Current Alzheimer research最新文献

Introduction: Alzheimer's Disease (AD) is a common neurodegenerative disorder (NDD) driven by multifaceted pathologies, including β-amyloid (Aβ) aggregation, tau protein hyperphosphorylation, oxidative stress, metal ion dyshomeostasis, and neuroinflammation. Current therapeutic strategies remain limited by insufficient Blood-Brain Barrier (BBB) penetration, singletarget approaches, and inefficacy against nanoscale pathological aggregates. This review highlights the emerging potential of low-dimensional nanomaterials (LDNMs) as multi-target therapeutic platforms for AD.

Method: We systematically evaluate zero-dimensional (0D), one-dimensional (1D), and twodimensional (2D) nanostructures and establish a "nano-nano" interaction paradigm that demonstrates how LDNMs interact with AD core pathological factors. Supporting tables summarize experimental data quantifying the effects of LDNMs on Aβ and tau pathologies, oxidative stress, metal ion homeostasis, neuroinflammation, and the delivery of BBB-penetrant drugs.

Results: LDNMs exhibit significant potential in mitigating core AD pathologies. They effectively inhibit Aβ aggregation and tau hyperphosphorylation, attenuate oxidative damage, restore metal ion homeostasis, reduce neuroinflammatory activity, and enable targeted drug delivery to the brain.

Discussion: The multi-target functionality of LDNMs overcomes major limitations of single-target therapies. Their nanoscale dimensions and modifiable surfaces enable synergistic interactions with pathological factors, offering a holistic intervention strategy. Limitations and translational challenges are discussed for future research directions for clinical application.

Conclusion: This review links the structure and drug loading of LDNMs to multi-targeted efficacy against core AD pathology. It establishes a mechanistic connection between nanomaterial size and multi-pathway efficacy that transcends the limitations of single-target strategies. Moreover, it also provides a comprehensive framework for designing LDNMs-based nanotherapeutics, highlighting their potential as multi-target platforms for AD therapy.

Alzheimer's disease (AD), a progressive neurodegenerative disorder, is inadequately comprehended, with hypotheses implicating amyloid-β, tau pathology, mitochondrial dysfunction, and epigenetic factors. Recent research underscores the significance of lipoproteins and the gut microbiota in the etiology of AD. Apolipoprotein E (ApoE), particularly the E4 subtype, emerges as a key genetic risk factor, influencing oxidative stress, synaptic defects, glucose metabolism, and amyloid-β clearance. Lipoprotein receptors, such as LRP-1, also influence the integrity of the blood-brain barrier, indicating potential for therapeutic applications. Novel therapies targeting lipoproteins, such as ALZ-801 and IDOL inhibitors, show promise in preclinical and clinical trials. Concurrently, the gut microbiome's impact on AD is increasingly recognized. Dysbiosis correlates with inflammation, mitochondrial oxidative stress, impaired autophagy, and neurotransmitter imbalances. Gut-derived metabolites, including phenylalanine and isoleucine, promote Th1 cell activation and microglial dysfunction, exacerbating AD pathology. Interventions, like probiotics, GV-971, and polyphenols, demonstrate efficacy in restoring microbial balance and mitigating cognitive decline. Crucially, bidirectional interactions between lipoproteins and the gut microbiome are implicated in AD. ApoE genotypes influence gut microbial composition, while microbiota- derived short-chain fatty acids and endotoxins modulate lipid metabolism and neuroinflammation. These interactions, mediated via the gut-brain axis, highlight novel therapeutic avenues. Current FDA-approved AD drugs face limitations in efficacy and side effects, underscoring the need for innovative strategies targeting lipoprotein-gut microbiome crosstalk. Integrating insights into lipoprotein biology and gut microbiota dynamics may offer transformative potential for AD treatment, emphasizing combinatorial approaches to modulate these interconnected pathways. Further research is warranted to elucidate mechanistic links and translate preclinical findings into clinical applications.

