Alzheimer's Disease Biomarkers Research Articles

Plasma phosphorylated tau181 outperforms [18F] fluorodeoxyglucose positron emission tomography in the identification of early Alzheimer disease.

This study was undertaken to compare the performance of plasma p-tau181 with that of [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET) in the identification of early biological Alzheimer disease (AD). We included 533 cognitively impaired participants from the Alzheimer's Disease Neuroimaging Initiative. Participants underwent PET scans, biofluid collection, and cognitive tests. Receiver operating characteristic analyses were used to determine the diagnostic accuracy of plasma p-tau181 and [18F]FDG-PET using clinical diagnosis and core AD biomarkers ([18F]florbetapir-PET and cerebrospinal fluid [CSF] p-tau181) as reference standards. Differences in the diagnostic accuracy between plasma p-tau181 and [18F]FDG-PET were determined by bootstrap-based tests. Correlations of [18F]FDG-PET and plasma p-tau181 with CSF p-tau181, amyloid β (Aβ) PET, and cognitive performance were evaluated to compare associations between measurements. We observed that both plasma p-tau181 and [18F]FDG-PET identified individuals with positive AD biomarkers in CSF or on Aβ-PET. In the MCI group, plasma p-tau181 outperformed [18F]FDG-PET in identifying AD measured by CSF (p = 0.0007) and by Aβ-PET (p = 0.001). We also observed that both plasma p-tau181 and [18F]FDG-PET metabolism were associated with core AD biomarkers. However, [18F]FDG-PET uptake was more closely associated with cognitive outcomes (Montreal Cognitive Assessment, Mini-Mental State Examination, Clinical Dementia Rating Sum of Boxes, and logical memory delayed recall, p < 0.001) than plasma p-tau181. Overall, although both plasma p-tau181 and [18F]FDG-PET were associated with core AD biomarkers, plasma p-tau181 outperformed [18F]FDG-PET in identifying individuals with early AD pathophysiology. Taken together, our study suggests that plasma p-tau181 may aid in detecting individuals with underlying early AD.

EEG biomarkers in Alzheimer’s and prodromal Alzheimer’s: a comprehensive analysis of spectral and connectivity features

BackgroundBiomarkers of Alzheimer’s disease (AD) and mild cognitive impairment (MCI, or prodromal AD) are highly significant for early diagnosis, clinical trials and treatment outcome evaluations. Electroencephalography (EEG), being noninvasive and easily accessible, has recently been the center of focus. However, a comprehensive understanding of EEG in dementia is still needed. A primary objective of this study is to investigate which of the many EEG characteristics could effectively differentiate between individuals with AD or prodromal AD and healthy individuals.MethodsWe collected resting state EEG data from individuals with AD, prodromal AD, and normal cognition. Two distinct preprocessing pipelines were employed to study the reliability of the extracted measures across different datasets. We extracted 41 different EEG features. We have also developed a stand-alone software application package, Feature Analyzer, as a comprehensive toolbox for EEG analysis. This tool allows users to extract 41 EEG features spanning various domains, including complexity measures, wavelet features, spectral power ratios, and entropy measures. We performed statistical tests to investigate the differences in AD or prodromal AD from age-matched cognitively normal individuals based on the extracted EEG features, power spectral density (PSD), and EEG functional connectivity.ResultsSpectral power ratio measures such as theta/alpha and theta/beta power ratios showed significant differences between cognitively normal and AD individuals. Theta power was higher in AD, suggesting a slowing of oscillations in AD; however, the functional connectivity of the theta band was decreased in AD individuals. In contrast, we observed increased gamma/alpha power ratio, gamma power, and gamma functional connectivity in prodromal AD. Entropy and complexity measures after correcting for multiple electrode comparisons did not show differences in AD or prodromal AD groups. We thus catalogued AD and prodromal AD-specific EEG features.ConclusionsOur findings reveal that the changes in power and connectivity in certain frequency bands of EEG differ in prodromal AD and AD. The spectral power, power ratios, and the functional connectivity of theta and gamma could be biomarkers for diagnosis of AD and prodromal AD, measure the treatment outcome, and possibly a target for brain stimulation.

Cognitive status and demographics modify the association between subjective cognition and amyloid.

