In recent years, electrochemical biosensors have shown great promise as innovative tools for the early identification of Alzheimer's disease (AD), a neurodegenerative disorder that severely affects cognitive ability and overall quality of life. This comprehensive review aims to consolidate the latest research on the creation and implementation of electrochemical biosensors designed to detect AD-related biomarkers. We examine cutting-edge approaches to surface modification that enhance the attachment of biorecognition molecules, thus enabling the simultaneous identification of multiple biomarkers. This review emphasizes the crucial role that electrochemical biosensors play in the early diagnosis of Alzheimer's disease, highlighting their potential to revolutionize clinical practices by facilitating timely interventions. In the future, research efforts should concentrate on refining these technologies for widespread clinical adoption, ensuring that they meet the needs of both healthcare professionals and patients.

Growth-associated protein 43 (GAP-43) is a key protein involved in neuronal growth and synaptic plasticity. Alterations in GAP-43 levels have been associated with Alzheimer's Disease (AD), potentially reflecting synaptic dysfunction. We evaluated the potential of GAP-43 as a biomarker for AD and explored its association with amyloid-beta (Aβ) levels, as well as its correlation with Aβ plaque burden in the brain.

We screened 1,639 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. A total of 226 individuals met the eligibility criteria and were enrolled. Participants were classified into three groups: 77 cognitively normal (CN) individuals, 111 with mild cognitive impairment (MCI), and 38 with a diagnosis of AD. The associations between cerebrospinal fluid (CSF) GAP-43 levels with other biomarkers as well as [¹⁸F] AV-45 (Florbetapir) PET Standardized Uptake Value Ratios (SUVR) were investigated.

Our findings revealed significantly elevated CSF GAP-43 levels in individuals with AD compared to CN and MCI groups. Furthermore, GAP-43 levels showed a significant positive correlation with tau pathology. Notably, we observed a significant association between GAP-43 and [¹⁸F] Florbetapir PET SUVR in the MCI group, suggesting that GAP-43 may serve as a reliable biomarker in the early stages of AD.

This study provides evidence supporting the role of GAP-43 as a potential biomarker for AD, particularly in relation to predicting the amyloid pathology pattern in the brain in the MCI stage.

Brain inflammation in Alzheimer's disease (AD) involves reactive nitrogen species (RNS) generation. Protein contents of 3-nitrotyrosine, a product of RNS generation, were assessed in frontal lobe brain homogenates from patients with AD, patients with vascular dementia (VaD) and non-dementia (ND) controls. Western blotting revealed a dominant 15 kDa nitrated protein band in both dementia (AD/VaD) and ND frontal lobe brain tissue. Surprisingly, this protein band was identified by mass spectrometry as hemoglobin, an erythrocytic protein. The same band stained positively when western blotted using an anti-hemoglobin antibody. On western blots, the median (IQR) normalized staining intensity for 3-nitrotyrosine in hemoglobin was increased in both AD [1.71 (1.20-3.05) AU] and VaD [1.50 (0.59-3.04) AU] brain tissue compared to ND controls [0.41 (0.09-0.75) AU] (Mann-Whitney U test: AD v ND, P < 0.0005; VaD v ND, P < 0.05; n = 11). The median normalized staining of the nitrated hemoglobin band was higher in advanced AD patients compared with early-stage AD (P < 0.005). The median brain tissue NO2- levels (nmol/mg protein) were significantly higher in AD samples than in ND controls (P < 0.05). Image analysis of western blots of lysates from peripheral blood erythrocytes suggested that hemoglobin nitration was increased in AD compared to ND (P < 0.05; n = 4 in each group). Total protein-associated 3-nitrotyrosine was measured by an electrochemiluminescence-based immunosorbent assay, but showed no statistically significant differences between AD, VaD and ND. Females showed larger increases in hemoglobin nitration and NO2- levels between disease and control groups compared to males, although the group sizes in these sub-analyses were small. In conclusion, the extent of hemoglobin nitration was increased in AD and VaD brain frontal lobe tissue compared with ND. We propose that reactive nitrogen species-mediated damage to hemoglobin may be involved in the pathogenesis of AD.

Identifying plasma biomarkers for Alzheimer's disease (AD) has garnered strong interest. In this study, a cytometric bead array (CBA) method for measuring the levels of amyloid-β (Aβ) peptides and phosphorylated tau (P-tau) was evaluated. Fifty patients with cognitive impairment (CI) and 22 cognitively unimpaired (CU) controls were recruited. CI patients were classified into Aβ + and Aβ - groups according to amyloid positron emission tomography (PET) scan results. Biomarker levels in the plasma of all participants and in the cerebrospinal fluid (CSF) of 28 CI patients were measured via CBA. Plasma P-tau181 levels were greatest in the Aβ + CI group and showed excellent performance in differentiating Aβ + CI patients from CU controls. The plasma and CSF levels of P-tau181 were correlated with each other and had similar diagnostic performance for distinguishing between Aβ + CI patients and Aβ- CI patients. Overall, CBA is a potential cost-effective method for measuring plasma biomarkers, particularly P-tau181, in AD patients.

The event-based model of disease progression (EBM) infers a temporal ordering of biomarker abnormalities, defining different disease stages, from cross-sectional data. A key modelling choice of the EBM is that biomarker abnormalities, termed events, are serially ordered. However, this choice enforces a strict equality between the number of input biomarkers and the number of modelled disease stages, limiting the EBM's ability to infer simple staging systems and identify latent disease processes driving multiple biomarker changes. To overcome this, we introduce the parsimonious event-based model of disease progression (P-EBM). The P-EBM generalises the EBM to allow multiple new biomarker abnormalities, termed "simultaneous events", at each model stage. We evaluate the P-EBM performance in simulated data and demonstrate its ability to reconstruct event orderings with arbitrary arrangements under realistic experimental conditions. In sporadic AD data from the Alzheimer's Disease Neuroimaging Initiative, the P-EBM estimated a sequence with 7 model stages from a dataset of 12 biomarkers that more closely fitted the data than the EBM. The inferred sets of simultaneous events, such as decreased cerebrospinal fluid total tau and p-tau181, correspond closely to known underlying disease processes. P-EBM patient stages were strongly associated with clinical diagnosis at baseline and future conversion and could be accurately estimated from a smaller number of biomarkers than the EBM. The P-EBM enables the data-driven discovery of simple disease staging systems which could highlight new latent disease processes and suggest practical strategies for patient staging.

