Potential Biomarker For Alzheimer's Disease Research Articles

Deciphering the role of lipid metabolism-related genes in Alzheimer's disease: a machine learning approach integrating Traditional Chinese Medicine.

Alzheimer's disease (AD) represents a progressive neurodegenerative disorder characterized by the accumulation of misfolded amyloid beta protein, leading to the formation of amyloid plaques and the aggregation of tau protein into neurofibrillary tangles within the cerebral cortex. The role of carbohydrates, particularly apolipoprotein E (ApoE), is pivotal in AD pathogenesis due to its involvement in lipid and cholesterol metabolism, and its status as a genetic predisposition factor for the disease. Despite its significance, the mechanistic contributions of Lipid Metabolism-related Genes (LMGs) to AD remain inadequately elucidated. This research endeavor seeks to bridge this gap by pinpointing biomarkers indicative of early-stage AD, with an emphasis on those linked to immune cell infiltration. To this end, advanced machine-learning algorithms and data derived from the Gene Expression Omnibus (GEO) database have been employed to facilitate the identification of these biomarkers. Differentially expressed genes (DEGs) were identified by comparing gene expression profiles between healthy individuals and Alzheimer's disease (AD) patients, using data from two Gene Expression Omnibus (GEO) datasets: GSE5281 and GSE138260. Functional enrichment analysis was conducted to elucidate the biological relevance of the DEGs. To ensure the reliability of the results, samples were randomly divided into training and validation sets. The analysis focused on lipid metabolism-related DEGs (LMDEGs) to explore potential biomarkers for AD. Machine learning algorithms, including Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and the Least Absolute Shrinkage and Selection Operator (LASSO) regression model, were applied to identify a key gene biomarker. Additionally, immune cell infiltration and its relationship with the gene biomarker were assessed using the CIBERSORT algorithm. The Integrated Traditional Chinese Medicine (ITCM) database was also referenced to identify Chinese medicines related to lipid metabolism and their possible connection to AD. This comprehensive strategy aims to integrate modern computational methods with traditional medicine to deepen our understanding of AD and its underlying mechanisms. The study identified 137 genes from a pool of 751 lipid metabolism-related genes (LMGs) significantly associated with autophagy and immune response mechanisms. Through the application of LASSO and SVM-RFE machine-learning techniques, four genes-choline acetyl transferase (CHAT), member RAS oncogene family (RAB4A), acyl-CoA binding domain-containing protein 6 (ACBD6), and alpha-galactosidase A (GLA)-emerged as potential biomarkers for Alzheimer's disease (AD). These genes demonstrated strong therapeutic potential due to their involvement in critical biological pathways. Notably, nine Chinese medicine compounds were identified to target these marker genes, offering a novel treatment approach for AD. Further, ceRNA network analysis revealed complex regulatory interactions involving these genes, underscoring their importance in AD pathology. CIBERSORT analysis highlighted a potential link between changes in the immune microenvironment and CHAT expression levels in AD patients, providing new insights into the immunological dimensions of the disease. The discovery of these gene markers offers substantial promise for the diagnosis and understanding of Alzheimer's disease (AD). However, further investigation is necessary to validate their clinical utility. This study illuminates the role of Lipid Metabolism-related Genes (LMGs) in AD pathogenesis, offering potential targets for therapeutic intervention. It enhances our grasp of AD's complex mechanisms and paves the way for future research aimed at refining diagnostic and treatment strategies.

Identification of neutrophil extracellular trap-related genes in Alzheimer's disease based on comprehensive bioinformatics analysis.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease. There are currently no effective interventions to slow down or prevent the occurrence and progression of AD. Neutrophil extracellular traps (NETs) have been proven to be tightly linked to AD. This project attempted to identify hub genes for AD based on NETs. Gene expression profiles of the training set and validation set were downloaded from the Gene Expression Omnibus (GEO) database, including non-demented (ND) controls and AD samples. NET-related genes (NETRGs) were collected from the literature. Differential analysis identified 21 AD differentially expressed NETRGs (AD-DE-NETRGs) majorly linked to functions such as defense response to bacterium as well as pathways including IL-17 signaling pathway, as evidenced by enrichment analyses of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Protein-protein interaction (PPI) network, Minutia Cylinder-Code (MCC) algorithm, and molecular complex detection (MCODE) algorithm in the CytoHubba plug-in were employed to identify five hub genes (NFKBIA, SOCS3, CCL2, TIMP1, ACTB). Their diagnostic ability was validated in the validation set using receiver operating characteristic (ROC) curves and gene differential expression analysis. A total of 16 miRNAs and 132 lncRNAs were predicted through the mirDIP and ENCORI databases, and a lncRNA-miRNA-mRNA regulatory network was constructed using Cytoscape software. Small molecular compounds such as Benzo(a)pyrene and Copper Sulfate were predicted to target hub genes using the CTD database. This project successfully identified five hub genes, which may serve as potential biomarkers for AD, proffering clues for new therapeutic targets.

