The critical need for defining preclinical biomarkers in Alzheimer's disease

Effect of osmolytes on the conformational stability of Aβ(25-35): A circular dichroism analysis

Alzheimer's (AD) is a neurodegenerative disease characterized by the onset and progression of mental decline. AD aetiopathogenesis is still questioned; however, according to one of the most accredited hypotheses, the accumulation of amyloid plaques formed by aggregated Aβ peptides is the primary cause of neuronal function loss. Accordingly, hundreds of molecules have been screened for their possible action to prevent or destroy amyloid aggregates. Following this track, osmolytes, naturally occurring small molecules produced by several organisms in response to external stressors, were recently evaluated as modulators of Aβ aggregation. In this study, we examined the conformational stability of Aβ(25-35) when exposed to the osmolytes acetylcholine (ACh), succinylcholine (SCh), and betaine (Bet). Aβ(25-35) is the shortest fragment known for replicating the aggregation process seen in Aβ peptides. By collecting circular dichroism (CD) spectra in water and different membrane-mimicking systems, we investigated the potential of the mentioned osmolytes to stabilize the soluble conformations of Aβ(25-35) and preserve them from denaturing conditions. Our data suggest that Bet is a promising small molecule that can safeguard the soluble form of Aβ peptide and is effective in counteracting environmental conditions by favoring the amyloid aggregation associated with pathology progression.

Functional and effective EEG connectivity patterns in Alzheimer's disease and mild cognitive impairment: a systematic review

Background

Alzheimer's disease (AD) might be best conceptualized as a disconnection syndrome, such that symptoms may be largely attributable to disrupted communication between brain regions, rather than to deterioration within discrete systems. EEG is uniquely capable of directly and non-invasively measuring neural activity with precise temporal resolution; connectivity quantifies the relationships between such signals in different brain regions. EEG research on connectivity in AD and mild cognitive impairment (MCI), often considered a prodromal phase of AD, has produced mixed results and has yet to be synthesized for comprehensive review. Thus, we performed a systematic review of EEG connectivity in MCI and AD participants compared with cognitively healthy older adult controls.

Methods

We searched PsycINFO, PubMed, and Web of Science for peer-reviewed studies in English on EEG, connectivity, and MCI/AD relative to controls. Of 1,344 initial matches, 124 articles were ultimately included in the systematic review.

Results

The included studies primarily analyzed coherence, phase-locked, and graph theory metrics. The influence of factors such as demographics, design, and approach was integrated and discussed. An overarching pattern emerged of lower connectivity in both MCI and AD compared to healthy controls, which was most prominent in the alpha band, and most consistent in AD. In the minority of studies reporting greater connectivity, theta band was most commonly implicated in both AD and MCI, followed by alpha. The overall prevalence of alpha effects may indicate its potential to provide insight into nuanced changes associated with AD-related networks, with the caveat that most studies were during the resting state where alpha is the dominant frequency. When greater connectivity was reported in MCI, it was primarily during task engagement, suggesting compensatory resources may be employed. In AD, greater connectivity was most common during rest, suggesting compensatory resources during task engagement may already be exhausted.

Conclusion

The review highlighted EEG connectivity as a powerful tool to advance understanding of AD-related changes in brain communication. We address the need for including demographic and methodological details, using source space connectivity, and extending this work to cognitively healthy older adults with AD risk toward advancing early AD detection and intervention.

Air pollution: a latent key driving force of dementia

Many researchers have studied the role of air pollutants on cognitive function, changes in brain structure, and occurrence of dementia. Due to the wide range of studies and often contradictory results, the present systematic review was conducted to try and clarify the relationship between air pollutants and dementia. To identify studies for this review, a systematic search was conducted in Scopus, PubMed, and Web of Science databases (without historical restrictions) until May 22, 2023. The PECO statement was created to clarify the research question, and articles that did not meet the criteria of this statement were excluded. In this review, animal studies, laboratory studies, books, review articles, conference papers and letters to the editors were avoided. Also, studies focused on the effect of air pollutants on cellular and biochemical changes (without investigating dementia) were also excluded. A quality assessment was done according to the type of design of each article, using the checklist developed by the Joanna Briggs Institute (JBI). Finally, selected studies were reviewed and discussed in terms of Alzheimer's dementia and non-Alzheimer's dementia. We identified 14,924 articles through a systematic search in databases, and after comprehensive reviews, 53 articles were found to be eligible for inclusion in the current systematic review. The results showed that chronic exposure to higher levels of air pollutants was associated with adverse effects on cognitive abilities and the presence of dementia. Studies strongly supported the negative effects of PM2.5 and then NO2 on the brain and the development of neurodegenerative disorders in old age. Because the onset of brain structural changes due to dementia begins decades before the onset of disease symptoms, and that exposure to air pollution is considered a modifiable risk factor, taking preventive measures to reduce air pollution and introducing behavioral interventions to reduce people's exposure to pollutants is advisable.

Barbiturate-sulfonate hybrids as potent cholinesterase inhibitors: design, synthesis and molecular modeling studies

Aim: Design and synthesis of a series of 5-benzylidene(thio)barbiturates 3a-r.Methodology: Evaluation of the inhibitory activity of the new chemical entities on acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) using Donepezil as the standard reference.Results & Conclusion: Compound 3r emerged as the most potent AChE inhibitor (IC50 = 9.12 μM), while compound 3q exhibited the highest inhibitory activity against BChE (IC50 = 19.43 μM). Toxicological bioassays confirmed the absence of cytotoxicity for the most potent compounds at the tested doses. Molecular docking analysis demonstrated that the tested derivatives effectively bind to the active sites of both enzymes. Overall, this study sheds light on the potential of barbiturate-sulfonate conjugates as promising drug candidates.

Integration of single-cell sequencing with machine learning and Mendelian randomization analysis identifies the NAP1L1 gene as a predictive biomarker for Alzheimer's disease

Background

The most effective approach to managing Alzheimer's disease (AD) lies in identifying reliable biomarkers for AD to forecast the disease in advance, followed by timely early intervention for patients.

Methods

Transcriptomic data on peripheral blood mononuclear cells (PBMCs) from patients with AD and the control group were collected, and preliminary data processing was completed using standardized analytical methods. PBMCs were initially segmented into distinct subpopulations, and the divisions were progressively refined until the most significantly altered cell populations were identified. A combination of high-dimensional weighted gene co-expression analysis (hdWGCNA), cellular communication, pseudotime analysis, and single-cell regulatory network inference and clustering (SCENIC) analysis was used to conduct single-cell transcriptomics analysis and identify key gene modules from them. Genes were screened using machine learning (ML) in the key gene modules, and internal and external dataset validations were performed using multiple ML methods to test predictive performance. Finally, bidirectional Mendelian randomization (MR) analysis, regional linkage analysis, and the Steiger test were employed to analyze the key gene.

Result

A significant decrease in non-classical monocytes was detected in PMBC of AD patients. Subsequent analyses revealed the inherent connection of non-classical monocytes to AD, and the NAP1L1 gene identified within its gene module appeared to exhibit some association with AD as well.

Conclusion

The NAP1L1 gene is a potential predictive biomarker for AD.

Odor identification score as an alternative method for early identification of amyloidogenesis in Alzheimer's disease

A simple screening test to identify the early stages of Alzheimer's disease (AD) is urgently needed. We investigated whether odor identification impairment can be used to differentiate between stages of the A/T/N classification (amyloid,  tau, neurodegeneration) in individuals with amnestic mild cognitive impairment or AD and in healthy controls. We collected data from 132 Japanese participants visiting the Toranomon Hospital dementia outpatient clinic. The odor identification scores correlated significantly with major neuropsychological scores, regardless of apolipoprotein E4 status, and with effective cerebrospinal fluid (CSF) biomarkers [amyloid β 42 (Aβ42) and the Aβ42/40 and phosphorylated Tau (p-Tau)/Aβ42 ratios] but not with ineffective biomarkers [Aβ40 and the p-Tau/total Tau ratio]. A weak positive correlation was observed between the corrected odor identification score (adjusted for age, sex, ApoE4 and MMSE), CSF Aβ42, and the Aβ42/40 ratio. The odor identification score demonstrated excellent discriminative power for the amyloidogenesis stage , according to the A/T/N classification, but was unsuitable for differentiating between the p-Tau accumulation and the neurodegeneration stages. After twelve odor species were analyzed, a version of the score comprising only four odors-India ink, wood, curry, and sweaty socks-proved highly effective in identifying AD amyloidogenesis, showing promise for the screening of preclinical AD.