Alzheimer's disease (AD) is neuropathologically characterized by the extracellular deposition of the amyloid-β peptide (Aβ) and the intraneuronal accumulation of abnormal phosphorylated tau (τ)-protein (p-τ). Most frequently, these hallmark lesions are accompanied by other co-pathologies in the brain that may contribute to cognitive impairment, such as vascular lesions, intraneuronal accumulation of phosphorylated transactive-response DNA-binding protein 43 (TDP-43), and/or α-synuclein (αSyn) aggregates. To estimate the extent of these AD and co-pathologies in patients, several biomarkers have been developed. Specific tracers target and visualize Aβ plaques, p-τ and αSyn pathology or inflammation by positron emission tomography. In addition to these imaging biomarkers, cerebrospinal fluid, and blood-based biomarker assays reflecting AD-specific or non-specific processes are either already in clinical use or in development. In this review, we will introduce the pathological lesions of the AD brain, the related biomarkers, and discuss to what extent the respective biomarkers estimate the pathology determined at post-mortem histopathological analysis. It became evident that initial stages of Aβ plaque and p-τ pathology are not detected with the currently available biomarkers. Interestingly, p-τ pathology precedes Aβ deposition, especially in the beginning of the disease when biomarkers are unable to detect it. Later, Aβ takes the lead and accelerates p-τ pathology, fitting well with the known evolution of biomarker measures over time. Some co-pathologies still lack clinically established biomarkers today, such as TDP-43 pathology or cortical microinfarcts. In summary, specific biomarkers for AD-related pathologies allow accurate clinical diagnosis of AD based on pathobiological parameters. Although current biomarkers are excellent measures for the respective pathologies, they fail to detect initial stages of the disease for which post-mortem analysis of the brain is still required. Accordingly, neuropathological studies remain essential to understand disease development especially in early stages. Moreover, there is an urgent need for biomarkers reflecting co-pathologies, such as limbic predominant, age-related TDP-43 encephalopathy-related pathology, which is known to modify the disease by interacting with p-τ. Novel biomarker approaches such as extracellular vesicle-based assays and cryptic RNA/peptides may help to better detect these co-pathologies in the future.

Neuroinflammation including interleukin (IL)-12/IL-23-signaling is central to Alzheimer's disease (AD) pathology. Inhibition of p40, a subunit of IL-12/IL-23, attenuates pathology in AD-like mice; however, its signaling mechanism and expression pattern remained elusive. Here we show that IL-12 receptors are predominantly expressed in neurons and oligodendrocytes in AD-like APPPS1 mice and in patients with AD, whereas IL-23 receptor transcripts are barely detectable. Consistently, deletion of the IL-12 receptor in neuroectodermal cells ameliorated AD pathology in APPPS1 mice, whereas removal of IL-23 receptors had no effect. Genetic ablation of IL-12 signaling alone reverted the loss of mature oligodendrocytes, restored myelin homeostasis, rescued the amyloid-β-dependent reduction of parvalbumin-positive interneurons and restored phagocytosis-related changes in microglia of APPPS1 mice. Furthermore, IL-12 protein expression was increased in human AD brains compared to healthy age-matched controls, and human oligodendrocyte-like cells responded profoundly to IL-12 stimulation. We conclude that oligodendroglial and neuronal IL-12 signaling, but not IL-23 signaling, are key in orchestrating AD-related neuroimmune crosstalk and that IL-12 represents an attractive therapeutic target in AD.

Accurate staging of Alzheimer's disease (AD) pathology is crucial for therapeutic trials and prognosis, but existing fluid biomarkers lack specificity, especially for assessing tau deposition severity, in amyloid-beta (Aβ)-positive patients. We analyze cerebrospinal fluid (CSF) samples from 136 participants in the Alzheimer's Disease Neuroimaging Initiative using more than 6,000 proteins. We apply machine learning to predict AD pathological stages defined by amyloid and tau positron emission tomography (PET). We identify two distinct protein panels: 16 proteins, including neurofilament heavy chain (NEFH) and SPARC-related modular calcium-binding protein 1 (SMOC1), that distinguished Aβ-negative/tau-negative (A-T-) from A+ individuals and nine proteins, such as HCLS1-associated protein X-1 (HAX1) and glucose-6-phosphate isomerase (GPI), that differentiated A+T+ from A+T- stages. These signatures outperform the established CSF biomarkers (area under the curve [AUC]: 0.92 versus 0.67-0.70) and accurately predicted disease progression over a decade. The findings are validated in both internal and external cohorts. These results underscore the potential of proteomic-based signatures to refine AD diagnostic criteria and improve patient stratification in clinical trials.

