Exploring Biomarkers for Alzheimer's Disease

The generalized order statistics arising from three populations with the lower truncated proportional hazard rate models and its application to the sensitivity to the early disease stage

In this paper, we present some results to make inference about the parameters of lower truncated proportional hazard rate models with the same baseline distributions based on three independent generalized order statistics samples. Then, especially by considering the results of the diagnostic tests for the non-diseased, early-diseased stage and fully diseased populations, we make inference about sensitivity to the early disease stage parameter. The maximum likelihood estimator, a generalized pivotal estimator and some Bayes estimators are obtained for different structures of prior distributions. The percentile bootstrap confidence interval, a generalized pivotal confidence interval and some Bayesian credible intervals are also presented. A Monte Carlo simulation study is used to evaluate the performances of the obtained point estimators and confidence/credible intervals and two competitors. We use two real datasets to illustrate the proposed methods.

The potential of five c-miRNAs as serum biomarkers for Late-Onset Alzheimer's disease diagnosis: miR-10a-5p, miR-29b-2-5p, miR-125a-5p, miR-342-3p, and miR-708-5p

The nervous system is rich in miRNAs, indicating an important role of these molecules in regulating processes associated with cognition, memory, and others. Therefore, qualitative and quantitative imbalances involving such miRNAs may be involved in dementia contexts, including Late-Onset Alzheimer's Disease (LOAD). To test the viability of circulating miRNAs (c-miRNAs) as biomarkers for LOAD, we proceed accordingly to the following reasoning. The first stage was to discover and identify profile of c-miRNAs by RNA sequencing (RNA-Seq). For this purpose, blood serum samples were used from LOAD patients (n = 5) and cognitively healthy elderly control group (CTRL_CH) (n = 5), all over 70 years old. We identified seven c-miRNAs differentially expressed (p ≤ 0.05) in the serum of LOAD patients compared to CTRL_CH (miR-10a-5p; miR-29b-2-5p; miR-125a-5p; miR-342-3p, miR-708-5p, miR-380-5p and miR-340-3p). Of these, five (p ≤ 0.01) were selected for in silico analysis (miR-10a-5p; miR-29b-2-5p; miR-125a-5p; miR-342-3p, miR-708-5p), for which 44 relevant target genes were found regulated by these c-miRNAs and related to LOAD. Through the analysis of these target genes in databases, it was possible to observe that they have functions related to the development and progress of LOAD, directly or indirectly connecting the different Alzheimer's pathways. Thus, this work found five promising serum c-miRNAs as options for biomarkers contributing to LOAD diagnosis. Our study shows the complex network between these molecules and LOAD, supporting the relevance of studies using c-miRNAs in dementia contexts.

An LC-MS/MS-based platform for the quantification of multiple amyloid beta peptides in surrogate cerebrospinal fluid

Introduction

The accurate quantification of amyloid beta (Aβ) peptides in cerebrospinal fluid (CSF) is crucial for Alzheimer's disease (AD) research, particularly in terms of preclinical and biomarker studies. Traditional methods, such as the enzyme-linked immunosorbent assay (ELISA), have limitations. These include high costs, labor intensity, lengthy processes, and the possibility of cross-reactivity.

Objectives

The primary objectives of this research were twofold: to comprehensively characterize Aβ peptides and to develop a reliable and accurate method for the simultaneous quantification of Aβ 1-40 and Aβ 1-42 peptides in surrogate CSF that is traceable to the International System of Units (SI).

Methods

We developed a novel method that combined solid phase extraction (SPE) with isotope dilution liquid chromatography/tandem mass spectrometry (ID-LC/MSMS). SPE was employed to efficiently eliminate matrix interferences, while [15N] Aβ1-40 and [15N] Aβ1-42 served as internal standards to improve accuracy. In addition, we introduced Peptide Impurity Corrected Amino Acid Analysis (PICAA) to ensure traceability to the SI and reliable quantification of Aβ peptides.

Results

The developed platform demonstrated a linear calibration range of 300-20000 pg/ml for both Aβ1-42 and Aβ1-40 peptides, accompanied by strong correlation coefficients greater than 0.995. Quality Control (QC) samples demonstrated an accuracy of at least 90.0 %.

Conclusion

The enhanced specificity and flexibility of the developed platform potentially have implications for Alzheimer's disease diagnosis and future investigations of novel Aβ peptide biomarkers.

Advanced Overview of Biomarkers and Techniques for Early Diagnosis of Alzheimer's Disease

The development of early non-invasive diagnosis methods and identification of novel biomarkers are necessary for managing Alzheimer's disease (AD) and facilitating effective prognosis and treatment. AD has multi-factorial nature and involves complex molecular mechanism, which causes neuronal degeneration. The primary challenges in early AD detection include patient heterogeneity and lack of precise diagnosis at the preclinical stage. Several cerebrospinal fluid (CSF) and blood biomarkers have been proposed to show excellent diagnosis ability by identifying tau pathology and cerebral amyloid beta (Aβ) for AD. Intense research endeavors are being made to develop ultrasensitive detection techniques and find potent biomarkers for early AD diagnosis. To mitigate AD worldwide, understanding various CSF biomarkers, blood biomarkers, and techniques that can be used for early diagnosis is imperative. This review attempts to provide information regarding AD pathophysiology, genetic and non-genetic factors associated with AD, several potential blood and CSF biomarkers, like neurofilament light, neurogranin, Aβ, and tau, along with biomarkers under development for AD detection. Besides, numerous techniques, such as neuroimaging, spectroscopic techniques, biosensors, and neuroproteomics, which are being explored to aid early AD detection, have been discussed. The insights thus gained would help in finding potential biomarkers and suitable techniques for the accurate diagnosis of early AD before cognitive dysfunction.

