Assessment of brain-derived extracellular vesicle enrichment for blood biomarker analysis in age-related neurodegenerative diseases: An international overview.

INTRODUCTION: Brain-derived extracellular vesicles (BEVs) in blood allows for minimally-invasive investigations of central nervous system (CNS) -specific markers of age-related neurodegenerative diseases (NDDs). Polymer-based EV- and immunoprecipitation (IP)-based BEV-enrichment protocols from blood have gained popularity. We systematically investigated protocol consistency across studies, and determined CNS-specificity of proteins associated with these protocols. METHODS: NDD articles investigating BEVs in blood using polymer-based and/or IP-based BEV enrichment protocols were systematically identified, and protocols compared. Proteins used for BEV-enrichment and/or post-enrichment were assessed for CNS- and brain-cell-type-specificity, extracellular domains (ECD+), and presence in EV-databases. RESULTS: A total of 82.1% of studies used polymer-based (ExoQuick) EV-enrichment, and 92.3% used L1CAM for IP-based BEV-enrichment. Centrifugation times differed across studies. A total of 26.8% of 82 proteins systematically identified were CNS-specific: 50% ECD+, 77.3% were listed in EV-databases. CONCLUSIONS: We identified protocol steps requiring standardization, and recommend additional CNS-specific proteins that can be used for BEV-enrichment or as BEV-biomarkers. HIGHLIGHTS: Across NDDs, we identified protocols commonly used for EV/BEV enrichment from blood. We identified protocol steps showing variability that require harmonization. We assessed CNS-specificity of proteins used for BEV-enrichment or found in BEV cargo. CNS-specific EV proteins with ECD+ or without were identified. We recommend evaluation of blood-BEV enrichment using these additional ECD+ proteins.

Proteome Profiling of Brain Vessels in a Mouse Model of Cerebrovascular Pathology.

Cerebrovascular pathology that involves altered protein levels (or signaling) of the transforming growth factor beta (TGFß) family has been associated with various forms of age-related dementias, including Alzheimer disease (AD) and vascular cognitive impairment and dementia (VCID). Transgenic mice overexpressing TGFß1 in the brain (TGF mice) recapitulate VCID-associated cerebrovascular pathology and develop cognitive deficits in old age or when submitted to comorbid cardiovascular risk factors for dementia. We characterized the cerebrovascular proteome of TGF mice using mass spectrometry (MS)-based quantitative proteomics. Cerebral arteries were surgically removed from 6-month-old-TGF and wild-type mice, and proteins were extracted and analyzed by gel-free nanoLC-MS/MS. We identified 3602 proteins in brain vessels, with 20 demonstrating significantly altered levels in TGF mice. For total and/or differentially expressed proteins (p ≤ 0.01, ≥ 2-fold change), using multiple databases, we (a) performed protein characterization, (b) demonstrated the presence of their RNA transcripts in both mouse and human cerebrovascular cells, and (c) demonstrated that several of these proteins were present in human extracellular vesicles (EVs) circulating in blood. Finally, using human plasma, we demonstrated the presence of several of these proteins in plasma and plasma EVs. Dysregulated proteins point to perturbed brain vessel vasomotricity, remodeling, and inflammation. Given that blood-isolated EVs are novel, attractive, and a minimally invasive biomarker discovery platform for age-related dementias, several proteins identified in this study can potentially serve as VCID markers in humans.

Assessment of brain-derived extracellular vesicle enrichment for blood biomarker analysis in age-related neurodegenerative diseases: An international overview.

