A Machine Learning Approach to Identify a Circulating MicroRNA Signature for Alzheimer Disease

Potential Implications of miRNAs in the Pathogenesis, Diagnosis, and Therapeutics of Alzheimer's Disease

Alzheimer's disease (AD) is a complex multifactorial disorder that poses a substantial burden on patients, caregivers, and society. Considering the increased aging population and life expectancy, the incidence of AD will continue to rise in the following decades. However, the molecular pathogenesis of AD remains controversial, superior blood-based biomarker candidates for early diagnosis are still lacking, and effective therapeutics to halt or slow disease progression are urgently needed. As powerful genetic regulators, microRNAs (miRNAs) are receiving increasing attention due to their implications in the initiation, development, and theranostics of various diseases, including AD. In this review, we summarize miRNAs that directly target microtubule-associated protein tau (MAPT), amyloid precursor protein (APP), and β-site APP-cleaving enzyme 1 (BACE1) transcripts and regulate the alternative splicing of tau and APP. We also discuss related kinases, such as glycogen synthase kinase (GSK)-3β, cyclin-dependent kinase 5 (CDK5), and death-associated protein kinase 1 (DAPK1), as well as apolipoprotein E, that are directly targeted by miRNAs to control tau phosphorylation and amyloidogenic APP processing leading to Aβ pathologies. Moreover, there is evidence of miRNA-mediated modulation of inflammation. Furthermore, circulating miRNAs in the serum or plasma of AD patients as noninvasive biomarkers with diagnostic potential are reviewed. In addition, miRNA-based therapeutics optimized with nanocarriers or exosomes as potential options for AD treatment are discussed.

Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer's disease

Mild cognitive impairment (MCI) is often considered an early stage of dementia, with estimated rates of progression to dementia up to 80-90% after approximately 6 years from the initial diagnosis. Diagnosis of cognitive impairment in dementia is typically based on clinical evaluation, neuropsychological assessments, cerebrospinal fluid (CSF) biomarkers, and neuroimaging. The main goal of diagnosing MCI is to determine its cause, particularly whether it is due to Alzheimer's disease (AD). However, only a limited percentage of the population has access to etiological confirmation, which has led to the emergence of peripheral fluid biomarkers as a diagnostic tool for dementias, including MCI due to AD. Recent advances in biofluid assays have enabled the use of sophisticated statistical models and multimodal machine learning (ML) algorithms for the diagnosis of MCI based on fluid biomarkers from CSF, peripheral blood, and saliva, among others. This approach has shown promise for identifying specific causes of MCI, including AD. After a PRISMA analysis, 29 articles revealed a trend towards using multimodal algorithms that incorporate additional biomarkers such as neuroimaging, neuropsychological tests, and genetic information. Particularly, neuroimaging is commonly used in conjunction with fluid biomarkers for both cross-sectional and longitudinal studies. Our systematic review suggests that cost-effective longitudinal multimodal monitoring data, representative of diverse cultural populations and utilizing white-box ML algorithms, could be a valuable contribution to the development of diagnostic models for AD due to MCI. Clinical assessment and biomarkers, together with ML techniques, could prove pivotal in improving diagnostic tools for MCI due to AD.

Can Genetic Markers Predict the Sporadic Form of Alzheimer's Disease? An Updated Review on Genetic Peripheral Markers

Alzheimer's disease (AD) is the most common form of dementia that affects millions of individuals worldwide. Although the research over the last decades has provided new insight into AD pathophysiology, there is currently no cure for the disease. AD is often only diagnosed once the symptoms have become prominent, particularly in the late-onset (sporadic) form of AD. Consequently, it is essential to further new avenues for early diagnosis. With recent advances in genomic analysis and a lower cost of use, the exploration of genetic markers alongside RNA molecules can offer a key avenue for early diagnosis. We have here provided a brief overview of potential genetic markers differentially expressed in peripheral tissues in AD cases compared to controls, as well as considering the changes to the dynamics of RNA molecules. By integrating both genotype and RNA changes reported in AD, biomarker profiling can be key for developing reliable AD diagnostic tools.

