Strategic roadmap for an early diagnosis of Alzheimer's disease based on biomarkers

Critical role of mitosis in spontaneous late-onset Alzheimer's disease; from a Shugoshin 1 cohesinopathy mouse model

From early-onset Alzheimer's disease (EOAD) studies, the amyloid-beta hypothesis emerged as the foremost theory of the pathological causes of AD. However, how amyloid-beta accumulation is triggered and progresses toward senile plaques in spontaneous late-onset Alzheimer's disease (LOAD) in humans remains unanswered. Various LOAD facilitators have been proposed, and LOAD is currently considered a complex disease with multiple causes. Mice do not normally develop LOAD. Possibly due to the multiple causes, proposed LOAD facilitators have not been able to replicate spontaneous LOAD in mice, representing a disease modeling issue. Recently, we reported spontaneous late-onset development of amyloid-beta accumulation in brains of Shugoshin 1 (Sgo1) haploinsufficient mice, a cohesinopathy-mediated chromosome instability model. The result for the first time expands disease relevance of mitosis studies to a major disease other than cancers. Reverse-engineering of the model would shed light on the process of late-onset amyloid-beta accumulation in the brain and spontaneous LOAD development, and contribute to development of interventions for LOAD. This review will discuss the Sgo1 model, our current "three-hit hypothesis" regarding LOAD development with an emphasis on critical role of prolonged mitosis in amyloid-beta accumulation, and implications for human LOAD intervention and treatment. Abbreviations: Alzheimer's disease (AD); Late-onset Alzheimer's disease (LOAD); Early-onset Alzheimer's disease (EOAD); Shugoshin-1 (Sgo1); Chromosome Instability (CIN); apolipoprotein (Apoe); Central nervous system (CNS); Amyloid precursor protein (APP); N-methyl-d-aspartate (NMDA); Hazard ratio (HR); Cyclin-dependent kinase (CDK); Chronic Atrial Intestinal Dysrhythmia (CAID); beta-secretase 1 (BACE); phosphor-Histone H3 (p-H3); Research and development (R&D); Non-steroidal anti-inflammatory drugs (NSAIDs); Brain blood barrier (BBB).

The impact of transcranial magnetic stimulation on diagnostic confidence in patients with Alzheimer disease

Background

Cholinergic dysfunction is a key abnormality in Alzheimer disease (AD) that can be detected in vivo with transcranial magnetic stimulation (TMS) protocols. Although TMS has clearly demonstrated analytical validity, its clinical utility is still debated. In the present study, we evaluated the incremental diagnostic value, expressed in terms of diagnostic confidence of Alzheimer disease (DCAD; range 0–100), of TMS measures in addition to the routine clinical diagnostic assessment in patients evaluated for cognitive impairment as compared with validated biomarkers of amyloidosis.

Methods

One hundred twenty patients with dementia were included and scored in terms of DCAD in a three-step assessment based on (1) demographic, clinical, and neuropsychological evaluations (clinical work-up); (2) clinical work-up plus amyloid markers (cerebrospinal fluid or amyloid positron emission tomographic imaging); and (3) clinical work-up plus TMS intracortical connectivity measures. Two blinded neurologists were asked to review the diagnosis and diagnostic confidence at each step.

Results

TMS measures increased the discrimination of DCAD in two clusters (AD-like vs FTD-like) when added to the clinical and neuropsychological evaluations with levels comparable to established biomarkers of brain amyloidosis (cluster distance of 55.1 for clinical work-up alone, 76.0 for clinical work-up plus amyloid markers, 80.0 for clinical work-up plus TMS). Classification accuracy for the “gold standard” diagnosis (dichotomous - AD vs FTD - variable) evaluated in the three-step assessment, expressed as AUC, increased from 0.82 (clinical work-up alone) to 0.98 (clinical work-up plus TMS) and to 0.99 (clinical work-up plus amyloidosis markers).

Conclusions

TMS in addition to routine assessment in patients with dementia has a significant effect on diagnosis and diagnostic confidence that is comparable to well-established amyloidosis biomarkers.

Electronic supplementary material

The online version of this article (10.1186/s13195-018-0423-6) contains supplementary material, which is available to authorized users.

Diagnostic performance of Elecsys immunoassays for cerebrospinal fluid Alzheimer's disease biomarkers in a nonacademic, multicenter memory clinic cohort: The ABIDE project

Introduction

We compared the automated Elecsys and manual Innotest immunoassays for cerebrospinal fluid (CSF) Alzheimer's disease biomarkers in a multicenter diagnostic setting.

Methods

We collected CSF samples from 137 participants in eight local memory clinics. Amyloid β(1–42) (Aβ42), total tau (t-tau), and phosphorylated tau (p-tau) were centrally analyzed with Innotest and Elecsys assays. Concordances between methods were assessed.

Results

Biomarker results strongly correlated between assays with Spearman's ρ 0.94 for Aβ42, 0.98 for t-tau, and 0.98 for p-tau. Using Gaussian mixture modeling, cohort-specific cut-points were estimated at 1092 pg/mL for Aβ42, 235 pg/mL for t-tau, and 24 pg/mL for p-tau. We found an excellent concordance of biomarker abnormality between assays of 97% for Aβ42 and 96% for both t-tau and p-tau.

Discussion

The high concordances between Elecsys and Innotest in this nonacademic, multicenter cohort support the use of Elecsys for CSF Alzheimer's disease diagnostics and allow conversion of results between methods.

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Method comparison of 137 CSF samples collected in eight nonacademic memory clinics.

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Innotest and Elecsys strongly correlated: ρ = 0.94 Aβ42; 0.98 t-tau; 0.98 p-tau.

