Current Alzheimer research最新文献

Background: Due to the heterogeneity of Alzheimer's disease (AD), the underlying pathogenic mechanisms have not been fully elucidated. Oligodendrocyte (OL) damage and myelin degeneration are prevalent features of AD pathology. When oligodendrocytes are subjected to amyloid-beta (Aβ) toxicity, this damage compromises the structural integrity of myelin and results in a reduction of myelin-associated proteins. Consequently, the impairment of myelin integrity leads to a slowdown or cessation of nerve signal transmission, ultimately contributing to cognitive dysfunction and the progression of AD. Consequently, elucidating the relationship between oligodendrocytes and AD from the perspective of oligodendrocytes is instrumental in advancing our understanding of the pathogenesis of AD.

Objective: Here, an attempt is made in this study to identify oligodendrocyte-related biomarkers of AD.

Methods: AD datasets were obtained from the Gene Expression Omnibus database and used for consensus clustering to identify subclasses. Hub genes were identified through differentially expressed genes (DEGs) analysis and oligodendrocyte gene set enrichment. Immune infiltration analysis was conducted using the CIBERSORT method. Signature genes were identified using machine learning algorithms and logistic regression. A diagnostic nomogram for predicting AD was developed and validated using external datasets and an AD model. A small molecular compound was identified using the eXtreme Sum algorithm.

Results: 46 genes were found to be significantly correlated with AD progression by examining the overlap between DEGs and oligodendrocyte genes. Two subclasses of AD, Cluster A, and Cluster B, were identified, and 9 signature genes were identified using a machine learning algorithm to construct a nomogram. Enrichment analysis showed that 9 genes are involved in apoptosis and neuronal development. Immune infiltration analysis found differences in immune cell presence between AD patients and controls. External datasets and RT-qPCR verification showed variation in signature genes between AD patients and controls. Five small molecular compounds were predicted.

Conclusion: It was found that 9 oligodendrocyte genes can be used to create a diagnostic tool for AD, which could help in developing new treatments.

Introduction: Alzheimer's disease (AD) is a complex neurological disorder that progressively worsens. Although its exact causes are not fully understood, new research indicates that genes related to non-neuronal cells change significantly with age, playing key roles in AD's pathology.

Method: This study focuses on a protein network centered on Glial Fibrillary Acidic Protein (GFAP) and Protein Tyrosine Phosphatase Receptor Type C (PTPRC). The Key Findings of this Study Include: 1. A significant correlation was observed between GFAP and PTPRC expression throughout AD progression, which links closely with clinical phenotypes and suggests their role in AD pathology. 2. A molecular network centered on GFAP and PTPRC, including Catenin Beta 1 (CTNNB1) and Integrin Beta 2 (ITGB2), showed distinct changes in interactions, highlighting its regulatory role in AD. 3. Analysis of GSE5281 data revealed a decline in the interaction strength within this network, pointing to potential desynchronization as a biomarker for AD. 4. SVM diagnostic models comparing GFAP expression and coupling values confirmed this desynchronization, suggesting it worsens with AD progression.

Result: Based on these findings, it is hypothesized that as AD progresses, the GFAP- and PTPRCcentered molecular framework undergoes significant changes affecting key biological pathways. These changes disrupt immune regulation and cellular functions, increasing immune cell activation and inflammation in the brain. This may impair neuronal communication and synaptic functionality, exacerbating AD's pathology.

Conclusion: To verify these findings, Support Vector Machine (SVM) diagnostic models and correlation analyses were used to examine changes in this network, indicating that its dysregulation significantly affects AD progression.

Introduction: Alzheimer's Disease (AD) is the leading cause of dementia and a significant public health concern, characterized by high incidence, mortality, and economic burden. This study analyzes the mortality patterns and demographic disparities in Alzheimer's disease-related deaths among the elderly population in the United States from 1999 through 2020.

Methods: Alzheimer's disease mortality data for individuals 65 and older were obtained from the CDC WONDER database, utilizing ICD-10 codes G30.0, G30.1, G30.8, and G30.9 for identification. Demographic and regional variables included age, gender, race/ethnicity, place of death, urban- rural status, and geographic region. Crude death rates (CR) and age-adjusted mortality rates (AAMR) per 100,000 individuals were calculated. Joinpoint Regression Program 5.0.2 was used to analyze trends, calculating Annual Percentage Changes (APCs) and Average Annual Percentage Changes (AAPCs).