State of the Art of microRNAs Signatures as Biomarkers and Therapeutic Targets in Parkinson's and Alzheimer's Diseases: A Systematic Review and Meta-Analysis

Identifying target microRNAs (miRNAs) might serve as a basis for developing advanced therapies for Parkinson's disease (PD) and Alzheimer's disease. This review aims to identify the main therapeutic targets of miRNAs that can potentially act in Parkinson's and Alzheimer's diseases. The publication research was conducted from May 2021 to March 2022, selected from Scopus, PubMed, Embase, OVID, Science Direct, LILACS, and EBSCO. A total of 25 studies were selected from 1549 studies evaluated. The total number of miRNAs as therapeutic targets evidenced was 90 for AD and 54 for PD. An average detection accuracy of above 84% for the miRNAs was observed in the selected studies of AD and PD. The major signatures were miR-26b-5p, miR-615-3p, miR-4722-5p, miR23a-3p, and miR-27b-3p for AD and miR-374a-5p for PD. Six miRNAs of intersection were found between AD and PD. This article identified the main microRNAs as selective biomarkers for diagnosing PD and AD and therapeutic targets through a systematic review and meta-analysis. This article can act as a microRNA guideline for laboratory research and pharmaceutical industries for treating Alzheimer's and Parkinson's diseases and offers the opportunity to evaluate therapeutic interventions earlier in the disease process.

Predicting AT(N) pathologies in Alzheimer's disease from blood-based proteomic data using neural networks

BACKGROUND AND OBJECTIVE

Blood-based biomarkers represent a promising approach to help identify early Alzheimer's disease (AD). Previous research has applied traditional machine learning (ML) to analyze plasma omics data and search for potential biomarkers, but the most modern ML methods based on deep learning has however been scarcely explored. In the current study, we aim to harness the power of state-of-the-art deep learning neural networks (NNs) to identify plasma proteins that predict amyloid, tau, and neurodegeneration (AT[N]) pathologies in AD.

METHODS

We measured 3,635 proteins using SOMAscan in 881 participants from the European Medical Information Framework for AD Multimodal Biomarker Discovery study (EMIF-AD MBD). Participants underwent measurements of brain amyloid β (Aβ) burden, phosphorylated tau (p-tau) burden, and total tau (t-tau) burden to determine their AT(N) statuses. We ranked proteins by their association with Aβ, p-tau, t-tau, and AT(N), and fed the top 100 proteins along with age and apolipoprotein E (APOE) status into NN classifiers as input features to predict these four outcomes relevant to AD. We compared NN performance of using proteins, age, and APOE genotype with performance of using age and APOE status alone to identify protein panels that optimally improved the prediction over these main risk factors. Proteins that improved the prediction for each outcome were aggregated and nominated for pathway enrichment and protein-protein interaction enrichment analysis.

RESULTS

Age and APOE alone predicted Aβ, p-tau, t-tau, and AT(N) burden with area under the curve (AUC) scores of 0.748, 0.662, 0.710, and 0.795. The addition of proteins significantly improved AUCs to 0.782, 0.674, 0.734, and 0.831, respectively. The identified proteins were enriched in five clusters of AD-associated pathways including human immunodeficiency virus 1 infection, p53 signaling pathway, and phosphoinositide-3-kinase-protein kinase B/Akt signaling pathway.

CONCLUSION

Combined with age and APOE genotype, the proteins identified have the potential to serve as blood-based biomarkers for AD and await validation in future studies. While the NNs did not achieve better scores than the support vector machine model used in our previous study, their performances were likely limited by small sample size.

Blood Analytes as Biomarkers of Mechanisms Involved in Alzheimer’s Disease Progression

