Alzheimer's Disease Progression Research Articles

The Role of Circadian Rhythm Disruptions in Alzheimers disease Progression: Mechanisms, Implications, and Therapeutic Strategies

Circadian rhythm disruptions have emerged as a critical factor in the development and progression of Alzheimers disease (AD). These disruptions affect key processes such as beta-amyloid (A) aggregation, tau protein hyperphosphorylation, and impaired protein clearance, all of which contribute to neurodegeneration. Melatonin, a hormone that regulates the circadian cycle, plays a neuroprotective role but decreases with age, especially in AD patients. Research suggests that melatonin supplementation may help mitigate the effects of A accumulation, tau-related pathology, and oxidative stress. Additionally, bright light therapy (BLT) has been shown to improve circadian rhythm regulation, enhancing sleep quality and cognitive function in individuals with AD. This review highlights the intricate relationship between circadian rhythm disruptions and AD pathology and examines therapeutic interventions such as melatonin and BLT that offer potential benefits in managing the disease.

Potential mechanisms of non-coding RNA regulation in Alzheimer's disease.

Alzheimer's disease, a progressively degenerative neurological disorder, is the most common cause of dementia in the elderly. While its precise etiology remains unclear, researchers have identified diverse pathological characteristics and molecular pathways associated with its progression. Advances in scientific research have increasingly highlighted the crucial role of non-coding RNAs in the progression of Alzheimer's disease. These non-coding RNAs regulate several biological processes critical to the advancement of the disease, offering promising potential as therapeutic targets and diagnostic biomarkers. Therefore, this review aims to investigate the underlying mechanisms of Alzheimer's disease onset, with a particular focus on microRNAs, long non-coding RNAs, and circular RNAs associated with the disease. The review elucidates the potential pathogenic processes of Alzheimer's disease and provides a detailed description of the synthesis mechanisms of the three aforementioned non-coding RNAs. It comprehensively summarizes the various non-coding RNAs that have been identified to play key regulatory roles in Alzheimer's disease, as well as how these non-coding RNAs influence the disease's progression by regulating gene expression and protein functions. For example, miR-9 targets the UBE4B gene, promoting autophagy-mediated degradation of Tau protein, thereby reducing Tau accumulation and delaying Alzheimer's disease progression. Conversely, the long non-coding RNA BACE1-AS stabilizes BACE1 mRNA, promoting the generation of amyloid-² and accelerating Alzheimer's disease development. Additionally, circular RNAs play significant roles in regulating neuroinflammatory responses. By integrating insights from these regulatory mechanisms, there is potential to discover new therapeutic targets and potential biomarkers for early detection and management of Alzheimer's disease. This review aims to enhance the understanding of the relationship between Alzheimer's disease and non-coding RNAs, potentially paving the way for early detection and novel treatment strategies.

The Effect of Zeolite Zinc on Memory Performance and Hippocampal Cell Death in a Rat Model of Alzheimer's-like Disease Induced by Aβ1-42.

Alzheimer's disease (AD) is one of the most common neurodegenerative disorders, characterized by the slow and progressive loss of brain structure and function, primarily affecting older individuals. Evidence has shown that disruption of zinc homeostasis in the brain contributes to synaptic dysfunction, as well as impairments in learning and memory. In this study, we evaluated the effect of zeolite zinc on memory performance and hippocampal cell death in a rat model of Alzheimer's disease (AD) induced by intracerebroventricular administration of Aβ1-42. We employed the Morris water maze, shuttle box, and open field tests to assess spatial memory, passive avoidance memory, and anxiety-like behavior, respectively. P-Tau and the amyloid precursor protein (APP) expression, along with hippocampal cell death, were also evaluated. Both Aβ1-42 and zeolite zinc were injected intracerebroventricularly. The results showed that zeolite zinc partially reversed Aβ1-42-induced impairments in memory performance and mitigated the effects of Aβ1-42 on locomotor activity, although it did not fully restore baseline levels. In addition, Aβ1-42 increased the expression of APP and P-Tau, as well as the number of dead cells, whereas zeolite zinc reduced these effects. In conclusion, our findings suggest that while zeolite zinc plays a role in modulating the pathophysiology of AD, its therapeutic effects only partially reverse the progression or symptoms of AD, indicating the need for further investigation into optimal dosing or combination therapies.

