Potential Biomarker For Alzheimer's Disease Research Articles

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.

The associations of serum isoleucine with Alzheimer`s disease on assisting diagnosis, predicting conversion and assessing cognition.

Advances in blood biomarker discovery have enabled the improved diagnosis and prognosis of Alzheimer's disease (AD). Most branched-chain amino acids, except isoleucine (Ile), are correlated with both mild cognitive impairment (MCI) and AD. Therefore, this study investigated the association between serum Ile levels and MCI/AD. This study stratified 700 participants from the Alzheimer's Disease Neuroimaging Initiative database into four diagnostic groups: cognitively normal, stable MCI, progressive MCI, and AD. Analysis of covariance and chi-square analyses were used to test the demographic data. Receiver operating curve analyses were used to calculate the diagnostic accuracy of different biomarkers and were compared using MedCalc 20. Additionally, Cox proportional hazards models were used to measure the ability of serum Ile levels to predict disease conversion. Finally, a linear mixed-effects model was used to evaluate the associations between serum Ile levels and cognition, brain structure, and metabolism. Serum Ile concentration was decreased in AD and demonstrated significant diagnostic efficacy. The combination of serum Ile and cerebrospinal fluid (CSF) phosphorylated tau (P-tau) levels improved the diagnostic accuracy in AD compared to T-tau alone. Serum Ile levels significantly predicted the conversion from MCI to AD (cutoff value = 78.3 μM). Finally, the results of this study also revealed a correlation between serum Ile levels and the Alzheimer's Disease Assessment Scale cognitive subscale Q4. Serum Ile level may be a potential biomarker of AD. Ile had independent diagnostic efficacy and significantly improved the diagnostic accuracy of CSF P-tau in AD. Patients with MCI with a lower serum Ile level had a higher risk of progression to AD and a worse cognition assessment.

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

BackgroundExtracellular free water (FW) resulting from white matter degeneration limits the sensitivity of diffusion tensor imaging (DTI) in predicting Alzheimer's disease (AD).PurposeTo 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.Study TypeRetrospective.PopulationThirty‐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).Field Strength/Sequence3.0T; DTI, T1‐weighted, T2‐weighted, T2 star‐weighted angiography, and Aβ PET (18F‐florbetaben or 11C‐PIB).AssessmentFW‐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).Statistical TestsChi‐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 &lt;0.05 was considered statistically significant.ResultsCompared 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%.Data ConclusionFW‐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.Evidence Level4Technical EfficacyStage 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.

Alteration of Metabolic Profiles during the Progression of Alzheimer's Disease.

(1) Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder that threatens the population health of older adults. However, the mechanisms of the altered metabolism involved in AD pathology are poorly understood. The aim of the study was to identify the potential biomarkers of AD and discover the metabolomic changes produced during the progression of the disease. (2) Methods: Gas chromatography-mass spectrometry (GC-MS) was used to measure the concentrations of the serum metabolites in a cohort of subjects with AD (n = 88) and a cognitively normal control (CN) group (n = 85). The patients were classified as very mild (n = 25), mild (n = 27), moderate (n = 25), and severe (n = 11). The serum metabolic profiles were analyzed using multivariate and univariate approaches. Least absolute shrinkage and selection operator (LASSO) logistic regression was applied to identify the potential biomarkers of AD. Biofunctional enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes. (3) Results: Our results revealed considerable separation between the AD and CN groups. Six metabolites were identified as potential biomarkers of AD (AUC > 0.85), and the diagnostic model of three metabolites could predict the risk of AD with high accuracy (AUC = 0.984). The metabolic enrichment analysis revealed that carbohydrate metabolism deficiency and the disturbance of amino acid, fatty acid, and lipid metabolism were involved in AD progression. Especially, the pathway analysis highlighted that l-glutamate participated in four crucial nervous system pathways (including the GABAergic synapse, the glutamatergic synapse, retrograde endocannabinoid signaling, and the synaptic vesicle cycle). (4) Conclusions: Carbohydrate metabolism deficiency and amino acid dysregulation, fatty acid, and lipid metabolism disorders were pivotal events in AD progression. Our study may provide novel insights into the role of metabolic disorders in AD pathogenesis and identify new markers for AD diagnosis.

Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data

Deep learning has shown potential in various scientific domains but faces challenges when applied to complex, high-dimensional multi-omics data. Alzheimer's Disease (AD) is a neurodegenerative disorder that lacks targeted therapeutic options. This study introduces the Circular-Sliding Window Association Test (c-SWAT) to improve the classification accuracy in predicting AD using serum-based metabolomics data, specifically lipidomics. The c-SWAT methodology builds upon the existing Sliding Window Association Test (SWAT) and utilizes a three-step approach: feature correlation analysis, feature selection, and classification. Data from 997 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) served as the basis for model training and validation. Feature correlations were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA), and Convolutional Neural Networks (CNN) were employed for feature selection. Random Forest was used for the final classification. The application of c-SWAT resulted in a classification accuracy of up to 80.8% and an AUC of 0.808 for distinguishing AD from cognitively normal older adults. This marks a 9.4% improvement in accuracy and a 0.169 increase in AUC compared to methods without c-SWAT. These results were statistically significant, with a p-value of 1.04×10ˆ-4. The approach also identified key lipids associated with AD, such as Cer(d16:1/22:0) and PI(37:6). Our results indicate that c-SWAT is effective in improving classification accuracy and in identifying potential lipid biomarkers for AD. These identified lipids offer new avenues for understanding AD and warrant further investigation. The specific funding of this article is provided in the acknowledgements section.

Evaluation of whole-brain oxygen metabolism in Alzheimer's disease using QSM and quantitative BOLD

ObjectiveThe objective of this study was to evaluate the whole-brain pattern of oxygen extraction fraction (OEF), cerebral blood flow (CBF), and cerebral metabolic rate of oxygen consumption (CMRO2) perturbation in Alzheimer's disease (AD) and investigate the relationship between regional cerebral oxygen metabolism and global cognition. MethodsTwenty-six AD patients and 25 age-matched healthy controls (HC) were prospectively recruited in this study. Mini-Mental State Examination (MMSE) was used to evaluate cognitive status. We applied the QQ-CCTV algorithm which combines quantitative susceptibility mapping and quantitative blood oxygen level-dependent models (QQ) for OEF calculation. CBF map was computed from arterial spin labeling and CMRO2 was generated based on Fick's principle. Whole-brain and regional OEF, CBF, and CMRO2 analyses were performed. The associations between these measures in substructures of deep brain gray matter and MMSE scores were assessed. ResultsWhole brain voxel-wise analysis showed that CBF and CMRO2 values significantly decreased in AD predominantly in the bilateral angular gyrus, precuneus gyrus and parieto-temporal regions. Regional analysis showed that CBF value decreased in the bilateral caudal hippocampus and left rostral hippocampus and CMRO2 value decreased in left caudal and rostral hippocampus in AD patients. Considering all subjects in the AD and HC groups combined, the mean CBF and CMRO2 values in the bilateral hippocampus positively correlated with the MMSE score. ConclusionCMRO2 mapping with the QQ-CCTV method - which is readily available in MR systems for clinical practice - can be a potential biomarker for AD. In addition, CMRO2 in the hippocampus may be a useful tool for monitoring cognitive impairment.

Identification of Peripheral Blood GZMK + CD8 + T Cells As Biomarkers of Alzheimer's Disease Based on Single-Cell Transcriptome

Based on single-cell RNA sequencing (scRNA-seq) to explore immune characteristics in the peripheral blood of patients with Alzheimer's disease (AD) as biomarkers. GSE168522, the scRNA-seq dataset of AD peripheral blood immune cells, was downloaded from the Gene Expression Omnibus (GEO) database and was analyzed in the RAD-Blood web server (http://www.bioinform.cn/RAD-Blood/). The changes in blood cell composition in AD patients were analyzed. The abnormal communications between different types of cells in AD patients were investigated by the CellChat R package. There were two kinds of CD8 + T cells in the blood of AD patients and healthy individuals, one of which highly expressed granzyme K ( GZMK) (false discovery rate [FDR]<0.05), and the other highly expressed GZMA, GZMB, and GZMH (FDR<0.05). In the blood of AD patients, the content of GZMK + CD8 + T cells was increased by 32.9% ( P=5.15E-21), their interactions with other cell types were increased, and they might be associated with AD through the abnormal signal transduction of major histocompatibility complex class Ⅰ (MHC-Ⅰ). Erythrocyte provided the main ligands, that are, human leukocyte antigen (HLA) class Ⅰ molecules, including HLA- A, HLA- B, HLA- C, and HLA- E, for the abnormal MHC-Ⅰ signaling pathway of GZMK + CD8 + T cells. The RESISTIN signaling pathway was specifically enriched in the blood of AD patients. The increased content of peripheral blood GZMK + CD8 + T cells, the increased interaction between GZMK + CD8 + T cells and erythrocytes, and the enhanced RESISTIN pathway are potential blood biomarkers of AD.