This study examined the effect of cognitive status, education, and sex on the association between subjective cognitive decline (SCD) and Alzheimer's disease (AD) biomarkers in non-demented older adults. Vanderbilt Memory and Aging Project participants (n = 129), dementia or stroke free, completed fasting lumbar puncture, SCD assessment, and cognitive assessment. Cerebrospinal fluid (CSF) biomarkers for AD were analyzed. Linear regression models related SCD to CSF AD biomarkers and follow-up models assessed interactions of SCD × cognitive status, sex, reading level, and education on AD biomarkers. In main effect models, higher SCD was associated with more amyloidosis (p-values <0.004). SCD was not associated with tau, p-tau, or neurofilament light (NFL) levels (p-values >0.38). SCD score interacted with cognitive status (p < 0.02), sex (p = 0.03), and education (p-values <0.005) on amyloidosis. In stratified models, higher SCD was associated with more amyloid in cognitively unimpaired (p-values <0.003), men (p = 0.0003), and higher education. No SCD score × reading-level interaction was found (p-values >0.51) though SCD related to amyloid markers in the higher reading-level group (p-values <0.004). Higher SCD was associated with greater cerebral amyloid accumulation, one of the earliest pathological AD changes. SCD appears most useful in detecting early AD-related brain changes prior to objective cognitive impairment, in men, and those with higher quantity and quality of education and highlight the importance of considering these factors.

Pathway enrichment in genome-wide analysis of longitudinal Alzheimer's disease biomarker endophenotypes.

The genetic pathways that influence longitudinal heterogeneous changes in Alzheimer's disease (AD) may provide insight into disease mechanisms and potential therapeutic targets. Longitudinal endophenotypes from the Alzheimer's Disease Neuroimaging Initiative (ADNI) representing amyloid, tau, neurodegeneration (A/T/N), and cognition were selected. Genome-wide association analysis was performed using a linear mixed model (LMM) approach, followed by gene and pathway enrichment with significant and functionally relevant SNPs. A total of 33 and 19 statistically significant pathways were identified associating with the intercept and longitudinal trajectory, respectively. The longitudinal intercept pathways represent eight groups: immune, metabolic, cell growth and survival, DNA maintenance, neuronal signaling, RAS/MAPK/ERKsignalingpathways, vesicle and lysosomal transport, and transcription modification. Longitudinal trajectory pathways represented six groups: Immune, metabolic, cell signaling, cytoskeleton, and glycosylation. Longitudinal enrichment identified pathways that uniquely associate with trajectories of key AD biomarkers and cognition, providing new insight into AD course-related mechanisms and potential new therapeutic targets. A systematic genome-wide analysis with longitudinal AD biomarker endophenotypes was performed. Enriched pathways were identified with functionally derived SNP to gene analysis. Fifty-two pathways were associated with longitudinal trajectory and intercept. Many of the identified pathways are specific steps in larger pathways implicated in AD. The identified pathways may provide therapeutic targets and areas for further study.

Blood-derived mitochondrial DNA copy number is associated with Alzheimer disease, Alzheimer-related biomarkers and serum metabolites

BackgroundBlood-derived mitochondrial DNA copy number (mtDNA-CN) is a proxy measurement of mitochondrial function in the peripheral and central systems. Abnormal mtDNA-CN not only indicates impaired mtDNA replication and transcription machinery but also dysregulated biological processes such as energy and lipid metabolism. However, the relationship between mtDNA-CN and Alzheimer disease (AD) is unclear.MethodsWe performed two-sample Mendelian randomization (MR) using publicly available summary statistics from GWAS for mtDNA-CN and AD to investigate the causal relationship between mtDNA-CN and AD. We estimated mtDNA-CN using whole-genome sequence data from blood and brain samples of 13,799 individuals from the Alzheimer’s Disease Sequencing Project. Linear and Cox proportional hazards models adjusting for age, sex, and study phase were used to assess the association of mtDNA-CN with AD. The association of AD biomarkers and serum metabolites with mtDNA-CN in blood was evaluated in Alzheimer’s Disease Neuroimaging Initiative using linear regression. We conducted a causal mediation analysis to test the natural indirect effects of mtDNA-CN change on AD risk through the significantly associated biomarkers and metabolites.ResultsMR analysis suggested a causal relationship between decreased blood-derived mtDNA-CN and increased risk of AD (OR = 0.68; P = 0.013). Survival analysis showed that decreased mtDNA-CN was significantly associated with higher risk of conversion from mild cognitive impairment to AD (HR = 0.80; P = 0.002). We also identified significant associations of mtDNA-CN with brain FDG-PET (β = 0.103; P = 0.022), amyloid-PET (β = 0.117; P = 0.034), CSF amyloid-β (Aβ) 42/40 (β=-0.124; P = 0.017), CSF t-Tau (β = 0.128; P = 0.015), p-Tau (β = 0.140; P = 0.008), and plasma NFL (β=-0.124; P = 0.004) in females. Several lipid species, amino acids, biogenic amines in serum were also significantly associated with mtDNA-CN. Causal mediation analyses showed that about a third of the effect of mtDNA-CN on AD risk was mediated by plasma NFL (P = 0.009), and this effect was more significant in females (P < 0.005).ConclusionsOur study indicates that mtDNA-CN measured in blood is predictive of AD and is associated with AD biomarkers including plasma NFL particularly in females. Further, we illustrate that decreased mtDNA-CN possibly increases AD risk through dysregulation of mitochondrial lipid metabolism and inflammation.

