Diagnosis Of Alzheimer's Disease Research Articles

Iron accumulation/overload and Alzheimer's disease risk factors in the precuneus region: A comprehensive narrative review.

Alzheimer's disease (AD) is a neurodegenerative disease that is characterized by amyloid plaques, neurofibrillary tangles, and neuronal loss. Early cerebral and body iron dysregulation and accumulation interact with AD pathology, particularly in the precuneus, a crucial functional hub in cognitive functions. Quantitative susceptibility mapping (QSM), a novel post-processing approach, provides insights into tissue iron levels and cerebral oxygen metabolism and reveals abnormal iron accumulation early in AD. Increased iron deposition in the precuneus can lead to oxidative stress, neuroinflammation, and accelerated neurodegeneration. Metabolic disorders (diabetes, non-alcoholic fatty liver disease (NAFLD), and obesity), genetic factors, and small vessel pathology contribute to abnormal iron accumulation in the precuneus. Therefore, in line with the growing body of literature in the precuneus region of patients with AD, QSM as a neuroimaging method could serve as a non-invasive biomarker to track disease progression, complement other imaging modalities, and aid in early AD diagnosis and monitoring.

Recommendations for clinical implementation of blood-based biomarkers for Alzheimer's disease.

Blood-based biomarkers (BBM) for Alzheimer's disease (AD) are being increasingly used in clinical practice to support an AD diagnosis. In contrast to traditional diagnostic modalities, such as amyloid positron emission tomography and cerebrospinal fluid biomarkers, BBMs offer a more accessible and lower cost alternative for AD biomarker testing. Their unique scalability addresses the anticipated surge in demand for biomarker testing with the emergence of disease-modifying treatments (DMTs) that require confirmation of amyloid pathology. To facilitate the uptake of BBMs in clinical practice, The Global CEO Initiative on Alzheimer's Disease convened a BBM Workgroup to provide recommendations for two clinical implementational pathways for BBMs: one for current use for triaging and another for future use to confirm amyloid pathology. These pathways provide a standardized diagnostic approach with guidance on interpreting BBM test results. Integrating BBMs into clinical practice will simplify the diagnostic process and facilitate timely access to DMTs for eligible patients.

Payer Coverage Considerations for Alzheimer Disease Blood-Based Biomarker Tests

This Viewpoint discusses proactively preparing to make informed payer coverage decisions to help address current disparities in Alzheimer disease diagnosis and treatment.

Diagnostic Value of Serum Apolipoprotein B100 Combined With Hippocampal Volume in Alzheimer's Disease.

To explore the diagnostic value of serum apolipoprotein B100 (Apo B100) combined with hippocampal volume in Alzheimer's disease (AD). A total of 59 AD patients and 59 healthy subjects were selected. The Mini-Mental State Examination (MMSE) was used for neuropsychological assessment. Blood glucose and serum lipid levels were detected by biochemical analyzer. Polymerase chain reaction (PCR) was used to detect apolipoprotein E (Apo E) ε3/ε4 genotypes in the plasma. Hippocampal volume was calculated using Slicer software. Independent-sample t test or Mann-Whitney U test were used to compare the levels of various indicators between the two groups. Spearman's correlation analysis was used to analyze the correlation between each level. The receiver operating characteristic curve (ROC) was plotted, and the area under the curve (AUC) was calculated to compare the diagnostic efficacy of individual and combined detection of serum Apo B100 levels and hippocampal volume in AD. Compared with the healthy control group, the levels of serum total cholesterol (TC), low-density lipoprotein (LDL), Apo B100, and plasma Apo E ε3/ε4 were higher in the AD group, and serum high-density lipoprotein (HDL) level was lower in the AD group (both p<0.05). The hippocampal volume in the AD group was lower than in the control group (p<0.01). The serum Apo B100 level was negatively correlated with MMSE score (r=-0.646), whereas hippocampal volume was positively correlated with MMSE score (r=0.630). ROC curve analysis showed that the AUC of the combined serum Apo B100 level and hippocampal volume for AD was higher than that of either alone (AUC=0.821, p<0.01). Serum Apo B100 level is elevated, and the hippocampal volume is reduced in AD patients. The combined detection of the two has a higher diagnostic efficiency for AD than other alone and has the potential to become an important indicator for the diagnosis of AD in the future.