Introduction/objective: Alzheimer's Disease (AD) presents a significant public health challenge in the U.S., with Latina/o/x elders being disproportionately affected. This study examines the key risk factors associated with AD in this population.

Methods: We analyzed data from the National Alzheimer's Coordinating Center (2017), focusing on 9,801 Latina/o/x older adults (32.7% males and 67.3% females). Statistical analyses conducted included Chi-square tests, t-tests, and Classification and Regression Tree (CART) analysis, which was used as the main statistical tool.

Results: The CART model, trained on 70% of the sample and tested on the remaining 30% (N = 9,801), identified seven terminal nodes and selected seven key predictors from 16 candidate variables. The model demonstrated modest discriminative ability (AUC = 0.68 for both training and test sets; misclassification error ≈ 36%). Sensitivity was 75%, while specificity was 55% in the test set. The most important predictors included age, education, smoking history, BMI, hypertension, and use of antidepressant or antipsychotic medications. A critical threshold emerged at < 5.5 years of education, which, in interaction with age and smoking, was associated with notably increased AD risk.

Conclusion: This study emphasizes the crucial role of sociodemographic factors-particularly gender, age, and education-in determining AD risk among Latina/o/x elders. CART analysis identified key thresholds for age and education levels impacting AD risk. The findings suggest the need for targeted interventions and policies, with a focus on education and lifestyle factors, to mitigate AD risk in this vulnerable population.

Alzheimer's disease (AD) is a formidable and complex neurodegenerative disorder driven by multifactorial interactions, including amyloid-beta (Aβ) aggregation, neurofibrillary tangles, and neuroinflammation etc. Current therapies mainly consist of cholinesterase inhibitors and NMDA receptor antagonists, which can alleviate symptoms but fail to reverse disease progression. In recent years, emerging approaches such as immunotherapy and gene therapy have shown potential but remain in clinical exploration. Phthalocyanine (Pc) compounds, with their ability to inhibit Aβ fibril formation, favorable biocompatibility, and optical properties, have demonstrated potential in AD diagnosis and treatment. This review discusses the pathogenesis, therapeutic strategies, and research progress of Pc compounds in AD. Furthermore, the elucidation of their mechanisms of action, the optimization of blood-brain barrier penetration, and the promotion of clinical translation are needed to provide new directions for AD therapy.

Introduction: Vascular Cognitive Impairment (VCI) is a common type of dementia that affects the quality of life and lacks effective treatments. The Dengzhan Shengmian capsule (DZSM), a traditional Chinese medicine, is clinically used to alleviate VCI symptoms, but its therapeutic mechanisms are not fully understood. This study aimed to evaluate the neuroprotective effects of DZSM in VCI patients by investigating its impact on cognitive function and brain structure, thereby providing neuroimaging evidence for its clinical application.

Methods: A randomized, double-masked, 6-month trial was conducted with 100 VCI patients, assigned to either the experimental group receiving DZSM (n = 50) or the placebo group (n = 50). The efficacy of DZSM in VCI patients was assessed through cognitive behavioral assessments and neuroimaging data collected at baseline and after 6 months. A comparison was made across groups to determine cognitive and neural changes associated with the intervention.

Results: Participants receiving DZSM exhibited significant improvements across multiple cognitive domains compared to the placebo, including global cognition (MMSE, p = 0.019; ADASCog, p < 0.001), episodic memory (AVLT-N1N5, p < 0.001), visuospatial ability (CDT, p = 0.034), and working memory (DST, p = 0.015). For brain structure, the gray matter volume in the right postcentral and precentral gyrus, bilateral cuneus, left supplementary motor area, superior occipital gyrus, right hippocampus, right thalamus, bilateral lingual gyrus, left precuneus, right inferior frontal gyrus (triangular part), left inferior parietal gyrus, left superior medial frontal gyrus, right superior temporal gyrus, left middle temporal gyrus, and right parahippocampal gyrus increased in the DZSM group (FDR-corrected, p<0.05), with no significant changes in white matter microstructure. Moreover, gray matter volume increases positively correlated with improvements in global cognition and visuospatial function.

