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

Plasma ATN(I) classification and precision pharmacology in Alzheimer's disease

Evaluating potential therapies for Alzheimer's disease (AD) depends on use of biomarkers for appropriate subject selection and monitoring disease progression. Biomarkers that predict onset of clinical symptoms are particularly important for AD because they enable intervention before irreversible neurodegeneration occurs. The amyloid-β-tau-neurodegeneration (ATN) classification system is currently used as a biological staging model for AD and is based on three classes of biomarkers evaluating amyloid-β (Aβ), tau pathology and neurodegeneration or neuronal injury. Promising blood-based biomarkers for each of these categories have been identified (Aβ42/Aβ40 ratio, phosphorylated tau, neurofilament light chain), and this matrix is now being expanded toward an ATN(I) system, where "I" represents a neuroinflammatory biomarker. The plasma ATN(I) system, together with APOE genotyping, offers a basis for individualized evaluation and a move away from the classic "one size fits all" approach toward a biomarker-driven individualisation of therapy for patients with AD.

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.

Alpha Synuclein: Neurodegeneration and Inflammation

Alpha-Synuclein (α-Syn) is one of the most important molecules involved in the pathogenesis of Parkinson's disease and related disorders, synucleinopathies, but also in several other neurodegenerative disorders with a more elusive role. This review analyzes the activities of α-Syn, in different conformational states, monomeric, oligomeric and fibrils, in relation to neuronal dysfunction. The neuronal damage induced by α-Syn in various conformers will be analyzed in relation to its capacity to spread the intracellular aggregation seeds with a prion-like mechanism. In view of the prominent role of inflammation in virtually all neurodegenerative disorders, the activity of α-Syn will also be illustrated considering its influence on glial reactivity. We and others have described the interaction between general inflammation and cerebral dysfunctional activity of α-Syn. Differences in microglia and astrocyte activation have also been observed when in vivo the presence of α-Syn oligomers has been combined with a lasting peripheral inflammatory effect. The reactivity of microglia was amplified, while astrocytes were damaged by the double stimulus, opening new perspectives for the control of inflammation in synucleinopathies. Starting from our studies in experimental models, we extended the perspective to find useful pointers to orient future research and potential therapeutic strategies in neurodegenerative disorders.

Neuropsychiatric Symptoms and Commonly Used Biomarkers of Alzheimer's Disease: A Literature Review from a Machine Learning Perspective

There is a growing interest in the application of machine learning (ML) in Alzheimer's disease (AD) research. However, neuropsychiatric symptoms (NPS), frequent in subjects with AD, mild cognitive impairment (MCI), and other related dementias have not been analyzed sufficiently using ML methods. To portray the landscape and potential of ML research in AD and NPS studies, we present a comprehensive literature review of existing ML approaches and commonly studied AD biomarkers. We conducted PubMed searches with keywords related to NPS, AD biomarkers, machine learning, and cognition. We included a total of 38 articles in this review after excluding some irrelevant studies from the search results and including 6 articles based on a snowball search from the bibliography of the relevant studies. We found a limited number of studies focused on NPS with or without AD biomarkers. In contrast, multiple statistical machine learning and deep learning methods have been used to build predictive diagnostic models using commonly known AD biomarkers. These mainly included multiple imaging biomarkers, cognitive scores, and various omics biomarkers. Deep learning approaches that combine these biomarkers or multi-modality datasets typically outperform single-modality datasets. We conclude ML may be leveraged to untangle the complex relationships of NPS and AD biomarkers with cognition. This may potentially help to predict the progression of MCI or dementia and develop more targeted early intervention approaches based on NPS.