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Concordances of biomarker abnormalities: 97% Aβ42; 96% t-tau and p-tau.

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Concordance of NIA-AA–based Alzheimer's disease profile (Aβ42 decreased and p-tau increased): 89%.

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Preanalytical protocol deviations did not show effects on biomarker correlations.

Venomous Arachnid Diagnostic Assays, Lessons from Past Attempts

Diagnostic tests for arachnid accidents remain unavailable for patients and clinicians. Together with snakes, these accidents are still a global medical concern, and are recognized as neglected tropical issues. Due to arachnid toxins’ fast mechanism of action, quick detection and quantification of venom is required to accelerate treatment decisions, rationalize therapy, and reduce costs and patient risks. This review aims to understand the current limitations for arachnid venom identification and quantification in biological samples. We benchmarked the already existing initiatives regarding test requirements (sample or biomarkers of choice), performances (time, detection limit, sensitivity and specificity) and their validation (on animal models or on samples from envenomed humans). Our analysis outlines unmet needs for improving diagnosis and consequently treatment of arachnid accidents. Hence, based on lessons from past attempts, we propose a road map for raising best practice guidelines, leading to recommendations for future progress in the development of arachnid diagnostic assays.

Exosomes as possible spread factor and potential biomarkers in Alzheimer's disease: current concepts

Increasing evidence points that important factors during development/spread of Alzheimer's disease in brain tissue are small extracellular vesicles, called exosomes. Exosomes comprise disease-related biomolecules such as the amyloid protein precursor, β-amyloid peptide and tau protein. Exosomes are hypothesized to facilitate the spread of β-amyloid peptide and tau protein from their cells of origin (e.g., neurons) to the extracellular space and to recipient cells to alter their phenotype and function. The roles of exosomes carry a rich biomolecules cargo in physiology and pathology is poorly understood. In this review, we will consider new information about the role of exosomes in Alzheimer's disease spreading and progression and underline their possible usefulness as the future diagnostic antemortem biomarkers in this devastating disorder.

Characterizing heterogeneity in the progression of Alzheimer's disease using longitudinal clinical and neuroimaging biomarkers

INTRODUCTION

Models characterizing intermediate disease stages of Alzheimer's disease (AD) are needed to inform clinical care and prognosis. Current models, however, use only a small subset of available biomarkers, capturing only coarse changes along the complete spectrum of disease progression. We propose the use of machine learning techniques and clinical, biochemical, and neuroimaging biomarkers to characterize progression to AD.

METHODS

We used a large multimodal longitudinal data set of biomarkers and demographic and genotype information from 1624 participants from the Alzheimer's Disease Neuroimaging Initiative. Using hidden Markov models, we characterized intermediate disease stages. We validated inferred disease trajectories by comparing time to first clinical AD diagnosis. We trained an L2-regularized logistic regression model to predict disease trajectory and evaluated its discriminative performance on a test set.

RESULTS

We identified 12 distinct disease states. Progression to AD occurred most often through one of two possible paths through these states. Paths differed in terms of rate of disease progression (by 5.44 years on average), amyloid and total-tau (t-tau) burden (by 10% and 69%, respectively), and hippocampal neurodegeneration (P < .001). On the test set, the predictive model achieved an area under the receiver operating characteristic curve of 0.85.

DISCUSSION

Progression to AD, in terms of biomarker trajectories, can be predicted based on participant-specific factors. Such disease staging tools could help in targeting high-risk patients for therapeutic intervention trials. As longitudinal data with richer features are collected, such models will help increase our understanding of the factors that drive the different trajectories of AD.

European Association of Nuclear Medicine and European Academy of Neurology recommendations for the use of brain 18 F-fluorodeoxyglucose positron emission tomography in neurodegenerative cognitive impairment and dementia: Delphi consensus

BACKGROUND AND PURPOSE

Recommendations for using fluorodeoxyglucose positron emission tomography (FDG-PET) to support the diagnosis of dementing neurodegenerative disorders are sparse and poorly structured.

METHODS

Twenty-one questions on diagnostic issues and on semi-automated analysis to assist visual reading were defined. Literature was reviewed to assess study design, risk of bias, inconsistency, imprecision, indirectness and effect size. Critical outcomes were sensitivity, specificity, accuracy, positive/negative predictive value, area under the receiver operating characteristic curve, and positive/negative likelihood ratio of FDG-PET in detecting the target conditions. Using the Delphi method, an expert panel voted for/against the use of FDG-PET based on published evidence and expert opinion.

RESULTS

Of the 1435 papers, 58 papers provided proper quantitative assessment of test performance. The panel agreed on recommending FDG-PET for 14 questions: diagnosing mild cognitive impairment due to Alzheimer's disease (AD), frontotemporal lobar degeneration (FTLD) or dementia with Lewy bodies (DLB); diagnosing atypical AD and pseudo-dementia; differentiating between AD and DLB, FTLD or vascular dementia, between DLB and FTLD, and between Parkinson's disease and progressive supranuclear palsy; suggesting underlying pathophysiology in corticobasal degeneration and progressive primary aphasia, and cortical dysfunction in Parkinson's disease; using semi-automated assessment to assist visual reading. Panellists did not support FDG-PET use for pre-clinical stages of neurodegenerative disorders, for amyotrophic lateral sclerosis and Huntington disease diagnoses, and for amyotrophic lateral sclerosis or Huntington-disease-related cognitive decline.

CONCLUSIONS

Despite limited formal evidence, panellists deemed FDG-PET useful in the early and differential diagnosis of the main neurodegenerative disorders, and semi-automated assessment helpful to assist visual reading. These decisions are proposed as interim recommendations.