Sex Differences in Cortical Thickness and Neuropsychiatric Symptom Burden Based on APOE4 Homozygosity in Alzheimer's Disease.

Sex differences in patterns of cortical thickness and neuropsychiatric symptom (NPS) burden were examined among individuals with Alzheimer's disease (AD) and two copies (homozygote carriers) of the e4 allele of the apolipoprotein gene (APOE). A total of 752 participants with a clinical etiologic diagnosis of AD were selected from the National Alzheimer's Coordinating Center (NACC) database. Bayesian multilevel regression was used to examine both the within- and between-sex differences in gray-matter cortical thickness and total NPS burden associated with APOE homozygosity. Female homozygote carriers displayed a high probability of having reduced cortical thickness primarily in medial-lateral temporal regions and a greater burden of NPS, relative to both non-homozygous females and homozygous males. These findings support the notion that APOE4 status affects cortical thickness and symptom burden in men and women with AD differentially, with females showing more pronounced effects in brain areas known to be vulnerable in early AD. Future investigations should attempt to elucidate the proposed pattern of decline longitudinally.

Using Cerebrospinal Fluid Biomarkers To Enhance Diagnostic Testing For Alzheimer's Disease In A Tertiary Memory Service

Abstract Background Cerebrospinal Fluid (CSF) biomarkers are useful in the definitive diagnosis of Alzheimer’s disease (AD) when basic investigations and neuroimaging are inconclusive. In our tertiary memory clinic, the standard CSF biomarker panel consists of Amyloid Beta (AB) 42, Total tau, and Phosphorylated tau. AB42/40 is an additional CSF biomarker which has been shown to improve diagnostic accuracy for AD but is not part of our standard CSF biomarker panel (1). We examined the use of AB42/40 in patients attending our service. Methods In our service, diagnosis of dementia is based on a multidisciplinary consensus meeting (including Geriatricians, Neurologists, Psychiatrists) who review clinical presentation and neuroimaging. In a minority of cases, CSF analysis is performed. We retrospectively evaluated how the addition of AB40/42 to our standard CSF panel impacted diagnostic accuracy for AD, taking a ratio of &amp;lt;0.067 to indicate AD (2). Results Of 1853 patients attending the Memory service between September 2022 and December 2023, CSF was available for 107 patients. Of these 107, average age was 68. 56 were female (52%) and 51 were male (48%). At the multidisciplinary consensus meeting, 81 of these 107 patients were diagnosed with AD (75%), with the remaining 26 (24%) patients being diagnosed with a non-AD process (Vascular disease, Lewy Body etc) or an inconclusive diagnosis. After the addition of AB42/40 to CSF, 8 of the 26 patients (31%) with non-AD or an inconclusive diagnosis were identified as potential AD. Of the 81 with a diagnosis of AD, none were reclassified after addition of AB42/40. Receiver Operating Characteristic (ROC) curve analysis was performed to determine relevant cut off values. Conclusion This study suggests the addition of AB42/40 ratio to our standard CSF biomarker panel improves identification of potential AD cases in our sample. This biomarker is being added to the standard CSF panel for our Memory service.

The association between antiglaucomatous agents and Alzheimer's disease.

To estimate the risk of Alzheimer's disease (AD) associated with long-term use of topical glaucoma medications among middle-aged and older glaucoma patients, and compare the AD risk among various glaucoma subtypes. This nationwide population-based cohort study utilized insurance claims data from Taiwan's National Health Insurance Research Database between 2008 and 2019. Participants were adults aged 45 years or older either with a diagnosis of glaucoma or without. Those with glaucoma must have received single antiglaucomatous medication (including α2-adrenergic agonists, cholinergic agonists, beta-blockers, prostaglandin analogs, and pilocarpine) for over 90 days. Those with pre-existing AD diagnoses prior to the index date were excluded. A total of 202,000 participants were included in the study, with 101,000 in each group (glaucoma and control groups). Glaucoma patients on topical alpha-2 adrenergic agonist monotherapy exhibited a significantly higher AD risk (aHR 1.15, 95% CI = 1.01-1.31) compared to those on beta-blockers. Glaucoma was further categorized into primary open-angle glaucoma (POAG), normal-tension glaucoma (NTG), primary angle-closure glaucoma (PACG), and unspecified glaucoma. Irrespective of the type of glaucoma, individuals with glaucoma had a significantly higher risk of AD compared to those without glaucoma (POAG: aHR 1.23, 95% CI = 1.08-1.40; NTG: aHR 1.49, 95% CI = 1.19-1.85; PACG: aHR 1.35, 95% CI = 1.19-1.52; unspecified glaucoma: aHR 1.36, 95% CI = 1.23-1.50). Topical alpha-2 adrenergic agonists might pose increased AD risk in individuals with glaucoma compared to beta-blockers. Accordingly, their utilization should be undertaken judiciously, especially in middle-aged and older populations. Our findings also indicate glaucoma may increase the risk of AD regardless of glaucoma subtype.