Alzheimer’s disease (AD) is the leading cause of dementia, but the pathogenetic factors are not yet well known, and the relationships between brain and systemic biochemical derangements and disease onset and progression are unclear. We aim to focus on blood biomarkers for an accurate prognosis of the disease. We used a dataset characterized by longitudinal findings collected over the past 10 years from 90 AD patients. The dataset included 277 observations (both clinical and biochemical ones, encompassing blood analytes encompassing routine profiles for different organs, together with immunoinflammatory and oxidative markers). Subjects were grouped into four severity classes according to the Clinical Dementia Rating (CDR) Scale: mild (CDR = 0.5 and CDR = 1), moderate (CDR = 2), severe (CDR = 3) and very severe (CDR = 4 and CDR = 5). Statistical models were used for the identification of potential blood markers of AD progression. Moreover, we employed the Pathfinder tool of the Reactome database to investigate the biological pathways in which the analytes of interest could be involved. Statistical results reveal an inverse significant relation between four analytes (high-density cholesterol, total cholesterol, iron and ferritin) with AD severity. In addition, the Reactome database suggests that such analytes could be involved in pathways that are altered in AD progression. Indeed, the identified blood markers include molecules that reflect the heterogeneous pathogenetic mechanisms of AD. The combination of such blood analytes might be an early indicator of AD progression and constitute useful therapeutic targets.

Alzheimer's Disease Diagnostics Using miRNA Biomarkers and Machine Learning

BACKGROUND

The current standard for Alzheimer's disease (AD) diagnosis is often imprecise, as with memory tests, and invasive or expensive, as with brain scans. However, the dysregulation patterns of miRNA in blood hold potential as useful biomarkers for the non-invasive diagnosis and even treatment of AD.

OBJECTIVE

The goal of this research is to elucidate new miRNA biomarkers and create a machine-learning (ML) model for the diagnosis of AD.

METHODS

We utilized pathways and target gene networks related to confirmed miRNA biomarkers in AD diagnosis and created multiple models to use for diagnostics based on the significant differences among miRNA expression between blood profiles (serum and plasma).

RESULTS

The best performing serum-based ML model, trained on filtered disease-specific miRNA datasets, was able to identify miRNA biomarkers with 92.0% accuracy and the best performing plasma-based ML model, trained on filtered disease-specific miRNA datasets, was able to identify miRNA biomarkers with 90.9% accuracy. Through analysis of AD implicated miRNA, thousands of descriptors reliant on target gene and pathways were created which can then be used to identify novel biomarkers and strengthen disease diagnosis.

CONCLUSION

Development of a ML model including miRNA and their genomic and pathway descriptors made it possible to achieve considerable accuracy for the prediction of AD.

Systematic Review: microRNAs as Potential Biomarkers in Mild Cognitive Impairment Diagnosis

The rate of progression from Mild Cognitive Impairment (MCI) to Alzheimer's disease (AD) is estimated at >10% per year, reaching up to 80-90% after 6 years. MCI is considered an indicator of early-stage AD. In this context, the diagnostic screening of MCI is crucial for detecting individuals at high risk of AD before they progress and manifest further severe symptoms. Typically, MCI has been determined using neuropsychological assessment tools such as the Montreal Cognitive Assessment (MoCA) or Mini-Mental Status Examination (MMSE). Unfortunately, other diagnostic methods are not available or are unable to identify MCI in its early stages. Therefore, identifying new biomarkers for MCI diagnosis and prognosis is a significant challenge. In this framework, miRNAs in serum, plasma, and other body fluids have emerged as a promising source of biomarkers for MCI and AD-related cognitive impairments. Interestingly, miRNAs can regulate several signaling pathways via multiple and diverse targets in response to pathophysiological stimuli. This systematic review aims to describe the current state of the art regarding AD-related target genes modulated by differentially expressed miRNAs in peripheral fluids samples in MCI subjects to identify potential miRNA biomarkers in the early stages of AD. We found 30 articles that described five miRNA expression profiles from peripheral fluid in MCI subjects, showing possible candidates for miRNA biomarkers that may be followed up as fluid biomarkers or therapeutic targets of early-stage AD. However, additional research is needed to validate these miRNAs and characterize the precise neuropathological mechanisms.

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.

Can (immune and other) gene expression help us to treat depression?

It is becoming clearer that it might be a combination of different biological processes such as genetic, environmental, and psychological factors, together with immune system, stress response, brain neuroplasticity and the regulation of neurotransmitters, that leads to the development of major depressive disorder (MDD). A growing number of studies have tried to investigate the underlying mechanisms of MDD by analysing the expression levels of genes (mRNA) involved in such biological processes. In this review, I have highlighted a possible key role that gene expression might play in the treatment of MDD. This is critical because many patients do not respond to antidepressant treatment or can experience side effects, causing treatment to be interrupted. Unfortunately, selecting the best antidepressant for each individual is still largely a matter of making an informed guess.

