Innovative diagnostic tools for early detection of Alzheimer's disease

Sphingolipid-Enriched Extracellular Vesicles and Alzheimer's Disease: A Decade of Research

Extracellular vesicles (EVs), particularly exosomes, have emerged in the last 10 years as a new player in the progression of Alzheimer's disease (AD) with high potential for being useful as a diagnostic and treatment tool. Exosomes and other EVs are enriched with the sphingolipid ceramide as well as other more complex glycosphingolipids such as gangliosides. At least a subpopulation of exosomes requires neutral sphingomyelinase activity for their biogenesis and secretion. As ceramide is often elevated in AD, exosome secretion may be affected as well. Here, we review the available data showing that exosomes regulate the aggregation and clearance of amyloid-beta (Aβ) and discuss the differences in data from laboratories regarding Aβ binding, induction of aggregation, and glial clearance. We also summarize available data on the role of exosomes in extracellular tau propagation, AD-related exosomal mRNA/miRNA cargo, and the use of exosomes as biomarker and gene therapy vehicles for diagnosis and potential treatment.

Blood-based biomarkers in Alzheimer disease: Current state of the science and a novel collaborative paradigm for advancing from discovery to clinic

The last decade has seen a substantial increase in research focused on the identification of blood-based biomarkers that have utility in Alzheimer's disease (AD). Blood-based biomarkers have significant advantages of being time- and cost-efficient as well as reduced invasiveness and increased patient acceptance. Despite these advantages and increased research efforts, the field has been hampered by lack of reproducibility and an unclear path for moving basic discovery toward clinical utilization. Here we reviewed the recent literature on blood-based biomarkers in AD to provide a current state of the art. In addition, a collaborative model is proposed that leverages academic and industry strengths to facilitate the field in moving past discovery only work and toward clinical use. Key resources are provided. This new public-private partnership model is intended to circumvent the traditional handoff model and provide a clear and useful paradigm for the advancement of biomarker science in AD and other neurodegenerative diseases.

Molecular Diagnostics of Ageing and Tackling Age-related Disease

As average life expectancy increases there is a greater focus on health-span and, in particular, how to treat or prevent chronic age-associated diseases. Therapies which were able to control 'biological age' with the aim of postponing chronic and costly diseases of old age require an entirely new approach to drug development. Molecular technologies and machine-learning methods have already yielded diagnostics that help guide cancer treatment and cardiovascular procedures. Discovery of valid and clinically informative diagnostics of human biological age (combined with disease-specific biomarkers) has the potential to alter current drug-discovery strategies, aid clinical trial recruitment and maximize healthy ageing. I will review some basic principles that govern the development of 'ageing' diagnostics, how such assays could be used during the drug-discovery or development process. Important logistical and statistical considerations are illustrated by reviewing recent biomarker activity in the field of Alzheimer's disease, as dementia represents the most pressing of priorities for the pharmaceutical industry, as well as the chronic disease in humans most associated with age.

Multivariate multi-scale weighted permutation entropy analysis of EEG complexity for Alzheimer's disease

The complexity change of brain activity in Alzheimer's disease (AD) is an interesting topic for clinical purpose. To investigate the dynamical complexity of brain activity in AD, a multivariate multi-scale weighted permutation entropy (MMSWPE) method is proposed to measure the complexity of electroencephalograph (EEG) obtained in AD patients. MMSWPE combines the weighted permutation entropy and the multivariate multi-scale method. It is able to quantify not only the characteristics of different brain regions and multiple time scales but also the amplitude information contained in the multichannel EEG signals simultaneously. The effectiveness of the proposed method is verified by both the simulated chaotic signals and EEG recordings of AD patients. The simulation results from the Lorenz system indicate that MMSWPE has the ability to distinguish the multivariate signals with different complexity. In addition, the EEG analysis results show that in contrast with the normal group, the significantly decreased complexity of AD patients is distributed in the temporal and occipitoparietal regions for the theta and the alpha bands, and also distributed from the right frontal to the left occipitoparietal region for the theta, the alpha and the beta bands at each time scale, which may be attributed to the brain dysfunction. Therefore, it suggests that the MMSWPE method may be a promising method to reveal dynamic changes in AD.