Exerkines mitigating Alzheimer's disease progression by regulating inflammation: Focusing on macrophage/microglial NLRP3 inflammasome pathway.

Recent research highlights the critical role of inflammation in accelerating amyloid beta and phosphorylated tubulin-associated protein tau cascade and Alzheimer's disease (AD) progression. Emerging evidence suggests that exercise influences AD by modulating inflammatory responses. We conducted a comprehensive search across multiple online databases. Our approach focused on previous and recent studies exploring the links among inflammation, AD, and the effects of exercise, specifically targeting research articles and books published in English. We pointed out that inflammation extends from the periphery to the central nervous system, facilitated by macrophage/microglial NLRP3 (nucleotide-binding domain, leucine rich-containing family,pyrin domain-containing protein 3)inflammasome signaling, which exacerbates classical AD mechanisms. Moreover, we provided further insights into the modulation of inflammasome signaling through exercise and exerkines, which may contribute to mitigating AD development. These insights deepen our understanding of AD mechanisms and offer the potential for identifying key therapeutic targets and biomarkers crucial for effective disease management and treatment. HIGHLIGHTS: Inflammation is potentially linked to the acceleration of classical Alzheimer's disease (AD) pathogenesis, including the pathways involving amyloid beta and phosphorylated tau, mediated by pro-inflammatory cytokines. Inflammation, initiated by the nucleotide-binding domain, leucine rich-containing family,pyrin domain-containing protein 3 (NLRP3) inflammasome signaling pathway within M1-type macrophages/microglia, may contribute to neuroinflammation and AD progression. Exercise has the potential to reduce inflammation and the development of AD by influencing NLRP3 inflammasome signaling via exerkines.

Modeling multi-stagedisease progression and identifying genetic risk factors via a novel collaborative learning method.

Alzheimer's disease (AD) typically progresses gradually for ages rather than suddenly. Thus, staging AD progression in different phases could aid in accurate diagnosis and treatment. In addition, identifying genetic variations that influence AD is critical to understanding the pathogenesis. However, staging the disease progression and identifying genetic variations is usually handled separately. To address this limitation, we propose a novel sparse multi-stage multi-task mixed-effects collaborative longitudinal regression method (MSColoR). Our method jointly models long disease progression as a multi-stage procedure and identifies genetic risk factors underpinning this complex trajectory. Specifically, MSColoR models multi-stage disease progression using longitudinal neuroimaging-derived phenotypes and associates the fitted disease trajectories with genetic variations at each stage. Furthermore, we collaboratively leverage summary statistics from large genome-wide association studies to improve the powers. Finally, an efficient optimization algorithm is introduced to solve MSColoR. We evaluate our method using both synthetic and real longitudinal neuroimaging and genetic data. Both results demonstrate that MSColoR can reduce modeling errors while identifying more accurate and significant genetic variations compared to other longitudinal methods. Consequently, MSColoR holds great potential as a computational technique for longitudinal brain imaging genetics and AD studies. The code is publicly available at https://github.com/dulei323/MSColoR.

Differential Expression of Neurodegeneration-Related Genes in SH-SY5Y Neuroblastoma Cells Under the Influence of Cyclophilin A: Could the Enzyme be a Likely Trigger and Therapeutic Target for Alzheimer's Disease?

The function and mechanism of Cyclophilin A (CypA) in modulating gene expression associated with Alzheimer's disease (AD) remain unclear. This multifunctional protein is found to be elevated in the cerebrospinal fluid (CSF) of individuals at risk for AD. The cytotoxic effects of CypA, including both wild-type and the mutant R55A, were assessed using the MTT assay. Prior to this evaluation, the purified recombinant protein was validated through enzymatic activity assays and western blot analysis. Following treatment with CypA and transient transfection using the CypA construct, real-time PCR (qRT-PCR) and western blotting were conducted to analyze the expression of factors involved in various signaling pathways, with an emphasis on inflammation, cell death, and intercellular communication. The findings indicate that CypA has a significant impact on the gene expression of factors associated with inflammation and the progression of AD in SH-SY5Y cells. It can be concluded that CypA is capable of regulating gene expression in SH-SY5Y cells, either in a manner dependent on or independent of its enzymatic activity. Additionally, the influence of this multifunctional protein on gene expression is contingent upon the specific site of action, as well as the dosage and duration of exposure to the cells.