The biomedical challenge of neurodegenerative disorders: an opportunity for cannabinoid-based therapies to improve on the poor current therapeutic outcomes

At the beginning of the 21st century, the therapeutic management of neurodegenerative disorders remains a major biomedical challenge, particularly given the worldwide ageing of the population over the past 50 years that is expected to continue in the forthcoming years. This review will focus on the promise of cannabinoid-based therapies to address this challenge. This promise is based on the broad neuroprotective profile of cannabinoids, which may cooperate to combat excitotoxicity, oxidative stress, glia-driven inflammation and protein aggregation. Such effects may be produced by the activity of cannabinoids through their canonical targets (e.g. cannabinoid receptors and endocannabinoid enzymes) and also via non-canonical elements and activities in distinct cell types critical for cell survival or neuronal replacement (e.g. neurons, glia and neural precursor cells). Ultimately, the therapeutic events driven by endocannabinoid signalling reflect the activity of an endogenous system that regulates the preservation, rescue, repair and replacement of neurons and glia. LINKED ARTICLES: This article is part of a themed section on 8^th European Workshop on Cannabinoid Research. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v176.10/issuetoc.

Clinical Research on Alzheimer's Disease: Progress and Perspectives

Alzheimer's disease (AD), the most common type of dementia, is becoming a major challenge for global health and social care. However, the current understanding of AD pathogenesis is limited, and no early diagnosis and disease-modifying therapy are currently available. During the past year, significant progress has been made in clinical research on the diagnosis, prevention, and treatment of AD. In this review, we summarize the latest achievements, including diagnostic biomarkers, polygenic hazard score, amyloid and tau PET imaging, clinical trials targeting amyloid-beta (Aβ), tau, and neurotransmitters, early intervention, and primary prevention and systemic intervention approaches, and provide novel perspectives for further efforts to understand and cure the disease.

Inflammation, neurodegeneration and protein aggregation in the retina as ocular biomarkers for Alzheimer's disease in the 3xTg-AD mouse model

Alzheimer's disease (AD) is the most common cause of dementia in the elderly. In the pathogenesis of AD a pivotal role is played by two neurotoxic proteins that aggregate and accumulate in the central nervous system: amyloid beta and hyper-phosphorylated tau. Accumulation of extracellular amyloid beta plaques and intracellular hyper-phosphorylated tau tangles, and consequent neuronal loss begins 10–15 years before any cognitive impairment. In addition to cognitive and behavioral deficits, sensorial abnormalities have been described in AD patients and in some AD transgenic mouse models. Retina can be considered a simple model of the brain, as some pathological changes and therapeutic strategies from the brain may be observed or applicable to the retina. Here we propose new retinal biomarkers that could anticipate the AD diagnosis and help the beginning and the follow-up of possible future treatments. We analyzed retinal tissue of triple-transgenic AD mouse model (3xTg-AD) for the presence of pathological hallmarks during disease progression. We found the presence of amyloid beta plaques, tau tangles, neurodegeneration, and astrogliosis in the retinal ganglion cell layer of 3xTg-AD mice, already at pre-symptomatic stage. Moreover, retinal microglia in pre-symptomatic mice showed a ramified, anti-inflammatory phenotype which, during disease progression, switches to a pro-inflammatory, less ramified one, becoming neurotoxic. We hypothesize retina as a window through which monitor AD-related neurodegeneration process.

Education-Adjusted Normality Thresholds for FDG-PET in the Diagnosis of Alzheimer Disease

Background: A corollary of the reserve hypothesis is that what is regarded as pathological cortical metabolism in patients might vary according to education. Objective: The aim of this study is to assess the incremental diagnostic value of education-adjusted over unadjusted thresholds on the diagnostic accuracy of FDG-PET as a biomarker for Alzheimer disease (AD). Methods: We compared cortical metabolism in 90 healthy controls and 181 AD patients from the Alzheimer Disease Neuroimaging Initiative (ADNI) database. The AUC of the ROC curve did not differ significantly between the whole group and the higher-education patients or the lower-education subjects. Results: The threshold of wMetaROI values providing 80% sensitivity was lower in higher-education patients and higher in the lower-education patients, compared to the standard threshold derived over the whole AD collective, without, however, significant changes in sensitivity and specificity. Conclusion: These data show that education, as a proxy of reserve, is not a major confounder in the diagnostic accuracy of FDG-PET in AD and the adoption of education-adjusted thresholds is not required in daily practice.

New PET markers for the diagnosis of dementia

PURPOSE OF REVIEW

To present the new PET markers that could become in the coming years, relevant to advanced clinical approaches to dementia diagnosis, drug trials, and treatment strategies and discuss their advantages and limitations.

RECENT FINDINGS

The most advanced new PET tracers are the markers of the amyloid plaques, the τ compounds and the tracers of the translocator protein as markers of neuroinflammation. The main advantages but also the weaknesses of each of these markers are discussed. The main pitfall remains the heterogeneity of the available results that cast doubt to a rapid introduction of these new ligands in clinical practice.

SUMMARY

With the advent of biomarkers in clinical management and findings of molecular neuroimaging studies in the evaluation of patients with suspected dementia, the impact of functional neuroimaging has increased considerably these last years and has been integrated into many clinical guidelines in the field of dementia. In addition to conventional single PET brain perfusion and dopaminergic neurotransmission, 18F-fluorodeoxyglucose (18F-FDG) PET is used in advanced diagnosis procedures. Furthermore, new tracers are being developed to quantify key neuropathological features in the brain tissue as highly specific diagnosis is crucial to comply with the global medical and public health objectives in this domain. A strategic road map for further developments, adapted from the approach to cancer biomarkers, should be proposed so as to optimize the rationale of the PET-based molecular diagnosis of Alzheimer's disease and related disorders.