Results: From 1999 to 2020, 1,852,432 deaths were attributed to AD among individuals aged 65 and older. The AAMR increased from 128.8 in 1999 to 254.3 in 2020, with an AAPC of 2.99% (95% CI = 2.61-3.48). The age-adjusted mortality rate (AAMR) was higher in females (218.5) than in males (163.5). Among racial and ethnic groups, non-Hispanic whites had the highest AAMR, followed by Non-Hispanic Blacks and Hispanics. Regionally, the West reported the highest AAMR, while the Northeast recorded the lowest. Most deaths occurred in nursing homes (57.3%), with a significant portion also occurring at decedents' homes (22.4%).

Conclusion: AD mortality rates in the U.S. have risen significantly, with notable disparities across age, gender, race, and geographic regions. These findings highlight the need for targeted interventions and research to address the growing burden of AD, particularly among the most affected demographic groups.

Introduction: Distinguishing between frontotemporal dementia (FTD) and Alzheimer's disease (AD) in their early stages remains a significant clinical challenge. Cerebrospinal fluid (CSF) biomarkers (total Tau, phosphorylated Tau, and beta-amyloid) are promising candidates for identifying early differences between these conditions.

Method: This study investigates the relationship between grey matter density and CSF markers in the behavioural variant of frontotemporal dementia (bvFTD) and Alzheimer's disease (AD). CSF and 3D T1-weighted magnetic resonance (MR) images were acquired from 14 bvFTD patients, 15 AD patients, and 13 cognitively normal (CN) matched subjects. The CSF markers and their relative ratios (total Tau/beta-amyloid, phosphorylated Tau/beta-amyloid) were compared across the three groups. Voxel-based morphometry (VBM) was performed to characterize the anatomical changes in bvFTD and AD patients compared to CN subjects. Grey matter density maps were obtained by automatic segmentation of 3.0 Tesla 3D T1-Weighted MR Images, and their correlation with CSF markers and relative ratios was investigated. Results demonstrated that, as compared to CN subjects, AD patients are characterised by higher CSF total Tau levels and lower beta-amyloid levels; however, beta-amyloid and relative ratios discriminated AD from bvFTD. In addition, AD and bvFTD patients showed different patterns of atrophy, with AD exhibiting more central (temporal areas) and bvFTD more anterior (frontal areas) atrophy.

Results: A correlation was found between grey matter density maps and CSF marker concentrations in the AD group, with total Tau and phosphorylated Tau levels showing a high association with low grey matter density in the left superior temporal gyrus.

Conclusion: The study concludes that while bvFTD lacks a CSF marker profile, CSF beta-amyloid levels are useful for differentiating AD from bvFTD. Furthermore, MR structural imaging can contribute significantly to distinguishing between the two pathologies.

Background: In an ageing population, dementia has become an imminent healthcare emergency. Capgras syndrome, the most common delusion of misidentification (DMS), is frequently found alongside dementia. Previous research showed that Capgras syndrome has significant negative effects on people living with dementia and their carers due to its complex presentation and impact on their lives. This qualitative systematic review explores the evidence base of the effective management and treatment of Capgras syndrome in dementia.

Aims: As per our knowledge, this is the first systematic review exploring the symptomatology of Capgras syndrome across different types of dementia. Additionally, it aims to identify the treatments used and their efficacy.

Methods: Four databases (EMBASE, MEDLINE, PsycINFO, and CINHAL) were screened in March, 2023. Twenty-six studies met the inclusion criteria and were included in the review. Thematic analysis was performed to explore and synthesise the qualitative findings of the studies.

Results: Three conceptual themes were identified: diagnostic tools, Capgras syndrome symptomatology, and Capgras syndrome treatment. Results showed that Capgras syndrome in dementia is not diagnosed and treated in a standardised manner. Following the pharmacological intervention, 28% of cases showed resolution of symptoms, and another 28% experienced improvement. However, 7% of cases reported worsening symptoms, and 10.7% experienced no change. While some patients had positive outcomes with specific medications, others either did not respond or experienced a deterioration of their condition.

