An overview of biomarkers in Alzheimer's disease

Navigating the Frontiers of Machine Learning in Neurodegenerative Disease Therapeutics

Recent advances in machine learning hold tremendous potential for enhancing the way we develop new medicines. Over the years, machine learning has been adopted in nearly all facets of drug discovery, including patient stratification, lead discovery, biomarker development, and clinical trial design. In this review, we will discuss the latest developments linking machine learning and CNS drug discovery. While machine learning has aided our understanding of chronic diseases like Alzheimer's disease and Parkinson's disease, only modest effective therapies currently exist. We highlight promising new efforts led by academia and emerging biotech companies to leverage machine learning for exploring new therapies. These approaches aim to not only accelerate drug development but to improve the detection and treatment of neurodegenerative diseases.

Changes in LASSI-L performance over time among older adults with amnestic MCI and amyloid positivity: A preliminary study

There is a pressing need to develop measures that are sensitive to the earliest subtle cognitive changes of Alzheimer's disease (AD) to improve early detection and track disease progression. The Loewenstein-Acevedo Scales of Semantic Interference (LASSI-L) has been shown to successfully discriminate between cognitively unimpaired (CU) older adults and those with amnestic mild cognitive impairment (MCI) and to correlate with total and regional brain amyloid load. The present study investigated how the LASSI-L scores change over time among three distinct diagnostic groups. Eighty-six community-dwelling older adults underwent a baseline evaluation including: a clinical interview, a neuropsychological evaluation, Magnetic Resonance Imaging (MRI), and amyloid Positron Emission Tomography (PET). A follow up evaluation was conducted 12 months later. Initial mean values were calculated using one-way ANOVAs and chi-square analyses. Post-hoc comparisons were conducted using Tukey's Honestly Significant Difference (HSD). A 3 × 2 repeated measures analysis was utilized to examine differences in LASSI-L performance over time. The MCI amyloid positive group demonstrated a significantly greater decline in LASSI-L performance than the MCI amyloid negative and CU groups respectively. The scales that best differentiated the three groups included the Cued A2, which taps into maximum learning capacity, and Cued B2, which assesses the failure to recover from proactive semantic interference. Our findings further support the LASSI-L's discriminative validity.

A Concise Overview of Biosensing Technologies for the Detection of Alzheimer's Disease Biomarkers

Alzheimer's disease (AD) is a brain-linked pathophysiological condition with neuronal degeneration, cognition dysfunctions, and other debilitations. Due to the growing prevalence of AD, there is a highly commended tendency to accelerate and develop analytical technologies for easy, cost-effective, and sensitive detection of AD biomarkers. In the last decade, remarkable advancements have been achieved on the gate to the progression of biosensors, predominantly optical and electrochemical, to detect AD biomarkers. Biosensors are commanding analytical devices that can conduct biological responses on transducers into measurable signals. These analytical devices can assist the case finding and management of AD. This review focuses on up-to-date developments, contests, and tendencies regarding AD biosensing principally, emphasizing the exclusive possessions of nanomaterials.

Construction of Unified Human Antimicrobial and Immunomodulatory Peptide Database and Examination of Antimicrobial and Immunomodulatory Peptides in Alzheimer's Disease Using Network Analysis of Proteomics Datasets

The reanalysis of genomics and proteomics datasets by bioinformatics approaches is an appealing way to examine large amounts of reliable data. This can be especially true in cases such as Alzheimer's disease, where the access to biological samples, along with well-defined patient information can be challenging. Considering the inflammatory part of Alzheimer's disease, our aim was to examine the presence of antimicrobial and immunomodulatory peptides in human proteomic datasets deposited in the publicly available proteomics database ProteomeXchange (http://www.proteomexchange.org/). First, a unified, comprehensive human antimicrobial and immunomodulatory peptide database, containing all known human antimicrobial and immunomodulatory peptides was constructed and used along with the datasets containing high-quality proteomics data originating from the examination of Alzheimer's disease and control groups. A throughout network analysis was carried out, and the enriched GO functions were examined. Less than 1% of all identified proteins in the brain were antimicrobial and immunomodulatory peptides, but the alterations characteristic of Alzheimer's disease could be recapitulated with their analysis. Our data emphasize the key role of the innate immune system and blood clotting in the development of Alzheimer's disease. The central role of antimicrobial and immunomodulatory peptides suggests their utilization as potential targets for mechanistic studies and future therapies.

