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

Facial Recognition Task for the Classification of Mild Cognitive Impairment with Ensemble Sparse Classifier

Conventional methods for detecting mild cognitive impairment (MCI) require cognitive exams and follow-up neuroimaging, which can be time-consuming and expensive. A great need exists for objective and cost-effective biomarkers for the early detection of MCI. This study uses a sequential imaging oddball paradigm to determine if familiar, unfamiliar, or inverted faces are effective visual stimuli for the early detection of MCI. Unlike the traditional approach where the amplitude and latency of certain deflection points of event-related potentials (ERPs) are selected as electrophysiological biomarkers (or features) of MCI, we used the entire ERPs as potential biomarkers and relied on an advanced machine-learning technique, i.e. an ensemble of sparse classifier (ESC), to choose the set of features to best discriminate MCI from healthy controls. Five MCI subjects and eight age-matched controls were given the MoCA exam before EEG recordings in a sensory-deprived room. Traditional time-domain comparisons of averaged ERPs between the two groups did not yield any statistical significance. However, ESC was able to discriminate MCI from controls with 95% classification accuracy based on the averaged ERPs elicited by familiar faces. By adopting advanced machine-learning techniques such as ESC, it may be possible to accurately diagnose MCI based on the ERPs that are specifically elicited by familiar faces.

Biological markers of aging and mental health: A seed and soil model of neurocognitive disorders

As the population of the world gets older, cognitive and mental health decline in older adults has become one of the most pressing scientific problems of the 21st century. It has been well-established that multiple pathways exist that can lead to future cognitive decline and the development of age-related mental health conditions. Making matters more complicated is the fact that aging is not a homogenous or simple process; rather, each person brings a diverse set of circumstances to the table, leading to differences in biologically aging. Research is needed to identify how the intersection of environmental and biological factors may protect older adults or put them at future risk for cognitive decline and the development of age-related mental health conditions. Furthermore, each potential factor may impact different domains of cognition and mental health or may impact individuals differentially based on genetic, environmental, social, emotional, and behavioral factors. Neuroimaging is one key tool that can be used to discover and assess markers of cognitive and mental health. The goal of this special issue is to expand the science on how biological markers can aid the understanding of aging and mental health through the lens of the individual from an international perspective, representing Germany, Taiwan, Thailand, the United Kingdom, and the United States. Common themes from these articles are used to support the new Seed and Soil Model of Neurocognitive Disorders.

Studying the Manifold Structure of Alzheimer's Disease: A Deep Learning Approach Using Convolutional Autoencoders

Many classical machine learning techniques have been used to explore Alzheimer's disease (AD), evolving from image decomposition techniques such as principal component analysis toward higher complexity, non-linear decomposition algorithms. With the arrival of the deep learning paradigm, it has become possible to extract high-level abstract features directly from MRI images that internally describe the distribution of data in low-dimensional manifolds. In this work, we try a new exploratory data analysis of AD based on deep convolutional autoencoders. We aim at finding links between cognitive symptoms and the underlying neurodegeneration process by fusing the information of neuropsychological test outcomes, diagnoses, and other clinical data with the imaging features extracted solely via a data-driven decomposition of MRI. The distribution of the extracted features in different combinations is then analyzed and visualized using regression and classification analysis, and the influence of each coordinate of the autoencoder manifold over the brain is estimated. The imaging-derived markers could then predict clinical variables with correlations above 0.6 in the case of neuropsychological evaluation variables such as the MMSE or the ADAS11 scores, achieving a classification accuracy over 80% for the diagnosis of AD.

Cerebrospinal Fluid Biomarkers in Alzheimer's Disease: An Invaluable Tool for Clinical Diagnosis and Trial Enrichment

Alzheimer's disease (AD) is the most common neurodegenerative disorder, affecting around 35 million people worldwide. Cerebrospinal fluid (CSF) biomarkers entered the diagnostic criteria as support for early diagnosis. The classical biochemical signature of AD includes total tau (T-tau), phosphorylated tau (P-tau), and the 42 amino acid peptide (Aβ42) of amyloid-β. Recent observations suggest that the use of CSF Aβ42:Aβ40 ratio rather than CSF Aβ42 alone could contribute to reduce inter-laboratory variation in Aβ values and increasing diagnostic performance of the CSF AD biomarkers in routine practice. However, research efforts aimed at enriching the CSF biomarker panel are ongoing. The CSF AD signature is also crucial for the design of clinical trials for AD, since it best guarantees AD pathology as the cause of cognitive impairment. Accordingly, CSF biomarkers have been now reported in the inclusion criteria of Phase I, Phase II, and Phase III clinical trials as enrichment strategy. So far, one of the most important reasons for the failure of AD clinical trials was the inclusion of participants with unlikely AD pathology. In order to implement the use of CSF biomarkers in AD routine diagnostic work-up and as accepted strategy for enriching trial populations, inter-laboratory variability should be minimized. Increasing efforts should also be devoted to promote data sharing practices, encouraging individual participant data meta-analyses.

α-synuclein in the pathophysiology of Alzheimer's disease

The Alzheimer’s disease (AD) afflicted brain is neuropathologically defined by extracellular amyloid-β (Aβ) plaques and intraneuronal neurofibrillary tangles composed of hyperphosphorylated tau protein. However, accumulating evidence suggests that the presynaptic protein α-synuclein (αSyn), mainly associated with synucleinopathies like Parkinson’s disease (PD), dementia with Lewy bodies (DLB) and multiple system atrophy (MSA), is involved in the pathophysiology of AD. Lewy-related pathology (LRP), primarily comprised of αSyn, is present in a majority of autopsied AD brains, and higher levels of αSyn in the cerebrospinal fluid (CSF) of patients with mild cognitive impairment (MCI) and AD have been linked to cognitive decline. Recent studies also suggest that the asymptomatic accumulation of Aβ plaques is associated with higher CSF αSyn levels in subjects at risk of sporadic AD and in individuals carrying autosomal dominant AD mutations. Experimental evidence has further linked αSyn mainly to tau hyperphosphorylation, but also to the pathological actions of Aβ and the APOEε4 allele, the latter being a major genetic risk factor for both AD and DLB. In this review, we provide a summary of the current evidence proposing an involvement of αSyn either as an active or passive player in the pathophysiological ensemble of AD, and furthermore describe in detail the current knowledge of αSyn structure and inferred function.

Dementia imaging in clinical practice: a European-wide survey of 193 centres and conclusions by the ESNR working group

PURPOSE

Through a European-wide survey, we assessed the current clinical practice of imaging in the primary evaluation of dementia, with respect to standardised imaging, evaluation and reporting.