Task-dependent learning and memory deficits in the TgF344-AD rat model of Alzheimer's disease: three key timepoints through middle-age in females

The TgF344 rat model of Alzheimer's disease (AD) provides a comprehensive neuropathology presentation, with age-dependent development of tau tangles, amyloid-beta (A[Formula: see text]) plaques, neuronal loss, and increased gliosis. The behavioral trajectory of this model, particularly relating to spatial learning and memory, has yet to be fully characterized. The current experiment evaluated spatial working and reference memory performance, as well as several physiological markers of health, at 3 key age points in female TgF344-AD rats: 6-months, 9-months, and 12-months. At 6 months of age, indications of working and reference memory impairments were observed in transgenic (Tg) rats on the water radial-arm maze, a complex task that requires working and reference memory simultaneously; at 12 months old, Tg impairments were observed for two working memory measures on this task. Notably, no impairments were observed at the 9-month timepoint on this maze. For the Morris maze, a measure of spatial reference memory, Tg rats demonstrated significant impairment relative to wildtype (WT) controls at all 3 age-points. Frontal cortex, entorhinal cortex, and dorsal hippocampus were evaluated for A[Formula: see text]1-42 expression via western blot in Tg rats only. Analyses of A[Formula: see text]1-42 expression revealed age-dependent increases in all 3 regions critical to spatial learning and memory. Measures of physiological health, including heart, uterine, and body weights, revealed unique age-specific outcomes for female Tg rats, with the 9-month timepoint identified as critical for further research within the trajectory of AD-like behavior, physiology, and pathology.

Oligomeropathies, inflammation and prion protein binding

The central role of oligomers, small soluble aggregates of misfolded proteins, in the pathogenesis of neurodegenerative disorders is recognized in numerous experimental conditions and is compatible with clinical evidence. To underline this concept, some years ago we coined the term oligomeropathies to define the common mechanism of action of protein misfolding diseases like Alzheimer, Parkinson or prion diseases. Using simple experimental conditions, with direct application of synthetic β amyloid or α-synuclein oligomers intraventricularly at micromolar concentrations, we could detect differences and similarities in the biological consequences. The two oligomer species affected cognitive behavior, neuronal dysfunction and cerebral inflammatory reactions with distinct mechanisms. In these experimental conditions the proposed mediatory role of cellular prion protein in oligomer activities was not confirmed. Together with oligomers, inflammation at different levels can be important early in neurodegenerative disorders; both β amyloid and α-synuclein oligomers induce inflammation and its control strongly affects neuronal dysfunction. This review summarizes our studies with β-amyloid or α-synuclein oligomers, also considering the potential curative role of doxycycline, a well-known antibiotic with anti-amyloidogenic and anti-inflammatory activities. These actions are analyzed in terms of the therapeutic prospects.

Atrial cardiopathy and cognitive impairment

Cognitive impairment involves complex interactions between multiple pathways and mechanisms, one of which being cardiac disorders. Atrial cardiopathy (AC) is a structural and functional disorder of the left atrium that may be a substrate for other cardiac disorders such as atrial fibrillation (AF) and heart failure (HF). The association between AF and HF and cognitive decline is clear; however, the relationship between AC and cognition requires further investigation. Studies have shown that several markers of AC, such as increased brain natriuretic peptide and left atrial enlargement, are associated with an increased risk for cognitive impairment. The pathophysiology of cognitive decline in patients with AC is not yet well understood. Advancing our understanding of the relationship between AC and cognition may point to important treatable targets and inform future therapeutic advancements. This review presents our current understanding of the diagnosis of AC, as well as clinical characteristics and potential pathways involved in the association between AC and cognitive impairment.

ALZT-OP1: An experimental combination regimen for the treatment of Alzheimer's Disease

INTRODUCTION

For Alzheimer's disease (AD) treatment, US FDA granted accelerated approval for aducanumab due to its amyloid-β (Aβ)-lowering effects, notwithstanding the reported poor correlation between amyloid plaque reduction and clinical change for this drug. The diversification of drug targets appears to be the future of the AD field and from this perspective, drugs modulating microglia dysfunction and combination treatment regimens offer some promise.

AREAS COVERED

The aim of the present article was to provide a comprehensive review of ALZT-OP1 (cromolyn sodium plus ibuprofen), an experimental combination treatment regimen for AD, discussing their mechanisms of action targeting Aβ and neuroinflammation, examining the role of microglia in AD and offering our own insights on the role of present and alternative approaches directed toward neuroinflammation.

