CSF Biomarkers for Alzheimer's Disease Diagnosis

Exploring easily accessible neurophysiological biomarkers for predicting Alzheimer's disease progression: a systematic review

Alzheimer disease (AD) remains a significant global health concern. The progression from preclinical stages to overt dementia has become a crucial point of interest for researchers. This paper reviews the potential of neurophysiological biomarkers in predicting AD progression, based on a systematic literature search following PRISMA guidelines, including 55 studies. EEG-based techniques have been predominantly employed, whereas TMS studies are less common. Among the investigated neurophysiological measures, spectral power measurements and event-related potentials-based measures, including P300 and N200 latencies, have emerged as the most consistent and reliable biomarkers for predicting the likelihood of conversion to AD. In addition, TMS-based indices of cortical excitability and synaptic plasticity have also shown potential in assessing the risk of conversion to AD. However, concerns persist regarding the methodological discrepancies among studies, the accuracy of these neurophysiological measures in comparison to established AD biomarkers, and their immediate clinical applicability. Further research is needed to validate the predictive capabilities of EEG and TMS measures. Advancements in this area could lead to cost-effective, reliable biomarkers, enhancing diagnostic processes and deepening our understanding of AD pathophysiology.

Artificial intelligence for brain disease diagnosis using electroencephalogram signals

Brain signals refer to electrical signals or metabolic changes that occur as a consequence of brain cell activity. Among the various non-invasive measurement methods, electroencephalogram (EEG) stands out as a widely employed technique, providing valuable insights into brain patterns. The deviations observed in EEG reading serve as indicators of abnormal brain activity, which is associated with neurological diseases. Brain‒computer interface (BCI) systems enable the direct extraction and transmission of information from the human brain, facilitating interaction with external devices. Notably, the emergence of artificial intelligence (AI) has had a profound impact on the enhancement of precision and accuracy in BCI technology, thereby broadening the scope of research in this field. AI techniques, encompassing machine learning (ML) and deep learning (DL) models, have demonstrated remarkable success in classifying and predicting various brain diseases. This comprehensive review investigates the application of AI in EEG-based brain disease diagnosis, highlighting advancements in AI algorithms.

Longitudinal Assessment of Peripapillary Microvasculature Using Optical Coherence Tomography Angiography in Cognitively Normal Adults

Introduction: To evaluate longitudinal peripapillary changes in cognitively normal older adults using optical coherence tomography (OCT) and OCT angiography (OCTA). Methods: Participants older than 50 years with no history of neurodegenerative disease or cognitive impairment were prospectively enrolled. OCT and OCTA images were obtained at the first visit and 2 years later. Results: The study comprised 189 eyes of 111 adults with a mean age (±SD) of 69.3 ± 5.8 years and mean follow-up of 2.1 ± 0.5 years. Woman experienced slower rate of decline than men in capillary perfusion density (0.000% ± 0.005% vs -0.002% ± 0.004%; P = .038) and retinal nerve fiber layer (RNFL) thickness (0.133 ± 1.617 µm vs -0.659 ± 1.431 µm; P = .008). At both timepoints, after controlling for sex, the capillary perfusion density (P < .001), capillary flux index (P < .001), and RNFL thickness (P = .005) were lower in older participants. The mean capillary perfusion density was higher in women than in men at both timepoints (P = .01 and P = .002, respectively), with no significant differences in the capillary flux index and RNFL thickness. Conclusions: In cognitively normal adults, there is a significant reduction in peripapillary capillary perfusion density, the capillary flux index, and RNFL thickness associated with aging beyond 50 years. Women had higher capillary perfusion density values with slower rates of change in capillary perfusion density and RNFL thickness. These values can serve as benchmarks, and variations could be suspicious for a pathologic process.

Amyloid-β and Phosphorylated Tau are the Key Biomarkers and Predictors of Alzheimer's Disease

Alzheimer's disease (AD) is a age-related neurodegenerative disease and is a major public health concern both in Texas, US and Worldwide. This neurodegenerative disease is mainly characterized by amyloid-beta (Aβ) and phosphorylated Tau (p-Tau) accumulation in the brains of patients with AD and increasing evidence suggests that these are key biomarkers in AD. Both Aβ and p-tau can be detected through various imaging techniques (such as positron emission tomography, PET) and cerebrospinal fluid (CSF) analysis. The presence of these biomarkers in individuals, who are asymptomatic or have mild cognitive impairment can indicate an increased risk of developing AD in the future. Furthermore, the combination of Aβ and p-tau biomarkers is often used for more accurate diagnosis and prediction of AD progression. Along with AD being a neurodegenerative disease, it is associated with other chronic conditions such as cardiovascular disease, obesity, depression, and diabetes because studies have shown that these comorbid conditions make people more vulnerable to AD. In the first part of this review, we discuss that biofluid-based biomarkers such as Aβ, p-Tau in cerebrospinal fluid (CSF) and Aβ & p-Tau in plasma could be used as an alternative sensitive technique to diagnose AD. In the second part, we discuss the underlying molecular mechanisms of chronic conditions linked with AD and how they affect the patients in clinical care.

The nasal lymphatic route of CSF outflow: implications for neurodegenerative disease diagnosis and monitoring

Cerebrospinal fluid (CSF) plays a crucial role in the brain's lymphatics as it traverses the central nervous system (CNS). Its primary function is to facilitate the outward transport of waste. Among the various CSF outflow pathways, the route through the cribriform plate along the olfactory nerves stands out as the most predominant. This review describes the outflow pathway of CSF into the nasal lymphatics. Additionally, we examine existing studies to describe mutual influences observed between the brain and extracranial regions due to this outflow pathway. Notably, pathological conditions in the CNS often influence CSF outflow, leading to observable changes in extracranial regions. The established connection between the brain and the nose is significant, and our review underscores its potential relevance in monitoring CNS ailments, including neurodegenerative diseases. Considering that aging - the most significant risk factor for the onset of neurodegeneration - is also a principal factor in CSF turnover alterations, we suggest a novel approach to studying neurodegenerative diseases in therapeutic terms.

Discerning the Prospects of miRNAs as a Multi-Target Therapeutic and Diagnostic for Alzheimer's Disease

Although over the last few decades, numerous attempts have been made to halt Alzheimer's disease (AD) progression and mitigate its symptoms, only a few have been proven beneficial. Most medications available, still only cater to the symptoms of the disease rather than fixing the cause at the root level. A novel approach involving the use of miRNAs, which work on the principle of gene silencing, is being explored by scientists. Naturally present miRNAs in the biological system help to regulate various genes than may be implicated in AD-like BACE-1 and APP. One miRNA thus, holds the power to keep a check on several genes, conferring it the ability to be used as a multi-target therapeutic. With aging and the onset of diseased pathology, dysregulation of these miRNAs is observed. This flawed miRNA expression is responsible for the unusual buildup of amyloid proteins, fibrillation of tau proteins in the brain, neuronal death and other hallmarks leading to AD. The use of miRNA mimics and miRNA inhibitors provides an attractive perspective for fixing the upregulation and downregulation of miRNAs that led to abnormal cellular activities. Furthermore, the detection of miRNAs in the CSF and serum of diseased patients might be considered an earlier biomarker for the disease. While most of the therapies designed around AD have not succeeded completely, the targeting of dysregulated miRNAs in AD patients might give a new direction to scholars to develop an effective treatment for Alzheimer's disease.

CSF metabolites associated with biomarkers of Alzheimer's disease pathology

Introduction

Metabolomics technology facilitates studying associations between small molecules and disease processes. Correlating metabolites in cerebrospinal fluid (CSF) with Alzheimer's disease (AD) CSF biomarkers may elucidate additional changes that are associated with early AD pathology and enhance our knowledge of the disease.

Methods

The relative abundance of untargeted metabolites was assessed in 161 individuals from the Wisconsin Registry for Alzheimer's Prevention. A metabolome-wide association study (MWAS) was conducted between 269 CSF metabolites and protein biomarkers reflecting brain amyloidosis, tau pathology, neuronal and synaptic degeneration, and astrocyte or microglial activation and neuroinflammation. Linear mixed-effects regression analyses were performed with random intercepts for sample relatedness and repeated measurements and fixed effects for age, sex, and years of education. The metabolome-wide significance was determined by a false discovery rate threshold of 0.05. The significant metabolites were replicated in 154 independent individuals from then Wisconsin Alzheimer's Disease Research Center. Mendelian randomization was performed using genome-wide significant single nucleotide polymorphisms from a CSF metabolites genome-wide association study.