Conclusion: The results highlight that there is no single treatment approach for Capgras syndrome in people living with dementia. This underscores the need for person-centred care, where treatment is tailored to individual needs. The review also reveals a heavy reliance on antipsychotic medications and a noticeable lack of psychosocial interventions. Given the limited benefits and significant risks associated with antipsychotics, future research should prioritise developing and testing psychosocial approaches. Additionally, establishing standardised diagnostic criteria and consistent outcome measures for Capgras syndrome in dementia is crucial for evaluating treatment effectiveness and improving care.

Background: Amnestic Mild Cognitive Impairment (aMCI) is a prodromal phase of Alzheimer's disease. Although recent studies have focused on cortical thickness as a key indicator, cortical complexity has not been exhaustively investigated.

Objectives: To investigate the altered patterns of cortical features in aMCI patients and their correlation with memory function for early identification.

Methods: 25 aMCI patients and 54 normal controls underwent neuropsychological assessments and 3D-T1 MRI scans. Cortical thickness and complexity measures were calculated using CAT12 software. Differences between groups were analyzed using two-sample t-tests, and multiple linear regression was employed to identify features associated with memory function. A support vector machine (SVM) model was constructed using multidimensional structural indicators to evaluate diagnostic performance.

Results: aMCI patients exhibited extensive reductions in cortical thickness (pFDR-corrected <0.05), with complexity reduction predominantly in the left parahippocampal, entorhinal, rostral anterior cingulate, fusiform, and orbitofrontal (pFWE-corrected<0.05). Cortical indicators exhibited robust correlations with auditory verbal learning test (AVLT) scores. Specifically, the fractal dimension of the left medial orbitofrontal region was independently and positively associated with AVLT-short delayed score (r=0.348, p=0.002), while the gyrification index of the left rostral anterior cingulate region showed independent positive correlations with AVLT-long delayed and recognition scores (r=0.408, p=0.000; r=0.332, p=0.003). Finally, the SVM model integrating these cortical features achieved an AUC of 0.91, with 82.28% accuracy, 76% sensitivity, and 85.19% specificity.

Conclusion: Cortical morphological indicators provide important neuroimaging evidence for the early diagnosis of aMCI. Integrating multiple structural indicators significantly improves diagnostic accuracy.

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.

Introduction: When Ant Colony Optimization algorithm (ACO) is adept at identifying the shortest path, the temporary solution is uncertain during the iterative process. All temporary solutions form a solution set.

Method: Where each solution is random. That is, the solution set has entropy. When the solution tends to be stable, the entropy also converges to a fixed value. Therefore, it was proposed in this paper that apply entropy as a convergence criterion of ACO. The advantage of the proposed criterion is that it approximates the optimal convergence time of the algorithm.

Results: In order to prove the superiority of the entropy convergence criterion, it was used to cluster gene chip data, which were sampled from patients of Alzheimer's Disease (AD). The clustering algorithm is compared with six typical clustering algorithms. The comparison shows that the ACO using entropy as a convergence criterion is of good quality.

Conclusion: At the same time, applying the presented algorithm, we analyzed the clustering characteristics of genes related to energy metabolism and found that as AD occurs, the entropy of the energy metabolism system decreases; that is, the system disorder decreases significantly.

Alzheimer's disease (AD) is a devastating neurological disorder that affects synaptic transmission between neurons. Several theories and concepts have been postulated to explain its etiology and pathogenesis. The disease has no cure, and the drugs available to manage AD symptoms provide only modest benefits. It originates in the brain's entorhinal cortex (EC), with tau pathology that can proceed overt symptoms by decades and then spreads to other connected areas and networks to cause severe cognitive decline. Despite decades of research, the reason why the EC is the first region to be affected during AD pathophysiology remains unknown. The EC is well connected with surrounding areas to support the brain's structural and functional integrity, participating in navigation, working memory, memory consolidation, olfaction, and olfactory-auditory coordination. These actions require massive energy expenditure; thus, the EC is extremely vulnerable to severe hypometabolism and an energy crisis. Unfortunately, the crucial events/factors that make the EC vulnerable to pathological sequelae more than other brain regions have not been thoroughly explored. An in-depth analysis of available research on the role of the EC in AD could provide meaningful insights into the susceptibility of this region and its role in propagating AD. In this review article, we highlight how the functional complexities of the EC account for its vulnerability in AD.