A Precision Medicine Initiative for Alzheimer’s disease: the road ahead to biomarker-guided integrative disease modeling

Phytoactive drugs used in the treatment of Alzheimer's disease and dementia

The prevalence of Alzheimer's disease and other forms of dementia is increasing worldwide, and finding effective treatments for these conditions is a major public health challenge. Natural bioactive drugs have been identified as a promising source of potential treatments, due to their ability to target multiple pathways and their low toxicity. This paper reviews the current state of research on natural bioactive drugs used in the treatment of Alzheimer's disease and other dementias. The paper summarizes the findings of studies on various natural compounds, including curcumin, resveratrol, caffeine, genistein, quercetin, GinkoBiloba, Withaniasomnifera, Ginseng Brahmi, Giloy, and huperzine, and their effects on cognitive function, neuroinflammation, and amyloid-beta accumulation. In this review, we discuss the mechanism of action involved in the treatment of Alzheimer's disease. The paper also discusses the challenges associated with developing natural bioactive drugs for dementia treatment, including issues related to bioavailability and standardization. Finally, the paper suggests directions for future research in this area, including the need for more rigorous clinical trials and the development of novel delivery systems to improve the efficacy of natural bioactive drugs. Overall, this review highlights the potential of natural bioactive drugs as a promising avenue for the development of safe and effective treatments for Alzheimer's disease and other dementias.

miR-519a-3p, found to regulate cellular prion protein during Alzheimer's disease pathogenesis, as a biomarker of asymptomatic stages

Clinical relevance of miRNAs as biomarkers is growing due to their stability and detection in biofluids. In this, diagnosis at asymptomatic stages of Alzheimer's disease (AD) remains a challenge since it can only be made at autopsy according to Braak NFT staging. Achieving the objective of detecting AD at early stages would allow possible therapies to be addressed before the onset of cognitive impairment. Many studies have determined that the expression pattern of some miRNAs is dysregulated in AD patients, but to date, none has been correlated with downregulated expression of cellular prion protein (PrPC) during disease progression. That is why, by means of cross studies of miRNAs up-regulated in AD with in silico identification of potential miRNAs-binding to 3'UTR of human PRNP gene, we selected miR-519a-3p for our study. Then, in vitro experiments were carried out in two ways. First, we validated miR-519a-3p target on 3'UTR-PRNP, and second, we analyzed the levels of PrPC expression after using of mimic technology on cell culture. In addition, RT-qPCR was performed to analyzed miR-519a-3p expression in human cerebral samples of AD at different stages of disease evolution. Additionally, samples of other neurodegenerative diseases such as other non-AD tauopathies and several synucleinopathies were included in the study. Our results showed that miR-519a-3p overlaps with PRNP 3'UTR in vitro and promotes downregulation of PrPC. Moreover, miR-519a-3p was found to be up-regulated exclusively in AD samples from stage I to VI, suggesting its potential use as a novel label of preclinical stages of the disease.

Personalized medicine in old age psychiatry and Alzheimer's disease

Elderly patients show us unfolded lives with unique individual characteristics. An increasing life span is associated with increasing physical and mental disease burden. Alzheimer's disease (AD) is an increasing challenge in old age. AD cannot be cured but it can be treated. The complexity of old age and AD offer targets for personalized medicine (PM). Targets for stratification of patients, detection of patients at risk for AD or for future targeted therapy are plentiful and can be found in several omic-levels.

Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities

In recent years, machine learning (ML) and deep learning (DL) have been the leading approaches to solving various challenges, such as disease predictions, drug discovery, medical image analysis, etc., in intelligent healthcare applications. Further, given the current progress in the fields of ML and DL, there exists the promising potential for both to provide support in the realm of healthcare. This study offered an exhaustive survey on ML and DL for the healthcare system, concentrating on vital state of the art features, integration benefits, applications, prospects and future guidelines. To conduct the research, we found the most prominent journal and conference databases using distinct keywords to discover scholarly consequences. First, we furnished the most current along with cutting-edge progress in ML-DL-based analysis in smart healthcare in a compendious manner. Next, we integrated the advancement of various services for ML and DL, including ML-healthcare, DL-healthcare, and ML-DL-healthcare. We then offered ML and DL-based applications in the healthcare industry. Eventually, we emphasized the research disputes and recommendations for further studies based on our observations.

Plasma cell-free RNA profiling of Vietnamese Alzheimer's patients reveals a linkage with chronic inflammation and apoptosis: a pilot study

Introduction

Circulating cell-free RNA (cfRNA) is a potential hallmark for early diagnosis of Alzheimer's Disease (AD) as it construes the genetic expression level, giving insights into the pathological progress from the outset. Profiles of cfRNA in Caucasian AD patients have been investigated thoroughly, yet there was no report exploring cfRNAs in the ASEAN groups. This study examined the gap, expecting to support the development of point-of-care AD diagnosis.

Methods

cfRNA profiles were characterized from 20 Vietnamese plasma samples (10 probable AD and 10 age-matched controls). RNA reads were subjected to differential expression (DE) analysis. Weighted gene correlation network analysis (WGCNA) was performed to identify gene modules that were significantly co-expressed. These modules' expression profiles were then correlated with AD status to identify relevant modules. Genes with the highest intramodular connectivity (module membership) were selected as hub genes. Transcript counts of differentially expressed genes were correlated with key AD measures-MMSE and MTA scores-to identify potential biomarkers.

Results

136 genes were identified as significant AD hallmarks (p < 0.05), with 52 downregulated and 84 upregulated in the AD cohort. 45.6% of these genes are highly expressed in the hippocampus, cerebellum, and cerebral cortex. Notably, all markers related to chronic inflammation were upregulated, and there was a significant shift in all apoptotic markers. Three co-expressed modules were found to be significantly correlated with Alzheimer's status (p < 0.05; R2> 0.5). Functional enrichment analysis on these modules reveals an association with focal adhesion, nucleocytoplasmic transport, and metal ion response leading to apoptosis, suggesting the potential participation of these pathways in AD pathology. 47 significant hub genes were found to be differentially expressed genes with the highest connectivity. Six significant hub genes (CREB1, YTHDC1, IL1RL1, PHACTR2, ANKRD36B, RNF213) were found to be significantly correlated with MTA and MMSE scores. Other significant transcripts (XRN1, UBB, CHP1, THBS1, S100A9) were found to be involved in inflammation and neuronal death. Overall, we have identified candidate transcripts in plasma cf-RNA that are differentially expressed and are implicated in inflammation and apoptosis, which can jumpstart further investigations into applying cf-RNA as an AD biomarker in Vietnam and ASEAN countries.

Normative Data on Serum and Plasma Tryptophan and Kynurenine Concentrations from 8089 Individuals Across 120 Studies: A Systematic Review and Meta-Analysis

In this systematic review and meta-analysis, a normative dataset is generated from the published literature on the kynurenine pathway in control participants extracted from case-control and methodological validation studies. Study characteristics were mapped, and studies were evaluated in terms of analytical rigour and methodological validation. Meta-analyses of variance between types of instruments, sample matrices and metabolites were conducted. Regression analyses were applied to determine the relationship between metabolite, sample matrix, biological sex, participant age and study age. The grand mean concentrations of tryptophan in the serum and plasma were 60.52 ± 15.38 μM and 51.45 ± 10.47 μM, respectively. The grand mean concentrations of kynurenine in the serum and plasma were 1.96 ± 0.51 μM and 1.82 ± 0.54 μM, respectively. Regional differences in metabolite concentrations were observed across America, Asia, Australia, Europe and the Middle East. Of the total variance within the data, mode of detection (MOD) accounted for up to 2.96%, sample matrix up to 3.23%, and their interaction explained up to 1.53%; the latter of which was determined to be negligible. This review was intended to inform future empirical research and method development studies and successfully synthesised pilot data. The pilot data reported in this study will inform future precision medicine initiatives aimed at targeting the kynurenine pathway by improving the availability and quality of normative data.

Cognitive impact of multidomain intervention and omega 3 according to blood Aβ42/40 ratio: a subgroup analysis from the randomized MAPT trial

BACKGROUND

In MAPT (Multidomain Alzheimer Preventive Trial), a cognitive effect of multidomain intervention (MI) was showed in non-demented subjects with positive amyloid PET. However, screening eligible patients for multidomain intervention by PET is difficult to generalize in real-world settings.