Epilepsy biomarkers - Toward etiology and pathology specificity

A biomarker is a characteristic that is measured as an indicator of normal biologic processes, pathogenic processes, or responses to an exposure or intervention, including therapeutic interventions. Biomarker modalities include molecular, histologic, radiographic, or physiologic characteristics. In 2015, the FDA-NIH Joint Leadership Council developed the BEST Resource (Biomarkers, EndpointS, and other Tools) to improve the understanding and use of biomarker terminology in biomedical research, clinical practice, and medical product development. The BEST biomarker categories include: (a) susceptibility/risk biomarkers, (b) diagnostic biomarkers, (c) monitoring biomarkers, (d) prognostic biomarkers, (e) predictive biomarkers, (f) pharmacodynamic/response biomarkers, and (g) safety biomarkers. Here we review 30 epilepsy biomarker studies that have identified (a) diagnostic biomarkers for epilepsy, epileptogenesis, epileptogenicity, drug-refractoriness, and status epilepticus - some of the epileptogenesis and epileptogenicity biomarkers can also be considered prognostic biomarkers for the development of epilepsy in subjects with a given brain insult, (b) predictive biomarkers for epilepsy surgery outcome, and (c) a response biomarker for therapy outcome. The biomarker modalities include plasma/serum/exosomal and cerebrospinal fluid molecular biomarkers, brain tissue molecular biomarkers, imaging biomarkers, electrophysiologic biomarkers, and behavioral/cognitive biomarkers. Both single and combinatory biomarkers have been described. Most of the reviewed biomarkers have an area under the curve >0.800 in receiver operating characteristics analysis, suggesting high sensitivity and specificity. As discussed in this review, we are in the early phase of the learning curve in epilepsy biomarker discovery. Many of the seven biomarker categories lack epilepsy-related biomarkers. There is a need for epilepsy biomarker discovery using proper, statistically powered study designs with validation cohorts, and the development and use of novel analytical methods. A strategic roadmap to discuss the research priorities in epilepsy biomarker discovery, regulatory issues, and optimization of the use of resources, similar to those devised in the cancer and Alzheimer's disease research areas, is also needed.

Diagnostic utility of FDG-PET in the differential diagnosis between different forms of primary progressive aphasia

Purpose

A joint effort of the European Association of Nuclear Medicine (EANM) and the European Academy of Neurology (EAN) aims at clinical guidance for the use of FDG-PET in neurodegenerative diseases. This paper addresses the diagnostic utility of FDG-PET over clinical/neuropsychological assessment in the differentiation of the three forms of primary progressive aphasia (PPA).

Methods

Seven panelists were appointed by the EANM and EAN and a literature search was performed by using harmonized PICO (Population, Intervention, Comparison, Outcome) question keywords. The studies were screened for eligibility, and data extracted to assess their methodological quality. Critical outcomes were accuracy indices in differentiating different PPA clinical forms. Subsequently Delphi rounds were held with the extracted data and quality assessment to reach a consensus based on both literature and expert opinion.

Results

Critical outcomes for this PICO were available in four of the examined papers. The level of formal evidence supporting clinical utility of FDG-PET in differentiating among PPA variants was considered as poor. However, the consensual recommendation was defined on Delphi round I, with six out of seven panelists supporting clinical use.

Conclusions

Quantitative evidence demonstrating utility or lack thereof is still missing. Panelists decided consistently to provide interim support for clinical use based on the fact that a typical atrophy or metabolic pattern is needed for PPA according to the diagnostic criteria, and the synaptic failure detected by FDG-PET is an earlier phenomenon than atrophy. Also, a normal FDG-PET points to a non-neurodegenerative cause.

The Role of Genetics in Advancing Precision Medicine for Alzheimer's Disease-A Narrative Review

Alzheimer's disease (AD) is the most common type of dementia, which has a substantial genetic component. AD affects predominantly older people. Accordingly, the prevalence of dementia has been rising as the population ages. To date, there are no effective interventions that can cure or halt the progression of AD. The only available treatments are the management of certain symptoms and consequences of dementia. The current state-of-the-art medical care for AD comprises three simple principles: prevent the preventable, achieve early diagnosis, and manage the manageable symptoms. This review provides a summary of the current state of knowledge of risk factors for AD, biological diagnostic testing, and prospects for treatment. Special emphasis is given to recent advances in genetics of AD and the way genomic data may support prevention, early intervention, and development of effective pharmacological treatments. Mutations in the APP, PSEN1, and PSEN2 genes cause early onset Alzheimer's disease (EOAD) that follows a Mendelian inheritance pattern. For late onset Alzheimer's disease (LOAD), APOE4 was identified as a major risk allele more than two decades ago. Population-based genome-wide association studies of late onset AD have now additionally identified common variants at roughly 30 genetic loci. Furthermore, rare variants (allele frequency <1%) that influence the risk for LOAD have been identified in several genes. These genetic advances have broadened our insights into the biological underpinnings of AD. Moreover, the known genetic risk variants could be used to identify presymptomatic individuals at risk for AD and support diagnostic assessment of symptomatic subjects. Genetic knowledge may also facilitate precision medicine. The goal of precision medicine is to use biological knowledge and other health information to predict individual disease risk, understand disease etiology, identify disease subcategories, improve diagnosis, and provide personalized treatment strategies. We discuss the potential role of genetics in advancing precision medicine for AD along with its ethical challenges. We outline strategies to implement genomics into translational clinical research that will not only improve accuracy of dementia diagnosis, thus enabling more personalized treatment strategies, but may also speed up the discovery of novel drugs and interventions.