Background/Objectives: Low-grade inflammation in the form of microglial activation may be involved in neurodegenerative and vascular dementias. Subcortical small-vessel disease (SSVD) is the main form of vascular dementia, associated with brain barrier dysfunction and endothelial and monocyte activation. IL-6 and IL-17A are known proinflammatory cytokines that contribute to the disruption of blood-brain barrier integrity and microvascular dysfunction, features that are central to SSVD pathophysiological pathways. We herein compared cerebrospinal fluid (CSF) IL-6 and IL-17A concentrations in SSVD and AD patients as well as control subjects and examined the potential associations among IL-6 and IL-17A levels with cognitive and ΜRΙ changes. The albumin quotient (Qalb) was also calculated. Methods: CSF IL-6 and IL-17A (18 SSVD, 17 AD, and 12 healthy controls) were measured with solid-phase sandwich ELISAs, while albumin levels were measured by immunonephelometry. MMSE, FAB, and the CLOX tests were used for cognitive assessment and MRI was used for atrophy and white matter hyperintensities. Results: Significantly elevated CSF levels of Qalb and IL-6 were found in SSVD patients compared to both AD (p = 0.02) and controls (p = 0.002), respectively. Moreover, CSF IL-6 levels displayed a significant inverse correlation with CLOX2 scores (r = -0.641, p = 0.02), as well as a positive correlation with the total normalized CSF volume (r = 0.7, p = 0.01). CSF IL-17A levels were found to be reduced in SSVD patients, compared to controls and AD patients (p < 0.0001 and p = 0.002, respectively). The IL-6/IL-17A ratio with a cut-off value > 1.004 displayed a sensitivity of 83.33% (95%CI; 60.78% to 94.16%) and a specificity of 68.97% (95%CI; 50.77% to 82.72%) for the discrimination of SSVD from AD patients and controls. Conclusions: In the present pilot single-center study, we found increased CSF IL-6 and IL-6/IL-17A ratio levels in SSVD patients that correlated with reduced scores in the CLOX2 test and increased CSF volume. These preliminary findings deserve further evaluation in larger cohorts in order to elucidate their potential as surrogate biomarkers for the discrimination of SSVD from AD pathology.

Age, sex, and apolipoprotein E (APOE) are the strongest risk factors for late-onset Alzheimer disease (AD). The role of APOE in AD varies with sex and ancestry. While the association of APOE with AD biomarkers also varies across sex and ancestry, no study has systematically investigated both sex-specific and ancestry differences of APOE on cerebrospinal fluid (CSF) biomarkers together, resulting in limited insights and generalizability.

To systematically investigate the association of sex and APOE-ε4 with 3 core CSF biomarkers across ancestries.

This cohort study examined 3 CSF biomarkers (amyloid β1-42 [Aβ42], phosphorylated tau 181 [p-tau], and total tau, in participants from 20 cohorts from July 1, 1985, to March 31, 2020. These individuals were grouped into African, Asian, and European ancestries based on genetic data. Data analyses were conducted from June 1, 2023, to November 10, 2024.

Sex (male or female) and APOE-ε4.

The associations of sex and APOE-ε4 with biomarker levels were assessed within each ancestry group, adjusting for age. Meta-analyses were performed to identify these associations across ancestries. Sensitivity analyses were conducted to exclude the potential influence of the APOE-ε2 allele.

This cohort study included 4592 individuals (mean [SD] age, 70.8 [10.2] years; 2425 [52.8%] female; 119 [2.6%] African, 52 [1.1%] Asian, and 4421 [96.3%] European). Higher APOE-ε4 dosage scores were associated with lower Aβ42 values (β [SE], -0.58 [0.02], P < .001), indicating more severe pathology; these associations were seen in men and women separately and jointly. The association with APOE-ε4 was statistically greater in men (β [SE], -0.63 [0.03]; P < .001) vs women (β [SE], -0.52 [0.03]; P < .001) of European ancestry (P = .01 for interaction). Women had higher levels of p-tau, indicating more severe neurofibrillary pathology. The association between APOE-ε4 dosage and p-tau was in the expected direction (higher APOE-ε4 dosage for higher p-tau values) in both sexes, but the difference between sexes was significant only in those of African ancestry (β [SE], 0.10 [0.18]; P = .57 for men; β [SE], 0.66 [0.17]; P < .001 for women; P = .03 for interaction). Women also had higher levels of total tau, indicating more neuronal damage. The association between APOE-ε4 dosage and total tau was stronger in women than in men in the African cohort (β [SE], 0.20 [0.22]; P = .36 for men and β [SE], 0.65 [0.22], P = .004 for women [P = .16 for interaction]) and European cohort (β [SE], 0.36 [0.03]; P < .001 in women and β [SE], 0.27 [0.03], P < .001 in men [P = .053 for interaction]); no significant associations were found in the Asian cohort. Sensitivity analysis excluding APOE-ε2 carriers yielded similar results.

In this cohort study, the association of the APOE-ε4 risk allele with tau accumulation was higher in women than in men. These findings underscore the importance of considering sex differences in APOE-ε4's association with AD biomarkers and tau pathology mechanisms in AD. Although this study provides robust evidence of complex interplay between sex and APOE-ε4 for European ancestry, further research is needed to fully understand other ancestry differences.

This study explores the application of machine learning to high-dimensional proteomics datasets for identifying Alzheimer's disease (AD) biomarkers. AD, a neurodegenerative disorder affecting millions worldwide, necessitates early and accurate diagnosis for effective management.

We leverage Tandem Mass Tag (TMT) proteomics data from the cerebrospinal fluid (CSF) samples from the frontal cortex of patients with idiopathic normal pressure hydrocephalus (iNPH), a condition often comorbid with AD, with rare access to both lumbar and ventricular samples. Our methodology includes extensive data preprocessing to address batch effects and missing values, followed by the use of the Synthetic Minority Over-sampling Technique (SMOTE) for data augmentation to overcome the small sample size. We apply linear, and non-linear machine learning models, and ensemble methods, to compare iNPH patients with and without biomarker evidence of AD pathology ( A β - T - or A β + T + ) in a classification task.