METHODS

MAPT study was a 3-year, randomized, placebo-controlled trial followed by a 2-year observational and optional extension. All participants were non-demented and randomly assigned (1:1:1:1) to the MI plus omega 3, MI plus placebo, omega 3 alone, or placebo alone group. The objectives were to assess the cognitive effect of MAPT interventions (omega 3 supplementation, MI, combined intervention) in non-demented subjects according to amyloid blood status at 12, 36, and 60 months. In this subgroup analysis (n = 483), amyloid status was defined by plasma Aβ42/40 ratio (cutoff ≤ 0.0107). The primary outcome measure was the change in cognitive composite score after a 1, 3, and 5-year clinical follow-up.

RESULTS

The intention-to-treat (ITT) population included 483 subjects (161 positive and 322 negative amyloid participants based on plasma Aβ42/40 ratio). In the positive amyloid ITT population, we showed a positive effect of MI plus omega 3 on the change in composite cognitive score in 12 (raw p = .0350, 0.01917, 95% CI = [0.0136 to 0.3699]) and 36 months (raw p = .0357, 0.2818, 95% CI = [0.0190 to 0.5446]). After correction of multiple comparisons and adjustments, these differences were not significant (adjusted p = .1144 and .0690). In the per-protocol positive amyloid group (n = 154), we observed a significant difference between the combined intervention and placebo groups at 12 (p = .0313, 0.2424, 0.0571 to 0.4276) and 36 months (p = .0195, 0.3747, 0.1055 to 0.6439) persisting after adjustment. In the ITT and per-protocol analyses, no cognitive effect was observed in the positive and negative amyloid group at 60-month visit.

CONCLUSIONS

These findings suggest a benefit of MI plus omega 3 in positive blood amyloid subjects. This promising trend needs to be confirmed before using blood biomarkers for screening in preventive trials.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT01513252 .

A blood-based, metabolite and demographic characteristic markers panel for the diagnosis of Alzheimer's disease

Aims: This work was designed to provide early diagnosis strategies for Alzheimer's disease (AD) based on the identification of blood metabolic biomarkers. Patients & methods: A total of 90 subjects aged 60 years or older were included in this study; 45 patients were assigned to the case group and control group, respectively. A total of 31 target metabolites were quantitatively analyzed by parallel reaction monitoring between the two groups. Results & conclusion: Three metabolites were screened out, including cystine, serine and alanine/sarcosine. Logistic regression and random forest analysis were used to establish AD diagnosis models, and the model combining metabolic biomarkers and demographic variables had higher detection efficiency (area under the curve = 0.869). A combination diagnostic model to provide a scientific reference for early screening and diagnosis of AD was constructed.

In silico study on evaluation of corosolic acid of Lagerstroemia speciosa against Alzheimer’s disease

Purpose

Alzheimer’s disease (AD), the most common cause of dementia, is a neurodegenerative disorder caused by the aggregation of amyloid-beta (Aβ) at outside of neuron cells and also due to tau aggregation inside the cell. Corosolic acid is aimed to be selected as a main active constituent of Lagerstroemia speciosa for the study.

Design/methodology/approach

In the present study, molecular docking of corosolic acid and tau protein was examined using PyRx-v.0.8 software. Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties were described and a molecular dynamics study of the bound complex was performed using Desmond.

Findings

The docking score and interactions suggested that the corosolic acid (CID:6918774) could bind to tau protein to prevent the fibrillar network, to prevent AD. During simulation corosolic acid-bound protein root mean square deviation (RMSD) values showed more stability when compared to the Apo form of protein. Molecular dynamics study of tau protein and corosolic acid complex gave the insights to develop a drug-like candidate against AD.

Originality/value

The use of corosolic acid of Lagerstroemia speciosa to prevent AD is supported by preliminary analysis on a computational basis. This compound should explore in terms of experimental strategies for the further drug development process. However, in vitro and in vivo evaluation studies are required to suggest the use of corosolic acid against AD.

Translating precision medicine for autism spectrum disorder: A pressing need

Autism spectrum disorder (ASD) is a heterogenous group of neurodevelopmental disorders (NDDs) with a high unmet medical need. Currently, ASD is diagnosed according to behavior-based criteria that overlook clinical and genomic heterogeneity, thus repeatedly resulting in failed clinical trials. Here, we summarize the scientific evidence pointing to the pressing need to create a precision medicine framework for ASD and other NDDs. We discuss the role of omics and systems biology to characterize more homogeneous disease subtypes with different underlying pathophysiological mechanisms and to determine corresponding tailored treatments. Finally, we provide recent initiatives towards tackling the complexity in NDDs for precision medicine and cost-effective drug discovery.

The Future of Precision Medicine in the Cure of Alzheimer's Disease

This decade has seen the beginning of ground-breaking conceptual shifts in the research of Alzheimer's disease (AD), which acknowledges risk elements and the evolving wide spectrum of complicated underlying pathophysiology among the range of diverse neurodegenerative diseases. Significant improvements in diagnosis, treatments, and mitigation of AD are likely to result from the development and application of a comprehensive approach to precision medicine (PM), as is the case with several other diseases. This strategy will probably be based on the achievements made in more sophisticated research areas, including cancer. PM will require the direct integration of neurology, neuroscience, and psychiatry into a paradigm of the healthcare field that turns away from the isolated method. PM is biomarker-guided treatment at a systems level that incorporates findings of the thorough pathophysiology of neurodegenerative disorders as well as methodological developments. Comprehensive examination and categorization of interrelated and convergent disease processes, an explanation of the genomic and epigenetic drivers, a description of the spatial and temporal paths of natural history, biological markers, and risk markers, as well as aspects about the regulation, and the ethical, governmental, and sociocultural repercussions of findings at a subclinical level all require clarification and realistic execution. Advances toward a comprehensive systems-based approach to PM may finally usher in a new era of scientific and technical achievement that will help to end the complications of AD.

The emerging relationship between vitamin K and neurodegenerative diseases: a review of current evidence

Neurodegenerative disease refers to a group of disorders that predominantly damage the neurons in the brain. Despite significant progress in the knowledge of neurodegenerative diseases, there is currently no disease-modifying drug available. Vitamin K was first established for its involvement in blood clotting, but there is now compelling evidence indicating its role in the neurological system. In particular, the results of recent studies on the effects of vitamin K2 on preventing apoptosis, oxidative stress, and microglial activation in neuron cells through its role in electron transport are very promising against Alzheimer's disease. In addition to its protective effect on cognitive functions, its inhibitory effects on inflammation and α-synuclein fibrillization in Parkinson's disease, which has been revealed in recent years, are remarkable. Although there are many studies on the mechanism and possible treatment methods of neurodegenerative diseases, especially Parkinson's and Alzheimer's disease, studies on the relationship between vitamin K and neurodegenerative diseases are very limited, yet have promising findings. Vitamin K has also been proposed for therapeutic use in multiple sclerosis patients to lower the intensity or to slow down the progression of the disease. Accordingly, the aim of this study is to review the current evidence for the use of vitamin K supplementation in neurodegenerative diseases, in particular Alzheimer's disease, Parkinson's disease, and multiple sclerosis.

Precision Medicine for Preventing Alzheimer's Disease: Analysis of the ADAPT Study

BACKGROUND

The Alzheimer's Disease Anti-inflammatory Prevention Trial (ADAPT) was the first-ever large-scale anti-inflammatory prevention trial targeting Alzheimer's disease.

OBJECTIVE

The overall goal of this study was to evaluate predictive blood biomarker profiles that identified individuals most likely to be responders on NSAID treatment or placebo at 12 and 24 months.

METHODS

Baseline (n = 193) and 12-month (n = 562) plasma samples were assayed. The predictive biomarker profile was generated using SVM analyses with response on treatment (yes/no) as the outcome variable.

RESULTS

Baseline (AUC = 0.99) and 12-month (AUC = 0.99) predictive biomarker profiles were highly accurate in predicting response on Celecoxib arm at 12 and 24 months. The baseline (AUC = 0.95) and 12-month (AUC = 0.9) predictive biomarker profile predicting response on Naproxen were also highly accurate at 12 and 24 months. The baseline (AUC = 0.93) and 12-month (AUC = 0.99) predictive biomarker profile was also highly accurate in predicting response on placebo. As with our prior work, the profiles varied by treatment arm.

CONCLUSIONS

The current results provide additional support for a precision medicine model for treating and preventing Alzheimer's disease.