METHODS

An online questionnaire was emailed to all European Society of Neuroradiology (ESNR) members (n = 1662) and non-members who had expressed their interest in ESNR activities in the past (n = 6400). The questionnaire featured 42 individual items, divided into multiple choice, single best choice and free text answers. Information was gathered on the context of the practices, available and preferred imaging modalities, applied imaging protocols and standards for interpretation, reporting and communication.

RESULTS

A total of 193 unique (non-duplicate) entries from the European academic and non-academic institutions were received from a total of 28 countries. Of these, 75% were neuroradiologists, 12% general radiologists and 11% (neuro) radiologists in training. Of responding centres, 38% performed more than five scans/week for suspected dementia. MRI was primarily used in 72% of centres. Over 90% of centres acquired a combination of T2w, FLAIR, T1w, DWI and T2*w sequences. Visual rating scales were used in 75% of centres, most often the Fazekas and medial temporal atrophy scale; 32% of respondents lacked full confidence in their use. Only 23% of centres performed volumetric analysis. A minority of centres (28%) used structured reports.

CONCLUSIONS

Current practice in dementia imaging is fairly homogeneous across Europe, in terms of image acquisition and image interpretation. Hurdles identified include training on the use of visual rating scales, implementation of volumetric assessment and structured reporting.

Effects of Second Language Learning on the Plastic Aging Brain: Functional Connectivity, Cognitive Decline, and Reorganization

Learning a new language requires the use of extensive neural networks and can represent a powerful tool to reorganize brain neuroplasticity. In this study, we analyze how a 4 months long second language learning program (16, 2 h sessions) can lead to functional changes in the brain of healthy elderly individuals. A large number of studies point out a decline of brain-skills with age; here it is analyzed how cognition together with functional brain organization can be improved later in life. Twenty-six older adults (59–79 years old) were enrolled in the present study. A complete neuropsychological examination was administered before and after the intervention to measure global cognition levels, short- and long-term memory, attention, language access and executive functions. At the end of the program, in the intervention group, the results showed a significant improvement in global cognition together with an increased functional connectivity in the right inferior frontal gyrus (rIFG), right superior frontal gyrus (rSFG) and left superior parietal lobule (lSPL). These findings can be added to the current neurobiological breakthroughs of reshaping brain networks with a short language learning practice in healthy elderly subjects. Therefore, learning a foreign-language may represent a potentially helpful cognitive intervention for promoting healthy aging.

Altered microRNAs related to synaptic function as potential plasma biomarkers for Alzheimer's disease

BACKGROUND

Several evidences suggest that failure of synaptic function occurs at preclinical stages of Alzheimer's disease (AD) preceding neuronal loss and the classical AD pathological hallmarks. Nowadays, there is an urgent need to identify reliable biomarkers that could be obtained with non-invasive methods to improve AD diagnosis at early stages. Here, we have examined plasma levels of a group of miRNAs related to synaptic proteins in a cohort composed of cognitive healthy controls (HC), mild cognitive impairment (MCI) and AD subjects.

METHODS

Plasma and brain levels of miRNAs were analysed in two different cohorts including 38 HC, 26 MCI, 56 AD dementia patients and 27 frontotemporal dementia (FTD) patients. D'Agostino and Pearson and Shapiro-Wilk tests were used to evaluate data normality. miRNA levels between groups were compared using a two-sided nonparametric Mann-Whitney test and sensitivity and specificity was determined by receiver operating characteristic curve analysis.

RESULTS

Significant upregulation of miR-92a-3p, miR-181c-5p and miR-210-3p was found in the plasma of both MCI and AD subjects. MCI patients that progress to AD showed higher plasma levels of these miRNAs. By contrast, no changes in miR-92a-3p, miR-181c-5p or miR-210-3p levels were observed in plasma obtained from a cohort of FTD.

CONCLUSION

Our study shows that plasma miR-92a-3p, miR-181c-5p and miR-210-3p constitute a specific molecular signature potentially useful as a potential biomarker for AD.

A Brief History of Simulation Neuroscience

Our knowledge of the brain has evolved over millennia in philosophical, experimental and theoretical phases. We suggest that the next phase is simulation neuroscience. The main drivers of simulation neuroscience are big data generated at multiple levels of brain organization and the need to integrate these data to trace the causal chain of interactions within and across all these levels. Simulation neuroscience is currently the only methodology for systematically approaching the multiscale brain. In this review, we attempt to reconstruct the deep historical paths leading to simulation neuroscience, from the first observations of the nerve cell to modern efforts to digitally reconstruct and simulate the brain. Neuroscience began with the identification of the neuron as the fundamental unit of brain structure and function and has evolved towards understanding the role of each cell type in the brain, how brain cells are connected to each other, and how the seemingly infinite networks they form give rise to the vast diversity of brain functions. Neuronal mapping is evolving from subjective descriptions of cell types towards objective classes, subclasses and types. Connectivity mapping is evolving from loose topographic maps between brain regions towards dense anatomical and physiological maps of connections between individual genetically distinct neurons. Functional mapping is evolving from psychological and behavioral stereotypes towards a map of behaviors emerging from structural and functional connectomes. We show how industrialization of neuroscience and the resulting large disconnected datasets are generating demand for integrative neuroscience, how the scale of neuronal and connectivity maps is driving digital atlasing and digital reconstruction to piece together the multiple levels of brain organization, and how the complexity of the interactions between molecules, neurons, microcircuits and brain regions is driving brain simulation to understand the interactions in the multiscale brain.

Peripheral transcriptomic biomarkers for early detection of sporadic Alzheimer disease?

Alzheimer disease (AD) is the major epidemic of the 21^st century, its prevalence rising along with improved human longevity. Early AD diagnosis is key to successful treatment, as currently available therapeutics only allow small benefits for diagnosed AD patients. By contrast, future therapeutics, including those already in preclinical or clinical trials, are expected to afford neuroprotection prior to widespread brain damage and dementia. Brain imaging technologies are developing as promising tools for early AD diagnostics, yet their high cost limits their utility for screening at-risk populations. Blood or plasma transcriptomics, proteomics, and/or metabolomics may pave the way for cost-effective AD risk screening in middle-aged individuals years ahead of cognitive decline. This notion is exemplified by data mining of blood transcriptomics from a published dataset. Consortia blood sample collection and analysis from large cohorts with mild cognitive impairment followed longitudinally for their cognitive state would allow the development of a reliable and inexpensive early AD screening tool.

