Early diagnosis of Alzheimer's disease using machine learning: a multi-diagnostic, generalizable approach

Validity and reliability of the Turkish version of the Australian National University-Alzheimer's Disease Risk Index (ANU-ADRI)

INTRODUCTION

There is still a requirement for concise, practical scales that can be readily incorporated into everyday schedules and predict the likelihood of dementia onset in individuals without dementia. This study aimed to assess the reliability of the ANU-ADRI (Australian National University Alzheimer's Disease Risk Index)-Short Form in Turkish geriatric patients.

METHODS

This methodological study involved 339 elderly patients attending the geriatric outpatient clinic for various reasons. The known-group validity and divergent validity were assessed. The ANU-ADRI was administered during the baseline test and again within one week for retest purposes. Alongside the ANU-ADRI, all participants underwent a comprehensive geriatric assessment, including Activities of Daily Living (ADL), mobility assessment (Performance-Oriented Mobility Assessment (POMA) and Timed Up and Go Test), nutritional assessment (Mini Nutritional Assessment (MNA)), and global cognition evaluation (Mini-Mental State Examination (MMSE)).

RESULTS

The scale demonstrated satisfactory linguistic validity. A correlation was observed between the mean scores of the ANU-ADRI test and retest (r = 0.997, p < 0.001). Additionally, there existed a moderate negative linear association between the ANU-ADRI and MMSE scores (r = -0.310, p < 0.001), POMA (r = -0.406, p < 0.001), Basic ADL (r = -0.359, p < 0.001), and Instrumental ADL (r = -0.294, p < 0.001). Moreover, a moderate positive linear association was found between the ANU-ADRI and the Timed Up and Go Test duration (r = 0.538, p < 0.001).

CONCLUSION

The ANU-ADRI-Short Form was proved as a valuable tool for clinical practice, facilitating the assessment of Alzheimer's disease risk within the Turkish geriatric population.

Intelligent decision support systems for dementia care: A scoping review

In the context of dementia care, Artificial Intelligence (AI) powered clinical decision support systems have the potential to enhance diagnosis and management. However, the scope and challenges of applying these technologies remain unclear. This scoping review aims to investigate the current state of AI applications in the development of intelligent decision support systems for dementia care. We conducted a comprehensive scoping review of empirical studies that utilised AI-powered clinical decision support systems in dementia care. The results indicate that AI applications in dementia care primarily focus on diagnosis, with limited attention to other aspects outlined in the World Health Organization (WHO) Global Action Plan on the Public Health Response to Dementia 2017-2025 (GAPD). A trifecta of challenges, encompassing data availability, cost considerations, and AI algorithm performance, emerges as noteworthy barriers in adoption of AI applications in dementia care. To address these challenges and enhance AI reliability, we propose a novel approach: a digital twin-based patient journey model. Future research should address identified gaps in GAPD action areas, navigate data-related obstacles, and explore the implementation of digital twins. Additionally, it is imperative to emphasize that addressing trust and combating the stigma associated with AI in healthcare should be a central focus of future research directions.

Cytokine expression patterns predict suppression of vulnerable neural circuits in a mouse model of Alzheimer's disease

Alzheimer's disease is a neurodegenerative disorder characterized by progressive amyloid plaque accumulation, tau tangle formation, neuroimmune dysregulation, synapse an neuron loss, and changes in neural circuit activation that lead to cognitive decline and dementia. Early molecular and cellular disease-instigating events occur 20 or more years prior to presentation of symptoms, making them difficult to study, and for many years amyloid-β, the aggregating peptide seeding amyloid plaques, was thought to be the toxic factor responsible for cognitive deficit. However, strategies targeting amyloid-β aggregation and deposition have largely failed to produce safe and effective therapies, and amyloid plaque levels poorly correlate with cognitive outcomes. However, a role still exists for amyloid-β in the variation in an individual's immune response to early, soluble forms of aggregates, and the downstream consequences of this immune response for aberrant cellular behaviors and creation of a detrimental tissue environment that harms neuron health and causes changes in neural circuit activation. Here, we perform functional magnetic resonance imaging of awake, unanesthetized Alzheimer's disease mice to map changes in functional connectivity over the course of disease progression, in comparison to wild-type littermates. In these same individual animals, we spatiotemporally profile the immune milieu by measuring cytokines, chemokines, and growth factors across various brain regions and over the course of disease progression from pre-pathology through established cognitive deficit. We identify specific signatures of immune activation predicting hyperactivity followed by suppression of intra- and then inter-regional functional connectivity in multiple disease-relevant brain regions, following the pattern of spread of amyloid pathology.