Liquid-liquid phase separation in Alzheimer's disease

The pathological aggregation and misfolding of tau and amyloid-β play a key role in Alzheimer's disease (AD). However, the underlying pathological mechanisms remain unclear. Emerging evidences indicate that liquid-liquid phase separation (LLPS) has great impacts on regulating human health and diseases, especially neurodegenerative diseases. A series of studies have revealed the significance of LLPS in AD. In this review, we summarize the latest progress of LLPS in AD, focusing on the impact of metal ions, small-molecule inhibitors, and proteinaceous partners on tau LLPS and aggregation, as well as toxic oligomerization, the role of LLPS on amyloid-β (Aβ) aggregation, and the cross-interactions between amyloidogenic proteins in AD. Eventually, the fundamental methods and techniques used in LLPS study are introduced. We expect to present readers a deeper understanding of the relationship between LLPS and AD.

Salvianolic acid B ameliorates retinal deficits in an early-stage Alzheimer's disease mouse model through downregulating BACE1 and Aβ generation

Alzheimer's disease (AD) is a neurodegenerative disease with subtle onset, early diagnosis remains challenging. Accumulating evidence suggests that the emergence of retinal damage in AD precedes cognitive impairment, and may serve as a critical indicator for early diagnosis and disease progression. Salvianolic acid B (Sal B), a bioactive compound isolated from the traditional Chinese medicinal herb Salvia miltiorrhiza, has been shown promise in treating neurodegenerative diseases, such as AD and Parkinson's disease. In this study we investigated the therapeutic effects of Sal B on retinopathy in early-stage AD. One-month-old transgenic mice carrying five familial AD mutations (5×FAD) were treated with Sal B (20 mg·kg-1·d-1, i.g.) for 3 months. At the end of treatment, retinal function and structure were assessed, cognitive function was evaluated in Morris water maze test. We showed that 4-month-old 5×FAD mice displayed distinct structural and functional deficits in the retinas, which were significantly ameliorated by Sal B treatment. In contrast, untreated, 4-month-old 5×FAD mice did not exhibit cognitive impairment compared to wild-type mice. In SH-SY5Y-APP751 cells, we demonstrated that Sal B (10 μM) significantly decreased BACE1 expression and sorting into the Golgi apparatus, thereby reducing Aβ generation by inhibiting the β-cleavage of APP. Moreover, we found that Sal B effectively attenuated microglial activation and the associated inflammatory cytokine release induced by Aβ plaque deposition in the retinas of 5×FAD mice. Taken together, our results demonstrate that functional impairments in the retina occur before cognitive decline, suggesting that the retina is a valuable reference for early diagnosis of AD. Sal B ameliorates retinal deficits by regulating APP processing and Aβ generation in early AD, which is a potential therapeutic intervention for early AD treatment.

Decoding the heterogeneity of Alzheimer's disease diagnosis and progression using multilayer networks

Alzheimer's disease (AD) is a multifactorial and heterogeneous disorder, which makes early detection a challenge. Studies have attempted to combine biomarkers to improve AD detection and predict progression. However, most of the existing work reports results in parallel or compares normalized findings but does not analyze data simultaneously. We tested a multi-dimensional network framework, applied to 490 subjects (cognitively normal [CN] = 147; mild cognitive impairment [MCI] = 287; AD = 56) from ADNI, to create a single model capable of capturing the heterogeneity and progression of AD. First, we constructed subject similarity networks for structural magnetic resonance imaging, amyloid-β positron emission tomography, cerebrospinal fluid, cognition, and genetics data and then applied multilayer community detection to find groups with shared similarities across modalities. Individuals were also followed-up longitudinally, with AD subjects having, on average, 4.5 years of follow-up. Our findings show that multilayer community detection allows for accurate identification of present and future AD (≈90%) and is also able to identify cases that were misdiagnosed clinically. From all MCI participants who developed AD or reverted to CN, the multilayer model correctly identified 90.8% and 88.5% of cases respectively. We observed similar subtypes across the full sample and when examining multimodal data from subjects with no AD pathology (i.e., amyloid negative). Finally, these results were also validated using an independent testing set. In summary, the multilayer framework is successful in detecting AD and provides unique insight into the heterogeneity of the disease by identifying subtypes that share similar multidisciplinary profiles of neurological, cognitive, pathological, and genetics information.

Developing Early Markers of Cognitive Decline and Dementia Derived From Survey Response Behaviors: Protocol for Analyses of Preexisting Large-scale Longitudinal Data

BACKGROUND

Accumulating evidence shows that subtle alterations in daily functioning are among the earliest and strongest signals that predict cognitive decline and dementia. A survey is a small slice of everyday functioning; nevertheless, completing a survey is a complex and cognitively demanding task that requires attention, working memory, executive functioning, and short- and long-term memory. Examining older people's survey response behaviors, which focus on how respondents complete surveys irrespective of the content being sought by the questions, may represent a valuable but often neglected resource that can be leveraged to develop behavior-based early markers of cognitive decline and dementia that are cost-effective, unobtrusive, and scalable for use in large population samples.

OBJECTIVE

This paper describes the protocol of a multiyear research project funded by the US National Institute on Aging to develop early markers of cognitive decline and dementia derived from survey response behaviors at older ages.

METHODS

Two types of indices summarizing different aspects of older adults' survey response behaviors are created. Indices of subtle reporting mistakes are derived from questionnaire answer patterns in a number of population-based longitudinal aging studies. In parallel, para-data indices are generated from computer use behaviors recorded on the backend server of a large web-based panel study known as the Understanding America Study (UAS). In-depth examinations of the properties of the created questionnaire answer pattern and para-data indices will be conducted for the purpose of evaluating their concurrent validity, sensitivity to change, and predictive validity. We will synthesize the indices using individual participant data meta-analysis and conduct feature selection to identify the optimal combination of indices for predicting cognitive decline and dementia.

RESULTS

As of October 2022, we have identified 15 longitudinal ageing studies as eligible data sources for creating questionnaire answer pattern indices and obtained para-data from 15 UAS surveys that were fielded from mid-2014 to 2015. A total of 20 questionnaire answer pattern indices and 20 para-data indices have also been identified. We have conducted a preliminary investigation to test the utility of the questionnaire answer patterns and para-data indices for the prediction of cognitive decline and dementia. These early results are based on only a subset of indices but are suggestive of the findings that we anticipate will emerge from the planned analyses of multiple behavioral indices derived from many diverse studies.

CONCLUSIONS

Survey response behaviors are a relatively inexpensive data source, but they are seldom used directly for epidemiological research on cognitive impairment at older ages. This study is anticipated to develop an innovative yet unconventional approach that may complement existing approaches aimed at the early detection of cognitive decline and dementia.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/44627.

HMGCS2-Induced Autophagic Degradation of Tau Involves Ketone Body and ANKRD24

BACKGROUND

Accumulation of hyperphosphorylated Tau (pTau) contributes to the formation of neurofibrillary tangles in Alzheimer's disease (AD), and targeting Tau/pTau metabolism has emerged as a therapeutic approach. We have previously reported that mitochondrial 3-hydroxy-3-methylglutaryl-COA synthase 2 (HMGCS2) is involved in AD by promoting autophagic clearance of amyloid-β protein precursor via ketone body-associated mechanism, whether HMGCS2 may also regulate Tau metabolism remains elusive.

OBJECTIVE

The present study was to investigate the role of HMGCS2 in Tau/p degradation.

METHODS

The protein levels of Tau and pTau including pT217 and pT181, as well as autophagic markers LAMP1 and LC3-II were assessed by western blotting. The differentially regulated genes by HMGCS2 were analyzed by RNA sequencing. Autophagosomes were assessed by transmission electron microscopy.

RESULTS

HMGCS2 significantly decreased Tau/pTau levels, which was paralleled by enhanced formation of autophagic vacuoles and prevented by autophagic regulators chloroquine, bafilomycin A1, 3-methyladenine, and rapamycin. Moreover, HMGCS2-induced alterations of LAMP1/LC3-II and Tau/pTau levels were mimicked by ketone body acetoacetate or β-hydroxybutyrate. Further RNA-sequencing identified ankyrin repeat domain 24 (ANKRD24) as a target gene of HMGCS2, and silencing of ANKRD24 reduced LAMP1/LC3-II levels, which was accompanied by the altered formation of autophagic vacuoles, and diminished the effect of HMGCS2 on Tau/pTau.