Transcranial magnetic stimulation treatment in Alzheimer's disease: a meta-analysis of its efficacy as a function of protocol characteristics and degree of personalization

Alzheimer's disease (AD) represents the most common type of neurodegenerative disorder. Although our knowledge on the causes of AD remains limited and no curative treatments are available, several interventions have been proposed in trying to improve patients' symptomatology. Among those, transcranial magnetic stimulation (TMS) has been shown a promising, safe and noninvasive intervention to improve global cognitive functioning. Nevertheless, we currently lack agreement between research studies on the optimal stimulation protocol yielding the highest efficacy in these patients. To answer this query, we conducted a systematic literature search in PubMed, PsycINFO and Scopus databases and meta-analysis of studies published in the last 10 years (2010-2021) according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Differently from prior published meta-analytic work, we investigated whether protocols that considered participants-specific neuroimaging scans for the selection of individualized stimulation targets held more successful outcomes compared to those relying on a generalized targeting selection criteria. We then compared the effect sizes of subsets of studies based on additional protocol characteristics (frequency, duration of intervention, number of stimulation sites, use of concomitant cognitive training and patients' educational level). Our results confirm TMS efficacy in improving global cognitive functioning in mild-to-moderate AD patients, but also highlight the flaws of current protocols characteristics, including a possible lack of sufficient personalization in stimulation protocols.

Is liquid biopsy mature enough for the diagnosis of Alzheimer’s disease?

The preclinical diagnosis and clinical practice for Alzheimer’s disease (AD) based on liquid biopsy have made great progress in recent years. As liquid biopsy is a fast, low-cost, and easy way to get the phase of AD, continual efforts from intense multidisciplinary studies have been made to move the research tools to routine clinical diagnostics. On one hand, technological breakthroughs have brought new detection methods to the outputs of liquid biopsy to stratify AD cases, resulting in higher accuracy and efficiency of diagnosis. On the other hand, diversiform biofluid biomarkers derived from cerebrospinal fluid (CSF), blood, urine, Saliva, and exosome were screened out and biologically verified. As a result, more detailed knowledge about the molecular pathogenesis of AD was discovered and elucidated. However, to date, how to weigh the reports derived from liquid biopsy for preclinical AD diagnosis is an ongoing question. In this review, we briefly introduce liquid biopsy and the role it plays in research and clinical practice. Then, we summarize the established fluid-based assays of the current state for AD diagnostic such as ELISA, single-molecule array (Simoa), Immunoprecipitation–Mass Spectrometry (IP–MS), liquid chromatography–MS, immunomagnetic reduction (IMR), multimer detection system (MDS). In addition, we give an updated list of fluid biomarkers in the AD research field. Lastly, the current outstanding challenges and the feasibility to use a stand-alone biomarker in the joint diagnostic strategy are discussed.

Blood-Based Biomarkers for Alzheimer's Disease Diagnosis and Progression: An Overview

Alzheimer’s Disease (AD) is a progressive neurodegenerative disease characterized by amyloid-β (Aβ) plaque deposition and neurofibrillary tangle accumulation in the brain. Although several studies have been conducted to unravel the complex and interconnected pathophysiology of AD, clinical trial failure rates have been high, and no disease-modifying therapies are presently available. Fluid biomarker discovery for AD is a rapidly expanding field of research aimed at anticipating disease diagnosis and following disease progression over time. Currently, Aβ_1–42, phosphorylated tau, and total tau levels in the cerebrospinal fluid are the best-studied fluid biomarkers for AD, but the need for novel, cheap, less-invasive, easily detectable, and more-accessible markers has recently led to the search for new blood-based molecules. However, despite considerable research activity, a comprehensive and up-to-date overview of the main blood-based biomarker candidates is still lacking. In this narrative review, we discuss the role of proteins, lipids, metabolites, oxidative-stress-related molecules, and cytokines as possible disease biomarkers. Furthermore, we highlight the potential of the emerging miRNAs and long non-coding RNAs (lncRNAs) as diagnostic tools, and we briefly present the role of vitamins and gut-microbiome-related molecules as novel candidates for AD detection and monitoring, thus offering new insights into the diagnosis and progression of this devastating disease.

Imaging Clinical Subtypes and Associated Brain Networks in Alzheimer’s Disease

Alzheimer’s disease (AD) does not present uniform symptoms or a uniform rate of progression in all cases. The classification of subtypes can be based on clinical symptoms or patterns of pathological brain alterations. Imaging techniques may allow for the identification of AD subtypes and their differentiation from other neurodegenerative diseases already at an early stage. In this review, the strengths and weaknesses of current clinical imaging methods are described. These include positron emission tomography (PET) to image cerebral glucose metabolism and pathological amyloid or tau deposits. Magnetic resonance imaging (MRI) is more widely available than PET. It provides information on structural or functional changes in brain networks and their relation to AD subtypes. Amyloid PET provides a very early marker of AD but does not distinguish between AD subtypes. Regional patterns of pathology related to AD subtypes are observed with tau and glucose PET, and eventually as atrophy patterns on MRI. Structural and functional network changes occur early in AD but have not yet provided diagnostic specificity.

A Proposed Brain-, Spine-, and Mental- Health Screening Methodology (NEUROSCREEN) for Healthcare Systems: Position of the Society for Brain Mapping and Therapeutics

The COVID-19 pandemic has accelerated neurological, mental health disorders, and neurocognitive issues. However, there is a lack of inexpensive and efficient brain evaluation and screening systems. As a result, a considerable fraction of patients with neurocognitive or psychobehavioral predicaments either do not get timely diagnosed or fail to receive personalized treatment plans. This is especially true in the elderly populations, wherein only 16% of seniors say they receive regular cognitive evaluations. Therefore, there is a great need for development of an optimized clinical brain screening workflow methodology like what is already in existence for prostate and breast exams. Such a methodology should be designed to facilitate objective early detection and cost-effective treatment of such disorders. In this paper we have reviewed the existing clinical protocols, recent technological advances and suggested reliable clinical workflows for brain screening. Such protocols range from questionnaires and smartphone apps to multi-modality brain mapping and advanced imaging where applicable. To that end, the Society for Brain Mapping and Therapeutics (SBMT) proposes the Brain, Spine and Mental Health Screening (NEUROSCREEN) as a multi-faceted approach. Beside other assessment tools, NEUROSCREEN employs smartphone guided cognitive assessments and quantitative electroencephalography (qEEG) as well as potential genetic testing for cognitive decline risk as inexpensive and effective screening tools to facilitate objective diagnosis, monitor disease progression, and guide personalized treatment interventions. Operationalizing NEUROSCREEN is expected to result in reduced healthcare costs and improving quality of life at national and later, global scales.

Effects of a 16-week multimodal exercise program on activities of daily living in institutionalized individuals with dementia

We aimed to examine the effects of a 16-week multimodal exercise program (MEP) on activities of daily living (ADL) in individuals with dementia (IWD). Furthermore, we investigated the participants’ individual response to the MEP and whether baseline cognitive and motor performance explain ADL performance. We conducted a multicenter randomized controlled trial (RCT) involving 319 participants aged ≥ 65 years with mild to moderate dementia. ADL were assessed at baseline and after the 16-week intervention using the Barthel Index (BI), the Erlangen Test of Activities of Daily Living (E-ADL) and the 7‑item Physical Performance Test (PPT-7). We additionally assessed cognitive and motor performance using standardized and validated assessments. Intervention effects were examined through two-factor analysis of variance with repeated measurements applying a per protocol and an intention-to-treat analysis. We compared baseline cognitive and motor performance between positive-responders (positive-R), non-responders (non-R), and negative-responders (negative-R) and examined cognitive and motor performance as potential cofounders of ADL by conducting multiple regression analyses. There were no significant time×group effects on ADL. Between 20 and 32% of participants responded positively to the intervention, i.e., improved ADL performance from baseline to follow-up. Positive-R had worse baseline motor performance compared to non-R. Cognitive and motor performance explained up to 51.4% of variance in ADL. The MEP had no significant overall effect on ADL in IWD. This may be related to insufficient exercise intensity. However, our results indicate that the response to the MEP depends on individual prerequisites which should thus be considered in further research on individual exercise approaches.

Heterogeneity and Factorial Structure in Alzheimer's Disease: A Cognitive Perspective

BACKGROUND

Alzheimer's disease (AD) patients show heterogeneous cognitive profiles which suggest the existence of cognitive subgroups. A deeper comprehension of this heterogeneity could contribute to move toward a precision medicine perspective.

OBJECTIVE

In this study, we aimed 1) to investigate AD cognitive heterogeneity as a product of the combination of within- (factors) and between-patients (sub-phenotypes) components, and 2) to promote its assessment in clinical practice by defining a small set of critical tests for this purpose.