Challenges associated with biomarker-based classification systems for Alzheimer's disease

INTRODUCTION

We aimed to evaluate the consistency of the A/T/N classification system.

METHODS

We included healthy controls, mild cognitive impairment, and dementia patients from Alzheimer's disease Neuroimaging Initiative. We assessed subject classification consistency with different biomarker combinations and the agreement and correlation between biomarkers.

RESULTS

Subject classification discordance ranged from 12.2% to 44.5% in the whole sample; 17.3%-46.4% in healthy controls; 11.9%-46.5% in mild cognitive impairment, and 1%-35.7% in dementia patients. Amyloid, but not neurodegeneration biomarkers, showed good agreement both in the whole sample and in the clinical subgroups. Amyloid biomarkers were correlated in the whole sample, but not along the Alzheimer's disease continuum (as defined by a positive amyloid positron emission tomography). Neurodegeneration biomarkers were poorly correlated both in the whole sample and along the Alzheimer's disease continuum. The relationship between biomarkers was stage-dependent.

DISCUSSION

Our findings suggest that the current A/T/N classification system does not achieve the required consistency to be used in the clinical setting.

Biomarkers study in atypical dementia: proof of a diagnostic work-up

BACKGROUND

An early differentiation between Alzheimer's Disease (AD) and other dementias is crucial for an adequate patients' management, albeit it may result difficult for the occurrence of "atypical presentations." Current diagnostic criteria recognize the importance of biomarkers for AD diagnosis, but still an optimal diagnostic work-up isn't available.

OBJECTIVE

Evaluate the utility and reproducibility of biomarkers and propose an "optimal" diagnostic work-up in atypical dementia.

METHODS

(1) a retrospective selection of "atypical dementia cases"; (2) a repetition of diagnostic assessment by two neurologists following two different diagnostic work-ups, each consisting of multiple steps; (3) a comparison between diagnostic accuracy and confidence reached at each step by both neurologists and evaluation of the inter-rater agreement.

RESULTS

In AD, regardless of the undertaken diagnostic work-up, a significant gain in accuracy was reached by both neurologists after the second step, whereas in frontotemporal dementia (FTD), adding subsequent steps was not always sufficient to increase significantly the baseline accuracy. A relevant increment in diagnostic confidence was detectable after studying pathophysiological markers in AD, and after assessing brain metabolism in FTD. The inter-rater agreement was higher at the second step for the AD group when the pathophysiological markers were available and for the FTD group when the results of FDG-PET were accessible.

CONCLUSIONS

In atypical cases of dementia, biomarkers significantly raise diagnostic accuracy, confidence, and agreement. This study introduces a proof of diagnostic work-up that combines imaging and CSF biomarkers and suggests distinct ways to proceed on the basis of a greater diagnostic likelihood.

Progress and Challenges in the Diagnosis of Dementia: A Critical Review

Longer life expectancies have led to an increased number of neurodegenerative disease cases globally. Accurate diagnosis of this devastating disorder is of crucial importance but is still feasible only by a brain biopsy after death. An enormous amount of attention and research has been in place over the years toward the better understanding of the mechanisms, as well as the early diagnosis, of neurodegeneration. However, numerous studies have been contradictory from time to time, while new diagnostic methods are constantly developed in a tireless effort to tackle the disease. Nonetheless, there is not yet a conclusive report covering a broader range of techniques for the diagnosis of different types of dementia. In this paper, we critically review current knowledge on the different hypotheses about the pathogenesis of distinct types of dementia, as well as risk factors and current diagnostic approaches in a clinical setting, including neuroimaging, cerebrospinal (CSF), and blood tests. Encouraging research results for the diagnosis and investigation of neurodegenerative disorders are also reported. Particular attention is given to the field of spectroscopy as an emerging tool to detect dementias, follow-up patients, and potentially monitor the patients' response to a therapeutic approach. Spectroscopic techniques, such as infrared and Raman spectroscopy, have facilitated numerous disease-related studies, including neurodegenerative disorders, and are currently undergoing trials for clinical implementation. This review constitutes a comprehensive report with an in-depth focus on promising imaging, molecular biomarker and spectroscopic tests in the field of dementive diseases.

A sensitive detection assay based on signal amplification technology for Alzheimer's disease's early biomarker in exosome

Alzheimer's disease (AD) considered as the third health "killer" has seriously threatened the health of the elderly. However, the modern diagnostic strategies of AD present several disadvantages: the low accuracy and specificity resulting in some false-negative diagnoses, and the poor sensitivity leading to a delayed treatment. In view of this situation, a enzyme-free and target-triggered signal amplification strategy, based on graphene oxide (GO) and entropy-driven strand displacement reaction (ESDR) principle, was proposed. In this strategy, when the hairpin structure probes (H)specially binds with beta-amyloid-(1-42) oligomers (Aβ42 oligomers), it's structure will be opened, causing the bases complementary to FAM-labeled replacement probes R (R1 and R2) exposed. At this time, R1 and R2 will hybridize with H, resulting in the bound Aβ42 oligomers released. The released Aβ42 oligomers would participate in the next cycle reaction, making the signal amplified. As a quencher, GO could absorb the free single-stranded DNA R1 and R2 and quench their fluorescence; however, the DNA duplex still exists free and keeps its signal-on. Through the detection of Aβ42 oligomers in exosomes, this ultrasensitive detection method with the advantages of low limit of detection (LOD, 20 pM), great accuracy, excellent precision and convenience provides an excellent prospect for AD's early diagnosis.