Leveraging electronic health records and knowledge networks for Alzheimer's disease prediction and sex-specific biological insights

Identification of Alzheimer's disease (AD) onset risk can facilitate interventions before irreversible disease progression. We demonstrate that electronic health records from the University of California, San Francisco, followed by knowledge networks (for example, SPOKE) allow for (1) prediction of AD onset and (2) prioritization of biological hypotheses, and (3) contextualization of sex dimorphism. We trained random forest models and predicted AD onset on a cohort of 749 individuals with AD and 250,545 controls with a mean area under the receiver operating characteristic of 0.72 (7 years prior) to 0.81 (1 day prior). We further harnessed matched cohort models to identify conditions with predictive power before AD onset. Knowledge networks highlight shared genes between multiple top predictors and AD (for example, APOE, ACTB, IL6 and INS). Genetic colocalization analysis supports AD association with hyperlipidemia at the APOE locus, as well as a stronger female AD association with osteoporosis at a locus near MS4A6A. We therefore show how clinical data can be utilized for early AD prediction and identification of personalized biological hypotheses.

Estimating dementia risk in an African American population using the DCTclock

The prevalence of Alzheimer's disease (AD) and related dementias (ADRD) is increasing. African Americans are twice as likely to develop dementia than other ethnic populations. Traditional cognitive screening solutions lack the sensitivity to independently identify individuals at risk for cognitive decline. The DCTclock is a 3-min AI-enabled adaptation of the well-established clock drawing test. The DCTclock can estimate dementia risk for both general cognitive impairment and the presence of AD pathology. Here we performed a retrospective analysis to assess the performance of the DCTclock to estimate future conversion to ADRD in African American participants from the Rush Alzheimer's Disease Research Center Minority Aging Research Study (MARS) and African American Clinical Core (AACORE). We assessed baseline DCTclock scores in 646 participants (baseline median age = 78.0 ± 6.4, median years of education = 14.0 ± 3.2, 78% female) and found significantly lower baseline DCTclock scores in those who received a dementia diagnosis within 3 years. We also found that 16.4% of participants with a baseline DCTclock score less than 60 were significantly more likely to develop dementia in 5 years vs. those with the highest DCTclock scores (75-100). This research demonstrates the DCTclock's ability to estimate the 5-year risk of developing dementia in an African American population. Early detection of elevated dementia risk using the DCTclock could provide patients, caregivers, and clinicians opportunities to plan and intervene early to improve cognitive health trajectories. Early detection of dementia risk can also enhance participant selection in clinical trials while reducing screening costs.

Feature Importance Analysis and Machine Learning for Alzheimer's Disease Early Detection: Feature Fusion of the Hippocampus, Entorhinal Cortex, and Standardized Uptake Value Ratio

Introduction

Alzheimer's disease (AD) is a progressive neurological disorder characterized by mild memory loss and ranks as a leading cause of mortality in the USA, accounting for approximately 120,000 deaths per year. It is also the primary form of dementia. Early detection is critical for timely intervention as the neurodegenerative process often starts 15-20 years before cognitive symptoms manifest. This study focuses on determining feature importance in AD classification using fused texture features from 3D magnetic resonance imaging hippocampal and entorhinal cortex and standardized uptake value ratio (SUVR) derived from positron emission tomography (PET) images.

