Using biomarkers to improve detection of Alzheimer's disease

An overview of the genes and biomarkers in Alzheimer's disease

Alzheimer's disease (AD) is the most common type of dementia and neurodegenerative disease characterized by neurofibrillary tangles (NFTs) and amyloid plaque. Familial AD is caused by mutations in the APP, PSEN1, and PSEN2 genes and these mutations result in the early onset of the disease. Sporadic AD usually affects older adults over the age of 65 years and is, therefore classified as late-onset AD (LOAD). Several risk factors associated with LOAD including the APOE gene have been identified. Moreover, GWAS studies have identified a wide array of genes and polymorphisms that are associated with LOAD risk. Currently, the diagnosis of AD involves the evaluation of memory and personality changes, cognitive impairment, and medical and family history to rule out other diseases. Laboratory tests to assess the biomarkers in the body fluids as well as MRI, CT, and PET scans to analyze the presence of plaques and NFTs are also included in the diagnosis of AD. It is important to diagnose AD before the onset of clinical symptoms, i.e. during the preclinical stage, to delay the progression and for better management of the disease. Research has been conducted to identify biomarkers of AD in the CSF, serum, saliva, and urine during the preclinical stage. Current research has identified several biomarkers and potential biomarkers in the body fluids that enhance diagnostic accuracy. Aside from genetics, other factors such as diet, physical activity, and lifestyle factors may influence the risk of developing AD. Clinical trials are underway to find potential biomarkers, diagnostic measures, and treatments for AD mainly in the preclinical stage. This review provides an overview of the genes and biomarkers of AD.

Paving the Way for Predicting the Progression of Cognitive Decline: The Potential Role of Machine Learning Algorithms in the Clinical Management of Neurodegenerative Disorders

Alzheimer's disease (AD) is the most common form of neurodegenerative disorder. The prodromal phase of AD is mild cognitive impairment (MCI). The capacity to predict the transitional phase from MCI to AD represents a challenge for the scientific community. The adoption of artificial intelligence (AI) is useful for diagnostic, predictive analysis starting from the clinical epidemiology of neurodegenerative disorders. We propose a Machine Learning Model (MLM) where the algorithms were trained on a set of neuropsychological, neurophysiological, and clinical data to predict the diagnosis of cognitive decline in both MCI and AD patients.

METHODS

We built a dataset with clinical and neuropsychological data of 4848 patients, of which 2156 had a diagnosis of AD, and 2684 of MCI, for the Machine Learning Model, and 60 patients were enrolled for the test dataset. We trained an ML algorithm using RoboMate software based on the training dataset, and then calculated its accuracy using the test dataset.

RESULTS

The Receiver Operating Characteristic (ROC) analysis revealed that diagnostic accuracy was 86%, with an appropriate cutoff value of 1.5; sensitivity was 72%; and specificity reached a value of 91% for clinical data prediction with MMSE.

CONCLUSION

This method may support clinicians to provide a second opinion concerning high prognostic power in the progression of cognitive impairment. The MLM used in this study is based on big data that were confirmed in enrolled patients and given a credibility about the presence of determinant risk factors also supported by a cognitive test score.

A classification for complex imbalanced data in disease screening and early diagnosis

Imbalanced classification has drawn considerable attention in the statistics and machine learning literature. Typically, traditional classification methods often perform poorly when a severely skewed class distribution is observed, not to mention under a high-dimensional longitudinal data structure. Given the ubiquity of big data in modern health research, it is expected that imbalanced classification in disease diagnosis may encounter an additional level of difficulty that is imposed by such a complex data structure. In this article, we propose a nonparametric classification approach for imbalanced data in longitudinal and high-dimensional settings. Technically, the functional principal component analysis is first applied for feature extraction under the longitudinal structure. The univariate exponential loss function coupled with group LASSO penalty is then adopted into the classification procedure in high-dimensional settings. Along with a good improvement in imbalanced classification, our approach provides a meaningful feature selection for interpretation while enjoying a remarkably lower computational complexity. The proposed method is illustrated on the real data application of Alzheimer's disease early detection and its empirical performance in finite sample size is extensively evaluated by simulations.