White paper by the Society for CSF Analysis and Clinical Neurochemistry: Overcoming barriers in biomarker development and clinical translation

Body fluid biomarkers have great potential for different clinical purposes, including diagnosis, prognosis, patient stratification and treatment effect monitoring. This is exemplified by current use of several excellent biomarkers, such as the Alzheimer’s disease cerebrospinal fluid (CSF) biomarkers, anti-neuromyelitis optica antibodies and blood neurofilament light. We still, however, have a strong need for additional biomarkers and several gaps in their development and implementation should be filled. Examples of such gaps are i) limited knowledge of the causes of neurological diseases, and thus hypotheses about the best biomarkers to detect subclinical stages of these diseases; ii) the limited success translating discoveries obtained by e.g. initial mass spectrometry proteomic low-throughput studies into immunoassays for widespread clinical implementation; iii) lack of interaction among all stakeholders to optimise and adapt study designs throughout the biomarker development process to medical needs, which may change during the long period needed for biomarker development.

The Society for CSF Analysis and Clinical Neurochemistry (established in 2015) has been founded as a concerted follow-up of large standardisation projects, including BIOMARKAPD and SOPHIA, and the BioMS-consortium.

The main aims of the CSF society are to exchange high level international scientific experience, to facilitate the incorporation of CSF diagnostics into clinical practice and to give advice on inclusion of CSF analysis into clinical guidelines. The society has a broad scope, as its vision is that the gaps in development and implementation of biomarkers are shared among almost all neurological diseases and thus they can benefit from the activities of the society.

Development and validation of a novel dementia of Alzheimer's type (DAT) score based on metabolism FDG-PET imaging

Fluorodeoxyglucose positron emission tomography (FDG-PET) imaging based 3D topographic brain glucose metabolism patterns from normal controls (NC) and individuals with dementia of Alzheimer's type (DAT) are used to train a novel multi-scale ensemble classification model. This ensemble model outputs a FDG-PET DAT score (FPDS) between 0 and 1 denoting the probability of a subject to be clinically diagnosed with DAT based on their metabolism profile. A novel 7 group image stratification scheme is devised that groups images not only based on their associated clinical diagnosis but also on past and future trajectories of the clinical diagnoses, yielding a more continuous representation of the different stages of DAT spectrum that mimics a real-world clinical setting. The potential for using FPDS as a DAT biomarker was validated on a large number of FDG-PET images (N=2984) obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database taken across the proposed stratification, and a good classification AUC (area under the curve) of 0.78 was achieved in distinguishing between images belonging to subjects on a DAT trajectory and those images taken from subjects not progressing to a DAT diagnosis. Further, the FPDS biomarker achieved state-of-the-art performance on the mild cognitive impairment (MCI) to DAT conversion prediction task with an AUC of 0.81, 0.80, 0.77 for the 2, 3, 5 years to conversion windows respectively.

Disposable Morpho menelaus Based Flexible Microfluidic and Electronic Sensor for the Diagnosis of Neurodegenerative Disease

Rapid early disease prevention or precise diagnosis is almost impossible in low-resource settings. Natural ordered structures in nature have great potential for the development of ultrasensitive biosensors. Here, motivated by the unique structures and extraordinary functionalities of ordered structures in nature, a biosensor based on butterfly wings is presented. In this study, a flexible Morpho menelaus (M. menelaus) based wearable sensor is integrated with a microfluidic system and electronic networks to facilitate the diagnosis of neurodegenerative disease (ND). In the microfluidic section, the structural characteristics of the M. menelaus wings up layer are combined with SiO₂ nanoparticles to form a heterostructure. The fluorescent enhancement property of the heterostructure is used to increase the fluorescent intensity for multiplex detection of two proteins: IgG and AD7c-NTP. For the electronic section, conductive ink is blade-coated on the under layer of wings for measuring resistance change rate to obtain the frequency of static tremors of ND patients. The disposable M. menelaus based flexible microfluidic and electronic sensor enables biochemical-physiological hybrid monitoring of ND. The sensor is also amenable to a variety of applications, such as comprehensive personal healthcare and human-machine interaction.

Use of Peptides for the Management of Alzheimer's Disease: Diagnosis and Inhibition

Alzheimer's disease (AD) is a form of dementia and the most common progressive neurodegenerative disease (ND). The targeting of amyloid-beta (Aβ) aggregation is one of the most widely used strategies to manage AD, and efforts are being made globally to develop peptide-based compounds for the early diagnosis and treatment of AD. Here, we briefly discuss the use of peptide-based compounds for the early diagnosis and treatment of AD and the use of peptide-based inhibitors targeting various Aβ aggregation checkpoints. In addition, we briefly discuss recent applications of peptide-based inhibitors against various AD targets including amyloid beta, β-site amyloid precursor protein cleaving enzyme 1 (BACE1), Glyceraldehyde-3-phosphate dehydrogenase (GAPDH), tyrosine phosphatase (TP) and potassium channel KV1.3.