METHODS

We performed factor mixture analysis (FMA) on neurocognitive assessment results of N = 230 patients with a clinical diagnosis of AD. This technique allowed to investigate the structure of cognitive heterogeneity in this sample and to characterize the core features of cognitive sub-phenotypes. Subsequently, we performed a tests selection based on logistic regression to highlight the best tests to detect AD patients in our sample. Finally, the accuracy of the same tests in the discrimination of sub-phenotypes was evaluated.

RESULTS

FMA revealed a structure characterized by five latent factors and four groups, which were identifiable by means of a few cognitive tests and were mainly characterized by memory deficits with visuospatial difficulties ("Visuospatial AD"), typical AD cognitive pattern ("Typical AD"), less impaired memory ("Mild AD"), and language/praxis deficits with relatively spared memory ("Nonamnestic AD").

CONCLUSION

The structure of cognitive heterogeneity in our sample of AD patients, as studied by FMA, could be summarized by four sub-phenotypes with distinct cognitive characteristics easily identifiable in clinical practice. Clinical implications under the precision medicine framework are discussed.

Using machine learning approaches for multi-omics data analysis: A review

With the development of modern high-throughput omic measurement platforms, it has become essential for biomedical studies to undertake an integrative (combined) approach to fully utilise these data to gain insights into biological systems. Data from various omics sources such as genetics, proteomics, and metabolomics can be integrated to unravel the intricate working of systems biology using machine learning-based predictive algorithms. Machine learning methods offer novel techniques to integrate and analyse the various omics data enabling the discovery of new biomarkers. These biomarkers have the potential to help in accurate disease prediction, patient stratification and delivery of precision medicine. This review paper explores different integrative machine learning methods which have been used to provide an in-depth understanding of biological systems during normal physiological functioning and in the presence of a disease. It provides insight and recommendations for interdisciplinary professionals who envisage employing machine learning skills in multi-omics studies.

Personalizing the Care and Treatment of Alzheimer's Disease: An Overview

Alzheimer's disease (AD) is a progressive, complex, and multifactorial neurodegenerative disorder, still without effective and stable therapeutic strategies. Currently, available medications for AD are based on symptomatic therapy, which include acetylcholinesterase (AChE) inhibitors and N-methyl-D-aspartate (NMDA) receptor antagonist. Additionally, medications such as antipsychotic drugs, antidepressants, sedative, and hypnotic agents, and mood stabilizers are used for the management of behavioral and psychological symptoms of dementia (BPSD). Clinical research has been extensively investigated treatments focusing on the hallmark pathology of AD, including the amyloid deposition, tau hyperphosphorylation, neuroinflammation, and vascular changes; however, so far without success, as all new potential drugs failed to show significant clinical benefit. The underlying heterogeneous etiology and diverse symptoms of AD suggest that a precision medicine strategy is required, which would take into account the complex genetic, epigenetic, and environmental landscape of each AD patient. The article provides a comprehensive overview of the literature on AD, the current and potential therapy of both cognitive symptoms as well as BPSD, with a special focus on gut microbiota and epigenetic modifications as new emerging drug targets. Their specific patterns could represent the basis for novel individually tailored approaches aimed to optimize precision medicine strategies for AD prevention and treatment. However, the successful application of precision medicine to AD demands a further extensive research of underlying pathological processes, as well as clinical and biological complexity of this multifactorial neurodegenerative disorder.

The Potential of Induced Pluripotent Stem Cells to Treat and Model Alzheimer's Disease

An estimated 6.2 million Americans aged 65 or older are currently living with Alzheimer's disease (AD), a neurodegenerative disease that disrupts an individual's ability to function independently through the degeneration of key regions in the brain, including but not limited to the hippocampus, the prefrontal cortex, and the motor cortex. The cause of this degeneration is not known, but research has found two proteins that undergo posttranslational modifications: tau, a protein concentrated in the axons of neurons, and amyloid precursor protein (APP), a protein concentrated near the synapse. Through mechanisms that have yet to be elucidated, the accumulation of these two proteins in their abnormal aggregate forms leads to the neurodegeneration that is characteristic of AD. Until the invention of induced pluripotent stem cells (iPSCs) in 2006, the bulk of research was carried out using transgenic animal models that offered little promise in their ability to translate well from benchtop to bedside, creating a bottleneck in the development of therapeutics. However, with iPSC, patient-specific cell cultures can be utilized to create models based on human cells. These human cells have the potential to avoid issues in translatability that have plagued animal models by providing researchers with a model that closely resembles and mimics the neurons found in humans. By using human iPSC technology, researchers can create more accurate models of AD ex vivo while also focusing on regenerative medicine using iPSC in vivo. The following review focuses on the current uses of iPSC and how they have the potential to regenerate damaged neuronal tissue, in the hopes that these technologies can assist in getting through the bottleneck of AD therapeutic research.

P-tau/Aβ42 and Aβ42/40 ratios in CSF are equally predictive of amyloid PET status

INTRODUCTION

Measurement of amyloid beta (Aβ40 and Aβ42) and tau (phosphorylated tau [p-tau] and total tau [t-tau]) in cerebrospinal fluid (CSF) can be utilized to differentiate clinical and preclinical Alzheimer's disease dementia (AD) from other neurodegenerative processes.

METHODS

CSF biomarkers were measured in 150 participants from the Mayo Clinic Study of Aging and the Alzheimer's Disease Research Center. P-tau/Aβ42 (Roche Elecsys, Fujirebio LUMIPULSE) and Aβ42/40 (Fujirebio LUMIPULSE) ratios were compared to one another and to amyloid positron emission tomography (PET) classification.

RESULTS

Strong correlation was observed between LUMIPULSE p-tau/Aβ42 and Aβ42/40, as well as Elecsys and LUMIPULSE p-tau/Aβ42 and Aβ42/40 (Spearman's ρ = -0.827, -0.858, and 0.960, respectively). Concordance between LUMIPULSE p-tau/Aβ42 and Aβ42/40 was 96% and between Elecsys p-tau/Aβ42 and both LUMIPULSE ratios was 97%. All ratios had > 94% overall, positive, and negative percent agreement with amyloid PET classification.

DISCUSSION

These data suggest that p-tau/Aβ42 and Aβ42/40 ratios provide similar clinical information in the assessment of amyloid pathology.

Personalized treatment interventions: nonpharmacological and natural treatment strategies in Alzheimer's disease

Introduction: Alzheimer's disease (AD) is a slow, irreversible, progressive, complex, and fatal neurodegenerative disorder. Available pharmacological treatment, known for almost two decades, does not cure the disease, but only alleviates the symptoms, with various efficacy and different side effects. Therefore, there is an unmet need to find other person-centered or personalized approaches to treat AD.Areas covered: This article describes the application of precision medicine-like approaches utilizing nonpharmacological treatment strategies and the use of natural products in personalized care for patients with AD.Expert opinion: Due to the heterogeneity of disease symptoms, somatic conditions, and patient preferences, there is definitely no "one size fits all" intervention. Therefore, individualized treatment choice is based on dementia stage, medical and psychiatric comorbidity, leading symptoms, patient preferences, and remaining capacity of the patient. In the absence of disease-modifying agents, a patient-centered, multidisciplinary team approach appears to be the best option to alleviate the heavy symptomatic burden in this unfortunate population. Hence, appropriate interventions can be offered along the AD continuum, while a better understanding of personal characteristics might help in establishing optimal individualized treatment, as well as its duration and intensity, to deliver interventions in the most effective ways.

Artificial intelligence and leukocyte epigenomics: Evaluation and prediction of late-onset Alzheimer's disease

We evaluated the utility of leucocyte epigenomic-biomarkers for Alzheimer’s Disease (AD) detection and elucidates its molecular pathogeneses. Genome-wide DNA methylation analysis was performed using the Infinium MethylationEPIC BeadChip array in 24 late-onset AD (LOAD) and 24 cognitively healthy subjects. Data were analyzed using six Artificial Intelligence (AI) methodologies including Deep Learning (DL) followed by Ingenuity Pathway Analysis (IPA) was used for AD prediction. We identified 152 significantly (FDR p<0.05) differentially methylated intragenic CpGs in 171 distinct genes in AD patients compared to controls. All AI platforms accurately predicted AD with AUCs ≥0.93 using 283,143 intragenic and 244,246 intergenic/extragenic CpGs. DL had an AUC = 0.99 using intragenic CpGs, with both sensitivity and specificity being 97%. High AD prediction was also achieved using intergenic/extragenic CpG sites (DL significance value being AUC = 0.99 with 97% sensitivity and specificity). Epigenetically altered genes included CR1L & CTSV (abnormal morphology of cerebral cortex), S1PR1 (CNS inflammation), and LTB4R (inflammatory response). These genes have been previously linked with AD and dementia. The differentially methylated genes CTSV & PRMT5 (ventricular hypertrophy and dilation) are linked to cardiovascular disease and of interest given the known association between impaired cerebral blood flow, cardiovascular disease, and AD. We report a novel, minimally invasive approach using peripheral blood leucocyte epigenomics, and AI analysis to detect AD and elucidate its pathogenesis.