Developing diagnostic biomarkers for Alzheimer's disease based on histone lactylation-related gene

BackgroundResearch underscores the significant influence of histone lactylation pathways in the progression of Alzheimer's disease (AD), though the molecular mechanisms associated with histone lactylation-related genes (HLRGs) in AD are still insufficiently investigated. MethodsThis study employed datasets GSE85426 and GSE97760 to identify candidate genes by intersecting weighted gene co-expression network analysis (WGCNA) module genes with AD-control differentially expressed genes (DEGs). Subsequently, machine learning refined key genes, validated by receiver operating characteristic (ROC) curve performance. Gene-set enrichment analysis (GSEA) explored the molecular mechanisms of these diagnostic markers. Concurrently, the association between the diagnostic genes and both differential immune cells and immune responses was examined. Furthermore, a ceRNA and gene-drug network was developed. Finally, the expression of the selected genes was validated using brain tissues from AD model mice. ResultsThis study identified five genes (ARID5B, NSMCE4A, SESN1, THADA, and XPA) with significant diagnostic utility, primarily enriched in olfactory transduction and N-glycan biosynthesis pathways. Correlation analysis demonstrated a strong positive association between all diagnostic genes and naive B cells. The ceRNA regulatory network comprised 7 miRNAs, 2 mRNAs, and 25 lncRNAs. Additionally, 33 drugs targeting the diagnostic genes were predicted. Following expression validation through training and validation sets, three genes (ARID5B, SESN1, XPA) were ultimately confirmed as biomarkers for this study. RT-qPCR and Western blot analyses revealed upregulated expression of ARID5B, SESN1, and XPA in the cerebral tissue of AD model mice. ConclusionThree histone lactylation-linked genes (ARID5B, SESN1, XPA) were identified as potential AD biomarkers, indicating a strong association with disease progression.

Proteomics of plasma-derived extracellular vesicles reveals S100A8 as a novel biomarker for Alzheimer's disease: A preliminary study

Extracellular vesicles (EVs) act as mediators for intercellular transfer of Aβ and tau proteins, promoting the propagation of these pathological misfolded proteins throughout the brain in Alzheimer's disease (AD). Levels of blood exosomal Aβ42, total Tau (t-Tau) and phosphorylated Tau (p-Tau) had a high correlation with their concentrations in cerebrospinal fluid (CSF), demonstrating that exosomal biomarkers have equal contribution as those in CSF for the diagnosis of AD. We aimed to comprehensively characterize the proteome of plasma-derived EVs to identify differentially expressed proteins (DEPs) and pathways in AD. Tandem mass tag (TMT) labeled quantitative proteomics was applied to analyze plasma-derived EV proteins in 9 AD patients and 9 healthy controls. 335 proteins were quantified, and 12 DEPs were identified including seven upregulated proteins and five down-regulated proteins. Oligomerized Aβ1–42 induced SH-SY5Y cell damage model was built to mimic the pathological changes of AD, and small interfering RNA (siRNA) against S100A8 was used to knock down S100A8 expression. Results displayed S100A8 was down regulated in plasma-derived EVs from AD patients, while enriched in EVs derived from Aβ1–42-induced SH-SY5Y cells. Furthermore, Aβ1–42-induced SH-SY5Y cells treated with S100A8 siRNA showed decreased Aβ levels in cell lysate and EVs, especially in EVs. SignificanceThe investigation aimed to comprehensively characterize the proteome of plasma-derived EVs to identify DEPs and potential biomarker of AD. S100A8 was found down regulated in plasma-derived EVs from AD patients using TMT labeled quantitative proteomics. The diagnostic value of S100A8 was also confirmed using receiver operating characteristic curve (ROC) analysis. Furthermore, Aβ1–42-induced SH-SY5Y cells treated with S100A8 siRNA showed decreased Aβ levels in cell lysate and EVs, especially in EVs. The preliminary findings suggest that suppression of S100A8 expression inhibits Aβ aggregation both in cell lysate and EVs from Aβ1–42-induced SH-SY5Y cells, and S100A8 more likely regulates Aβ aggregation via EVs. Therefore, plasma-derived EV S100A8 might be a potential biomarker of AD. Manipulation of S100A8 expression may be a novel therapeutic strategy in the treatment of AD.