Robust parametric modeling of Alzheimer's disease progression

Quantitative characterization of disease progression using longitudinal data can provide long-term predictions for the pathological stages of individuals. This work studies the robust modeling of Alzheimer's disease progression using parametric methods. The proposed method linearly maps the individual's age to a disease progression score (DPS) and jointly fits constrained generalized logistic functions to the longitudinal dynamics of biomarkers as functions of the DPS using M-estimation. Robustness of the estimates is quantified using bootstrapping via Monte Carlo resampling, and the estimated inflection points of the fitted functions are used to temporally order the modeled biomarkers in the disease course. Kernel density estimation is applied to the obtained DPSs for clinical status classification using a Bayesian classifier. Different M-estimators and logistic functions, including a novel type proposed in this study, called modified Stannard, are evaluated on the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) for robust modeling of volumetric magnetic resonance imaging (MRI) and positron emission tomography (PET) biomarkers, cerebrospinal fluid (CSF) measurements, as well as cognitive tests. The results show that the modified Stannard function fitted using the logistic loss achieves the best modeling performance with an average normalized mean absolute error (NMAE) of 0.991 across all biomarkers and bootstraps. Applied to the ADNI test set, this model achieves a multiclass area under the ROC curve (AUC) of 0.934 in clinical status classification. The obtained results for the proposed model outperform almost all state-of-the-art results in predicting biomarker values and classifying clinical status. Finally, the experiments show that the proposed model, trained using abundant ADNI data, generalizes well to data from the National Alzheimer's Coordinating Center (NACC) with an average NMAE of 1.182 and a multiclass AUC of 0.929.

Early diagnosis of disease using microbead array technology: A review

Early diagnosis of diseases (before they become advanced and incurable) is essential to reduce morbidity and mortality rates. With the advent of novel technologies in clinical laboratory diagnosis, microbead-based arrays have come to be recognized as an efficient approach, that demonstrates useful advantages over traditional assay methods for multiple disease-related biomarkers. Multiplexed microbead assays provide a robust, rapid, specific, and cost-effective approach for high-throughput and simultaneous screening of many different targets. Biomolecular binding interactions occur after applying a biological sample (such as blood plasma, saliva, cerebrospinal fluid etc.) containing the target analyte(s) to a set of microbeads with different ligand-specificities that have been coded in planar or suspension arrays. The ligand-receptor binding activity is tracked by optical signals generated by means of flow cytometry analysis in the case of suspension arrays, or by image processing devices in the case of planar arrays. In this review paper, we discuss diagnosis of cancer, neurological and infectious diseases by using optically-encoded microbead-based arrays (both multiplexed and single-analyte assays) as a reliable tool for detection and quantification of various analytes.

The importance of endpoint selection: How effective does a drug need to be for success in a clinical trial of a possible Alzheimer's disease treatment?

To date, Alzheimer’s disease (AD) clinical trials have been largely unsuccessful. Failures have been attributed to a number of factors including ineffective drugs, inadequate targets, and poor trial design, of which the choice of endpoint is crucial. Using data from the Alzheimer’s Disease Neuroimaging Initiative, we have calculated the minimum detectable effect size (MDES) in change from baseline of a range of measures over time, and in different diagnostic groups along the AD development trajectory. The Functional Activities Questionnaire score had the smallest MDES for a single endpoint where an effect of 27% could be detected within 3 years in participants with Late Mild Cognitive Impairment (LMCI) at baseline, closely followed by the Clinical Dementia Rating Sum of Boxes (CDRSB) score at 28% after 2 years in the same group. Composite measures were even more successful than single endpoints with an MDES of 21% in 3 years. Using alternative cognitive, imaging, functional, or composite endpoints, and recruiting patients that have LMCI could improve the success rate of AD clinical trials.