Epigenetics of Alzheimer's Disease

There are currently no validated biomarkers which can be used to accurately diagnose Alzheimer’s disease (AD) or to distinguish it from other dementia-causing neuropathologies. Moreover, to date, only symptomatic treatments exist for this progressive neurodegenerative disorder. In the search for new, more reliable biomarkers and potential therapeutic options, epigenetic modifications have emerged as important players in the pathogenesis of AD. The aim of the article was to provide a brief overview of the current knowledge regarding the role of epigenetics (including mitoepigenetics) in AD, and the possibility of applying these advances for future AD therapy. Extensive research has suggested an important role of DNA methylation and hydroxymethylation, histone posttranslational modifications, and non-coding RNA regulation (with the emphasis on microRNAs) in the course and development of AD. Recent studies also indicated mitochondrial DNA (mtDNA) as an interesting biomarker of AD, since dysfunctions in the mitochondria and lower mtDNA copy number have been associated with AD pathophysiology. The current evidence suggests that epigenetic changes can be successfully detected, not only in the central nervous system, but also in the cerebrospinal fluid and on the periphery, contributing further to their potential as both biomarkers and therapeutic targets in AD.

Aptamarker prediction of brain amyloid-β status in cognitively normal individuals at risk for Alzheimer's disease

The traditional approach to biomarker discovery for any pathology has been through hypothesis-based research one candidate at a time. The objective of this study was to develop an agnostic approach for the simultaneous screening of plasma for consistent molecular differences between a group of individuals exhibiting a pathology and a group of healthy individuals. To achieve this, we focused on developing a predictive tool based on plasma for the amount of brain amyloid-β deposition as observed in PET scans. The accumulation of brain amyloid-β (Aβ) plaques is a key risk factor for the development of Alzheimer’s disease. A contrast was established between cognitively normal individuals above the age of 70 that differed for the amount of brain amyloid-β observed in PET scans (INSIGHT study group). Positive selection was performed against a pool of plasma from individuals with high brain amyloid and negative selection against a pool of plasma from individuals with low brain amyloid This enriched, selected library was then applied to plasma samples from 11 individuals with high levels of brain amyloid and 11 individuals with low levels of brain Aβ accumulation. Each of these individually selected libraries was then characterized by next generation sequencing, and the relative frequency of 10,000 aptamer sequences that were observed in each selection was screened for ability to explain variation in brain amyloid using sparse partial least squares discriminant analysis. From this analysis a subset of 44 aptamers was defined, and the individual aptamers were synthesized. This subset was applied to plasma samples from 70 cognitively normal individuals all above the age of 70 that differed for brain amyloid deposition. 54 individuals were used as a training set, and 15 as a test set. Three of the 15 individuals in the test set were mis-classified resulting in an overall accuracy of 80% with 86% sensitivity and 75% specificity. The aptamers included in the subset serve directly as biomarkers, thus we have named them Aptamarkers. There are two potential applications of these results: extending the predictive capacity of these aptamers across a broader range of individuals, and/or using the individual aptamers to identify targets through covariance analysis and reverse omics approaches. We are currently expanding applications of the Aptamarker platform to other diseases and target matrices.

hiPSCs for predictive modelling of neurodegenerative diseases: dreaming the possible

Human induced pluripotent stem cells (hiPSCs) were first generated in 2007, but the full translational potential of this valuable tool has yet to be realized. The potential applications of hiPSCs are especially relevant to neurology, as brain cells from patients are rarely available for research. hiPSCs from individuals with neuropsychiatric or neurodegenerative diseases have facilitated biological and multi-omics studies as well as large-scale screening of chemical libraries. However, researchers are struggling to improve the scalability, reproducibility and quality of this descriptive disease modelling. Addressing these limitations will be the first step towards a new era in hiPSC research - that of predictive disease modelling - involving the correlation and integration of in vitro experimental data with longitudinal clinical data. This approach is a key element of the emerging precision medicine paradigm, in which hiPSCs could become a powerful diagnostic and prognostic tool. Here, we consider the steps necessary to achieve predictive modelling of neurodegenerative disease with hiPSCs, using Huntington disease as an example.

A Precision Medicine Approach to Treating Alzheimer's Disease Using Rosiglitazone Therapy: A Biomarker Analysis of the REFLECT Trials

BACKGROUND

The REFLECT trials were conducted to examine the treatment of mild-to-moderate Alzheimer's disease utilizing a peroxisome proliferator-activated receptor gamma agonist.

OBJECTIVE

To generate a predictive biomarker indicative of positive treatment response using samples from the previously conducted REFLECT trials.

METHODS

Data were analyzed on 360 participants spanning multiple negative REFLECT trials, which included treatment with rosiglitazone and rosiglitazone XR. Support vector machine analyses were conducted to generate a predictive biomarker profile.

RESULTS

A pre-defined 6-protein predictive biomarker (IL6, IL10, CRP, TNFα, FABP-3, and PPY) correctly classified treatment response with 100%accuracy across study arms for REFLECT Phase II trial (AVA100193) and multiple Phase III trials (AVA105640, AV102672, and AVA102670). When the data was combined across all rosiglitazone trial arms, a global RSG-predictive biomarker with the same 6-protein predictive biomarker was able to accurately classify 98%of treatment responders.

CONCLUSION

A predictive biomarker comprising of metabolic and inflammatory markers was highly accurate in identifying those patients most likely to experience positive treatment response across the REFLECT trials. This study provides additional proof-of-concept that a predictive biomarker can be utilized to help with screening and predicting treatment response, which holds tremendous benefit for clinical trials.

Alzheimer's disease: from basic science to precision medicine approach

Alzheimer’s disease (AD) is the most common form of dementia in the elderly. Together with cerebral amyloid accumulation, several factors contribute to AD pathology including vascular alterations, systemic inflammation, genetic/epigenetic status and mitochondrial dysfunction. Much is now being devoted to neuroinflammation. However, anti-inflammatory drugs as numerous other therapies, mainly targeted on β-amyloid, have failed to show efficacious effects in AD. Timing, proper selection of patients, and the need for a multitarget approach appear to be the main weak points of current therapeutic efforts. The efficacy of a treatment could be better evaluate if efficient biomarkers are available. We propose here the application of precision medicine principles in AD to simultaneously verify the efficacy of a treatment and the reliability of specific biomarkers according to individually tailored biomarker-guided targeted therapies. People at risk of developing AD or in the very early phase of the disease should be stratified according to: (1) neuropsychological tests; (2) apolipoprotein E (ApoE) genotyping; (3) biochemical analysis of plasma and cerebrospinal fluid (CSF); (4) MRI and positron emission tomography and (5) assessment of their inflammatory profile by an integration of various genetic and biochemical parameters in plasma, CSF and an analysis of microbiota composition. The selected population should be treated with antiamyloidogenic and anti-inflammatory drugs in randomised, longitudinal, placebo-controlled studies using ad hoc profiles (eg, vascular profile, mitochondrial profile, etc…) If these criteria are adopted widely and the results shared, it may be possible to rapidly develop innovative and personalised drug treatment protocols with more realistic chances of being efficacious.

Age and sex impact plasma NFL and t-Tau trajectories in individuals with subjective memory complaints: a 3-year follow-up study

Background

Plasma neurofilament light (NFL) and total Tau (t-Tau) proteins are candidate biomarkers for early stages of Alzheimer’s disease (AD). The impact of biological factors on their plasma concentrations in individuals with subjective memory complaints (SMC) has been poorly explored. We longitudinally investigate the effect of sex, age, APOE ε4 allele, comorbidities, brain amyloid-β (Aβ) burden, and cognitive scores on plasma NFL and t-Tau concentrations in cognitively healthy individuals with SMC, a condition associated with AD development.

Methods

Three hundred sixteen and 79 individuals, respectively, have baseline and three-time point assessments (at baseline, 1-year, and 3-year follow-up) of the two biomarkers. Plasma biomarkers were measured with an ultrasensitive assay in a mono-center cohort (INSIGHT-preAD study).

