Current Alzheimer research最新文献

Introduction: Alzheimer's disease (AD) is a neurodegenerative disorder characterized by synaptic dysfunction and the accumulation of amyloid plaques. The molecular mechanisms linking gene dysregulation, pathogenic variants, and protein interaction networks to these core pathologies remain incompletely understood. This study aimed to integrate transcriptomic data with mutation and structural modeling to uncover disease mechanisms and identify therapeutic targets.

Methods: We performed differential gene expression analysis on the GSE138260 microarray dataset using GEO2R to identify DEGs in AD brain tissue. Missense mutations in DEGs were retrieved from the Alzheimer's Disease Variant Portal (ADVP). Protein-protein interaction networks were constructed using the STRING database to identify connections with the amyloid precursor protein (APP). Molecular dynamics simulations were conducted to evaluate the structural consequences of the BDNF V66M mutation.

Results: A total of 1,588 DEGs were identified, including upregulation of immune-related genes and downregulation of neuroplasticity-associated genes (e.g., BDNF, GRIN2B, GRM8). PPI analysis revealed a core network centered on APP, including BDNF as a direct interactor. The V66M variant in BDNF, confirmed to be downregulated in AD brains, showed increased rigidity and localized flexibility in structural models.

Discussion: The integration of transcriptomics and protein modeling revealed a critical link between BDNF dysfunction and APP interaction in AD. The V66M mutation was found to structurally alter BDNF, potentially disrupting its neuroprotective roles. The findings suggested that impaired BDNF signaling, driven by transcriptional repression and structural mutation, contributes to amyloid pathology and synaptic failure.

Conclusion: This multi-omics investigation has identified BDNF as a converging point of gene dysregulation and pathogenic mutation within an APP-centric network. Structural alterations induced by the V66M mutation may exacerbate amyloid accumulation and neuronal dysfunction, supporting therapeutic strategies aimed at enhancing BDNF signaling in AD.

Introduction: Type 2 diabetes mellitus (T2D) is a known risk factor for developing Alzheimer's disease (AD). Recent research shows that both diseases share complex and related pathophysiological processes. Network medicine approaches can help to elucidate common dysregulated processes among different diseases, such as AD and T2D. Thus, the aim of this work was to determine differentially expressed genes (DEGs) in AD and T2D and to apply a network medicine approach to identify the microRNAs (miRNAs) involved in the AD-T2D association.

Methods: Gene expression microarray data sets consisting of 384 control samples and 399 samples belonging to AD and T2D disease were analyzed to obtain DEGs shared by both diseases; the miRNAs associated with these DEGs were predicted using a network medicine approach. Finally, potential small molecules targeting these potentially deregulated miRNAs were identified.

Results: AD and T2D shared a subset of 82 downregulated DEGs. These genes were significantly associated (p < 0.01) with the ontology terms of chemical synaptic deregulation. DEGs were associated with 12 miRNAs expressed in specific tissues for AD and T2D. Such miRNAs were also primarily associated with the ontology terms related to synaptic deregulation and cancer, and AKT signaling pathways. Steroid anti-inflammatory drugs, antineoplastics, and glucose metabolites were predicted to be potential regulators of the 12 shared miRNAs.

Discussion: The network medicine approach integrating DEGs and miRNAs enabled the identification of shared, potentially deregulated biological processes and pathways underlying the pathophysiology of AD and T2D. These common molecular mechanisms were also linked to drugs currently used in clinical practice, suggesting that this strategy may inform future drug repurposing efforts. Nonetheless, further in-depth biological validation is required to confirm these findings.

Conclusion: Network medicine allowed identifying 12 miRNAs involved in the AD-T2D association, and these could be drug targets for the design of new treatments; however, the identified miRNAs need further experimental confirmation.