A call for comparative effectiveness research to learn whether routine clinical care decisions can protect from dementia and cognitive decline

Common diseases like diabetes, hypertension, and atrial fibrillation are probable risk factors for dementia, suggesting that their treatments may influence the risk and rate of cognitive and functional decline. Moreover, specific therapies and medications may affect long-term brain health through mechanisms that are independent of their primary indication. While surgery, benzodiazepines, and anti-cholinergic drugs may accelerate decline or even raise the risk of dementia, other medications act directly on the brain to potentially slow the pathology that underlies Alzheimer’s and other dementia. In other words, the functional and cognitive decline in vulnerable patients may be influenced by the choice of treatments for other medical conditions. Despite the importance of these questions, very little research is available. The Alzheimer’s Drug Discovery Foundation convened an advisory panel to discuss the existing evidence and to recommend strategies to accelerate the development of comparative effectiveness research on how choices in the clinical care of common chronic diseases may protect from cognitive decline and dementia.

Late-Life Vascular Risk Factors and Alzheimer Disease Neuropathology in Individuals with Normal Cognition

Vascular risk factors (VRFs) have been associated with clinically diagnosed Alzheimer disease (AD), but few studies have examined the association between VRF and AD neuropathology (ADNP) in cognitively normal individuals. We used longitudinal data from the National Alzheimer's Disease Center's Uniform Data Set and Neuropathology Data Set to examine the association between VRF and ADNP (moderate to frequent neuritic plaques; Braak stage III-VI) in those with normal cognition. Our sample included 53 participants with ADNP and 140 without ADNP. Body mass index (BMI), resting heart rate (HR), and pulse pressure (PP) were measured at each visit; values were averaged across participant visits and examined annual change in BMI, PP, and HR. Hypertension, diabetes, and hypercholesterolemia were self-reported. In the multivariable logistic regression analyses, average BMI and HR were associated with lower odds of ADNP, and annual increases in HR and BMI were associated with higher odds of ADNP. A previously experienced decline in BMI or HR in late-life (therefore, currently low BMI and low HR) as well as a late-life increase in BMI and HR may indicate underlying AD pathology. Additional clinicopathological research is needed to elucidate the role of changes in late-life VRF and AD pathogenesis.

On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey

INTRODUCTION

The number of Alzheimer's Disease (AD) patients is increasing with increased life expectancy and 115.4 million people are expected to be affected in 2050. Unfortunately, AD is commonly diagnosed too late, when irreversible damages have been caused in the patient.

OBJECTIVE

An automatic, continuous and unobtrusive early AD detection method would be required to improve patients' life quality and avoid big healthcare costs. Thus, the objective of this survey is to review the multimodal signals that could be used in the development of such a system, emphasizing on the accuracy that they have shown up to date for AD detection. Some useful tools and specific issues towards this goal will also have to be reviewed.

METHODS

An extensive literature review was performed following a specific search strategy, inclusion criteria, data extraction and quality assessment in the Inspec, Compendex and PubMed databases.

RESULTS

This work reviews the extensive list of psychological, physiological, behavioural and cognitive measurements that could be used for AD detection. The most promising measurements seem to be magnetic resonance imaging (MRI) for AD vs control (CTL) discrimination with an 98.95% accuracy, while electroencephalogram (EEG) shows the best results for mild cognitive impairment (MCI) vs CTL (97.88%) and MCI vs AD distinction (94.05%). Available physiological and behavioural AD datasets are listed, as well as medical imaging analysis steps and neuroimaging processing toolboxes. Some issues such as "label noise" and multi-site data are discussed.

CONCLUSIONS

The development of an unobtrusive and transparent AD detection system should be based on a multimodal system in order to take full advantage of all kinds of symptoms, detect even the smallest changes and combine them, so as to detect AD as early as possible. Such a multimodal system might probably be based on physiological monitoring of MRI or EEG, as well as behavioural measurements like the ones proposed along the article. The mentioned AD datasets and image processing toolboxes are available for their use towards this goal. Issues like "label noise" and multi-site neuroimaging incompatibilities may also have to be overcome, but methods for this purpose are already available.

