Biomarkers of Alzheimer's disease: the present and the future

Biosensor approaches on the diagnosis of neurodegenerative diseases: Sensing the past to the future

Early diagnosis of neurodegeneration-oriented diseases that develop with the aging world is essential for improving the patient's living conditions as well as the treatment of the disease. Alzheimer's and Parkinson's diseases are prominent examples of neurodegeneration characterized by dementia leading to the death of nerve cells. The clinical diagnosis of these diseases only after the symptoms appear, delays the treatment process. Detection of biomarkers, which are distinctive molecules in biological fluids, involved in neurodegeneration processes, has the potential to allow early diagnosis of neurodegenerative diseases. Studies on biosensors, whose main responsibility is to detect the target analyte with high specificity, has gained momentum in recent years with the aim of high detection of potential biomarkers of neurodegeneration process. This study aims to provide an overview of neuro-biosensors developed on the basis of biomarkers identified in biological fluids for the diagnosis of neurodegenerative diseases such as Alzheimer's disease (AD), and Parkinson's disease (PD), and to provide an overview of the urgent needs in this field, emphasizing the importance of early diagnosis in the general lines of the neurodegeneration pathway. In this review, biosensor systems developed for the detection of biomarkers of neurodegenerative diseases, especially in the last 5 years, are discussed.

Deconstructing Alzheimer's Disease: How to Bridge the Gap between Experimental Models and the Human Pathology?

Discovered more than a century ago, Alzheimer's disease (AD) is not only still present in our societies but has also become the most common dementia, with 50 million people worldwide affected by the disease. This number is expected to double in the next generation, and no cure is currently available to slow down or stop the disease progression. Recently, some advances were made due to the approval of the aducanumab treatment by the American Food and Drug Administration. The etiology of this human-specific disease remains poorly understood, and the mechanisms of its development have not been completely clarified. Several hypotheses concerning the molecular mechanisms of AD have been proposed, but the existing studies focus primarily on the two main markers of the disease: the amyloid β peptides, whose aggregation in the brain generates amyloid plaques, and the abnormally phosphorylated tau proteins, which are responsible for neurofibrillary tangles. These protein aggregates induce neuroinflammation and neurodegeneration, which, in turn, lead to cognitive and behavioral deficits. The challenge is, therefore, to create models that best reproduce this pathology. This review aims at gathering the different existing AD models developed in vitro, in cellulo, and in vivo. Many models have already been set up, but it is necessary to identify the most relevant ones for our investigations. The purpose of the review is to help researchers to identify the most pertinent disease models, from the most often used to the most recently generated and from simple to complex, explaining their specificities and giving concrete examples.

Radiopharmaceuticals for Assessment of Altered Metabolism and Biometal Fluxes in Brain Aging and Alzheimer's Disease with Positron Emission Tomography

Aging is a risk factor for Alzheimer's disease (AD). There are changes of brain metabolism and biometal fluxes due to brain aging, which may play a role in pathogenesis of AD. Positron emission tomography (PET) is a versatile tool for tracking alteration of metabolism and biometal fluxes due to brain aging and AD. Age-dependent changes in cerebral glucose metabolism can be tracked with PET using 2-deoxy-2-[18F]-fluoro-D-glucose (18F-FDG), a radiolabeled glucose analogue, as a radiotracer. Based on different patterns of altered cerebral glucose metabolism, 18F-FDG PET was clinically used for differential diagnosis of AD and Frontotemporal dementia (FTD). There are continued efforts to develop additional radiopharmaceuticals or radiotracers for assessment of age-dependent changes of various metabolic pathways and biometal fluxes due to brain aging and AD with PET. Elucidation of age-dependent changes of brain metabolism and altered biometal fluxes is not only significant for a better mechanistic understanding of brain aging and the pathophysiology of AD, but also significant for identification of new targets for the prevention, early diagnosis, and treatment of AD.

Blood-Borne Activity-Dependent Neuroprotective Protein (ADNP) is Correlated with Premorbid Intelligence, Clinical Stage, and Alzheimer's Disease Biomarkers

Biomarkers for Alzheimer's disease (AD) are vital for disease detection in the clinical setting. Discovered in our laboratory, activity-dependent neuroprotective protein (ADNP) is essential for brain formation and linked to cognitive functions. Here, we revealed that blood borne expression of ADNP and its paralog ADNP2 is correlated with premorbid intelligence, AD pathology, and clinical stage. Age adjustment showed significant associations between: 1) higher premorbid intelligence and greater serum ADNP, and 2) greater cortical amyloid and lower ADNP and ADNP2 mRNAs. Significant increases in ADNP mRNA levels were observed in patients ranging from mild cognitive impairment (MCI) to AD dementia. ADNP2 transcripts showed high correlation with ADNP transcripts, especially in AD dementia lymphocytes. ADNP plasma/serum and lymphocyte mRNA levels discriminated well between cognitively normal elderly, MCI, and AD dementia participants. Measuring ADNP blood-borne levels could bring us a step closer to effectively screening and tracking AD.