Proteomics Analysis of Blood Serums from Alzheimer's Disease Patients Using iTRAQ Labeling Technology

Alzheimer' disease (AD) is the most common form of dementia affecting up to 6% of the population over the age of 65. In order to discover differentially expressed proteins that might serve as potential biomarkers, the serums from AD patients and healthy controls were compared and analyzed using the proteomics approach of isobaric tagging for relative and absolute quantitation (iTRAQ). For the first time, AD biomarkers in serums are investigated in the Han Chinese population using iTRAQ labeled proteomics strategy. Twenty-two differentially expressed proteins were identified and out of which nine proteins were further validated with more sample test. Another three proteins that have been reported in the literature to be potentially associated with AD were also investigated for alteration in expression level. Functions of those proteins were mainly related to the following processes: amyloid-β (Aβ) metabolism, cholesterol transport, complement and coagulation cascades, immune response, inflammation, hemostasis, hyaluronan metabolism, and oxidative stress. These results support current views on the molecular mechanism of AD. For the first time, differential expression of zinc-alpha-2-glycoprotein (AZGP1), fibulin-1 (FBLN1), platelet basic protein (PPBP), thrombospondin-1 (THBS1), S100 calcium-binding protein A8 (S100A8), and S100 calcium-binding protein A9 (S100A9) were detected in the serums of AD patients compared with healthy controls. These proteins might play a role in AD pathophysiology and serve as potential biomarkers for AD diagnosis. Specifically, our results strengthened the crucial role of Aβ metabolism and blood coagulation in AD pathogenesis and proteins related to these two processes may be used as peripheral blood biomarkers for AD.

A Systematic Review of Antiamyloidogenic and Metal-Chelating Peptoids: Two Structural Motifs for the Treatment of Alzheimer's Disease

Alzheimer's disease (AD) is an incurable form of dementia affecting millions of people worldwide and costing billions of dollars in health care-related payments, making the discovery of a cure a top health, societal, and economic priority. Peptide-based drugs and immunotherapies targeting AD-associated beta-amyloid (Aβ) aggregation have been extensively explored; however, their therapeutic potential is limited by unfavorable pharmacokinetic (PK) properties. Peptoids (N-substituted glycine oligomers) are a promising class of peptidomimetics with highly tunable secondary structures and enhanced stabilities and membrane permeabilities. In this review, the biological activities, structures, and physicochemical properties for several amyloid-targeting peptoids will be described. In addition, metal-chelating peptoids with the potential to treat AD will be discussed since there are connections between the dysregulation of certain metals and the amyloid pathway.

Using Informant and Performance Screening Methods to Detect Mild Cognitive Impairment and Dementia

Purpose of Review

Dementia detection in the community is challenging. The purpose of this paper is to review methods of dementia screening and provide a useable algorithm for screening for dementia a variety of clinical settings.

Recent Findings

In recent years, a number of brief performance and informant-based assessments have been developed and validated in research, clinical, and community samples. These assessments are now complemented by patient self-reports that afford the ability to detect subjective cognitive impairment.

Summary

An optimal approach to dementia screening is to combine performance, informant, and self-reports, many of which can be completed in the waiting room or by non-physician staff prior to the start of the office visit. This diverse information may help inform the provider as to the presence or absence of a cognitive disorder, assist in staging the extent of the disorder, and help to develop a differential diagnosis and management plan.

Thrombospondin-1 secreted by human umbilical cord blood-derived mesenchymal stem cells rescues neurons from synaptic dysfunction in Alzheimer's disease model

Alzheimer’s disease (AD) is an incurable neurodegenerative disease characterised clinically by learning and memory impairments. Amyloid beta (Aβ) peptide-induced synaptic dysfunction is a pathological process associated with early-stage AD. Here, we show that paracrine action of human umbilical cord blood-derived-mesenchymal stem cells (hUCB-MSCs) protects the hippocampus from synaptic-density loss in in vitro and in vivo AD models. To identify paracrine factors underlying this rescue effect, we analysed hUCB-MSCs’ secretome co-cultured with Aβ42-treated mouse hippocampal neurons. Thrombospondin-1 (TSP-1), a protein secreted by hUCB-MSCs in in vitro and 5XFAD AD mouse models, was selected for study. Treatment with exogenous recombinant TSP-1 or co-cultures with hUCB-MSCs significantly increased expression of synaptic-density markers, such as synaptophysin (SYP) and post-synaptic density protein-95 (PSD-95) in Aβ42-treated mouse hippocampal neurons. Knockdown of TSP-1 expression in hUCB-MSCs through small interfering RNA (siRNA) abolished the reversal of Aβ42-induced hippocampal synaptic-density loss. We demonstrate that the rescue effect of hUCB-MSC-secreted TSP-1 was mediated by neuroligin-1 (NLGN1) or α2δ-1 receptors. Interestingly, NLGN1 and α2δ-1 expression, which was reduced in Aβ42-treated hippocampal neurons, increased in co-cultures with hUCB-MSCs or exogenous TSP-1. Together, these findings suggest that hUCB-MSCs can attenuate Aβ42-induced synaptic dysfunction by regulating TSP-1 release, thus providing a potential alternative therapeutic option for early-stage AD.

