Toward precision medicine in Alzheimer's disease

Network dynamics-based subtyping of Alzheimer's disease with microglial genetic risk factors

BACKGROUND

The potential of microglia as a target for Alzheimer's disease (AD) treatment is promising, yet the clinical and pathological diversity within microglia, driven by genetic factors, poses a significant challenge. Subtyping AD is imperative to enable precise and effective treatment strategies. However, existing subtyping methods fail to comprehensively address the intricate complexities of AD pathogenesis, particularly concerning genetic risk factors. To address this gap, we have employed systems biology approaches for AD subtyping and identified potential therapeutic targets.

METHODS

We constructed patient-specific microglial molecular regulatory network models by utilizing existing literature and single-cell RNA sequencing data. The combination of large-scale computer simulations and dynamic network analysis enabled us to subtype AD patients according to their distinct molecular regulatory mechanisms. For each identified subtype, we suggested optimal targets for effective AD treatment.

RESULTS

To investigate heterogeneity in AD and identify potential therapeutic targets, we constructed a microglia molecular regulatory network model. The network model incorporated 20 known risk factors and crucial signaling pathways associated with microglial functionality, such as inflammation, anti-inflammation, phagocytosis, and autophagy. Probabilistic simulations with patient-specific genomic data and subsequent dynamics analysis revealed nine distinct AD subtypes characterized by core feedback mechanisms involving SPI1, CASS4, and MEF2C. Moreover, we identified PICALM, MEF2C, and LAT2 as common therapeutic targets among several subtypes. Furthermore, we clarified the reasons for the previous contradictory experimental results that suggested both the activation and inhibition of AKT or INPP5D could activate AD through dynamic analysis. This highlights the multifaceted nature of microglial network regulation.

CONCLUSIONS

These results offer a means to classify AD patients by their genetic risk factors, clarify inconsistent experimental findings, and advance the development of treatments tailored to individual genotypes for AD.

Human brain organoids containing microglia that have arisen innately adapt to a β-amyloid challenge better than those in which microglia are integrated by co-culture

BACKGROUND

Alzheimer disease (AD) is a heterogenous and multifactorial disease, and its pathology is partly driven by microglia and their activated phenotype. Brain organoids (BOs) are gaining prominence as a relevant model of the human brain for the study of AD; however, BOs are commonly devoid of microglia. To overcome this limitation, current protocols incorporate microglia through either (1) co-culture (BO co-culture), or (2) molecular manipulation at critical windows of BO development to have microglia arise innately (BO innate cultures). It is currently unclear whether the microglia incorporated into BOs by either of these two protocols differ in function.

METHODS

At in vitro day 90, BO innate cultures and BO-co-cultures were challenged with the AD-related β-amyloid peptide (Aβ) for up to 72 h. After Aβ challenge, BOs were collected for immunoblotting. Immunoblots compared immunodensity and protein banding of Aβ and ionized calcium-binding adapter molecule 1 (IBA1, a marker of microglial activation) in BOs. The translational potential of these observations was supported using 56 human cortical samples from neurocognitively normal donors and patients with early-onset AD and late-onset AD. Statistical analyses were conducted using the Kruskal-Wallis test, a two-way ANOVA, or a simple linear regression, and where applicable, followed by Dunn's or Sidak's test.

RESULTS

We show that BO co-cultures promote Aβ oligomerization as early as 24 h and this coincides with a significant increase in IBA1 levels. In contrast, the Aβs do not oligomerize in BO innate cultures and the IBA1 response was modest and only emerged after 48 h. In human cortical samples, we found IBA1 levels correlated with age at onset, age at death, and the putative diagnostic Aβ(1-42)/Aβ(1-40) ratio (particularly in their oligomeric forms) in a sex-dependent manner.

CONCLUSIONS

Our unique observations suggest that BOs with innate microglia model the response of a healthy brain to Aβ, and by extension the initial stages of Aβ challenge. It would be impossible to model these early stages of pathogenesis in BOs where microglia are already compromised, such as those with microglia incorporated by co-culture.

Bio-Nano Toolbox for Precision Alzheimer's Disease Gene Therapy

Alzheimer's disease (AD) is the most burdensome aging-associated neurodegenerative disorder, and its treatment encounters numerous failures during drug development. Although there are newly approved in-market β-amyloid targeting antibody solutions, pathological heterogeneity among patient populations still challenges the treatment outcome. Emerging advances in gene therapies offer opportunities for more precise personalized medicine; while, major obstacles including the pathological heterogeneity among patient populations, the puzzled mechanism for druggable target development, and the precision delivery of functional therapeutic elements across the blood-brain barrier remain and limit the use of gene therapy for central neuronal diseases. Aiming for "precision delivery" challenges, nanomedicine provides versatile platforms that may overcome the targeted delivery challenges for AD gene therapy. In this perspective, to picture a toolbox for AD gene therapy strategy development, the most recent advances from benchtop to clinics are highlighted, possibly available gene therapy targets, tools, and delivery platforms are outlined, their challenges as well as rational design elements are addressed, and perspectives in this promising research field are discussed.

Evolving therapeutic interventions for the management and treatment of Alzheimer's disease

Alzheimer's Disease (AD) patients experience diverse symptoms, including memory loss, cognitive impairment, behavioral abnormalities, mood changes, and mental issues. The fundamental objective of this review is to discuss novel therapeutic approaches, with special emphasis on recently approved marketed formulations for the treatment of AD, especially Aducanumab, the first FDA approved moiety that surpasses the blood-brain barrier (BBB) and reduces amyloid plaques in the brain, thereby reducing associated cognitive decline. However, it is still in the phase IV trial and is to be completed by 2030. Other drugs such as lecanemab are also under clinical trial and has recently been approved by the FDA and is also discussed here. In this review, we also focus on active and passive immunotherapy for AD as well as several vaccines, such as amyloid-beta epitope-based vaccines, amyloid-beta DNA vaccines, and stem cell therapy for AD, which are in clinical trials. Furthermore, ongoing pre-clinical trials associated with AD and other novel strategies such as curcumin-loaded nanoparticles, Crispr/ cas9, precision medicine, as well as some emerging therapies like anti-sense therapy are also highlighted. Additionally, we discuss some off-labeled drugs like non-steroidal anti-inflammatory drugs (NSAID), anti-diabetic drugs, and lithium, which can manage symptoms of AD and different non-pharmacological approaches are also covered which can help to manage AD. In summary, we have tried to cover all the therapeutic interventions which are available for the treatment and management of AD under sections approved, clinical phase, pre-clinical phase or futuristic interventions, off-labelled drugs, and non-pharmacological interventions for AD, offering positive findings and well as challenges that remain.