CONCLUSION

HMGCS2 promoted autophagic clearance of Tau/pTau, in which ketone body and ANKRD24 played an important role.

Monitoring the Conformational Changes of the Aβ(25-35) Peptide in SDS Micelles: A Matter of Time

Alzheimer's disease is a neurodegenerative disease characterized by the formation of amyloid plaques constituted prevalently by amyloid peptides. Due to the well-known challenges related to the study in solution of these peptides, several membrane-mimicking systems such as micelle constituted by detergent-i.e., DPC and SDS-have been deeply investigated. Additionally, the strategy of studying short fragments instead of the full-length peptide turned out to be advantageous in exploring the structural properties of the different moieties in Aβ in order to reproduce its pathologic effects. Several studies reveal that among Aβ fragments, Aβ(25-35) is the shortest fragment able to reproduce the aggregation process. To enrich the structural data currently available, in the present work we decided to evaluate the conformational changes adopted by Aβ(25-35) in SDS combining CD and NMR spectroscopies at different times. From the solved structures, it emerges that Aβ(25-35) passes from an unordered conformation at the time of the constitution of the system to a more ordered and energetically favorable secondary structure at day 7, which is kept for 2 weeks. These preliminary data suggest that a relatively long time affects the kinetic in the aggregation process of Aβ(25-35) in a micellar system, favoring the stabilization and the formation of a soluble helix conformation.

Circulating serum metabolites as predictors of dementia: a machine learning approach in a 21-year follow-up of the Whitehall II cohort study

BACKGROUND

Age is the strongest risk factor for dementia and there is considerable interest in identifying scalable, blood-based biomarkers in predicting dementia. We examined the role of midlife serum metabolites using a machine learning approach and determined whether the selected metabolites improved prediction accuracy beyond the effect of age.

METHODS

Five thousand three hundred seventy-four participants from the Whitehall II study, mean age 55.8 (standard deviation (SD) 6.0) years in 1997-1999 when 233 metabolites were quantified using nuclear magnetic resonance metabolomics. Participants were followed for a median 21.0 (IQR 20.4, 21.7) years for clinically-diagnosed dementia (N=329). Elastic net penalized Cox regression with 100 repetitions of nested cross-validation was used to select models that improved prediction accuracy for incident dementia compared to an age-only model. Risk scores reflecting the frequency with which predictors appeared in the selected models were constructed, and their predictive accuracy was examined using Royston's R², Akaike's information criterion, sensitivity, specificity, C-statistic and calibration.

RESULTS

Sixteen of the 100 models had a better c-statistic compared to an age-only model and 15 metabolites were selected at least once in all 16 models with glucose present in all models. Five risk scores, reflecting the frequency of selection of metabolites, and a 1-SD increment in all five risk scores was associated with higher dementia risk (HR between 3.13 and 3.26). Three of these, constituted of 4, 5 and 15 metabolites, had better prediction accuracy (c-statistic from 0.788 to 0.796) compared to an age-only model (c-statistic 0.780), all p<0.05.

CONCLUSIONS

Although there was robust evidence for the role of glucose in dementia, metabolites measured in midlife made only a modest contribution to dementia prediction once age was taken into account.

Pinus halepensis Essential Oil Ameliorates Aβ1-42-Induced Brain Injury by Diminishing Anxiety, Oxidative Stress, and Neuroinflammation in Rats

The Pinus L. genus comprises around 250 species, being popular worldwide for their medicinal and aromatic properties. The present study aimed to evaluate the P. halepensis Mill. essential oil (PNO) in an Alzheimer’s disease (AD) environment as an anxiolytic and antidepressant agent. The AD-like symptoms were induced in Wistar male rats by intracerebroventricular administration of amyloid beta1-42 (Aβ1-42), and PNO (1% and 3%) was delivered to Aβ1-42 pre-treated rats via inhalation route for 21 consecutive days, 30 min before behavioral assessments. The obtained results indicate PNO’s potential to relieve anxious–depressive features and to restore redox imbalance in the rats exhibiting AD-like neuropsychiatric impairments. Moreover, PNO presented beneficial effects against neuroinflammation and neuroapoptosis in the Aβ1-42 rat AD model.

Temporal Cortex Microarray Analysis Revealed Impaired Ribosomal Biogenesis and Hyperactivity of the Glutamatergic System: An Early Signature of Asymptomatic Alzheimer's Disease

Pathogenic aging is regarded as asymptomatic AD when there is no cognitive deficit except for neuropathology consistent with Alzheimer's disease. These individuals are highly susceptible to developing AD. Braak and Braak's theory specific to tau pathology illustrates that the brain's temporal cortex region is an initiation site for early AD progression. So, the hub gene analysis of this region may reveal early altered biological cascades that may be helpful to alleviate AD in an early stage. Meanwhile, cognitive processing also drags its attention because cognitive impairment is the ultimate result of AD. Therefore, this study aimed to explore changes in gene expression of aged control, asymptomatic AD (AsymAD), and symptomatic AD (symAD) in the temporal cortex region. We used microarray data sets to identify differentially expressed genes (DEGs) with the help of the R programming interface. Further, we constructed the protein-protein interaction (PPI) network by performing the STRING plugin in Cytoscape and determined the hub genes via the CytoHubba plugin. Furthermore, we conducted Gene Ontology (GO) enrichment analysis via Bioconductor's cluster profile package. Resultant, the AsymAD transcriptome revealed the early-stage changes of glutamatergic hyperexcitability. Whereas the connectivity of major hub genes in this network indicates a shift from initially reduced rRNA biosynthesis in the AsymAD group to impaired protein synthesis in the symAD group. Both share the phenomenon of breaking tight junctions and others. In conclusion, this study offers new understandings of the early biological vicissitudes that occur in the brain before the manifestation of symAD and gives new promising therapeutic targets for early AD intervention.

Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application

Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through  GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.

Electrophysiological resting state brain network and episodic memory in healthy aging adults

Recent studies have emphasized the changes in large-scale brain networks related to healthy aging, with the ultimate purpose to aid in differentiating normal neurocognitive aging from neurodegenerative disorders that also arise with age. Emerging evidence from functional Magnetic Resonance Imaging (fMRI) indicates that connectivity patterns within specific brain networks, especially the Default Mode Network (DMN), distinguish those with Alzheimer's disease from healthy individuals. In addition, disruptive alterations in the large-scale brain systems that support high-level cognition are shown to accompany cognitive decline at the behavioral level, which is commonly observed in the aging populations, even in the absence of disease. Although fMRI is useful for assessing functional changes in brain networks, its high costs and limited accessibility discourage studies that need large populations. In this study, we investigated the aging-effect on large-scale networks of the human brain using high-density electroencephalography and electrophysiological source imaging, which is a less costly and more accessible alternative to fMRI. In particular, our study examined a group of healthy subjects in the age range from middle- to older-aged adults, which is an under-studied range in the literature. Employing a high-resolution computation model, our results revealed age associations in the connectivity pattern of DMN in a consistent manner with previous fMRI findings. Particularly, in combination with a standard battery of cognitive tests, our data showed that in the posterior cingulate / precuneus area of DMN higher brain connectivity was associated with lower performance on an episodic memory task. The findings demonstrate the feasibility of using electrophysiological imaging to characterize large-scale brain networks and suggest that changes in network connectivity are associated with normal aging.

Subtle mistakes in self-report surveys predict future transition to dementia

INTRODUCTION

We investigate whether indices of subtle reporting mistakes derived from responses in self-report surveys are associated with dementia risk.

METHODS

We examined 13,831 participants without dementia from the prospective, population-based Health and Retirement Study (mean age 69 ± 10 years, 59% women). Participants' response patterns in 21 questionnaires were analyzed to identify implausible responses (multivariate outliers), incompatible responses (Guttman errors), acquiescent responses, random errors, and the proportion of skipped questions. Subsequent incident dementia was determined over up to 10 years of follow-up.