Plasticity of Human Microglia and Brain Perivascular Macrophages in Aging and Alzheimer's Disease.

The complex roles of myeloid cells, including microglia and perivascular macrophages, are central to the neurobiology of Alzheimer's disease (AD), yet they remain incompletely understood. Here, we profiled 832,505 human myeloid cells from the prefrontal cortex of 1,607 unique donors covering the human lifespan and varying degrees of AD neuropathology. We delineated 13 transcriptionally distinct myeloid subtypes organized into 6 subclasses and identified AD-associated adaptive changes in myeloid cells over aging and disease progression. The GPNMB subtype, linked to phagocytosis, increased significantly with AD burden and correlated with polygenic AD risk scores. By organizing AD-risk genes into a regulatory hierarchy, we identified and validated MITF as an upstream transcriptional activator of GPNMB, critical for maintaining phagocytosis. Through cell-to-cell interaction networks, we prioritized APOE-SORL1 and APOE-TREM2 ligand-receptor pairs, associated with AD progression. In both human and mouse models, TREM2 deficiency disrupted GPNMB expansion and reduced phagocytic function, suggesting that GPNMB's role in neuroprotection was TREM2-dependent. Our findings clarify myeloid subtypes implicated in aging and AD, advancing the mechanistic understanding of their role in AD and aiding therapeutic discovery.

Delayed Magnetic Resonance Imaging of Alzheimer's Disease by Using Poly(2-(methacryloyloxy)ethyl phosphorylcholine)-Functionalized Nanoprobes.

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases, commonly affecting the aged, with pathophysiological changes presenting 15 to 20 years before clinical symptoms. Early diagnosis and intervention are crucial in effectively slowing the progression of AD. In the current study, poly(2-(methacryloyloxy)ethyl phosphorylcholine) (PMPC)-functionalized NaGdF4 nanoparticles (NaGdF4-PMPC) were developed as magnetic resonance imaging (MRI) contrast agents for targeting alpha 7 nicotinic acetylcholine receptors (α7 nAChRs) in AD mice. NaGdF4-PMPC showed excellent biocompatibility, targeting ability, and MRI performance, with the longitudinal molar relaxivity (r1) and transverse molar relaxivity (r2) being 1.21-fold and 1.33-fold higher than those of the clinical contrast agent Gd-DTPA, respectively, resulting in higher-sensitive MR angiography. After intravenous injection, 3D dynamic contrast-enhanced (DCE) MR images with high-resolution vasculature of the mouse brain were obtained. In addition, by using NaGdF4-PMPC, susceptibility-weighted imaging (SWI) signals in AD mouse brains were greatly retained compared to those in healthy mice for 24 h, emphasizing the excellent targeting ability of NaGdF4-PMPC. Furthermore, the CD31, α7 nAChRs, and Thioflavin S staining were also utilized to investigate the relationship among vascular inflammation, α7 nAChRs, and amyloid-β (Aβ) deposition in AD mice. This work highlights a promising targeted imaging strategy for the timely diagnosis of AD.

Alzheimer's disease-like pathology induced by Porphyromonas gingivalis in middle-aged mice is mediated by NLRP3 inflammasome via the microbiota-gut-brain axis.