Small RNA Profiles of Brain Tissue-Derived Extracellular Vesicles in Alzheimer's Disease

Background

Extracellular vesicles (EVs) and non-coding RNAs (ncRNAs) are emerging contributors to Alzheimer's disease (AD) pathophysiology. Differential abundance of ncRNAs carried by EVs may provide valuable insights into underlying disease mechanisms. Brain tissue-derived EVs (bdEVs) are particularly relevant, as they may offer valuable insights about the tissue of origin. However, there is limited research on diverse ncRNA species in bdEVs in AD.

Objective

This study explored whether the non-coding RNA composition of EVs isolated from post-mortem brain tissue is related to AD pathogenesis.

Methods

bdEVs from age-matched late-stage AD patients (n = 23) and controls (n = 10) that had been separated and characterized in our previous study were used for RNA extraction, small RNA sequencing, and qPCR verification.

Results

Significant differences of non-coding RNAs between AD and controls were found, especially for miRNAs and tRNAs. AD pathology-related miRNA and tRNA differences of bdEVs partially matched expression differences in source brain tissues. AD pathology had a more prominent association than biological sex with bdEV miRNA and tRNA components in late-stage AD brains.

Conclusions

Our study provides further evidence that EV non-coding RNAs from human brain tissue, including but not limited to miRNAs, may be altered and contribute to AD pathogenesis.

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.

Exploring the potential impact of multi-factor precision interventions in Alzheimer's disease with system dynamics

Numerous clinical trials based on a single-cause paradigm have not resulted in efficacious treatments for Alzheimer's disease (AD). Recently, prevention trials that simultaneously intervened on multiple risk factors have shown mixed results, suggesting that careful design is necessary. Moreover, intensive pilot precision medicine (PM) trial results have been promising but may not generalize to a broader population. These observations suggest that a model-based approach to multi-factor precision medicine (PM) is warranted. We systematically developed a system dynamics model (SDM) of AD for PM using data from two longitudinal studies (N=3660). This method involved a model selection procedure in identifying interaction terms between the SDM components and estimating individualized parameters. We used the SDM to explore simulated single- and double-factor interventions on 14 modifiable risk factors. We quantified the potential impact of double-factor interventions over single-factor interventions as 1.5 [95% CI: 1.5-2.6] and of SDM-based PM over a one-size-fits-all approach as 3.5 [3.1, 3.8] ADAS-cog-13 points in 12 years. Although the model remains to be validated, we tentatively conclude that multi-factor PM could come to play an important role in AD prevention.

Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine

Mounting evidence has highlighted the implementation of big data handling and management in the healthcare industry to improve the clinical services. Various private and public companies have generated, stored, and analyzed different types of big healthcare data, such as omics data, clinical data, electronic health records, personal health records, and sensing data with the aim to move in the direction of precision medicine. Additionally, with the advancement in technologies, researchers are curious to extract the potential involvement of artificial intelligence and machine learning on big healthcare data to enhance the quality of patient's lives. However, seeking solutions from big healthcare data requires proper management, storage, and analysis, which imposes hinderances associated with big data handling. Herein, we briefly discuss the implication of big data handling and the role of artificial intelligence in precision medicine. Further, we also highlighted the potential of artificial intelligence in integrating and analyzing the big data that offer personalized treatment. In addition, we briefly discuss the applications of artificial intelligence in personalized treatment, especially in neurological diseases. Lastly, we discuss the challenges and limitations imposed by artificial intelligence in big data management and analysis to hinder precision medicine.

Required improvements for cerebrospinal fluid-based biomarker tests of Alzheimer's disease

INTRODUCTION

Cerebrospinal fluid (CSF) biomarkers represent a well-established tool for diagnosing Alzheimer's disease (AD), independently from the clinical stage, by reflecting the presence of brain amyloidosis (A+) and tauopathy (T+). In front of this important achievement, so far, (i) CSF AD biomarkers have not yet been adopted for routine clinical use in all Centers dedicated to AD, mainly due to inter-lab variation and lack of internationally accepted cutoff values; (ii) we do need to add other biomarkers more suitable to correlate with the clinical stage and disease monitoring; (iii) we also need to detect the co-presence of other 'non-AD' pathologies.

AREAS COVERED

Efforts to establish standardized cutoff values based on large-scale multi-center studies are discussed. The influence of aging and comorbidities on CSF biomarker levels is also analyzed, and possible solutions are presented, i.e. complementing the A/T/(N) system with markers of axonal damage and synaptic derangement.

EXPERT OPINION

The first, mandatory need is to reach common cutoff values and defined (automated) methodologies for CSF AD biomarkers. To properly select subjects deserving CSF analysis, blood tests might represent the first-line approach. In those subjects undergoing CSF analysis, multiple biomarkers, able to give a comprehensive and personalized pathophysiological/prognostic information, should be included.

Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer's disease?

Alzheimer's disease (AD) is a progressive, neurodegenerative disorder that affects memory, thinking, behavior, and other cognitive functions. Although there is no cure, detecting AD early is important for the development of a therapeutic plan and a care plan that may preserve cognitive function and prevent irreversible damage. Neuroimaging, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), has served as a critical tool in establishing diagnostic indicators of AD during the preclinical stage. However, as neuroimaging technology quickly advances, there is a challenge in analyzing and interpreting vast amounts of brain imaging data. Given these limitations, there is great interest in using artificial Intelligence (AI) to assist in this process. AI introduces limitless possibilities in the future diagnosis of AD, yet there is still resistance from the healthcare community to incorporate AI in the clinical setting. The goal of this review is to answer the question of whether AI should be used in conjunction with neuroimaging in the diagnosis of AD. To answer the question, the possible benefits and disadvantages of AI are discussed. The main advantages of AI are its potential to improve diagnostic accuracy, improve the efficiency in analyzing radiographic data, reduce physician burnout, and advance precision medicine. The disadvantages include generalization and data shortage, lack of in vivo gold standard, skepticism in the medical community, potential for physician bias, and concerns over patient information, privacy, and safety. Although the challenges present fundamental concerns and must be addressed when the time comes, it would be unethical not to use AI if it can improve patient health and outcome.