Biomarker-Based Precision Therapy for Alzheimer's Disease: Multidimensional Evidence Leading a New Breakthrough in Personalized Medicine.

(1) Background: Alzheimer's disease (AD) is a worldwide neurodegenerative disorder characterized by the buildup of abnormal proteins in the central nervous system and cognitive decline. Since no radical therapy exists, only symptomatic treatments alleviate symptoms temporarily. In this review, we will explore the latest advancements in precision medicine and biomarkers for AD, including their potential to revolutionize the way we diagnose and treat this devastating condition. (2) Methods: A literature search was performed combining the following Medical Subject Heading (MeSH) terms on PubMed: "Alzheimer's disease", "biomarkers", "APOE", "APP", "GWAS", "cerebrospinal fluid", "polygenic risk score", "Aβ42", "τP-181", " p-tau217", "ptau231", "proteomics", "total tau protein", and "precision medicine" using Boolean operators. (3) Results: Genome-wide association studies (GWAS) have identified numerous genetic variants associated with AD risk, while a transcriptomic analysis has revealed dysregulated gene expression patterns in the brains of individuals with AD. The proteomic and metabolomic profiling of biological fluids, such as blood, urine, and CSF, and neuroimaging biomarkers have also yielded potential biomarkers of AD that could be used for the early diagnosis and monitoring of disease progression. (4) Conclusion: By leveraging a combination of the above biomarkers, novel ultrasensitive immunoassays, mass spectrometry methods, and metabolomics, researchers are making significant strides towards personalized healthcare for individuals with AD.

Integration of single-cell sequencing with machine learning and Mendelian randomization analysis identifies the NAP1L1 gene as a predictive biomarker for Alzheimer's disease.

The most effective approach to managing Alzheimer's disease (AD) lies in identifying reliable biomarkers for AD to forecast the disease in advance, followed by timely early intervention for patients. Transcriptomic data on peripheral blood mononuclear cells (PBMCs) from patients with AD and the control group were collected, and preliminary data processing was completed using standardized analytical methods. PBMCs were initially segmented into distinct subpopulations, and the divisions were progressively refined until the most significantly altered cell populations were identified. A combination of high-dimensional weighted gene co-expression analysis (hdWGCNA), cellular communication, pseudotime analysis, and single-cell regulatory network inference and clustering (SCENIC) analysis was used to conduct single-cell transcriptomics analysis and identify key gene modules from them. Genes were screened using machine learning (ML) in the key gene modules, and internal and external dataset validations were performed using multiple ML methods to test predictive performance. Finally, bidirectional Mendelian randomization (MR) analysis, regional linkage analysis, and the Steiger test were employed to analyze the key gene. A significant decrease in non-classical monocytes was detected in PMBC of AD patients. Subsequent analyses revealed the inherent connection of non-classical monocytes to AD, and the NAP1L1 gene identified within its gene module appeared to exhibit some association with AD as well. The NAP1L1 gene is a potential predictive biomarker for AD.

From Gut Microbiota to Brain Waves: The Potential of the Microbiome and EEG as Biomarkers for Cognitive Impairment.