Porphyromonas gingivalis (P. gingivalis) has been found to enter the brain and induce inflammation, contributing to Alzheimer's disease (AD). P. gingivalis is also closely linked to gut dysbiosis. However, does P. gingivalis induce AD-like pathology through the microbiota-gut-brain axis? There is limited literature on this topic. To determine the precise causal link among P. gingivalis, intestinal inflammation, and AD-related pathology. 12- to 13-month-old female C57BL/6J mice were subjected to ligature placement and oral administration of P. gingivalis over a 24-week period. Then, cognitive performance was evaluated with behavioral tests, while AD neuropathological changes, neuroinflammation, and intestinal inflammation were assessed through qPCR, immunofluorescence, and western blot, and gut microbiota was analyzed by 16S rRNA. Mice exposed to P. gingivalis showed impaired behavior in open field test, novel object recognition, and Y-maze tests. The bacterium infiltrated their brains, increasing Aβ42, AβPP, and Aβ fragments, promoting tau phosphorylation and microglial activation, and reducing levels of ZO-1, PSD95, SYP, and NeuN proteins. Inflammatory factors like NLRP3, caspase-1, IL-1β, IL-6, and TNF-α were elevated in both brains and intestine, while ZO-1 and occludin levels decreased in intestine. P. gingivalis also altered gut microbial compositions. P. gingivalis induced gut dysbiosis and activated the NLRP3 inflammasome in the intestine and brains of mice. This led to impairment of both the intestinal and brain-blood barriers, triggering neuroinflammation and promoting the progression of AD. These findings highlight the critical role of NLRP3 inflammasome activation in the microbiota-gut-brain axis in the AD-like pathology induced by P. gingivalis.

Morphological features of synaptic structures associated with amyloid plaques in the human cerebral cortex

BACKGROUND: Synapse damage and loss are fundamental to the pathogenesis of Alzheimer’s disease (AD) and lead to reduced cognitive function. Therefore, the study of synaptic abnormalities is crucial for understanding the pathogenesis and progression of AD. Synaptophysin, a transmembrane protein of synaptic vesicles, is most widely used as an immunohistochemical marker of synapses. AIM: to study the morphological features of synaptophysin–containing elements in the human cerebral cortex with amyloid plaques. METHODS: This study was made on the cerebral cortex samples of men and women aged 65 to 94 years (n = 10). Synapses and amyloid plaques were simultaneously detected with a new original light microscopy technique. This technique is based on immunohistochemical staining for synaptophysin and histochemical staining of amyloid plaques with Alcian blue. RESULTS: Abnormal synaptophysin-immunopositive structures presumed to be dystrophic presynapses were found within most of the identified amyloid plaques. These structures have large sizes and indistinct shapes and are present exclusively within amyloid plaques. Interestingly, corpora amylacea were also identified in all samples. They have spherical shapes and are located mainly in the perivascular, periventricular, and subpial regions of the brain. In all analyzed samples, synaptophysin-immunopositive terminals surrounding corpora amylacea had normal morphology and density and showed no signs of pathology. CONCLUSION: In this work, abnormal synaptophysin-containing structures associated with amyloid plaques were found in the human cerebral cortex using the original staining technique. Further study of these structures holds promise for identifying new biomarkers of synaptic disorganization, including in Alzheimer's disease.

Hyperactive neuronal networks facilitate tau spread in an Alzheimer's disease mouse model.

Pathological tau spreads throughout the brain along neuronal connections in Alzheimer's disease (AD), but the mechanisms that underlie this process are poorly understood. Given the high incidence and deleterious consequences of epileptiform activity in AD, we hypothesized neuronal hyperactivity and seizures are key factors in tau spread. To examine these interactions, we created a novel mouse model involving the cross of targeted recombination in active populations (TRAP) mice and the 5 times familial AD (5XFAD; 5X-TRAP) model allowing for the permanent fluorescent labelling of neuronal activity. To establish a causal role of seizures in tau spread, we seeded mice with human AD brain-derived tau lysate and induced seizures with pentylenetetrazol (PTZ) kindling. Comprehensive brain mapping of tau pathology and neuronal activity revealed that basal hyperactivity in 5X-TRAP mice was associated with increased tau spread, which was exacerbated by seizure induction through activated networks and correlated with memory deficits. Computational modeling revealed that anterograde tau spread was elevated in 5X-TRAP mice and that regional neuronal activity was predictive of tau spread to that brain region. On a cellular level, we found that in both saline and PTZ-treated 5X-TRAP mice, hyperactive neurons disproportionately contributed to the spread of tau. Further, we found that Synaptogyrin-3, a synaptic vesicle protein that interacts with tau, was increased following PTZ kindling in 5X-TRAP mice, possibly indicative of a synaptic mechanism underlying seizure-exacerbated tau spread. Importantly, postmortem AD brain tissue from patients with a history of seizures showed increased tau pathology in patterns indicative of increased spread and increased Synaptogyrin-3 levels compared to those without seizures. Overall, our study identifies neuronal hyperactivity and seizures as key factors underlying the pathobiological and cognitive progression of AD. Therapies targeting these factors should be tested clinically to slow tau spread and AD progression.