We present a machine learning workflow for working with high-dimensional TMT proteomics data that addresses their inherent data characteristics. Our results demonstrate that batch effect correction has no or minor impact on the models' performance and robust feature selection is critical for model stability and performance, especially in the high-dimensional proteomics data setting for AD diagnostics. The results further indicated that removing features with missing values produced stronger models than imputing them, and the batch effect had minimal impact on the models Our best-performing disease-progression detection model, a random forest, achieves an AUC of 0.84 (± 0.03).

We identify several novel protein biomarkers candidates, such as FABP3 and GOT1, with potential diagnostic value for AD pathology detection, suggesting the necessity of different biomarkers for AD diagnoses for patients with iNPH, and considering different biomarkers for ventricular and lumbar CSF samples. This work underscores the importance of a meticulous machine learning process in enhancing biomarker discovery. Our study also provides insights in translating biomarkers from other central nervous system diseases like iNPH, and both ventricular and lumbar CSF samples for biomarker discovery, providing a foundation for future research and clinical applications.

It is unclear whether aggregated plasma protein risk scores (PPRSs) could be useful in predicting the risks of mild cognitive impairment (MCI) and Alzheimer's disease (AD).

The Cox proportional hazard model with the Least Absolute Shrinkage and Selection Operator penalty was used to build the PPRSs for MCI and AD in 1515 Framingham Heart Study Generation 2 with 1128 proteins measured in plasma at exam 5 (cognitively normal [CN] = 1258, MCI = 129, AD = 128).

MCI PPRS had a hazard ratio (HR) of 6.97 [5.34, 9.12], with a discriminating power (C-index = 82.52%). AD PPRS had a HR of 5.74 [4.67, 7.05] (C-index = 88.15%). Both PPRSs were also significantly associated with cognitive changes, brain atrophy, and plasma AD biomarkers. Proteins in the MCI and AD PPRSs were involved in several pathways related to leukocyte, chemotaxis, immunity, inflammation, and cellular migration.

This study suggests that PPRSs serve well to predict the risk of developing MCI and AD as well as cognitive changes and AD-related pathogenesis in the brain.

PPRSs were developed for the risk of AD and AD preclinical stage, MCI. PPRSs were developed for MCI and AD associated with cognitive changes, loss of brain volume, and increasing level of plasma AD biomarkers. Leukocyte, chemotaxis, immunity, inflammation, and cellular migration enriched in proteins were identified as being involved in MCI and AD PPRSs.

Alzheimer disease (AD) diagnosis is confirmed using a medley of modalities, such as the detection of amyloid-β (Aβ) neuritic plaques and neurofibrillary tangles with positron electron tomography (PET) or the appraisal of irregularities in cognitive function with examinations. Although these methods have been efficient in confirming AD pathology, the rising demand for earlier intervention during pathogenesis has led researchers to explore the diagnostic potential of fluid biomarkers in cerebrospinal fluid (CSF) and plasma. Since CSF sample collection is invasive and limited in quantity, biomarker detection in plasma has become more attractive and modern advancements in technology has permitted more efficient and accurate analysis of plasma biomolecules. In this study, we found that a composite of standard factors, Aβ40 and total tau levels in plasma, divided by the variation factor, plasma Aβ42 level, provide better correlation with amyloid neuroimaging and neuropsychological test results than a level comparison between total tau and Aβ42 in plasma. We collected EDTA-treated blood plasma samples of 53 subjects, of randomly selected 27 AD patients and 26 normal cognition (NC) individuals, who received amyloid-PET scans for plaque quantification, and measured plasma levels of Aβ40, Aβ42, and total tau with digital enzyme-linked immunosorbent assay (ELISA) in a blinded manner. There was difficulty distinguishing AD patients from controls when analyzing biomarkers independently. However, significant differentiation was observed between the two groups when comparing individual ratios of total-tau×Aβ40/Aβ42. Our results indicate that collectively comparing fluctuations of these fluid biomarkers could aid in monitoring AD pathogenesis.

Diffusion tensor imaging (DTI) can be used to detect early signs of increased water diffusivity in white matter tracts in patients with mild cognitive impairment (MCI). This study examined how DTI, alongside cerebrospinal fluid (CSF) biomarkers (like tau proteins and amyloid-β), can help identify early brain changes in MCI. We included 159 individuals (92 with MCI and 67 healthy controls) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and extracted their demographics, CSF biomarkers, and DTI metrics. We compared the biomarkers (CSF biomarkers and DTI markers in 57 white matter tracts) between the two study groups using a general linear model, adjusting for age, sex, and handedness. CSF biomarker levels showed a statistically significant difference between the two study groups. Also, diffusion properties of left Cingulum and left Uncinate fasciculus in both groups were statistically different. Additionally, we explored possible associations between CSF and DTI markers in the MCI group. Our results indicated several statistically significant associations between DTI metrics and CSF biomarkers within specific white matter tracts. These findings underscore the complexity of imaging and molecular markers associated with MCI.

Blood-based assays are expected to be integrated into clinical routines across various contexts, including Alzheimer's disease (AD). Vascular dementia (VaD), which is the second most common cause leading to dementia after AD, could also significantly benefit from this advancement. However, no informative fluid biomarker has been identified for VaD. Given the disruption of iron homeostasis in both AD and VaD, this study aims to characterize the potential of the iron-related hormone Hepcidin as a biomarker for these two conditions. We will compare its added value to established AT(N) blood biomarkers.

Blood biomarkers (amyloid-composite, p-Tau181, Neurofilament Light Chain [NfL] and Hepcidin) levels in blood were analyzed in two independent cohorts and compared between AD patients and non-AD individuals. Additionally, blood Hepcidin and NfL were evaluated in the contexts of VaD and CADASIL, with their relative diagnostic value assessed.