RESULTS

During follow-up, 2074 participants developed dementia and 3717 died. Each of the survey response indices was associated with future dementia risk controlling for confounders and accounting for death as a competing risk. Stronger associations were evident for participants who were younger and cognitively normal at baseline.

DISCUSSION

Mistakes in the completion of self-report surveys in longitudinal studies may be early indicators of dementia among middle-aged and older adults.

When Does Alzheimer's Disease Really Start? The Role of Biomarkers

(Appeared originally in Int J Mol Sci 2019, 20 5536).

Pairwise Correlation Analysis of the Alzheimer's Disease Neuroimaging Initiative (ADNI) Dataset Reveals Significant Feature Correlation

The Alzheimer's Disease Neuroimaging Initiative (ADNI) contains extensive patient measurements (e.g., magnetic resonance imaging [MRI], biometrics, RNA expression, etc.) from Alzheimer's disease (AD) cases and controls that have recently been used by machine learning algorithms to evaluate AD onset and progression. While using a variety of biomarkers is essential to AD research, highly correlated input features can significantly decrease machine learning model generalizability and performance. Additionally, redundant features unnecessarily increase computational time and resources necessary to train predictive models. Therefore, we used 49,288 biomarkers and 793,600 extracted MRI features to assess feature correlation within the ADNI dataset to determine the extent to which this issue might impact large scale analyses using these data. We found that 93.457% of biomarkers, 92.549% of the gene expression values, and 100% of MRI features were strongly correlated with at least one other feature in ADNI based on our Bonferroni corrected α (p-value ≤ 1.40754 × 10-13). We provide a comprehensive mapping of all ADNI biomarkers to highly correlated features within the dataset. Additionally, we show that significant correlation within the ADNI dataset should be resolved before performing bulk data analyses, and we provide recommendations to address these issues. We anticipate that these recommendations and resources will help guide researchers utilizing the ADNI dataset to increase model performance and reduce the cost and complexity of their analyses.

Discovery of a Metabolic Signature Predisposing High Risk Patients with Mild Cognitive Impairment to Converting to Alzheimer's Disease

Assessing dementia conversion in patients with mild cognitive impairment (MCI) remains challenging owing to pathological heterogeneity. While many MCI patients ultimately proceed to Alzheimer’s disease (AD), a subset of patients remain stable for various times. Our aim was to characterize the plasma metabolites of nineteen MCI patients proceeding to AD (P-MCI) and twenty-nine stable MCI (S-MCI) patients by untargeted metabolomics profiling. Alterations in the plasma metabolites between the P-MCI and S-MCI groups, as well as between the P-MCI and AD groups, were compared over the observation period. With the help of machine learning-based stratification, a 20-metabolite signature panel was identified that was associated with the presence and progression of AD. Furthermore, when the metabolic signature panel was used for classification of the three patient groups, this gave an accuracy of 73.5% using the panel. Moreover, when specifically classifying the P-MCI and S-MCI subjects, a fivefold cross-validation accuracy of 80.3% was obtained using the random forest model. Importantly, indole-3-propionic acid, a bacteria-generated metabolite from tryptophan, was identified as a predictor of AD progression, suggesting a role for gut microbiota in AD pathophysiology. Our study establishes a metabolite panel to assist in the stratification of MCI patients and to predict conversion to AD.

Omics sciences for systems biology in Alzheimer's disease: State-of-the-art of the evidence

Alzheimer's disease (AD) is characterized by non-linear, genetic-driven pathophysiological dynamics with high heterogeneity in biological alterations and disease spatial-temporal progression. Human in-vivo and post-mortem studies point out a failure of multi-level biological networks underlying AD pathophysiology, including proteostasis (amyloid-β and tau), synaptic homeostasis, inflammatory and immune responses, lipid and energy metabolism, oxidative stress. Therefore, a holistic, systems-level approach is needed to fully capture AD multi-faceted pathophysiology. Omics sciences - genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics - embedded in the systems biology (SB) theoretical and computational framework can generate explainable readouts describing the entire biological continuum of a disease. Such path in Neurology is encouraged by the promising results of omics sciences and SB approaches in Oncology, where stage-driven pathway-based therapies have been developed in line with the precision medicine paradigm. Multi-omics data integrated in SB network approaches will help detect and chart AD upstream pathomechanistic alterations and downstream molecular effects occurring in preclinical stages. Finally, integrating omics and neuroimaging data - i.e., neuroimaging-omics - will identify multi-dimensional biological signatures essential to track the clinical-biological trajectories, at the subpopulation or even individual level.

Changes in plasma phospholipids and sphingomyelins with aging in men and women: A comprehensive systematic review of longitudinal cohort studies

BACKGROUND

Aging affects the serum levels of various metabolites which may be involved in the pathogenesis of chronic diseases. The aim of this review article is to summarize the relationship between aging and alterations in the plasma phospholipids and sphingomyelins.

METHODS

PRISMA guidelines were employed during all steps. MEDLINE (PubMed), Scopus, Embase and Web of Sciences databases and Google Scholar were searched up to October 2020. Cohort studies investigating the relationship between aging and within-person changes in sphingomyelin (SM), phosphatidyl choline (PC), lyso PC (LPC) and phosphatidyl ethanolamine (PE) were included. Newcastle-Ottawa scale was used to assess the quality of included studies.

RESULTS

A total of 1425 studies were identified. After removing 610 duplicates and 723 irrelevant studies, full texts of 92 articles were evaluated. Of these 92, 6 studies (including data from 7 independent cohorts) met the inclusion criteria and are included in this review. All study populations were healthy and included both men and women. Results by sex were reported in 3 cohorts for PC, 5 cohorts for LPC, 3 cohorts for SM, and only 1 cohort for PE. In men, PC, SM, PE and LPC decreased with aging, although results for LPC were inconsistent. In women, LPC, SM, and PE increased age, whereas changes in PC were inconsistent.

CONCLUSION

Within-person serum levels of phospholipids and sphingomyelins, decrease during aging in men and increase in women. Notably, however, there were some inconsistencies across studies of LPC in men and of PC in women.

Towards defining the Mechanisms of Alzheimer's disease based on a contextual analysis of molecular pathways

Alzheimer's disease (AD) is posing an increasingly profound problem to society. Our genuine understanding of the pathogenesis of AD is inadequate and as a consequence, diagnostic and therapeutic strategies are currently insufficient. The understandable focus of many studies is the identification of molecules with high diagnostic utility however the opportunity to obtain a further understanding of the mechanistic origins of the disease from such putative biomarkers is often overlooked. This study examines the involvement of biomarkers in AD to shed light on potential mechanisms and pathways through which they are implicated in the pathology of this devastating neurodegenerative disorder. The computational tools required to analyse ever-growing datasets in the context of AD are also discussed.

CSF metabolites associate with CSF tau and improve prediction of Alzheimer's disease status

INTRODUCTION

Cerebrospinal fluid (CSF) total tau (t-tau) and phosphorylated tau (p-tau) are biomarkers of Alzheimer's disease (AD), yet much is unknown about AD-associated changes in tau metabolism and tau tangle etiology.

METHODS

We assessed the variation of t-tau and p-tau explained by 38 previously identified CSF metabolites using linear regression models in middle-age controls from the Wisconsin Alzheimer's Disease Research Center, and predicted AD/mild cognitive impairment (MCI) versus an independent set of older controls using metabolites selected by the least absolute shrinkage and selection operator (LASSO).

RESULTS

The 38 CSF metabolites explained 70.3% and 75.7% of the variance in t-tau and p-tau, respectively. Of these, seven LASSO-selected metabolites improved the prediction ability of AD/MCI versus older controls (area under the curve score increased from 0.92 to 0.97 and 0.78 to 0.93) compared to the base model.

DISCUSSION

These tau-correlated CSF metabolites increase AD/MCI prediction accuracy and may provide insight into tau tangle etiology.