EXPERT OPINION

Enrolling high-risk participants with elevated brain amyloid could help to slow cognitive decline in secondary prevention trials during AD preclinical stages. Long-term follow-up indicated that non-steroidal anti-inflammatory drugs use begun when the brain was still normal may benefit these patients, suggesting that the timing of therapy could be crucial. However, previous clinical failures and the present incomplete understanding of the Aβ pathophysiological role in AD put this novel experimental combination regimen at substantial risk of failure.

Z-Guggulsterone attenuates cognitive defects and decreases neuroinflammation in APPswe/PS1dE9 mice through inhibiting the TLR4 signaling pathway

Growing evidence indicates that inflammatory damage is implicated in the pathogenesis of Alzheimer's disease (AD). Z-Guggulsterone (Z-GS) is a natural steroid, which is extracted from Commiphora mukul and has anti-inflammatory effects in vivo and in vitro. In the present study, we investigated the disease-modifying effects of chronic Z-GS administration on the cognitive and neuropathological impairments in the transgenic mouse models of AD. We found that chronic Z-GS administration prevented learning and memory deficits in the APPswe/PS1dE9 mice. In addition, Z-GS treatment significantly decreased cerebral amyloid-β (Aβ) levels and plaque burden via inhibiting amyloid precursor protein (APP) processing by reducing beta-site APP cleaving enzyme 1 (BACE1) expression in the APPswe/PS1dE9 mice. We also found that Z-GS treatment markedly alleviated neuroinflammation and reduced synaptic defects in the APPswe/PS1dE9 mice. Furthermore, the activated TLR4/NF-κB signaling pathways in APPswe/PS1dE9 mice were remarkably inhibited by Z-GS treatment, which was achieved via suppressing the phosphorylation of JNK. Collectively, our data demonstrate that chronic Z-GS treatment restores cognitive defects and reverses multiple neuropathological impairments in the APPswe/PS1dE9 mice. This study provides novel insights into the neuroprotective effects and neurobiological mechanisms of Z-GS on AD, indicating that Z-GS is a promising disease-modifying agent for the treatment of AD.

Genetically modified mice for research on human diseases: A triumph for Biotechnology or a work in progress?

Genetically modified mice are engineered as models for human diseases. These mouse models include inbred strains, mutants, gene knockouts, gene knockins, and ‘humanized’ mice. Each mouse model is engineered to mimic a specific disease based on a theory of the genetic basis of that disease. For example, to test the amyloid theory of Alzheimer’s disease, mice with amyloid precursor protein genes are engineered, and to test the tau theory, mice with tau genes are engineered. This paper discusses the importance of mouse models in basic research, drug discovery, and translational research, and examines the question of how to define the “best” mouse model of a disease. The critiques of animal models and the caveats in translating the results from animal models to the treatment of human disease are discussed. Since many diseases are heritable, multigenic, age-related and experience-dependent, resulting from multiple gene-gene and gene-environment interactions, it will be essential to develop mouse models that reflect these genetic, epigenetic and environmental factors from a developmental perspective. Such models would provide further insight into disease emergence, progression and the ability to model two-hit and multi-hit theories of disease. The summary examines the biotechnology for creating genetically modified mice which reflect these factors and how they might be used to discover new treatments for complex human diseases such as cancers, neurodevelopmental and neurodegenerative diseases.

Methods for Stratification and Validation Cohorts: A Scoping Review

Personalized medicine requires large cohorts for patient stratification and validation of patient clustering. However, standards and harmonized practices on the methods and tools to be used for the design and management of cohorts in personalized medicine remain to be defined. This study aims to describe the current state-of-the-art in this area. A scoping review was conducted searching in PubMed, EMBASE, Web of Science, Psycinfo and Cochrane Library for reviews about tools and methods related to cohorts used in personalized medicine. The search focused on cancer, stroke and Alzheimer’s disease and was limited to reports in English, French, German, Italian and Spanish published from 2005 to April 2020. The screening process was reported through a PRISMA flowchart. Fifty reviews were included, mostly including information about how data were generated (25/50) and about tools used for data management and analysis (24/50). No direct information was found about the quality of data and the requirements to monitor associated clinical data. A scarcity of information and standards was found in specific areas such as sample size calculation. With this information, comprehensive guidelines could be developed in the future to improve the reproducibility and robustness in the design and management of cohorts in personalized medicine studies.