Blood Hepcidin and NfL do not significantly increase the AUC obtained with both p-Tau181 and amyloid composite in the context of AD. In contrast, AUC for VaD diagnosis is significantly higher when combining these two blood biomarkers compared to NfL alone. Hepcidin was not significantly modified in CADASIL patients compared to control subjects.

Blood Hepcidin and NfL have limited interest in the clinical context of AD but determination of these biomarkers shows to be highly informative for the diagnosis of VaD. This result could have important implications for diagnosis and implementation of clinical trials.

Since combined antiretroviral therapy for human immunodeficiency virus-associated neurocognitive dysfunction (HAND) only slows the disease's progression, early identification and timely intervention are crucial for effective therapy. In this article, we review the latest evidence in body fluid biomarkers of HAND, providing an overview of research conducted on cerebrospinal fluid and blood samples to draw conclusions on promising biomarkers. Although the significance of biomarkers such as amyloid metabolites, tau proteins, neurofilament light chain, myelin oligodendrocyte glycoprotein, and brain-derived neurotrophic factor in the early detection of HAND may not be immediately clear, they could potentially play a crucial role in evaluating prognosis and tracking the effectiveness of treatment.

Monitoring and assessing the progression of symptoms in neurodegenerative diseases, including Alzheimer's and Parkinson's disease, are critical for improving patient outcomes. Traditional biomarkers, such as cerebrospinal fluid analysis and brain imaging, are widely used to investigate the underlying mechanisms of disease and enable early diagnosis. In contrast, digital biomarkers derived from phenotypic changes-such as EEG, eye movement, gait, and speech analysis-offer a noninvasive and accessible alternative. Leveraging portable and widely available devices, such as smartphones and wearable sensors, digital biomarkers are emerging as a promising tool for ND diagnosis and monitoring. This review highlights the comprehensive developments in digital biomarkers, emphasizing their unique advantages and integration potential alongside traditional biomarkers.

Alzheimer's disease (AD) is the leading cause of dementia worldwide, and the development of early screening methods can address its significant health and social consequences. In this paper, we present a rotary-valve assisted paper-based immunoassay device (RAPID) for early screening of AD, featuring a highly integrated on-chip rotary micro-valve that enables fully automated and efficient detection of the AD biomarker (amyloid beta 42, Aβ42) in artificial plasma. The microfluidic paper-based analytical device (μPAD) of the RAPID pre-stores the required assay reagents on a μPAD and automatically controls the liquid flow through a single valve. Once the test sample is added, the test reagents are sequentially transferred to the test area in the order set by the enzyme-linked immunosorbent assay (ELISA) protocol. In addition, the RAPID can remotely control the operation of the μPAD valve via a micro-servomotor, quantify the signals generated, display the results, and wirelessly transmit the data to a smartphone. To calibrate the RAPID, we performed a sandwich ELISA for Aβ42 in artificial plasma, and obtained a low limit of detection (LOD) of 9.6 pg mL-1, a coefficient of determination (COD) of 0.994, and an individual assay time of ∼30 minutes. In addition, we simulated 24 artificial samples to quantify Aβ42 protein concentrations in artificial plasma samples. The results show good consistency between the conventional ELISA and RAPID detection. The experimental results demonstrate that the RAPID is expected to promote further popularization of the screening of early-stage AD.

Side effects from antiseizure medication (ASM) are common in epilepsy but biomarkers for detection and monitoring are missing. This study investigated associations between CNS-related side effects from ASM and blood concentrations of the brain injury markers neurofilament-light (NFL), total tau, glial acidic fibrillary protein (GFAP), S100 calcium-binding protein B (S100B) and neuron-specific enolase (NSE).

This is a population-based cohort study of adults with epilepsy recruited from five Swedish outpatient neurology clinics from December 2020 to April 2023. Side effects classified as CNS-related: tiredness, dizziness, headache, concentration, memory, mood, motor/tremor, or sleep. Marker concentrations in the groups CNS side effects/no side effects were analyzed with Mann-Whitney U-test and significant differences were included in multivariable logistic regression models adjusting for age, epilepsy duration, seizure status, acquired structural lesion, and mono-/polytherapy.

The cohort consisted of 367 patients, 187 (51 %) were females, the median age was 43 years (IQR 30-61), and 123 (34 %) reported CNS side effects. Total tau was higher among participants reporting CNS side effects (median 4.44 (95 %CI 4.12-4.88) pg/ml) compared with participants without side effects (3.84 (95 %CI 3.52-4.07) pg/ml, p < 0.01). The difference remained significant in multivariable regression models. NSE was higher among participants without side effects but did not remain significant in the multivariable regression model. No differences were observed for NFL, GFAP or S100B.

Higher total tau plasma concentration could be associated with increased risk of CNS side effects from ASM. Longitudinal studies could determine if this reflects vulnerability or detrimental effects of ASM.

PREDICT, clinicaltrials.gov identifier NCT04559919.

The central nervous system (CNS) is immune-privileged by several immuno-modulators as interleukins (ILs). ILs are cytokines secreted by immune cells for cell-cell signaling communications and affect the functions of the CNS. ILs were reported to orchestrate different molecular and cellular mechanisms of both physiological and pathological events, through overproduction or over-expression of their receptors. They interact with numerous receptors mediating pro-inflammatory and/or anti-inflammatory actions. Interleukins have been implicated to participate in neurodegenerative diseases. They play a critical role in Alzheimer's disease (AD) pathology which is characterized by the over-production of pro-inflammatory ILs. These may aggravate neurodegeneration, in addition to their contribution to detrimental mechanisms as oxidative stress, and excitotoxicity. However, recent research on the relation between ILs and AD revealed major discrepancies. Most of the major ILs were shown to play both pro- and anti-inflammatory roles in different experimental settings and models. The interactions between different ILs through shared pathways also add to the difficulty of drawing solid conclusions. In addition, targeting the different ILs has not yielded consistent results. The repeated failures of therapeutic drugs in treating AD necessitate the search for novel agents targeting multiple mechanisms of the disease pathology. In this context, the understanding of interleukins and their roles throughout the disease progression and interaction with other systems in the brain may provide promising therapeutic targets for the prevention or treatment of AD.