A study of the longitudinal changes in multiple cerebrospinal fluid and volumetric magnetic resonance imaging biomarkers on converter and non-converter Alzheimer's disease subjects with consideration for their amyloid beta status

INTRODUCTION

This study aims to determine whether newly introduced biomarkers Visinin-like protein-1 (VILIP-1), chitinase-3-like protein 1 (YKL-40), synaptosomal-associated protein 25 (SNAP-25), and neurogranin (NG) in cerebrospinal fluid are useful in evaluating the asymptomatic and early symptomatic stages of Alzheimer's disease (AD). It further aims to shed new insight into the differences between stable subjects and those who progress to AD by associating cerebrospinal fluid (CSF) biomarkers and specific magnetic resonance imaging (MRI) regions with disease progression, more deeply exploring how such biomarkers relate to AD pathology.

METHODS

We examined baseline and longitudinal changes over a 7-year span and the longitudinal interactions between CSF and MRI biomarkers for subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We stratified all CSF (140) and MRI (525) cohort participants into five diagnostic groups (including converters) further dichotomized by CSF amyloid beta (Aβ) status. Linear mixed models were used to compare within-person rates of change across diagnostic groups and to evaluate the association of CSF biomarkers as predictors of magnetic resonance imaging (MRI) biomarkers. CSF biomarkers and disease-prone MRI regions are assessed for CSF proteins levels and brain structural changes.

RESULTS

VILIP-1 and SNAP-25 displayed within-person increments in early symptomatic, amyloid-positive groups. CSF amyloid-positive (Aβ+) subjects showed elevated baseline levels of total tau (tTau), phospho-tau181 (pTau), VILIP-1, and NG. YKL-40, SNAP-25, and NG are positively intercorrelated. Aβ+ subjects showed negative MRI biomarker changes. YKL-40, tTau, pTau, and VILIP-1 are longitudinally associated with MRI biomarkers atrophy.

DISCUSSION

Converters (CNc, MCIc) highlight the evolution of biomarkers during the disease progression. Results show that underlying amyloid pathology is associated with accelerated cognitive impairment. CSF levels of Aβ42, pTau, tTau, VILIP-1, and SNAP-25 show utility to discriminate between mild cognitive impairment (MCI) converter and control subjects (CN). Higher levels of YKL-40 in the Aβ+ group were longitudinally associated with declines in temporal pole and entorhinal thickness. Increased levels of tTau, pTau, and VILIP-1 in the Aβ+ groups were longitudinally associated with declines in hippocampal volume. These CSF biomarkers should be used in assessing the characterization of the AD progression.

High sensitivity C-reactive protein and glycated hemoglobin levels as dominant predictors of all-cause dementia: a nationwide population-based cohort study

BACKGROUND

Chronic inflammation might play a major role in the pathogenesis linking diabetes mellitus (DM) to cognition. In addition, DM might be the main driver of dementia risk. The purpose of the present study was to evaluate whether inflammation, glycation, or both are associated with the risk of developing all-cause dementia (ACD).

METHODS

A nationwide population-based cohort study was conducted with 4113 participants. The data were obtained from the Taiwanese Survey on Prevalence of Hypertension, Hyperglycemia, and Hyperlipidemia (TwSHHH) in 2007, which was linked with the Taiwan National Health Insurance Research Database (NHIRD). The markers of inflammation, expressed as hs-CRP, and glycation, presented as HbA1c, were measured. High levels of hs-CRP and HbA1c were defined as values greater than or equal to the 66th percentile. Developed ACD was identified based on the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes.

RESULTS

During 32,926.90 person-years, 106 individuals developed ACD in up to 8 years of follow-up. The study participants were separated into four categories by the top tertiles of hs-CRP and HbA1c based on the 66th percentile: high levels of both hs-CRP and HbA1c, only high levels of hs-CRP, only high levels of HbA1c, and non-high levels of hs-CRP nor HbA1c. Those who with a high level of only hs-CRP had the higher hazard for developing ACD (adjusted HR = 2.58; 95% CI = 1.29 ~ 5.17; P = 0.007), followed by the group with a high level of only HbA1c (adjusted HR = 2.52; 95% CI = 1.34 ~ 4.74; P = 0.004) and the group with high levels of both hs-CRP and HbA1c (adjusted HR = 2.36; 95% CI = 1.20 ~ 4.62; P = 0.012). Among those aged less than 65 years, hs-CRP was the only significant predictor of ACD risk (P = 0.046), whereas it did not yield any significant result in the elderly.

CONCLUSIONS

A higher risk of developing ACD was found not only in patients with high levels of inflammation but also high levels of glycated hemoglobin. Future studies should focus on the clinical implementation of hs-CRP or HbA1c to monitor cognitive deficits.