Automation on an Open-Access Platform of Alzheimer's Disease Biomarker Immunoassays

The lack of (inter-)laboratory standardization has hampered the application of universal cutoff values for Alzheimer's disease (AD) cerebrospinal fluid (CSF) biomarkers and their transfer to general clinical practice. The automation of the AD biomarker immunoassays is suggested to generate more robust results than using manual testing. Open-access platforms will facilitate the integration of automation for novel biomarkers, allowing the introduction of the protein profiling concept. A feasibility study was performed on an automated open-access platform of the commercial immunoassays for the 42-amino-acid isoform of amyloid-β (Aβ_1-42), Aβ_1-40, and total tau in CSF. Automated Aβ_1-42, Aβ_1-40, and tau immunoassays were performed within predefined acceptance criteria for bias and imprecision. Similar accuracy was obtained for ready-to-use calibrators as for reconstituted lyophilized kit calibrators. When compared with the addition of a standard curve in each test run, the use of a master calibrator curve, determined before and applied to each batch analysis as the standard curve, yielded an acceptable overall bias of -2.6% and -0.9% for Aβ_1-42 and Aβ_1-40, respectively, with an imprecision profile of 6.2% and 8.4%, respectively. Our findings show that transfer of commercial manual immunoassays to fully automated open-access platforms is feasible, as it performs according to universal acceptance criteria.

Is It Possible to Predict the Future in First-Episode Psychosis?

The outcome of first-episode psychosis (FEP) is highly variable, ranging from early sustained recovery to antipsychotic treatment resistance from the onset of illness. For clinicians, a possibility to predict patient outcomes would be highly valuable for the selection of antipsychotic treatment and in tailoring psychosocial treatments and psychoeducation. This selective review summarizes current knowledge of prognostic markers in FEP. We sought potential outcome predictors from clinical and sociodemographic factors, cognition, brain imaging, genetics, and blood-based biomarkers, and we considered different outcomes, like remission, recovery, physical comorbidities, and suicide risk. Based on the review, it is currently possible to predict the future for FEP patients to some extent. Some clinical features-like the longer duration of untreated psychosis (DUP), poor premorbid adjustment, the insidious mode of onset, the greater severity of negative symptoms, comorbid substance use disorders (SUDs), a history of suicide attempts and suicidal ideation and having non-affective psychosis-are associated with a worse outcome. Of the social and demographic factors, male gender, social disadvantage, neighborhood deprivation, dysfunctional family environment, and ethnicity may be relevant. Treatment non-adherence is a substantial risk factor for relapse, but a small minority of patients with acute onset of FEP and early remission may benefit from antipsychotic discontinuation. Cognitive functioning is associated with functional outcomes. Brain imaging currently has limited utility as an outcome predictor, but this may change with methodological advancements. Polygenic risk scores (PRSs) might be useful as one component of a predictive tool, and pharmacogenetic testing is already available and valuable for patients who have problems in treatment response or with side effects. Most blood-based biomarkers need further validation. None of the currently available predictive markers has adequate sensitivity or specificity used alone. However, personalized treatment of FEP will need predictive tools. We discuss some methodologies, such as machine learning (ML), and tools that could lead to the improved prediction and clinical utility of different prognostic markers in FEP. Combination of different markers in ML models with a user friendly interface, or novel findings from e.g., molecular genetics or neuroimaging, may result in computer-assisted clinical applications in the near future.

Advances in Resting State Neuroimaging of Mild Cognitive Impairment

The rapidly increasing number of patients with Alzheimer's disease (AD) worldwide has become a major public concern. Mild cognitive impairment (MCI), characterized with accelerated memory decline than normal aging, is a stage between cognitively unimpaired and dementia. Identification of MCI in the Alzheimer's continuum from normal aging, is important for early diagnosis and improved intervention of AD. The imaging technique has been extensively used for diagnose and understanding the mechanisms of MCI. Firstly, we review the recent progresses in the research framework of MCI depending on the clinical and/or biomarker findings. Secondly, we cover studies that use of rs-fMRI (resting state functional magnetic resonance imaging) for the brain activities and functional connectivity between normal aging and MCI. Other methodologies and multi-modal studies for investigating the mechanism and early diagnosis of MCI are also discussed. Finally, we discuss how genetic and environmental factors such as education could interact with in MCI. Overall, MCI is a heterogeneous state and employing resting state neuroimaging with other AD biomarker approaches will be able to target in the more precise population and AD-related pathology process.

Amsterdam Dementia Cohort: Performing Research to Optimize Care

The Alzheimer center of the VU University Medical Center opened in 2000 and was initiated to combine both patient care and research. Together, to date, all patients forming the Amsterdam Dementia Cohort number almost 6,000 individuals. In this cohort profile, we provide an overview of the results produced based on the Amsterdam Dementia Cohort. We describe the main results over the years in each of these research lines: 1) early diagnosis, 2) heterogeneity, and 3) vascular factors. Among the most important research efforts that have also impacted patients' lives and/or the research field, we count the development of novel, easy to use diagnostic measures such as visual rating scales for MRI and the Amsterdam IADL Questionnaire, insight in different subgroups of AD, and findings on incidence and clinical sequelae of microbleeds. Finally, we describe in the outlook how our research endeavors have improved the lives of our patients.

Neuro-Immuno-Gene- and Genome-Editing-Therapy for Alzheimer's Disease: Are We There Yet?

Alzheimer's disease (AD) is a highly complex neurodegenerative disorder and the current treatment strategies are largely ineffective thereby leading to irreversible and progressive cognitive decline in AD patients. AD continues to defy successful treatment despite significant advancements in the field of molecular medicine. Repeatedly, early promising preclinical and clinical results have catapulted into devastating setbacks leading to multi-billion dollar losses not only to the top pharmaceutical companies but also to the AD patients and their families. Thus, it is very timely to review the progress in the emerging fields of gene therapy and stem cell-based precision medicine. Here, we have made sincere efforts to feature the ongoing progress especially in the field of AD gene therapy and stem cell-based regenerative medicine. Further, we also provide highlights in elucidating the molecular mechanisms underlying AD pathogenesis and describe novel AD therapeutic targets and strategies for the new drug discovery. We hope that the quantum leap in the scientific advancements and improved funding will bolster novel concepts that will propel the momentum toward a trajectory leading to a robust AD patient-specific next generation precision medicine with improved cognitive function and excellent life quality.