WIMOAD: Weighted Integration of Multi-Omics data for Alzheimer's Disease (AD) Diagnosis.

As the most common subtype of dementia, Alzheimer's disease (AD) is characterized by a progressive decline in cognitive functions, especially in memory, thinking, and reasoning ability. Early diagnosis and interventions enable the implementation of measures to reduce or slow further regression of the disease, preventing individuals from severe brain function decline. The current framework of AD diagnosis depends on A/T/(N) biomarkers detection from cerebrospinal fluid or brain imaging data, which is invasive and expensive during the data acquisition process. Moreover, the pathophysiological changes of AD accumulate in amino acids, metabolism, neuroinflammation, etc., resulting in heterogeneity in newly registered patients. Recently, next generation sequencing (NGS) technologies have found to be a non-invasive, efficient and less-costly alternative on AD screening. However, most of existing studies rely on single omics only. To address these concerns, we introduce WIMOAD, a weighted integration of multi-omics data for AD diagnosis. WIMOAD synergistically leverages specialized classifiers for patients' paired gene expression and methylation data for multi-stage classification. The resulting scores were then stacked with MLP-based meta-models for performance improvement. The prediction results of two distinct meta-models were integrated with optimized weights for the final decision-making of the model, providing higher performance than using single omics only. Remarkably, WIMOAD achieves significantly higher performance than using single omics alone in the classification tasks. The model's overall performance also outperformed most existing approaches, highlighting its ability to effectively discern intricate patterns in multi-omics data and their correlations with clinical diagnosis results. In addition, WIMOAD also stands out as a biologically interpretable model by leveraging the SHapley Additive exPlanations (SHAP) to elucidate the contributions of each gene from each omics to the model output. We believe WIMOAD is a very promising tool for accurate AD diagnosis and effective biomarker discovery across different progression stages, which eventually will have consequential impacts on early treatment intervention and personalized therapy design on AD.

Gut Microbiota Mediates Neuroinflammation in Alzheimer's Disease: Unraveling Key Factors and Mechanistic Insights.

The gut microbiota, the complex community of microorganisms that inhabit the gastrointestinal tract, has emerged as a key player in the pathogenesis of neurodegenerative disorders, including Alzheimer's disease (AD). AD is characterized by progressive cognitive decline and neuronal loss, associated with the accumulation of amyloid-β plaques, neurofibrillary tangles, and neuroinflammation in the brain. Increasing evidence suggests that alterations in the composition and function of the gut microbiota, known as dysbiosis, may contribute to the development and progression of AD by modulating neuroinflammation, a chronic and maladaptive immune response in the central nervous system. This review aims to comprehensively analyze the current role of the gut microbiota in regulating neuroinflammation and glial cell function in AD. Its objective is to deepen our understanding of the pathogenesis of AD and to discuss the potential advantages and challenges of using gut microbiota modulation as a novel approach for the diagnosis, treatment, and prevention of AD.

Cerebrospinal Fluid Neurofilaments Light-Chain Differentiate Patients Affected by Alzheimer's Disease with Different Rate of Progression (RoP): A Preliminary Study.

Alzheimer's disease (AD) is the most common neurodegenerative disorder and a leading cause of dementia. One major challenge for clinicians is accurately assessing the rate of disease progression (RoP) early in the diagnostic process, which is crucial for patient management and clinical trial stratification. This study evaluated the role of cerebrospinal fluid biomarkers-Aβ42, t-Tau, pTau, Neurogranin (Ng), and Neurofilament light-chain (NF-L)-in predicting RoP at the time of AD diagnosis. We included 56 AD patients and monitored cognitive impairment using MMSE scores at diagnosis and during six-month follow-up visits. RoP scores were calculated based on these assessments. Our correlation analyses revealed significant associations between RoP and pTau, Aβ42/Ng ratio, and NF-L levels. When patients were stratified by median RoP values into low-to-moderate (L-M: <2) and upper-moderate (U-M: >2) groups, those in the U-M group had notably higher CSF NF-L levels compared to the L-M group. Logistic regression analysis further demonstrated that elevated CSF NF-L levels were predictive of a faster RoP. These findings highlight the potential of CSF NF-L as a prognostic biomarker for rapid disease progression in AD. By identifying patients at risk for accelerated cognitive decline, CSF NF-L could significantly enhance early intervention strategies and improve patient management in clinical settings.

