Current Alzheimer research最新文献

Domestic abuse (DA) and Alzheimer's disease (AD) and related dementias) are two of humankind's most significant societal healthcare issues. DA is widespread, with one in three women and one in four men experiencing physical, psychological, emotional, sexual, and financial abuse in their lifetime. AD is the most common form of dementia and is expected to affect more than 152 million people worldwide by 2050. Given the incidence and prevalence of these two problems, any causal relationship between them carries profound societal consequences. Herein, we describe five types of overlapping relationships between DA and AD: 1. intimate partner violence (IPV) as a risk factor for AD; 2. AD as a risk factor for worsening ongoing DA; 3. abuse of caregivers by people with AD; 4. abuse of people with AD by caregivers; and 5. reactivation of previous DA behavior in a person with AD. Chronologically, these five types cover the spectrum from occurring decades before the onset of AD symptoms to emerging only after AD symptoms have manifested. Mechanistically, these five subtypes reflect the paradoxical fact that DA and AD may be causes or consequences of each other. Phenomenologically, they encompass the full spectrum of DA, including physical, psychological, emotional, sexual, and financial abuse. Given the challenges in recognizing, managing, and treating both DA and AD, society's need to recognize the DA/AD Problem and to identify and prevent the five subtypes of DA and AD overlap is an emerging healthcare priority.

Introduction: The locus coeruleus is the primary site of norepinephrine (NE) synthesis in the brain. Its dysfunction has been implicated in the pathogenesis of Alzheimer's disease. Vascular risk factors, thyroid dysfunction, and vitamin deficiencies have also been associated with an increased risk of dementia. This study aimed to evaluate the relationship between the catecholaminergic system-by measuring cerebrospinal fluid (CSF) levels of L-3,4-dihydroxyphenylalanine (L-DOPA), dopamine (DA), and NE-and vascular risk factors, thyroid dysfunction, and vitamin deficiencies.

Methods: We conducted a cross-sectional observational study in which CSF levels of L-DOPA, DA and NE were measured in 117 participants. Data on Blood Pressure (BP), heart rate, glycaemic and lipid profiles, smoking history, thyroid function and vitamin B12 and folic acid levels were collected for each participant.

Results: We found significant correlations between NE and CSF glucose levels (r = 0.308, p = 0.003) in participants without diabetes mellitus, between L-DOPA and orthostatic variation of diastolic BP (r = -0.288, p = 0.014) and high-density lipoprotein (r = 0.404, p = 0.001) and between NE and triglycerides (r = 0.271, p = 0.030) and folic acid (r = 0.298, p = 0.009).

Discussion: This is the first study to demonstrate correlations between CSF NE levels and CSF glucose, probably due to the effect of NE on astrocytes, and between CSF NE levels and folic acid, possibly related to its role in catecholamine synthesis. CSF L-DOPA levels were correlated with cardiovascular risk factors such as the orthostatic regulation of diastolic BP.

Conclusion: These findings may contribute to a better understanding of the pathophysiology of neurodegenerative diseases.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by amyloid-beta (Aβ) plaque deposition, neurofibrillary tangles of hyperphosphorylated tau protein, and chronic neuroinflammation, leading to synaptic dysfunction and cognitive decline. Current diagnostic methods rely on clinical symptoms and limited biomarkers, while available treatments only provide symptomatic relief without halting disease progression. MicroRNAs (miRNAs), small non-coding RNAs of 19-22 nucleotides, have emerged as crucial regulators of gene expression through post-transcriptional mechanisms and show distinct dysregulation patterns in AD patients' blood, cerebrospinal fluid (CSF), and brain tissues. Key miRNAs such as miR-132, miR-146a, miR-34a, and miR-125b demonstrate consistent alterations in expression levels, correlating with disease progression and offering potential as non-invasive diagnostic tools. This review comprehensively examines the dual role of miRNAs as diagnostic biomarkers and therapeutic targets for AD. We also provide an analysis of specific miRNA signatures in different biofluids (plasma, serum, CSF) and brain regions that correlate with disease stages, highlighting their potential for early and non-invasive diagnosis. Therapeutically, miRNAs modulate multiple AD-related pathways, including neuroinflammation via NF-κB signaling, Aβ production through BACE1 inhibition, and tau phosphorylation via GSK3β regulation. miRNAs also influence synaptic plasticity, mitochondrial function, and autophagy, presenting multifaceted opportunities for intervention. However, challenges, including miRNA heterogeneity, stability, and targeted delivery, remain critical impediments. Advances in nanocarriers, exosomal miRNAs, and viral vectors show promise in overcoming these obstacles, enabling precise miRNA modulation. In addition, we underscore the need for standardized protocols, further validation in clinical cohorts, and the development of cost-effective detection methods to translate miRNA-based approaches into practical diagnostics and therapies. By integrating miRNA biomarkers with existing diagnostic tools and exploring combinatorial therapeutic strategies, researchers can harness the potential of miRNAs to revolutionize AD intervention, paving the way for early detection and effective treatment of this devastating disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by abnormalities in protein metabolism leading to the accumulation of extracellular amyloid-beta (Aβ) plaques and intracellular neurofibrillary tangles (hyperphosphorylated tau protein). While these pathological constructs have held significant attention for decades, emerging evidence highlights the understanding of neuroinflammation (notably involving microglia, and cytokine signaling) as a critical initial event with respect to the inception and progression of AD. This review discusses the dynamic and dualistic effects of immune response in AD based on the relationship between neuroinflammatory processes and classical neuropathological characteristics. Microglia are ubiquitous immune cells in the central nervous system responsible for maintaining homeostasis as the brain's "housekeepers" by removing cellular debris, pruning synapses, and monitoring cell interactions. However, microglia in AD function produce a chronically activated phenotype that elicits neurotoxicity, impairs synaptic functioning, and is are protracted source of neuroinflammation. The appearance of disease-associated microglia (DAM) may illustrate complexities of TREM2 signaling for the anabolism of Aβ clearance and the modulation of inflammatory systems. Cytokine imbalance - higher expression of pro-inflammatory (e.g., IL-1β, TNF-α) and lower expression of antiinflammatory (e.g., IL-10, TGF-β) - adds to a self-perpetuating inflammatory loop that exacerbates Aβ and tau pathology, brain-blood barrier permeability, and peripheral-CNS immune communications. The mechanisms of an inflammatory event may drive brain tau hyperphosphorylation, tau propagation, along with other pathophysiological neurodegenerative features of traumatic brain injury and Alzheimer's disease. While examples of therapies targeting microglia and their cytokine activity are actively being explored, clinical efforts have been mixed. Neuroimaging development (e.g., TSPO-PET), cytokine collection and compositional approaches, and application of single-cell transcriptomics are providing new ways of thinking about complex neuroimmunology. Exploring, informing, and defining the timing, context, and variations of neuroinflammatory responses will be ultimately needed to create effective, targeted therapies for Alzheimer's disease (AD).