Evaluating the Clinical Significance of Diazepam Binding Inhibitor in Alzheimer’s Disease: A Comparison with Inflammatory, Oxidative, and Neurodegenerative Biomarkers

Introduction: Alzheimer’s disease (AD) is one of the pathologies that the scientific world is still desperate for. The aim of this study was the investigation of diazepam binding inhibitor (DBI) as a prognostic factor for AD prognosis. Methods: A total of 120 participants were divided into 3 groups. Forty new diagnosed Alzheimer patients (NDG) who have been diagnosed but have not started AD treatment, 40 patients who diagnosed 5 years ago (D5YG), and 40 healthy control groups (CG) were included in the study. Levels of DBI, oxidative stress, inflammatory, and neurodegenerative biomarkers were compared between 3 groups. Results: Plasma levels of DBI, oligomeric Aβ, total tau, glial fibrillary acidic protein, α-synuclein, interleukin (IL) 1β, IL6, tumor necrosis factor α, oxidative stress index, high-sensitive C-reactive protein, and DNA damage were found higher in D5YG and NDG as compared to CG (p < 0.001). On the contrary, plasma levels of total thiol, native thiol, vitamin D and vitamin B12 were lower in D5YG and NDG as compared to CG (p < 0.001). Discussion: DBI may be a potential plasma biomarker and promising drug target for AD. It could help physicians make a comprehensive evaluation with cognitive and neurodegenerative tests.

Transcriptional risk scores in Alzheimer's disease: From pathology to cognition.

Our previously developed blood-based transcriptional risk scores (TRS) showed associations with diagnosis and neuroimaging biomarkers for Alzheimer's disease (AD). Here, we developed brain-based TRS. We integrated AD genome-wide association study summary and expression quantitative trait locus data to prioritize target genes using Mendelian randomization. We calculated TRS using brain transcriptome data of two independent cohorts (N=878) and performed association analysis of TRS with diagnosis, amyloidopathy, tauopathy, and cognition. We compared AD classification performance of TRS with polygenic risk scores (PRS). Higher TRS values were significantly associated with AD, amyloidopathy, tauopathy, worse cognition, and faster cognitive decline, which were replicated in an independent cohort. The AD classification performance of PRS was increased with the inclusion of TRS up to 16% with the area under the curve value of 0.850. Our results suggest brain-based TRS improves the AD classification of PRS and may be a potential AD biomarker. Transcriptional risk score (TRS) is developed using brain RNA-Seq data. Higher TRS values are shown in Alzheimer's disease (AD). TRS improves the AD classification power of PRS up to 16%. TRS is associated with AD pathology presence. TRS is associated with worse cognitive performance and faster cognitive decline.

Integrative metabolomics science in Alzheimer's disease: Relevance and future perspectives.

Alzheimer's disease (AD) is determined by various pathophysiological mechanisms starting 10-25 years before the onset of clinical symptoms. As multiple functionally interconnected molecular/cellular pathways appear disrupted in AD, the exploitation of high-throughput unbiased omics sciences is critical to elucidating the precise pathogenesis of AD. Among different omics, metabolomics is a fast-growing discipline allowing for the simultaneous detection and quantification of hundreds/thousands of perturbed metabolites in tissues or biofluids, reproducing the fluctuations of multiple networks affected by a disease. Here, we seek to critically depict the main metabolomics methodologies with the aim of identifying new potential AD biomarkers and further elucidating AD pathophysiological mechanisms. From a systems biology perspective, as metabolic alterations can occur before the development of clinical signs, metabolomics - coupled with existing accessible biomarkers used for AD screening and diagnosis - can support early disease diagnosis and help develop individualized treatment plans. Presently, the majority of metabolomic analyses emphasized that lipid metabolism is the most consistently altered pathway in AD pathogenesis. The possibility that metabolomics may reveal crucial steps in AD pathogenesis is undermined by the difficulty in discriminating between the causal or epiphenomenal or compensatory nature of metabolic findings.