INTRODUCTION: Brain-derived extracellular vesicles (BEVs) in blood allows for minimally- invasive investigations of CNS-specific markers of age-related neurodegenerative diseases (NDDs). Polymer-based EV- and immunoprecipitation (IP)-based BEV-enrichment protocols from blood have gained popularity. We systematically investigated protocol consistency across studies, and determined CNS-specificity of proteins associated with these protocols. METHODS: NDD articles investigating BEVs in blood using polymer-based and/or IP-based BEV enrichment protocols were systematically identified, and protocols compared. Proteins used for BEV-enrichment and/or post-enrichment were assessed for CNS- and brain-cell-type- specificity; extracellular domains (ECD+); and presence in EV-databases. RESULTS: 82.1% of studies used polymer-based (ExoQuick) EV-enrichment, and 92.3% used L1CAM for IP-based BEV-enrichment. Centrifugation times differed across studies. 26.8% of 82 proteins systematically identified were CNS-specific: 50% ECD+, 77.3% were listed in EV- databases. DISCUSSION: We identified protocol steps requiring standardization, and recommend additional CNS-specific proteins that can be used for BEV-enrichment or as BEV-biomarkers.

Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia.

With the increase in large multimodal cohorts and high-throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers. Remaining challenges include a lack of diversity in the data sets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies. Overcoming these challenges will involve collecting data from underrepresented populations, developing more powerful AI approaches, validating the use of noninvasive biomarkers, and adhering to reporting guidelines. By harnessing rich multimodal data through AI approaches and international collaborative innovation, we are well positioned to identify clinically useful biomarkers that are accurate, generalizable, unbiased, and acceptable in clinical practice. HIGHLIGHTS: Artificial intelligence and machine learning approaches may accelerate dementia biomarker discovery. Remaining challenges include data set suitability due to size and bias in cohort selection. Multimodal data, diverse data sets, improved machine learning approaches, real-world validation, and interdisciplinary collaboration are required.

Changes in levels of the antioxidant glutathione in brain and blood across the age span of healthy adults: A systematic review.

Aging is characterized by a gradual decline of the body's biological functions, which can lead to increased production of reactive oxygen species (ROS). Antioxidants neutralize ROS and maintain balance between oxidation and reduction. If ROS production exceeds the ability of antioxidant systems to neutralize, a damaging state of oxidative stress (OS) may exist. The reduced form of glutathione (GSH) is the most abundant antioxidant, and decline of GSH is considered a marker of OS. Our review summarizes the literature on GSH variations with age in healthy adults in brain (in vivo, ex vivo) and blood (plasma, serum), and reliability of in vivo magnetic resonance spectroscopy (MRS) measurement of GSH. A systematic PubMed search identified 35 studies. All in vivo MRS studies (N = 13) reported good to excellent reproducibility of GSH measures. In brain, 3 out of 4 MRS studies reported decreased GSH with age, measured in precuneus, cingulate, and occipital regions, while 1 study reported increased GSH with age in frontal and sensorimotor regions. In post-mortem brain, out of 3 studies, 2 reported decreased GSH with age in hippocampal and frontal regions, while 1 study reported increased GSH with age in a frontal region. Oxidized glutathione disulfide (GSSG) was reported to be increased in caudate with age in 1 study, suggesting OS. Although findings in the brain lacked a clear consensus, the majority of studies suggested a decline of GSH with age. The low number of studies (particularly ex vivo) and potential regional differences may have contributed to variability in the findings in brain. In blood, in contrast, GSH levels predominately were reported to decrease with advancing age (except in the oldest-old, who may represent a select group of particularly successful agers), while GSSG findings lacked consensus. The larger number of studies assessing age-specific GSH level changes in blood (N = 16) allowed for more robust consensus across studies than in brain. Overall, the literature suggests that aging is associated with increased OS in brain and body, but the timing and regional distribution of changes in the brain require further study. The contribution of brain OS to brain aging, and the effect of interventions to raise brain GSH levels on decline of brain function, remain understudied. Given that reliable tools to measure brain GSH exist, we hope this paper will serve as a catalyst to stimulate more work in this field.

Artificial intelligence for dementia research methods optimization.

Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care. HIGHLIGHTS: Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation.

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review.

INTRODUCTION: Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. METHODS: We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. RESULTS: A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. DISCUSSION: The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. HIGHLIGHTS: There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias.

Artificial Intelligence for Dementia Research Methods Optimization.