Recent work has bolstered the possibility that peripheral changes may be relevant to Alzheimer's disease pathogenesis in the brain. While age-associated blood-borne proteins have been targeted to restore function to the aged brain, it remains unclear whether other dysfunctional systemic states can be exploited for similar benefits. Here, we investigate whether APOE allelic variation or presence of brain amyloid are associated with plasma proteomic changes and the molecular processes associated with these changes.

Using the SOMAscan assay, we measured 1305 plasma proteins from 53 homozygous, APOE3 and APOE4 subjects without dementia. We investigated the relationship of either the APOE-ε4 allele or amyloid positivity with plasma proteome changes by linear mixed effects modeling and ontology-based pathway and module-trait correlation analyses.

APOE4 is associated with plasma protein differences linked to atherosclerosis, tyrosine kinase activity, cholesterol transport, extracellular matrix, and synaptogenesis pathways. Independent of APOE4, we found that subjects likely harboring brain amyloid exhibit plasma proteome signatures associated with AD-linked pathways, including neurovascular dysfunction.

Our results indicate that APOE4 status or presence of brain amyloid are associated with plasma proteomic shifts prior to the onset of symptoms, suggesting that systemic pathways in certain risk contexts may be plausible targets for disease modification.

Alzheimer's disease (AD) is the most common form of dementia, leading to sustained cognitive decline. An increasing number of studies suggest that exercise is an effective strategy to promote the improvement of cognition in AD. Mechanisms of the benefits of exercise intervention on cognitive function may include modulation of vascular factors by affecting cardiovascular risk factors, regulating cardiorespiratory health, and enhancing cerebral blood flow. Exercise also promotes neurogenesis by stimulating neurotrophic factors, affecting neuroplasticity in the brain. Additionally, regular exercise improves the neuropathological characteristics of AD by improving mitochondrial function, and the brain redox status. More and more attention has been paid to the effect of Aβ and tau pathology as well as sleep disorders on cognitive function in persons diagnosed with AD. Besides, there are various forms of exercise intervention in cognitive improvement in patients with AD, including aerobic exercise, resistance exercise, and multi-component exercise. Consequently, the purpose of this review is to summarize the findings of the mechanisms of exercise intervention on cognitive function in patients with AD, and also discuss the application of different exercise interventions in cognitive impairment in AD to provide a theoretical basis and reference for the selection of exercise intervention in cognitive rehabilitation in AD.

Alzheimer's disease (AD) is a common neurodegenerative disorder worldwide and the using of magnetic resonance imaging (MRI) in the management of AD is increasing. The present study aims to summarize MRI in AD researches via bibliometric analysis and predict future research hotspots.

We searched for records related to MRI studies in AD patients from 2004 to 2023 in the Web of Science Core Collection (WoSCC) database. CiteSpace was applied to analyze institutions, references and keywords. VOSviewer was used for the analysis of countries, authors and journals.

A total of 13,659 articles were obtained in this study. The number of published articles showed overall exponential growth from 2004 to 2023. The top country and institution were the United States and the University of California System, accounting for 40.30% and 9.88% of the total studies, respectively. Jack CR from the United States was the most productive author. The most productive journal was the Journal of Alzheimers Disease. Keyword burst analysis revealed that "machine learning" and "deep learning" were the keywords that frequently appeared in the past 6 years. Timeline views of the references revealed that "#0 tau pathology" and "#1 deep learning" are currently the latest research focuses.

This study provides an in-depth overview of publications on MRI studies in AD. The United States is the leading country in this field with a concentration of highly productive researchers and high-level institutions. The current research hotspot is deep learning, which is being applied to develop noninvasive diagnosis and safer treatment of AD.

The rising incidence of Alzheimer's disease (AD) poses significant challenges to traditional diagnostic methods, which primarily rely on neuropsychological assessments and brain MRIs. The advent of deep learning in medical diagnosis opens new possibilities for early AD detection. In this study, we introduce retinal vessel segmentation methods based on U-Net ad iterative registration Learning (ReIU), which extract retinal vessel maps from OCT angiography (OCT-A) facilities. Our method achieved segmentation accuracies of 79.1% on the DRIVE dataset, 68.3% on the HRF dataset. Utilizing a multimodal dataset comprising both healthy and AD subjects, ReIU extracted vascular density from fundus images, facilitating primary AD screening with a classification accuracy of 79%. These results demonstrate ReIU's substantial accuracy and its potential as an economical, non-invasive screening tool for Alzheimer's disease. This study underscores the importance of integrating multi-modal data and deep learning techniques in advancing the early detection and management of Alzheimer's disease.

Discovery of novel protein biomarkers for clinical applications is an active research field across a manifold of diseases. Despite some successes and progress, the biomarker development pipeline still frequently ends in failure. Biomarker candidates that are discovered by appropriate technologies such as unbiased mass spectrometry cannot be validated or translated to immunoassays in many cases. Selection of strong disease biomarker candidates that further constitute suitable targets for antibody binding in immunoassays is thus important to allow routine clinical use. This essential selection step can be supported and rationalized using bioinformatics tools such as protein databases. Here, we present a workflow in the form of decision trees to computationally investigate biomarker candidates and their available affinity reagents in depth. This analysis can identify the most promising biomarker candidates for assay development, while minimal time and effort are required.

The history from a relative or caregiver is an important tool for differentiating neurodegenerative disease. We characterized patterns of caregiver questionnaire responses, at diagnosis and follow-up, on the Cambridge Behavioural Inventory (CBI).