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.

The Association Between Constipation and Positron Emission Tomography and Blood-Based Biomarkers in Older Cognitively Unimpaired Adults with Higher Amyloid-β Burden.

Constipation may be linked to cognitive decline and a higher risk of Alzheimer's disease (AD). We aimed to investigate the association between constipation and positron emission tomography (PET) and blood-based AD biomarkers in older cognitively unimpaired (CU) adults with higher Aβ burden. Constipation was diagnosed according to Rome IV criteria and the severity of constipation was evaluated by using a validated self-reported questionnaire. The participants underwent the examination of plasma AD biomarkers and 18F-florbetapir PET and 18F-MK6240 PET scans; the latter was only performed in the validation cohort. Correlation and multiple linear regression analyses were used to investigate the association between constipation and AD biomarkers. Two cohorts were included in our study. A total of 404 older participants with 126 of whom Aβ-PET positive were enrolled in the development cohort. Multiple linear regression analysis showed constipation was associated with plasma t-Tau, p-Tau-181, and neurofilament light chain (NfL) in participants with Aβ-PET (+). Meanwhile, no/mild constipation was associated with lower Aβ-PET standard uptake value ratio. The association between constipation and plasma biomarkers was different in the subgroups stratified by age, sex and APOE ε4 genotype. The above associations were further validated in the validation cohort containing 36 Aβ-PET (+) participants. Importantly, no/mild constipation was associated with lessTau burden evaluated by 18F-MK6240 PET Braak stages. Our data indicate that no/mild constipation may be associated with lower plasma t-Tau, p-Tau-181, and NfL as well as less Aβ and Tau burden in older CU adults with Aβ deposition. Improving constipation and being away from defecation disorders may help reduce the risk of AD development.

Blood biomarkers for Alzheimer’s disease with the Lumipulse automated platform: Age-effect and clinical value interpretation

BackgroundAdvances in analytical methods have recently paved the way to Alzheimer’s disease (AD) biomarkers testing in blood along with the more established CSF testing. To ensure a forthcoming application of this low-invasive diagnostic that might allow to recognize early onset of dementia, appropriate pathological cut-points need to be defined. MethodsIn this cross-sectional study we measured blood and CSF neurofilament light chain (NFL), phosphorylated tau (pTau 181), Amyloid-β1-42 (AB 1–42) and Amyloid-β1-40 (AB 1–40) on a fully automated chemiluminescent platform (Lumipulse, Fujirebio) in 80 cognitively impaired patients and 55 cognitively unimpaired subjects. Clinical cut points were calculated with receiver-operator characteristic (ROC) curve analysis and a head-to-head comparison of blood and CSF testing was performed. ResultsBlood NFL best discriminant thresholds to distinguish neurodegenerative diseases from controls varied age-dependently, being 19 and 33 pg/mL in subjects 50–65 years and > 65 years respectively. AD was best framed by AB 1–42/1–40 ratio < 0.079 and ptau181 > 1 pg/mL. Though a strong correlation for all biomarkers, only blood AB ratio was equal to CSF testing for AD diagnosis. ConclusionsThe specific context of use might be considered to define the cut-offs of blood biomarkers of neurodegenerative diseases. Future efforts towards reference materials for each AD blood biomarker will improve clinical cut-offs.