Results

Metabolome-wide association study results showed several significantly associated metabolites for all the biomarkers except Aβ42/40 and IL-6. Genetic variants associated with metabolites and Mendelian randomization analysis provided evidence for a causal association of metabolites for soluble triggering receptor expressed on myeloid cells 2 (sTREM2), amyloid β (Aβ40), α-synuclein, total tau, phosphorylated tau, and neurogranin, for example, palmitoyl sphingomyelin (d18:1/16:0) for sTREM2, and erythritol for Aβ40 and α-synuclein.

Discussion

This study provides evidence that CSF metabolites are associated with AD-related pathology, and many of these associations may be causal.

Protein fibril length in cerebrospinal fluid is increased in Alzheimer's disease

Alzheimer's disease (AD) associated proteins exist in cerebrospinal fluid (CSF). This paper evidences that protein aggregate morphology distinctly differs in CSF of patients with AD dementia (ADD), mild cognitive impairment due to AD (MCI AD), with subjective cognitive decline without amyloid pathology (SCD) and with non-AD MCI using liquid-based atomic force microscopy (AFM). Spherical-shaped particles and nodular-shaped protofibrils were present in the CSF of SCD patients, whereas CSF of ADD patients abundantly contained elongated mature fibrils. Quantitative analysis of AFM topographs confirms fibril length is higher in CSF of ADD than in MCI AD and lowest in SCD and non-AD dementia patients. CSF fibril length is inversely correlated with CSF amyloid beta (Aβ) 42/40 ratio and CSF p-tau protein levels (obtained from biochemical assays) to predict amyloid and tau pathology with an accuracy of 94% and 82%, respectively, thus identifying ultralong protein fibrils in CSF as a possible signature of AD pathology.

Clinical usefulness of cerebrospinal fluid biomarkers in Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is a complex disease that shares clinical features with other dementias. It is important to establish a specific and reliable diagnosis. Nowadays, AD diagnosis is based on cerebrospinal fluid (CSF) biomarkers. However, the corresponding cut-offs differ amongst studies. This study aims to evaluate the CSF biomarkers in the AD differential diagnosis.

METHODS

Clinical relevant biomarkers (amyloid β42 (Aβ42), t-Tau, p-Tau, amyloid β40 (Aβ40), neurofilament light chain (NfL)) were determined in CSF samples from participants classified as AD (n = 124) and non-AD (n = 148) patients from the Neurology Unit. They were included and evaluated consecutively (August 2018-October 2020). The clinical utility of these biomarkers was evaluated by AUC-ROC curves and the corresponding cut-off points were defined.

RESULTS

The results showed satisfactory accuracy (AUC-ROC 0.91 for Aβ42, 0.890 for t-Tau and 0.933 for p-Tau); whilst Aβ40 and NfL did not show good discriminatory capacity (AUC-ROC 0.557 and 0.738, respectively). The ratios Aβ42/Aβ40 and t-Tau/Aβ42 improved the diagnosis indices of each individual biomarker, with AUC-ROC of 0.980 and 0.971, respectively. Also, elevated levels of NfL were found in the frontotemporal dementia group compared with the other participant groups.

CONCLUSIONS

The ratio Aβ42/Aβ40 showed the highest discriminating capacity between AD and non-AD patients and might be useful in clinical practice. Regarding NfL, it is not a specific biomarker for AD; however, it might be helpful for the differential diagnosis of frontotemporal dementia. Nevertheless, further analysis in an external cohort is required in order to validate these results.

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.

Predicting time-to-conversion for dementia of Alzheimer's type using multi-modal deep survival analysis

Dementia of Alzheimer's Type (DAT) is a complex disorder influenced by numerous factors, and it is difficult to predict individual progression trajectory from normal or mildly impaired cognition to DAT. An in-depth examination of multiple modalities of data may yield an accurate estimate of time-to-conversion to DAT for preclinical subjects at various stages of disease development. We used a deep-learning model designed for survival analyses to predict subjects' time-to-conversion to DAT using the baseline data of 401 subjects with 63 features from MRI, genetic, and CDC (Cognitive tests, Demographic, and CSF) data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our study demonstrated that CDC data outperform genetic or MRI data in predicting DAT time-to-conversion for subjects with Mild Cognitive Impairment (MCI). On the other hand, genetic data provided the most predictive power for subjects with Normal Cognition (NC) at the time of the visit. Furthermore, combining MRI and genetic features improved the time-to-event prediction over using either modality alone. Finally, adding CDC to any combination of features only worked as well as using only the CDC features.

Lipids as Early and Minimally Invasive Biomarkers for Alzheimer's Disease

Alzheimer's disease (AD) is the most common neurodegenerative disorder worldwide. Specifically, typical late-onset AD is a sporadic form with a complex etiology that affects over 90% of patients. The current gold standard for AD diagnosis is based on the determination of amyloid status by analyzing cerebrospinal fluid samples or brain positron emission tomography. These procedures can be used widely as they have several disadvantages (expensive, invasive). As an alternative, blood metabolites have recently emerged as promising AD biomarkers. Small molecules that cross the compromised AD blood-brain barrier could be determined in plasma to improve clinical AD diagnosis at early stages through minimally invasive techniques. Specifically, lipids could play an important role in AD since the brain has a high lipid content, and they are present ubiquitously inside amyloid plaques. Therefore, a systematic review was performed with the aim of identifying blood lipid metabolites as potential early AD biomarkers. In conclusion, some lipid families (fatty acids, glycerolipids, glycerophospholipids, sphingolipids, lipid peroxidation compounds) have shown impaired levels at early AD stages. Ceramide levels were significantly higher in AD subjects, and polyunsaturated fatty acids levels were significantly lower in AD. Also, high arachidonic acid levels were found in AD patients in contrast to low sphingomyelin levels. Consequently, these lipid biomarkers could be used for minimally invasive and early AD clinical diagnosis.

Neuronal ER-Signalosome Proteins as Early Biomarkers in Prodromal Alzheimer's Disease Independent of Amyloid-β Production and Tau Phosphorylation

There exists considerable interest to unveil preclinical period and prodromal stages of Alzheimer's disease (AD). The mild cognitive impairment (MCI) is characterized by significant memory and/or other cognitive domains impairments, and is often considered the prodromal phase of AD. The cerebrospinal fluid (CSF) levels of β-amyloid (βA), total tau (t-tau), and phosphorylated tau (p-tau) have been used as biomarkers of AD albeit their significance as indicators during early stages of AD remains far from accurate. The new biomarkers are being intensively sought as to allow identification of pathological processes underlying early stages of AD. Fifty-three participants (75.4 ± 8.3 years) were classified in three groups as cognitively normal healthy controls (HC), MCI, and subjective memory complaints (SMC). The subjects were subjected to a battery of neurocognitive tests and underwent lumbar puncture for CSF extraction. The CSF levels of estrogen-receptor (ER)-signalosome proteins, βA, t-tau and p-tau, were submitted to univariate, bivariate, and multivariate statistical analyses. We have found that the components of the ER-signalosome, namely, caveolin-1, flotilin-1, and estrogen receptor alpha (ERα), insulin growth factor-1 receptor β (IGF1Rβ), prion protein (PrP), and plasmalemmal voltage dependent anion channel 1 (VDAC) could be detected in the CSF from all subjects of the HC, MCI, and SMC groups. The six proteins appeared elevated in MCI and slightly increased in SMC subjects compared to HC, suggesting that signalosome proteins undergo very early modifications in nerve cells. Using a multivariate approach, we have found that the combination of ERα, IGF-1Rβ, and VDAC are the main determinants of group segregation with resolution enough to predict the MCI stage. The analyses of bivariate relationships indicated that collinearity of ER-signalosome proteins vary depending on the stage, with some pairs displaying opposed relationships between HC and MCI groups, and the SMC stage showing either no relationships or behaviors similar to either HC or MCI stages. The multinomial logistic regression models of changes in ER-signalosome proteins provide reliable predictive criteria, particularly for the MCI. Notably, most of the statistical analyses revealed no significant relationships or interactions with classical AD biomarkers at either disease stage. Finally, the multivariate functions were highly correlated with outcomes from neurocognitive tests for episodic memory. These results demonstrate that alterations in ER-signalosome might provide useful diagnostic information on preclinical stages of AD, independently from classical biomarkers.