Prediction of differentially expressed microRNAs in blood as potential biomarkers for Alzheimer's disease by meta-analysis and adaptive boosting ensemble learning

BACKGROUND

Blood circulating microRNAs that are specific for Alzheimer's disease (AD) can be identified from differentially expressed microRNAs (DEmiRNAs). However, non-reproducible and inconsistent reports of DEmiRNAs hinder biomarker development. The most reliable DEmiRNAs can be identified by meta-analysis. To enrich the pool of DEmiRNAs for potential AD biomarkers, we used a machine learning method called adaptive boosting for miRNA disease association (ABMDA) to identify eligible candidates that share similar characteristics with the DEmiRNAs identified from meta-analysis. This study aimed to identify blood circulating DEmiRNAs as potential AD biomarkers by augmenting meta-analysis with the ABMDA ensemble learning method.

METHODS

Studies on DEmiRNAs and their dysregulation states were corroborated with one another by meta-analysis based on a random-effects model. DEmiRNAs identified by meta-analysis were collected as positive examples of miRNA-AD pairs for ABMDA ensemble learning. ABMDA identified similar DEmiRNAs according to a set of predefined criteria. The biological significance of all resulting DEmiRNAs was determined by their target genes according to pathway enrichment analyses. The target genes common to both meta-analysis- and ABMDA-identified DEmiRNAs were collected to construct a network to investigate their biological functions.

RESULTS

A systematic database search found 7841 studies for an extensive meta-analysis, covering 54 independent comparisons of 47 differential miRNA expression studies, and identified 18 reliable DEmiRNAs. ABMDA ensemble learning was conducted based on the meta-analysis results and the Human MicroRNA Disease Database, which identified 10 additional AD-related DEmiRNAs. These 28 DEmiRNAs and their dysregulated pathways were related to neuroinflammation. The dysregulated pathway related to neuronal cell cycle re-entry (CCR) was the only statistically significant pathway of the ABMDA-identified DEmiRNAs. In the biological network constructed from 1865 common target genes of the identified DEmiRNAs, the multiple core ubiquitin-proteasome system, that is involved in neuroinflammation and CCR, was highly connected.

CONCLUSION

This study identified 28 DEmiRNAs as potential AD biomarkers in blood, by meta-analysis and ABMDA ensemble learning in tandem. The DEmiRNAs identified by meta-analysis and ABMDA were significantly related to neuroinflammation, and the ABMDA-identified DEmiRNAs were related to neuronal CCR.

The Eminent Role of microRNAs in the Pathogenesis of Alzheimer's Disease

Alzheimer's disease (AD) is an irrevocable neurodegenerative condition characterized by the presence of senile plaques comprising amassed β-amyloid peptides (Aβ) and neurofibrillary tangles mainly comprising extremely phosphorylated Tau proteins. Recent studies have emphasized the role of microRNAs (miRNAs) in the development of AD. A number of miRNAs, namely, miR-200a-3p, miR-195, miR-338-5p, miR-34a-5p, miR-125b-5p, miR-132, miR-384, miR-339-5p, miR-135b, miR-425-5p, and miR-339-5p, have been shown to participate in the development of AD through interacting with BACE1. Other miRNAs might affect the inflammatory responses in the course of AD. Aberrant expression of several miRNAs in the plasma samples of AD subjects has been shown to have the aptitude for differentiation of AD subjects from healthy subjects. Finally, a number of AD-modifying agents affect miRNA profile in cell cultures or animal models. We have performed a comprehensive search and summarized the obtained data about the function of miRNAs in AD in the current review article.