Changes in the Chemical Barrier Composition of Tears in Alzheimer's Disease Reveal Potential Tear Diagnostic Biomarkers

Alzheimer’s disease (AD) is one of the most common neurodegenerative diseases, with increasing prevalence affecting millions of people worldwide. Currently, only autopsy is able to confirm the diagnosis with a 100% certainty, therefore, biomarkers from body fluids obtained by non-invasive means provide an attractive alternative for the diagnosis of Alzheimer`s disease. Global changes of the protein profile were examined by quantitative proteomics; firstly, electrophoresis and LC-MS/MS were used, thereafter, SRM-based targeted proteomics method was developed and applied to examine quantitative changes of tear proteins. Alterations in the tear flow rate, total tear protein concentration and composition of the chemical barrier specific to AD were demonstrated, and the combination of lipocalin-1, dermcidin, lysozyme-C and lacritin was shown to be a potential biomarker, with an 81% sensitivity and 77% specificity.

Nano-biosensors to detect beta-amyloid for Alzheimer's disease management

Beta-amyloid (β-A) peptides are potential biomarkers to monitor Alzheimer's diseases (AD) for diagnostic purposes. Increased β-A level is neurotoxic and induces oxidative stress in brain resulting in neurodegeneration and causes dementia. As of now, no sensitive and inexpensive method is available for β-A detection under physiological and pathological conditions. Although, available methods such as neuroimaging, enzyme-linked immunosorbent assay (ELISA), and polymerase chain reaction (PCR) detect β-A, but they are not yet extended at point-of-care (POC) due to sophisticated equipments, need of high expertize, complicated operations, and challenge of low detection limit. Recently, β-A antibody based electrochemical immuno-sensing approach has been explored to detect β-A at pM levels within 30-40 min compared to 6-8h of ELISA test. The introduction of nano-enabling electrochemical sensing technology could enable rapid detection of β-A at POC and may facilitate fast personalized health care delivery. This review explores recent advancements in nano-enabling electrochemical β-A sensing technologies towards POC application to AD management. These analytical tools can serve as an analytical tool for AD management program to obtain bio-informatics needed to optimize therapeutics for neurodegenerative diseases diagnosis management.

Serum high-density lipoprotein is associated with better cognitive function in a cross-sectional study of aging women

Purpose/Aim of the study: Poor cardiovascular health, including obesity and altered lipid profiles at mid-life, are linked to increased risk of Alzheimer's disease (AD). The biological mechanisms linking cardiovascular health and cognitive function are unclear though are likely to be multifactorial. This study examined the association between various lipoproteins and cognitive functioning in ageing women.

MATERIALS AND METHODS

We investigated the relationship between readily available biomarkers (i.e. serum lipoprotein) and cognitive decline in domains associated with increased risk of AD (e.g. episodic verbal memory performance and subjective memory complaint). We report cross-sectional data investigating the relationship between serum total cholesterol, triglycerides, high-density lipoprotein (HDL-C) and low-density lipoprotein with verbal memory and learning ability in 130 women with and without memory complaints (n = 71 and 59, respectively) drawn from a study investigating cognitively healthy Western Australians (average age 62.5 years old).

RESULTS

After statistical modelling that controlled for the effects of age, depression and apolipoprotein E genotype, HDL-C was significantly associated with better verbal learning and memory performance, specifically short and long delay-free recalls (F = 3.062; p < .05 and F = 3.2670; p < .05, respectively).

CONCLUSION

Our cross-sectional findings suggest that the positive effect of HDL-C on verbal memory may be present much earlier than previously reported and provide further support for the role of HDL-C in healthy brain ageing. Further exploration of the protective effect of HDL-C on cognitive function in ageing is warranted through follow-up, longitudinal studies.

Prediction of conversion from mild cognitive impairment to dementia with neuronally derived blood exosome protein profile

Introduction

Levels of Alzheimer's disease (AD)-related proteins in plasma neuronal derived exosomes (NDEs) were quantified to identify biomarkers for prediction and staging of mild cognitive impairment (MCI) and AD.