Predicting conversion from MCI to AD by integration of rs-fMRI and clinical information using 3D-convolutional neural network

PURPOSE

Alzheimer's is the most common irreversible neurodegenerative disease. Its symptoms range from memory impairments to degradation of multiple cognitive abilities and ultimately death. Mild cognitive impairment (MCI) is the earliest detectable stage that happens between normal aging and early dementia, and even though MCI subjects have a chance of changing back to cognitively normal or even staying the same, there is a risk that their condition progresses to Alzheimer's disease (AD) annually. Therefore predicting AD among MCI subjects is pivotal for starting treatments at an opportune time in case of progression, and if staying stable is the case, the need for consistent medical observations would eliminate. Thus, we aim to diagnose possible conversion from MCI to AD by exploiting a class of deep learning (DL) methods called convolutional neural network (CNN).

METHODS

We proposed a three-dimensional CNN (3D-CNN) to combine and analyze resting-state functional magnetic resonance imaging (rs-fMRI), clinical assessment results, and demographic information to predict conversion from MCI to AD in an average 5-years interval. Initially, a 3D-CNN was developed based on fMRI single volumes of 266 samples from 81 subjects; then, we used neuron layers to combine clinical data with fMRI to improve the results.

RESULTS

At first, the CNN model demonstrated an AUC of 87.67% and an accuracy of 85.7%, then after combining clinical and rs-fMRI features, we observed the following improved scores: an AUC of 91.72%, an accuracy of 87.6%, a sensitivity of 75.58% and a specificity of 92.57%.

CONCLUSION

Our developed algorithm managed to predict prognosis from MCI to AD with high levels of accuracy, proving the potential of DL approaches in solving the matter and the efficiency of integrating clinical information with imaging according to the proposed method.

Differential Diagnosis Findings Between Alzheimer's Disease and Major Depressive Disorder: A Review

Background

Differentiating diagnosis between Alzheimer's disease and major depressive disorder in the elderly is a great clinical challenge. This study aimed to identify the establishment of differential diagnosis protocols between Alzheimer's disease and major depressive disorder.

Methods

We searched studies in the Ovid MEDLINE, EMBASE, PsycINFO, and Web of Science databases between 2009 and 2019. A total of 155 references were found for searching relevant articles using Boolean search. After exclusion of redundancies and assessing of title, abstract, and full text for eligibility, 11 articles were selected. The total sample size was 1077 distributed in 8 different countries.

Results

Significant results were found for differential diagnosis between Alzheimer's disease and major depressive disorder, such as overall mental status, episodic memory, visuospatial construction, delayed recognition task, semantic verbal fluency, visual task in short-term memory, atrophy of the hippocampus, cortical activation in specific tasks, and cerebrospinal fluid biomarkers.

Conclusion

These findings are good pathways for discriminating Alzheimer's disease from major depression in the elderly.

Intranasal insulin modulates cerebrospinal fluid markers of neuroinflammation in mild cognitive impairment and Alzheimer's disease: a randomized trial

Intranasal insulin (INI) has shown promise as a treatment for Alzheimer's disease (AD) in pilot clinical trials. In a recent phase 2 trial, participants with mild cognitive impairment (MCI) or AD who were treated with INI with one of two delivery devices showed improved cerebral spinal fluid (CSF) biomarker profiles and slower symptom progression compared with placebo. In the cohort which showed benefit, we measured changes in CSF markers of inflammation, immune function and vascular integrity and assessed their relationship with changes in cognition, brain volume, and CSF amyloid and tau concentrations. The insulin-treated group had increased CSF interferon-γ (p = 0.032) and eotaxin (p = 0.049), and reduced interleukin-6 (p = 0.048) over the 12 month trial compared to placebo. Trends were observed for increased CSF macrophage-derived chemokine for the placebo group (p = 0.083), and increased interleukin-2 in the insulin-treated group (p = 0.093). Insulin-treated and placebo groups showed strikingly different patterns of associations between changes in CSF immune/inflammatory/vascular markers and changes in cognition, brain volume, and amyloid and tau concentrations. In summary, INI treatment altered the typical progression of markers of inflammation and immune function seen in AD, suggesting that INI may promote a compensatory immune response associated with therapeutic benefit.

Diagnostic Blood Biomarkers in Alzheimer’s Disease

Potential biomarkers for Alzheimer’s disease (AD) include amyloid β1–42 (Aβ1–42), t-Tau, p-Tau181, neurofilament light chain (NFL), and neuroimaging biomarkers. Their combined use is useful for diagnosing and monitoring the progress of AD. Therefore, further development of a combination of these biomarkers is essential. We investigated whether plasma NFL/Aβ1–42 can serve as a plasma-based primary screening biomarker reflecting brain neurodegeneration and amyloid pathology in AD for monitoring disease progression and early diagnosis. We measured the NFL and Aβ1–42 concentrations in the CSF and plasma samples and performed correlation analysis to evaluate the utility of these biomarkers in the early diagnosis and monitoring of AD spectrum disease progression. Pearson’s correlation analysis was used to analyse the associations between the fluid biomarkers and neuroimaging data. The study included 136 participants, classified into five groups: 28 cognitively normal individuals, 23 patients with preclinical AD, 22 amyloid-negative patients with amnestic mild cognitive impairment, 32 patients with prodromal AD, and 31 patients with AD dementia. With disease progression, the NFL concentrations increased and Aβ1–42 concentrations decreased. The plasma and CSF NFL/Aβ1–42 were strongly correlated (r = 0.558). Plasma NFL/Aβ1–42 was strongly correlated with hippocampal volume/intracranial volume (r = 0.409). In early AD, plasma NFL/Aβ1–42 was associated with higher diagnostic accuracy than the individual biomarkers. Moreover, in preclinical AD, plasma NFL/Aβ1–42 changed more rapidly than the CSF t-Tau or p-Tau181 concentrations. Our findings highlight the utility of plasma NFL/Aβ1–42 as a non-invasive plasma-based biomarker for early diagnosis and monitoring of AD spectrum disease progression.

Longitudinal Analysis of Brain-Predicted Age in Amnestic and Non-amnestic Sporadic Early-Onset Alzheimer's Disease

Objective: Predicted age difference (PAD) is a score computed by subtracting chronological age from "brain" age, which is estimated using neuroimaging data. The goal of this study was to evaluate the PAD as a marker of phenotypic heterogeneity and severity among early-onset Alzheimer's disease (EOAD) patients. Methods: We first used 3D T1-weighted (3D-T1) magnetic resonance images (MRI) of 3,227 healthy subjects aged between 18 and 85 years to train, optimize, and evaluate the brain age model. A total of 123 participants who met the criteria for early-onset (<65 years) sporadic form of probable Alzheimer's disease (AD) and presented with two distinctive clinical presentations [an amnestic form (n = 74) and a non-amnestic form (n = 49)] were included at baseline and followed-up for a maximum period of 4 years. All the participants underwent a work-up at baseline and every year during the follow-up period, which included clinical examination, neuropsychological testing and genotyping, and structural MRI. In addition, cerebrospinal fluid biomarker assay was recorded at baseline. PAD score was calculated by applying brain age model to 3D-T1 images of the EOAD patients and healthy controls, who were matched based on age and sex. At baseline, between-group differences for neuropsychological and PAD scores were assessed using linear models. Regarding longitudinal analysis of neuropsychological and PAD scores, differences between amnestic and non-amnestic participants were analyzed using linear mixed-effects modeling. Results: PAD score was significantly higher for non-amnestic patients (2.35 ± 0.91) when compared to amnestic patients (2.09 ± 0.74) and controls (0.00 ± 1). Moreover, PAD score was linearly correlated with the Mini-Mental State Examination (MMSE) and the Clinical Dementia Rating Sum of Boxes (CDR-SB), for both amnestic and non-amnestic sporadic forms. Longitudinal analyses showed that the gradual development of the disease in patients was accompanied by a significant increase in PAD score over time, for both amnestic and non-amnestic patients. Conclusion: PAD score was able to separate amnestic and non-amnestic sporadic forms. Regardless of the clinical presentation, as PAD score was a way of quantifying an early brain age acceleration, it was an appropriate method to detect the development of AD and follow the evolution of the disease as a marker of severity as MMSE and CDR-SB.

Enhanced Expression of microRNA-1273g-3p Contributes to Alzheimer's Disease Pathogenesis by Regulating the Expression of Mitochondrial Genes

Alzheimer’s disease (AD) is the most common form of dementia in the elderly population, but its underlying cause has not been fully elucidated. Recent studies have shown that microRNAs (miRNAs) play important roles in regulating the expression levels of genes associated with AD development. In this study, we analyzed miRNAs in plasma and cerebrospinal fluid (CSF) from AD patients and cognitively normal (including amyloid positive) individuals. miR-1273g-3p was identified as an AD-associated miRNA and found to be elevated in the CSF of early-stage AD patients. The overexpression of miR-1273g-3p enhanced amyloid beta (Aβ) production by inducing oxidative stress and mitochondrial impairments in AD model cell lines. A biotin-streptavidin pull-down assay demonstrated that miR-1273g-3p primarily interacts with mitochondrial genes, and that their expression is downregulated by miR-1273g-3p. In particular, the miR-1273g-3p-target gene TIMM13 showed reduced expression in brain tissues from human AD patients. These results suggest that miR-1273g-3p expression in an early stage of AD notably contributes to Aβ production and mitochondrial impairments. Thus, miR-1273g-3p might be a biomarker for early diagnosis of AD and a potential therapeutic target to prevent AD progression.