Early Detection of Alzheimer's Disease with Blood Plasma Proteins Using Support Vector Machines

The successful development of amyloid-based biomarkers and tests for Alzheimer's disease (AD) represents an important milestone in AD diagnosis. However, two major limitations remain. Amyloid-based diagnostic biomarkers and tests provide limited information about the disease process and they are unable to identify individuals with the disease before significant amyloid-beta accumulation in the brain develops. The objective in this study is to develop a method to identify potential blood-based non-amyloid biomarkers for early AD detection. The use of blood is attractive because it is accessible and relatively inexpensive. Our method is mainly based on machine learning (ML) techniques (support vector machines in particular) because of their ability to create multivariable models by learning patterns from complex data. Using novel feature selection and evaluation modalities, we identified 5 novel panels of non-amyloid proteins with the potential to serve as biomarkers of early AD. In particular, we found that the combination of A2M, ApoE, BNP, Eot3, RAGE and SGOT may be a key biomarker profile of early disease. Disease detection models based on the identified panels achieved sensitivity (SN) > 80%, specificity (SP) > 70%, and area under receiver operating curve (AUC) of at least 0.80 at prodromal stage (with higher performance at later stages) of the disease. Existing ML models performed poorly in comparison at this stage of the disease, suggesting that the underlying protein panels may not be suitable for early disease detection. Our results demonstrate the feasibility of early detection of AD using non-amyloid based biomarkers.

Accurate Blood-Based Diagnostic Biosignatures for Alzheimer's Disease via Automated Machine Learning

Alzheimer's disease (AD) is the most common form of neurodegenerative dementia and its timely diagnosis remains a major challenge in biomarker discovery. In the present study, we analyzed publicly available high-throughput low-sample -omics datasets from studies in AD blood, by the AutoML technology Just Add Data Bio (JADBIO), to construct accurate predictive models for use as diagnostic biosignatures. Considering data from AD patients and age-sex matched cognitively healthy individuals, we produced three best performing diagnostic biosignatures specific for the presence of AD: A. A 506-feature transcriptomic dataset from 48 AD and 22 controls led to a miRNA-based biosignature via Support Vector Machines with three miRNA predictors (AUC 0.975 (0.906, 1.000)), B. A 38,327-feature transcriptomic dataset from 134 AD and 100 controls led to six mRNA-based statistically equivalent signatures via Classification Random Forests with 25 mRNA predictors (AUC 0.846 (0.778, 0.905)) and C. A 9483-feature proteomic dataset from 25 AD and 37 controls led to a protein-based biosignature via Ridge Logistic Regression with seven protein predictors (AUC 0.921 (0.849, 0.972)). These performance metrics were also validated through the JADBIO pipeline confirming stability. In conclusion, using the automated machine learning tool JADBIO, we produced accurate predictive biosignatures extrapolating available low sample -omics data. These results offer options for minimally invasive blood-based diagnostic tests for AD, awaiting clinical validation based on respective laboratory assays. They also highlight the value of AutoML in biomarker discovery.

Machine Learning Analysis of Blood microRNA Data in Major Depression: A Case-Control Study for Biomarker Discovery

BACKGROUND

There is a lack of reliable biomarkers for major depressive disorder (MDD) in clinical practice. However, several studies have shown an association between alterations in microRNA levels and MDD, albeit none of them has taken advantage of machine learning (ML).

METHOD

Supervised and unsupervised ML were applied to blood microRNA expression profiles from a MDD case-control dataset (n = 168) to distinguish between (1) case vs control status, (2) MDD severity levels defined based on the Montgomery-Asberg Depression Rating Scale, and (3) antidepressant responders vs nonresponders.

RESULTS

MDD cases were distinguishable from healthy controls with an area-under-the receiver-operating characteristic curve (AUC) of 0.97 on testing data. High- vs low-severity cases were distinguishable with an AUC of 0.63. Unsupervised clustering of patients, before supervised ML analysis of each cluster for MDD severity, improved the performance of the classifiers (AUC of 0.70 for cluster 1 and 0.76 for cluster 2). Antidepressant responders could not be successfully separated from nonresponders, even after patient stratification by unsupervised clustering. However, permutation testing of the top microRNA, identified by the ML model trained to distinguish responders vs nonresponders in each of the 2 clusters, showed an association with antidepressant response. Each of these microRNA markers was only significant when comparing responders vs nonresponders of the corresponding cluster, but not using the heterogeneous unclustered patient set.

CONCLUSIONS

Supervised and unsupervised ML analysis of microRNA may lead to robust biomarkers for monitoring clinical evolution and for more timely assessment of treatment in MDD patients.