Implications of Glycosylation in Alzheimer's Disease

Alzheimer's disease (AD) is the most common cause of dementia, affecting millions of people worldwide, and no cure is currently available. The major pathological hallmarks of AD are considered to be amyloid beta plaques and neurofibrillary tangles, generated by respectively APP processing and Tau phosphorylation. Recent evidence imply that glycosylation of these proteins, and a number of other AD-related molecules is altered in AD, suggesting a potential implication of this process in disease pathology. In this review we summarize the understanding of glycans in AD pathogenesis, and discuss how glycobiology can contribute to early diagnosis and treatment of AD, serving as potential biomarkers and therapeutic targets. Furthermore, we look into the potential link between the emerging topic neuroinflammation and glycosylation, combining two interesting, and until recent years, understudied topics in the scope of AD. Lastly, we discuss how new model platforms such as induced pluripotent stem cells can be exploited and contribute to a better understanding of a rather unexplored area in AD.

Nutrients in Alzheimer’s Disease: The Interaction of Diet, Drugs and Disease

In recent decades, clinical trials in Alzheimer’s disease (AD) have failed at an unprecedented rate. The etiology of AD has since come under renewed scrutiny, both to elucidate the underlying pathologies and to identify novel therapeutic strategies. Here, diet has emerged as a potential causative/protective agent. A variety of nutrients, including lipids, minerals, vitamins, antioxidants and sugars as well as broader dietary patterns and microbiotal interactions have demonstrated associations with AD. Although clinical trials have yet to definitively implicate any singular dietary element as therapeutic or causative, it is apparent that dietary preferences, likely in complex synergies, may influence the risk, onset and course of AD. This review catalogs the impact of major dietary elements on AD. It further examines an unexplored reciprocal association where AD may modulate diet, as well as how potential therapeutics may complicate these interactions. In doing so, we observe diet may have profound effects on the outcome of a clinical trial, either as a confounder of a drug/disease interaction or as a generally disruptive covariate. We therefore conclude that future clinical trials in AD should endeavor to control for diet, either in study design or subsequent analyses.

On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey

INTRODUCTION

The number of Alzheimer's Disease (AD) patients is increasing with increased life expectancy and 115.4 million people are expected to be affected in 2050. Unfortunately, AD is commonly diagnosed too late, when irreversible damages have been caused in the patient.

OBJECTIVE

An automatic, continuous and unobtrusive early AD detection method would be required to improve patients' life quality and avoid big healthcare costs. Thus, the objective of this survey is to review the multimodal signals that could be used in the development of such a system, emphasizing on the accuracy that they have shown up to date for AD detection. Some useful tools and specific issues towards this goal will also have to be reviewed.

METHODS

An extensive literature review was performed following a specific search strategy, inclusion criteria, data extraction and quality assessment in the Inspec, Compendex and PubMed databases.

RESULTS

This work reviews the extensive list of psychological, physiological, behavioural and cognitive measurements that could be used for AD detection. The most promising measurements seem to be magnetic resonance imaging (MRI) for AD vs control (CTL) discrimination with an 98.95% accuracy, while electroencephalogram (EEG) shows the best results for mild cognitive impairment (MCI) vs CTL (97.88%) and MCI vs AD distinction (94.05%). Available physiological and behavioural AD datasets are listed, as well as medical imaging analysis steps and neuroimaging processing toolboxes. Some issues such as "label noise" and multi-site data are discussed.