Comorbidity-driven multi-modal subtype analysis in mild cognitive impairment of Alzheimer's disease

BACKGROUND

Mild cognitive impairment (MCI) is a heterogeneous condition with high individual variabilities in clinical outcomes driven by patient demographics, genetics, brain structure features, blood biomarkers, and comorbidities. Multi-modality data-driven approaches have been used to discover MCI subtypes; however, disease comorbidities have not been included as a modality though multiple diseases including hypertension are well-known risk factors for Alzheimer's disease (AD). The aim of this study was to examine MCI heterogeneity in the context of AD-related comorbidities along with other AD-relevant features and biomarkers.

METHODS

A total of 325 MCI subjects with 32 AD-relevant comorbidities and features were considered. Mixed-data clustering is applied to discover and compare MCI subtypes with and without including AD-related comorbidities. Finally, the relevance of each comorbidity-driven subtype was determined by examining their MCI to AD disease prognosis, descriptive statistics, and conversion rates.

RESULTS

We identified four (five) MCI subtypes: poor-, average-, good-, and best-AD prognosis by including comorbidities (without including comorbidities). We demonstrated that comorbidity-driven MCI subtypes differed from those identified without comorbidity information. We further demonstrated the clinical relevance of comorbidity-driven MCI subtypes. Among the four comorbidity-driven MCI subtypes there were substantial differences in the proportions of participants who reverted to normal function, remained stable, or converted to AD. The groups showed different behaviors, having significantly different MCI to AD prognosis, significantly different means for cognitive test-related and plasma features, and by the proportion of comorbidities.

CONCLUSIONS

Our study indicates that AD comorbidities should be considered along with other diverse AD-relevant characteristics to better understand MCI heterogeneity.

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.

Characterization of the angular gyrus in an older adult population: a multimodal multilevel approach

The angular gyrus (AG) has been associated with multiple cognitive functions, such as language, spatial and memory functions. Since the AG is thought to be a cross-modal hub region suffering from significant age-related structural atrophy, it may also play a key role in age-related cognitive decline. However, the exact relation between structural atrophy of the AG and cognitive decline in older adults is not fully understood, which may be related to two aspects: First, the AG is cytoarchitectonically divided into two areas, PGa and PGp, potentially sub-serving different cognitive functions. Second, the older adult population is characterized by high between-subjects variability which requires targeting individual phenomena during the aging process. We therefore performed a multimodal (gray matter volume [GMV], resting-state functional connectivity [RSFC] and structural connectivity [SC]) characterization of AG subdivisions PGa and PGp in a large older adult population, together with relations to age, cognition and lifestyle on the group level. Afterwards, we switched the perspective to the individual, which is especially important when it comes to the assessment of individual patients. The AG can be considered a heterogeneous structure in of the older brain: we found the different AG parts to be associated with different patterns of whole-brain GMV associations as well as their associations with RSFC, and SC patterns. Similarly, differential effects of age, cognition and lifestyle on the GMV of AG subdivisions were observed. This suggests each region to be structurally and functionally differentially involved in the older adult's brain network architecture, which was supported by differential molecular and genetic patterns, derived from the EBRAINS multilevel atlas framework. Importantly, individual profiles deviated considerably from the global conclusion drawn from the group study. Hence, general observations within the older adult population need to be carefully considered, when addressing individual conditions in clinical practice.

Feasibility of Precision Medicine in Hypertension Management-Scope and Technological Aspects

Personalized management of diseases by considering relevant patient features enables optimal treatment, instead of management according to an average patient. Precision management of hypertension is important, because both susceptibility to complications and response to treatment vary between individuals. While the use of genomic and proteomic personal features for widespread precision hypertension management is not practical, other features, such as age, ethnicity, and cardiovascular diseases, have been utilized in guidelines for hypertension management. In precision medicine, more blood-pressure-related clinical and physiological characteristics in the patient's profile can be utilized for the determination of the threshold of hypertension and optimal treatment. Several non-invasive and simple-to-use techniques for the measurement of hypertension-related physiological features are suggested for use in precision management of hypertension. In order to provide precise management of hypertension, accurate measurement of blood pressure is required, but the available non-invasive blood pressure measurement techniques, auscultatory sphygmomanometry and oscillometry, have inherent significant inaccuracy-either functional or technological-limiting the precision of personalized management of hypertension. A novel photoplethysmography-based technique for the measurement of systolic blood pressure that was recently found to be more accurate than the two available techniques can be utilized for more precise and personalized hypertension management.

The Immune System as a Therapeutic Target for Alzheimer’s Disease

Alzheimer’s disease (AD) is a heterogeneous neurodegenerative disorder and is the most common cause of dementia. Furthermore, aging is considered the most critical risk factor for AD. However, despite the vast amount of research and resources allocated to the understanding and development of AD treatments, setbacks have been more prominent than successes. Recent studies have shown that there is an intricate connection between the immune and central nervous systems, which can be imbalanced and thereby mediate neuroinflammation and AD. Thus, this review examines this connection and how it can be altered with AD. Recent developments in active and passive immunotherapy for AD are also discussed as well as suggestions for improving these therapies moving forward.

Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis

The integration of artificial intelligence in precision medicine has revolutionized healthcare delivery. Precision medicine identifies the phenotype of particular patients with less-common responses to treatment. Recent studies have demonstrated that translational research exploring the convergence between artificial intelligence and precision medicine will help solve the most difficult challenges facing precision medicine. Here, we discuss different aspects of artificial intelligence in precision medicine that improve healthcare delivery. First, we discuss how artificial intelligence changes the landscape of precision medicine and the evolution of artificial intelligence in precision medicine. Second, we highlight the synergies between artificial intelligence and precision medicine and promises of artificial intelligence and precision medicine in healthcare delivery. Third, we briefly explain the promise of big data analytics and the integration of nanomaterials in precision medicine. Last, we highlight the challenges and opportunities of artificial intelligence in precision medicine.

Perspectives and challenges in patient stratification in Alzheimer’s disease

Background

Patient stratification is the division of a patient population into distinct subgroups based on the presence or absence of particular disease characteristics. As patient stratification can be used to account for the underlying pathology of a disease, it can help physicians to tailor therapeutic interventions to individuals and optimize their care management and treatment regime. Alzheimer’s disease, the most common form of dementia, is a heterogeneous disease and its management benefits from patient stratification in clinical trials, and the development of personalized care and treatment strategies for people living with the disease.

Main body

In this review, we discuss the importance of the stratification of people living with Alzheimer’s disease, the challenges associated with early diagnosis and patient stratification, and the evolution of patient stratification once disease-modifying therapies become widely available.