Early diagnosis of Alzheimer's Disease based on multi-attention mechanism.

Alzheimer's Disease is a neurodegenerative disorder, and one of its common and prominent early symptoms is language impairment. Therefore, early diagnosis of Alzheimer's Disease through speech and text information is of significant importance. However, the multimodal data is often complex and inconsistent, which leads to inadequate feature extraction. To address the problem, We propose a model for early diagnosis of Alzheimer's Disease based on multimodal attention(EDAMM). Specifically, we first evaluate and select three optimal feature extraction methods, Wav2Vec2.0, TF-IDF and Word2Vec, to extract acoustic and linguistic features. Next, by leveraging self-attention mechanism and cross-modal attention mechanisms, we generate fused features to enhance and capture the inter-modal correlation information. Finally, we concatenate the multimodal features into a composite feature vector and employ a Neural Network(NN) classifier to diagnose Alzheimer's Disease. To evaluate EDAMM, we perform experiments on two public datasets, i.e., NCMMSC2021 and ADReSSo. The results show that EDAMM improves the performance of Alzheimer's Disease diagnosis over state-of-the-art baseline approaches on both datasets.

A machine learning-based prediction of tau load and distribution in Alzheimer's disease using plasma, MRI and clinical variables.

Tau positron emission tomography (PET) is a reliable neuroimaging technique for assessing regional load of tau pathology in the brain, commonly used in Alzheimer's disease (AD) research and clinical trials. However, its routine clinical use is limited by cost and accessibility barriers. Here we explore using machine learning (ML) models to predict clinically useful tau-PET composites from low-cost and non-invasive features, e.g., basic clinical variables, plasma biomarkers, and structural magnetic resonance imaging (MRI). Results demonstrated that models including plasma biomarkers yielded the most accurate predictions of tau-PET burden (best model: R-squared=0.66-0.68), with especially high contribution from plasma P-tau217. In contrast, MRI variables stood out as best predictors (best model: R-squared=0.28-0.42) of asymmetric tau load between the two hemispheres (an example of clinically relevant spatial information). The models showed high generalizability to external test cohorts with data collected at multiple sites. Based on these results, we also propose a proof-of-concept two-step classification workflow, demonstrating how the ML models can be translated to a clinical setting. This study uncovers current potential in predicting tau-PET information from scalable cost-effective variables, which could improve diagnosis and prognosis of AD.

Automated alzheimer's disease detection and diagnosis method based on Bayesian optimization and CNN-based pre-trained features

Automated alzheimer's disease detection and diagnosis method based on Bayesian optimization and CNN-based pre-trained features

Extracellular vesicle and CRISPR gene therapy: Current applications in Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease.

Neurodegenerative diseases are characterized by progressive deterioration of the nervous system. Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and Huntington's disease (HD) are prominently life-threatening examples of neurodegenerative diseases. The complexity of the pathophysiology in neurodegenerative diseases causes difficulties in diagnosing. Although the drugs temporarily help to correct specific symptoms including memory loss and degeneration, a complete treatment has not been found yet. New therapeutic approaches have been developed to understand and treat the underlying pathogenesis of neurodegenerative diseases. With this purpose, clustered-regularly interspaced short palindromic repeats/CRISPR-associated protein (CRISPR/Cas) technology has recently suggested a new treatment option. Editing of the genome is carried out by insertion and deletion processes on DNA. Safe delivery of the CRISPR/Cas system to the targeted cells without affecting surrounding cells is frequently investigated. Extracellular vesicles (EVs), that is exosomes, have recently been used in CRISPR/Cas studies. In this review, CRISPR/Cas and EV approaches used for diagnosis and/or treatment in AD, PD, ALS, and HD are reviewed. CRISPR/Cas and EV technologies, which stand out as new therapeutic approaches, may offer a definitive treatment option in neurodegenerative diseases.