Bias-generating factors in biofluid amyloid-β measurements for Alzheimer's disease diagnosis

Alzheimer's disease (AD) is the most prevalent cause of dementia worldwide, yet the dearth of readily accessible diagnostic biomarkers is a substantial hindrance towards progressing to effective preventive and therapeutic approaches. Due to a long delay between cerebral amyloid-β (Aβ) accumulation and the onset of cognitive impairments, biomarkers that reflect Aβ pathology and enable routine screening for disease progression are of urgent need for application in the clinical diagnosis of AD. According to accumulating evidences, cerebrospinal fluid (CSF) and plasma offer windows to the brain as they allow monitoring of biochemical changes in the brain. Considering the high availability and accuracy in depicting Aβ deposition in the brain, Aβ levels in CSF and plasma are regarded as promising fluid biomarkers for the diagnosis of AD patients at an early stage. However, clinical data with intra- and interindividual variations in the concentrations of CSF and plasma Aβ implicate the need to reevaluate current Aβ detection methods and establish a standardized operating procedure. Therefore, this review introduces three bias-generating factors in biofluid Aβ measurement that may hamper the accurate Aβ quantification and how such complications can be overcome for the widespread implementation of fluid Aβ detection in clinical practice.

CSF Diagnostics: A Potentially Valuable Tool in Neurodegenerative and Inflammatory Disorders Involving Motor Neurons: A Review

Cerebrospinal fluid (CSF) diagnostics has emerged as a valid tool for a variety of neurological diseases. However, CSF diagnostics has been playing a subordinate role in the diagnosis of many neurological conditions. Thus, in the multitude of neuromuscular diseases in which motor neurons are affected, a CSF sample is rarely taken routinely. However, CSF diagnostics has the potential to specify the diagnosis and monitor the treatment of neuromuscular disorders. In this review, we therefore focused on a variety of neuromuscular diseases, among them amyotrophic lateral sclerosis (ALS), peripheral neuropathies, and spinal muscular atrophy (SMA), for which CSF diagnostics has emerged as a promising option for determining the disease itself and its progression. We focus on potentially valuable biomarkers among different disorders, such as neurofilaments, cytokines, other proteins, and lipids to determine their suitability, differentiating between different neurological disorders and their potential to determine early disease onset, disease progression, and treatment outcome. We further recommend novel approaches, e.g., the use of mass spectrometry as a promising alternative techniques to standard ELISA assays, potentially enhancing biomarker significance in clinical applications.

A Concise Overview of Biosensing Technologies for the Detection of Alzheimer's Disease Biomarkers

Alzheimer&#039;s disease (AD) is a brain-linked pathophysiological condition with neuronal degeneration, cognition dysfunctions, and other debilitations. Due to the growing prevalence of AD, there is a highly commended tendency to accelerate and develop analytical technologies for easy, cost-effective, and sensitive detection of AD biomarkers. In the last decade, remarkable advancements have been achieved on the gate to the progression of biosensors, predominantly optical and electrochemical, to detect AD biomarkers. Biosensors are commanding analytical devices that can conduct biological responses on transducers into measurable signals. These analytical devices can assist the case finding and management of AD. This review focuses on up-to-date developments, contests, and tendencies regarding AD biosensing principally, emphasizing the exclusive possessions of nanomaterials.

Validation of Fucoxanthin from Microalgae Phaeodactylum tricornutum for the Detection of Amyloid Burden in Transgenic Mouse Models of Alzheimer’s Disease

The visualization of misfolded Aβ peptides by using fluorescence chemical dyes is very important in Alzheimer’s disease (AD) diagnosis. Here, we describe the fluorescent substance, fucoxanthin, which detects Aβ aggregates in the brain of AD transgenic mouse models. We found that fucoxanthin from the microalgae Phaeodactylum tricornutum has fluorescent excitation and emission wavelengths without any interference for Aβ interaction. Thus, we applied it to monitor Aβ aggregation in AD transgenic mouse models. Aβ plaques were visualized using fucoxanthin in the brain tissue of APP/PS1 and 5×FAD mice by histological staining with different staining methods. By comparing fucoxanthin-positive and thioflavin S-positive stained regions in the brains, we found that they are colocalized and that fucoxanthin can detect Aβ aggregates. Our finding suggests that fucoxanthin from P. tricornutum can be a new Aβ fluorescent imaging reagent in AD diagnosis.

Transfection and Activation of CofActor, a Light and Stress Gated Optogenetic Tool, in Primary Hippocampal Neuron Cultures

Proteins involved in neurodegeneration can be coupled with optogenetic reagents to create rapid and sensitive reporters to provide insight into the biochemical processes that mediate the progression of neurodegenerative disorders, including Alzheimer's Disease (AD). We have recently developed a novel optically-responsive tool (the 'CofActor' system) that couples cof ilin and act in (key players in early stage cytoskeletal abnormalities associated with neurodegenerative disorders) with light-gated optogenetic proteins to provide spatial and temporal resolution of oxidative and energetic stress-dependent biochemical events. In contrast to currently available small-molecule based biosensors for monitoring changes in the redox environment of the cell, CofActor is a light-activated, genetically encoded redox sensor that can be activated with precise spatial and temporal control. Here we describe a protocol for the expression and activation of the CofActor system in dissociated hippocampal neuron cultures prepared from newborn mice. Cultures were transfected with Lipofectamine on the fifth day in vitro (DIV5), then exposed to cellular stress inducing stimuli, leading to the formation of actin-cofilin rods that can be observed using live cell imaging techniques. The protocol described here allows for studies of stress-related cytoskeletal dysregulation in live neurons exposed to neurodegenerative stimuli, such as toxic Aβ42 oligomers. Moreover, expression of the sensor in neurons isolated from transgenic mouse models of AD and/or mice KO for proteins involved in AD can advance our understanding of the molecular basis of early cytoskeletal dysfunctions associated with neurodegeneration.

Diagnosis of Alzheimer's Disease in Developed and Developing Countries: Systematic Review and Meta-Analysis of Diagnostic Test Accuracy

BACKGROUND

The present systematic review and meta-analysis of diagnostic test accuracy summarizes the last three decades in advances on diagnosis of Alzheimer's disease (AD) in developed and developing countries.

OBJECTIVE

To determine the accuracy of biomarkers in diagnostic tools in AD, for example, cerebrospinal fluid, positron emission tomography (PET), and magnetic resonance imaging (MRI), etc.

METHODS

The authors searched PubMed for published studies from 1990 to April 2020 on AD diagnostic biomarkers. 84 published studies were pooled and analyzed in this meta-analysis and diagnostic accuracy was compared by summary receiver operating characteristic statistics.

RESULTS

Overall, 84 studies met the criteria and were included in a meta-analysis. For EEG, the sensitivity ranged from 67 to 98%, with a median of 80%, 95% CI [75, 91], tau-PET diagnosis sensitivity ranged from 76 to 97%, with a median of 94%, 95% CI [76, 97]; and MRI sensitivity ranged from 41 to 99%, with a median of 84%, 95% CI [81, 87]. Our results showed that tau-PET diagnosis had higher performance as compared to other diagnostic methods in this meta-analysis.

CONCLUSION

Our findings showed an important discrepancy in diagnostic data for AD between developed and developing countries, which can impact global prevalence estimation and management of AD. Also, our analysis found a better performance for the tau-PET diagnostic over other methods to diagnose AD patients, but the expense of tau-PET scan seems to be the limiting factor in the diagnosis of AD in developing countries such as those found in Asia, Africa, and Latin America.

Plasma contact factors as novel biomarkers for diagnosing Alzheimer's disease

Background

Alzheimer’s disease (AD) is the most common cause of dementia and most of AD patients suffer from vascular abnormalities and neuroinflammation. There is an urgent need to develop novel blood biomarkers capable of diagnosing Alzheimer’s disease (AD) at very early stage. This study was performed to find out new accurate plasma diagnostic biomarkers for AD by investigating a direct relationship between plasma contact system and AD.