Machine Learning Profiling of Alzheimer's Disease Patients Based on Current Cerebrospinal Fluid Markers and Iron Content in Biofluids

Alzheimer's disease (AD) is the most common form of dementia, characterized by a complex etiology that makes therapeutic strategies still not effective. A true understanding of key pathological mechanisms and new biomarkers are needed, to identify alternative disease-modifying therapies counteracting the disease progression. Iron is an essential element for brain metabolism and its imbalance is implicated in neurodegeneration, due to its potential neurotoxic effect. However, the role of iron in different stages of dementia is not clearly established. This study aimed to investigate the potential impact of iron both in cerebrospinal fluid (CSF) and in serum to improve early diagnosis and the related therapeutic possibility. In addition to standard clinical method to detect iron in serum, a precise quantification of total iron in CSF was performed using graphite-furnace atomic absorption spectrometry in patients affected by AD, mild cognitive impairment, frontotemporal dementia, and non-demented neurological controls. The application of machine learning techniques, such as clustering analysis and multiclassification algorithms, showed a new potential stratification of patients exploiting iron-related data. The results support the involvement of iron dysregulation and its potential interaction with biomarkers (Tau protein and Amyloid-beta) in the pathophysiology and progression of dementia.

Serum amino acid profiles in patients with mild cognitive impairment and in patients with mild dementia or moderate dementia

Neurodegenerative disorders are one of the greatest global challenges for social and health care in the twenty-first century. Nowadays, determination of cerebrospinal fluid biomarkers for early diagnosis is served by a complex sample preparation procedure with limited diagnostic accuracy. Furthermore, neuroimaging methods are expensive, time-consuming and are not readily available for use as a complimentary and common screening method. Recently, researchers have shown an increased interest in the identification and characterization of new blood biomarkers of dementia to minimize the limitations associated with the current methods of biomarker determination. Amino acids play many important roles in the central nervous system, acting as neuromodulators, neurotransmitters and regulators of energy metabolism. The aim of this study was to evaluate if serum amino acid levels change along the continuum from no cognitive impairment to moderate dementia, and to identify putative biomarkers for early diagnosis of neurodegenerative diseases. Serum levels of 16 amino acids were determined in 3 groups of patients-22 with mild cognitive impairment, 45 with mild dementia and 28 with moderate dementia-by high-performance liquid chromatography (HPLC) with fluorescence detection using AccQ Tag column (Waters). The most exciting result is the significantly elevated concentration of arginine in patients with both stages of dementia as compared to mild cognitive impairment individuals. Recent accumulating evidence suggests the implication of changed arginine metabolism in the pathogenesis of neurodegenerative diseases. We conclude that amino acids profiling might be helpful in searching for biomarkers of neurogenerative diseases. In the present study, we discovered that arginine in plasma may have a putative diagnostic value for dementia.

The role of peripheral fatty acids as biomarkers for Alzheimer's disease and brain inflammation

Alzheimer's disease (AD) is a complex and heterogeneous neurodegenerative disease. A wide range of techniques have been proposed to facilitate early diagnosis of AD, including biomarkers from the cerebrospinal fluid and blood. Although phosphorylated tau and amyloid beta are amongst the most promising biomarkers of AD, other peripheral biomarkers have been identified and in this review we synthesize the current knowledge on circulating fatty acids. Fatty acids are involved in different biological process including neurotransmission and inflammation. Interestingly, some fatty acids appear to be modulated during disease progression, including long chain saturated fatty acids, and polyunsaturated fatty acids, such as docosahexaenoic acid . However, discrepant results have been reported in the literature and there is the need for further validation and method standardization. Nonetheless, our literature review suggests that fatty acid analyses could potentially provide a valuable source of data to further inform the pathology and progression of AD.

Molecular subtyping of Alzheimer's disease using RNA sequencing data reveals novel mechanisms and targets

Alzheimer's disease (AD), the most common form of dementia, is recognized as a heterogeneous disease with diverse pathophysiologic mechanisms. In this study, we interrogate the molecular heterogeneity of AD by analyzing 1543 transcriptomes across five brain regions in two AD cohorts using an integrative network approach. We identify three major molecular subtypes of AD corresponding to different combinations of multiple dysregulated pathways, such as susceptibility to tau-mediated neurodegeneration, amyloid-β neuroinflammation, synaptic signaling, immune activity, mitochondria organization, and myelination. Multiscale network analysis reveals subtype-specific drivers such as GABRB2, LRP10, MSN, PLP1, and ATP6V1A We further demonstrate that variations between existing AD mouse models recapitulate a certain degree of subtype heterogeneity, which may partially explain why a vast majority of drugs that succeeded in specific mouse models do not align with generalized human trials across all AD subtypes. Therefore, subtyping patients with AD is a critical step toward precision medicine for this devastating disease.

Reciprocal Incremental Value of 18F-FDG-PET and Cerebrospinal Fluid Biomarkers in Mild Cognitive Impairment Patients Suspected for Alzheimer's Disease and Inconclusive First Biomarker

BACKGROUND

In Alzheimer's disease (AD) diagnosis, both cerebrospinal fluid (CSF) biomarkers and FDG-PET sometimes give inconclusive results.

OBJECTIVE

To evaluate the incremental diagnostic value of FDG-PET over CSF biomarkers, and vice versa, in patients with mild cognitive impairment (MCI) and suspected AD, in which the first biomarker resulted inconclusive.

METHODS

A consecutive series of MCI patients was retrospectively selected from two Memory Clinics where, as per clinical routine, either the first biomarker choice is FDG-PET and CSF biomarkers are only used in patients with uninformative FDG-PET, or vice versa. We defined criteria of uncertainty in interpretation of FDG-PET and CSF biomarkers, according to current evidence. The final diagnosis was established according to clinical-neuropsychological follow-up of at least one year (mean 4.4±2.2).

RESULTS

When CSF was used as second biomarker after FDG-PET, 14 out of 36 (39%) received informative results. Among these 14 patients, 11 (79%) were correctly classified with respect to final diagnosis, thus with a relative incremental value of CSF over FDG-PET of 30.6%. When FDG-PET was used as second biomarker, 26 out of 39 (67%) received informative results. Among these 26 patients, 15 (58%) were correctly classified by FDG-PET with respect to final diagnosis, thus with a relative incremental value over CSF of 38.5%.

CONCLUSION

Our real-world data confirm the added values of FDG-PET (or CSF) in a diagnostic pathway where CSF (or FDG-PET) was used as first biomarkers in suspected AD. These findings should be replicated in larger studies with prospective enrolment according to a Phase III design.

A 5-min Cognitive Task With Deep Learning Accurately Detects Early Alzheimer's Disease

Introduction: The goal of this study was to investigate and compare the classification performance of machine learning with behavioral data from standard neuropsychological tests, a cognitive task, or both.

Methods: A neuropsychological battery and a simple 5-min cognitive task were administered to eight individuals with mild cognitive impairment (MCI), eight individuals with mild Alzheimer's disease (AD), and 41 demographically match controls (CN). A fully connected multilayer perceptron (MLP) network and four supervised traditional machine learning algorithms were used.

Results: Traditional machine learning algorithms achieved similar classification performances with neuropsychological or cognitive data. MLP outperformed traditional algorithms with the cognitive data (either alone or together with neuropsychological data), but not neuropsychological data. In particularly, MLP with a combination of summarized scores from neuropsychological tests and the cognitive task achieved ~90% sensitivity and ~90% specificity. Applying the models to an independent dataset, in which the participants were demographically different from the ones in the main dataset, a high specificity was maintained (100%), but the sensitivity was dropped to 66.67%.

Discussion: Deep learning with data from specific cognitive task(s) holds promise for assisting in the early diagnosis of Alzheimer's disease, but future work with a large and diverse sample is necessary to validate and to improve this approach.

A Community-Based Study Identifying Metabolic Biomarkers of Mild Cognitive Impairment and Alzheimer's Disease Using Artificial Intelligence and Machine Learning

BACKGROUND

Currently, there is no objective, clinically available tool for the accurate diagnosis of Alzheimer's disease (AD). There is a pressing need for a novel, minimally invasive, cost friendly, and easily accessible tool to diagnose AD, assess disease severity, and prognosticate course. Metabolomics is a promising tool for discovery of new, biologically, and clinically relevant biomarkers for AD detection and classification.

OBJECTIVE

Utilizing artificial intelligence and machine learning, we aim to assess whether a panel of metabolites as detected in plasma can be used as an objective and clinically feasible tool for the diagnosis of mild cognitive impairment (MCI) and AD.

METHODS

Using a community-based sample cohort acquired from different sites across the US, we adopted an approach combining Proton Nuclear Magnetic Resonance Spectroscopy (1H NMR), Liquid Chromatography coupled with Mass Spectrometry (LC-MS) and various machine learning statistical approaches to identify a biomarker panel capable of identifying those patients with AD and MCI from healthy controls.