Methods

Plasma exosomes were extracted, precipitated, and enriched for neuronal source by anti-L1CAM antibody absorption. NDEs were characterized by size (Nanosight) and shape (TEM) and extracted NDE protein biomarkers were quantified by ELISAs. Plasma NDE cargo was injected into normal mice, and results were characterized by immunohistochemistry to determine pathogenic potential.

Results

Plasma NDE levels of P-T181-tau, P-S396-tau, and Aβ1–42 were significantly higher, whereas those of neurogranin (NRGN) and the repressor element 1-silencing transcription factor (REST) were significantly lower in AD and MCI converting to AD (ADC) patients compared to cognitively normal controls (CNC) subjects and stable MCI patients. Mice injected with plasma NDEs from ADC patients displayed increased P-tau (PHF-1 antibody)–positive cells in the CA1 region of the hippocampus compared to plasma NDEs from CNC and stable MCI patients.

Conclusions

Abnormal plasma NDE levels of P-tau, Aβ1–42, NRGN, and REST accurately predict conversion of MCI to AD dementia. Plasma NDEs from demented patients seeded tau aggregation and induced AD-like neuropathology in normal mouse CNS.

tACS Phase Locking of Frontal Midline Theta Oscillations Disrupts Working Memory Performance

Background: Frontal midline theta (FMT) oscillations (4–8 Hz) are strongly related to cognitive and executive control during mental tasks such as memory processing, arithmetic problem solving or sustained attention. While maintenance of temporal order information during a working memory (WM) task was recently linked to FMT phase, a positive correlation between FMT power, WM demand and WM performance was shown. However, the relationship between these measures is not well understood, and it is unknown whether purposeful FMT phase manipulation during a WM task impacts FMT power and WM performance. Here we present evidence that FMT phase manipulation mediated by transcranial alternating current stimulation (tACS) can block WM demand-related FMT power increase (FMTΔpower) and disrupt normal WM performance.

Methods: Twenty healthy volunteers were assigned to one of two groups (group A, group B) and performed a 2-back task across a baseline block (block 1) and an intervention block (block 2) while 275-sensor magnetoencephalography (MEG) was recorded. After no stimulation was applied during block 1, participants in group A received tACS oscillating at their individual FMT frequency over the prefrontal cortex (PFC) while group B received sham stimulation during block 2. After assessing and mapping phase locking values (PLV) between the tACS signal and brain oscillatory activity across the whole brain, FMT power and WM performance were assessed and compared between blocks and groups.

Results: During block 2 of group A but not B, FMT oscillations showed increased PLV across task-related cortical areas underneath the frontal tACS electrode. While WM task-related FMTΔpower and WM performance were comparable across groups in block 1, tACS resulted in lower FMTΔpower and WM performance compared to sham stimulation in block 2.

Conclusion: tACS-related manipulation of FMT phase can disrupt WM performance and influence WM task-related FMTΔpower. This finding may have important implications for the treatment of brain disorders such as depression and attention deficit disorder associated with abnormal regulation of FMT activity or disorders characterized by dysfunctional coupling of brain activity, e.g., epilepsy, Alzheimer’s or Parkinson’s disease (AD/PD).

Quantifying synchrony patterns in the EEG of Alzheimer's patients with linear and non-linear connectivity markers

We analyzed the relation of several synchrony markers in the electroencephalogram (EEG) and Alzheimer's disease (AD) severity as measured by Mini-Mental State Examination (MMSE) scores. The study sample consisted of 79 subjects diagnosed with probable AD. All subjects were participants in the PRODEM-Austria study. Following a homogeneous protocol, the EEG was recorded both in resting state and during a cognitive task. We employed quadratic least squares regression to describe the relation between MMSE and the EEG markers. Factor analysis was used for estimating a potentially lower number of unobserved synchrony factors. These common factors were then related to MMSE scores as well. Most markers displayed an initial increase of EEG synchrony with MMSE scores from 26 to 21 or 20, and a decrease below. This effect was most prominent during the cognitive task and may be owed to cerebral compensatory mechanisms. Factor analysis provided interesting insights in the synchrony structures and the first common factors were related to MMSE scores with coefficients of determination up to 0.433. We conclude that several of the proposed EEG markers are related to AD severity for the overall sample with a wide dispersion for individual subjects. Part of these fluctuations may be owed to fluctuations and day-to-day variability associated with MMSE measurements. Our study provides a systematic analysis of EEG synchrony based on a large and homogeneous sample. The results indicate that the individual markers capture different aspects of EEG synchrony and may reflect cerebral compensatory mechanisms in the early stages of AD.