A residual GRU method with deep cross fusion for Alzheimer’s disease progression prediction using missing variable-length time series data

Alzheimer’s disease (AD) is a neurodegenerative disorder with a long prodromal phase. Early prediction of AD progression is crucial for improving clinical diagnosis. However, missing data and variable-length of time series data make it difficult to predict the progression of AD in clinical practice. Regarding this problem, existing researches mainly employed LSTM (Long Short-Term Memory) networks to impute missing data and make predictions, but those methods only encoded fixed-length time series data and suffered from the error accumulation, resulting in relatively unsatisfactory prediction results. To address this issue, this paper develops a novel method, named residual sharing GRU (Gated Recurrent Unit) with enhanced deep cross fusion module (RGRU-EDCF), to perform AD progression prediction. Specifically, we first design an enhanced cross deep module to impute missing data and learn complementary information. Moreover, we design a residual sharing GRU model to realize the input of variable-length time series data, thus, enhancing flexibility of model. Besides, the residual structure in the residual sharing GRU model is used for reducing error accumulation. We also add a local multi-head attention mechanism for specific time series features learning for classification and regression prediction. Finally, we use specific time series features to realize prediction of classification and regression tasks. Unlike existing methods that trained the missing data imputation and prediction model separately, our method considers an end-to-end training strategy to train both enhanced cross deep module and the residual sharing GRU model to further promote the performance of AD progression prediction. The proposed method is validated using time series data from the ADNI dataset. The accuracy can reach 95.17% in the prediction task, MAE can reach 2.64 in cognitive score prediction, and 4.04 in MRI biomarkers prediction, it is competitive and superior over other state-of-the-art methods. We also validate our proposed method on MIMIC-III dataset.

DenseIncepS115: a novel network-level fusion framework for Alzheimer's disease prediction using MRI images.

One of the most prevalent disorders relating to neurodegenerative conditions and dementia is Alzheimer's disease (AD). In the age group 65 and older, the prevalence of Alzheimer's disease is increasing. Before symptoms showed up, the disease had grown to a severe stage and resulted in an irreversible brain disorder that is not treatable with medication or other therapies. Therefore, early prediction is essential to slow down AD progression. Computer-aided diagnosis systems can be used as a second opinion by radiologists in their clinics to predict AD using MRI scans. In this work, we proposed a novel deep learning architecture named DenseIncepS115for for AD prediction from MRI scans. The proposed architecture is based on the Inception Module with Self-Attention (InceptionSA) and the Dense Module with Self-Attention (DenseSA). Both modules are fused at the network level using a depth concatenation layer. The proposed architecture hyperparameters are initialized using Bayesian Optimization, which impacts the better learning of the selected datasets. In the testing phase, features are extracted from the depth concatenation layer, which is further optimized using the Catch Fish Optimization (CFO) algorithm and passed to shallow wide neural network classifiers for the final prediction. In addition, the proposed DenseIncepS115 architecture is interpreted through Lime and Gradcam explainable techniques. Two publicly available datasets were employed in the experimental process: Alzheimer's ADNI and Alzheimer's classes MRI. On both datasets, the proposed architecture obtained an accuracy level of 99.5% and 98.5%, respectively. Detailed ablation studies and comparisons with state-of-the-art techniques show that the proposed architecture outperforms.

Evaluating clinical meaningfulness of anti-β-amyloid therapies amidst amyloid-related imaging abnormalities concern in Alzheimer's disease.

Alzheimer's disease is the most prevalent form of dementia in the elderly, which is clinically characterized by a gradual and progressive deterioration of cognitive functions. The central and early role of β-amyloid in the pathogenesis of Alzheimer's disease is supported by a plethora of studies including genetic analyses, biomarker research and genome-wide association studies in both familial (early-onset) and sporadic (late-onset) forms of Alzheimer's. Monoclonal antibodies directed against β-amyloid demonstrate slowing of the clinical deterioration of patients with early Alzheimer's disease. Aducanumab, lecanemab and donanemab clinical trials showed slowing of Alzheimer's disease progression on composite scores by 25-40% based on the measure used. Anti-β-amyloid antibodies can cause side effects of bleeding and swelling in the brain, called amyloid-related imaging abnormalities. Amyloid-related imaging abnormalities typically occur early in treatment and are often asymptomatic, and though in rare cases, they can lead to serious or life-threatening events. The aim of this review is to evaluate the clinical meaningfulness of anti-β-amyloid therapies amidst amyloid-related imaging abnormalities concern in Alzheimer's disease.