INTRODUCTION: Machine learning (ML) has been extremely successful in identifying key features from high-dimensional datasets and executing complicated tasks with human expert levels of accuracy or greater. METHODS: We summarize and critically evaluate current applications of ML in dementia research and highlight directions for future research. RESULTS: We present an overview of ML algorithms most frequently used in dementia research and highlight future opportunities for the use of ML in clinical practice, experimental medicine, and clinical trials. We discuss issues of reproducibility, replicability and interpretability and how these impact the clinical applicability of dementia research. Finally, we give examples of how state-of-the-art methods, such as transfer learning, multi-task learning, and reinforcement learning, may be applied to overcome these issues and aid the translation of research to clinical practice in the future. DISCUSSION: ML-based models hold great promise to advance our understanding of the underlying causes and pathological mechanisms of dementia.

Social isolation is linked to classical risk factors of Alzheimer's disease-related dementias.

Alzheimer's disease and related dementias is a major public health burden-compounding over upcoming years due to longevity. Recently, clinical evidence hinted at the experience of social isolation in expediting dementia onset. In 502,506 UK Biobank participants and 30,097 participants from the Canadian Longitudinal Study of Aging, we revisited traditional risk factors for developing dementia in the context of loneliness and lacking social support. Across these measures of subjective and objective social deprivation, we have identified strong links between individuals' social capital and various indicators of Alzheimer's disease and related dementias risk, which replicated across both population cohorts. The quality and quantity of daily social encounters had deep connections with key aetiopathological factors, which represent 1) personal habits and lifestyle factors, 2) physical health, 3) mental health, and 4) societal and external factors. Our population-scale assessment suggest that social lifestyle determinants are linked to most neurodegeneration risk factors, highlighting them as promising targets for preventive clinical action.

Ocular Biomarkers for Alzheimer Disease Dementia: An Umbrella Review of Systematic Reviews and Meta-analyses.

Importance: Several ocular biomarkers have been proposed for the early detection of Alzheimer disease (AD) and mild cognitive impairment (MCI), particularly fundus photography, optical coherence tomography (OCT), and OCT angiography (OCTA). Objective: To perform an umbrella review of systematic reviews to assess the diagnostic accuracy of ocular biomarkers for early diagnosis of Alzheimer disease. Data Sources: MEDLINE, Embase, and PsycINFO were searched from January 2000 to November 2021. The references of included reviews were also searched. Study Selection: Systematic reviews investigating the diagnostic accuracy of ocular biomarkers to detect AD and MCI, in secondary care or memory clinics, against established clinical criteria or clinical judgment. Data Extraction and Synthesis: The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline checklist was followed and the Risk Of Bias in Systematic reviews tool was used to assess review quality. Main Outcomes and Measures: The prespecified outcome was the accuracy of ocular biomarkers for diagnosing AD and MCI. The area under the curve (AUC) was derived from standardized mean difference. Results: From the 591 titles, 14 systematic reviews were included (median [range] number of studies in each review, 14 [5-126]). Only 4 reviews were at low risk of bias on all Risk of Bias in Systematic Reviews domains. The imaging-derived parameters with the most evidence for detecting AD compared with healthy controls were OCT peripapillary retinal nerve fiber layer thickness (38 studies including 1883 patients with AD and 2510 controls; AUC = 0.70; 95% CI, 0.53-0.79); OCTA foveal avascular zone (5 studies including 177 patients with AD and 371 controls; AUC = 0.73; 95% CI, 0.50-0.89); and saccadic eye movements prosaccade latency (30 studies including 651 patients with AD/MCI and 771 controls; AUC = 0.64; 95% CI, 0.58-0.69). Antisaccade error was investigated in fewer studies (12 studies including 424 patients with AD/MCI and 382 controls) and yielded the best accuracy (AUC = 0.79; 95% CI, 0.70-0.88). Conclusions and Relevance: This umbrella review has highlighted limitations in design and reporting of the existing research on ocular biomarkers for diagnosing AD. Parameters with the best evidence showed poor to moderate diagnostic accuracy in cross-sectional studies. Future longitudinal studies should investigate whether changes in OCT and OCTA measurements over time can yield accurate predictions of AD onset.