Ultrasensitive Detection of Alzheimer's Amyloids on a Plasmonic-Gold Platform

More than 55 million people live with dementia worldwide in 2021, and there are nearly 10 million new cases every year. Alzheimer's disease (AD) is the most common cause of dementia. Despite urgent need, early detection of AD and long-term monitoring of AD progression have been challenging. This is due to the limited availability of brain imaging facilities and the highly invasive procedure with the cerebrospinal fluid assay to assess the level of AD biomarkers, such as beta-amyloid (Aβ). Reliable measurements of AD biomarkers in blood samples are still difficult because of their very low abundance. Here, we develop a rapid, specific, and ultrasensitive immunoassay using plasmonic-gold nanoisland (pGOLD) chips with near-infrared fluorescence-enhanced detection for Aβ_1-40 and Aβ_1-42. We show step-by-step processes and results during the platform establishment, including antibody specificity and sensitivity tests, antibody pair examination, condition optimization, and procedure refinement. Finally, we demonstrate the platform performance with detection sensitivity at the subpicogram per milliliter level. This platform, therefore, has a great application potential for early detection of AD using blood samples.

Accurate characterization of β-amyloid (Aβ40, Aβ42) standards using species-specific isotope dilution by means of HPLC-ICP-MS/MS

The amyloid β peptide, as one of the main components in senile plaque, represents a defining pathological feature for Alzheimer’s disease, and is therefore commonly used as a biomarker for this disease in clinical analysis. However, the selection of suitable standards is limited here, since only a few are commercially available, and these suffer from varying purity. Hence, the accurate characterization of these standards is of great importance. In this study, we developed a method for the traceable quantification of the peptide content using species-specific isotope dilution and ICP-MS/MS detection. It is based on the separation of the sulfur-containing amino acids methionine and cysteine after oxidation and hydrolysis of the peptide. Using a strong anion exchange column, both amino acids could be separated from each other, as well as from their oxidized forms and sulfate. The sulfur content was determined via ICP-MS/MS using oxygen as reaction gas. Species-specific isotope dilution was enabled by using a ³⁴S-labeled yeast hydrolysate, containing methionine sulfone and cysteic acid with different isotopic composition. The peptide contents of Aβ standards (Aβ40,42), as well as myoglobin and lysozyme with different degrees of purity, were determined. For validation purposes, the standard reference material NIST 2389a, which contains the amino acids in a similar concentration, was subjected to the developed sample preparation and analysis method. In addition to accounting for errors during sample preparation, high levels of accuracy and precision could be obtained using this method, making it fit-for-purpose for the characterization of peptide standards.

Practical recommendations for timely, accurate diagnosis of symptomatic Alzheimer's disease (MCI and dementia) in primary care: a review and synthesis

The critical role of primary care clinicians (PCCs) in Alzheimer's disease (AD) prevention, diagnosis and management must evolve as new treatment paradigms and disease-modifying therapies (DMTs) emerge. Our understanding of AD has grown substantially: no longer conceptualized as a late-in-life syndrome of cognitive and functional impairments, we now recognize that AD pathology builds silently for decades before cognitive impairment is detectable. Clinically, AD first manifests subtly as mild cognitive impairment (MCI) due to AD before progressing to dementia. Emerging optimism for improved outcomes in AD stems from a focus on preventive interventions in midlife and timely, biomarker-confirmed diagnosis at early signs of cognitive deficits (i.e. MCI due to AD and mild AD dementia). A timely AD diagnosis is particularly important for optimizing patient care and enabling the appropriate use of anticipated DMTs. An accelerating challenge for PCCs and AD specialists will be to respond to innovations in diagnostics and therapy for AD in a system that is not currently well positioned to do so. To overcome these challenges, PCCs and AD specialists must collaborate closely to navigate and optimize dynamically evolving AD care in the face of new opportunities. In the spirit of this collaboration, we summarize here some prominent and influential models that inform our current understanding of AD. We also advocate for timely and accurate (i.e. biomarker-defined) diagnosis of early AD. In doing so, we consider evolving issues related to prevention, detecting emerging cognitive impairment and the role of biomarkers in the clinic.

Recent Advancements in Electrochemical Biosensors for Alzheimer's Disease Biomarkers Detection

Background

It is estimated that the average time between the diagnosis of Alzheimer’s disease (AD) and the patient’s death is 5-9 years. Therefore, both the initial phase of the disease and the preclinical state can be included in the critical period in disease diagnosis. Accordingly, huge progress has recently been observed in biomarker research to identify risk factors for dementia in older people with normal cognitive functions and mild cognitive impairments.

Methods

Electrochemical biosensors are excellent analytical tools that are used in the detection of AD biomarkers as they are easy to use, portable, and can do analysis in real time.

Results

This review presents the analytical techniques currently used to determine AD biomarkers in terms of their advantages and disadvantages; the most important clinical biomarkers of AD and their role in the disease. All recently used biorecognition molecules in electrochemical biosensor development, i.e., receptor protein, antibodies, aptamers and nucleic acids, are summarized for the first time. Novel electrochemical biosensors for AD biomarker detection, as ideal analytical platforms for point-of-care diagnostics, are also reviewed.

Conclusion

The article focuses on various strategies of biosensor chemical surface modifications to immobilize biorecognition molecules, enabling specific, quantitative AD biomarker detection in synthetic and clinical samples. In addition, this is the first review that presents innovative single-platform systems for simultaneous detection of multiple biomarkers and other important AD-associated biological species based on electrochemical techniques. The importance of these platforms in disease diagnosis is discussed.