Conclusion

Patient stratification plays an important role in drug development in clinical trials and may play an even larger role in clinical practice. A timely diagnosis and stratification of people living with Alzheimer’s disease is paramount in determining people who are at risk of progressing from mild cognitive impairment to Alzheimer’s dementia. There are key issues associated with stratifying patients which include the heterogeneity and complex neurobiology behind Alzheimer’s disease, our inadequately prepared healthcare systems, and the cultural perceptions of Alzheimer’s disease. Stratifying people living with Alzheimer’s disease may be the key in establishing precision and personalized medicine in the field, optimizing disease prevention and pharmaceutical treatment to slow or stop cognitive decline, while minimizing adverse effects.

The road to precision medicine: Eliminating the "One Size Fits All" approach in Alzheimer's disease

The expeditious advancement of Alzheimer's Disease (AD) is a threat to the global healthcare system, that is further supplemented by therapeutic failure. The prevalence of this disorder has been expected to quadrupole by 2050, thereby exerting a tremendous economic pressure on medical sector, worldwide. Thus, there is a dire need of a change in conventional approaches and adopt a novel methodology of disease prevention, treatment and diagnosis. Precision medicine offers a personalized approach to disease management, It is dependent upon genetic, environmental and lifestyle factors associated with the individual, aiding to develop tailored therapeutics. Precision Medicine Initiatives are launched, worldwide, to facilitate the integration of personalized models and clinical medicine. The review aims to provide a comprehensive understanding of the neuroinflammatory processes causing AD, giving a brief overview of the disease interventions. This is further followed by the role of precision medicine in AD, constituting the genetic perspectives, operation of personalized form of medicine and optimization of clinical trials with the 3 R's, showcasing an in-depth understanding of this novel approach in varying aspects of the healthcare industry, to provide an opportunity to the global AD researchers to elucidate suitable therapeutic regimens in clinically and pathologically complex diseases, like AD.

The Multifaceted Role of Neuroprotective Plants in Alzheimer’s Disease Treatment

Alzheimer’s disease (AD) is an age-related, progressive neurodegenerative disorder characterized by impaired cognition, memory loss, and altered personality. Many of the available pharmaceutical treatments do not alter the onset of disease progression. Recently, alternatives to developed drug candidates have been explored including medicinal plants and herbal treatments for the treatment of AD. This article examines the role of herbal plant extracts and the neuroprotective effects as alternative modes of intervention for AD progression. These extracts contain key metabolites that culminate alterations in AD progression. The traditional plant extracts explored in this article induce a variety of beneficial properties, including antioxidants, anti-inflammatory, and enhanced cognition, while also inducing activity on AD drug targets such as Aβ degradation. While these neuroprotective aspects for AD are relatively recent, there is great potential in the drug discovery aspect of these plant extracts for future use in AD treatment.

Dementia Risk Scores and Their Role in the Implementation of Risk Reduction Guidelines

Dementia prevention is a global health priority. In 2019, the World Health Organisation published its first evidence-based guidelines on dementia risk reduction. We are now at the stage where we need effective tools and resources to assess dementia risk and implement these guidelines into policy and practice. In this paper we review dementia risk scores as a means to facilitate this process. Specifically, we (a) discuss the rationale for dementia risk assessment, (b) outline some conceptual and methodological issues to consider when reviewing risk scores, (c) evaluate some dementia risk scores that are currently in use, and (d) provide some comments about future directions. A dementia risk score is a weighted composite of risk factors that reflects the likelihood of an individual developing dementia. In general, dementia risks scores have a wide range of implementations and benefits including providing early identification of individuals at high risk, improving risk perception for patients and physicians, and helping health professionals recommend targeted interventions to improve lifestyle habits to decrease dementia risk. A number of risk scores for dementia have been published, and some are widely used in research and clinical trials e.g., CAIDE, ANU-ADRI, and LIBRA. However, there are some methodological concerns and limitations associated with the use of these risk scores and more research is needed to increase their effectiveness and applicability. Overall, we conclude that, while further refinement of risk scores is underway, there is adequate evidence to use these assessments to implement guidelines on dementia risk reduction.

Advances in Deep Neuropathological Phenotyping of Alzheimer Disease: Past, Present, and Future

Alzheimer disease (AD) is a neurodegenerative disorder characterized pathologically by the presence of neurofibrillary tangles and amyloid beta (Aβ) plaques in the brain. The disease was first described in 1906 by Alois Alzheimer, and since then, there have been many advancements in technologies that have aided in unlocking the secrets of this devastating disease. Such advancements include improving microscopy and staining techniques, refining diagnostic criteria for the disease, and increased appreciation for disease heterogeneity both in neuroanatomic location of abnormalities as well as overlap with other brain diseases; for example, Lewy body disease and vascular dementia. Despite numerous advancements, there is still much to achieve as there is not a cure for AD and postmortem histological analyses is still the gold standard for appreciating AD neuropathologic changes. Recent technological advances such as in-vivo biomarkers and machine learning algorithms permit great strides in disease understanding, and pave the way for potential new therapies and precision medicine approaches. Here, we review the history of human AD neuropathology research to include the notable advancements in understanding common co-pathologies in the setting of AD, and microscopy and staining methods. We also discuss future approaches with a specific focus on deep phenotyping using machine learning.

Brain Tissue-Derived Extracellular Vesicles in Alzheimer's Disease Display Altered Key Protein Levels Including Cell Type-Specific Markers

BACKGROUND

Brain tissue-derived extracellular vesicles (bdEVs) play neurodegenerative and protective roles, including in Alzheimer's disease (AD). Extracellular vesicles (EVs) may also leave the brain to betray the state of the CNS in the periphery. Only a few studies have profiled the proteome of bdEVs and source brain tissue. Additionally, studies focusing on bdEV cell type-specific surface markers are rare.

OBJECTIVE

We aimed to reveal the pathological mechanisms inside the brain by profiling the tissue and bdEV proteomes in AD patients. In addition, to indicate targets for capturing and molecular profiling of bdEVs in the periphery, CNS cell-specific markers were profiled on the intact bdEV surface.

METHODS

bdEVs were separated and followed by EV counting and sizing. Brain tissue and bdEVs from age-matched AD patients and controls were then proteomically profiled. Total tau (t-tau), phosphorylated tau (p-tau), and antioxidant peroxiredoxins (PRDX) 1 and 6 were measured by immunoassay in an independent bdEV separation. Neuron, microglia, astrocyte, and endothelia markers were detected on intact EVs by multiplexed ELISA.