The plasma miRNAome in ADNI: Signatures to aid the detection of at-risk individuals.

MicroRNAs are short non-coding RNAs that control proteostasis at the systems level and are emerging as potential prognostic and diagnostic biomarkers for Alzheimer's disease (AD). We performed small RNA sequencing on plasma samples from 847 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. We identified microRNA signatures that correlate with AD diagnoses and help predict the conversion from mild cognitive impairment (MCI) to AD. Our data demonstrate that plasma microRNA signatures can be used to not only diagnose MCI, but also, critically, predict the conversion from MCI to AD. Moreover, combined with neuropsychological testing, plasma microRNAome evaluation helps predict MCI to AD conversion. These findings are of considerable public interest because they provide a path toward reducing indiscriminate utilization of costly and invasive testing by defining the at-risk segment of the aging population. We provide the first analysis of the plasma microRNAome for the ADNI study. The levels of several microRNAs can be used as biomarkers for the prediction of conversion from MCI to AD. Adding the evaluation of plasma microRNA levels to neuropsychological testing in a clinical setting increases the accuracy of MCI to AD conversion prediction.

Exploring the Association between Pro-Inflammation and the Early Diagnosis of Alzheimer’s Disease in Buccal Cells Using Immunocytochemistry and Machine Learning Techniques

The progressive aging of the global population and the high impact of neurodegenerative diseases, such as Alzheimer’s disease (AD), underscore the urgent need for innovative diagnostic and therapeutic strategies. AD, the most prevalent neurodegenerative disorder among the elderly, is expected to affect 75 million people in developing countries by 2030. Despite extensive research, the precise etiology of AD remains elusive due to its heterogeneity and complexity. The key pathological features of AD, including amyloid-beta plaques and hyperphosphorylated tau protein, are established years before clinical symptoms appear. Recent studies highlight the pivotal role of neuroinflammation in AD pathogenesis, with the chronic activation of the brain’s immune system contributing to the disease’s progression. Pro-inflammatory cytokines, such as TNF-α, IL-1β, and IL-6, are elevated in AD and mild cognitive impairment (MCI) patients, suggesting a strong link between peripheral inflammation and CNS degeneration. There is a pressing need for minimally invasive, cost-effective diagnostic methods. Buccal mucosa cells and saliva, which share an embryological origin with the CNS, show promise for AD diagnosis and prognosis. This study integrates cellular observations with advanced data processing and machine learning to identify significant biomarkers and patterns, aiming to enhance the early diagnosis and prevention strategies for AD.

Delirium event and associated treatment modifications among older adults with Alzheimer's disease: An interrupted time-series analysis of Medicare data.

Both Alzheimer's disease (AD) and deliriogenic medications increase the risk of delirium in older adults. This study examined the association between delirium and the subsequent monthly use of anticholinergic, sedative, and opioid medications in the 1 year after delirium in older adults with AD. This comparative interrupted time series analysis involved adults (aged 65 years and older) with a diagnosis of AD initiating on cholinesterase inhibitors (ChEIs) based on 2013-2017 Medicare data. Separate patient-level segmented regression models were used for each outcome to evaluate changes in the cumulative anticholinergic burden (CAB), sedative load, and opioid load after the delirium/index event using a 12-month baseline and follow-up period among patients who had a delirium event and those without delirium (control group). Propensity score-based stabilized weights were utilized to balance baseline factors in the delirium and control groups. The study included 80,019 older adults with AD with incident ChEI use; 17.11% had delirium. There was an immediate decline in monthly CAB after the delirium event (mean estimate -0.86, p-value: 0.01) compared to the control group. A similar decline was observed when examining the sedative load (-0.06, p-value: 0.002) after the delirium event. However, there was no decline in opioid load (-0.50, p-value: 0.18). In the long term, CAB (0.13; p-value: <0.0001), sedative load (0.01; p-value: <0.001), and opioid load (0.07; p-value: 0.006) increased over the 1-year post-delirium period in the delirium group compared to those without delirium. This study found the burden of deliriogenic medications over the 1-year follow-up showed increasing trends in older adults with AD, even though there was some level shift in CAB and sedative load after the delirium event.