Introduction: Cerebral Amyloid Angiopathy (CAA) is a common form of cerebral small vessel disease (CSVD), characterized by the accumulation of amyloid-β (Aβ) protein in the walls of cortical and leptomeningeal arteries and arterioles. The sporadic form primarily affects the elderly and is closely associated with Alzheimer's disease (AD). Despite previous studies on cognition, the specific neuropsychological profile of CAA remains unclear. This study aims to describe the cognitive profile of CAA patients and characterize their neuropsychological aspects in the absence of a clinical diagnosis of AD.

Methods: We present a case series of six patients with probable CAA, without clinical evidence of AD, who underwent extensive neuropsychological assessment. Additionally, a narrative review was conducted to synthesize current knowledge of the cognitive and neuropsychological aspects of sporadic CAA.

Results: The narrative review indicates that CAA predominantly affects executive functioning, processing speed, episodic memory, global cognition, and visuospatial functions. In our case series, all patients exhibited impairments in these domains, except for global cognition. Notably, a specific dissociation was observed in the Rey Auditory Verbal Learning Test (RAVLT), with impaired delayed recall but preserved recognition.

Discussion: Sporadic CAA in patients without AD contributes to cognitive impairment, particularly affecting executive functioning, processing speed, visuospatial functions, and episodic memory. In our sample, memory impairment in CAA follows a dysexecutive pattern, characterized by retrieval deficits with preserved storage. This contrasts with the amnestic profile seen in AD and amnestic mild cognitive impairment (aMCI), where both retrieval and storage are compromised.

Conclusion: This distinct memory profile may represent a useful neuropsychological marker for differentiating CAA-related cognitive impairment from that associated with AD and its prodromal forms. This differentiation has potential implications for diagnosis, prognosis, and the development of tailored therapeutic strategies.

Parkinson's disease (PD) is a multifaceted neurodegenerative condition marked by the progressive loss of dopaminergic neurons, leading to impairments in movement and cognition. This study offers an in-depth examination of the pathophysiological pathways associated with PD, emphasising the roles of oxidative stress, mitochondrial dysfunction, and neuroinflammation. The study examines the interaction between genetic and environmental factors in the development of PD, highlighting the significance of oxidative stress, mitochondrial dysfunction, and excitotoxicity in the degeneration of dopaminergic neurons. It also looks into the impact of neuroinflammation and microglial activation on the causes of PD. Despite considerable progress in research, there remains a lack of effective treatments that can modify the course of the disease, highlighting the pressing need for new therapeutic approaches that address mitochondrial malfunction, oxidative stress, and neuroinflammation. This study assesses the neuroprotective efficacy of various substances, notably natural agents like resveratrol, curcumin, ginsenoside, and melatonin, for managing PD. Although these natural chemicals show promise, further robust clinical trials are needed to confirm their effectiveness and safety, as well as to investigate their potential incorporation into conventional PD treatment.

Introduction: Alzheimer's disease (AD) is a leading cause of death, making early detection critical to improve survival rates. Conventional manual techniques struggle with early diagnosis due to the brain's complex structure, necessitating the use of dependable deep learning (DL) methods. This research proposes a novel RESIGN model is a combination of Res-InceptionSeg for detecting AD utilizing MRI images.

Methods: The input MRI images were pre-processed using a Non-Local Means (NLM) filter to reduce noise artifacts. A ResNet-LSTM model was used for feature extraction, targeting White Matter (WM), Grey Matter (GM), and Cerebrospinal Fluid (CSF). The extracted features were concatenated and classified into Normal, MCI, and AD categories using an Inception V3-based classifier. Additionally, SegNet was employed for abnormal brain region segmentation.

Results: The RESIGN model achieved an accuracy of 99.46%, specificity of 98.68%, precision of 95.63%, recall of 97.10%, and an F1 score of 95.42%. It outperformed ResNet, AlexNet, Dense- Net, and LSTM by 7.87%, 5.65%, 3.92%, and 1.53%, respectively, and further improved accuracy by 25.69%, 5.29%, 2.03%, and 1.71% over ResNet18, CLSTM, VGG19, and CNN, respectively.