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder and a leading cause of dementia. Aging is a significant risk factor for AD, emphasizing the importance of early detection since symptoms cannot be reversed once the advanced stage is reached. Currently, there is no established method for early AD diagnosis. However, emerging evidence suggests that the microbiome has an impact on cognitive function. The gut microbiome and the brain communicate bidirectionally through the gut-brain axis, with systemic inflammation identified as a key connection that may contribute to AD. Gut dysbiosis is more prevalent in individuals with AD compared to their cognitively healthy counterparts, leading to increased gut permeability and subsequent systemic inflammation, potentially causing neuroinflammation. Detecting brain activity traditionally involves invasive and expensive methods, but electroencephalography (EEG) poses as a non-invasive alternative. EEG measures brain activity and multiple studies indicate distinct patterns in individuals with AD. Furthermore, EEG patterns in individuals with mild cognitive impairment differ from those in the advanced stage of AD, suggesting its potential as a method for early indication of AD. This review aims to consolidate existing knowledge on the microbiome and EEG as potential biomarkers for early-stage AD, highlighting the current state of research and suggesting avenues for further investigation.

Oxidative stress-mediated neuroinflammation in Alzheimer's disease.

Reactive oxygen species (ROS) are metabolic by-products that constitute an indispensable component of physiological processes, albeit their heightened presence may proffer substantial perils to biological entities. Such a proliferation gives rise to a gradual escalation of oxidative stress within the organism, thereby compromising mitochondrial functionality and inflicting harm upon various bodily systems, with a particular predilection for the central nervous system. In its nascent stages, it is plausible that inflammation has been a facilitator in the progression of the malady. The precise role of inflammation in Alzheimer's disease (AD) remains somewhat enigmatic, although it is conceivable that activated microglia and astrocytes might be implicated in the removal of amyloid-β (Aβ) deposits. Nonetheless, prolonged microglial activation is associated with Tau phosphorylation and Aβ aggregation. Research studies have indicated that AD brains upregulate complementary molecules, inflammatory cytokines, acute phase reacting agents, and other inflammatory mediators that may cause neurodegeneration. In this review, oxidative damage products will be discussed as potential peripheral biomarkers for AD and its early stages. The disordered excretion of pro-inflammatory cytokines, chemokines, oxygen, and nitrogen-reactive species, along with the stimulation of the complement system by glial cells, has the potential to disrupt the functionality of neuronal termini. This perturbation, in turn, culminates in compromised synaptic function, a phenomenon empirically linked to the manifestation of cognitive impairments. The management of neurodegenerative conditions in the context of dementia necessitates therapeutic interventions that specifically target the excessive production of inflammatory and oxidative agents. Furthermore, we shall deliberate upon the function of microglia and oxidative injury in the etiology of AD and the ensuing neurodegenerative processes.

Development and Assessment of a Prediction Model for Alzheimer's Disease Diagnosis Based on Thermoregulation-Related Genes.

Alzheimer's disease (AD) is a prevalent neurodegenerative condition among the elderly population and the most common form of dementia, however, we lack potent interventions to arrest its inherent pathogenic vectors. Robust evidence indicates thermoregulatory perturbations during and before the onset of symptoms. Therefore, temperature-regulated biomarkers may offer clues to therapeutic targets during the presymptomatic stage. The purpose of this study is to develop and assess a thermoregulation-related gene prediction model for Alzheimer's Disease diagnosis. This study aims to utilize microarray bioinformatic analysis to identify the potential biomarkers of AD by analyzing four microarray datasets (GSE48350, GSE5281, GSE122063, and GSE181279) of AD patients. Furthermore, thermoregulation-associated hub genes were identified, and the expression patterns in the brain were explored. In addition, we explored the infiltration of immune cells with thermoregulation-related hub genes. Diagnostic marker validation was then performed at the single-cell level. Finally, the prediction of targeted drugs was performed based on the hub genes. Through the analysis of four datasets pertaining to AD, a total of five genes associated with temperature regulation were identified. Notably, CCK, CXCR4, SLC27A4, and SLC17A6 emerged as diagnostic markers indicative of AD-related brain injury. Furthermore, in the examination of peripheral blood samples from AD patients, SLC27A4 and CXCR4 were identified as pivotal diagnostic indicators. Regrettably, animal experimentation was not pursued to validate the data; rather, an assessment of temperature regulation-related genes was conducted. Future investigations will be undertaken to establish the correlation between these genes and AD pathology. Overall, CCK, CXCR4, SLC27A4, and SLC17A6 can be considered pivotal biomarkers for diagnosing the pathogenesis and molecular functions of AD.