CONCLUSIONS

The development of an unobtrusive and transparent AD detection system should be based on a multimodal system in order to take full advantage of all kinds of symptoms, detect even the smallest changes and combine them, so as to detect AD as early as possible. Such a multimodal system might probably be based on physiological monitoring of MRI or EEG, as well as behavioural measurements like the ones proposed along the article. The mentioned AD datasets and image processing toolboxes are available for their use towards this goal. Issues like "label noise" and multi-site neuroimaging incompatibilities may also have to be overcome, but methods for this purpose are already available.

Cerebrospinal fluid analysis in Alzheimer's disease: technical issues and future developments

Alzheimer's disease (AD) is a leading cause of morbidity, mortality, and a major epidemic worldwide. Although clinical assessment continues to remain the keystone for patient management and clinical trials, such evaluation has important limitations. In this context, cerebrospinal fluid (CSF) biomarkers are important tools to better identify high-risk individuals, to diagnose AD promptly and accurately, especially at the prodromal mild cognitive impairment stage of the disease, and to effectively prognosticate and treat AD patients. Recent advances in functional genomics, proteomics, metabolomics, and bioinformatics will hopefully revolutionize unbiased inquiries into several putative CSF markers of cerebral pathology that may be concisely informative with regard to the various stages of AD progression through years and decades. Moreover, the identification of efficient drug targets and development of optimal therapeutic strategies for AD will increasingly rely on a better understanding and integration of the systems biology paradigm, which will allow predicting the series of events and resulting responses of the biological network triggered by the introduction of new therapeutic compounds. In this scenario, unbiased systems biology-based diagnostic and prognostic models in AD will consist of relevant comprehensive panels of molecules and key branches of the disease-affected cellular neuronal network. Such characteristic and unbiased biomarkers will more accurately and comprehensively reflect pathophysiology from the early asymptomatic and presymptomatic to the final prodromal and symptomatic clinical stages in individual patients (and their individual genetic disease predisposition), ultimately increasing the chances of success of future disease modifying and preventive treatments.

A review of neuroimaging biomarkers of Alzheimer's disease

Neuroimaging biomarkers have potential role in the early diagnosis as well as periodic follow-up of neurodegenerative diseases such as Alzheimer's disease (AD). Structural imaging biomarkers can be used to predict those who are at risk or in preclinical stages of AD. It could possibly be useful even in predicting the conversion of Mild Cognitive Impairment (MCI) an early stage of AD to AD. In addition there has been a lot of progress in molecular imaging in AD. This article presents a review of recent progress in selected imaging biomarkers for early diagnosis, classification, and progression, of AD. A comprehensive integrative strategy initiated early in the cognitive decline is perhaps the most effective method of controlling progression to Alzheimer's disease.

Diagnosis of Alzheimer's disease in Brazil: Supplementary exams

This article presents a review of the recommendations on supplementary exams employed for the clinical diagnosis of Alzheimer's disease (AD) in Brazil published in 2005. A systematic assessment of the consensus reached in other countries, and of articles on AD diagnosis in Brazil available on the PUBMED and LILACS medical databases, was carried out. Recommended laboratory exams included complete blood count, serum creatinine, thyroid stimulating hormone (TSH), albumin, hepatic enzymes, Vitamin B12, folic acid, calcium, serological reactions for syphilis and serology for HIV in patients aged younger than 60 years with atypical clinical signs or suggestive symptoms. Structural neuroimaging, computed tomography or - preferably - magnetic resonance exams, are indicated for diagnostic investigation of dementia syndrome to rule out secondary etiologies. Functional neuroimaging exams (SPECT and PET), when available, increase diagnostic reliability and assist in the differential diagnosis of other types of dementia. The cerebrospinal fluid exam is indicated in cases of pre-senile onset dementia with atypical clinical presentation or course, for communicant hydrocephaly, and suspected inflammatory, infectious or prion disease of the central nervous system. Routine electroencephalograms aid the differential diagnosis of dementia syndrome with other conditions which impair cognitive functioning. Genotyping of apolipoprotein E or other susceptibility polymorphisms is not recommended for diagnostic purposes or for assessing the risk of developing the disease. Biomarkers related to the molecular alterations in AD are largely limited to use exclusively in research protocols, but when available can contribute to improving the accuracy of diagnosis of the disease.