In Vitro Biosensing of β-Amyloid Peptide Aggregation Dynamics using a Biological Nanopore

Alzheimer's disease and other neurodegenerative disorders are becoming more prevalent as advances in technology and medicine increase living standards and life expectancy. Alzheimer's disease is thought to initiate development early in the patient's life and progresses continuously into old age. This process is characterized molecularly by the amyloid hypothesis, which asserts that self-aggregating amyloid peptides are core to the pathophysiology in Alzheimer's progression. Precise quantification of amyloid peptides in human bodily fluid samples (i.e. cerebrospinal fluid, blood) may inform diagnosis and prognosis, and has been studied using established biosensing technologies like liquid chromatography, mass spectrometry, and immunoassays. However, existing methods are challenged to provide single molecule, quantitative analysis of the disease-causing aggregation process. Ultra-sensitive nanopore biosensors can step in to fill this role as a dynamic mapping tool. The work in this paper establishes characteristic signals of β-amyloid 40 monomers, oligomers, and soluble aggregates, as well as a proof-of-concept foundation where a biological nanopore biosensor is used to monitor the extent of in vitro β-amyloid 40 peptide aggregation at the single molecule level. This foundation allows for future work to expand in drug screening, diagnostics, and aggregation dynamic experiments.

Predicting the Prognosis of MCI Patients Using Longitudinal MRI Data

The aim of this study is to develop a computer-aided diagnosis system with a deep-learning approach for distinguishing "Mild Cognitive Impairment (MCI) due to Alzheimer's Disease (AD)" patients among a list of MCI patients. In this system we are using the power of longitudinal data extracted from magnetic resonance (MR). For this work, a total of 294 MCI patients were selected from the ADNI database. Among them, 125 patients developed AD during their follow-up and the rest remained stable. The proposed computer-aided diagnosis system (CAD) attempts to identify brain regions that are significant for the prediction of developing AD. The longitudinal data were constructed using a 3D Jacobian-based method aiming to track the brain differences between two consecutive follow-ups. The proposed CAD system distinguishes MCI patients who developed AD from those who remained stable with an accuracy of 87.2 percent. Moreover, it does not depend on data acquired by invasive methods or cognitive tests. This work demonstrates that the use of data in different time periods contains information that is beneficial for prognosis prediction purposes that outperform similar methods and are slightly inferior only to those systems that use invasive methods or neuropsychological tests.

Cerebrospinal Fluid Biomarkers of Alzheimer's Disease: Current Evidence and Future Perspectives

Alzheimer's disease is a progressive, clinically heterogeneous, and particularly complex neurodegenerative disease characterized by a decline in cognition. Over the last two decades, there has been significant growth in the investigation of cerebrospinal fluid (CSF) biomarkers for Alzheimer's disease. This review presents current evidence from many clinical neurochemical studies, with findings that attest to the efficacy of existing core CSF biomarkers such as total tau, phosphorylated tau, and amyloid-β (Aβ42), which diagnose Alzheimer's disease in the early and dementia stages of the disorder. The heterogeneity of the pathophysiology of the late-onset disease warrants the growth of the Alzheimer's disease CSF biomarker toolbox; more biomarkers showing other aspects of the disease mechanism are needed. This review focuses on new biomarkers that track Alzheimer's disease pathology, such as those that assess neuronal injury (VILIP-1 and neurofilament light), neuroinflammation (sTREM2, YKL-40, osteopontin, GFAP, progranulin, and MCP-1), synaptic dysfunction (SNAP-25 and GAP-43), vascular dysregulation (hFABP), as well as CSF α-synuclein levels and TDP-43 pathology. Some of these biomarkers are promising candidates as they are specific and predict future rates of cognitive decline. Findings from the combinations of subclasses of new Alzheimer's disease biomarkers that improve their diagnostic efficacy in detecting associated pathological changes are also presented.

Retinal Thickness Changes Over Time in a Murine AD Model APP NL-F/NL-F

Background: Alzheimer's disease (AD) may present retinal changes before brain pathology, suggesting the retina as an accessible biomarker of AD. The present work is a diachronic study using spectral domain optical coherence tomography (SD-OCT) to determine the total retinal thickness and retinal nerve fiber layer (RNFL) thickness in an APP^{NL−F/NL−F} mouse model of AD at 6, 9, 12, 15, 17, and 20 months old compared to wild type (WT) animals.

Methods: Total retinal thickness and RNFL thickness were determined. The mean total retinal thickness was analyzed following the Early Treatment Diabetic Retinopathy Study sectors. RNFL was measured in six sectors of axonal ring scans around the optic nerve.

Results: In the APP^{NL−F/NL−F} group compared to WT animals, the total retinal thickness changes observed were the following: (i) At 6-months-old, a significant thinning in the outer temporal sector was observed; (ii) at 15-months-old a significant thinning in the inner temporal and in the inner and outer inferior retinal sectors was noticed; (iii) at 17-months-old, a significant thickening in the inferior and nasal sectors was found in both inner and outer rings; and (iv) at 20-months-old, a significant thinning in the inner ring of nasal, temporal, and inferior retina and in the outer ring of superior and temporal retina was seen. In RNFL thickness, there was significant thinning in the global analysis and in nasal and inner-temporal sectors at 6 months old. Thinning was also found in the supero-temporal and nasal sectors and global value at 20 months old.

Conclusions: In the APP^{NL−F/NL−F} AD model, the retinal thickness showed thinning, possibly produced by neurodegeneration alternating with thickening caused by deposits and neuroinflammation in some areas of the retina. These changes over time are similar to those observed in the human retina and could be a biomarker for AD. The APP^{NL−F/NL−F} AD model may help us better understand the different retinal changes during the progression of AD.