Methods

A total 101 of human CSF and plasma samples from normal and AD patients were analyzed. The contact factor activities in plasma were measured with the corresponding specific peptide substrates.

Results

The activities of contact factors (FXIIa, FXIa, plasma kallikrein) and FXa clearly increased and statistically correlated as AD progresses. We present here, for the first time, the FXIIa cut-off scores to as: > 26.3 U/ml for prodromal AD [area under the curve (AUC) = 0.783, p < 0.001] and > 27.2 U/ml for AD dementia (AUC = 0.906, p < 0.001). We also describe the cut-off scores from the ratios of CSF Aβ_1–42 versus the contact factors. Of these, the representative ratio cut-off scores of Aβ_1–42/FXIIa were to be: < 33.8 for prodromal AD (AUC = 0.965, p < 0.001) and < 27.44 for AD dementia (AUC = 1.0, p < 0.001).

Conclusion

The activation of plasma contact system is closely associated with clinical stage of AD, and FXIIa activity as well as the cut-off scores of CSF Aβ_1–42/FXIIa can be used as novel accurate diagnostic AD biomarkers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40364-020-00258-5.

Intrusion Errors and Progression of Cognitive Deficits in Older Adults with Mild Cognitive Impairment and PreMCI States

INTRODUCTION

Among persons with amnestic mild cognitive impairment (aMCI), intrusion errors on subscales that measure proactive semantic interference (PSI) may be among the earliest behavioral markers of elevated Alzheimer's disease brain pathology. While there has been considerable cross-sectional work in the area, it is presently unknown whether semantic intrusion errors are predictive of progression of cognitive impairment in aMCI or PreMCI (not cognitively normal but not meeting full criteria for MCI).

METHODS

This study examined the extent to which the percentage of semantic intrusion errors (PIE) based on total responses on a novel cognitive stress test, the Loewenstein-Acevedo Scales for Semantic Interference and Learning (LASSI-L), could predict clinical/cognitive outcomes over an average 26-month period in older adults initially diagnosed with aMCI, PreMCI, and normal cognition.

RESULTS

On the LASSI-L subscale sensitive to PSI, a PIE cut point of 44% intrusion errors distinguished between those at-risk individuals with PreMCI who progressed to MCI over time compared to individuals with PreMCI who reverted to normal on longitudinal follow-up. Importantly, PIE was able to accurately predict 83.3% of aMCI individuals who later progressed to dementia.

DISCUSSION

These preliminary findings indicate that PIE on LASSI-L subscales that measure PSI may be a useful predictor of clinical progression overtime in at-risk older adults.

Nanobiosensors for Non-Amyloidbeta-Tau Biomarkers as Advanced Reporters of Alzheimer's Disease

Emerging nanomaterials providing benefits in sensitivity, specificity and cost-effectiveness are being widely investigated for biosensors in the application of Alzheimer's disease (AD) diagnosis. Core biomarkers amyloid-beta (Aβ) and Tau have been considered as key neuropathological hallmarks of AD. However, they did not sufficiently reflect clinical severity and therapeutic response, proving the difficulty of the Aβ- and Tau-targeting therapies in clinical trials. In recent years, there has still been a shortage of sensors for non-Aβ-Tau pathophysiological biomarkers that serve as advanced reporters for the early diagnosis of AD, predict AD progression, and monitor the treatment response. Nanomaterial-based sensors measuring multiple non-Aβ-Tau biomarkers could improve the capacity of AD progression characterization and supervised treatment, facilitating the comprehensive management of AD. This is the first review to principally represent current nanobiosensors for non-Aβ-Tau biomarker and that strategically deliberates future perspectives on the merit of non-Aβ-Tau biomarkers, in combination with Aβ and Tau, for the accurate diagnosis and prognosis of AD.

Effect of Times to Blood Processing on the Stability of Blood Proteins Associated with Dementia

BACKGROUND

The stability of proteins in the collecting tubes after blood draw is critical to the measured concentrations of the proteins. Although the guidelines issued by the Clinical and Laboratory Standards Institute (CLSI) suggest centrifugation should take place within 2 h of drawing blood, it is very difficult to follow these guidelines in hospitals or clinics. It is necessary to study the effect of times to blood processing on the stability of the proteins of interest.

METHODS

In this work, the plasma proteins of interest were those relevant to dementia, such as amyloid β 1-40 (Aβ1-40), Aβ1-42, Tau protein (Tau), and α-synuclein. The times to blood processing after blood draw ranged from 0.5 to 8 h. The storage temperatures of blood were room temperature (approx. 25°C) and 30°C. After storage, blood samples were centrifuged at room temperature to obtain plasma samples. Ultrasensitive immunomagnetic reduction was applied to assay these proteins in the plasma.

RESULTS

The levels of plasma Aβ1-40, Tau, and α-synuclein did not significantly change until 8 h after blood draw when stored at room temperature. Plasma Aβ1-42 levels did not change significantly after 8 h of storage at room temperature before blood processing. Higher storage temperatures, such as 30°C, for blood samples accelerated the significant variations in the measured concentrations of Aβ1-40, Tau, and α-synuclein in plasma.

CONCLUSION

According to these results, for clinical practice, it is suggested that blood samples be stored at room temperature for no longer than 4.5 h after blood draw until centrifugation for the assay of dementia biomarkers in plasma.

Early neurotransmission impairment in non-invasive Alzheimer Disease detection

Alzheimer Disease (AD) is a pathology suffered by millions of people worldwide and it has a great social and economic impact. Previous studies reported a relationship between alterations in different amino acids and derivatives involved in neurotransmission systems and cognitive impairment. Therefore, in this study the neurotransmission impairment associated to early AD has been evaluated. For this purpose, different amino acids and derivatives were determined in saliva samples from AD patients and healthy subjects, by means of an analytical method based on chromatography coupled to tandem mass spectrometry. Results showed statistically significant differences in salivary levels for the compounds myo-inositol, creatine and acetylcholine; and other compounds (myo-inositol, glutamine, creatine, acetylcholine) showed significant correlations with some cognitive tests scores. Therefore, these compounds were included in a multivariate analysis and the corresponding diagnosis model showed promising indices (AUC 0.806, sensitivity 61%, specificity 92%). In conclusion, some amino acids and derivatives involved in neurotransmission impairment could be potential biomarkers in early and non-invasive AD detection.

Predicting Thioflavin Fluorescence of Retinal Amyloid Deposits Associated With Alzheimer's Disease from Their Polarimetric Properties

Purpose

To use machine learning in those with brain amyloid to predict thioflavin fluorescence (indicative of amyloid) of retinal deposits from their interactions with polarized light.

Methods

We imaged 933 retinal deposits in 28 subjects with post mortem evidence of brain amyloid using thioflavin fluorescence and polarization sensitive microscopy. Means and standard deviations of 14 polarimetric properties were input to machine learning algorithms. Two oversampling strategies were applied to overcome data imbalance. Three machine learning algorithms: linear discriminant analysis, supporting vector machine, and random forest (RF) were trained to predict thioflavin positive deposits. For each method; accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve were computed.

Results

For the polarimetric positive deposits, using 1 oversampling method, RF had the highest area under the receiver operating characteristic curve (0.986), which was not different from that with the second oversampling method. RF had 95% accuracy, 94% sensitivity, and 97% specificity. After including deposits with no polarimetric signals, polarimetry correctly predicted 93% of thioflavin positive deposits. Linear retardance and linear anisotropy were the dominant polarimetric properties in RF with 1 oversampling method, and no polarimetric properties were dominant in the second method.

Conclusions

Thioflavin positivity of retinal amyloid deposits can be predicted from their images in polarized light. Polarimetry is a promising dye-free method of detecting amyloid deposits in ex vivo retinal tissue. Further testing is required for translation to live eye imaging.

Translational Relevance

This dye-free method distinguishes retinal amyloid deposits, a promising biomarker of Alzheimer's disease, in human retinas imaged with polarimetry.

Clinical Utility of Plasma Lipid Peroxidation Biomarkers in Alzheimer's Disease Differential Diagnosis

BACKGROUND

Differential diagnosis of Alzheimer's disease (AD) is a complex task due to the clinical similarity among neurodegenerative diseases. Previous studies showed the role of lipid peroxidation in early AD development. However, the clinical validation of potential specific biomarkers in minimally invasive samples constitutes a great challenge in early AD diagnosis.