Terahertz spectroscopy of brain tissue from a mouse model of Alzheimer's disease

The terahertz (THz) absorption and index of refraction of brain tissues from a mouse model of Alzheimer’s disease (AD) and a control wild-type (normal) mouse were compared using THz time-domain spectroscopy (THz-TDS). Three dominating absorption peaks associated to torsional–vibrational modes were observed in AD tissue, at about 1.44, 1.8, and 2.114 THz, closer to the peaks of free tryptophan molecules than in normal tissue. A possible reason is that there is more free tryptophan in AD brain tissue, while in normal brain tissue more tryptophan is attached to other molecules. Our study suggests that THz-absorption modes may be used as an AD biomarker fingerprint in brain, and that THz-TDS is a promising technique for early diagnosis of AD.

Speaking in Alzheimer's Disease, is That an Early Sign? Importance of Changes in Language Abilities in Alzheimer's Disease

It is known that Alzheimer’s disease (AD) influences the temporal characteristics of spontaneous speech. These phonetical changes are present even in mild AD. Based on this, the question arises whether an examination based on language analysis could help the early diagnosis of AD and if so, which language and speech characteristics can identify AD in its early stage. The purpose of this article is to summarize the relation between prodromal and manifest AD and language functions and language domains. Based on our research, we are inclined to claim that AD can be more sensitively detected with the help of a linguistic analysis than with other cognitive examinations. The temporal characteristics of spontaneous speech, such as speech tempo, number of pauses in speech, and their length are sensitive detectors of the early stage of the disease, which enables an early simple linguistic screening for AD. However, knowledge about the unique features of the language problems associated with different dementia variants still has to be improved and refined.

Ecological Assessment of Autonomy in Instrumental Activities of Daily Living in Dementia Patients by the Means of an Automatic Video Monitoring System

Currently, the assessment of autonomy and functional ability involves clinical rating scales. However, scales are often limited in their ability to provide objective and sensitive information. By contrast, information and communication technologies may overcome these limitations by capturing more fully functional as well as cognitive disturbances associated with Alzheimer disease (AD). We investigated the quantitative assessment of autonomy in dementia patients based not only on gait analysis but also on the participant performance on instrumental activities of daily living (IADL) automatically recognized by a video event monitoring system (EMS). Three groups of participants (healthy controls, mild cognitive impairment, and AD patients) had to carry out a standardized scenario consisting of physical tasks (single and dual task) and several IADL such as preparing a pillbox or making a phone call while being recorded. After, video sensor data were processed by an EMS that automatically extracts kinematic parameters of the participants’ gait and recognizes their carried out activities. These parameters were then used for the assessment of the participants’ performance levels, here referred as autonomy. Autonomy assessment was approached as classification task using artificial intelligence methods that takes as input the parameters extracted by the EMS, here referred as behavioral profile. Activities were accurately recognized by the EMS with high precision. The most accurately recognized activities were “prepare medication” with 93% and “using phone” with 89% precision. The diagnostic group classifier obtained a precision of 73.46% when combining the analyses of physical tasks with IADL. In a further analysis, the created autonomy group classifier which obtained a precision of 83.67% when combining physical tasks and IADL. Results suggest that it is possible to quantitatively assess IADL functioning supported by an EMS and that even based on the extracted data the groups could be classified with high accuracy. This means that the use of such technologies may provide clinicians with diagnostic relevant information to improve autonomy assessment in real time decreasing observer biases.