APOE alleles are associated with sex-specific structural differences in brain regions affected in Alzheimer's disease and related dementia.

Alzheimer's disease is marked by intracellular tau aggregates in the medial temporal lobe (MTL) and extracellular amyloid aggregates in the default network (DN). Here, we examined codependent structural variations between the MTL's most vulnerable structure, the hippocampus (HC), and the DN at subregion resolution in individuals with Alzheimer's disease and related dementia (ADRD). By leveraging the power of the approximately 40,000 participants of the UK Biobank cohort, we assessed impacts from the protective APOE ɛ2 and the deleterious APOE ɛ4 Alzheimer's disease alleles on these structural relationships. We demonstrate ɛ2 and ɛ4 genotype effects on the inter-individual expression of HC-DN co-variation structural patterns at the population level. Across these HC-DN signatures, recurrent deviations in the CA1, CA2/3, molecular layer, fornix's fimbria, and their cortical partners related to ADRD risk. Analyses of the rich phenotypic profiles in the UK Biobank cohort further revealed male-specific HC-DN associations with air pollution and female-specific associations with cardiovascular traits. We also showed that APOE ɛ2/2 interacts preferentially with HC-DN co-variation patterns in estimating social lifestyle in males and physical activity in females. Our structural, genetic, and phenotypic analyses in this large epidemiological cohort reinvigorate the often-neglected interplay between APOE ɛ2 dosage and sex and link APOE alleles to inter-individual brain structural differences indicative of ADRD familial risk.

Vascular Brain Lesions, Cognitive Reserve, and Their Association with Cognitive Profile in Persons with Early-Stage Cognitive Decline.

Background: Cognitive reserve may protect against the effects of brain pathology, but few studies have looked at whether cognitive reserve modifies the adverse effects of vascular brain pathology. Objective: We determined if cognitive reserve attenuates the associations of vascular brain lesions with worse cognition in persons with subjective concerns or mild impairment. Methods: We analyzed 200 participants aged 50-90 years from the Comprehensive Assessment of Neurodegeneration and Dementia (COMPASS-ND) study. Cognition was measured using the Montreal Cognitive Assessment and a neuropsychological test battery. High vascular lesion burden was defined as two or more supratentorial infarcts or beginning confluent or confluent white matter hyperintensity. Cognitive reserve proxies included education, occupational attainment, marital status, social activities, physical activity, household income, and multilingualism. Results: Mean age was 72.8 years and 48% were female; 73.5% had mild cognitive impairment and 26.5% had subjective concerns. Professional/managerial occupations, annual household income≥$60,000 per year, not being married/common law, and high physical activity were independently associated with higher cognition. Higher vascular lesion burden was associated with lower executive function, but the association was not modified by cognitive reserve. Conclusion: Markers of cognitive reserve are associated with higher cognition. Vascular lesion burden is associated with lower executive function. However, cognitive reserve does not mitigate the effects of vascular lesion burden on executive function. Public health efforts should focus on preventing vascular brain injury as well as promoting lifestyle factors related to cognitive reserve, as cognitive reserve alone may not mitigate the effects of vascular brain injury.

Brainhack: Developing a culture of open, inclusive, community-driven neuroscience.

Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open, inclusive environment. This NeuroView describes the myriad benefits for participants and the research community and how Brainhacks complement conventional formats to augment scientific progress.

Embracing diversity and inclusivity in an academic setting: Insights from the Organization for Human Brain Mapping.