METHODS

Plasma samples from participants classified into AD (n = 138), non-AD (including MCI and other dementias not due to AD) (n = 70) and healthy (n = 50) were analysed. Lipid peroxidation compounds (isoprostanes, isofurans, neuroprostanes, neurofurans) were determined by ultra-performance liquid chromatography coupled with tandem mass spectrometry. Statistical analysis for biomarkers' clinical validation was based on Elastic Net.

RESULTS

A two-step diagnosis model was developed from plasma lipid peroxidation products to diagnose early AD specifically, and a bootstrap validated AUC of 0.74 was obtained.

CONCLUSION

A promising AD differential diagnosis model was developed. It was clinically validated as a screening test. However, further external validation is required before clinical application.

Isoprostanoids Levels in Cerebrospinal Fluid Do Not Reflect Alzheimer's Disease

Previous studies showed a relationship between lipid oxidation biomarkers from plasma samples and Alzheimer's Disease (AD), constituting a promising diagnostic tool. In this work we analyzed whether these plasma biomarkers could reflect specific brain oxidation in AD. In this work lipid peroxidation compounds were determined in plasma and cerebrospinal fluid (CSF) samples from AD and non-AD (including other neurological pathologies) participants, by means of an analytical method based on liquid chromatography coupled with mass spectrometry. Statistical analysis evaluated correlations between biological matrices. The results did not show satisfactory correlations between plasma and CSF samples for any of the studied lipid peroxidation biomarkers (isoprostanes, neuroprostanes, prostaglandines, dihomo-isoprostanes). However, some of the analytes showed correlations with specific CSF biomarkers for AD and with neuropsychological tests (Mini-Mental State Examination (MMSE), Repeatable Battery for the Assessment of Neuropsychological Status (RBANS)). In conclusion, lipid peroxidation biomarkers in CSF samples do not reflect their levels in plasma samples, and no significant differences were observed between participant groups. However, some of the analytes could be useful as cognitive decline biomarkers.

Alzheimer's Disease Diagnosis Using Misfolding Proteins in Blood

Alzheimer's disease (AD) is pathologically characterized by a long progressive phase of neuronal changes, including accumulation of extracellular amyloid-β (Aβ) and intracellular neurofibrillary tangles, before the onset of observable symptoms. Many efforts have been made to develop a blood-based diagnostic method for AD by incorporating Aβ and tau as plasma biomarkers. As blood tests have the advantages of being highly accessible and low cost, clinical implementation of AD blood tests would provide preventative screening to presymptomatic individuals, facilitating early identification of AD patients and, thus, treatment development in clinical research. However, the low concentration of AD biomarkers in the plasma has posed difficulties for accurate detection, hindering the development of a reliable blood test. In this review, we introduce three AD blood test technologies emerging in South Korea, which have distinctive methods of heightening detection sensitivity of specific plasma biomarkers. We discuss in detail the multimer detection system, the self-standard analysis of Aβ biomarkers quantified by interdigitated microelectrodes, and a biomarker ratio analysis comprising Aβ and tau.

Oxidative Damage of DNA as Early Marker of Alzheimer's Disease

Alzheimer’s Disease (AD) is the most common cause of dementia, and its characteristic histopathological hallmarks are neurofibrillary tangles and senile plaques. Among involved mechanisms, oxidative stress plays an important role in damaging cell components (e.g., proteins, nucleic acids). In this study, different oxidized products of proteins and DNA were determined in the urine samples from mild cognitive impairment due to AD patients (n = 53) and healthy controls (n = 27) by means of ultra-performance liquid chromatography-tandem mass spectrometry analysis. A multivariate model developed by partial least squares generated a diagnostic model for AD with an AUC-ROC (area under the curve-receiver operating characteristic) of 0.843. From the studied analytes, 8-OHdG (8-hydroxy-2’-deoxyguanosine) and the ratio 8-OHdG/2dG (2’-deoxyguanosine) were able to distinguish between AD and healthy participants, showing statistically significant differences between groups, postulating DNA oxidation as a molecular pathway involved in early AD.

Analysis of miRNA signatures in CSF identifies upregulation of miR-21 and miR-146a/b in patients with multiple sclerosis and active lesions

BACKGROUND

MicroRNAs (miRNAs) have been reported as deregulated in active brain lesions derived from patients with multiple sclerosis (MS). In there, these post-transcriptional regulators may elicit very important effects but proper identification of miRNA candidates as potential biomarkers and/or therapeutic targets is scarcely available.

OBJECTIVE

The aim of the study was to detect the presence of a set of candidate miRNAs in cell-free cerebrospinal fluid (CSF) and to determine their association with gadolinium-enhancing (Gd+) lesions in order to assess their value as biomarkers of MS activity.

METHODS

Assessment of 28 miRNA candidates in cell-free CSF collected from 46 patients with MS (26 Gd+ and 20 Gd- patients) was performed by TaqMan assays and qPCR. Variations in their relative abundance were analyzed by the Mann-Whitney U test and further evaluated by receiver operating characteristic (ROC) analysis. Signaling pathways and biological functions of miRNAs were analyzed using bioinformatic tools (miRTarBase, Enrichr, REVIGO, and Cytoscape softwares).

RESULTS

Seven out of 28 miRNA candidates were detected in at least 75% of CSF samples. Consistent increase of miR-21 and miR-146a/b was found in Gd+ MS patients. This increase was in parallel to the number of Gd+ lesions and neurofilament light chain (NF-L) levels. Gene Ontology enrichment analysis revealed that the target genes of these miRNAs are involved in biological processes of key relevance such as apoptosis, cell migration and proliferation, and in cytokine-mediated signaling pathways.

CONCLUSION

Levels of miR-21 and miR-146a/b in cell-free CSF may represent valuable biomarkers to identify patients with active MS lesions.

Associations between Plasma Biomarkers and Cognition in Patients with Alzheimer's Disease and Amnestic Mild Cognitive Impairment: A Cross-Sectional and Longitudinal Study

Brain degeneration in patients with Alzheimer's disease (AD) results from the accumulation of pathological amyloid- (Aβ) plaques and tau protein tangles, leading to altered plasma levels of biomarkers. However, few studies have investigated the association between plasma biomarkers and cognitive impairment in patients with AD. In this cross-sectional study, we investigated correlations between mini-mental state examination (MMSE) scores and levels of plasma biomarkers in patients with amnestic mild cognitive impairment (aMCI) and AD. Thirteen individuals with normal cognition, 40 patients with aMCI, and 37 patients with AD were enrolled. Immunomagnetic reduction was used to assess the levels of plasma biomarkers, including amyloid A_1-40, A_1-42, total tau protein (t-Tau), and phosphorylated tau protein (threonine 181, p-Tau181). Our analysis revealed a significant negative correlation between MMSE and both measures of tau, and a trend toward negative correlation between MMSE and A_1-42. In a longitudinal study involving three patients with aMCI and two patients with AD, we observed strong negative correlations (r < -0.8) between changes in MMSE scores and plasma levels of t-Tau. Our results suggest that plasma levels of t-Tau and p-Tau181 can be used to assess the severity of cognitive impairment in patients with AD. Furthermore, the results of our preliminary longitudinal study suggest that levels of t-Tau can be used to monitor the progression of cognitive decline in patients with aMCI/AD.

Plasma alterations in cholinergic and serotonergic systems in early Alzheimer Disease: Diagnosis utility

BACKGROUND

Alzheimer Disease (AD) is the most common cause of dementia and it involves a high social and economic cost worldwide, and the health system still does not count with an effective treatment. This may be explained by the lack of a reliable early diagnosis and the complex physiological mechanisms involved in the disease development. In this sense, the cholinergic and serotonergic systems may be altered in the disease course.

METHODS

In this study, metabolites from these pathways were determined in order to develop a non-invasive and early diagnosis model, as well as to advance in the knowledge of the physiopathological mechanisms of the disease. For this, plasma samples from mild cognitive impairment due to AD patients (MCI-AD, n = 25) and healthy controls (n = 25) were analysed.

RESULTS

choline and tryptophan pathways were deregulated in MCI-AD. Therefore, a model based on betaine, cytidine, uridine, choline, acetylcholine, serotonin and tryptophan was developed, showing an AUC-ROC of 0.862, and sensitivity and specificity of 96% and 72%, respectively.

CONCLUSION

Alterations in metabolites from these pathways are related to cognitive impairment and neurodegeneration, and they could be useful in AD diagnosis. Nevertheless, further research is required in order to validate this diagnosis model.