Bayesian graphical network analyses reveal complex biological interactions specific to Alzheimer's disease

With different approaches to finding prognostic or diagnostic biomarkers for Alzheimer's disease (AD), many studies pursue only brief lists of biomarkers or disease specific pathways, potentially dismissing information from groups of correlated biomarkers. Using a novel Bayesian graphical network method, with data from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging, the aim of this study was to assess the biological connectivity between AD associated blood-based proteins. Briefly, three groups of protein markers (18, 37, and 48 proteins, respectively) were assessed for the posterior probability of biological connection both within and between clinical classifications. Clinical classification was defined in four groups: high performance healthy controls (hpHC), healthy controls (HC), participants with mild cognitive impairment (MCI), and participants with AD. Using the smaller group of proteins, posterior probabilities of network similarity between clinical classifications were very high, indicating no difference in biological connections between groups. Increasing the number of proteins increased the capacity to separate both hpHC and HC apart from the AD group (0 for complete separation, 1 for complete similarity), with posterior probabilities shifting from 0.89 for the 18 protein group, through to 0.54 for the 37 protein group, and finally 0.28 for the 48 protein group. Using this approach, we identified beta-2 microglobulin (β2M) as a potential master regulator of multiple proteins across all classifications, demonstrating that this approach can be used across many data sets to identify novel insights into diseases like AD.

Linking hypothetical knowledge patterns to disease molecular signatures for biomarker discovery in Alzheimer's disease

BACKGROUND

A number of compelling candidate Alzheimer's biomarkers remain buried within the literature. Indeed, there should be a systematic effort towards gathering this information through approaches that mine publicly available data and substantiate supporting evidence through disease modeling methods. In the presented work, we demonstrate that an integrative gray zone mining approach can be used as a way to tackle this challenge successfully.

METHODS

The methodology presented in this work combines semantic information retrieval and experimental data through context-specific modeling of molecular interactions underlying stages in Alzheimer's disease (AD). Information about putative, highly speculative AD biomarkers was harvested from the literature using a semantic framework and was put into a functional context through disease- and stage-specific models. Staging models of AD were further validated for their functional relevance and novel biomarker candidates were predicted at the mechanistic level.

RESULTS

Three interaction models were built representing three stages of AD, namely mild, moderate, and severe stages. Integrated analysis of these models using various arrays of evidence gathered from experimental data and published knowledge resources led to identification of four candidate biomarkers in the mild stage. Mode of action of these candidates was further reasoned in the mechanistic context of models by chains of arguments. Accordingly, we propose that some of these 'emerging' potential biomarker candidates have a reasonable mechanistic explanation and deserve to be investigated in more detail.

CONCLUSIONS

Systematic exploration of derived hypothetical knowledge leads to generation of a coherent overview on emerging knowledge niches. Integrative analysis of this knowledge in the context of disease mechanism is a promising approach towards identification of candidate biomarkers taking into consideration the complex etiology of disease. The added value of this strategy becomes apparent particularly in the area of biomarker discovery for neurodegenerative diseases where predictive biomarkers are desperately needed.

A diagnostic scale for Alzheimer's disease based on cerebrospinal fluid biomarker profiles

Introduction

The relevance of the cerebrospinal fluid (CSF) biomarkers for the diagnosis of Alzheimer’s disease (AD) and related disorders is clearly established. However, the question remains on how to use these data, which are often heterogeneous (not all biomarkers being pathologic). The objective of this study is to propose to physicians in memory clinics a biologic scale of probabilities that the patient with cognitive impairments has an Alzheimer’s disease (AD) pathologic process.

Methods

For that purpose, we took advantage of the multicenter data of our Paris-North, Lille, and Montpellier (PLM) study, which has emerged through the initial sharing of information from these memory centers. Different models combining the CSF levels of amyloid-β 42, tau, and p-tau(181) were tested to generate categories of patients with very low (<10%), low (<25%), high (>75%), and very high predictive values (>90%) for positive AD. In total, 1,273 patients (646 AD and 627 non-AD) from six independent memory-clinic cohorts were included.

Results

A prediction model based on logistic regressions achieved a very good stratification of the population but had the disadvantages of needing mathematical optimization and being difficult to use in daily clinical practice. Remarkably, a simple and intuitive model based on the number (from zero to three) of three pathologic CSF biomarkers resulted in a very efficient predictive scale for AD in patients seen in memory clinics. The scale’s overall predictive value for AD for the different categories were as follows: class 0, 9.6% (95% confidence interval (CI), 6.0% to 13.2%); class 1, 24.7% (95% CI, 18.0% to 31.3%); class 2, 77.2% (95% CI, 67.8% to 86.5%); and class 3, 94.2% (95% CI, 90.7% to 97.7%). In addition, with this scale, significantly more patients were correctly classified than with the logistic regression. Its superiority in model performance was validated by the computation of the net reclassification index (NRI). The model was also validated in an independent multicenter dataset of 408 patients (213 AD and 195 non-AD).

Conclusions

In conclusion, we defined a new scale that could be used to facilitate the interpretation and routine use of multivariate CSF data, as well as helping the stratification of patients in clinical research trials.