Quantitative Proteomic Analysis of APP/PS1 Transgenic Mice.

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. 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. 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. 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. 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.

Predicting conversion of Alzheimer’s disease based on multi-modal fusion of neuroimaging and genetic data

Identifying progressive mild cognitive impairment (pMCI) and stable mild cognitive impairment (sMCI) play a significant role in the early diagnosis of Alzheimer’s disease (AD) and can be helpful in early treatment to reduce the risk of conversion to AD. We proposed a classification method of sMCIs and pMCIs based on multi-modality data fusion of single-nucleotide polymorphisms (SNP), ratio of gray matter volume (RGV) obtained by morphometric measures, and sMRI images to predict the progression of AD. We validated the effectiveness of the proposed method by applying it to the task of identifying the disease status on the Alzheimer’s Disease Neuroimaging Initiative dataset. The results showed that the classification performances of our method was better than other state-of-the-art methods, and the accuracy rate for the classification of pMCI and sMCI reached 94.37%. The accuracy of our method was better than that of existing classification methods based on multi-modality images, and the accuracy rate for the classification of pMCI and sMCI reached 94.37%. Our study demonstrated that compared with unimodal and bimodal data, the method based on trimodal data fusion can better distinguish sMCI and pMCI, obtaining higher prediction accuracy for AD conversion. In addition, as a morphological feature, ratio of gray matter volume played a key role in distinguish of sMCI and pMCI, which can be used for the early diagnosis of AD.

The complex relationship between gut microbiota and Alzheimer's disease: A systematic review.

Alzheimer's disease (AD) is a progressive, degenerative disorder of the central nervous system. Despite extensive research conducted on this disorder, its precise pathogenesis remains unclear. In recent years, the microbiota-gut-brain axis has attracted considerable attention within the field of AD. The gut microbiota communicates bidirectionally with the central nervous system through the gut-brain axis, and alterations in its structure and function can influence the progression of AD. Consequently, regulating the gut microbiota to mitigate the progression of AD has emerged as a novel therapeutic approach. Currently, numerous studies concentrate on the intrinsic relationship between the microbiota-gut-brain axis and AD. In this paper, we summarize the multifaceted role of the gut microbiota in AD and present detailed therapeutic strategies targeting the gut microbiota, including the treatment of AD with Traditional Chinese Medicine (TCM), which has garnered increasing attention in recent years. Finally, we discuss potential therapeutic strategies for modulating the gut microbiota to alleviate the progression of AD, the current challenges in this area of research, and provide an outlook on future research directions in this field.

Using Inflammatory Plasma Biomarkers to Predict Preclinical Alzheimer’s Disease

AbstractBackgroundPlasma biomarkers have recently emerged to detect symptomatic Alzheimer Disease (AD), but have yet to be validated in preclinical AD populations, where Aβ accumulates in the brain but older adults are cognitively unimpaired (CU). In addition to AD pathologic plasma biomarkers (amyloid and tau), inflammatory markers can accurately detect symptomatic AD. We used pathologic and inflammatory plasma biomarkers to predict amyloid PET status in CU older adults.MethodParticipants were 125 CU older adults (mean age = 68) from the Butler Hospital Alzheimer’s Prevention Registry who completed amyloid PET through a separate research study. Blood samples were collected and analyzed for the following: an inflammatory panel consisting of 21 proteins, Aβ40, Aβ42, tau (total), p‐tau181, p‐tau 231, p‐tau217, GFAP and NfL. Multiple regression was used to evaluate the best predictors of amyloid PET status (positive vs. negative) in CU older adults. Model 1 included predictors age, education, and gender. Model 2 and 3 added predictors APOE status, Aβ42/40 ratio and p‐tau181 respectively. Random forest (RF) modeling was used to establish the five proteomic markers that best predicted amyloid PET status, and these markers were added in Model 4.ResultWe tested RF models that included all 21 inflammatory proteins, as well as the top 10, 8 and 5 inflammatory proteins. Models were compared based on sensitivity, specificity, PPV, NPV, and AUC. The model including the top 5 inflammatory proteins (CRP, SAA, sVCAM1, TNFα, and TARC) was the best fit with sensitivity of .88, specificity of .31, PPV of .71, NPV of .57 and AUC of .63. Multiple regression analysis showed that the best model for predicting amyloid PET positivity included age, years of education, gender, APOE E4 status, Aβ42/40 ratio and p‐tau181(p<.01). Adding the top 5 proteomic markers did not significantly improve the model.ConclusionResults revealed that the proteomic inflammatory markers in plasma did not add predictive value to standard AD pathologic plasma biomarkers in predicting amyloid PET positivity in CU older adults. This may reflect that the changes associated with inflammatory biomarkers occur later downstream in the pathogenesis and disease progression of AD.