Prediction and Classification of Alzheimer's Disease Based on Combined Features From Apolipoprotein-E Genotype, Cerebrospinal Fluid, MR, and FDG-PET Imaging Biomarkers

Alzheimer's disease (AD), including its mild cognitive impairment (MCI) phase that may or may not progress into the AD, is the most ordinary form of dementia. It is extremely important to correctly identify patients during the MCI stage because this is the phase where AD may or may not develop. Thus, it is crucial to predict outcomes during this phase. Thus far, many researchers have worked on only using a single modality of a biomarker for the diagnosis of AD or MCI. Although recent studies show that a combination of one or more different biomarkers may provide complementary information for the diagnosis, it also increases the classification accuracy distinguishing between different groups. In this paper, we propose a novel machine learning-based framework to discriminate subjects with AD or MCI utilizing a combination of four different biomarkers: fluorodeoxyglucose positron emission tomography (FDG-PET), structural magnetic resonance imaging (sMRI), cerebrospinal fluid (CSF) protein levels, and Apolipoprotein-E (APOE) genotype. The Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset was used in this study. In total, there were 158 subjects for whom all four modalities of biomarker were available. Of the 158 subjects, 38 subjects were in the AD group, 82 subjects were in MCI groups (including 46 in MCIc [MCI converted; conversion to AD within 24 months of time period], and 36 in MCIs [MCI stable; no conversion to AD within 24 months of time period]), and the remaining 38 subjects were in the healthy control (HC) group. For each image, we extracted 246 regions of interest (as features) using the Brainnetome template image and NiftyReg toolbox, and later we combined these features with three CSF and two APOE genotype features obtained from the ADNI website for each subject using early fusion technique. Here, a different kernel-based multiclass support vector machine (SVM) classifier with a grid-search method was applied. Before passing the obtained features to the classifier, we have used truncated singular value decomposition (Truncated SVD) dimensionality reduction technique to reduce high dimensional features into a lower-dimensional feature. As a result, our combined method achieved an area under the receiver operating characteristic (AU-ROC) curve of 98.33, 93.59, 96.83, 94.64, 96.43, and 95.24% for AD vs. HC, MCIs vs. MCIc, AD vs. MCIs, AD vs. MCIc, HC vs. MCIc, and HC vs. MCIs subjects which are high relative to single modality results and other state-of-the-art approaches. Moreover, combined multimodal methods have improved the classification performance over the unimodal classification.

Electric Field Disruption of Amyloid Aggregation: Potential Noninvasive Therapy for Alzheimer's Disease

The aggregation of β-amyloid peptides is a key event in the formative stages of Alzheimer's disease. Promoting folding and inhibiting aggregation was reported as an effective strategy in reducing Aβ-elicited toxicity. This study experimentally investigates the influence of the external electric field (EF) and magnetic field (MF) of varying strengths on the in vitro fibrillogenesis of hydrophobic core sequence, Aβ_16-22, and its parent peptide, Aβ_1-42. Biophysical methods such as ThT fluorescence, static light scattering, circular dichroism, and infrared spectroscopy suggest that EF has a stabilizing effect on the secondary structure, initiating a conformational switch of Aβ_16-22 and Aβ_1-42 from β to non-β conformation. This observation was further corroborated by dynamic light scattering and transmission electron microscopic studies. To mimic in vivo conditions, we repeated ThT fluorescence assay with Aβ_1-42 in human cerebrospinal fluid to verify EF-mediated modulation. The self-seeding of Aβ_1-42 and cross-seeding with Aβ_1-40 to verify that the autocatalytic amplification of self-assembly as a result of secondary nucleation also yields comparable results in EF-exposed and unexposed samples. Aβ-elicited toxicity of EF-treated samples in two neuroblastoma cell lines (SH-SY5Y and IMR-32) and human embryonic kidney cell line (HEK293) were found to be 15-38% less toxic than the EF untreated ones under identical conditions. Experiments with fluorescent labeled Aβ_1-42 to correlate reduced cytotoxicity and cell internalization suggest a comparatively smaller uptake of the EF-treated peptides. Our results provide a scientific roadmap for future noninvasive, therapeutic solutions for the treatment of Alzheimer's disease.

Predicting cognitive decline with non-clinical markers in Parkinson's disease (PRECODE-2)

OBJECTIVES

To investigate whether baseline [¹²³I]FP-CIT SPECT and CSF markers can predict cognitive impairment (CI) in PD patients, and provide a profile of those most at risk.

METHODS

262 de novo PD patients from the Parkinson's Progression Markers Initiative database were stratified into two CI groups at the 36-month follow-up: MoCA-defined diagnosis: PD patients who had a MoCA score < 26; neuropsychological test-defined diagnosis: PD patients with MoCA-defined diagnosis and at least two test scores (of six; irrespective of test domain) greater than 1.5 standard deviation below the mean score in healthy controls. Predictive variables of CI were divided into deciles, providing us with ideal cutoff values for each variable.

RESULTS

At the 36-month follow-up, 108/262 (41.2%) PD patients had CI as defined by the MoCA, of which 40/108 (37.0%) had neuropsychological test-defined CI. Baseline CSF Aβ42 (hazard ratio [HR]: 0.996, confidence interval [CI]: 0.992-0.999, p = 0.025), CSF total tau ([HR]: 1.023, [CI]: 1.002-1.044, p = 0.031) and caudate [¹²³I]FP-CIT SPECT uptake ([HR]: 0.332, [CI]: 0.115-0.960, p = 0.042) were predictors of CI. Patients with reduced CSF Aβ42 (< 384.6 pg/mL), increased CSF total tau (> 45.0 pg/mL) and reduced caudate [¹²³I]FP-CIT SPECT uptake (< 1.82) had a 65% risk of developing CI at 36-month follow-up.

CONCLUSION

We report a characteristic profile (reduced CSF Aβ42, increased CSF total tau and reduced caudate [¹²³I]FP-CIT SPECT uptake) that enables identification of early PD patients at risk of developing CI. These findings confirm previous reports of low CSF Aβ42, elevated CSF total tau and reduced dopaminergic integrity being associated with cognitive decline in PD.

Long-Term Storage Effects on Stability of Aβ1-40, Aβ1-42, and Total Tau Proteins in Human Plasma Samples Measured with Immunomagnetic Reduction Assays

BACKGROUND

The stability of Alzheimer's disease (AD) biomarkers in plasma, measured by immunomagnetic reduction (IMR) after long-term storage at -80°C, has not been established before.

METHOD

Ninety-nine human plasma samples from 53 normal controls (NCs), 5 patients with amnestic mild cognitive impairment (aMCI), and 41 AD patients were collected. Each plasma sample was aliquoted and stored as single-use aliquots at -80°C. The baseline measurements for Aβ1-40, Aβ1-42, and total Tau protein (T-Tau) concentrations for each sample were done within 3 months of blood draw by IMR. They are referred to as baseline concentrations. A separate aliquot from each sample was assayed with IMR to assess the stability of the measured analytes during storage at -80°C between 1.1 and 5.4 years. This is referred to as a repeated result.

RESULTS

IMR shows that plasma levels of Aβ1-40 and Aβ1-42 exhibit stability over 5-year storage at -80°C and that plasma levels of T-Tau are less stable (approximately 1.5 years).

CONCLUSION

Although the measured concentrations of T-Tau in human plasma may alter during storage, the diagnostic utility of the results are only slightly affected when the product of Aβ1-42 and T-Tau concentrations are used. The results show that the overall agreement between baseline and repeated measurements in the ability of discriminating NCs from aMCI/AD patients is higher than 80%.

Machine Learning Reveals Protein Signatures in CSF and Plasma Fluids of Clinical Value for ALS

We use shotgun proteomics to identify biomarkers of diagnostic and prognostic value in individuals diagnosed with amyotrophic lateral sclerosis. Matched cerebrospinal and plasma fluids were subjected to abundant protein depletion and analyzed by nano-flow liquid chromatography high resolution tandem mass spectrometry. Label free quantitation was used to identify differential proteins between individuals with ALS (n = 33) and healthy controls (n = 30) in both fluids. In CSF, 118 (p-value < 0.05) and 27 proteins (q-value < 0.05) were identified as significantly altered between ALS and controls. In plasma, 20 (p-value < 0.05) and 0 (q-value < 0.05) proteins were identified as significantly altered between ALS and controls. Proteins involved in complement activation, acute phase response and retinoid signaling pathways were significantly enriched in the CSF from ALS patients. Subsequently various machine learning methods were evaluated for disease classification using a repeated Monte Carlo cross-validation approach. A linear discriminant analysis model achieved a median area under the receiver operating characteristic curve of 0.94 with an interquartile range of 0.88–1.0. Three proteins composed a prognostic model (p = 5e-4) that explained 49% of the variation in the ALS-FRS scores. Finally we investigated the specificity of two promising proteins from our discovery data set, chitinase-3 like 1 protein and alpha-1-antichymotrypsin, using targeted proteomics in a separate set of CSF samples derived from individuals diagnosed with ALS (n = 11) and other neurological diseases (n = 15). These results demonstrate the potential of a panel of targeted proteins for objective measurements of clinical value in ALS.