The Italian Version of the Five-Word Test: A Simple Diagnostic Test for Dementia due to Alzheimer's Disease in Routine Clinical Practice

Background

The five-word test (FWT) is a neuropsychological tool (derived from the Grober and Buschke paradigm), measuring hippocampal memory trace consolidation. The study aimed to validate the test for the Italian language and to verify its ability to discriminate patients affected by mild cognitive impairment and dementia due to Alzheimer's disease from healthy matches.

Methods

217 subjects (127 controls, 47 MCI due to AD, and 43 AD) underwent neuropsychological evaluation. The Spearman rank coefficient (ρ) was used to assess the correlation between immediate (IRS), delayed (DRS), and total score (TRS) of the FWT and correspondent matches of a specific short story test, while receiving operator characteristic (ROC) curves were built to investigate the diagnostic accuracy of both.

Results

Correlation between almost all the scores was significant in all the diagnostic subgroups; the ROC curves of the two tests were not statistically different. A TRS of the FWT with a cut-off of ≤9/10 could accurately discriminate AD patients (sensitivity: 97%, specificity: 94%) and MCI due to AD (sensitivity: 76%, specificity: 68%) from control matches.

Conclusion

FWT is a simple and valid test of hippocampal memory which appears recommendable in routine clinical practice.

Alzheimer's disease and cigarette smoke components: effects of nicotine, PAHs, and Cd(II), Cr(III), Pb(II), Pb(IV) ions on amyloid-β peptide aggregation

Cigarette smoking is a significant risk factor for Alzheimer's disease (AD), which is associated with extracellular brain deposits of amyloid plaques containing aggregated amyloid-β (Aβ) peptides. Aβ aggregation occurs via multiple pathways that can be influenced by various compounds. Here, we used AFM imaging and NMR, fluorescence, and mass spectrometry to monitor in vitro how Aβ aggregation is affected by the cigarette-related compounds nicotine, polycyclic aromatic hydrocarbons (PAHs) with one to five aromatic rings, and the metal ions Cd(II), Cr(III), Pb(II), and Pb(IV). All PAHs and metal ions modulated the Aβ aggregation process. Cd(II), Cr(III), and Pb(II) ions displayed general electrostatic interactions with Aβ, whereas Pb(IV) ions showed specific transient binding coordination to the N-terminal Aβ segment. Thus, Pb(IV) ions are especially prone to interact with Aβ and affect its aggregation. While Pb(IV) ions affected mainly Aβ dimer and trimer formation, hydrophobic toluene mainly affected formation of larger aggregates such as tetramers. The uncharged and hydrophilic nicotine molecule showed no direct interactions with Aβ, nor did it affect Aβ aggregation. Our Aβ interaction results suggest a molecular rationale for the higher AD prevalence among smokers, and indicate that certain forms of lead in particular may constitute an environmental risk factor for AD.

Brain MRI Pattern Recognition Translated to Clinical Scenarios

We explored the performance of structure-based computational analysis in four neurodegenerative conditions [Ataxia (AT, n = 16), Huntington's Disease (HD, n = 52), Alzheimer's Disease (AD, n = 66), and Primary Progressive Aphasia (PPA, n = 50)], all characterized by brain atrophy. The independent variables were the volumes of 283 anatomical areas, derived from automated segmentation of T1-high resolution brain MRIs. The segmentation based volumetric quantification reduces image dimensionality from the voxel level [on the order of O(10⁶)] to anatomical structures [O(10²)] for subsequent statistical analysis. We evaluated the effectiveness of this approach on extracting anatomical features, already described by human experience and a priori biological knowledge, in specific scenarios: (1) when pathologies were relatively homogeneous, with evident image alterations (e.g., AT); (2) when the time course was highly correlated with the anatomical changes (e.g., HD), an analogy for prediction; (3) when the pathology embraced heterogeneous phenotypes (e.g., AD) so the classification was less efficient but, in compensation, anatomical and clinical information were less redundant; and (4) when the entity was composed of multiple subgroups that had some degree of anatomical representation (e.g., PPA), showing the potential of this method for the clustering of more homogeneous phenotypes that can be of clinical importance. Using the structure-based quantification and simple linear classifiers (partial least square), we achieve 87.5 and 73% of accuracy on differentiating AT and pre-symptomatic HD patents from controls, respectively. More importantly, the anatomical features automatically revealed by the classifiers agreed with the patterns previously described on these pathologies. The accuracy was lower (68%) on differentiating AD from controls, as AD does not display a clear anatomical phenotype. On the other hand, the method identified PPA clinical phenotypes and their respective anatomical signatures. Although most of the data are presented here as proof of concept in simulated clinical scenarios, structure-based analysis was potentially effective in characterizing phenotypes, retrieving relevant anatomical features, predicting prognosis, and aiding diagnosis, with the advantage of being easily translatable to clinics and understandable biologically.

Tau protein aggregation in Alzheimer's disease: An attractive target for the development of novel therapeutic agents

Alzheimer's Disease (AD) is a neurodegenerative brain disorder in which many biological dysfunctions are involved. Among them, two main types of lesions were discovered and widely studied: the amyloid plaques and the neurofibrillary tangles (NFTs). These two lesions are caused by the dysfunction and the accumulation of two proteins which are, respectively, the beta-amyloid peptide and the tau protein. The process that leads these two proteins to aggregate is complex and is the subject of current studies. After a brief description of the aggregation mechanisms, we will provide an overview of new therapeutic agents targeting the different dysfunctions and toxic species found during aggregation.