RESULTS

Of the 212 measured metabolites, 5 were identified as optimal to discriminate between controls, and individuals with MCI or AD. Our models performed with AUC values in the range of 0.72-0.76, with the sensitivity and specificity values ranging from 0.75-0.85 and 0.69-0.81, respectively. Univariate and pathway analysis identified lipid metabolism as the most perturbed biochemical pathway in MCI and AD.

CONCLUSION

A comprehensive method of acquiring metabolomics data, coupled with machine learning techniques, has identified a strong panel of diagnostic biomarkers capable of identifying individuals with MCI and AD. Further, our data confirm what other groups have reported, that lipid metabolism is significantly perturbed in those individuals suffering with dementia. This work may provide additional insight into AD pathogenesis and encourage more in-depth analysis of the AD lipidome.

Blood biomarkers indicate that the preclinical stages of Alzheimer's disease present overlapping molecular features

It is still debated whether non-specific preclinical symptoms of Alzheimer's disease (AD) can have diagnostic relevance. We followed the evolution from cognitively normal to AD by NMR-based metabolomics of blood sera. Multivariate statistical analysis of the NMR profiles yielded models that discriminated subjective memory decline (SMD), mild cognitive impairment (MCI) and AD. We validated a panel of six statistically significant metabolites that predicted SMD, MCI and AD in a blind cohort with sensitivity values ranging from 88 to 95% and receiver operating characteristic values from 0.88 to 0.99. However, lower values of specificity, accuracy and precision were observed for the models involving SMD and MCI, which is in line with the pathological heterogeneity indicated by clinical data. This excludes a "linear" molecular evolution of the pathology, pointing to the presence of overlapping "gray-zones" due to the reciprocal interference of the intermediate stages. Yet, the clear difference observed in the metabolic pathways of each model suggests that pathway dysregulations could be investigated for diagnostic purposes.

Large-Scale Structural Covariance Networks Predict Age in Middle-to-Late Adulthood: A Novel Brain Aging Biomarker

The aging process is accompanied by changes in the brain's cortex at many levels. There is growing interest in summarizing these complex brain-aging profiles into a single, quantitative index that could serve as a biomarker both for characterizing individual brain health and for identifying neurodegenerative and neuropsychiatric diseases. Using a large-scale structural covariance network (SCN)-based framework with machine learning algorithms, we demonstrate this framework's ability to predict individual brain age in a large sample of middle-to-late age adults, and highlight its clinical specificity for several disease populations from a network perspective. A proposed estimator with 40 SCNs could predict individual brain age, balancing between model complexity and prediction accuracy. Notably, we found that the most significant SCN for predicting brain age included the caudate nucleus, putamen, hippocampus, amygdala, and cerebellar regions. Furthermore, our data indicate a larger brain age disparity in patients with schizophrenia and Alzheimer's disease than in healthy controls, while this metric did not differ significantly in patients with major depressive disorder. These findings provide empirical evidence supporting the estimation of brain age from a brain network perspective, and demonstrate the clinical feasibility of evaluating neurological diseases hypothesized to be associated with accelerated brain aging.

Early Diagnosis of Alzheimer's Disease in Blood Using a Disposable Electrochemical Microfluidic Platform

Alzheimer's disease (AD) is a neurodegenerative condition that affects a large number of elderly people worldwide and has a high social and economic impact. The diagnosis of AD in early stage can significantly improve the evolution and prognosis of the disease. We report the use of A Disintegrin And Metalloprotease 10 (ADAM10) as a blood biomarker for the early diagnosis of AD. A simple, low-cost, sensitive, and disposable microfluidic platform (DμP) was developed for ADAM10 detection in plasma and cerebrospinal fluid based on electrochemical immunosensors. The assay was designed to accurately detect ADAM10 in serum, with a limit of detection of 0.35 fg/mL. ADAM10 was detected in subjects divided into cognitively healthy subjects, subjects with mild cognitive impairment, and AD patients in different disease stages. An increase in protein levels was found throughout the disease, and good DμP accuracy in differentiating individuals was observed. The DμP provided significantly better sensitivity than the well-established enzyme-linked immunosorbent assay test. ADAM10 and its detection using the DμP were proven to be an alternative tool for the early diagnosis and monitoring of AD, bringing new exciting possibilities to improve the quality of life of AD patients.

Metabolomics and incident dementia in older Chinese adults: The Shanghai Aging Study

INTRODUCTION

Metabolomics provide a promising tool to understand the pathogenesis and to identify novel biomarkers of dementia. This study aimed to determine circulating metabolites associated with incident dementia in a Chinese cohort, and whether a selected metabolite panel could predict dementia.

METHODS

Thirty-eight metabolites in baseline serum were profiled by nuclear magnetic resonance in 1440 dementia-free participants followed 5 years in the Shanghai Aging Study.

RESULTS

Higher serum levels of glutamine and O-acetyl-glycoproteins were associated with increased risk of dementia, whereas glutamate, tyrosine, acetate, glycine, and phenylalanine were negatively related to incident dementia. A panel of five metabolites selected by least absolute shrinkage and selection operator within cross-validation regression analysis could predict incident dementia with an area under the receiver-operating characteristic curve of 0.72.

DISCUSSION

We identified seven candidate serum metabolic biomarkers for dementia. These findings and the underlying biological mechanisms need to be further replicated and elucidated in future studies.

The European medical information framework: A novel ecosystem for sharing healthcare data across Europe

INTRODUCTION

The European medical information framework (EMIF) was an Innovative Medicines Initiative project jointly supported by the European Union and the European Federation of Pharmaceutical Industries and Associations, that generated a common technology and governance framework to identify, assess and (re)use healthcare data, to facilitate real-world data research. The objectives of EMIF included providing a unified platform to support a wide range of studies within two verification programmes-Alzheimer's disease (EMIF-AD), and metabolic consequences of obesity (EMIF-MET).

METHODS

The EMIF platform was built around two main data-types: electronic health record data and research cohort data, and the platform architecture composed of a set of tools designed to enable data discovery and characterisation. This included the EMIF catalogue, which allowed users to find relevant data sources, including the data-types collected. Data harmonisation via a common data model were central to the project especially for population data sources. EMIF also developed an ethical code of practice to ensure data protection, patient confidentiality and compliance with the European Data Protection Directive, and GDPR.

RESULTS

Currently 18 population-based disease agnostic and 60 cohort-based Alzheimer's data partners from across 14 countries are contained within the catalogue, and this will continue to expand. The work conducted in EMIF-AD and EMIF-MET includes standardizing cohorts, summarising baseline characteristics of patients, developing diagnostic algorithms, epidemiological studies, identifying and validating novel biomarkers and selecting potential patient samples for pharmacological intervention.

CONCLUSIONS

EMIF was designed to provide a sustainable model as demonstrated by the sustainability plans for EMIF-AD. Although network-wide studies using EMIF were not conducted during this project to evaluate its sustainability, learning from EMIF will be used in the follow-on IMI-2 project, European Health Data and Evidence Network (EHDEN). Furthermore, EMIF has facilitated collaborations between partners and continues to promote a wider adoption of principles, technology and architecture through some of its continued work.

Biomarker potential of brain-secreted extracellular vesicles in blood in Alzheimer's disease

INTRODUCTION

Brain cells secrete extracellular microvesicles (EVs) that cross the blood-brain barrier. Involved in cell-to-cell communication, EVs contain surface markers and a biologically active cargo of molecules specific to their tissue (and cell) of origin, reflecting the tissue or cell's physiological state. Isolation of brain-secreted EVs (BEVs) from blood provides a minimally invasive way to sample components of brain tissue in Alzheimer's disease (AD), and is considered a form of "liquid biopsy."

METHODS

We performed a comprehensive review of the PubMed literature to assess the biomarker and therapeutic potential of blood-isolated BEVs in AD.

RESULTS

We summarize methods used for BEV isolation, validation, and novel biomarker discovery, as well as provide insights from 26 studies in humans on the biomarker potential in AD of four cell-specific BEVs isolated from blood: neuron-, neural precursor-, astrocyte-, and brain vasculature-derived BEVs. Of these, neuron-derived BEVs has been investigated on several fronts, and these include levels of amyloid-β and tau proteins, as well as synaptic proteins. In addition, we provide a synopsis of the current landscape of BEV-based evaluation/monitoring of AD therapeutics based on two published trials and a review of registered clinical trials.