Discussion: DZSM capsules significantly improved multiple cognitive domains in VCI patients, particularly memory, visuospatial, and executive functions. The observed increases in gray matter volume suggest that DZSM may exert neuroprotective effects through structural brain remodeling, which is closely associated with cognitive enhancement.

Conclusion: This study identifies brain structural abnormalities in VCI patients that correlate with cognitive deficits. DZSM capsule treatment significantly improved cognitive function. While the underlying mechanisms remain to be fully elucidated, these effects may be related to structural changes in the brain.

A serine/threonine kinase with a wide variety of substrates, Glycogen Synthase Kinase-3 (GSK-3) is widely expressed. GSK-3 is a key player in cell metabolism and signaling, modulating numerous cellular functions and playing significant roles in both healthy and diseased states. The two histopathological features of Alzheimer's disease, the intracellular neurofibrillary tangles composed of hyperphosphorylated tau, and the extracellular senile plaques composed of beta-amyloid, have been linked to GSK-3. It alters multiple tau protein locations found in neurofibrillary tangles. Additionally, GSK-3 can react to this peptide and regulate the production of beta-amyloid. The overexpression of GSK-3 in several transgenic models has been linked to tau hyperphosphorylation, neuronal death, and a reduction in cognitive function. It has been shown that lithium, a medication commonly used to treat affective disorders, inhibits GSK-3 at therapeutically relevant concentrations and stops tau phosphorylation. In this review, we provide an overview of the most recent research on the potential of GSK-3 inhibitors for treating Alzheimer's disease.

Introduction: This study aimed to explore the value of diffusion tensor imaging (DTI)- based radiomics in the early diagnosis of Alzheimer's disease (AD) and predicting the progression of mild cognitive impairment (MCI) to AD.

Methods: A cohort of 186 patients with MCI was obtained from the publicly accessible Alzheimer's. Disease Neuroimaging Initiative (ADNI) database, and 49 of these individuals developed AD over a 5-year observation period. The subjects were divided into a training set and a test set in a ratio of 7 to 3. Radiomic features were extracted from the corpus callosum within the DTI post-processed images. The Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression algorithm was employed to develop radiomic signatures. The performance of the radiomic signature was assessed using receiver operating characteristic (ROC) analysis and decision curve analysis (DCA).

Results: In the training set, 35 patients were converted, and in the test set, 14 patients were converted. Among all the patients, notable differences were observed in age, CDR-SB, ADAS, MMSE, FAQ, and MOCA between the stable group and the transformed group (p < 0.05). In the test set, the AUCs of the radiomics signatures constructed based on fractional anisotropy, axial diffusivity, mean diffusivity, and radial diffusivity were 0.824, 0.852, 0.833, and 0.862, respectively. The AUC of the clinical model was 0.868, and that of the combined model was 0.936. DCA demonstrated that the combined model had the best performance.

Discussion conclusion: The combined radiomics and clinical model, utilizing DTI data, can relatively accurately forecast which patients with MCI are likely to progress to AD. This approach offers potential for early AD prevention in MCI patients.

Objective: At present, there is limited research on the association between dietary intake of anthocyanidins and Alzheimer's disease (AD). More epidemiological studies are needed to better understand this relationship.

Methods: We explored the relationship between dietary Anthocyanidins intake and AD among 3806 American adults in the National Health and Nutrition Examination Survey (NHANES) and the United States Department of Agriculture's Food and Nutrient Database for Dietary Studies (FNDDS) from 2007 to 2010, and 2017 to 2018. We use weighted logistic regression model, restricted cubic spline (RCS) and weighted quantile sum (WQS) regression analysis to analyze the relationship between anthocyanidins monomer and AD.

Results: The weighted logistic regression model showed that the total intake of anthocyanidins was the fourth (OR:0.979; 95% CI: 0.966-0.992) quantile (relative to the lowest quantile) is related to the reduction of AD risk. RCS analysis showed that the total intake of anthocyanidins was negatively linearly correlated with AD (nonlinear P value was 0.002). The WQS regression analysis shows that cyanidin and malvidin are the main contributors to the comprehensive effects of six anthocyanidins.