Lights and Shadows of Cerebrospinal Fluid Biomarkers in the Current Alzheimer's Disease Framework

BACKGROUND

The most significant biomarkers that are included in the Alzheimer's disease (AD) research framework are amyloid-β plaques deposition, p-tau, t-tau, and neurodegeneration.Although cerebrospinal fluid (CSF) biomarkers are included in the most recent AD research criteria, their use is increasing in the routine clinical practice and is applied also to the preclinical phases of AD, including mild cognitive impairment. The role of these biomarkers is still unclear concerning the preclinical stage of AD diagnosis, the CSF methodology, and the costs-benefits of the biomarkers' tests. The controversies regarding the use of biomarkers in the clinical practice are related to the concepts of analytical validity, clinical validity, and clinical utility and to the question of whether they are able to diagnose AD without the support of AD clinical phenotypes.

OBJECTIVE

The objective of the present work is to expose the strengths and weaknesses of the use of CSF biomarkers in the diagnosis of AD in a clinical context.

METHODS

We used PubMed as main source for articles published and the final reference list was generated on the basis of relevance to the topics covered in this work.

RESULTS

The use of CSF biomarkers for AD diagnosis is certainly important but its indication in routine clinical practice, especially for prodromal conditions, needs to be regulated and also contextualized considering the variety of possible clinical AD phenotypes.

CONCLUSION

We suggest that the diagnosis of AD should be understood both as clinical and pathological.

The Potential Role of Gut Microbiota in Alzheimer’s Disease: from Diagnosis to Treatment

Gut microbiota is emerging as a key regulator of many disease conditions and its dysregulation is implicated in the pathogenesis of several gastrointestinal and extraintestinal disorders. More recently, gut microbiome alterations have been linked to neurodegeneration through the increasingly defined gut microbiota brain axis, opening the possibility for new microbiota-based therapeutic options. Although several studies have been conducted to unravel the possible relationship between Alzheimer’s Disease (AD) pathogenesis and progression, the diagnostic and therapeutic potential of approaches aiming at restoring gut microbiota eubiosis remain to be fully addressed. In this narrative review, we briefly summarize the role of gut microbiota homeostasis in brain health and disease, and we present evidence for its dysregulation in AD patients. Based on these observations, we then discuss how dysbiosis might be exploited as a new diagnostic tool in early and advanced disease stages, and we examine the potential of prebiotics, probiotics, fecal microbiota transplantation, and diets as complementary therapeutic interventions on disease pathogenesis and progression, thus offering new insights into the diagnosis and treatment of this devastating and progressive disease.

Platelet total PLA2 activity, serum oxidative level and plasma Cu/Zn ratio: a vicious cycle with a potential role to monitor MCI and Alzheimer's disease progression

Alzheimer's disease (AD) has no cure, mainly because of late diagnosis. Early diagnostic biomarkers are crucial. Phospholipases A2 (PLA2) are hydrolases with several functions in the brain, nevertheless their deregulation contributes to neurodegeneration. We evaluated platelet total PLA2 activity (ptotPLA2) in healthy elderly subjects (HE, n = 102), patients suffering from Mild Cognitive Impairment (MCI, n = 90) and AD (n = 91). Platelets are considered "circulating neurons" and ptotPLA2 seems to mirror the cerebral activity. ptotPLA2 of the three cohorts was similar, but in MCI the higher ptotPLA2 the worse the global cognitive status (Mini Mental State Examination score, MMSE) and in AD the lower ptotPLA2 the more severe the pathology stage (Clinical Dementia Rating, CDR). Accordingly, MCI with MMSE ≥ 26 overlapped HE, in MCI with MMSE < 26 and in AD with CDR 1 ptotPLA2 increased, in AD with CDR 2 ptotPLA2 decreased. In MCI ptotPLA2 positively correlated with blood oxidation and inflammation, in AD it was the opposite. Finally, Discrimination Index (DI) - calculated multiplying ptotPLA2, oxidative level and Cu/Zn ratio (an inflammation parameter) - differentiated MCI patients who progressed to dementia in the following 24 months and AD patients with the worse pathology development. Summarizing, ptotPLA2 changes during MCI and AD progression, is linked, in opposite way, to oxidative/inflammatory status in MCI and AD and might help, when included in DI, to identify MCI converters to dementia and AD patients with the more severe prognosis. ptotPLA2 may have a diagnostic/prognostic value and be a potential therapeutic target.