Design, synthesis and biological evaluation of new H2S-releasing rivastigmine derivatives as neuroprotective molecules.

Alzheimer's disease (AD) represents one of the main challenges for the 21st century medical research as no disease-modifying agent has been successfully progressed to the market, while the number of people affected by AD is estimated to grow exponentially over the next years. The complex network of triggering factors involved in the insurgence and progression of AD can be rightly addressed as one of the main reasons behind the difficulty in identifying new pharmacological approaches. For this reason, the discovery and development of drugs endowed with pleiotropic activity remain the most valuable, but at the same time challenging, approaches to tackle down AD. Interestingly, the combination of active pharmacophores through molecular hybridization - or Multi-Target Directed Ligand strategy (MTDL) - has not been explored enough for this disease, despite proving to be a successfully strategy in other field, such as oncology. To contribute to the development of new strategies against AD, we decided to explore the hybridization of the marketed drug rivastigmine - prescribed to ameliorate AD symptomatology - with moieties capable to release hydrogen sulfide (H2S), a gasotransmitter with a key role in the neurological physiology of ageing. In particular, we identified compound 1, as a potent small molecule capable of inhibit AChE, preventing inflammation and ROS production in cultured neurons and microglia, triggering autophagy response and blocking Aβ fibrils propagation. Interestingly, the beneficial effects observed in vitro have been confirmed in vivo, since the rivastigmine derivative 1 improved the lifespan in a Caenorhabditis elegans model of AD.

Exploring the impact of APOE ɛ4 on functional connectivity in Alzheimer’s disease across cognitive impairment levels

The apolipoprotein E (APOE) ɛ4 allele is a recognized genetic risk factor for Alzheimer’s Disease (AD). Studies have shown that APOE ɛ4 mediates the modulation of intrinsic functional brain networks in cognitively normal individuals and significantly disrupts the whole-brain topological structure in AD patients. However, how APOE ɛ4 regulates brain functional connectivity (FC) and consequently affects the levels of cognitive impairment in AD patients remains unknown. In this study, we systematically analyzed functional magnetic resonance imaging (fMRI) data from two distinct cohorts: an In-house dataset includes 59 AD patients (73.37±6.42 years), and the ADNI dataset includes 117 AD patients (74.91±7.91 years). Experimental comparisons were conducted by grouping AD patients based on both APOE ɛ4 status and cognitive impairment levels of AD. Network-Based Statistic (NBS) method and the Graph Neural Network Explainer (GNN-Explainer) were combined to identify significant FC changes across different comparisons. Importantly, the GNN-Explainer method was introduced as an enhancement over the NBS method to better model complex high-order nonlinear characteristics for discovering FC features that significantly contribute to classification tasks. The results showed that APOE ɛ4 primarily influenced temporal lobe FCs, while it influenced different cognitive impairment levels of AD by adjusting prefrontal-parietal FCs. These findings were validated by p-values < 0.05 from NBS method, and 5-fold cross-validation along with ablation studies from the GNN-Explainer method. In conclusion, our findings provide new insights into the role of APOE ɛ4 in altering FC dynamics during the progression of AD, highlighting potential targets for early intervention.