Surface protein profiling and subtyping of extracellular vesicles in body fluids reveals non-CSF biomarkers of Alzheimer's disease.

Noninvasive and effortless diagnosis of Alzheimer's disease (AD) remains challenging. Here we report the multiplexed profiling of extracellular vesicle (EV) surface proteins at the single EV level in five types of easily accessible body fluids using a proximity barcoding assay (PBA). A total of 183 surface proteins were detected on the EVs from body fluids collected from APP/PS1 transgenic mice and patients with AD. The AD-associated differentially expressed EV proteins could discriminate between the control and AD/AD model samples with high accuracy. Based on machine learning predictive models, urinary EV proteins exhibited the highest diagnostic potential compared to those on other biofluid EVs, both in mice and humans. Single EV analysis further revealed AD-associated EV subpopulations in the tested body fluids, and a urinary EV subpopulation with the signature proteins PLAU, ITGAX and ANXA1 could diagnose patients with AD in blinded datasets with 88% accuracy. Our results suggest that EVs and their subpopulations from noninvasive body fluids, particularly urine, are potential diagnostic biomarkers for AD.

Analysis and experimental validation of necroptosis-related molecular classification, immune signature and feature genes in Alzheimer's disease.

Necroptosis, a programmed cell death pathway, has been demonstrated to be activated in Alzheimer's disease (AD). However,the precise role of necroptosis and its correlation with immune cell infiltration in AD remains unclear. In this study, we conducted non-negative matrix factorization clustering analysis to identify three subtypes of AD based on necroptosis-relevant genes. Notably, these subtypes exhibited varying necroptosis scores, clinical characteristics and immune infiltration signatures. Cluster B, characterized by high necroptosis scores, showed higher immune cell infiltration and was associated with a more severe pathology, potentially representing a high-risk subgroup. To identify potential biomarkers for AD within cluster B, we employed two machine learning algorithms: the least absolute shrinkage and selection operator regression and Random Forest. Subsequently, we identified eight feature genes (CARTPT, KLHL35, NRN1, NT5DC3, PCYOX1L, RHOQ, SLC6A12, and SLC38A2) that were utilized to develop a diagnosis model with remarkable predictive capacity for AD. Moreover, we conducted validation using bulk RNA-seq, single-nucleus RNA-seq, and in vivo experiments to confirm the expression of these feature genes. In summary, our study identified a novel necroptosis-related subtype of AD and eight diagnostic biomarkers, explored the roles of necroptosis in AD progression and shed new light for the clinical diagnosis and treatment of this disease.

Identification of key regulatory molecules in the early development stage of Alzheimer's disease.

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases, the incidence of which increases with age, and the pathological changes in the brain are irreversible. Recent studies have highlighted the essential role of long noncoding RNAs (lncRNAs) in AD by acting as competing endogenous RNAs (ceRNAs). Our aim was to construct lncRNA-associated ceRNA regulatory networks composed of potential biomarkers for the early stage of AD. AD related datasets come from AlzData and GEO databases. The R package 'Limma' identifies differentially expressed genes (DEGs), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases for functional enrichment analysis. Protein-protein interactions (PPIs) in DEGs were constructed in the STRING database, and Cytoscape software identified DEGs. Convergent functional genomics (CFG) analysis of differentially expressed hub genes (referred to as early-DEGs) in the brain before the development of AD pathology. The AlzData database analyses the expression levels of early-DEGs in different nerve cells. The lncRNA-miRNA-mRNA regulatory network was established according to the ceRNA hypothesis. We identified four lncRNAs (XIST, NEAT1, KCNQ1OT1 and HCG18) and four miRNAs (hsa-let-7c-5p, hsa-miR-107, hsa-miR-129-2-3p and hsa-miR-214-3p) were preliminarily identified as potential biomarkers for early AD, competitively regulating Atp6v0b, Atp6v1e1 Atp6v1f and Syt1. This study indicates that NEAT1, XIST, HCG18 and KCNQ1OT1 act as ceRNAs in competitive binding with miRNAs to regulate the expression of Atp6v0b, Atp6v1e1, Atp6v1f and Syt1 before the occurrence of pathological changes in AD.