Timing of Alzheimer's disease biomarker progressions: A two-decade observational study from the Alzheimer's Disease Neuroimaging Initiative (ADNI).

Alzheimer's Disease Neuroimaging Initiative (ADNI) has been pivotal in identifying and refining Alzheimer's disease (AD) biomarkers for clinical trials. This study leverages longitudinal data from participants who have progressed to amyloid-positivity during their study participation to track evolution of biomarkers and cognitive function. We modeled AD biomarker (positron emission tomography [PET], structural, cerebrospinal fluid [CSF], cognition) trajectories before and after observed amyloid-positivity onset time to detect time at which each biomarker had detectable trajectory changes. Analysis of a sub-cohort of the 20-year ADNI study (N=90) recapitulated Alzheimer's progression beginning with amyloid alterations -4.8 to -5.3 years relative to amyloid-positivity, succeeded by neurodegeneration (t=-4.0 to -4.1 years), and CSF tau (t=-0.4 to -0.5 years). Cognitive decline was observed to significantly correspond with emergence of amyloid-positivity (t=0.2 to 2.4 years). Our results corroborate temporal progression curves of AD biomarkers, providing insights on earliest detectable changes in objective and subjective cognitive function assessments.

A meta-analysis of bulk RNA-seq datasets identifies potential biomarkers and repurposable therapeutics against Alzheimer's disease.

Alzheimer's disease (AD) poses a major challenge due to its impact on the elderly population and the lack of effective early diagnosis and treatment options. In an effort to address this issue, a study focused on identifying potential biomarkers and therapeutic agents for AD was carried out. Using RNA-Seq data from AD patients and healthy individuals, 12 differentially expressed genes (DEGs) were identified, with 9 expressing upregulation (ISG15, HRNR, MTATP8P1, MTCO3P12, DTHD1, DCX, ST8SIA2, NNAT, and PCDH11Y) and 3 expressing downregulation (LTF, XIST, and TTR). Among them, TTR exhibited the lowest gene expression profile. Interestingly, functional analysis tied TTR to amyloid fiber formation and neutrophil degranulation through enrichment analysis. These findings suggested the potential of TTR as a diagnostic biomarker for AD. Additionally, druggability analysis revealed that the FDA-approved drug Levothyroxine might be effective against the Transthyretin protein encoded by the TTR gene. Molecular docking and dynamics simulation studies of Levothyroxine and Transthyretin suggested that this drug could be repurposed to treat AD. However, additional studies using in vitro and in vivo models are necessary before these findings can be applied in clinical applications.

Association of CSF α-synuclein seed amplification assay positivity with disease progression and cognitive decline: A longitudinal Alzheimer's Disease Neuroimaging Initiative study.

Cerebrospinal fluid (CSF) α-synuclein (α-syn) seed amplification assay (SAA) is a sensitive and specific tool for detecting Lewy body co-pathology in Alzheimer's disease. A total of 1637 cross-sectional and 407 longitudinal CSF samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were tested with SAA. We examined longitudinal dynamics of amyloid beta (Aβ), α-syn seeds, and phosphorylated tau181 (p-tau181), along with global and domain-specific cognition in stable SAA+, stable SAA-, and those who converted to SAA+ from SAA-. SAA+ individuals had faster cognitive decline than SAA-, notably in mild cognitive impairment, and presented with earlier symptom onset. SAA+ conversion was associated with CSF Aβ42 positivity but did not impact the progression of either CSF Aβ42 or CSF p-tau181 status. CSF Aβ42, p-tau181, and α-syn SAA were all strong predictors of clinical progression, particularly CSF Aβ42. In vitro, CSF α-syn SAA kinetic parameters were associated with participant demographics, clinical profiles, and cognitive decline. These results highlight the interplay between amyloid and α-syn and their association with disease progression. Seed amplification assay (SAA) positivity was associated with greater cognitive decline and earlier symptom onset. Thirty-four Alzheimer's Disease Neuroimaging Initiative (ADNI) individuals progressed from SAA- to SAA+, that is, ≈ 5% conversion. SAA conversion was associated with amyloid beta (Aβ) pathology and greater cognitive decline. SAA status did not impact the progression of either CSF Aβ42 or phosphorylated tau181 biomarkers. Change in clinical diagnosis was associated with both Alzheimer's disease biomarkers and SAA. SAA kinetic parameters were associated with clinical features and progression.