Semi-quantification and grading of amyloid PET: A project of the European Alzheimer's Disease Consortium (EADC)

Background

amyloid-PET reading has been classically implemented as a binary assessment, although the clinical experience has shown that the number of borderline cases is non negligible not only in epidemiological studies of asymptomatic subjects but also in naturalistic groups of symptomatic patients attending memory clinics. In this work we develop a model to compare and integrate visual reading with two independent semi-quantification methods in order to obtain a tracer-independent multi-parametric evaluation.

Methods

We retrospectively enrolled three cohorts of cognitively impaired patients submitted to ¹⁸F-florbetaben (53 subjects), ¹⁸F-flutemetamol (62 subjects), ¹⁸F-florbetapir (60 subjects) PET/CT respectively, in 6 European centres belonging to the EADC. The 175 scans were visually classified as positive/negative following approved criteria and further classified with a 5-step grading as negative, mild negative, borderline, mild positive, positive by 5 independent readers, blind to clinical data. Scan quality was also visually assessed and recorded. Semi-quantification was based on two quantifiers: the standardized uptake value (SUVr) and the ELBA method. We used a sigmoid model to relate the grading with the quantifiers.

We measured the readers accord and inconsistencies in the visual assessment as well as the relationship between discrepancies on the grading and semi-quantifications.

Conclusion

It is possible to construct a map between different tracers and different quantification methods without resorting to ad-hoc acquired cases. We used a 5-level visual scale which, together with a mathematical model, delivered cut-offs and transition regions on tracers that are (largely) independent from the population. All fluorinated tracers appeared to have the same contrast and discrimination ability with respect to the negative-to-positive grading. We validated the integration of both visual reading and different quantifiers in a more robust framework thus bridging the gap between a binary and a user-independent continuous scale.

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Scans acquired with all commercial amyloid-PET fluorinated tracers are compared.

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2 independent semi-quantification methods provided whole-brain amyloid load values.

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5 readers independently evaluated all scans using a graded scale.

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A mathematical model is used to link visual grading to semi-quantification.

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Mapping between tracers and reader evaluation are given.

Cerebrospinal fluid biogenic amines depletion and brain atrophy in adult patients with phenylketonuria

Biogenic amines synthesis in phenylketonuria (PKU) patients with high phenylalanine (Phe) concentration is thought to be impaired due to inhibition of tyrosine and tryptophan hydroxylases and competition with amino acids at the blood-brain barrier. Dopamine and serotonin deficits might explain brain damage and progressive neuropsychiatric impairment in adult PKU patients. Ten early treated adult PKU patients (mean age 38.2 years) and 15 age-matched controls entered the study. Plasma and cerebrospinal fluid (CSF) Phe, 5-hydroxyindoleacetic acid (5-HIAA), 5-hydroxytryptophan (5-HTP), 3,4-dihydroxy-l-phenylalanine (l-DOPA) and homovanillic acid (HVA) were analyzed. Voxel-based morphometry statistical nonparametric mapping was used to test the age-corrected correlation between gray matter atrophy and CSF biogenic amines levels. 5-HIAA and 5-HTP were significantly reduced in PKU patients compared to controls. Significant negative correlations were found between CSF 5-HIAA, HVA, and 5-HTP and Phe levels. A decrease in 5-HIAA and 5-HTP concentrations correlated with precuneus and frontal atrophy, respectively. Lower HVA levels correlated with occipital atrophy. Biogenic amines deficits correlate with specific brain atrophy patterns in adult PKU patients, in line with serotonin and dopamine projections. These findings may support a more rigorous Phe control in adult PKU to prevent neurotransmitter depletion and accelerated brain damage due to aging.

Personalized risk for clinical progression in cognitively normal subjects-the ABIDE project

Background

Biomarkers such as cerebrospinal fluid (CSF) and magnetic resonance imaging (MRI) have predictive value for progression to dementia in patients with mild cognitive impairment (MCI). The pre-dementia stage takes far longer, and the interpretation of biomarker findings is particular relevant for individuals who present at a memory clinic, but are deemed cognitively normal. The objective of the current study is to construct biomarker-based prognostic models for personalized risk of clinical progression in cognitively normal individuals presenting at a memory clinic.

Methods

We included 481 individuals with subjective cognitive decline (SCD) from the Amsterdam Dementia Cohort. Prognostic models were developed by Cox regression with patient characteristics, MRI, and/or CSF biomarkers to predict clinical progression to MCI or dementia. We estimated 5- and 3-year individualized risks based on patient-specific values. External validation was performed on Alzheimer’s Disease Neuroimaging Initiative (ADNI) and an European dataset.

Results

Based on demographics only (Harrell’s C = 0.70), 5- and 3-year progression risks varied from 6% [3–11] and 4% [2–8] (age 55, MMSE 30) to 38% [29–49] and 28% [21–37] (age 70, MMSE 27). Normal CSF biomarkers strongly decreased progression probabilities (Harrell’s C = 0.82). By contrast, abnormal CSF markedly increased risk (5 years, 96% [56–100]; 3 years, 89% [44–99]). The CSF model could reclassify 58% of the individuals with an “intermediate” risk (35–65%) based on the demographic model. MRI measures were not retained in the models.

Conclusion

The current study takes the first steps in a personalized approach for cognitively normal individuals by providing biomarker-based prognostic models.

Electronic supplementary material

The online version of this article (10.1186/s13195-019-0487-y) contains supplementary material, which is available to authorized users.

CSF and blood biomarkers for Parkinson's disease

In the management of Parkinson's disease, reliable diagnostic and prognostic biomarkers are urgently needed. The diagnosis of Parkinson's disease mostly relies on clinical symptoms, which hampers the detection of the earliest phases of the disease-the time at which treatment with forthcoming disease-modifying drugs could have the greatest therapeutic effect. Reliable prognostic markers could help in predicting the response to treatments. Evidence suggests potential diagnostic and prognostic value of CSF and blood biomarkers closely reflecting the pathophysiology of Parkinson's disease, such as α-synuclein species, lysosomal enzymes, markers of amyloid and tau pathology, and neurofilament light chain. A combination of multiple CSF biomarkers has emerged as an accurate diagnostic and prognostic model. With respect to early diagnosis, the measurement of CSF α-synuclein aggregates is providing encouraging preliminary results. Blood α-synuclein species and neurofilament light chain are also under investigation because they would provide a non-invasive tool, both for early and differential diagnosis of Parkinson's disease versus atypical parkinsonian disorders, and for disease monitoring. In view of adopting CSF and blood biomarkers for improving Parkinson's disease diagnostic and prognostic accuracy, further validation in large independent cohorts is needed.