Extracellular vesicles in Alzheimer's disease.

Extracellular vesicles (EVs) are small vesicles released by cells that facilitate cell signaling. They are categorized based on their biogenesis and size. In the context of the central nervous system (CNS), EVs have been extensively studied for their role in both normal physiological functions and diseases like Alzheimer's disease (AD). AD is a neurodegenerative disorder characterized by cognitive decline and neuronal death. EVs have emerged as potential biomarkers for AD due to their involvement in disease progression. Specifically, EVs derived from neurons, astrocytes, and neuron precursor cells exhibit changes in quantity and composition in AD. Neuron-derived EVs have been found to contain key proteins associated with AD pathology, such as amyloid beta (Aß) and tau. Increased levels of Aß in neuron-derived EVs isolated from the plasma have been observed in individuals with AD and mild cognitive impairment, suggesting their potential as early biomarkers. However, the analysis of tau in neuron-derived EVs is still inconclusive. In addition to Aß and tau, neuron-derived EVs also carry other proteins linked to AD, including synaptic proteins. These findings indicate that EVs could serve as biomarkers for AD, particularly for early diagnosis and disease monitoring. However, further research is required to validate their use and explore potential therapeutic applications. To summarize, EVs are small vesicles involved in cell signaling within the CNS. They hold promise as biomarkers for AD, potentially enabling early diagnosis and monitoring of disease progression. Ongoing research aims to refine their use as biomarkers and uncover additional therapeutic applications.

Beyond CSF and Neuroimaging Assessment: Evaluating Plasma miR-145-5p as a Potential Biomarker for Mild Cognitive Impairment and Alzheimer's Disease.

Alzheimer's disease (AD) is the most common cause of dementia. New strategies for the early detection of MCI and sporadic AD are crucial for developing effective treatment options. Current techniques used for diagnosis of AD are invasive and/or expensive, so they are not suitable for population screening. Cerebrospinal fluid (CSF) biomarkers such as amyloid β1-42 (Aβ1-42), total tau (T-tau), and phosphorylated tau181 (P-tau181) levels are core biomarkers for early diagnosis of AD. Several studies have proposed the use of blood-circulating microRNAs (miRNAs) as potential novel early biomarkers for AD. We therefore applied a novel approach to identify blood-circulating miRNAs associated with CSF biomarkers and explored the potential of these miRNAs as biomarkers of AD. In total, 112 subjects consisting of 28 dementia due to AD cases, 63 MCI due to AD cases, and 21 cognitively healthy controls were included. We identified seven Aβ1-42-associated plasma miRNAs, six P-tau181-associated plasma miRNAs, and nine Aβ1-42-associated serum miRNAs. These miRNAs were involved in AD-relevant biological processes, such as PI3K/AKT signaling. Based on this signaling pathway, we constructed an miRNA-gene target network, wherein miR-145-5p has been identified as a hub. Furthermore, we showed that miR-145-5p performs best in the prediction of both AD and MCI. Moreover, miR-145-5p also improved the prediction performance of the mini-mental state examination (MMSE) score. The performance of this miRNA was validated using different datasets including an RT-qPCR dataset from plasma samples of 23 MCI cases and 30 age-matched controls. These findings indicate that blood-circulating miRNAs that are associated with CSF biomarkers levels and specifically plasma miR-145-5p alone or combined with the MMSE score can potentially be used as noninvasive biomarkers for AD or MCI screening in the general population, although studies in other AD cohorts are necessary for further validation.

DHEC mesylate attenuates pathologies and aberrant bisecting N-glycosylation in Alzheimer's disease models

Tremendous progress has been made to develop the therapy of Alzheimer's disease (AD). Existing several anti-AD remedies, with certain limitations, are far from adequate. Evidence suggests that dihydroergocristine (DHEC) mesylate, one of the main components of Ergoloid mesylates, can reduce the production of amyloid-β in vitro. However, the therapeutic effect of DHEC mesylate in AD and its underlying mechanism are still largely unknown. Herein, we characterized the pharmacological effect of DHEC mesylate in AD and found that the spatial memory disorders and Alzheimer-type pathologies were alleviated by DHEC mesylate administration. Moreover, we demonstrated that DHEC mesylate improved aberrant bisecting N-glycosylation, which was identified as a potential biomarker of AD. We further explored the underlying mechanism and confirmed that DHEC mesylate protected against AD via AMPK and ERK signaling, in which, AMPK was the dominant down-stream molecule of DHEC mesylate. In summary, our findings provide foundations for development of DHEC mesylate as a therapeutic approach for AD.