Methods

To achieve this objective, we employed four distinct classifiers (Linear Support Vector Classification, Linear Discriminant Analysis, Logistic Regression, and Logistic Regression Classifier with Stochastic Gradient Descent Learning). These classifiers were used to derive both average and top-ranked importance scores for each feature based on their outputs. Our framework is designed to distinguish between two classes, AD-negative (or mild cognitive impairment stable [MCIs]) and AD-positive (or MCI conversion [MCIc]), using a probabilistic neural network classifier and the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

Results

The findings from the feature importance highlight the crucial role of the GLCM texture features extracted from the hippocampus and entorhinal cortex, demonstrating their superior performance compared to the volume and SUVR. GLCM texture AD classification achieved approximately 90% sensitivity in identifying MCIc cases while maintaining low false positives (below 30%) when fused with other features. Moreover, the receiver operating characteristic curves validate the GLCMs' superior performance in distinguishing between MCIs and MCIc. Additionally, fusing different types of features improved classification performance compared to relying solely on any single feature category.

Conclusion

Our study emphasizes the pivotal role of GLCM texture features in early Alzheimer's detection.

A New Approach for Multimodal Usage of Gene Expression and Its Image Representation for the Detection of Alzheimer's Disease

Alzheimer's disease (AD) is a complex neurodegenerative disorder and the multifaceted nature of it requires innovative approaches that integrate various data modalities to enhance its detection. However, due to the cost of collecting multimodal data, multimodal datasets suffer from an insufficient number of samples. To mitigate the impact of a limited sample size on classification, we introduce a novel deep learning method (One2MFusion) which combines gene expression data with their corresponding 2D representation as a new modality. The gene vectors were first mapped to a discriminative 2D image for training a convolutional neural network (CNN). In parallel, the gene sequences were used to train a feed forward neural network (FNN) and the outputs of the FNN and CNN were merged, and a joint deep network was trained for the binary classification of AD, normal control (NC), and mild cognitive impairment (MCI) samples. The fusion of the gene expression data and gene-originated 2D image increased the accuracy (area under the curve) from 0.86 (obtained using a 2D image) to 0.91 for AD vs. NC and from 0.76 (obtained using a 2D image) to 0.88 for MCI vs. NC. The results show that representing gene expression data in another discriminative form increases the classification accuracy when fused with base data.

The microRNA-485-3p concentration in salivary exosome-enriched extracellular vesicles is related to amyloid β deposition in the brain of patients with Alzheimer's disease

OBJECTIVES

Alzheimer's disease (AD) is an irreversible neurodegenerative disease characterized by progressive long-term memory loss and cognitive dysfunction. Neuroimaging tests for abnormal amyloid-β (Aβ) deposition are considered the most reliable methods for the diagnosis of AD; however, the cost for such testing is very high and generally not covered by national insurance systems. Accordingly, it is only recommended for individuals exhibiting clinical symptoms of AD supported by clinical cognitive assessments. Recently, it was suggested that dysregulated microRNA-485-3p (miRNA-485-3p) in the brain and cerebrospinal fluid is closely related to pathogenesis of AD. However, a relationship between circulating miRNA-485-3p in salivary exosome-enriched extracellular vesicles (EE-EV) and Aβ deposition in the brain has not been observed.

DESIGN & METHODS

Using quantitative real-time polymerase chain reaction, we analyzed miRNA-485-3p concentration in salivary EE-EV. We used receiver operating characteristic (ROC) curve analysis to evaluate its predictive value for Aβ positron emission tomography (Aβ-PET) positivity in patients with AD.

RESULTS

Our results showed that the miRNA-485-3p concentration in salivary EE-EV isolated from patients with AD was significantly increased compared with that in the healthy controls (p<0.0001). In the analysis of all participants, the miRNA-485-3p concentration was significantly increased in Aβ-PET-positive participants compared to Aβ-PET-negative participants (p<0.0001). Further analysis using only AD patients also showed that the miRNA-485-3p concentration was significantly increased in Aβ-PET-positive AD patients vs. Aβ-PET-negative AD patients (p=0.0063). The ROC curve analysis for differentiating Aβ-PET-positive and negative participants showed that the area under the curve for miRNA-485-3p was 0.9217.

CONCLUSION

These findings suggested that the miRNA-485-3p concentration in salivary EE-EV was closely related to Aβ deposition in the brain and had high diagnostic accuracy for predicting Aβ-PET positivity.