Uncharacterized RNAs in Plasma of Alzheimer's Patients Are Associated with Cognitive Impairment and Show a Potential Diagnostic Power

Alzheimer’s disease (AD) diagnosis is actually based on clinical evaluation and brain-imaging tests, and it can often be confirmed only post-mortem. Therefore, new non-invasive molecular biomarkers are necessary to improve AD diagnosis. As circulating microRNA biomarkers have been proposed for many diseases, including AD, we aimed to identify new diagnostic non-small RNAs in AD. Whole transcriptome analysis was performed on plasma samples of five AD and five unaffected individuals (CTRL) using the Clariom D Pico Assay, followed by validation in real-time PCR on 37 AD patients and 37 CTRL. Six differentially expressed (DE) transcripts were identified: GS1-304P7.3 (upregulated), NONHSAT090268, TC0100011037, TC0400008478, TC1400008125, and UBE2V1 (downregulated). Peripheral blood mononuclear cells (PBMCs) may influence the expression of circulating RNAs and their analysis has been proposed to improve AD clinical management. Accordingly, DE transcript expression was also evaluated in PBMCs, showing no difference between AD and CTRL. ROC (receiver operating characteristic) curve analysis was performed to evaluate the diagnostic accuracy of each DE transcript and a signature including all of them. A correlation between cognitive impairment and GS1-304P7.3, NONHSAT090268, TC0100011037, and TC0400008478 was detected, suggesting a potential association between their extracellular abundance and AD clinical phenotype. Finally, this study identified six transcripts showing altered expression in the plasma of AD patients. Given the need for new, accurate blood biomarkers for AD diagnosis, these transcripts may be considered for further analyses in larger cohorts, also in combination with other biomarkers, aiming to identify specific RNA-based biomarkers to be eventually applied to clinical practice.

Quantile graphs for EEG-based diagnosis of Alzheimer's disease

Known as a degenerative and progressive dementia, Alzheimer’s disease (AD) affects about 25 million elderly people around the world. This illness results in a decrease in the productivity of people and places limits on their daily lives. Electroencephalography (EEG), in which the electrical brain activity is recorded in the form of time series and analyzed using signal processing techniques, is a well-known neurophysiological AD biomarker. EEG is noninvasive, low-cost, has a high temporal resolution, and provides valuable information about brain dynamics in AD. Here, we present an original approach based on the use of quantile graphs (QGs) for classifying EEG data. QGs map frequency, amplitude, and correlation characteristics of a time series (such as the EEG data of an AD patient) into the topological features of a network. The five topological network metrics used here—clustering coefficient, mean jump length, betweenness centrality, modularity, and Laplacian Estrada index—showed that the QG model can distinguish healthy subjects from AD patients, with open or closed eyes. The QG method also indicates which channels (corresponding to 19 different locations on the patients’ scalp) provide the best discriminating power. Furthermore, the joint analysis of delta, theta, alpha, and beta wave results indicate that all AD patients under study display clear symptoms of the disease and may have it in its late stage, a diagnosis known a priori and supported by our study. Results presented here attest to the usefulness of the QG method in analyzing complex, nonlinear signals such as those generated from AD patients by EEGs.

Microglial Activation in the Retina of a Triple-Transgenic Alzheimer's Disease Mouse Model (3xTg-AD)

Alzheimer’s disease (AD) is the most common type of dementia in the world. The main biomarkers associated with AD are protein amyloid-β (Aβ) plaques and protein tau neurofibrillary tangles, which are responsible for brain neuroinflammation mediated by microglial cells. Increasing evidence has shown that the retina can also be affected in AD, presenting some molecular and cellular changes in the brain, such as microglia activation. However, there are only a few studies assessing such changes in the retinal microglia in animal models of AD. These studies use retinal sections, which have some limitations. In this study, we performed, for the first time in a triple-transgenic AD mouse model (3xTg-AD), a quantitative morphometric analysis of microglia activation (using the anti-Iba-1 antibody) in retinal whole-mounts, allowing visualization of the entire microglial cell, as well as its localization along the extension of the retina in different layers. Compared to age-matched animals, the retina of 3xTg-AD mice presents a higher number of microglial cells and a thicker microglial cell body area. Moreover, the microglia migrate, reorient, and retract their processes, changing their localization from a parallel to a perpendicular position relative to the retinal surface. These findings demonstrate clear microglia remodeling in the retina of 3xTg-AD mice.

Nanopore Fabrication and Application as Biosensors in Neurodegenerative Diseases

Since its conception as an applied biomedical technology nearly 30 years ago, nanopore is emerging as a promising, high-throughput, biomarker-targeted diagnostic tool for clinicians. The attraction of a nanopore-based detection system is its simple, inexpensive, robust, user-friendly, high-throughput blueprint with minimal sample preparation needed prior to analysis. The goal of clinical-based nanopore biosensing is to go from sample acquisition to a meaningful readout quickly. The most extensive work in nanopore applications has been targeted at DNA, RNA, and peptide identification. Although, biosensing of pathological biomarkers, which is covered in this review, is on the rise. This review is broken into two major sections: (i) the current state of existing biological, solid state, and hybrid nanopore systems and (ii) the applications of nanopore biosensors toward detecting neurodegenerative biomarkers.