Blood‐based biomarkers of Alzheimer's disease: Standardization and comprehensiveness

AbstractPopulation aging is sweeping across the globe, resulting in a striking prevalence of Alzheimer's disease (AD) and dementia and a heavy economic burden. Given the time window of 10–20 years from pathological initiation to clinically detected cognitive impairment, early detection can significantly impact the prevention and control of AD. The invasiveness and high cost of cerebrospinal fluid biomarkers and positron emission tomography‐computed tomography imaging limit large‐scale disease screening. However, blood‐based biomarkers (BBMs) lack these disadvantages, shedding light on their usefulness in the large‐scale identification and prevention of AD. Prominent advancement has recently been made regarding BBMs of AD co‐pathology (amyloid β, tau protein, neurofilament light polypeptide, and glial fibrillary acidic protein) to improve their accuracy as clinical diagnostics of AD to a level comparable to that of canonical methods, facilitating the large‐scale clinical implementation of diagnostic tests with higher precision. To briefly summarize, the prospects of AD BBMs rely on standardization and comprehensiveness. Calibrating the sample collection procedure and clarifying the boundaries for indices and abnormalities are beneficial for constructing a canonical diagnostic assay. The comprehensive assembly of heterogeneous clinical evidence guarantees the accuracy of diagnosis and improves the workflow for early identification.

A glycan biomarker predicts cognitive decline in amyloid- and tau-negative patients.

Early detection of Alzheimer's disease is vital for timely treatment. Existing biomarkers for Alzheimer's disease reflect amyloid- and tau-related pathology, but it is unknown whether the disease can be detected before cerebral amyloidosis is observed. N-glycosylation has been suggested as an upstream regulator of both amyloid and tau pathology, and levels of the N-glycan structure bisecting N-acetylglucosamine (GlcNAc) correlate with tau in blood and CSF already at pre-clinical stages of the disease. Therefore, we aimed to evaluate whether bisecting GlcNAc could predict future cognitive decline in patients from a memory clinic cohort, stratified by amyloid/tau status. We included 251 patients (mean age: 65.6 ± 10.6 years, 60.6% female) in the GEDOC cohort, from the Memory Clinic at Karolinska University Hospital, Stockholm, Sweden. Patients were classified as amyloid/tau positive or negative based on CSF biomarkers. Cognitive decline, measured by longitudinal Mini-Mental State Examination scores, was followed for an average of 10.7 ± 4.1 years and modelled using non-linear mixed effects models. Additionally, bisecting GlcNAc levels were measured in hippocampus and cortex with lectin-based immunohistochemistry in 10 Alzheimer's disease and control brains. We found that CSF bisecting GlcNAc levels were elevated in tau-positive individuals compared with tau-negative individuals, but not in amyloid-positive individuals compared with amyloid-negative individuals. In the whole sample, high levels of CSF bisecting GlcNAc predicted earlier cognitive decline. Strikingly, amyloid/tau stratification showed that high CSF bisecting GlcNAc levels predicted earlier cognitive decline in amyloid-negative patients (β = 2.53 ± 0.85 years, P = 0.003) and tau-negative patients (β = 2.43 ± 1.01 years, P = 0.017), but not in amyloid- or tau-positive patients. Finally, histochemical analysis of bisecting GlcNAc showed increased levels in neurons in hippocampus and cortex of Alzheimer's disease compared with control brain (fold change = 1.44-1.49, P < 0.001). In conclusion, high CSF levels of bisecting GlcNAc reflected neuronal pathology and predicted cognitive decline in amyloid- and tau-negative individuals, suggesting that abnormal glycosylation precedes cerebral amyloidosis and tau hyper-phosphorylation in Alzheimer's disease. Bisecting GlcNAc is a promising novel early biomarker for Alzheimer's disease.

Electrochemical Analysis of Amyloid Plaques and ApoE4 with Chitosan-Coated Gold Nanostars for Alzheimer's Detection.