Data-driven multivariate analysis (n = 4952 questionnaires) was undertaken for participants (n = 2481) with Alzheimer's disease (typical/amnestic n = 543, language n = 50, and posterior cortical n = 50 presentations), Parkinson's disease (n = 740), dementia with Lewy bodies (n = 55), multiple system atrophy (n = 55), progressive supranuclear palsy (n = 422), corticobasal syndrome (n = 176), behavioral variant frontotemporal dementia (n = 218), semantic (n = 125) and non-fluent variant progressive aphasia (n = 88), and motor neuron disease (n = 12).

Item-level support vector machine learning gave high diagnostic accuracy between diseases (area under the curve mean 0.83), despite transdiagnostic changes in memory, behavior, and everyday function. There was progression in CBI subscores over time, which varied by diagnosis.

Our results highlight the differential diagnostic information for a wide range of neurodegenerative diseases contained in a simple, structured collateral history.

We analyzed 4952 questionnaires from caregivers of 2481 participants with neurodegenerative disease. Behavioral and neuropsychiatric manifestations of neurodegenerative disease had overlapping diagnostic boundaries. Simple questionnaire response patterns were sufficient for accurate diagnosis of each disease. We reinforce the value of a collateral history to support a diagnosis of dementia. The Cambridge Behavioural Inventory is sensitive to change over time and suitable as an outcome measure in clinical trials.

The chiral compounds may be biomarker candidates in human metabolism, which indicates the health status of humans. There are many applications in LC/MS that show that chiral small molecules are promising biomarkers for human diseases. Both clinical and commercial analyses of chiral metabolites are necessary due to the enantiomeric ratios of chiral molecules in biological samples may show both human health status and diseases. This review provides current and advanced LC/MS techniques for the separation and analysis of chiral molecules as disease biomarkers. In particular, sample preparation and chromatographic analysis of potential chiral biomarkers in biological samples are presented. The preparation and applications of several chiral columns used in enantiomeric separation of chiral metabolites/biomarkers by advanced LC/MS techniques are discussed. The improvement of these analyses will enable both the discovery of new chiral biomarkers and the prognosis of human diseases.

Many changes can now be seen in the development and use of tests, especially those incorporating fluid biomarkers, to diagnose Alzheimer's disease (AD), a devastating disease caused by the progressive but rapid degeneration of cortical tissue. Some biomarkers we already know have a significant association with AD, such as amyloid beta (Aβ) and tau, as well as the ratio of concentrations of other Aβ isoforms. In addition, several novel biomarkers are emerging that can also be used as diagnostic fluid biomarkers for AD, but many studies are still needed before we can consider them reliable. The U.S. Federal Food and Drug Administration recently announced its final ruling to regulate laboratory-developed tests (LDTs) as medical devices, which can significantly impact LDT development. In this narrative review, we discuss the current status of fluid biomarkers used to diagnose early AD, their potential and limitations, and the impact caused by the FDA's decision and strategies to help developers navigate the complex changes in the regulatory landscape of LDTs.

CSF biomarkers of Aβ42 and phosphorylated tau (p-tau181) are used clinically for the detection of Alzheimer disease (AD) pathology during life. CSF biomarker validation studies have largely used clinical diagnoses and/or amyloid PET imaging as the reference standard. The few existing CSF-to-autopsy studies have been restricted to late-stage AD. This CSF-to-autopsy study investigated associations between CSF biomarkers of AD and AD neuropathologic changes among brain donors who had normal cognition at the time of lumbar puncture (LP).

This was a retrospective study of brain donors from the National Alzheimer's Coordinating Center who had normal cognition at the time of LP and who had measurements of CSF Aβ42 and p-tau181 performed with Lumipulse assays. All brain donors were from Washington University Knight ADRC. Staging of AD neuropathologic change (ADNC) was made based on National Institute on Aging-Alzheimer's Association criteria. For this study, participants were divided into 2 categories: "AD-" (no AD/low ADNC) and "AD+" (intermediate/high ADNC). Accuracy of each biomarker for discriminating AD status was evaluated using area under the curve (AUC) statistics generated using predicted probabilities from binary logistic regressions that controlled for age, sex, APOE ε4, and interval between LP and death.

The average age at LP was 79.3 years (SD = 5.6), and the average age at death was 87.1 years (SD = 6.5). Of the 49 brain donors, 24 (49%) were male and 47 (95.9%) were White. 20 (40.8%) had autopsy-confirmed AD. The average interval from LP until death was 7.76 years (SD = 4.31). CSF p-tau181/Aβ42 was the optimal predictor of AD, having excellent discrimination accuracy (AUC = 0.97, 95% CI 0.94-1.00, p = 0.003). CSF p-tau181 alone had the second-best discrimination accuracy (AUC = 0.92, 95% CI 0.84-1.00, p = 0.001), followed by CSF Aβ42 alone (AUC = 0.92, 95% CI 0.85-1.00, p = 0.007), while CSF t-tau had the numerically lowest discrimination accuracy (AUC = 0.87, 95% CI 0.76-0.97, p = 0.005). Effects remained after controlling for prevalent comorbid neuropathologies. CSF p-tau181/Aβ42 was strongly associated with CERAD ratings of neuritic amyloid plaque scores and Braak staging of NFTs.

This study supports Lumipulse-measured CSF Aβ42 and p-tau181 and, particularly, the ratio of p-tau181 to Aβ42, for the early detection of AD pathophysiologic processes.

This study provides Class II evidence that Lumipulse measures of p-tau181/Aβ42 in the CSF accurately discriminated cognitively normal participants with and without Alzheimer disease neuropathologic change.