Putative autoantibodies in the cerebrospinal fluid of Alzheimer's disease patients

Background: Recent efforts have described an immunogenic component to the pathobiology of Alzheimer's disease (AD) and Parkinson's disease (PD). However, current methods of studying fluid autoantibodies, such as enzyme-linked immunosorbent assays and immunohistochemistry, are hypothesis-driven and not optimal for discovering new autoantibody biomarkers by proteome-wide screening. Recently, we developed a general mass spectrometry-based approach to identify tissue-specific autoantibodies in serum, at a proteome-wide level. In this study, we adapted the method to explore novel autoantibody biomarkers in the cerebrospinal fluid (CSF) of AD and PD patients. Methods: CSF samples were obtained from 10 headache control individuals, 10 AD patients and 10 PD patients. Antibodies present in the CSF were isolated by immobilization to protein-G magnetic beads. These antibodies were incubated with a brain tissue extract, prepared from frontal cortex, pons, cerebellum and brain stem. Protein antigens captured by the protein-G magnetic bead-bound antibodies were digested with trypsin and analyzed using mass spectrometry. Autoantibody candidates were selected by 1) detection in one or less individuals of the control group and 2) identification in at least half of the patient groups. Results: There were 16 putative autoantibody biomarkers selected from the AD group. Glia-derived nexin autoantibody was detected in eight of ten AD patients and was absent in the control group. Other AD pathology-related targets were also identified, such as actin-interaction protein, quinone oxidoreductase, sushi repeat-containing protein, metalloproteinase inhibitor 2, IP3 receptor 1 and sarcoplasmic/endoplasmic reticulum calcium ATPase 2. An additional eleven autoantibody targets were also identified in the present experiment, although their link to AD is not clear. No autoantibodies in the PD group satisfied our selection criteria. Conclusion: Our unbiased mass spectrometry method was able to detect new putative CSF autoantibody biomarkers of AD. Further investigation into the involvement of humoral autoimmunity in AD and PD pathobiology may be warranted.

Characterization of Inner Retinal Hyperreflective Alterations in Early Cognitive Impairment on Adaptive Optics Scanning Laser Ophthalmoscopy

Purpose

To examine inner retinal hyperreflective features on adaptive optics scanning laser ophthalmoscopy (AOSLO) in individuals with early cognitive impairment.

Methods

In this prospective, cross-sectional study, we enrolled 12 participants with either amnestic mild cognitive impairment (aMCI, n = 10) or early dementia due to Alzheimer's disease (eAD, n = 2) and 12 age-, sex-, and race-matched cognitively normal controls. All participants completed AOSLO imaging of the inner retina. AOSLO montages of the peripapillary area were graded for hyperreflective features including granular membranes, mottled membranes, and nummular features. Regions of interest on AOSLO were compared qualitatively to corresponding optical coherence tomography (OCT) cross sections. OCT was also used to analyze peripapillary retinal nerve fiber layer (RNFL) thickness.

Results

Cognitively impaired individuals had a significantly higher number of granular membranes with a larger overall area compared to controls. The proportion of cognitively impaired individuals with two or more granular membranes was 41.7% compared to none in the control group. Granular membrane area was also inversely correlated with cognitive performance on the Montreal Cognitive Assessment. There was no difference between the two groups in terms of other membrane types or RNFL thickness.

Conclusions

Individuals with early cognitive impairment related to Alzheimer's show hyperreflective granular membranes on high-resolution imaging, which we hypothesize to be manifestations of inner retinal gliosis. The presence of these subtle hyperreflective membranes may obscure underlying RNFL thinning in these eyes on OCT imaging. The distinctive phenotype of granular membranes surrounding the optic nerve on AOSLO may represent a new potential biomarker of early Alzheimer's.

Plasma microRNA miR-26b as a potential diagnostic biomarker of degenerative myelopathy in Pembroke welsh corgis

BACKGROUND

Degenerative myelopathy (DM) is a progressive neurodegenerative disease frequently found in Pembroke Welsh Corgis (PWCs). Most DM-affected PWCs are homozygous for the mutant superoxide dismutase 1 (SOD1) allele; however, the genetic examination for the SOD1 mutation does not exclusively detect symptomatic dogs. In order to identify novel biomarkers, the plasma microRNA (miRNA) profiles of PWCs with DM were investigated.

RESULTS

Quantification of the plasma levels of 277 miRNAs by an RT-qPCR array identified 11 up-regulated miRNAs and 7 down-regulated miRNAs in DM-affected PWCs from those in wild-type SOD1 PWCs. A pathway analysis identified 3 miRNAs: miR-26b, miR-181a, and miR-196a, which potentially regulate several genes associated with SOD1. In order to validate the diagnostic accuracy of the candidate miRNAs in the aged PWC population, candidate miRNAs in plasma were measured by RT-qPCR and a receiver operating characteristic (ROC) curve analysis was performed. miR-26b had the largest area under the ROC curve for distinguishing DM PWCs from healthy PWCs (sensitivity, 66.7%; specificity, 87.0%). The plasma level of miR-26b was significantly higher in the DM group than in the healthy control group. A positive correlation was observed between increases in the plasma level of miR-26b and disease progression.

CONCLUSIONS

These results suggest that plasma miR-26b is a potential novel diagnostic biomarker of DM.

Parafoveal vessel loss and correlation between peripapillary vessel density and cognitive performance in amnestic mild cognitive impairment and early Alzheimer's Disease on optical coherence tomography angiography

PURPOSE

Patients with Alzheimer's Disease (AD) exhibit decreased retinal blood flow and vessel density (VD). However, it is not known whether these changes are also present in individuals with early AD (eAD) or amnestic type mild cognitive impairment (aMCI), an enriched pre-AD population with a higher risk for progressing to dementia. We performed a prospective case-control clinical study to investigate whether optical coherence tomography angiography (OCTA) parameters in the macula and disc are altered in those with aMCI and eAD.

