AD and VD, we can refine diagnostic accuracy and optimize treatment strategies for this ever-growing patient population. Future research efforts should focus on identifying disease-specific biomarkers and developing more effective neuroimaging methods to achieve a definitive diagnosis and guide the development of targeted therapies.