Clinical applicability of diagnostic biomarkers in early-onset cognitive impairment

BACKGROUND AND PURPOSE

Several diagnostic biomarkers are currently available for clinical use in early-onset cognitive impairment. The decision on which biomarker is used in each patient depends on several factors such as its predictive value or tolerability.

METHODS

There were a total of 40 subjects with early-onset cognitive complaints (<65 years of age): 26 with Alzheimer's disease (AD), five with frontotemporal dementia and nine with diagnostic suspicion of non-neurodegenerative disorder. Clinical and neuropsychological evaluation, lumbar puncture for cerebrospinal fluid (CSF) AD core biochemical marker determination, medial temporal atrophy evaluation on magnetic resonance imaging, amyloid-positron emission tomography (PET) and ¹⁸ F-fluorodeoxyglucose-PET were performed. Neurologists provided pre- and post-biomarker diagnosis, together with diagnostic confidence and clinical/therapeutic management. Patients scored the tolerability of each procedure.

RESULTS

Cerebrospinal fluid biomarkers and amyloid-PET increased diagnostic confidence in AD (77.4%-86.2% after CSF, 92.4% after amyloid-PET, P < 0.01) and non-neurodegenerative conditions (53.6%-75% after CSF, 95% after amyloid-PET, P < 0.05). Biomarker results led to diagnostic (32.5%) and treatment (32.5%) changes. All tests were well tolerated.

CONCLUSIONS

Biomarker procedures are well tolerated and have an important diagnostic/therapeutic impact on early-onset cognitive impairment.

Radiosynthesis of a carbon-11-labeled AMPAR allosteric modulator as a new PET radioligand candidate for imaging of Alzheimer's disease

To develop PET tracers for imaging of Alzheimer's disease, a new carbon-11-labeled AMPAR allosteric modulator 4-cyclopropyl-7-(3-[¹¹C]methoxyphenoxy)-3,4-dihydro-2H-benzo[e][1,2,4]thiadiazine 1,1-dioxide ([¹¹C]8) has been synthesized. The reference standard 4-cyclopropyl-7-(3-methoxyphenoxy)-3,4-dihydro-2H-benzo[e][1,2,4]thiadiazine 1,1-dioxide (8) and its corresponding desmethylated precursor 4-cyclopropyl-7-(3-hydroxyphenoxy)-3,4-dihydro-2H-benzo[e][1,2,4]thiadiazine 1,1-dioxide (9) were synthesized from 4-methoxyabiline and chlorosulfonyl isocyanate in eight and nine steps with 3% and 1% overall chemical yield, respectively. The target tracer [¹¹C]8 was prepared from the precursor 9 with [¹¹C]CH₃OTf through O-[¹¹C]methylation and isolated by HPLC combined with SPE in 10-15% radiochemical yield, based on [¹¹C]CO₂ and decay corrected to end of bombardment (EOB). The radiochemical purity was >99%, and the molar activity (A_M) at EOB was 370-740 GBq/μmol with a total synthesis time of 35-40-minutes from EOB.

Dual-drug loaded nanoparticles of Epigallocatechin-3-gallate (EGCG)/Ascorbic acid enhance therapeutic efficacy of EGCG in a APPswe/PS1dE9 Alzheimer's disease mice model

Epigallocatechin-3-gallate (EGCG) is a candidate for treatment of Alzheimer's disease (AD) but its inherent instability limits bioavailability and effectiveness. We found that EGCG displayed increased stability when formulated as dual-drug loaded PEGylated PLGA nanoparticles (EGCG/AA NPs). Oral administration of EGCG/AA NPs in mice resulted in EGCG accumulation in all major organs, including the brain. Pharmacokinetic comparison of plasma and brain accumulation following oral administration of free or EGCG/AA NPs showed that, whilst in both cases initial EGCG concentrations were similar, long-term (5–25 h) concentrations were ca. 5 fold higher with EGCG/AA NPs. No evidence was found that EGCG/AA NPs utilised a specific pathway across the blood-brain barrier (BBB). However, EGCG, empty NPs and EGCG/AA NPs all induced tight junction disruption and opened the BBB in vitro and ex vivo. Oral treatment of APPswe/PS1dE9 (APP/PS1) mice, a familial model of AD, with EGCG/AA NPs resulted in a marked increase in synapses, as judged by synaptophysin (SYP) expression, and reduction of neuroinflammation as well as amyloid β (Aβ) plaque burden and cortical levels of soluble and insoluble Aβ_(1-42) peptide. These morphological changes were accompanied by significantly enhanced spatial learning and memory. Mechanistically, we propose that stabilisation of EGCG in NPs complexes and a destabilized BBB led to higher therapeutic EGCG concentrations in the brain. Thus EGCG/AA NPs have the potential to be developed as a safe and strategy for the treatment of AD.

Prediction of future cognitive impairment among the community elderly: A machine-learning based approach

The early detection of cognitive impairment is a key issue among the elderly. Although neuroimaging, genetic, and cerebrospinal measurements show promising results, high costs and invasiveness hinder their widespread use. Predicting cognitive impairment using easy-to-collect variables by non-invasive methods for community-dwelling elderly is useful prior to conducting such a comprehensive evaluation. This study aimed to develop a machine learning-based predictive model for future cognitive impairment. A total of 3424 community elderly without cognitive impairment were included from the nationwide dataset. The gradient boosting machine (GBM) was exploited to predict cognitive impairment after 2 years. The GBM performance was good (sensitivity = 0.967; specificity = 0.825; and AUC = 0.921). This study demonstrated that a machine learning-based predictive model might be used to screen future cognitive impairment using variables, which are commonly collected in community health care institutions. With efforts of enhancing the predictive performance, such a machine learning-based approach can further contribute to the improvement of the cognitive function in community elderly.

Conversion From The Korean Dementia Screening Questionnaire To The Informant Questionnaire On Cognitive Decline In The Elderly

PURPOSE

The ability to convert scores between cognitive measurements would facilitate the longitudinal assessment of cognition in clinical practice and the comparison and synthesis of cognitive data from international, multicenter, or longitudinal studies. The primary aim of this study was to apply a simple and reliable method for converting scores from the Korean Dementia Screening Questionnaire (KDSQ) to those of the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE).

PATIENTS AND METHODS

A total of 627 participants, with and without cognitive dysfunction, received both the KDSQ and the IQCODE at the same visit. The scores of both tools were calculated, and equipercentile equating was used to create a method for converting scores from the KDSQ to the IQCODE.