RESULTS

Overall, concentration of recovered bdEVs was not affected by AD. Proteome differences between AD and control were more pronounced for bdEVs than for brain tissue. Levels of t-tau, p-tau, PRDX1, and PRDX6 were significantly elevated in AD bdEVs compared with controls. Release of certain cell-specific bdEV markers was increased in AD.

CONCLUSION

Several bdEV proteins are involved in AD mechanisms and may be used for disease monitoring. The identified CNS cell markers may be useful tools for peripheral bdEV capture.

A Multimodal Risk Network Predicts Executive Function Trajectories in Non-demented Aging

Background: Multiple modalities of Alzheimer's disease (AD) risk factors may operate through interacting networks to predict differential cognitive trajectories in asymptomatic aging. We test such a network in a series of three analytic steps. First, we test independent associations between three risk scores (functional-health, lifestyle-reserve, and a combined multimodal risk score) and cognitive [executive function (EF)] trajectories. Second, we test whether all three associations are moderated by the most penetrant AD genetic risk [Apolipoprotein E (APOE) ε4+ allele]. Third, we test whether a non-APOE AD genetic risk score further moderates these APOE × multimodal risk score associations. Methods: We assembled a longitudinal data set (spanning a 40-year band of aging, 53-95 years) with non-demented older adults (baseline n = 602; Mage = 70.63(8.70) years; 66% female) from the Victoria Longitudinal Study (VLS). The measures included for each modifiable risk score were: (1) functional-health [pulse pressure (PP), grip strength, and body mass index], (2) lifestyle-reserve (physical, social, cognitive-integrative, cognitive-novel activities, and education), and (3) the combination of functional-health and lifestyle-reserve risk scores. Two AD genetic risk markers included (1) APOE and (2) a combined AD-genetic risk score (AD-GRS) comprised of three single nucleotide polymorphisms (SNPs; Clusterin[rs11136000], Complement receptor 1[rs6656401], Phosphatidylinositol binding clathrin assembly protein[rs3851179]). The analytics included confirmatory factor analysis (CFA), longitudinal invariance testing, and latent growth curve modeling. Structural path analyses were deployed to test and compare prediction models for EF performance and change. Results: First, separate analyses showed that higher functional-health risk scores, lifestyle-reserve risk scores, and the combined score, predicted poorer EF performance and steeper decline. Second, APOE and AD-GRS moderated the association between functional-health risk score and the combined risk score, on EF performance and change. Specifically, only older adults in the APOEε4- group showed steeper EF decline with high risk scores on both functional-health and combined risk score. Both associations were further magnified for adults with high AD-GRS. Conclusion: The present multimodal AD risk network approach incorporated both modifiable and genetic risk scores to predict EF trajectories. The results add an additional degree of precision to risk profile calculations for asymptomatic aging populations.

Cognitive profiles of patients with mild cognitive impairment due to Alzheimer's versus Parkinson's disease defined using a base rate approach: Implications for neuropsychological assessments

INTRODUCTION

Large studies on cognitive profiles of patients with mild cognitive impairment (MCI) due to Alzheimer's disease (AD-MCI) compared to Parkinson's disease (PD-MCI) are rare.

METHODS

Data from two multicenter cohort studies in AD and PD were merged using a unified base rate approach for the MCI diagnosis. Cognitive profiles were compared using scores derived from the Consortium to Establish a Registry for Alzheimer's Disease battery.

RESULTS

Patients with AD-MCI showed lower standardized scores on all memory test scores and a language test. Patients with PD-MCI showed lower standardized scores in a set-shifting measure as an executive task. A cross-validated logistic regression with test scores as predictors was able to classify 72% of patients correctly to AD-MCI versus PD-MCI.

DISCUSSION

The applied test battery successfully discriminated between AD-MCI and PD-MCI. Neuropsychological test batteries in clinical practice should always include a broad spectrum of cognitive domains to capture any cognitive changes.

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.

Effect of a Music Therapy Intervention Using Gerdner and Colleagues' Protocol for Caregivers and Elderly Patients with Dementia: A Single-Blind Randomized Controlled Study

Music therapy (MT) is considered one of the complementary strategies to pharmacological treatment for behavioral and psychological symptoms (BPSD) of dementia. However, studies adopting MT protocols tailored for institutionalized people with dementia are limited and their usefulness for supporting caregivers is under investigated to date. Our study aimed at evaluating the effects of an MT intervention according to Gerdner and colleagues’ protocol in a sample of 60 elderly people with moderate-to-severe dementia of the Auxologico Institute (Milan, Italy) and associated caregivers, randomly assigned to an Experimental Group (EG) (n = 30) undergoing 30 min of MT two times a week for 8 weeks and to a Control Group (n = 30) (CG) receiving standard care. Before and after the intervention, residents-associated caregivers were administered the Caregiver Burden Inventory (CBI) and the Neuropsychiatric Inventory (NPI). Depression and worry were also assessed in caregivers prior to the intervention, by the Beck Depression Inventory-II and the Penn State Worry Questionnaire, respectively. A mixed model ANCOVA revealed a Time*Group effect (p = 0.006) with regard to CBI decreasing after the intervention for the EG and Time*Group effects (p = 0.001) with regard to NPI_frequencyXseverity and NPI_distress, with a greater effect for the EG than the CG. Implications for MT protocols implementations are discussed.

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.

Vascular mild cognitive impairment and its relationship to hemoglobin A1c levels and apolipoprotein E genotypes in the Dominican Republic

Dementia and vascular mild cognitive impairment (VaMCI) currently impose a tremendous human and economic burden on patients from aging populations and their families worldwide. Understanding the interplay of cardiometabolic risk factors and apolipoprotein E (APOE) may direct us to a more personalized medicine and preventative care in MCI and dementia.

APOE: The New Frontier in the Development of a Therapeutic Target towards Precision Medicine in Late-Onset Alzheimer's

Alzheimer's disease (AD) has a critical unmet medical need. The consensus around the amyloid cascade hypothesis has been guiding pre-clinical and clinical research to focus mainly on targeting beta-amyloid for treating AD. Nevertheless, the vast majority of the clinical trials have repeatedly failed, prompting the urgent need to refocus on other targets and shifting the paradigm of AD drug development towards precision medicine. One such emerging target is apolipoprotein E (APOE), identified nearly 30 years ago as one of the strongest and most reproduceable genetic risk factor for late-onset Alzheimer's disease (LOAD). An exploration of APOE as a new therapeutic culprit has produced some very encouraging results, proving that the protein holds promise in the context of LOAD therapies. Here, we review the strategies to target APOE based on state-of-the-art technologies such as antisense oligonucleotides, monoclonal antibodies, and gene/base editing. We discuss the potential of these initiatives in advancing the development of novel precision medicine therapies to LOAD.