METHODS

This is a single center study of 32 participants. Individuals with aMCI/eAD (n = 16) were 1:1 matched to cognitively normal controls (n = 16). We evaluated OCTA images of the parafoveal superficial capillary plexus (SCP) and two vascular layers in the peripapillary region, the radial peripapillary capillary (RPC) and superficial vascular complex (SVC). Outcome vascular and structural parameters included VD, vessel length density (VLD), adjusted flow index (AFI) and structural retinal nerve fiber layer (RNFL) thickness. We compared these parameters between the two groups and examined the correlation between OCTA parameters and cognitive performance on the Montreal Cognitive Assessment (MoCA).

RESULTS

Cognitively impaired participants demonstrated statistically significant decrease in parafoveal SCP VD and AFI as compared to controls, but no statistically significant difference in peripapillary parameters. Furthermore, we found a significant positive correlation between MoCA scores for the entire study cohort and both the parafoveal SCP VD and peripapillary RPC VLD.

CONCLUSION

OCTA shows significant decline in parafoveal flow and VD in individuals with early cognitive impairment related to AD, suggesting that these parameters could have potential utility as early disease biomarkers. In contrast, the presence of larger vascular channels in the peripapillary region may have obscured subtle capillary changes in that region. Overall, the correlation between vascular OCTA parameters and cognitive performance supports further OCTA studies in this population.

High-throughput epitope profiling of antibodies in the plasma of Alzheimer's disease patients using random peptide microarrays

The symptoms of Alzheimer's disease (AD), a major cause of dementia in older adults, are linked directly with neuronal cell death, which is thought to be due to aberrant neuronal inflammation. Autoantibodies formed during neuronal inflammation show excellent stability in blood; therefore, they may be convenient blood-based diagnostic markers of AD. Here, we performed microarray analysis of 29,240 unbiased random peptides to be used for comprehensive screening of AD-specific IgG and IgM antibodies in the blood. The results showed that (1) sequence-specific and isotype-specific antibodies are regulated differentially in AD, and combinations of these antibodies showing high area under the receiver operating characteristic curve values (0.862-0.961) can be used to classify AD, (2) AD-specific IgG antibodies arise from IgM antibody-secreting cells that existed before disease onset and (3) target protein profiling of the antibodies identified some AD-related proteins, some of which are involved in AD-related signalling pathways. Therefore, we propose that these epitopes may facilitate the development of biomarkers for AD diagnosis and form the basis for a mechanistic study related to AD progression.

Biomarkers for the diagnosis of Alzheimer's disease, dementia Lewy body, frontotemporal dementia and vascular dementia

Background

Dementia is a chronic brain disorder classified by four distinct diseases that impact cognition and mental degeneration. Each subgroup exhibits similar brain deficiencies and mutations. This review will focus on four dementia subgroups: Alzheimer’s disease, vascular dementia, frontotemporal dementia and dementia Lewy body.

Aim

The aim of this systematic review is to create a concise overview of unique similarities within dementia used to locate and identify new biomarker methods in diagnosing dementia.

Methods

123 300 articles published after 2010 were identified from PubMed, JSTOR, WorldCat Online Computer Library and PALNI (Private Academic Library Network of Indiana) using the following search items (in title or abstract): ‘Neurodegenerative Diseases’ OR ‘Biomarkers’ OR ‘Alzheimer’s Disease’ OR ‘Frontal Temporal Lobe Dementia’ OR ‘Vascular Dementia’ OR ‘Dementia Lewy Body’ OR ‘Cerebral Spinal Fluid’ OR ‘Mental Cognitive Impairment’. 47 studies were included in the qualitative synthesis.

Results

Evidence suggested neuroimaging with amyloid positron emission tomography (PET) scanning and newly found PET tracers to be more effective in diagnosing Alzheimer’s and amnesiac mental cognitive impairment than carbon-11 Pittsburgh compound-B radioisotope tracer. Newly created methods to make PET scans more accurate and practical in clinical settings signify a major shift in diagnosing dementia and neurodegenerative diseases.

Conclusion

Vast improvements in neuroimaging techniques have led to newly discovered biomarkers and diagnostics. Neuroimaging with amyloid PET scanning surpasses what had been considered the dominant method of neuroimaging and MRI. Newly created methods to make PET scans more accurate and practical in clinical settings signify a major shift in diagnosing dementia pathology. Continued research and studies must be conducted to improve current findings and streamline methods to further subcategorise neurodegenerative disorders and diagnosis.

Complementarity of Matrix- and Nanostructure-Assisted Laser Desorption/Ionization Approaches

In recent years, matrix-assisted laser desorption/ionization (MALDI) has become the main tool for the study of biological macromolecules, such as protein nano-machines, especially in the determination of their molecular masses, structure, and post-translational modifications. A key role in the classical process of desorption and ionization of the sample is played by a matrix, usually a low-molecular weight weak organic acid. Unfortunately, the interpretation of mass spectra in the mass range of below m/z 500 is difficult, and hence the analysis of low molecular weight compounds in a matrix-assisted system is an analytical challenge. Replacing the classical matrix with nanomaterials, e.g., silver nanoparticles, allows improvement of the selectivity and sensitivity of spectrometric measurement of biologically important small molecules. Nowadays, the nanostructure-assisted laser desorption/ionization (NALDI) approach complements the classic MALDI in the field of modern bioanalytics. In particular, the aim of this work is to review the recent advances in MALDI and NALDI approaches.