RESULTS

KDSQ scores were highly correlated with IQCODE scores (Pearson r = 0.905, P < 0.01). We developed scores for converting the KDSQ to the IQCODE using equipercentile equating and log-linear smoothing. We provide an easy-to-use table that enables the conversion of KDSQ scores to IQCODE scores.

CONCLUSION

We delivered a simple and reliable method for converting scores from the KDSQ to the IQCODE. The conversion score table reported here enables direct and easy comparison of these cognitive measurements in older adults.

Neuroimaging and Neurolaw: Drawing the Future of Aging

Human brain-aging is a complex, multidimensional phenomenon. Knowledge of the numerous aspects that revolve around it is therefore essential if not only the medical issues, but also the social, psychological, and legal issues related to this phenomenon are to be managed correctly. In the coming decades, it will be necessary to find solutions to the management of the progressive aging of the population so as to increase the number of individuals that achieve successful aging. The aim of this article is to provide a current overview of the physiopathology of brain aging and of the role and perspectives of neuroimaging in this context. The progressive development of neuroimaging has opened new perspectives in clinical and basic research and it has modified the concept of brain aging. Neuroimaging will play an increasingly important role in the definition of the individual's brain aging in every phase of the physiological and pathological process. However, when the process involved in age-related brain cognitive diseases is being investigated, factors that might affect this process on a clinical and behavioral level (genetic susceptibility, risks factors, endocrine changes) cannot be ignored but must, on the contrary, be integrated into a neuroimaging evaluation to ensure a correct and global management, and they are therefore discussed in this article. Neuroimaging appears important to the correct management of age-related brain cognitive diseases not only within a medical perspective, but also legal, according to a wider approach based on development of relationship between neuroscience and law. The term neurolaw, the neologism born from the relationship between these two disciplines, is an emerging field of study, that deals with various issues in the impact of neurosciences on individual rights. Neuroimaging, enhancing the detection of physiological and pathological brain aging, could give an important contribution to the field of neurolaw in elderly where the full control of cognitive and volitional functions is necessary to maintain a whole series of rights linked to legal capacity. For this reason, in order to provide the clinician and researcher with a broad view of the brain-aging process, the role of neurolaw will be introduced into the brain-aging context.

Current and Emerging Pharmacological Targets for the Treatment of Alzheimer's Disease

No cure or disease-modifying therapy for Alzheimer's disease (AD) has yet been realized. However, a multitude of pharmacological targets have been identified for possible engagement to enable drug discovery efforts for AD. Herein, we review these targets comprised around three main therapeutic strategies. First is an approach that targets the main pathological hallmarks of AD: amyloid-β (Aβ) oligomers and hyperphosphorylated tau tangles which primarily focuses on reducing formation and aggregation, and/or inducing their clearance. Second is a strategy that modulates neurotransmitter signaling. Comprising this strategy are the cholinesterase inhibitors and N-methyl-D-aspartate receptor blockade treatments that are clinically approved for the symptomatic treatment of AD. Additional targets that aim to stabilize neuron signaling through modulation of neurotransmitters and their receptors are also discussed. Finally, the third approach comprises a collection of 'sensitive targets' that indirectly influence Aβ or tau accumulation. These targets are proteins that upon Aβ accumulation in the brain or direct Aβ-target interaction, a modification in the target's function is induced. The process occurs early in disease progression, ultimately causing neuronal dysfunction. This strategy aims to restore normal target function to alleviate Aβ-induced toxicity in neurons. Overall, we generally limit our analysis to targets that have emerged in the last decade and targets that have been validated using small molecules in in vitro and/or in vivo models. This review is not an exhaustive list of all possible targets for AD but serves to highlight the most promising and critical targets suitable for small molecule drug intervention.

Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks

We built and validated a deep learning algorithm predicting the individual diagnosis of Alzheimer's disease (AD) and mild cognitive impairment who will convert to AD (c-MCI) based on a single cross-sectional brain structural MRI scan. Convolutional neural networks (CNNs) were applied on 3D T1-weighted images from ADNI and subjects recruited at our Institute (407 healthy controls [HC], 418 AD, 280 c-MCI, 533 stable MCI [s-MCI]). CNN performance was tested in distinguishing AD, c-MCI and s-MCI. High levels of accuracy were achieved in all the classifications, with the highest rates achieved in the AD vs HC classification tests using both the ADNI dataset only (99%) and the combined ADNI + non-ADNI dataset (98%). CNNs discriminated c-MCI from s-MCI patients with an accuracy up to 75% and no difference between ADNI and non-ADNI images. CNNs provide a powerful tool for the automatic individual patient diagnosis along the AD continuum. Our method performed well without any prior feature engineering and regardless the variability of imaging protocols and scanners, demonstrating that it is exploitable by not-trained operators and likely to be generalizable to unseen patient data. CNNs may accelerate the adoption of structural MRI in routine practice to help assessment and management of patients.

An Automated Pipeline for the Analysis of PET Data on the Cortical Surface

We present a fully automatic pipeline for the analysis of PET data on the cortical surface. Our pipeline combines tools from FreeSurfer and PETPVC, and consists of (i) co-registration of PET and T1-w MRI (T1) images, (ii) intensity normalization, (iii) partial volume correction, (iv) robust projection of the PET signal onto the subject's cortical surface, (v) spatial normalization to a template, and (vi) atlas statistics. We evaluated the performance of the proposed workflow by performing group comparisons and showed that the approach was able to identify the areas of hypometabolism characteristic of different dementia syndromes: Alzheimer's disease (AD) and both the semantic and logopenic variants of primary progressive aphasia. We also showed that these results were comparable to those obtained with a standard volume-based approach. We then performed individual classifications and showed that vertices can be used as features to differentiate cognitively normal and AD subjects. This pipeline is integrated into Clinica, an open-source software platform for neuroscience studies available at www.clinica.run.

The clinical practice of risk reduction for Alzheimer's disease: A precision medicine approach

Like virtually all age-related chronic diseases, late-onset Alzheimer's disease (AD) develops over an extended preclinical period and is associated with modifiable lifestyle and environmental factors. We hypothesize that multimodal interventions that address many risk factors simultaneously and are individually tailored to patients may help reduce AD risk. We describe a novel clinical methodology used to evaluate and treat patients at two Alzheimer's Prevention Clinics. The framework applies evidence-based principles of clinical precision medicine to tailor individualized recommendations, follow patients longitudinally to continually refine the interventions, and evaluate N-of-1 effectiveness (trial registered at ClinicalTrials.gov NCT03687710). Prior preliminary results suggest that the clinical practice of AD risk reduction is feasible, with measurable improvements in cognition and biomarkers of AD risk. We propose using these early findings as a foundation to evaluate the comparative effectiveness of personalized risk management within an international network of clinician researchers in a cohort study possibly leading to a randomized controlled trial.