Background: Previous research explores the relationship between the cognitive status of people with dementia (PwD) and their performance in complex, domain-specific interaction tasks. However, these activities and their sophisticated instructions are challenging for PwD, potentially biasing performance metrics. This study aims to answer whether the cognitive status of PwD is related to their performance in simple touch and tangible tasks disassociated from a domain.

Methods: This study involved 7 formal caregivers and data from 21 PwD corresponding to completion times of interaction tasks. Relationships between completion times and the cognitive status of PwD were explored using Spearman's rank correlation, mutual information, gini importance, permutation importance, and a principal component analysis (PCA) visualization.

Results: Completion times of drag & drop, grasp, and tap interaction tasks have a strong negative monotonic association with the MMSE score (Bonferroni-corrected p-value < 0.05). The most relevant gestures were drag & drop, tap, and grasp. The PCA visualization allowed formal caregivers to detect relationships between patients' performance and their MMSE scores (p-value < 0.05).

Discussion: The cognitive status of PwD is related to their performance in simple interaction tasks disassociated from a domain. As the MMSE score decreases, task completion times increase. In addition, the PCA visualization was considered useful to inform decision-making.

Conclusion: This work provides the foundation for technology-enhanced cognitive activities supported by touch and tangible gestures that can be used to determine the cognitive status of PwD.

Introduction: Alzheimer's Disease (AD) is the commonest pathology underlying dementia, but it frequently coexists with cerebrovascular disease (CVD). Existing literature supports a possible role for vascular risk factors (VRFs), including hypertension, diabetes and dyslipidaemia in AD pathogenesis. This study aims to determine whether VRFs contribute to typical AD pathogenesis or co-morbid CVD in mixed AD.

Methods: Well-characterised probable AD subjects participating in two large clinical trials of Hydromethylthionine were classified into "typical AD" and "mixed" patterns based on FDG-PET images. VRFs, including hypertension, diabetes and dyslipidaemia, and MRI-derived White Matter Hyperintensities (WMHs) and brain fraction (as a measure of brain atrophy) were analysed to investigate the relationship between VRFs and AD subtypes.

Results: Of 794 participants, 533 (67.1%) were classified as typical AD and 261 (32.8%) were classified as mixed. Among VRFs, cardiovascular risks were significantly more frequent in typical AD (59%) than in mixed subtype (47%) (p = 0.002).

Discussion: We found that it was mainly hypertension that differed according to subtypes. Although brain atrophy is the main driver of cognitive impairment in patients with AD subtype, the microvascular pathology in the form of WMHs was significantly higher in patients with hypertension, irrespective of subtype.

Conclusion: Although hypertension is the main risk factor for cerebrovascular disease, contrary to our expectation, hypertension is common in typical AD than the mixed subtype, and this association is driven by the hitherto unsuspected contribution of microvascular pathology to cognitive impairment in typical AD.

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.

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), 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: Alzheimer's disease (AD) is a debilitating neurodegenerative condition marked by progressive cognitive decline and memory impairment, affecting millions worldwide. Despite extensive research, no definitive cure exists, underscoring the need for innovative approaches to drug discovery and development.

Methods: This review focuses on the application of molecular docking techniques in the context of AD drug discovery. The methodology involves the use of computational modeling tools to predict and analyze the interactions between small drug-like molecules and key protein targets implicated in AD pathogenesis, particularly amyloid-beta (Aβ) and tau proteins.

Results: Molecular docking has enabled the virtual screening of large chemical libraries to identify potential inhibitors of Aβ aggregation and tau hyperphosphorylation. Numerous studies have validated docking-predicted interactions with in vitro and in vivo experiments, resulting in the discovery of novel compounds with promising pharmacological profiles. Docking has also aided in the optimization of ligand binding affinity and selectivity toward AD-relevant targets.

Discussion: The integration of molecular docking with experimental techniques enhances the reliability and efficiency of the drug discovery process. Docking allows for the early identification of bioactive molecules, reducing time and cost compared to traditional methods. However, limitations such as rigid receptor assumptions and scoring function inaccuracies require further refinement.

Conclusion: Molecular docking stands out as a powerful computational tool in the quest for effective AD therapies. Simulating protein-ligand interactions accelerates the identification of potential drug candidates and supports the rational design of targeted interventions, paving the way for future clinical applications in combating Alzheimer's disease.