Leukocytes and Endothelial Cells Participate in the Pathogenesis of Alzheimer's Disease: Identifying New Biomarkers Mirroring Metabolic Alterations.

Alzheimer's disease (AD) is a neurodegenerative disorder marked by amyloid-β accumulation, tau dysfunction, and neuroinflammation, involving endothelial cells and leukocytes. The breakdown of the blood-brain barrier allows immune cell infiltration, intensifying inflammation. A decreased ratio of Connexin-37 (Cx37, also known as GJA4: Gap Junction Protein Alpha 4) and Prolyl Hydroxylase Domain-Containing Protein 3 (PHD3, also known as EGLN3: Egl-9 Family Hypoxia Inducible Factor 3), Cx37/PHD3, consistently observed in different AD-related models, may represent a novel potential biomarker of AD, albeit the exact mechanisms underlying this phenomenon, most likely based on gap junction-mediated cellular interaction that modulate the cellular metabolite status, remain to be fully elucidated.

CSF 14–3-3 zeta(ζ) isoform is associated with tau pathology and cognitive decline in Alzheimer's disease

14–3-3 is a family of conserved proteins that consist of seven isoforms which are highly expressed in the brain, and 14–3-3 zeta(ζ) is one of the isoforms encoded by the YWHAZ gene. Previous studies demonstrated that 14–3-3ζ is deposited in the neurofibrillary tangles of Alzheimer's disease (AD) brains, and that 14–3-3ζ interacts with tau from the purified neurofibrillary tangles of AD brain extract. The present study examined the cerebrospinal fluid (CSF) 14–3-3ζ levels of 719 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI), including cognitively normal (CN) participants, patients with mild cognitive impairment (MCI) and patients with AD dementia, and aimed to identify whether CSF 14–3-3ζ is associated with tau pathology. CSF 14–3-3ζ levels were increased in AD, and particularly elevated among tau pathology positive individuals. CSF 14–3-3ζ levels were associated with CSF phosphorylated tau 181 (p-tau) (r = 0.741, P < 0.001) and plasma p-tau (r = 0.293, P < 0.001), which are fluid biomarkers of tau pathology, and could predict tau pathology positive status with high accuracy (area under the receiver operating characteristic curve [AUC], 0.891). CSF 14–3-3ζ levels were also correlated to synaptic biomarker CSF GAP-43 (r = 0.609, P < 0.001) and neuroinflammatory biomarker CSF sTREM-2 (r = 0.507, P < 0.001). High CSF 14–3-3ζ levels at baseline were associated with progressive decline of cognitive function and neuroimaging findings during follow up. In conclusion, this study suggests that CSF 14–3-3ζ is a potential biomarker of AD that may be useful in clinical practice.

Comparing Symmetric Dimethylarginine and Amyloid-β42 as Predictors of Alzheimer's Disease Development.

Physicians may soon be able to diagnose Alzheimer's disease (AD) in its early stages using fluid biomarkers like amyloid. However, it is acknowledged that additional biomarkers need to be characterized which would facilitate earlier monitoring of AD pathogenesis. To determine if a potential novel inflammation biomarker for AD, symmetric dimethylarginine, has utility as a baseline serum biomarker for discriminating prodromal AD from cognitively unimpaired controls in comparison to cerebrospinal fluid amyloid-β42 (Aβ42). Data including demographics, magnetic resonance imaging and fluorodeoxyglucose-positron emission tomography scans, Mini-Mental State Examination and Functional Activities Questionnaire scores, and biomarker concentrations were obtained from the Alzheimer's Disease Neuroimaging Initiative for a total of 146 prodromal AD participants and 108 cognitively unimpaired controls. Aβ42 (p = 0.65) and symmetric dimethylarginine (p = 0.45) were unable to predict age-matched cognitively unimpaired controls and prodromal AD participants. Aβ42 was negatively associated with regional brain atrophy and hypometabolism as well as cognitive and functional decline in cognitively unimpaired control participants (p < 0.05) that generally decreased in time. There were no significant associations between Aβ42 and symmetric dimethylarginine with imaging or neurocognitive biomarkers in prodromal AD patients. Correlations were smaller between Aβ42 and neuropathological biomarkers over time and were absent in prodromal AD participants, suggesting a plateau effect dependent on age and disease stage. Evidence supporting symmetric dimethylarginine as a novel biomarker for AD as a single measurement was not found.