Noninvasive characterization of Alzheimer's disease by circulating, cell-free messenger RNA next-generation sequencing

The lack of accessible noninvasive tools to examine the molecular alterations occurring in the brain limits our understanding of the causes and progression of Alzheimer's disease (AD), as well as the identification of effective therapeutic strategies. Here, we conducted a comprehensive profiling of circulating, cell-free messenger RNA (cf-mRNA) in plasma of 126 patients with AD and 116 healthy controls of similar age. We identified 2591 dysregulated genes in the cf-mRNA of patients with AD, which are enriched in biological processes well known to be associated with AD. Dysregulated genes included brain-specific genes and resembled those identified to be dysregulated in postmortem AD brain tissue. Furthermore, we identified disease-relevant circulating gene transcripts that correlated with the severity of cognitive impairment. These data highlight the potential of high-throughput cf-mRNA sequencing to evaluate AD-related pathophysiological alterations in the brain, leading to precision healthcare solutions that could improve AD patient management.

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.

Distinct Cognitive Trajectories in Late Life and Associated Predictors and Outcomes: A Systematic Review

Background

Cognitive aging is a dynamic process in late life with significant heterogeneity across individuals.

Objective

To review the evidence for latent classes of cognitive trajectories and to identify the associated predictors and outcomes.

Methods

A systematic search was performed in MEDLINE and EMBASE for articles that identified two or more cognitive trajectories in adults. The study was conducted following the PRISMA statement.

Results

Thirty-seven studies were included, ranging from 219 to 9,704 participants, with a mean age of 60 to 93.4 years. Most studies (n = 30) identified distinct cognitive trajectories using latent class growth analysis. The trajectory profile commonly consisted of three to four classes with progressively decreasing baseline and increasing rate of decline-a 'stable-high' class characterized as maintenance of cognitive function at high level, a 'minor-decline' class or 'stable-medium' class that declines gradually over time, and a 'rapid-decline' class with the steepest downward slope. Generally, membership of better classes was predicted by younger age, being female, more years of education, better health, healthier lifestyle, higher social engagement and lack of genetic risk variants. Some factors (e.g., education) were found to be associated with cognitive function over time only within individual classes.

Conclusion

Cognitive aging in late life is a dynamic process with significant inter-individual variability. However, it remains unclear whether similar patterns of cognitive aging are observed across all cognitive domains. Further research into unique factors which promote the maintenance of high-cognitive function is needed to help inform public policy.

A Systematic Review on Predictors of Working Memory Training Responsiveness in Healthy Older Adults: Methodological Challenges and Future Directions

Background: Research on predictors of working memory training responsiveness, which could help tailor cognitive interventions individually, is a timely topic in healthy aging. However, the findings are highly heterogeneous, reporting partly conflicting results following a broad spectrum of methodological approaches to answer the question “who benefits most” from working memory training.

Objective: The present systematic review aimed to systematically investigate prognostic factors and models for working memory training responsiveness in healthy older adults.

Method: Four online databases were searched up to October 2019 (MEDLINE Ovid, Web of Science, CENTRAL, and PsycINFO). The inclusion criteria for full texts were publication in a peer-reviewed journal in English/German, inclusion of healthy older individuals aged ≥55 years without any neurological and/or psychiatric diseases including cognitive impairment, and the investigation of prognostic factors and/or models for training responsiveness after targeted working memory training in terms of direct training effects, near-transfer effects to verbal and visuospatial working memory as well as far-transfer effects to other cognitive domains and behavioral variables. The study design was not limited to randomized controlled trials.

Results: A total of 16 studies including n = 675 healthy older individuals with a mean age of 63.0–86.8 years were included in this review. Within these studies, five prognostic model approaches and 18 factor finding approaches were reported. Risk of bias was assessed using the Quality in Prognosis Studies checklist, indicating that important information, especially regarding the domains study attrition, study confounding, and statistical analysis and reporting, was lacking throughout many of the investigated studies. Age, education, intelligence, and baseline performance in working memory or other cognitive domains were frequently investigated predictors across studies.

Conclusions: Given the methodological shortcomings of the included studies, no clear conclusions can be drawn, and emerging patterns of prognostic effects will have to survive sound methodological replication in future attempts to promote precision medicine approaches in the context of working memory training. Methodological considerations are discussed, and our findings are embedded to the cognitive aging literature, considering, for example, the cognitive reserve framework and the compensation vs. magnification account. The need for personalized cognitive prevention and intervention methods to counteract cognitive decline in the aging population is high and the potential enormous.

Registration: PROSPERO, ID CRD42019142750.

Gut microbiome-mediated epigenetic regulation of brain disorder and application of machine learning for multi-omics data analysis

The gut-brain axis (GBA) is a biochemical link that connects the central nervous system (CNS) and enteric nervous system (ENS). Clinical and experimental evidence suggests gut microbiota as a key regulator of the GBA. Microbes living in the gut not only interact locally with intestinal cells and the ENS but have also been found to modulate the CNS through neuroendocrine and metabolic pathways. Studies have also explored the involvement of gut microbiota dysbiosis in depression, anxiety, autism, stroke, and pathophysiology of other neurodegenerative diseases. Recent reports suggest that microbe-derived metabolites can influence host metabolism by acting as epigenetic regulators. Butyrate, an intestinal bacterial metabolite, is a known histone deacetylase inhibitor that has shown to improve learning and memory in animal models. Due to high disease variability amongst the population, a multi-omics approach that utilizes artificial intelligence and machine learning to analyze and integrate omics data is necessary to better understand the role of the GBA in pathogenesis of neurological disorders, to generate predictive models, and to develop precise and personalized therapeutics. This review examines our current understanding of epigenetic regulation of the GBA and proposes a framework to integrate multi-omics data for prediction, prevention, and development of precision health approaches to treat brain disorders.