Discussion: The integration of spatial-temporal feature extraction, hybrid classification, and deep segmentation makes RESIGN highly reliable in detecting AD. A 5-fold cross-validation proved its robustness, and its performance exceeded that of existing models on the ADNI dataset. However, there are potential limitations related to dataset bias and limited generalizability due to uniform imaging conditions.

Conclusion: The proposed RESIGN model demonstrates significant improvement in early AD detection through robust feature extraction and classification by offering a reliable tool for clinical diagnosis.

Circadian rhythms are crucial for essential physiological functions such as metabolism, sleep-wake cycles, hormone balance, and cognitive abilities, which are regulated by the central Suprachiasmatic Nucleus (SCN) and peripheral clocks. Disruptions to circadian rhythms, which may be caused by aging, lifestyle factors, and environmental influences, are linked to metabolic disorders and Neurodegenerative Diseases (NDs). This review examines the reciprocal relationship between circadian control and metabolism, highlighting the molecular processes that maintain circadian rhythms and how these processes change with age. Aging diminishes SCN efficiency and disrupts peripheral clock alignment, leading to impaired physiological functions, increased oxidative stress, and neuroinflammation, all of which contribute to the progression of NDs such as Alzheimer's (AD), Parkinson's disease (PD), Huntington's disease (HD), etc. Emerging therapeutic strategies aim to restore circadian function through interventions, including bright light therapy, melatonin supplementation, and pharmacological agents targeting clock gene regulators and neuropeptides. Furthermore, lifestyle modifications, such as Structured Physical Activity (SPA) and Time-Restricted Feeding (TRF), can enhance circadian health by synchronizing metabolic and hormonal rhythms. Future directions include chrono-pharmacology, gene editing, and Artificial Intelligence (AI)-driven personalized medicine, all of which emphasize the development of tailored circadian therapies. Advancing circadian research holds the potential to facilitate better health outcomes and improve quality of life, while also addressing the growing concerns of the aging population and NDs.

Mild Cognitive Impairment (MCI) is marked by a measurable decline in cognitive function that exceeds typical age-related changes but does not yet qualify as dementia. The brain's Default Mode Network (DMN) remains active during rest and plays a crucial role in introspective processes, such as memory retrieval and self-referential thinking. Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive neuroimaging technique that measures spontaneous fluctuations in blood oxygenation, providing insights into functional connectivity within brain networks. Investigating the DMN using rs-fMRI in individuals with MCI allows researchers to identify early neural changes associated with cognitive decline, which may serve as biomarkers for the early detection of Alzheimer's disease or related dementias. The rs-fMRI technique has been widely used in MCI research to explore the underlying neurobiological mechanisms of cognitive impairment. This study aims to synthesize findings from rs-fMRI studies focusing on alterations in DMN connectivity in MCI populations. This analysis deepens our understanding of the early-stage neural disruptions in MCI and holds significant implications for developing early diagnostic tools and interventions aimed at delaying the progression to dementia.

Introduction: Previous studies have shown a correlation between neurodegenerative diseases and cancers. However, the biological processes for these diseases are not completely known, and the genetic factors for their onset are not defined.

Materials and methods: This study reports the genetic relationships of different neurodegenerative diseases, such as Multiple Sclerosis (MS), Alzheimer's Disease (AD), and Parkinson's disease (PD) and cancer types (squamous cell lung carcinoma, esophageal adenocarcinoma, and colorectal cancer), with other human traits, based on cross-trait Linkage Disequilibrium Score regression (LDSC). We then applied a clumping approach to select candidate genes for each disease, and via an miRNA analysis, we identified miRNAs that could regulate those genes.

Results: LDSC revealed an inverse association of human traits with neurodegenerative diseases and cancer. Indeed, the cancer types studied were positively correlated with "Body Mass Index (BMI)," while AD, PD, and MS showed a negative correlation. miR-1-3p, miR-34a-5p, and miR-27a-3p were revealed as common biomarkers among the different pathological conditions.