Biomarkers and the diagnosis of preclinical dementia

Alzheimer's disease pathology accumulates years before the onset of clinical symptoms and has been termed ‘preclinical dementia’. Biomarkers have been developed to detect this pathology – namely, brain amyloid deposition and markers of neurodegeneration. In this article we describe these biomarkers and review the evidence for their clinical use in predicting risk both in the cognitively ‘normal’ and in those who already have established cognitive decline. We also discuss the limitations and ethical considerations of these tests and consider whether we should start incorporating Alzheimer's disease biomarkers into clinical practice. We find that, because many cognitively healthy people will have Alzheimer's pathology, and it is not clear whether this does help predict future risk of Alzheimer's disease, diagnosing preclinical dementia carries numerous ethical implications and is currently not being advocated outside research settings.

LEARNING OBJECTIVES

•

DECLARATION OF INTEREST

J. C. H. works in the Research Institute for the Care of Older People (RICE), which undertakes clinical drug trials for drug companies. He is a sub-investigator on a number of trials (some of which involve neuroimaging and biomarkers) and principal investigator and chief investigator on two trials (neither of which involves biomarkers). All of these trials concern Alzheimer's disease or dementia. He does not receive any direct personal payment from the trials: the payment goes to RICE, which does, however, fund almost half of his post. RICE is an independent charity and separate from the University of Bristol.

Using Multi-Scale Genetic, Neuroimaging and Clinical Data for Predicting Alzheimer's Disease and Reconstruction of Relevant Biological Mechanisms

Alzheimer's Disease (AD) is among the most frequent neuro-degenerative diseases. Early diagnosis is essential for successful disease management and chance to attenuate symptoms by disease modifying drugs. In the past, a number of cerebrospinal fluid (CSF), plasma and neuro-imaging based biomarkers have been proposed. Still, in current clinical practice, AD diagnosis cannot be made until the patient shows clear signs of cognitive decline, which can partially be attributed to the multi-factorial nature of AD. In this work, we integrated genotype information, neuro-imaging as well as clinical data (including neuro-psychological measures) from ~900 normal and mild cognitively impaired (MCI) individuals and developed a highly accurate machine learning model to predict the time until AD is diagnosed. We performed an in-depth investigation of the relevant baseline characteristics that contributed to the AD risk prediction. More specifically, we used Bayesian Networks to uncover the interplay across biological scales between neuro-psychological assessment scores, single genetic variants, pathways and neuro-imaging related features. Together with information extracted from the literature, this allowed us to partially reconstruct biological mechanisms that could play a role in the conversion of normal/MCI into AD pathology. This in turn may open the door to novel therapeutic options in the future.

Temporal association patterns and dynamics of amyloid-β and tau in Alzheimer's disease

The elusive relationship between underlying pathology and clinical disease hampers diagnosis of Alzheimer's disease (AD) and preventative intervention development. We seek to understand the relationship between two classical AD biomarkers, amyloid-β1-42 (Aβ1-42) and total-tau (t-tau), and define their trajectories across disease development, as defined by disease onset at diagnosis of mild cognitive impairment (MCI). Using longitudinal data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we performed a correlation analysis of biomarkers CSF Aβ1-42 and t-tau, and longitudinal quantile analysis. Using a mixed effects model, with MCI onset as an anchor, we develop linear trajectories to describe the rate of change across disease development. These trajectories were extended through the incorporation of data from cognitively normal, healthy adults (aged 20-62 years) from the literature, to fit sigmoid curves by means of non-linear least squares estimators, to create curves encompassing the 50 years prior to MCI onset. A strong right-angled relationship between the biomarkers Aβ1-42 and t-tau is detected, implying a highly non-linear relationship. The rate of change of Aβ1-42 is correlated with the baseline concentration per quantile, reflecting a reduction in the rate of loss across disease within subjects. Regression models reveal significant amyloid loss relative to MCI onset (- 2.35 pg/mL/year), compared to minimal loss relative to AD onset (- 0.97 pg/mL/year). Tau accumulates consistently relative to MCI and AD onset, (2.05 pg/mL/year) and (2.46 pg/mL/year), respectively. The fitted amyloid curve shows peak loss of amyloid 8.06 years prior to MCI diagnosis, while t-tau exhibits peak accumulation 14.17 years following MCI diagnosis, with the upper limit not yet reached 30 years post diagnosis. Biomarker trajectories aid unbiased, objective assessment of disease progression. Quantitative trajectories are likely to be of use in clinical trial design, as they allow for a more detailed insight into the effectiveness of treatments designed to delay development of biological disease.

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.

Biomarkers of neuronal injury and amyloid metabolism in the cerebrospinal fluid of patients infected with HIV-1 subtypes B and C

Based on prior reports that the HIV-1 Tat protein modulates amyloid-beta (Aβ) metabolism, this study aimed to compare CSF neural injury biomarkers between 27 patients with HIV subtype B, 26 patients with HIV subtype C, 18 healthy HIV-negative controls, and 24 patients with Alzheimer's disease (AD). Immunoassays were used to measure soluble amyloid precursor protein α and β (sAPPα, sAPPβ), Aβ oligomers 38, 40, 42, and Aβ-total; phosphorylated tau (P-tau181), and total tau (T-tau). Comparisons between HIV(+) and HIV(-) (including AD) were adjusted by linear regression for gender and age; HIV subtype comparisons were adjusted for nadir CD4 and plasma viral load suppression. The p values were corrected for multiple testing with the Benjamini-Hochberg procedure. CSF Aβ-42 and Hulstaert (P-tau181) index were lower in HIV1-C than B (p = 0.03, and 0.049 respectively); subtypes did not differ on other CSF biomarkers or ratios. Compared to AD, HIV(+) had lower CSF levels of T-tau, P-tau181 (p < 0.001), and sAPPα (p = 0.041); HIV(+) had higher CSF Aβ-42 (p = 0.002) and higher CSF indexes: [Aß-42/(240 + 1.18 T-tau)], P-tau181/Aβ-42, T-tau/Aβ-42, P-tau181/T-tau, sAPPα/β (all p ≤ 0.01) than AD. Compared to HIV(-), HIV(+) had lower CSF Aβ-42, and T-tau (all p ≤ 0.004). As conclusion, amyloid metabolism was influenced by HIV infection in a subtype-dependent manner. Aß-42 levels were lower in HIV1-C than B, suggesting that there may be greater deposition of Aß-42 in HIV1-C. These findings are supported by CSF Hulstaert (P-tau181) index. Differences between HIV and AD in the patterns of Aß and Tau biomarkers suggest that CNS HIV infection and AD may not share some of same mechanisms of neuronal injury.

Validation of Immunoassay-Based Tools for the Comprehensive Quantification of Aβ40 and Aβ42 Peptides in Plasma

Recent advances in neuroimaging and cerebrospinal fluid (CSF) biomarker assays have provided evidence of a long preclinical stage of Alzheimer's disease (AD). This period is being increasingly targeted for secondary prevention trials of new therapies. In this context, the interest of a noninvasive, cost-effective amyloid-β (Aβ) blood-based test does not need to be overstated. Nevertheless, a thorough validation of these bioanalytical methods should be performed as a prerequisite for confident interpretation of clinical results. The aim of this study was to validate ELISA sandwich colorimetric ABtest40 and ABtest42 for the quantification of Aβ40 and Aβ42 in human plasma. The validation parameters assessed included precision, accuracy, sensitivity, specificity, recovery, and dilution linearity. ABtest40 and ABtest42 proved to be specific for their target peptide using Aβ peptides with sequence similar to the target. Mean relative error in the quantification was found to be below 7.5% for both assays, with high intra-assay, inter-assay, and inter-batch precision (CV <9.0% on average). Sensitivity was assessed by determination of the limit of quantification fulfilling precision and accuracy criteria; it was established at 7.60 pg/ml and 3.60 pg/ml for ABtest40 and ABtest42, respectively. Plasma dilution linearity was demonstrated in PBS; however, dilution in a proprietary formulated buffer significantly increased the recovery of both Aβ40 and Aβ42 masked by matrix interactions, allowing a more comprehensive assessment of the free and total peptide levels in the plasma. In conclusion, both assays were successfully validated as tools for the quantification Aβ40 and Aβ42 in plasma.