Monitoring the progression of Alzheimer's disease (AD) is crucial for mitigating dementia symptoms, alleviating pain, and improving mobility. Traditionally, AD biomarkers like amyloid plaques are predominantly identified in cerebrospinal fluid (CSF) due to their concentrated presence. However, detecting these markers in blood is hindered by the blood-brain barrier (BBB), resulting in lower concentrations. To address this challenge and identify pertinent AD biomarkers-specifically amyloid plaques and apolipoprotein E4 (ApoE4)-in blood plasma, we propose an innovative approach. This involves enhancing a screen-printed carbon electrode (SPCE) with an immobilization matrix comprising gold nanostars (AuNSs) coated with chitosan. Morphological and electrical analyses confirmed superior dispersion and conductivity with 0.5% chitosan, supported by UV-Vis spectroscopy, cyclic voltammetry, and Nyquist plots. Subsequent clinical assays measured electrical responses to quantify amyloid-β 42 (Aβ42) (15.63-1000 pg/mL) and APoE4 levels (0.41 to 40 ng/mL) in human blood plasma samples. Differential pulse voltammetry (DPV) responses exhibited peak currents proportional to biomarker concentrations, demonstrating high linear correlations (0.985 for Aβ42 and 0.919 for APoE4) with minimal error bars. Cross-reactivity tests with mixed solutions of amyloid-β 40 (Aβ40), Aβ42, and ApoE4 indicated minimal interference between biomarkers (<3% variation), further confirming the high specificity of the developed sensor. Validation studies demonstrated a strong concurrence with the gold-standard enzyme-linked immunosorbent assay (ELISA), while interference tests indicated a minimal variation in peak currents. This improved device presents promising potential as a point-of-care system, offering a less invasive, cost-effective, and simplified approach to detecting and tracking the progression of AD. The substantial surface binding area further supports the efficacy of our method, offering a promising avenue for advancing AD diagnostics.

Neuroimaging-Based Brain Morphometry in Alzheimer’s Disease

Background/Objectives: Alzheimer’s disease (AD) is a leading cause of death worldwide, affecting millions of older Americans and resulting in a substantial economic burden. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) aims to investigate and develop treatments for AD. Methods: This study included 60 participants, divided equally into AD and control cohorts, and utilized magnetic resonance imaging (MRI) scans to detect gray matter volumetric alterations, a key biomarker of AD. The participants’ cortical volume and surface area were quantified using an automated pipeline in MIMICS (Materialise Interactive Medical Imaging Control System). Results: A multivariate regression analysis was conducted to explore the relationship between cortical measurements and potential factors influencing AD susceptibility. The study found that both cortical volume and surface area were statistically significant predictors of AD (p = 0.0004 and p = 0.011, respectively). Age was also a significant factor, with the 65–70 age group showing the strongest association (p &lt; 0.001). The model achieved an accuracy of 0.68 in predicting AD. Conclusions: While voxel-based morphometry (VBM) using MIMICS showed promise, further development of the automated pipeline could enhance accuracy and correlation indices. These findings contribute to our understanding of brain atrophy in AD pathophysiology and highlight the potential of MRI morphometry as a tool for AD biomarker development.

Biofluid biomarkers for Alzheimer’s disease: past, present, and future

Abstract Alzheimer’s disease (AD) is a gradually progressive neurodegenerative disease with tremendous social and economic burden. Therefore, early and accurate diagnosis is imperative for effective treatment or prevention of the disease. Cerebrospinal fluid and blood biomarkers emerge as favorable diagnostic tools due to their relative accessibility and potential for widespread clinical use. This review focuses on the AT(N) biomarker system, which includes biomarkers reflecting AD core pathologies, amyloid deposition, and pathological tau, as well as neurodegeneration. Novel biomarkers associated with inflammation/immunity, synaptic dysfunction, vascular pathology, and α-synucleinopathy, which might contribute to either the pathogenesis or the clinical progression of AD, have also been discussed. Other emerging candidates including non-coding RNAs, metabolites, and extracellular vesicle-based markers have also enriched the biofluid biomarker landscape for AD. Moreover, the review discusses the current challenges of biofluid biomarkers in AD diagnosis and offers insights into the prospective future development.