Glymphatic Efficiency Is a Critical Factor for Using Abnormal Tau in Peripheral Tissues as Biomarker for Alzheimer's Disease

Alzheimer's Disease or other dementias are characterized by the accumulation of abnormal tau and amyloid β peptides in brains. Therefore, abnormal tau and amyloid peptides in peripheral tissues or blood have been explored as diagnostic biomarkers. On the other hand, recent studies have revealed glymphatics a special drainage system for brain's wastes. We aimed to investigate whether effectiveness of glymphatic system affects the quantity of abnormal tau in the peripheral tissues. We have previously shown that aged IL33 KO (Il33 -/-) mice develop Alzheimer's like disease. Despite a large quantity of abnormal tau in brains, Il33 -/- mice showed a much lower amount of abnormal tau drained to the peripheral tissues kidneys than in wild type mice. Our further study showed that it was caused by defective glymphatic drainage since Il33 KO impaired glymphatics. Thus, it is necessary to identify biomarkers, which can evaluate efficiency of glymphatic drainage. Simultaneous measurement of these biomarkers and abnormal tau in peripheral tissues or blood may be critical for accurate diagnosis of Alzheimer's disease.

Loss of Ca2+/Calmodulin Dependent Protein Kinase Kinase 2 Leads to Aberrant Transferrin Phosphorylation and Trafficking: A Potential Biomarker for Alzheimer's Disease

Ca²⁺/calmodulin-dependent protein kinase kinase 2 (CaMKK2) is a serine/threonine kinase that is activated following an increase in the intracellular Ca²⁺ concentration and activates multiple signaling cascades that control physiologically important neuronal processes. CaMKK2 has been implicated in schizophrenia, bipolar disease, neurodegeneration, and cancer. Using isoelectric focusing (IEF) and mass spectrometry-based proteomic analysis, it was found that knockdown (KD) of CaMKK2 in cultured adult primary dorsal root ganglion (DRG) neurons resulted in the reduction of transferrin (TF) phosphorylation at multiple functionally relevant residues which corresponded to loss of an acidic fraction (pH~3-4) of TF. In vitro studies using CRISPR/Cas9 based CaMKK2 knockout (KO) HEK293 and HepG2 cells lines validated previous findings and revealed that loss of CaMKK2 interfered with TF trafficking and turnover. TF is an iron transporter glycoprotein. Abnormal accumulation of iron and/or deregulated Ca²⁺ homeostasis leads to neurodegeneration in Alzheimer's disease (AD). Therefore, it was hypothesized that aberrant CaMKK2 in AD may lead to aberrant phosphorylated transferrin (P-TF: pH~3-4 fraction) which may serve as a hallmark biomarker for AD. A significant reduction of P-TF in the brain and serum of CaMKK2 KO mice and a triple-transgenic mouse model of AD (3xTg-AD) supported this hypothesis. In addition, analysis of early (< 65 years) and late-stage (>65 years) postmortem human AD cerebrospinal fluid (CSF) and serum samples revealed that aberrant P-TF (pH~3-4 fraction) profile was associated with both early and late-stage AD compared to age-matched controls. This indicates P-TF (pH~3-4 fraction) profile may be useful as a minimally invasive biomarker for AD. In addition, this study provides a link between aberrant CaMKK2 with TF trafficking and turnover which provides a novel insight into the neurodegeneration process.

Olfactory Dysfunction as a Global Biomarker for Sniffing out Alzheimer's Disease: A Meta-Analysis

Cases of Alzheimer’s disease (AD) are rising exponentially due to increasing global life expectancy. There are approximately 50 million sufferers worldwide, with prevalence rising most rapidly in low-income countries such as Africa and Asia. There is currently no definite diagnosis of AD until after death, thus an early biomarker for AD is urgently required in order to administer timelier and more effective interventions. Olfactory dysfunction (problems with the sense of smell) is one of the earliest, preclinical symptoms observed in AD. Olfaction is a promising early biomarker for use worldwide as it is easy, cheap to measure, and not reliant on specialist clinicians or laboratory analysis. We carried out a meta-analysis to determine the credibility of olfaction in diagnosing AD in the preclinical stages, by comparing olfaction in healthy controls against AD patients and patients with mild cognitive impairment (MCI). Data from 10 articles were subjected to two comparative meta-analyses. In the case of AD, the results illustrated that the overall magnitude of effect size was more apparent, d = −1.63, 95% CI [−1.95, −1.31], in comparison to that of MCI, d = −0.81, 95% CI [−1.08, −0.55]. This shows that olfaction worsens progressively as patients progress from MCI to AD, highlighting the potential for olfactory dysfunction to identify AD in the preclinical stages prior to MCI.

An Artificial Neural Network Integrated Pipeline for Biomarker Discovery Using Alzheimer's Disease as a Case Study

The field of machine learning has allowed researchers to generate and analyse vast amounts of data using a wide variety of methodologies. Artificial Neural Networks (ANN) are some of the most commonly used statistical models and have been successful in biomarker discovery studies in multiple disease types. This review seeks to explore and evaluate an integrated ANN pipeline for biomarker discovery and validation in Alzheimer's disease, the most common form of dementia worldwide with no proven cause and no available cure. The proposed pipeline consists of analysing public data with a categorical and continuous stepwise algorithm and further examination through network inference to predict gene interactions. This methodology can reliably generate novel markers and further examine known ones and can be used to guide future research in Alzheimer's disease.