Scientific research aims to bring forward innovative ideas and constantly challenges existing knowledge structures and stereotypes. However, women, ethnic and cultural minorities, as well as individuals with disabilities, are systematically discriminated against or even excluded from promotions, publications, and general visibility. A more diverse workforce is more productive, and thus discrimination has a negative impact on science and the wider society, as well as on the education, careers, and well-being of individuals who are discriminated against. Moreover, the lack of diversity at scientific gatherings can lead to micro-aggressions or harassment, making such meetings unpleasant, or even unsafe environments for early career and underrepresented scientists. At the Organization for Human Brain Mapping (OHBM), we recognized the need for promoting underrepresented scientists and creating diverse role models in the field of neuroimaging. To foster this, the OHBM has created a Diversity and Inclusivity Committee (DIC). In this article, we review the composition and activities of the DIC that have promoted diversity within OHBM, in order to inspire other organizations to implement similar initiatives. Activities of the committee over the past four years have included (a) creating a code of conduct, (b) providing diversity and inclusivity education for OHBM members, (c) organizing interviews and symposia on diversity issues, and (d) organizing family-friendly activities and providing childcare grants during the OHBM annual meetings. We strongly believe that these activities have brought positive change within the wider OHBM community, improving inclusivity and fostering diversity while promoting rigorous, ground-breaking science. These positive changes could not have been so rapidly implemented without the enthusiastic support from the leadership, including OHBM Council and Program Committee, and the OHBM Special Interest Groups (SIGs), namely the Open Science, Student and Postdoc, and Brain-Art SIGs. Nevertheless, there remains ample room for improvement, in all areas, and even more so in the area of targeted attempts to increase inclusivity for women, individuals with disabilities, members of the LGBTQ+ community, racial/ethnic minorities, and individuals of lower socioeconomic status or from low and middle-income countries. Here, we present an overview of the DIC's composition, its activities, future directions and challenges. Our goal is to share our experiences with a wider audience to provide information to other organizations and institutions wishing to implement similar comprehensive diversity initiatives. We propose that scientific organizations can push the boundaries of scientific progress only by moving beyond existing power structures and by integrating principles of equity and inclusivity in their core values.

A dataset of long-term consistency values of resting-state fMRI connectivity maps in a single individual derived at multiple sites and vendors using the Canadian Dementia Imaging Protocol.

The impact of multisite acquisition on resting-state functional MRI (rsfMRI) connectivity has recently gained attention. We provide consistency values (Pearson's correlation) between rsfMRI connectivity maps of an adult volunteer (Csub) scanned 25 times over 3.5 years at 13 sites using the Canadian Dementia Imaging Protocol (CDIP, www.cdip-pcid.ca). This dataset was generated as part of the following article: Multivariate consistency of resting-state fMRI connectivity maps acquired on a single individual over 2.5 years, 13 sites and 3 vendors [1]. Acquired on three 3T scanner vendors (GE, Siemens and Philips), the Csub dataset is part of an ongoing effort to monitor the quality and comparability of MRI data collected across the Canadian Consortium on Neurodegeneration in Aging (CCNA) imaging network. The participant was scanned 25 times in the above-mentioned article: multiple times at six sites over a period of 2.5 years, and once at the remaining seven sites. Since then the participant was scanned an additional 45 times, allowing us to extend the dataset to 70 rsfMRI scans over a period of >4 years. In addition, we provide intra- and inter-subject consistency values of rsfMRI connectivity maps derived from 26 adult participants belonging to the publicly released Hangzhou Normal University dataset (HNU1). All HNU1 participants underwent 10 rsfMRI scans over one month on a single 3T scanner (GE). Connectivity maps of seven canonical networks were generated for each scan in the two datasets (Csub and HNU1). All consistency values, along with the scripts used to preprocess the rsfMRI data and generate connectivity maps and pairwise consistency values, have been made available on two public repositories, Github and Zenodo. We have also made available four Jupyter notebooks that use the provided consistency values to (a) generate interactive graphical summaries - 1 notebook, (b) perform statistical analyses - 2 notebooks, and (c) perform data-driven cluster analysis for the recovery of subject identity (i.e. rsfMRI fingerprinting) - 1 notebook. In addition, we provide two interactive dashboards that allow visualization of individual connectivity maps from the two datasets. Finally, we also provide minimally preprocessed rsfMRI data in Brain Imaging Data Standard (BIDS) format on all 70 scans in the extended dataset.

Effective Self-Management for Early Career Researchers in the Natural and Life Sciences.

Early career researchers (ECRs) are faced with a range of competing pressures in academia, making self-management key to building a successful career. The Organization for Human Brain Mapping undertook a group effort to gather helpful advice for ECRs in self-management.