Current state of Alzheimer's fluid biomarkers

Alzheimer’s disease (AD) is a progressive neurodegenerative disease with a complex and heterogeneous pathophysiology. The number of people living with AD is predicted to increase; however, there are no disease-modifying therapies currently available and none have been successful in late-stage clinical trials. Fluid biomarkers measured in cerebrospinal fluid (CSF) or blood hold promise for enabling more effective drug development and establishing a more personalized medicine approach for AD diagnosis and treatment. Biomarkers used in drug development programmes should be qualified for a specific context of use (COU). These COUs include, but are not limited to, subject/patient selection, assessment of disease state and/or prognosis, assessment of mechanism of action, dose optimization, drug response monitoring, efficacy maximization, and toxicity/adverse reactions identification and minimization. The core AD CSF biomarkers Aβ42, t-tau, and p-tau are recognized by research guidelines for their diagnostic utility and are being considered for qualification for subject selection in clinical trials. However, there is a need to better understand their potential for other COUs, as well as identify additional fluid biomarkers reflecting other aspects of AD pathophysiology. Several novel fluid biomarkers have been proposed, but their role in AD pathology and their use as AD biomarkers have yet to be validated. In this review, we summarize some of the pathological mechanisms implicated in the sporadic AD and highlight the data for several established and novel fluid biomarkers (including BACE1, TREM2, YKL-40, IP-10, neurogranin, SNAP-25, synaptotagmin, α-synuclein, TDP-43, ferritin, VILIP-1, and NF-L) associated with each mechanism. We discuss the potential COUs for each biomarker.

Aβ and the dementia syndrome: Simple versus complex perspectives

BACKGROUND

The amyloid cascade hypothesis (ACH) has dominated strategy in dementia research for decades despite evidence of its limitations including known heterogeneity of the dementia syndrome in the population and the narrow focus on a single molecule - the amyloid beta protein (Aβ) as causal for all Alzheimer-type dementia. Other hypotheses relevant to Aβ are the presenilin (PS) hypothesis (PSH) relating to the involvement of PS in the generation of Aβ, and the amyloid precursor protein (APP) matrix approach (AMA), relating to the complex and dynamic breakdown of APP, from which Aβ derives.

MATERIALS AND METHODS

In this article we explore perspectives relating to complex disorders occurring mainly in older populations through a detailed case study of the role of Aβ in AD.

RESULTS

Scrutiny of the evidence generated so far reveals and a lack of understanding of the wider APP proteolytic system and how narrow research into the dementia syndrome has been to date. Confounding factors add significant limitations to the understanding of the current evidence base.

CONCLUSIONS

A better characterisation of the entire APP proteolytic system in the human brain is urgently required to place Aβ in its complex physiological context. From a molecular perspective, a combination of the alternative hypotheses, the PSH and the AMA may better describe the complexity of the APP proteolytic system leading to new therapeutic approaches. The reductionist approach is widespread throughout biomedical research and this example highlights how neglect of complexity can undermine investigations of complex disorders, particularly those arising in the oldest in our populations.

Toward Exosome-Based Neuronal Diagnostic Devices

Targeting exosome for liquid biopsy has gained significant attention for its diagnostic and therapeutic potential. For detecting neuronal disease diagnosis such as Alzheimer's disease (AD), the main technique for identifying AD still relies on positron-emission tomography (PET) imaging to detect the presence of amyloid-β (Aβ). While the detection of Aβ in cerebrospinal fluid has also been suggested as a marker for AD, the lack of quantitative measurements has compromised existing assays. In cerebrospinal fluid, in addition to Aβ, T-Tau, and P-Tau, alpha-synuclein has been considered a biomarker of neurodegeneration. This review suggests that and explains how the exosome can be used as a neuronal diagnostic component. To this end, we summarize current progress in exosome preparation/isolation and quantification techniques and comment on the outlooks for neuronal exosome-based diagnostic techniques.

Imaging and Molecular Mechanisms of Alzheimer's Disease: A Review

Alzheimer's disease is the most common form of dementia and is a significant burden for affected patients, carers, and health systems. Great advances have been made in understanding its pathophysiology, to a point that we are moving from a purely clinical diagnosis to a biological one based on the use of biomarkers. Among those, imaging biomarkers are invaluable in Alzheimer's, as they provide an in vivo window to the pathological processes occurring in Alzheimer's brain. While some imaging techniques are still under evaluation in the research setting, some have reached widespread clinical use. In this review, we provide an overview of the most commonly used imaging biomarkers in Alzheimer's disease, from molecular PET imaging to structural MRI, emphasising the concept that multimodal imaging would likely prove to be the optimal tool in the future of Alzheimer's research and clinical practice.

Raman Spectroscopy to Diagnose Alzheimer's Disease and Dementia with Lewy Bodies in Blood

Accurate identification of Alzheimer's disease (AD) is still of major clinical importance considering the current lack of noninvasive and low-cost diagnostic approaches. Detection of early stage AD is particularly desirable as it would allow early intervention or recruitment of patients into clinical trials. There is also an unmet need for discrimination of AD from dementia with Lewy bodies (DLB), as many cases of the latter are misdiagnosed as AD. Biomarkers based on a simple blood test would be useful in research and clinical practice. Raman spectroscopy has been implemented to analyze blood plasma of a cohort that consisted of early stage AD, late-stage AD, DLB, and healthy controls. Classification algorithms achieved high accuracy for the different groups: early stage AD vs healthy with 84% sensitivity, 86% specificity; late-stage AD vs healthy with 84% sensitivity, 77% specificity; DLB vs healthy with 83% sensitivity, 87% specificity; early-stage AD vs DLB with 81% sensitivity, 88% specificity; late-stage AD vs DLB with 90% sensitivity, 93% specificity; and lastly, early-stage AD vs late-stage AD 66% sensitivity and 83% specificity. G-score values were also estimated between 74% and 91%, demonstrating that the overall performance of the classification model was satisfactory. The wavenumbers responsible for differentiation were assigned to important biomolecules, which can serve as a panel of biomarkers. These results suggest a cost-effective, blood-based test for neurodegeneration in dementias.