Free Water MR Imaging of White Matter Microstructural Changes is a Sensitive Marker of Amyloid Positivity in Alzheimer's Disease.

Extracellular free water (FW) resulting from white matter degeneration limits the sensitivity of diffusion tensor imaging (DTI) in predicting Alzheimer's disease (AD). To evaluate the sensitivity of FW-DTI in detecting white matter microstructural changes in AD. To validate the effectiveness of FW-DTI indices to predict amyloid-beta (Aβ) positivity in mild cognitive impairment (MCI) subtypes. Retrospective. Thirty-eight Aβ-negative cognitively healthy (CH) controls (68.74 ± 8.28 years old, 55% female), 15 Aβ-negative MCI patients (MCI-n) (68.87 ± 8.83 years old, 60% female), 29 Aβ-positive MCI patients (MCI-p) (73.03 ± 7.05 years old, 52% female), and 29 Aβ-positive AD patients (72.93 ± 9.11 years old, 55% female). 3.0T; DTI, T1-weighted, T2-weighted, T2 star-weighted angiography, and Aβ PET (18F-florbetaben or 11C-PIB). FW-corrected and standard diffusion indices were analyzed using trace-based spatial statistics. Area under the curve (AUC) in distinguishing MCI subtypes were compared using support vector machine (SVM). Chi-squared test, one-way analysis of covariance, general linear regression analyses, nonparametric permutation tests, partial Pearson's correlation, receiver operating characteristic curve analysis, and linear SVM. A P value <0.05 was considered statistically significant. Compared with CH/MCI-n/MCI-p, AD showed significant change in tissue compartment indices of FW-DTI. No difference was found in the FW index among pair-wise group comparisons (the minimum FWE-corrected P = 0.114). There was a significant association between FW-DTI indices and memory and visuospatial function. The SVM classifier with tissue radial diffusivity as an input feature had the best classification performance of MCI subtypes (AUC = 0.91), and the classifying accuracy of FW-DTI was all over 89.89%. FW-DTI indices prove to be potential biomarkers of AD. The classification of MCI subtypes based on SVM and FW-DTI indices has good accuracy and could help early diagnosis. 4 TECHNICAL EFFICACY: Stage 2.

Static and dynamic functional connectivity combined with the triple network model in amnestic mild cognitive impairment and Alzheimer's disease.

Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) are characterized by abnormal functional connectivity (FC) of default-mode network (DMN), salience network (SN), and central executive network (CEN). Static FC (sFC) and dynamic FC (dFC) combined with triple network model can better study the dynamic and static changes of brain networks, and improve its potential diagnostic value in the diagnosis of AD spectrum disorders. Differences in sFC values and dFC variability patterns among the three brain networks of the three groups (53 AD patients, 40 aMCI patients, and 40 NCs) were computed by ANOVA using Gaussian Random Field theory (GRF) correction. The correlation between FC values (sFC values and dFC variability) in the three networks and cognitive scores (MMSE and MoCA) in AD and aMCI groups was analyzed separately. Within the DMN network, there were significant differences of sFC values in right/left medial superior frontal gyrus and dFC variability in left opercular part inferior frontal gyrus and right dorsolateral superior frontal gyrus among the three groups. Within the CEN network, there were significant differences of sFC values in left superior parietal gyrus. Within the SN network, there were significant differences of dFC variability in right Cerebelum_7b and left opercular part inferior frontal gyrus. In addition, there was a significant negative correlation between FC values (sFC values of CEN and dFC variability of SN) and MMSE and MoCA scores. It suggests that sFC, dFC combined with triple network model can be considered as potential biomarkers for AD and aMCI.