A Standardized Protocol for Efficient and Reliable Quality Control of Brain Registration in Functional MRI Studies.

Automatic alignment of brain anatomy in a standard space is a key step when processing magnetic resonance imaging for group analyses. Such brain registration is prone to failure, and the results are therefore typically reviewed visually to ensure quality. There is however no standard, validated protocol available to perform this visual quality control (QC). We propose here a standardized QC protocol for brain registration, with minimal training overhead and no required knowledge of brain anatomy. We validated the reliability of three-level QC ratings (OK, Maybe, Fail) across different raters. Nine experts each rated N = 100 validation images, and reached moderate to good agreement (kappa from 0.4 to 0.68, average of 0.54 ± 0.08), with the highest agreement for "Fail" images (Dice from 0.67 to 0.93, average of 0.8 ± 0.06). We then recruited volunteers through the Zooniverse crowdsourcing platform, and extracted a consensus panel rating for both the Zooniverse raters (N = 41) and the expert raters. The agreement between expert and Zooniverse panels was high (kappa = 0.76). Overall, our protocol achieved a good reliability when performing a two level assessment (Fail vs. OK/Maybe) by an individual rater, or aggregating multiple three-level ratings (OK, Maybe, Fail) from a panel of experts (3 minimum) or non-experts (15 minimum). Our brain registration QC protocol will help standardize QC practices across laboratories, improve the consistency of reporting of QC in publications, and will open the way for QC assessment of large datasets which could be used to train automated QC systems.

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-ß 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.

A multiomics approach to heterogeneity in Alzheimer's disease: focused review and roadmap.

Aetiological and clinical heterogeneity is increasingly recognized as a common characteristic of Alzheimer's disease and related dementias. This heterogeneity complicates diagnosis, treatment, and the design and testing of new drugs. An important line of research is discovery of multimodal biomarkers that will facilitate the targeting of subpopulations with homogeneous pathophysiological signatures. High-throughput 'omics' are unbiased data-driven techniques that probe the complex aetiology of Alzheimer's disease from multiple levels (e.g. network, cellular, and molecular) and thereby account for pathophysiological heterogeneity in clinical populations. This review focuses on data reduction analyses that identify complementary disease-relevant perturbations for three omics techniques: neuroimaging-based subtypes, metabolomics-derived metabolite panels, and genomics-related polygenic risk scores. Neuroimaging can track accrued neurodegeneration and other sources of network impairments, metabolomics provides a global small-molecule snapshot that is sensitive to ongoing pathological processes, and genomics characterizes relatively invariant genetic risk factors representing key pathways associated with Alzheimer's disease. Following this focused review, we present a roadmap for assembling these multiomics measurements into a diagnostic tool highly predictive of individual clinical trajectories, to further the goal of personalized medicine in Alzheimer's disease.

Recent advances from metabolomics and lipidomics application in alzheimer's disease inspiring drug discovery.

Introduction: Although age is a major risk factor for Alzheimer's disease (AD), it is not an inevitable consequence of aging nor is it exclusively an old-age disease. Several other major risk factors for AD are strongly associated with metabolism and include lack of exercise, obesity, diabetes, high blood pressure and cholesterol, over-consumption of alcohol and depression in addition to low educational level, social isolation, and cognitive inactivity. Approaches for Alzheimer prevention and treatment through manipulation of metabolism and utilization of active metabolites have great potential either as a primary or secondary treatment avenue or as a preventative strategy in high-risk individuals.Areas covered: This review outlines the current knowledge concerning the relationship between AD and metabolism and the novel treatments attempting to correct changes in AD patients determined through metabolomics or lipidomic analyses.Expert opinion: Metabolites are one of the main driving factors and indicators of AD and can offer many possible avenues for prevention and treatment. However, with the highly interconnected effects of metabolites and metabolism, as well as the many different routes for metabolism dysfunction, successful treatment would have to include the correction of metabolic errors as well as errors in transport and metabolite processing in order to affect and revert AD progression.