Results

We show an effect of age on plasma NFL, with women having a higher increase of plasma t-Tau concentrations compared to men, over time. The APOE ε4 allele does not affect the biomarker concentrations while plasma vitamin B12 deficiency is associated with higher plasma t-Tau concentrations. Both biomarkers are correlated and increase over time. Baseline NFL is related to the rate of Aβ deposition at 2-year follow-up in the left-posterior cingulate and the inferior parietal gyri. Baseline plasma NFL and the rate of change of plasma t-Tau are inversely associated with cognitive score.

Conclusion

We find that plasma NFL and t-Tau longitudinal trajectories are affected by age and female sex, respectively, in SMC individuals. Exploring the influence of biological variables on AD biomarkers is crucial for their clinical validation in blood.

microRNA-Based Biomarkers in Alzheimer's Disease (AD)

Alzheimer's disease (AD) is a multifactorial, age-related neurological disease characterized by complex pathophysiological dynamics taking place at multiple biological levels, including molecular, genetic, epigenetic, cellular and large-scale brain networks. These alterations account for multiple pathophysiological mechanisms such as brain protein accumulation, neuroinflammatory/neuro-immune processes, synaptic dysfunction, and neurodegeneration that eventually lead to cognitive and behavioral decline. Alterations in microRNA (miRNA) signaling have been implicated in the epigenetics and molecular genetics of all neurobiological processes associated with AD pathophysiology. These changes encompass altered miRNA abundance, speciation and complexity in anatomical regions of the CNS targeted by the disease, including modified miRNA expression patterns in brain tissues, the systemic circulation, the extracellular fluid (ECF) and the cerebrospinal fluid (CSF). miRNAs have been investigated as candidate biomarkers for AD diagnosis, disease prediction, prognosis and therapeutic purposes because of their involvement in multiple brain signaling pathways in both health and disease. In this review we will: (i) highlight the significantly heterogeneous nature of miRNA expression and complexity in AD tissues and biofluids; (ii) address how information may be extracted from these data to be used as a diagnostic, prognostic and/or screening tools across the entire continuum of AD, from the preclinical stage, through the prodromal, i.e., mild cognitive impairment (MCI) phase all the way to clinically overt dementia; and (iii) consider how specific miRNA expression patterns could be categorized using miRNA reporters that span AD pathophysiological initiation and disease progression.

Exercise benefits on Alzheimer's disease: State-of-the-science

Although there is no unanimity, growing evidence supports the value of regular physical exercise to prevent Alzheimer's disease as well as cognitive decline in affected patients. Together with an introductory summary on epidemiological evidence, the aim of this review is to summarize the current knowledge on the potential biological mechanisms underlying exercise benefits in this condition. Regular physical exercise has proven to be beneficial for traditional cardiovascular risk factors (e.g., reduced vascular flow, diabetes) involved in the pathogenesis of Alzheimer's disease. Exercise also promotes neurogenesis via increases in exercise-induced metabolic factors (e.g., ketone bodies, lactate) and muscle-derived myokines (cathepsin-B, irisin), which in turn stimulate the production of neurotrophins such as brain-derived neurotrophic factor. Finally, regular exercise exerts anti-inflammatory effects and improves the brain redox status, thereby ameliorating the pathophysiological hallmarks of Alzheimer's disease (e.g., amyloid-β deposition). In summary, physical exercise might provide numerous benefits through different pathways that might, in turn, help prevent risk and progression of Alzheimer's disease. More evidence is needed, however, based on human studies.

Brain Aβ load association and sexual dimorphism of plasma BACE1 concentrations in cognitively normal individuals at risk for AD

INTRODUCTION

Successful development of effective β-site amyloid precursor protein cleaving enzyme 1 (BACE1)-targeted therapies for early stages of Alzheimer's disease requires biomarker-guided intervention strategies.

METHODS

We investigated whether key biological factors such as sex, apolipoprotein E (APOE ε4) allele, and age affect longitudinal plasma BACE1 concentrations in a large monocenter cohort of individuals at risk for Alzheimer's disease. We explored the relationship between plasma BACE1 concentrations and levels of brain amyloid-β (Aβ) deposition, using positron emission tomography global standard uptake value ratios.

RESULTS

Baseline and longitudinal mean concentrations of plasma BACE1 were significantly higher in women than men. We also found a positive significant impact of plasma BACE1 on baseline Aβ-positron emission tomography global standard uptake value ratios.

DISCUSSION

Our results suggest a sexual dimorphism in BACE1-related upstream mechanisms of brain Aβ production and deposition. We argue that plasma BACE1 should be considered in further biomarker validation and qualification studies as well as in BACE1 clinical trials.

Polygenic Risk Scores in Alzheimer's Disease: Current Applications and Future Directions

Genome-wide association studies have identified nearly 40 genome-wide significant single nucleotide polymorphisms (SNPs) which are associated with Alzheimer's Disease (AD). Due to the polygenicity of AD, polygenic risk scores (PRS) have shown high potential for AD risk prediction. PRSs have been shown to successfully discriminate between AD cases and controls achieving a prediction accuracy of up to 84% based on area under the receiver operating curve. The prediction accuracy in AD is higher compared with other complex genetic disorders. PRS can be restricted to SNPs which reside in biologically relevant gene-sets; the predictive value of these gene-sets in the general population is not as high as genome-wide PRS, but they may play an important role to identify mechanisms of disease development and inform biological experiments. Multiple methods are available to derive PRSs, such as selecting SNPs based on statistical evidence of association with the disease or using prior evidence for SNP selection. All methods have advantages, but PRS produced using different methodologies are often not comparable, and results should be interpreted with care. Similarly, this is true when PRS is based on different background populations. With the exponential growth in development of digital electronic devices it is easy to calculate an individual's disease risk using public databases. A major limitation for the utility of PRSs is that the risk score is sample and method dependent. Therefore, replicability and interpretability of PRS is an important issue. PRS can be used to determine the probability of developing disease which incorporates information about disease risk in the general population or in a specific AD risk group. It is essential to consult with genetic counselors to ensure genetic risk is communicated appropriately.

Different Clinical Contexts of Use of Blood Neurofilament Light Chain Protein in the Spectrum of Neurodegenerative Diseases

One of the most pressing challenges in the clinical research of neurodegenerative diseases (NDDs) is the validation and standardization of pathophysiological biomarkers for different contexts of use (CoUs), such as early detection, diagnosis, prognosis, and prediction of treatment response. Neurofilament light chain (NFL) concentration is a particularly promising candidate, an indicator of axonal degeneration, which can be analyzed in peripheral blood with advanced ultrasensitive methods. Serum/plasma NFL concentration is closely correlated with cerebrospinal fluid NFL and directly reflects neurodegeneration within the central nervous system. Here, we provide an update on the feasible CoU of blood NFL in NDDs and translate recent findings to potentially valuable clinical practice applications. As NFL is not a disease-specific biomarker, however, blood NFL is an easily accessible biomarker with promising different clinical applications for several NDDs: (1) early detection and diagnosis (i.e., amyotrophic lateral sclerosis, Creutzfeldt-Jakob disease, atypical parkinsonisms, sporadic late-onset ataxias), (2) prognosis (Huntington's disease and Parkinson's disease), and (3) prediction of time to symptom onset (presymptomatic mutation carriers in genetic Alzheimer's disease and spinocerebellar ataxia type 3).

Thrombin, a Mediator of Coagulation, Inflammation, and Neurotoxicity at the Neurovascular Interface: Implications for Alzheimer's Disease

The societal burden of Alzheimer’s disease (AD) is staggering, with current estimates suggesting that 50 million people world-wide have AD. Identification of new therapeutic targets is a critical barrier to the development of disease-modifying therapies. A large body of data implicates vascular pathology and cardiovascular risk factors in the development of AD, indicating that there are likely shared pathological mediators. Inflammation plays a role in both cardiovascular disease and AD, and recent evidence has implicated elements of the coagulation system in the regulation of inflammation. In particular, the multifunctional serine protease thrombin has been found to act as a mediator of vascular dysfunction and inflammation in both the periphery and the central nervous system. In the periphery, thrombin contributes to the development of cardiovascular disease, including atherosclerosis and diabetes, by inducing endothelial dysfunction and related inflammation. In the brain, thrombin has been found to act on endothelial cells of the blood brain barrier, microglia, astrocytes, and neurons in a manner that promotes vascular dysfunction, inflammation, and neurodegeneration. Thrombin is elevated in the AD brain, and thrombin signaling has been linked to both tau and amyloid beta, pathological hallmarks of the disease. In AD mouse models, inhibiting thrombin preserves cognition and endothelial function and reduces neuroinflammation. Evidence linking atrial fibrillation with AD and dementia indicates that anticoagulant therapy may reduce the risk of dementia, with targeting thrombin shown to be particularly effective. It is time for “outside-the-box” thinking about how vascular risk factors, such as atherosclerosis and diabetes, as well as the coagulation and inflammatory pathways interact to promote increased AD risk. In this review, we present evidence that thrombin is a convergence point for AD risk factors and as such that thrombin-based therapeutics could target multiple points of AD pathology, including neurodegeneration, vascular activation, and neuroinflammation. The urgent need for disease-modifying drugs in AD demands new thinking about disease pathogenesis and an exploration of novel drug targets, we propose that thrombin inhibition is an innovative tactic in the therapeutic battle against this devastating disease.