Genetic Factors Affecting Late-Onset Alzheimer's Disease Susceptibility

Alzheimer's disease is considered a progressive brain disease in the older population. Late-onset Alzheimer's disease (LOAD) as a multifactorial dementia has a polygenic inheritance. Age, environment, and lifestyle along with a growing number of genetic factors have been reported as risk factors for LOAD. Our aim was to present results of LOAD association studies that have been done in northwestern Iran, and we also explored possible interactions with apolipoprotein E (APOE) status. We re-evaluated the association of these markers in dominant, recessive, and additive models. In all, 160 LOAD and 163 healthy control subjects of Azeri Turkish ethnicity were studied. The Chi-square test with Yates' correction and Fisher's exact test were used for statistical analysis. A Bonferroni-corrected p value, based on the number of statistical tests, was considered significant. Our results confirmed that chemokine receptor type 2 (CCR2), estrogen receptor 1 (ESR1), toll-like receptor 2 (TLR2), tumor necrosis factor alpha (TNF α), APOE, bridging integrator 1 (BIN1), and phosphatidylinositol-binding clathrin assembly protein (PICALM) are LOAD susceptibility loci in Azeri Turk ancestry populations. Among them, variants of CCR2, ESR1, TNF α, and APOE revealed associations in three different genetic models. After adjusting for APOE, the association (both allelic and genotypic) with CCR2, BIN1, and ESRα (PvuII) was evident only among subjects without the APOE ε4, whereas the association with CCR5, without Bonferroni correction, was significant only among subjects carrying the APOE ε4 allele. This result is an evidence of a synergistic and antagonistic effect of APOE on variant associations with LOAD.

Clearing the amyloid in Alzheimer's: progress towards earlier diagnosis and effective treatments – an update for clinicians

SUMMARY 

A beta (Aβ or β-amyloid) is a key molecule in Alzheimer's disease (AD) pathogenesis. According to the ‘amyloid hypothesis’, the gradual accumulation of Aβ triggers events which results in neuronal loss in regions of the brain involved with memory and learning. Diverse agents have been developed to reduce brain Aβ accumulation or to enhance its clearance. Some have progressed to human trials, however all have failed to improve cognition in patients. This has led researchers to question whether Aβ is really the problem. However, the trials have been targeting end stages of AD, by which stage extensive irreversible neuronal damage has already occurred. Intervention is required preclinically, therefore preclinical AD biomarkers are needed. In this regard, amyloid imaging and cerebrospinal fluid biomarkers are leading the way, with plasma biomarkers and eye tests also being investigated. This review covers the current state of knowledge of Aβ as an early diagnostic biomarker and as a therapeutic target in AD.