Conclusion: Our results show that a higher dietary intake of anthocyanidins is associated with a lower risk of AD.

Introduction: Alzheimer's disease (AD) is a progressive neurodegenerative disorder that currently affects over 55 million individuals worldwide. Conventional diagnostic approaches often rely on subjective clinical assessments and isolated biomarkers, limiting their accuracy and early-stage effectiveness. With the rising global burden of AD, there is an urgent need for objective, automated tools that enhance diagnostic precision using neuroimaging data.

Methods: This study proposes a novel diagnostic framework combining a fine-tuned VGG19 deep convolutional neural network with an eXtreme Gradient Boosting (XGBoost) classifier. The model was trained and validated on the OASIS MRI dataset (Dataset 2), which was manually balanced to ensure equitable class representation across the four AD stages. The VGG19 model was pre-trained on ImageNet and fine-tuned by unfreezing its last ten layers. Data augmentation strategies, including random rotation and zoom, were applied to improve generalization. Extracted features were classified using XGBoost, incorporating class weighting, early stopping, and adaptive learning. Model performance was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC.

Results: The proposed VGG19-XGBoost model achieved a test accuracy of 99.6%, with an average precision of 1.00, a recall of 0.99, and an F1-score of 0.99 on the balanced OASIS dataset. ROC curves indicated high separability across AD stages, confirming strong discriminatory power and robustness in classification.

Discussion: The integration of deep feature extraction with ensemble learning demonstrated substantial improvement over conventional single-model approaches. The hybrid model effectively mitigated issues of class imbalance and overfitting, offering stable performance across all dementia stages. These findings suggest the method's practical viability for clinical decision support in early AD diagnosis.

Conclusion: This study presents a high-performing, automated diagnostic tool for Alzheimer's disease based on neuroimaging. The VGG19-XGBoost hybrid architecture demonstrates exceptional accuracy and robustness, underscoring its potential for real-world applications. Future work will focus on integrating multimodal data and validating the model on larger and more diverse populations to enhance clinical utility and generalizability.

Alzheimer's disease (AD) is a neurodegenerative condition characterized by neuroinflammation, tau hyperphosphorylation, Aβ (Amyloid beta) accumulation, and synaptic dysfunction. New research indicates that the gut-brain axis, a network of two-way communication that involves immunological signals, neural pathways, and microbial metabolites, makes dysbiosis of the gut microbiota essential to the pathogenesis of AD. Alterations in the gut microbiota's composition hinder the production of crucial metabolites, such as short-chain fatty acids, trimethylamine- N-oxide, and secondary bile acids, which affect neuroinflammatory cascades, mitochondrial bioenergetics, and synaptic plasticity. Furthermore, Toll-like receptor 4 -4-mediated microglial responses are triggered by Gram-negative bacterial lipopolysaccharides. This cascade promotes oxidative stress, chronic neuroinflammation, and disruption of the (BBB) blood-brain barrier, all of which encourage the accumulation of neurotoxic proteins. Microbiome-modulating therapies, such as probiotics, prebiotics, and synbiotics, have been shown to have neuroprotective properties. They work by restoring microbial diversity, increasing (Short-chain fatty acids) SCFA-mediated anti-inflammatory pathways, and reducing glial activation. In addition to promoting gut microbiota equilibrium, dietary approaches like the Mediterranean and ketogenic diets, which are enhanced with polyphenols and omega-3 fatty acids, also lower systemic inflammation and increase neural resilience. Furthermore, the potential of postbiotics and fecal microbiota transplantation to attenuate AD-related neurodegeneration and restore gut-derived metabolic balance is being investigated. Translating these methods into standardized clinical applications is difficult, though, because individual microbiome composition varies. It will be essential to address these complications through mechanistic research and extensive clinical trials to establish gut microbiota as a promising therapeutic target in AD.