Molecular and Imaging Biomarkers in Alzheimer's Disease: A Focus on Recent Insights

Alzheimer’s disease (AD) is the most common neurodegenerative disease among the elderly, affecting millions of people worldwide and clinically characterized by a progressive and irreversible cognitive decline. The rapid increase in the incidence of AD highlights the need for an easy, efficient and accurate diagnosis of the disease in its initial stages in order to halt or delay the progression. The currently used diagnostic methods rely on measures of amyloid-β (Aβ), phosphorylated (p-tau) and total tau (t-tau) protein levels in the cerebrospinal fluid (CSF) aided by advanced neuroimaging techniques like positron emission tomography (PET) and magnetic resonance imaging (MRI). However, the invasiveness of these procedures and the high cost restrict their utilization. Hence, biomarkers from biological fluids obtained using non-invasive methods and novel neuroimaging approaches provide an attractive alternative for the early diagnosis of AD. Such biomarkers may also be helpful for better understanding of the molecular mechanisms underlying the disease, allowing differential diagnosis or at least prolonging the pre-symptomatic stage in patients suffering from AD. Herein, we discuss the advantages and limits of the conventional biomarkers as well as recent promising candidates from alternative body fluids and new imaging techniques.

Neuropathological Diagnoses of Demented Hispanic, Black, and Non-Hispanic White Decedents Seen at an Alzheimer's Disease Center

Our nation is becoming increasingly diverse; however, few autopsy studies examine multiple ethnoracial groups, especially Hispanics. We examined differences in neuropathological diagnoses of 423 deceased participants with dementia from three ethnoracial groups (35 Black, 28 Hispanic, and 360 non-Hispanic White) evaluated at the University of California Davis Alzheimer's Disease Center. We used novel applications of bootstrap resampling and logistic regression standardization to project neuropathological diagnostic rates for non-Hispanic Whites to minority sample characteristics to improve inference of findings. Alzheimer's disease (AD) without significant cerebrovascular disease (CVD) or other dementia-related pathologies (AD (non-mixed)) was present in 15 Black (43%), 4 Hispanic (14%), and 156 (43%) non-Hispanic Whites. CVD sufficient to contribute to dementia was confirmed in 14 Black (40%), 15 Hispanic (54%), and 101 (28%) non-Hispanic White decedents. The observed CVD prevalence of 40% in Blacks exceeded the predicted 29% [95% CI: 22%-36%]. Despite being outside the 95% confidence interval, the difference between observed and predicted was not statistically significant after bootstrap testing. Conversely, for Hispanics, the observed proportion at 54% exceeded significantly the predicted prevalence of 24% from non-Hispanic Whites [95% CI: 16%-34%], avg. p = 0.008). An identical analysis using AD (non-mixed) as the outcome predicted AD (non-mixed) in Blacks averaging 41% [95% CI: 34%-48%], nearly equal to observed prevalence. For Hispanics, however, the observed proportion at 14%, was well below predictions (mean = 42%, 95% CI: 32%-53%], avg. p = 0.008). We conclude mixed diagnoses and CVD are more common in Hispanic and Black decedents than Non-Hispanic Whites with dementia in our cohort. The increased prevalence of vascular co-morbidity may be a potential opportunity to intervene more effectively in dementia treatment of those individuals.

PreMedKB: an integrated precision medicine knowledgebase for interpreting relationships between diseases, genes, variants and drugs

One important aspect of precision medicine aims to deliver the right medicine to the right patient at the right dose at the right time based on the unique ‘omics’ features of each individual patient, thus maximizing drug efficacy and minimizing adverse drug reactions. However, fragmentation and heterogeneity of available data makes it challenging to readily obtain first-hand information regarding some particular diseases, drugs, genes and variants of interest. Therefore, we developed the Precision Medicine Knowledgebase (PreMedKB) by seamlessly integrating the four fundamental components of precision medicine: diseases, genes, variants and drugs. PreMedKB allows for search of comprehensive information within each of the four components, the relationships between any two or more components, and importantly, the interpretation of the clinical meanings of a patient's genetic variants. PreMedKB is an efficient and user-friendly tool to assist researchers, clinicians or patients in interpreting a patient's genetic profile in terms of discovering potential pathogenic variants, recommending therapeutic regimens, designing panels for genetic testing kits, and matching patients for clinical trials. PreMedKB is freely accessible and available at http://www.fudan-pgx.org/premedkb/index.html#/home.

A propositional AI system for supporting epilepsy diagnosis based on the 2017 epilepsy classification: Illustrated by Dravet syndrome

PURPOSE

The 2017 epilepsy and seizure diagnosis framework emphasizes epilepsy syndromes and the etiology-based approach. We developed a propositional artificial intelligence (AI) system based on the above concepts to support physicians in the diagnosis of epilepsy.

METHODS

We analyzed and built ontology knowledge for the classification of seizure patterns, epilepsy, epilepsy syndrome, and etiologies. Protégé ontology tool was applied in this study. In order to enable the system to be close to the inferential thinking of clinical experts, we classified and constructed knowledge of other epilepsy-related knowledge, including comorbidities, epilepsy imitators, epilepsy descriptors, characteristic electroencephalography (EEG) findings, treatments, etc. We used the Ontology Web Language with Description Logic (OWL-DL) and Semantic Web Rule Language (SWRL) to design rules for expressing the relationship between these ontologies.

RESULTS

Dravet syndrome was taken as an illustration for epilepsy syndromes implementation. We designed an interface for the physician to enter the various characteristics of the patients. Clinical data of an 18-year-old boy with epilepsy was applied to the AI system. Through SWRL and reasoning engine Drool's execution, we successfully demonstrate the process of differential diagnosis.

CONCLUSION

We developed a propositional AI system by using the OWL-DL/SWRL approach to deal with the complexity of current epilepsy diagnosis. The experience of this system, centered on the clinical epilepsy syndromes, paves a path to construct an AI system for further complicated epilepsy diagnosis.

Blood-based systems biology biomarkers for next-generation clinical trials in Alzheimer's disease

Alzheimer's disease (AD)-a complex disease showing multiple pathomechanistic alterations-is triggered by nonlinear dynamic interactions of genetic/epigenetic and environmental risk factors, which, ultimately, converge into a biologically heterogeneous disease. To tackle the burden of AD during early preclinical stages, accessible blood-based biomarkers are currently being developed. Specifically, next-generation clinical trials are expected to integrate positive and negative predictive blood-based biomarkers into study designs to evaluate, at the individual level, target druggability and potential drug resistance mechanisms. In this scenario, systems biology holds promise to accelerate validation and qualification for clinical trial contexts of use-including proof-of-mechanism, patient selection, assessment of treatment efficacy and safety rates, and prognostic evaluation. Albeit in their infancy, systems biology-based approaches are poised to identify relevant AD "signatures" through multifactorial and interindividual variability, allowing us to decipher disease pathophysiology and etiology. Hopefully, innovative biomarker-drug codevelopment strategies will be the road ahead towards effective disease-modifying drugs.
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The path to biomarker-based diagnostic criteria for the spectrum of neurodegenerative diseases

Introduction: The postmortem examination still represents the reference standard for detecting the pathological nature of chronic neurodegenerative diseases (NDD). This approach displays intrinsic conceptual limitations since NDD represent a dynamic spectrum of partially overlapping phenotypes, shared pathomechanistic alterations that often give rise to mixed pathologies.Areas covered: We scrutinized the international clinical diagnostic criteria of NDD and the literature to provide a roadmap toward a biomarker-based classification of the NDD spectrum. A few pathophysiological biomarkers have been established for NDD. These are time-consuming, invasive, and not suitable for preclinical detection. Candidate screening biomarkers are gaining momentum. Blood neurofilament light-chain represents a robust first-line tool to detect neurodegeneration tout court and serum progranulin helps detect genetic frontotemporal dementia. Ultrasensitive assays and retinal scans may identify Aβ pathology early, in blood and the eye, respectively. Ultrasound also represents a minimally invasive option to investigate the substantia nigra. Protein misfolding amplification assays may accurately detect α-synuclein in biofluids.Expert opinion: Data-driven strategies using quantitative rather than categorical variables may be more reliable for quantification of contributions from pathophysiological mechanisms and their spatial-temporal evolution. A systems biology approach is suitable to untangle the dynamics triggering loss of proteostasis, driving neurodegeneration and clinical evolution.