DISCUSSION

Blood-isolated BEVs have emerged as a novel player in the study of AD, with enormous potential as a diagnostic, evaluation of therapeutics, and treatment tool. The literature has largely concentrated on neuron-derived BEVs in the blood in AD. Given the multifactorial pathophysiology of AD, additional studies, in neuron-derived and other brain cell-specific BEVs are warranted to establish BEVs as a robust blood-based biomarker of AD.

An Exploratory Analysis of Potential New Biomarkers of Cognitive Function

We examined the relationship between serially measured, novel serum biomarkers and a measure of cognitive functioning in older adults. We assayed stored serum samples from two Fels Longitudinal Study visits in N = 100 adult participants (visit 1 ages 59.3 ± 8.5 years; 53% female), and Montreal Cognitive Assessment (MoCA) scores also assessed at the second visit. Assays included acylcarnitines, amino acids, and 2-hydroxybutyric acid (b-HBA). Cross-sectional correlations between acylcarnitines and amino acids and MoCA were identified. Serial change in short-chain acylcarnitines and visit 2 MoCA were also correlated. Participants with MoCA scores <26 were more likely to have an increase in short-chain acylcarnitines between visits 1 and 2 [adjusted odds ratio (OR) = 5.24; 95% confidence interval (CI) 1.07-25.9]. b-HBA was also correlated with acylcarnitines. Several cross-sectional and serial associations between novel serum biomarkers and cognitive functioning were identified. b-HBA may also be a cost-effective marker of dysregulation associated with cognitive decline.

When Does Alzheimer's Disease Really Start? The Role of Biomarkers

While Alzheimer’s disease (AD) classical diagnostic criteria rely on clinical data from a stablished symptomatic disease, newer criteria aim to identify the disease in its earlier stages. For that, they incorporated the use of AD’s specific biomarkers to reach a diagnosis, including the identification of Aβ and tau depositions, glucose hypometabolism, and cerebral atrophy. These biomarkers created a new concept of the disease, in which AD’s main pathological processes have already taken place decades before we can clinically diagnose the first symptoms. Therefore, AD is now considered a dynamic disease with a gradual progression, and dementia is its final stage. With that in mind, new models were proposed, considering the orderly increment of biomarkers and the disease as a continuum, or the variable time needed for the disease’s progression. In 2011, the National Institute on Aging and the Alzheimer’s Association (NIA-AA) created separate diagnostic recommendations for each stage of the disease continuum—preclinical, mild cognitive impairment, and dementia. However, new scientific advances have led them to create a unifying research framework in 2018 that, although not intended for clinical use as of yet, is a step toward shifting the focus from the clinical symptoms to the biological alterations and toward changing the future diagnostic and treatment possibilities. This review aims to discuss the role of biomarkers in the onset of AD.

Narrative impairment, white matter damage and CSF biomarkers in the Alzheimer's disease spectrum

BACKGROUND

Narrative discourse (ND) refers to one's ability to verbally reproduce a sequence of temporally and logically-linked events. Impairments in ND may occur in subjects with Amnestic Mild Cognitive Impairment (aMCI) and Alzheimer's Disease (AD), but correlates across this function, neuroimaging and cerebrospinal fluid (CSF) AD biomarkers remain understudied.

OBJECTIVES

We sought to measure correlates among ND, Diffusion Tensor Imaging (DTI) indexes and AD CSF biomarkers in patients within the AD spectrum.

RESULTS

Groups differed in narrative production (NProd) and comprehension. aMCI and AD presented poorer inference abilities than controls. AD subjects were more impaired than controls and aMCI regarding WB (p<0.01). ROIs DTI assessment distinguished the three groups. Mean Diffusivity (MD) in the uncinate, bilateral parahippocampal cingulate and left inferior occipitofrontal fasciculi negatively correlated with NProd. Changes in specific tracts correlated with T-tau/Aβ1-42 ratio in CSF.

CONCLUSIONS

AD and aMCI patients presented more ND impairments than controls. Those findings were associated with changes in ventral language-associated and in the inferior parahippocampal pathways. The latest were correlated with biomarkers' levels in the CSF.

METHODS

AD (N=14), aMCI (N=31) and Control (N=39) groups were compared for whole brain (WB) and regions of interest (ROI) DTI parameters, ND and AD CSF biomarkers.

Quantitative proteomics of synaptosome S-nitrosylation in Alzheimer's disease

Increasing evidence suggests that both synaptic loss and neuroinflammation constitute early pathologic hallmarks of Alzheimer's disease. A downstream event during inflammatory activation of microglia and astrocytes is the induction of nitric oxide synthase type 2, resulting in an increased release of nitric oxide and the post-translational S-nitrosylation of protein cysteine residues. Both early events, inflammation and synaptic dysfunction, could be connected if this excess nitrosylation occurs on synaptic proteins. In the long term, such changes could provide new insight into patho-mechanisms as well as biomarker candidates from the early stages of disease progression. This study investigated S-nitrosylation in synaptosomal proteins isolated from APP/PS1 model mice in comparison to wild type and NOS2^-/- mice, as well as human control, mild cognitive impairment and Alzheimer's disease brain tissues. Proteomics data were obtained using an established protocol utilizing an isobaric mass tag method, followed by nanocapillary high performance liquid chromatography tandem mass spectrometry. Statistical analysis identified the S-nitrosylation sites most likely derived from an increase in nitric oxide (NO) in dependence of presence of AD pathology, age and the key enzyme NOS2. The resulting list of candidate proteins is discussed considering function, previous findings in the context of neurodegeneration, and the potential for further validation studies.

A longitudinal assessment of retinal function and structure in the APP/PS1 transgenic mouse model of Alzheimer's disease

BACKGROUND

A great body of evidence suggests that there are retinal functional and structural changes that occur in Alzheimer's disease (AD). However, whether such changes are primary or secondary remains to be elucidated. We studied a range of retinal functional and structural parameters in association with AD- specific pathophysiological markers in the double transgenic APP/PS1 and control mice across age.

METHODS

Electroretinogram (ERG) and optical coherence tomography (OCT) was performed in APP/PS1 and wild type (WT) control mice every 3 months from 3 to 12 months of age. For functional assessment, the a- and b-wave of the ERG, amplitude of oscillatory potentials (OP) and the positive scotopic threshold response (pSTR) were quantified at each time point. For structural assessment, the inner and outer retinal thickness was segmented and measured from OCT scans. Episodic memory was evaluated at 6, 9 and 12 months of age using the novel object recognition test. Amyloid beta (Aβ) distribution in the hippocampus and the retina were visualised at 3, 6 and 12 months of age. Inter- and intra- group analysis was performed to study rate of change for each parameter between the two groups.

RESULTS

Inter-group analysis revealed a significant difference in b-wave and OPs of APP/PS1 compared to WT controls starting from 3 months (p < 0.001). There was also a significant difference in the amplitude of pSTR between the two groups starting from 6 months (p < 0.001). Furthermore, a significant difference in the inner retinal thickness, between the two groups, was observed starting from 9 months (p < 0.001).

CONCLUSIONS

We observed an age-related decline in retinal functional and structural parameters in both APP/PS1 and WT controls, however, inter-group analysis revealed that inner retinal functional and structural decline is exacerbated in APP/PS1 mice, and that retinal functional changes precede structural changes in this strain. Further studies are required to confirm whether such phenomenon occurs in humans and if studying retinal functional changes can aid-in early assessment of AD.

The CSF p-tau181/Aβ42 Ratio Offers a Good Accuracy “In Vivo” in the Differential Diagnosis of Alzheimer’s Dementia

Background:

The incoming disease-modifying therapies against Alzheimer’s disease (AD) require reliable diagnostic markers to correctly enroll patients all over the world. CSF AD biomarkers, namely amyloid-β 42 (Aβ42), total tau (t-tau), and tau phosphorylated at threonine 181 (p-tau181), showed good diagnostic accuracy in detecting AD pathology, but their real usefulness in daily clinical practice is still a matter of debate. Therefore, further validation in complex clinical settings, that is patients with different types of dementia, is needed to uphold their future worldwide adoption.