Optimization of touchscreen-based behavioral paradigms in mice: implications for building a battery of tasks taxing learning and memory functions

Although many clinical pathological states are now detectable using imaging and biochemical analyses, neuropsychological tests are often considered as valuable complementary approaches to confirm diagnosis, especially for disorders like Alzheimer’s or Parkinson’s disease, and schizophrenia. The touchscreen-based automated test battery, which was introduced two decades ago in humans to assess cognitive functions, has recently been successfully back-translated in monkeys and rodents. We focused on optimizing the protocol of three distinct behavioral paradigms in mice: two variants of the Paired Associates Learning (PAL) and the Visuo-Motor Conditional Learning (VMCL) tasks. Acquisition of these tasks was assessed in naive versus pre-trained mice. In naive mice, we managed to define testing conditions allowing significant improvements of learning performances over time in the three aforementioned tasks. In pre-trained mice, we observed differential acquisition rates after specific task combinations. Particularly, we identified that animals previously trained in the VMCL paradigm subsequently poorly learned the sPAL rule. Together with previous findings, these data confirm the feasibility of using such behavioral assays to evaluate the power of different models of cognitive dysfunction in mice. They also highlight the risk of interactions between tasks when rodents are run through a battery of different cognitive touchscreen paradigms.

Mild cognitive impairment

Mild cognitive impairment is the term applied to the cognitive state that lies between normal aging and dementia. There has been significant controversy around describing, defining and characterizing mild cognitive impairment. This review will cover current understanding of the condition and discuss clinical features, research strategies and future directions.

Current guidelines for dementia screening: shortcomings and recommended changes

The availability of various guidelines regarding the diagnosis of Alzheimer's disease (AD) leaves most primary care providers with the task of having to decide which guidelines to follow. This review will help them navigate these different guidelines and understand how they differ from previous guidelines. Challenges related to the use of these guidelines are discussed, including biomarker testing, the lack of recognition in the community of what constitutes memory impairment and how to best screen for it, and recommendations for easy and early detection and diagnosis in the clinical settings are made. Adoption of biomarkers in clinical practice will give primary care providers the means to establish with certainty the underlying pathology responsible for the observed clinical symptoms and increase their ability to establish the presence of AD pathology before symptoms occur and, therefore, potentially help prevent or slow down the progression to AD.

Evidence of altered phosphatidylcholine metabolism in Alzheimer's disease

Abberant lipid metabolism is implicated in Alzheimer's disease (AD) pathophysiology, but the connections between AD and lipid metabolic pathways are not fully understood. To investigate plasma lipids in AD, a multiplatform screen (n = 35 by liquid chromatography-mass spectrometry and n = 35 by nuclear magnetic resonance) was developed, which enabled the comprehensive analysis of plasma from 3 groups (individuals with AD, individuals with mild cognitive impairment (MCI), and age-matched controls). This screen identified 3 phosphatidylcholine (PC) molecules that were significantly diminished in AD cases. In a subsequent validation study (n = 141), PC variation in a bigger sample set was investigated, and the same 3 PCs were found to be significantly lower in AD patients: PC 16:0/20:5 (p < 0.001), 16:0/22:6 (p < 0.05), and 18:0/22:6 (p < 0.01). A receiver operating characteristic (ROC) analysis of the PCs, combined with apolipoprotein E (ApoE) data, produced an area under the curve predictive value of 0.828. Confirmatory investigations into the background biochemistry indiciated no significant change in plasma levels of 3 additional PCs of similar structure, total choline containing compounds or total plasma omega fatty acids, adding to the evidence that specific PCs play a role in AD pathology.

Cerebrospinal fluid biomarkers in Alzheimer's disease, vascular dementia and ischemic stroke patients: a critical analysis

Vascular factors are thought to contribute to the development of disease pathology in neurodegenerative dementia such as Alzheimer's disease (AD). Another entity, called vascular dementia (VaD), comprises a less defined group of dementia patients having various vascular diseases that especially emerge in the elderly population and require valid options for examination and differential diagnosis. In the context of a retrospective study, we analyzed the cerebrospinal fluid (CSF) biomarkers t-tau, p-tau and Aß42 of a total of 131 patients with AD (n = 47), mild cognitive impairment (MCI) (n = 22), VaD (n = 44) and stroke (n = 18). We found a remarkable alteration in CSF biomarker profile in AD, VaD and in acute ischemic events. CSF profile in AD patients was altered in a very similar way as in stroke patients, without statistical differences. In stroke, increase depend largely on size and duration after the initial event. Total tau levels were useful to differ between VaD and stroke. Aß42 decreased in a similar way in AD, VaD and stroke and had a trend to lower levels in MCI but not in controls.