Glioma Migration Through the Corpus Callosum and the Brainstem Detected by Diffusion and Magnetic Resonance Imaging: Initial Findings

Purpose: Glioma cell infiltration, in which the glioma tumor cells spread long distances from the primary location using white matter (WM) or blood vessels, is known as a significant challenge for surgery or localized chemotherapy and radiation therapy. Following the World Health Organization (WHO), the glioma grading system ranges from stages I to IV, in which lower-grade gliomas represent benign tumors, and higher grade gliomas are considered the most malignant.

Materials and Methods: We gathered magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) data for seven patients with right precentral gyrus-located tumors and six age- and sex-matched healthy subjects for analysis. Tract-Based Spatial Statistics (TBSS) was utilized to evaluate whole-brain WM implication due to probable tumor infiltration. Also, along-tract statistics were used in order to trace the implicated WM tracts. Finally, for cortical evaluation of probable tumor cell migration, voxel-based morphometry (VBM) was utilized, which allowed us to do whole-brain cortical estimation.

Results: The TBSS results revealed significantly higher fractional anisotropy (FA) and lower mean diffusivity (MD) in the left side superior corona radiata. Also, higher FA was observed in the right corticostriatal tract. Along-tract statistics were also compiled on the corpus callosum (CC), which is anatomically known as a hub between hemispheres. The body of the CC, which connected with the superior corona radiata anatomically, showed significantly higher FA values relative to healthy subjects, which are in line with the TBSS results. Consistent with these results, whole-brain gray matter changes were analyzed via VBM, which showed significant hypertrophy of both sides of the brainstem.

Conclusion: In future investigations, focusing on the genetic basis of the glioma patients in line with imaging studies on a larger sample size, which is known as genetics imaging, would be a suitable approach for tracing this process.

Challenges of Integrative Disease Modeling in Alzheimer's Disease

Dementia-related diseases like Alzheimer's Disease (AD) have a tremendous social and economic cost. A deeper understanding of its underlying pathophysiologies may provide an opportunity for earlier detection and therapeutic intervention. Previous approaches for characterizing AD were targeted at single aspects of the disease. Yet, due to the complex nature of AD, the success of these approaches was limited. However, in recent years, advancements in integrative disease modeling, built on a wide range of AD biomarkers, have taken a global view on the disease, facilitating more comprehensive analysis and interpretation. Integrative AD models can be sorted in two primary types, namely hypothetical models and data-driven models. The latter group split into two subgroups: (i) Models that use traditional statistical methods such as linear models, (ii) Models that take advantage of more advanced artificial intelligence approaches such as machine learning. While many integrative AD models have been published over the last decade, their impact on clinical practice is limited. There exist major challenges in the course of integrative AD modeling, namely data missingness and censoring, imprecise human-involved priori knowledge, model reproducibility, dataset interoperability, dataset integration, and model interpretability. In this review, we highlight recent advancements and future possibilities of integrative modeling in the field of AD research, showcase and discuss the limitations and challenges involved, and finally, propose avenues to address several of these challenges.

Brain targeting stealth lipomers of combined antiepileptic-anti-inflammatory drugs as alternative therapy for conventional anti-Parkinson's

This study presents an alternative therapy to conventional anti-Parkinson's treatment strategies; where motor and non-motor symptomatic complications are considered. Thus; providing sustainability, patient compliance, therapeutic safety and efficiency, based on triggering secretion of endogenous dopamine (DA). Exogenous DA has long been considered the best therapy, however, its poor blood brain barrier (BBB) permeability, fluctuated plasma levels, and non-motor complications negligence, decreased response to therapy with time. Consequently; brain targeting Tween®80-coated pegylated lipomers were tailored for intravenous administration (IV) of L-Dopa, and two drugs of reported neuroprotective effect: lamotrigine (LTG) and tenoxicam (TX). Single-step nanoprecipitation method was used; for its reproducibility and ease of scaling-up. Formulation targeting and anti-PD efficiency was evaluated against marketed standards and L-Dopa. In-vitro and in-vivo pharmacokinetic and dynamic studies were carried out for setting optimization standards upon varying inter-components ratio. Results revealed that lipomers are, generally, significantly efficient in brain targeting compared to oral tablets. LTG-lipomers (LF20) showed the maximum anti-PD compared to its TX and L-Dopa analogues. Combining LTG and TX had synergistic effect; highlighting a new prescription for both drugs. Thus; offering a safe, targeted, and therapeutically efficient sustained dosage form, capable of mitigating PD risk and treating it though weekly administration. Hence; presenting a novel promising anti-neurodegenerative strategy; on employing various mechanisms that were previously achieved through additional therapeutic supplements.

Plasma tau correlates with basal forebrain atrophy rates in people at risk for Alzheimer disease

Objective

To investigate whether baseline concentrations of plasma total tau (t-tau) and neurofilament light (NfL) chain proteins are associated with annual percent change (APC) of the basal forebrain cholinergic system (BFCS) in cognitively intact older adults at risk for Alzheimer disease (AD).

Methods

This was a large-scale study of 276 cognitively intact older adults from the monocentric INSIGHT-preAD (Investigation of Alzheimer's Predictors in Subjective Memory Complainers) cohort. Participants underwent baseline assessment of plasma t-tau and NfL concentrations as well as baseline and 24-month follow-up MRI scans. Linear models with and without influential observations (calculated using the Cook distance) were carried out to investigate the effect of plasma NfL and t-tau concentrations, and their interaction effect with β-amyloid status and APOE genotype, on the APC of the whole BFCS and its anterior (Ch1/2) and posterior (Ch4) subdivisions separately.

Results

Higher plasma t-tau concentrations at baseline were associated with higher BFCS rate of atrophy (model without influencers: n = 251, F value = 4.6815; p value = 0.031). Subregional analyses showed similar results for both the APC of the Ch1/2 (model without influencers: n = 256, F value = 3.9535, p corrected = 0.047) and Ch4 BFCS sectors (model without influencers: n = 253, F value = 4.9090, p corrected = 0.047). Baseline NfL, β-amyloid load, and APOE ε4 carrier status did not affect APC of the BFCS.

Conclusion

Increased concentrations of baseline plasma t-tau may predict in vivo structural BFCS atrophy progression in older adults at risk for AD, independently of β-amyloid status and APOE genotype.

Individualized clinical management of patients at risk for Alzheimer's dementia

INTRODUCTION

Multidomain intervention for Alzheimer's disease (AD) risk reduction is an emerging therapeutic paradigm.

METHODS

Patients were prescribed individually tailored interventions (education/pharmacologic/nonpharmacologic) and rated on compliance. Normal cognition/subjective cognitive decline/preclinical AD was classified as Prevention. Mild cognitive impairment due to AD/mild-AD was classified as Early Treatment. Change from baseline to 18 months on the modified Alzheimer's Prevention Cognitive Composite (primary outcome) was compared against matched historical control cohorts. Cognitive aging composite (CogAging), AD/cardiovascular risk scales, and serum biomarkers were secondary outcomes.

RESULTS

One hundred seventy-four were assigned interventions (age 25-86). Higher-compliance Prevention improved more than both historical cohorts (P = .0012, P < .0001). Lower-compliance Prevention also improved more than both historical cohorts (P = .0088, P < .0055). Higher-compliance Early Treatment improved more than lower compliance (P = .0007). Higher-compliance Early Treatment improved more than historical cohorts (P < .0001, P = .0428). Lower-compliance Early Treatment did not differ (P = .9820, P = .1115). Similar effects occurred for CogAging. AD/cardiovascular risk scales and serum biomarkers improved.

DISCUSSION

Individualized multidomain interventions may improve cognition and reduce AD/cardiovascular risk scores in patients at-risk for AD dementia.