Methods:

We measured CSF AD biomarkers’ concentrations in a sample of 526 patients with a clinical diagnosis of dementia (277 with AD and 249 with Other Type of Dementia, OTD). Brain FDG-PET was also considered in a subsample of 54 patients with a mismatch between the clinical diagnosis and the CSF findings.

Results:

A p-tau181/Aβ42 ratio higher than 0.13 showed the best diagnostic performance in differentiating AD from OTD (86% accuracy index, 74% sensitivity, 81% specificity). In cases with a mismatch between clinical diagnosis and CSF findings, brain FDG-PET partially agreed with the p-tau181/Aβ42 ratio, thus determining an increase in CSF accuracy.

Conclusions:

The p-tau181/Aβ42 ratio alone might reliably detect AD pathology in heterogeneous samples of patients suffering from different types of dementia. It might constitute a simple, cost-effective and reproducible in vivo proxy of AD suitable to be adopted worldwide not only in daily clinical practice but also in future experimental trials, to avoid the enrolment of misdiagnosed AD patients.

A simplified and sensitive immunoprecipitation mass spectrometry protocol for the analysis of amyloid-beta peptides in brain tissue

In the field of Alzheimer's disease, there is an urgent need for novel analytical tools to identify disease-specific biomarkers and to evaluate therapeutics. Preclinical trials commonly employ amyloid beta (Aβ) peptide signatures as a read-out. In this paper, we report a simplified and detailed protocol for robust immunoprecipitation of Aβ in brain tissue prior to mass spectrometric detection exemplified by a study using transgenic mice. The established method employed murine monoclonal and rabbit polyclonal antibodies and was capable of yielding well-reproducible peaks of high intensity with low background signal intensities corresponding to various Aβ forms.

Serum Metabolite Markers of Dementia Through Quantitative NMR Analysis: The Importance of Threonine-Linked Metabolic Pathways

There is a great need for diagnostic biomarkers of impending dementia. Metabolite markers in blood have been investigated in several studies, but inconclusive findings encourage further investigation, particularly in the pre-diagnostic phase. In the present study, the serum metabolomes of 110 dementia or pre-diagnostic dementia individuals and 201 healthy individuals matched for age, gender, and education were analyzed by nuclear magnetic resonance spectroscopy in combination with multivariate data analysis. 58 metabolites were quantified in each of the 311 samples. Individuals with dementia were discriminated from controls using a panel of seven metabolites, while the pre-diagnostic dementia subjects were distinguished from controls using a separate set of seven metabolites, where threonine was a common significant metabolite in both panels. Metabolite and pathway alterations specific for dementia and pre-diagnostic dementia were identified, in particular a disturbed threonine catabolism at the pre-diagnostic stage that extends to several threonine-linked pathways at the dementia stage.

Joint associations of smartphone use and gender on multidimensional cognitive health among community-dwelling older adults: a cross-sectional study

Background

Smartphone use has become an increasingly pervasive part of our daily lives, and as a portable media device, smartphones provide good support for cognitive training during aging. However, little is known about the joint association of smartphone use and gender on the cognitive health of older adults, particularly with regard to multi-domain cognition.

Methods

A face-to-face survey of 3230 older adults aged 60+ years was conducted in Xiamen, China, in 2016. The Montreal Cognitive Assessment (MoCA) score was used to measure both general and multi-domain cognition. Smartphone use was self-reported and the number of the smartphone functions used (NSFU) was classified as 0, 1, and 2+. General and subdomain cognitive functions were modelled on NSFU only, gender only, and NSFU and gender combined by using a series of proportional-odds cumulative logit models. Furthermore, joint associations of gender and NSFU on both general and multi-domain cognition were estimated, and a four-category quantile classification was used to evaluate the total MoCA score.

Results

Among all 3230 respondents, 2600 remained after exclusion of respondents with very low MoCA scores (below the education-adjusted cut-offs for dementia). Only 29.96% of older adults used smartphones, 473 (60.72%) of which were men. Respondents who had a higher NSFU maintained a better general and sub-domain cognition except for memory and orientation. Although women had lower values compared to men in visuospatial ability (OR (95% CI): 0.46 (0.37–0.57)), they outperformed their male counterparts in memory (OR (95% CI): 1.38 (1.10–1.73)). The results of the joint association showed that women’s inferiority in visuospatial ability diminished when they had a NSFU of 2+. However, a significantly better improvement in memory for male was achieved when they had a NSFU of 1 rather than 2 + .

Conclusions

A higher NSFU was positively associated with increased general and partial subdomain cognitive functions. However, gender differences were found in visuospatial ability and memory, which could be alleviated by smartphone use.

Metabolomics in the Development and Progression of Dementia: A Systematic Review

Dementia has become a major global public health challenge with a heavy economic burden. It is urgently necessary to understand dementia pathogenesis and to identify biomarkers predicting risk of dementia in the preclinical stage for prevention, monitoring, and treatment. Metabolomics provides a novel approach for the identification of biomarkers of dementia. This systematic review aimed to examine and summarize recent retrospective cohort human studies assessing circulating metabolite markers, detected using high-throughput metabolomics, in the context of disease progression to dementia, including incident mild cognitive impairment, all-cause dementia, and cognitive decline. We systematically searched the PubMed, Embase, and Cochrane databases for retrospective cohort human studies assessing associations between blood (plasma or serum) metabolomics profile and cognitive decline and risk of dementia from inception through October 15, 2018. We identified 16 studies reporting circulating metabolites and risk of dementia, and six regarding cognitive performance change. Concentrations of several blood metabolites, including lipids (higher phosphatidylcholines, sphingomyelins, and lysophophatidylcholine, and lower docosahexaenoic acid and high-density lipoprotein subfractions), amino acids (lower branched-chain amino acids, creatinine, and taurine, and higher glutamate, glutamine, and anthranilic acid), and steroids were associated with cognitive decline and the incidence or progression of dementia. Circulating metabolites appear to be associated with the risk of dementia. Metabolomics could be a promising tool in dementia biomarker discovery. However, standardization and consensus guidelines for study design and analytical techniques require future development.

Amyloid blood biomarker detects Alzheimer's disease

Alzheimer's disease (AD) is currently incurable, but there is general agreement that a minimally invasive blood biomarker for screening in preclinical stages would be crucial for future therapy. Diagnostic tools for detection of AD are either invasive like cerebrospinal fluid (CSF) biomarkers or expensive such as positron emission tomography (PET) scanning. Here, we determine the secondary structure change of amyloid‐β (Aβ) in human blood. This change used as blood amyloid biomarker indicates prodromal AD and correlates with CSF AD biomarkers and amyloid PET imaging in the cross‐sectional BioFINDER cohort. In a further population‐based longitudinal cohort (ESTHER), the blood biomarker detected AD several years before clinical diagnosis in baseline samples with a positive likelihood ratio of 7.9; that is, those who were diagnosed with AD over the years were 7.9 times more likely to test positive. This assay may open avenues for blood screening of early AD stages as a funnel for further more invasive and expensive tests.

Prediction and Early Detection of Alzheimer's Dementia: Professional Disclosure Practices and Ethical Attitudes

BACKGROUND

Biomarker-supported testing for preclinical and prodromal Alzheimer's disease (AD) finds its way into clinical practice. Professional attitudes and practices regarding disclosure and ethical issues are controversial in many countries.

OBJECTIVES

Against this background, the objective was to survey the actual practice and the attitudes of physicians in German hospitals and memory clinics in order to explore possible practical insecurities and ethical concerns.

METHODS

A detailed survey with 37 items was conducted among medical professionals at German hospitals and memory clinics (n = 108). Analyses were performed using SPSS 21.0 (IBM). Findings were based on frequency and percentage distribution.

RESULTS

Nearly half of the respondents stated that persons with mild cognitive impairment and pathological cerebrospinal fluid biomarkers were informed they had or would soon develop AD. While 81% acknowledged a 'right not to know', 75% said that results were always communicated. A majority agreed there was a benefit of prediction or later life planning [end-of-life, financial, family, housing (73-75%)] but also expected high psychological stress (82%) and self-stigmatization (70%) for those tested.

CONCLUSIONS

There is considerable heterogeneity and insecurity regarding prediction and early detection in the context of AD in Germany. Information of professionals and standardization of professional testing and disclosure practices are needed.