Variants in PPP2R2B and IGF2BP3 are associated with higher tau deposition

Tau deposition is a key biological feature of Alzheimer's disease that is closely related to cognitive impairment. However, it remains poorly understood why certain individuals may be more susceptible to tau deposition while others are more resistant. The recent availability of in vivo assessment of tau burden through positron emission tomography provides an opportunity to test the hypothesis that common genetic variants may influence tau deposition. We performed a genome-wide association study of tau-positron emission tomography on a sample of 754 individuals over age 50 (mean age 72.4 years, 54.6% men, 87.6% cognitively unimpaired) from the population-based Mayo Clinic Study of Aging. Linear regression was performed to test nucleotide polymorphism associations with AV-1451 (18F-flortaucipir) tau-positron emission tomography burden in an Alzheimer's-signature composite region of interest, using an additive genetic model and covarying for age, sex and genetic principal components. Genome-wide significant associations with higher tau were identified for rs76752255 (P = 9.91 × 10-9, β = 0.20) in the tau phosphorylation regulatory gene PPP2R2B (protein phosphatase 2 regulatory subunit B) and for rs117402302 (P  = 4.00 × 10-8, β = 0.19) near IGF2BP3 (insulin-like growth factor 2 mRNA-binding protein 3). The PPP2R2B association remained genome-wide significant after additionally covarying for global amyloid burden and cerebrovascular disease risk, while the IGF2BP3 association was partially attenuated after accounting for amyloid load. In addition to these discoveries, three single nucleotide polymorphisms within MAPT (microtubule-associated protein tau) displayed nominal associations with tau-positron emission tomography burden, and the association of the APOE (apolipoprotein E) ɛ4 allele with tau-positron emission tomography was marginally nonsignificant (P  = 0.06, β = 0.07). No associations with tau-positron emission tomography burden were identified for other single nucleotide polymorphisms associated with Alzheimer's disease clinical diagnosis in prior large case-control studies. Our findings nominate PPP2R2B and IGF2BP3 as novel potential influences on tau pathology which warrant further functional characterization. Our data are also supportive of previous literature on the associations of MAPT genetic variation with tau, and more broadly supports the inference that tau accumulation may have a genetic architecture distinct from known Alzheimer's susceptibility genes, which may have implications for improved risk stratification and therapeutic targeting.

Exploring Alzheimer's Disease Molecular Variability via Calculation of Personalized Transcriptional Signatures

Despite huge investments and major efforts to develop remedies for Alzheimer’s disease (AD) in the past decades, AD remains incurable. While evidence for molecular and phenotypic variability in AD have been accumulating, AD research still heavily relies on the search for AD-specific genetic/protein biomarkers that are expected to exhibit repetitive patterns throughout all patients. Thus, the classification of AD patients to different categories is expected to set the basis for the development of therapies that will be beneficial for subpopulations of patients. Here we explore the molecular heterogeneity among a large cohort of AD and non-demented brain samples, aiming to address the question whether AD-specific molecular biomarkers can progress our understanding of the disease and advance the development of anti-AD therapeutics. We studied 951 brain samples, obtained from up to 17 brain regions of 85 AD patients and 22 non-demented subjects. Utilizing an information-theoretic approach, we deciphered the brain sample-specific structures of altered transcriptional networks. Our in-depth analysis revealed that 7 subnetworks were repetitive in the 737 diseased and 214 non-demented brain samples. Each sample was characterized by a subset consisting of ~1–3 subnetworks out of 7, generating 52 distinct altered transcriptional signatures that characterized the 951 samples. We show that 30 different altered transcriptional signatures characterized solely AD samples and were not found in any of the non-demented samples. In contrast, the rest of the signatures characterized different subsets of sample types, demonstrating the high molecular variability and complexity of gene expression in AD. Importantly, different AD patients exhibiting similar expression levels of AD biomarkers harbored distinct altered transcriptional networks. Our results emphasize the need to expand the biomarker-based stratification to patient-specific transcriptional signature identification for improved AD diagnosis and for the development of subclass-specific future treatment.

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.

The role of anxiety in the integrative memory model

We suggest that the inclusion of anxiety, as one relevant mood factor, could enhance the implementation of the integrative memory model in research and the clinic. The role of anxiety in Alzheimer's disease neuroanatomy, symptomology, and progression is used as an example. Customization of the integrative memory model can establish strong foundations for pathology-specific models of memory deficits, enhancing the development of precision medicine applications.

High-resolution metabolomic profiling of Alzheimer's disease in plasma

Background

Alzheimer’s disease (AD) is a complex neurological disorder with contributions from genetic and environmental factors. High‐resolution metabolomics (HRM) has the potential to identify novel endogenous and environmental factors involved in AD. Previous metabolomics studies have identified circulating metabolites linked to AD, but lack of replication and inconsistent diagnostic algorithms have hindered the generalizability of these findings. Here we applied HRM to identify plasma metabolic and environmental factors associated with AD in two study samples, with cerebrospinal fluid (CSF) biomarkers of AD incorporated to achieve high diagnostic accuracy.

Methods

Liquid chromatography‐mass spectrometry (LC–MS)‐based HRM was used to identify plasma and CSF metabolites associated with AD diagnosis and CSF AD biomarkers in two studies of prevalent AD (Study 1: 43 AD cases, 45 mild cognitive impairment [MCI] cases, 41 controls; Study 2: 50 AD cases, 18 controls). AD‐associated metabolites were identified using a metabolome‐wide association study (MWAS) framework.

Results

An MWAS meta‐analysis identified three non‐medication AD‐associated metabolites in plasma, including elevated levels of glutamine and an unknown halogenated compound and lower levels of piperine, a dietary alkaloid. The non‐medication metabolites were correlated with CSF AD biomarkers, and glutamine and the unknown halogenated compound were also detected in CSF. Furthermore, in Study 1, the unknown compound and piperine were altered in MCI patients in the same direction as AD dementia.

Conclusions

In plasma, AD was reproducibly associated with elevated levels of glutamine and a halogen‐containing compound and reduced levels of piperine. These findings provide further evidence that exposures and behavior may modify AD risks.

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.