In vivo assessment of neuroinflammation in progressive multiple sclerosis: a proof of concept study with [18F]DPA714 PET

Background

Over the past decades, positron emission tomography (PET) imaging has become an increasingly useful research modality in the field of multiple sclerosis (MS) research, as PET can visualise molecular processes, such as neuroinflammation, in vivo. The second generation PET radioligand [¹⁸F]DPA714 binds with high affinity to the 18-kDa translocator-protein (TSPO), which is mainly expressed on activated microglia. The aim of this proof of concept study was to evaluate this in vivo marker of neuroinflammation in primary and secondary progressive MS.

Methods

All subjects were genotyped for the rs6971 polymorphism within the TSPO gene, and low-affinity binders were excluded from participation in this study. Eight patients with progressive MS and seven age and genetic binding status matched healthy controls underwent a 60 min dynamic PET scan using [¹⁸F]DPA714, including both continuous on-line and manual arterial blood sampling to obtain metabolite-corrected arterial plasma input functions.

Results

The optimal model for quantification of [¹⁸F]DPA714 kinetics was a reversible two-tissue compartment model with additional blood volume parameter. For genetic high-affinity binders, a clear increase in binding potential was observed in patients with MS compared with age-matched controls. For both high and medium affinity binders, a further increase in binding potential was observed in T2 white matter lesions compared with non-lesional white matter. Volume of distribution, however, did not differentiate patients from healthy controls, as the large non-displaceable compartment of [¹⁸F]DPA714 masks its relatively small specific signal.

Conclusion

The TSPO radioligand [¹⁸F]DPA714 can reliably identify increased focal and diffuse neuroinflammation in progressive MS when using plasma input-derived binding potential, but observed differences were predominantly visible in high-affinity binders.

Electronic supplementary material

The online version of this article (10.1186/s12974-018-1352-9) contains supplementary material, which is available to authorized users.

Appropriate use criteria for lumbar puncture and cerebrospinal fluid testing in the diagnosis of Alzheimer's disease

INTRODUCTION

The Alzheimer's Association convened a multidisciplinary workgroup to develop appropriate use criteria to guide the safe and optimal use of the lumbar puncture procedure and cerebrospinal fluid (CSF) testing for Alzheimer's disease pathology detection in the diagnostic process.

METHODS

The workgroup, experienced in the ethical use of lumbar puncture and CSF analysis, developed key research questions to guide the systematic review of the evidence and developed clinical indications commonly encountered in clinical practice based on key patient groups in whom the use of lumbar puncture and CSF may be considered as part of the diagnostic process. Based on their expertise and interpretation of the evidence from systematic review, members rated each indication as appropriate or inappropriate.

RESULTS

The workgroup finalized 14 indications, rating 6 appropriate and 8 inappropriate.

DISCUSSION

In anticipation of the emergence of more reliable CSF analysis platforms, the manuscript offers important guidance to health-care practitioners and suggestions for implementation and future research.

Immunosenescence of Natural Killer Cells, Inflammation, and Alzheimer's Disease

Alzheimer's disease (AD) represents the most common cause of dementia in the elderly. AD is a neurodegenerative disorder characterized by progressive memory loss and cognitive decline. Although the aetiology of AD is not clear, both environmental factors and heritable predisposition may contribute to disease occurrence. In addition, inflammation and immune system alterations have been linked to AD. The prevailing hypothesis as cause of AD is the deposition in the brain of amyloid beta peptides (Aβ). Although Aβ have a role in defending the brain against infections, their accumulation promotes an inflammatory response mediated by microglia and astrocytes. The production of proinflammatory cytokines and other inflammatory mediators such as prostaglandins and complement factors favours the recruitment of peripheral immune cells further promoting neuroinflammation. Age-related inflammation and chronic infection with herpes virus such as cytomegalovirus may also contribute to inflammation in AD patients. Natural killer (NK) cells are innate lymphoid cells involved in host defence against viral infections and tumours. Once activated NK cells secrete cytokines such as IFN-γ and TNF-α and chemokines and exert cytotoxic activity against target cells. In the elderly, changes in NK cell compartment have been described which may contribute to the lower capacity of elderly individuals to respond to pathogens and tumours. Recently, the role of NK cells in the immunopathogenesis of AD is discussed. Although in AD patients the frequency of NK cells is not affected, a high NK cell response to cytokines has been described together with NK cell dysregulation of signalling pathways which is in part involved in this altered behaviour.

Blood-based biomarkers for Alzheimer disease: mapping the road to the clinic

Biomarker discovery and development for clinical research, diagnostics and therapy monitoring in clinical trials have advanced rapidly in key areas of medicine - most notably, oncology and cardiovascular diseases - allowing rapid early detection and supporting the evolution of biomarker-guided, precision-medicine-based targeted therapies. In Alzheimer disease (AD), breakthroughs in biomarker identification and validation include cerebrospinal fluid and PET markers of amyloid-β and tau proteins, which are highly accurate in detecting the presence of AD-associated pathophysiological and neuropathological changes. However, the high cost, insufficient accessibility and/or invasiveness of these assays limit their use as viable first-line tools for detecting patterns of pathophysiology. Therefore, a multistage, tiered approach is needed, prioritizing development of an initial screen to exclude from these tests the high numbers of people with cognitive deficits who do not demonstrate evidence of underlying AD pathophysiology. This Review summarizes the efforts of an international working group that aimed to survey the current landscape of blood-based AD biomarkers and outlines operational steps for an effective academic-industry co-development pathway from identification and assay development to validation for clinical use.

Data-driven approaches for tau-PET imaging biomarkers in Alzheimer's disease

Previous positron emission tomography (PET) studies have quantified filamentous tau pathology using regions-of-interest (ROIs) based on observations of the topographical distribution of neurofibrillary tangles in post-mortem tissue. However, such approaches may not take full advantage of information contained in neuroimaging data. The present study employs an unsupervised data-driven method to identify spatial patterns of tau-PET distribution, and to compare these patterns to previously published "pathology-driven" ROIs. Tau-PET patterns were identified from a discovery sample comprised of 123 normal controls and patients with mild cognitive impairment or Alzheimer's disease (AD) dementia from the Swedish BioFINDER cohort, who underwent [¹⁸ F]AV1451 PET scanning. Associations with cognition were tested in a separate sample of 90 individuals from ADNI. BioFINDER [¹⁸ F]AV1451 images were entered into a robust voxelwise stable clustering algorithm, which resulted in five clusters. Mean [¹⁸ F]AV1451 uptake in the data-driven clusters, and in 35 previously published pathology-driven ROIs, was extracted from ADNI [¹⁸ F]AV1451 scans. We performed linear models comparing [¹⁸ F]AV1451 signal across all 40 ROIs to tests of global cognition and episodic memory, adjusting for age, sex, and education. Two data-driven ROIs consistently demonstrated the strongest or near-strongest effect sizes across all cognitive tests. Inputting all regions plus demographics into a feature selection routine resulted in selection of two ROIs (one data-driven, one pathology-driven) and education, which together explained 28% of the variance of a global cognitive composite score. Our findings suggest that [¹⁸ F]AV1451-PET data naturally clusters into spatial patterns that are biologically meaningful and that may offer advantages as clinical tools.