Conclusion: The present study suggests novel genetic associations between neurological diseases, cancer types and new targets to explain the genetic sharing between the diseases.

Introduction: Currently, there is no information on changes in the mitophagy (BNIP3), apoptosis (CASP3), and autophagy (BECN1) genes in the frontal cortex after brain ischemia with animal survival for 2 years. Furthermore, it is not known whether the BNIP3, CASP3, and BECN1 genes possess any influence on neurons in the frontal cortex due to ischemia.

Aims: The goal of the investigation was to evaluate alterations in the behavior of BNIP3, CASP3, and BECN1 genes in the frontal cortex following ischemia with survival of 2 years.

Materials and methods: Gene expression was assessed using an RT-PCR protocol at 2-30 days and 6-24-months after ischemia.

Results: BECN1 gene expression after ischemic injury was lower than the control group during 7-30- days and 18 months, whereas overexpression was noted after 2 days, 6-, 12- and 24 months. In the case of BNIP3 gene expression, it was lower than the control group for 2-7 days and higher than the control throughout the remaining time after ischemia. Increased expression of the CASP3 gene was observed except on days 7-30 following ischemia when its expression was lower compared to control values.

Discussion: The data seem to indicate that the observed changes in gene expression may reflect the activation and inhibition of different mechanisms involved in the advancement of neurodegeneration after ischemia.

Conclusion: Overexpression of BECN1gene is likely to be associated with the induction of neuroprotective phenomena, whereas overexpression of BNIP3 and CASP3 genes can cause harmful effects.

Introduction: The mechanisms underlying the relationship between obesity and cognitive function remain unclear, particularly among older adults, where reliable evidence is limited. This study aimed to explore whether the relationship between obesity and cognitive function is mediated by sleep quality and blood pressure (BP) in older Chinese adults.

Methods: We conducted an observational study using data from a cluster randomized controlled trial (RCT) with 5 follow-up periods involving older adults in rural China. The trial took place in Sichuan, China, from May 2021 to May 2023. Telephone Interview for Cognitive Status (TICS- 10) was used to assess the participants' cognitive function. Additionally, linear mixed-effects models and mediation analyses were performed.

Results: The mean age of participants was 70.89, and 225 out of 506 participants were males. Weight, waist circumference (WC), and hip circumference (HC) were positively associated with cognitive function, while compared to normal/underweight participants, participants with overweight had a significant association with cognitive function. Sleep quality mediated the association between weight and cognitive function (β = 0.01, (95% CI: 0.00 to 0.01), P < 0.001), accounting for a mediating effect proportion of 4.04% (95% CI: 2.19% to 8.00%). Diastolic blood pressure (DBP) mediated the association between overweight (β = 0.02, (95% CI: 0.00 to 0.05), P < 0.001), HC (β = 0.01, (95% CI: 0.00 to 0.01), P = 0.02), and WC (β = 0.01, (95% CI: 0.00, 0.01), P <0.001) and cognitive function, explaining approximately 4.46% (95% CI: 0.41% to 12.00%), 7.16% (95% CI: 0.36%, 17.00%), and 9.60% (95% CI: 1.11%, 25.00%) mediating proportion of the total effect, respectively.

Discussion: Our study highlights the potential mediating roles of sleep quality and DBP in the relationship between obesity and cognitive function. The findings contribute to understanding the obesity-cognition link in older adults, particularly in rural settings. However, limitations, such as self-reported sleep measures and unmeasured confounders, warrant caution. Further research is needed to clarify the underlying mechanisms and inform targeted interventions.

Conclusion: Our study demonstrates a significant positive association between weight, body mass index (BMI), HC, and WC and cognitive function in older adults. These findings suggest that maintaining a moderately high level of overweight may be protective against cognitive decline in this population. Additionally, the study also provides insights into optimizing cognitive function through factors, such as sleep and BP management.