Pterosin B improves cognitive dysfunction by promoting microglia M1/M2 polarization through inhibiting Klf5/Parp14 pathway

BackgroundPterosin B (PB) exhibits strong neuroprotective effects in vitro, but its therapeutic effect and underlying mechanism on Alzheimer's disease (AD) remain elusive. PurposeThis study aimed to investigate the anti-AD effect and mechanism of PB. Study designThe therapeutic effect and mechanism of PB were investigated in APP/PS1 mice and lipopolysaccharide (LPS)-induced BV-2 cells. MethodsAfter 8 weeks of oral administration of PB or donepezil, the cognitive function was assessed using behavioral tests. Pathological damage was evaluated using histological analysis and immunohistochemical staining. Flow cytometry was applied to detect M1/M2 polarization. The expression levels of glycolysis- and oxidative phosphorylation-related proteins as well as enzyme activities were determined using Western blot and biochemical kits, respectively. The levels of inflammatory cytokines and Kruppel-like factor 5 (Klf5) were measured using enzyme-linked immunosorbent assay. AD biomarkers in serum were analyzed using single-molecular array. RNA sequencing identified the downstream molecules of Klf5, and interaction was evaluated using dual-luciferase reporter assay. ResultsOur findings demonstrated that PB effectively ameliorated cognitive impairment and reduced pathological damage in APP/PS1 mice. Furthermore, PB facilitated the transition of the phenotype of LPS-induced BV-2 cells from M1 to M2 by modulating metabolic reprogramming. Additionally, Klf5 had high expression levels in the serum of patients with AD, which strongly correlated with cognitive performance and AD biomarkers. PB downregulated Klf5 expression both in vitro and in vivo. Subsequently, poly-ADP ribosyl polymerase 14 (Parp14) was identified as a downstream molecule of Klf5 involved in regulating metabolic reprogramming, and PB regulated microglia M1/M2 polarization by inhibiting the Klf5/Parp14 pathway. ConclusionThe findings suggested that PB ameliorated cognitive dysfunction in AD by modulating microglia M1/M2 polarization via inhibiting Klf5/Parp14 pathway.

The PANoptosis-related hippocampal molecular subtypes and key biomarkers in Alzheimer’s disease patients

Alzheimer’s Disease (AD) is a neurodegenerative disorder, and various molecules associated with PANoptosis are involved in neuroinflammation and neurodegenerative diseases. This work aims to identify key genes, and characterize PANoptosis-related molecular subtypes in AD. Moreover, we establish a scoring system for distinguishing PANoptosis molecular subtypes and constructing diagnostic models for AD differentiation. A total of 5 hippocampal datasets were obtained from the Gene Expression Omnibus (GEO) database. In total, 1324 protein-encoding genes associated with PANoptosis (1313 apoptosis genes, 11 necroptosis genes, and 31 pyroptosis genes) were extracted from the GeneCards database. The Limma package was used to identify differentially expressed genes. Weighted Gene Co-Expression Network Analysis (WGCNA) was conducted to identify gene modules significantly associated with AD. The ConsensusClusterPlus algorithm was used to identify AD subtypes. Gene Set Variation Analysis (GSVA) was used to assess functional and pathway differences among the subtypes. The Boruta, Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithms were used to select the three PANoptosis-related Key AD genes (PKADg). A scoring model was constructed based on the Boruta algorithm. PANoptosis diagnostic models were developed using the RF, SVM-RFE, and Logistic Regression (LR) algorithms. The ROC curves were used to assess the model performance. A total of 48 important genes were identified by intersecting 725 differentially expressed genes and 2127 highly correlated module genes from WGCNA with 1324 protein-encoding genes related to PANoptosis. Machine learning algorithms identified 3 key AD genes related to PANoptosis, including ANGPT1, STEAP3, and TNFRSF11B. These genes had strong discriminatory capacities among samples, with Receiver Operating Characteristic Curve (ROC) analysis indicating Area Under the Curve (AUC) values of 0.839, 0.8, and 0.868, respectively. Using the 48 important genes, the ConsensusClusterPlus algorithm identified 2 PANoptosis subtypes among AD patients, i.e., apoptosis subtype and mild subtype. Apoptosis subtype patients displayed evident cellular apoptosis and severe functionality damage in the hippocampal tissue. Meanwhile, mild subtype patients showed milder functionality damage. These two subtypes had significant differences in apoptosis and necroptosis; however, there was no apparent variation in pyroptosis functionality. The scoring model achieved an AUC of 100% for sample differentiation. The RF PANoptosis diagnostic model demonstrated an AUC of 100% in the training set and 85.85% in the validation set for distinguishing AD. This study identified two PANoptosis-related hippocampal molecular subtypes of AD, identified key genes, and established machine learning models for subtype differentiation and discrimination of AD. We found that in the context of AD, PANoptosis may influence disease progression through the modulation of apoptosis and necrotic apoptosis.