AD is a progressive neurodegenerative disorder characterized by memory loss. Due to the advancement in next-generation sequencing, an enormous amount of AD-associated genomics data is available. However, the information about the involvement of these genes in AD association is still a research topic. Therefore, AlzGenPred is developed to identify the AD-associated genes using machine-learning. A total of 13,504 features derived from eight sequence-encoding schemes were generated and evaluated using 16 machine learning algorithms. Network-based features significantly outperformed sequence-based features, effectively distinguishing AD-associated genes. In contrast, sequence-based features failed to classify accurately. To improve performance, we generated 24 fused features (6020 D) from sequence-based encodings, increasing accuracy by 5-7% using a two-step lightGBM-based recursive feature selection method. However, accuracy remained below 70% even after hyperparameter tuning. Therefore, network-based features were used to generate the CatBoost-based ML method AlzGenPred with 96.55% accuracy and 98.99% AUROC. The developed method is tested on the AlzGene dataset where it showed 96.43% accuracy. Then the model was validated using the transcriptomics dataset. AlzGenPred provides a reliable and user-friendly tool for identifying potential AD biomarkers, accelerating biomarker discovery, and advancing our understanding of AD. It is available at https://www.bioinfoindia.org/alzgenpred/ and https://github.com/shuklarohit815/AlzGenPred .

The amyloid beta (Aβ) 42/40 ratio has been widely studied as a biomarker in Alzheimer's disease (AD); however, other Aβ peptides could also represent relevant biomarkers. We measured levels of Aβ38/40/42 in plasma samples from cognitively-unimpaired older adults and determined the relationships between Aβ levels and amyloid positron-emission-tomography (PET) and performance on a learning and memory task. We found that all Aβ peptides individually and the Aβ42/40 ratio, but not the Aβ42/38 ratio, were significantly correlated with brain amyloid (Aβ-PET). Multiple linear modeling, adjusting for age, sex, education, APOE4 and Aβ-PET showed significant associations between the Aβ42/38 ratio and memory. Further, associations between the Aβ42/38 ratio and learning scores were stronger in males and in Aβ-PET-negative individuals. In contrast, no significant associations were detected between the Aβ42/40 ratio and any learning measure. These studies implicate the Aβ42/38 ratio as a biomarker to assess early memory deficits and underscore the utility of the Aβ38 fragment as an important biomarker in the AD field.

In recent years, the diagnostic accuracy of Alzheimer's disease has been enhanced by the development of different types of biomarkers that indicate the presence of neuropathological processes. In addition to improving patient selection for clinical trials, biomarkers can assess the effects of new treatments on pathological processes. However, there is concern about the indiscriminate and poorly supported use of biomarkers, especially in asymptomatic individuals or those with subjective cognitive decline. Difficulties interpreting these tests, high costs, and unequal access make this scenario even more challenging in healthcare. This article presents the recommendations from the Scientific Department of Cognitive Neurology and Aging of the Brazilian Academy of Neurology (Departamento Científico de Neurologia Cognitiva e Envelhecimento da Academia Brasileira de Neurologia) regarding the rational use and interpretation of Alzheimer's disease biomarkers in clinical practice. The clinical diagnosis of cognitive-behavioral syndrome is recommended as the initial step to guide the request for biomarkers.

This study aims to investigate the potential causal relationship between cerebrospinal fluid (CSF) metabolites and Guillain-Barré syndrome (GBS) using a bidirectional two-sample Mendelian randomization (MR) approach. Publicly available summary data from genome-wide association studies (GWAS) were utilized for comprehensive analysis. The CSF metabolite GWAS summary data were extracted from a GWAS conducted by Panyard et al encompassing 338 CSF metabolites in European participants (n = 291). GWAS summary statistics for GBS were obtained from the FinnGen consortium (n = 215,931) comprising European populations. The primary method for MR analysis was the inverse variance weighted method. Various sensitivity analyses were conducted to assess the heterogeneity and pleiotropy of the findings. In the forward MR analysis, we identified a causal relationship between 15 CSF metabolites, including ribitol levels (odds ratio = 3.833, 95% confidence interval: 1.949-7.540, P = 9.87E-05), and the risk of developing GBS. In the reverse MR analysis, we found a causal relationship between GBS and 21 CSF metabolites, including gamma-glutamylphenylalanine levels (odds ratio = 0.934, 95% confidence interval: 0.904-0.966, P = 7.10E-05). No evidence of heterogeneity or horizontal pleiotropy was found in the MR analysis. Our findings suggest that the identified CSF metabolites and metabolic pathways can serve as valuable biomarkers for clinical screening and prevention of GBS. They may also be considered as candidate molecules for future research into the underlying mechanisms and for selecting drug targets.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory loss, and impaired daily functioning. The pathology of AD is marked by the accumulation of amyloid beta plaques and tau protein tangles in the brain, along with neuroinflammation and synaptic dysfunction. Genetic factors, such as mutations in APP, PSEN1, and PSEN2 genes, as well as the APOE ε4 allele, contribute to increased risk of acquiring AD. Currently available treatments provide symptomatic relief but do not halt disease progression. Research efforts are focused on developing disease-modifying therapies that target the underlying pathological mechanisms of AD. Advances in identification and validation of reliable biomarkers for AD hold great promise for enhancing early diagnosis, monitoring disease progression, and assessing treatment response in clinical practice in effort to alleviate the burden of this devastating disease. In this paper, we analyze data from the CAS Content Collection to summarize the research progress in Alzheimer's disease. We examine the publication landscape in effort to provide insights into current knowledge advances and developments. We also review the most discussed and emerging concepts and assess the strategies to combat the disease. We explore the genetic risk factors, pharmacological targets, and comorbid diseases. Finally, we inspect clinical applications of products against AD with their development pipelines and efforts for drug repurposing. The objective of this review is to provide a broad overview of the evolving landscape of current knowledge regarding AD, to outline challenges, and to evaluate growth opportunities to further efforts in combating the disease.