Epigenetic heterogeneity in cancer

Phenotypic and functional heterogeneity is one of the hallmarks of human cancers. Tumor genotype variations among tumors within different patients are known as interpatient heterogeneity, and variability among multiple tumors of the same type arising in the same patient is referred to as intra-patient heterogeneity. Subpopulations of cancer cells with distinct phenotypic and molecular features within a tumor are called intratumor heterogeneity (ITH). Since Nowell proposed the clonal evolution of tumor cell populations in 1976, tumor heterogeneity, especially ITH, was actively studied. Research has focused on the genetic basis of cancer, particularly mutational activation of oncogenes or inactivation of tumor-suppressor genes (TSGs). The phenomenon of ITH is commonly explained by Darwinian-like clonal evolution of a single tumor. Despite the monoclonal origin of most cancers, new clones arise during tumor progression due to the continuous acquisition of mutations. It is clear that disruption of the "epigenetic machinery" plays an important role in cancer development. Aberrant epigenetic changes occur more frequently than gene mutations in human cancers. The epigenome is at the intersection of the environment and genome. Epigenetic dysregulation occurs in the earliest stage of cancer. The current trend of epigenetic therapy is to use epigenetic drugs to reverse and/or delay future resistance to cancer therapies. A majority of cancer therapies fail to achieve durable responses, which is often attributed to ITH. Epigenetic therapy may reverse drug resistance in heterogeneous cancer. Complete understanding of genetic and epigenetic heterogeneity may assist in designing combinations of targeted therapies based on molecular information extracted from individual tumors.

Perceptions of precision medicine among diverse dementia caregivers and professional providers

Introduction

Underrepresented groups experience health disparities and a history of exploitation by researchers and the health-care system that may contribute to distrust of new treatments and technologies. This study aims to understand how diverse family caregivers and health-care professionals view the benefits and risks of precision medicine as well as cultural dimensions to consider when developing and implementing precision medicine interventions in dementia care.

Methods

Eight focus group sessions and one individual interview were conducted over a 6-month period. Fifty-four focus group participants included African-American, American Indian, rural Caucasian, Latino, and West African caregivers and health professionals. The majority of participants were female (73%) and were of Hispanic/Latino ethnicity (68%). About a third of participants identified their race as white. Participants were presented with four hypothetical scenarios related to precision medicine diagnostic and treatment approaches in dementia care: (1) genetic testing for dementia risk, (2) health-care informatics to determine individualized medication dosages based on health and family history, (3) a smartphone application providing dementia caregiving tips, and (4) remote activity monitoring technology in the home. Focus groups' responses were coded using thematic analysis.

Results

Participants indicated skepticism regarding the use of precision medicine in their communities. Concerns included cost of precision medicine and insurance coverage; lack of alignment with cultural norms; fraught relationships between communities, health professionals, and researchers; data ownership and privacy; and the trade-off between knowing risk and treatment benefit.

Discussion

Establishing relationships with underserved communities is crucial to advancing precision medicine in dementia care. Appropriate engagement with diverse racial, ethnic, and geographic communities may require significant investment but is necessary to deliver precision medicine effectively.

The cholinergic system in the pathophysiology and treatment of Alzheimer's disease

Cholinergic synapses are ubiquitous in the human central nervous system. Their high density in the thalamus, striatum, limbic system, and neocortex suggest that cholinergic transmission is likely to be critically important for memory, learning, attention and other higher brain functions. Several lines of research suggest additional roles for cholinergic systems in overall brain homeostasis and plasticity. As such, the brain's cholinergic system occupies a central role in ongoing research related to normal cognition and age-related cognitive decline, including dementias such as Alzheimer's disease. The cholinergic hypothesis of Alzheimer's disease centres on the progressive loss of limbic and neocortical cholinergic innervation. Neurofibrillary degeneration in the basal forebrain is believed to be the primary cause for the dysfunction and death of forebrain cholinergic neurons, giving rise to a widespread presynaptic cholinergic denervation. Cholinesterase inhibitors increase the availability of acetylcholine at synapses in the brain and are one of the few drug therapies that have been proven clinically useful in the treatment of Alzheimer's disease dementia, thus validating the cholinergic system as an important therapeutic target in the disease. This review includes an overview of the role of the cholinergic system in cognition and an updated understanding of how cholinergic deficits in Alzheimer's disease interact with other aspects of disease pathophysiology, including plaques composed of amyloid-β proteins. This review also documents the benefits of cholinergic therapies at various stages of Alzheimer's disease and during long-term follow-up as visualized in novel imaging studies. The weight of the evidence supports the continued value of cholinergic drugs as a standard, cornerstone pharmacological approach in Alzheimer's disease, particularly as we look ahead to future combination therapies that address symptoms as well as disease progression.

Sex Differences in the Genetic Architecture of Alzheimer's Disease

PURPOSE OF REVIEW

Summarize sex-specific contributors to the genetic architecture of Alzheimer's disease (AD).

RECENT FINDINGS

There are sex differences in the effects of Apolipoprotein E (APOE), genes along the APOE pathway, and genes along the neurotrophic signaling pathway in predicting AD. Reported sex differences are largely driven by stronger associations among females. Evidence also suggests that genetic predictors of amyloidosis are largely shared across sexes, while sex-specific genetic effects emerge downstream of amyloidosis and drive the clinical manifestation of AD.

SUMMARY

There is a lack of comprehensive assessments of sex differences in genome-wide analyses of AD and a need for more systematic reporting a sex-stratified genetic effects. The emerging emphasis on sex as a biological variable provides an opportunity for transdisciplinary collaborations aimed at addressing major analytical challenges that have hampered advancements in the field. Ultimately, sex-specific genetic association studies represent a logical first step towards precision medicine.

REST rs3796529 Genotype and Rate of Functional Deterioration in Alzheimer's Disease

Recently, REST (RE1-silencing transcription factor) gene has been shown to be lost in Alzheimer’s disease (AD), and a missense minor REST allele rs3796529-T has been shown to reduce the rate of hippocampal volume loss. However, whether the REST rs3796529 genotype is associated with the rate of functional deterioration in AD is unknown. A total of 584 blood samples from Taiwanese patients with AD were collected from January 2002 to December 2013. The diagnosis of AD was based on the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association criteria. The allele frequency of rs3796529-T was compared between the AD cohort and 993 individuals from the general population in Taiwan. Kaplan-Meier analysis, the log rank test and a multivariate Cox model were then used to evaluate the association between rs3796529-T and functional deterioration in the AD cohort. The allele frequency of rs3796529-T was significantly lower in the AD cohort compared to the general population cohort (36.82% vs. 40.73%, p=0.029). Kaplan-Meier analysis and the log rank test showed that the AD patients carrying the rs3796529 T/T genotype had a longer progression-free survival than those with the C/C genotype (p=0.012). In multivariate analysis, the rs3796529 T/T genotype (adjusted HR=0.593, 95% CI: 0.401-0.877, p=0.009) was an independent protective factor for functional deterioration. The rs3796529 T/T genotype was associated with slower functional deterioration in patients with AD. This finding may lead to a to better understanding of the molecular pathways involved, and prompt further development of novel biomarkers to monitor AD.