Blood-Based Biomarkers in Alzheimer's Disease: Advancing Non-Invasive Diagnostics and Prognostics.

Alzheimer's disease (AD), the most prevalent form of dementia, is expected to rise dramatically in incidence due to the global population aging. Traditional diagnostic approaches, such as cerebrospinal fluid analysis and positron emission tomography, are expensive and invasive, limiting their routine clinical use. Recent advances in blood-based biomarkers, including amyloid-beta, phosphorylated tau, and neurofilament light, offer promising non-invasive alternatives for early AD detection and disease monitoring. This review synthesizes current research on these blood-based biomarkers, highlighting their potential to track AD pathology and enhance diagnostic accuracy. Furthermore, this review uniquely integrates recent findings on protein-protein interaction networks and microRNA pathways, exploring novel combinations of proteomic, genomic, and epigenomic biomarkers that provide new insights into AD's molecular mechanisms. Additionally, we discuss the integration of these biomarkers with advanced neuroimaging techniques, emphasizing their potential to revolutionize AD diagnostics. Although large-scale validation is still needed, these biomarkers represent a critical advancement toward more accessible, cost-effective, and early diagnostic tools for AD.

Multi-analyte proteomic analysis identifies blood-based neuroinflammation, cerebrovascular and synaptic biomarkers in preclinical Alzheimer’s disease

BackgroundBlood-based biomarkers are gaining grounds for the detection of Alzheimer’s disease (AD) and related disorders (ADRDs). However, two key obstacles remain: the lack of methods for multi-analyte assessments and the need for biomarkers for related pathophysiological processes like neuroinflammation, vascular, and synaptic dysfunction. A novel proteomic method for pre-selected analytes, based on proximity extension technology, was recently introduced. Referred to as the NULISAseq CNS disease panel, the assay simultaneously measures ~ 120 analytes related to neurodegenerative diseases, including those linked to both core (i.e., tau and amyloid-beta (Aβ)) and non-core AD processes. This study aimed to evaluate the technical and clinical performance of this novel targeted proteomic panel.MethodsThe NULISAseq CNS disease panel was applied to 176 plasma samples from 113 individuals in the MYHAT-NI cohort of predominantly cognitively normal participants from an economically underserved region in southwestern Pennsylvania, USA. Classical AD biomarkers, including p-tau181, p-tau217, p-tau231, GFAP, NEFL, Aβ40, and Aβ42, were independently measured using Single Molecule Array (Simoa) and correlations and diagnostic performances compared. Aβ pathology, tau pathology, and neurodegeneration (AT(N) statuses) were evaluated with [11C] PiB PET, [18F]AV-1451 PET, and an MRI-based AD-signature composite cortical thickness index, respectively. Linear mixed models were used to examine cross-sectional and Wilcoxon rank sum tests for longitudinal associations between NULISA and neuroimaging-determined AT(N) biomarkers.ResultsNULISA concurrently measured 116 plasma biomarkers with good technical performance (97.2 ± 13.9% targets gave signals above assay limits of detection), and significant correlation with Simoa assays for the classical biomarkers. Cross-sectionally, p-tau217 was the top hit to identify Aβ pathology, with age, sex, and APOE genotype-adjusted AUC of 0.930 (95%CI: 0.878–0.983). Fourteen markers were significantly decreased in Aβ-PET + participants, including TIMP3, BDNF, MDH1, and several cytokines. Longitudinally, FGF2, IL4, and IL9 exhibited Aβ PET-dependent yearly increases in Aβ-PET + participants. Novel plasma biomarkers with tau PET-dependent longitudinal changes included proteins associated with neuroinflammation, synaptic function, and cerebrovascular integrity, such as CHIT1, CHI3L1, NPTX1, PGF, PDGFRB, and VEGFA; all previously linked to AD but only reliable when measured in cerebrospinal fluid. The autophagosome cargo protein SQSTM1 exhibited significant association with neurodegeneration after adjusting age, sex, and APOE ε4 genotype.ConclusionsTogether, our results demonstrate the feasibility and potential of immunoassay-based multiplexing to provide a comprehensive view of AD-associated proteomic changes, consistent with the recently revised biological and diagnostic framework. Further validation of the identified inflammation, synaptic, and vascular markers will be important for establishing disease state markers in asymptomatic AD.