A review on omics-based biomarkers discovery for Alzheimer's disease from the bioinformatics perspectives: Statistical approach vs machine learning approach

Alzheimer's Disease (AD) is a neurodegenerative disease that affects cognition and is the most common cause of dementia in the elderly. As the number of elderly individuals increases globally, the incidence and prevalence of AD are expected to increase. At present, AD is diagnosed clinically, according to accepted criteria. The essential elements in the diagnosis of AD include a patients history, a physical examination and neuropsychological testing, in addition to appropriate investigations such as neuroimaging. The omics-based approach is an emerging field of study that may not only aid in the diagnosis of AD but also facilitate the exploration of factors that influence the development of the disease. Omics techniques, including genomics, transcriptomics, proteomics and metabolomics, may reveal the pathways that lead to neuronal death and identify biomolecular markers associated with AD. This will further facilitate an understanding of AD neuropathology. In this review, omics-based approaches that were implemented in studies on AD were assessed from a bioinformatics perspective. Current state-of-the-art statistical and machine learning approaches used in the single omics analysis of AD were compared based on correlations of variants, differential expression, functional analysis and network analysis. This was followed by a review of the approaches used in the integration and analysis of multi-omics of AD. The strengths and limitations of multi-omics analysis methods were explored and the issues and challenges associated with omics studies of AD were highlighted. Lastly, future studies in this area of research were justified.

Artificial Intelligence for Alzheimer's Disease: Promise or Challenge?

Decades of experimental and clinical research have contributed to unraveling many mechanisms in the pathogenesis of Alzheimer's disease (AD), but the puzzle is still incomplete. Although we can suppose that there is no complete set of puzzle pieces, the recent growth of open data-sharing initiatives collecting lifestyle, clinical, and biological data from AD patients has provided a potentially unlimited amount of information about the disease, far exceeding the human ability to make sense of it. Moreover, integrating Big Data from multi-omics studies provides the potential to explore the pathophysiological mechanisms of the entire biological continuum of AD. In this context, Artificial Intelligence (AI) offers a wide variety of methods to analyze large and complex data in order to improve knowledge in the AD field. In this review, we focus on recent findings and future challenges for AI in AD research. In particular, we discuss the use of Computer-Aided Diagnosis tools for AD diagnosis and the use of AI to potentially support clinical practices for the prediction of individual risk of AD conversion as well as patient stratification in order to finally develop effective and personalized therapies.

An infrared spectral biomarker accurately predicts neurodegenerative disease class in the absence of overt symptoms

Although some neurodegenerative diseases can be identified by behavioral characteristics relatively late in disease progression, we currently lack methods to predict who has developed disease before the onset of symptoms, when onset will occur, or the outcome of therapeutics. New biomarkers are needed. Here we describe spectral phenotyping, a new kind of biomarker that makes disease predictions based on chemical rather than biological endpoints in cells. Spectral phenotyping uses Fourier Transform Infrared (FTIR) spectromicroscopy to produce an absorbance signature as a rapid physiological indicator of disease state. FTIR spectromicroscopy has over the past been used in differential diagnoses of manifest disease. Here, we report that the unique FTIR chemical signature accurately predicts disease class in mouse with high probability in the absence of brain pathology. In human cells, the FTIR biomarker accurately predicts neurodegenerative disease class using fibroblasts as surrogate cells.