AMYPAD Diagnostic and Patient Management Study: Rationale and design

INTRODUCTION

Reimbursement of amyloid-positron emission tomography (PET) is lagging due to the lack of definitive evidence on its clinical utility and cost-effectiveness. The Amyloid Imaging to Prevent Alzheimer's Disease-Diagnostic and Patient Management Study (AMYPAD-DPMS) is designed to fill this gap.

METHODS

AMYPAD-DPMS is a phase 4, multicenter, prospective, randomized controlled study. Nine hundred patients with subjective cognitive decline plus, mild cognitive impairment, and dementia possibly due to Alzheimer's disease will be randomized to ARM1, amyloid-PET performed early in the diagnostic workup; ARM2, amyloid-PET performed after 8 months; and ARM3, amyloid-PET performed whenever the physician chooses to do so.

ENDPOINTS

The primary endpoint is the difference between ARM1 and ARM2 in the proportion of patients receiving a very-high-confidence etiologic diagnosis after 3 months. Secondary endpoints address diagnosis and diagnostic confidence, diagnostic/therapeutic management, health economics and patient-related outcomes, and methods for image quantitation.

EXPECTED IMPACTS

AMYPAD-DPMS will supply physicians and health care payers with real-world data to plan management decisions.

Visual Features in Alzheimer's Disease: From Basic Mechanisms to Clinical Overview

Alzheimer's disease (AD) is the leading cause of dementia worldwide. It compromises patients' daily activities owing to progressive cognitive deterioration, which has elevated direct and indirect costs. Although AD has several risk factors, aging is considered the most important. Unfortunately, clinical diagnosis is usually performed at an advanced disease stage when dementia is established, making implementation of successful therapeutic interventions difficult. Current biomarkers tend to be expensive, insufficient, or invasive, raising the need for novel, improved tools aimed at early disease detection. AD is characterized by brain atrophy due to neuronal and synaptic loss, extracellular amyloid plaques composed of amyloid-beta peptide (Aβ), and neurofibrillary tangles of hyperphosphorylated tau protein. The visual system and central nervous system share many functional components. Thus, it is plausible that damage induced by Aβ, tau, and neuroinflammation may be observed in visual components such as the retina, even at an early disease stage. This underscores the importance of implementing ophthalmological examinations, less invasive and expensive than other biomarkers, as useful measures to assess disease progression and severity in individuals with or at risk of AD. Here, we review functional and morphological changes of the retina and visual pathway in AD from pathophysiological and clinical perspectives.

Computer-assisted prediction of clinical progression in the earliest stages of AD

Introduction

Individuals with subjective cognitive decline (SCD) are at increased risk for clinical progression. We studied how combining different diagnostic tests can help to identify individuals who are likely to show clinical progression.

Methods

We included 674 patients with SCD (46% female, 64 ± 9 years, Mini-Mental State Examination 28 ± 2) from three memory clinic cohorts. A multivariate model based on the Disease State Index classifier incorporated the available baseline tests to predict progression to MCI or dementia over time. We developed and internally validated the model in one cohort and externally validated it in the other cohorts.

Results

After 2.9 ± 2.0 years, 151(22%) patients showed clinical progression. Overall performance of the classifier when combining cognitive tests, magnetic resonance imagining, and cerebrospinal fluid showed a balanced accuracy of 74.0 ± 5.5, with high negative predictive value (93.3 ± 2.8).

Discussion

We found that a combination of diagnostic tests helps to identify individuals at risk of progression. The classifier had particularly good accuracy in identifying patients who remained stable.

The emerging roles of protein homeostasis-governing pathways in Alzheimer's disease

Pathways governing protein homeostasis are involved in maintaining the structural, quantitative, and functional stability of intracellular proteins and involve the ubiquitin-proteasome system, autophagy, endoplasmic reticulum, and mTOR pathway. Due to the broad physiological implications of protein homeostasis pathways, dysregulation of proteostasis is often involved in the development of multiple pathological conditions, including Alzheimer's disease (AD). Similar to other neurodegenerative diseases that feature pathogenic accumulation of misfolded proteins, Alzheimer's disease is characterized by two pathological hallmarks, amyloid-β (Aβ) plaques and tau aggregates. Knockout or transgenic overexpression of various proteostatic components in mice results in AD-like phenotypes. While both Aβ plaques and tau aggregates could in turn enhance the dysfunction of these proteostatic pathways, eventually leading to apoptotic or necrotic neuronal death and pathogenesis of Alzheimer's disease. Therefore, targeting the components of proteostasis pathways may be a promising therapeutic strategy against Alzheimer's disease.

Alzheimer's disease (AD) therapeutics - 2: Beyond amyloid - Re-defining AD and its causality to discover effective therapeutics

Compounds targeted for the treatment of Alzheimer's Disease (AD) have consistently failed in clinical trials despite evidence for target engagement and pharmacodynamic activity. This questions the relevance of compounds acting at current AD drug targets - the majority of which reflect the seminal amyloid and, to a far lesser extent, tau hypotheses - and limitations in understanding AD causality as distinct from general dementia. The preeminence of amyloid and tau led to many alternative approaches to AD therapeutics being ignored or underfunded to the extent that their causal versus contributory role in AD remains unknown. These include: neuronal network dysfunction; cerebrovascular disease; chronic, local or systemic inflammation involving the innate immune system; infectious agents including herpes virus and prion proteins; neurotoxic protein accumulation associated with sleep deprivation, circadian rhythm and glymphatic/meningeal lymphatic system and blood-brain-barrier dysfunction; metabolic related diseases including diabetes, obesity hypertension and hypocholesterolemia; mitochondrial dysfunction and environmental factors. As AD has become increasingly recognized as a multifactorial syndrome, a single treatment paradigm is unlikely to work in all patients. However, the biomarkers required to diagnose patients and parse them into mechanism/disease-based sub-groups remain rudimentary and unvalidated as do non-amyloid, non-tau translational animal models. The social and economic impact of AD is also discussed in the context of new FDA regulatory draft guidance and a proposed biomarker-based Framework (re)-defining AD and its stages as part of the larger landscape of treating dementia via the 2013 G8 initiative to identify a disease-modifying therapy for dementia/AD by 2025.

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.