Autoencoder imputation of missing heterogeneous data for Alzheimer's disease classification

AbstractMissing Alzheimer's disease (AD) data is prevalent and poses significant challenges for AD diagnosis. Previous studies have explored various data imputation approaches on AD data, but the systematic evaluation of deep learning algorithms for imputing heterogeneous and comprehensive AD data is limited. This study investigates the efficacy of denoising autoencoder‐based imputation of missing key features of heterogeneous data that comprised tau‐PET, MRI, cognitive and functional assessments, genotype, sociodemographic, and medical history. The authors focused on extreme (≥40%) missing at random of key features which depend on AD progression; identified as the history of a mother having AD, APoE ε4 alleles, and clinical dementia rating. Along with features selected using traditional feature selection methods, latent features extracted from the denoising autoencoder are incorporated for subsequent classification. Using random forest classification with 10‐fold cross‐validation, robust AD predictive performance of imputed datasets (accuracy: 79%–85%; precision: 71%–85%) across missingness levels, and high recall values with 40% missingness are found. Further, the feature‐selected dataset using feature selection methods, including autoencoder, demonstrated higher classification score than that of the original complete dataset. These results highlight the effectiveness and robustness of autoencoder in imputing crucial information for reliable AD prediction in AI‐based clinical decision support systems.

Gut-Brain Nexus: Mapping Multi-Modal Links to Neurodegeneration at Biobank Scale.

Alzheimer's disease (AD) and Parkinson's disease (PD) are influenced by genetic and environmental factors. Using data from UK Biobank, SAIL Biobank, and FinnGen, we conducted an unbiased, population-scale study to: 1) Investigate how 155 endocrine, nutritional, metabolic, and digestive system disorders are associated with AD and PD risk prior to their diagnosis, considering known genetic influences; 2) Assess plasma biomarkers' specificity for AD or PD in individuals with these conditions; 3) Develop a multi-modal classification model integrating genetics, proteomics, and clinical data relevant to conditions affecting the gut-brain axis. Our findings show that certain disorders elevate AD and PD risk before AD and PD diagnosis including: insulin and non-insulin dependent diabetes mellitus, noninfective gastro-enteritis and colitis, functional intestinal disorders, and bacterial intestinal infections, among others. Polygenic risk scores revealed lower genetic predisposition to AD and PD in individuals with co-occurring disorders in the study categories, underscoring the importance of regulating the gut-brain axis to potentially prevent or delay the onset of neurodegenerative diseases. The proteomic profile of AD/PD cases was influenced by comorbid endocrine, nutritional, metabolic, and digestive systems conditions. Importantly, we developed multi-modal prediction models integrating clinical, genetic, proteomic and demographic data, the combination of which performs better than any single paradigm approach in disease classification. This work aims to illuminate the intricate interplay between various physiological factors involved in the gut-brain axis and the development of AD and PD, providing a multifactorial systemic understanding that goes beyond traditional approaches.

Diagnose Alzheimer’s disease and mild cognitive impairment using deep CascadeNet and handcrafted features from EEG signals

Alzheimer’s disease (AD) is the most prevalent clinically diagnosed neurodegenerative disorder. Early detection of mild cognitive impairment (MCI) is crucial for implementing effective interventions and potentially preventing further cognitive decline. Due to its efficiency, the electroencephalogram (EEG) is a promising tool for AD diagnosis. This paper proposes a computer-aided diagnostic model for identifying AD using EEG data analysis. The proposed approach comprises two key steps: signal processing and classification. Initially, the EEG signal is decomposed into sub-bands using the discrete wavelet transform (DWT), followed by windowing for data augmentation. Subsequently, an improved CascadeNet model is employed for feature extraction and classification from the windowed EEG data. CascadeNet’s architecture was specifically chosen for its ability to achieve high accuracy in AD and MCI detection, even with relatively small EEG datasets. Moreover, the paper explores the potential of deep Cascade learning for AD prediction. The effectiveness of the proposed strategy is evaluated using metrics such as F-measures, specificity, sensitivity, and overall detection accuracy. The developed model attains impressive accuracies of 98.84 % and 97.78 % on the Figshare and Brazilian datasets, respectively, significantly outperforming existing methods. To validate the model’s efficacy, it was applied to the Figshare and Brazilian datasets encompassing control (CO), MCI, and AD classes. The results confirm the proposed method as a valuable tool for identifying potential biomarkers aiding in the clinical diagnosis of AD. Notably, unlike previous methods limited to two-class identification, this technique effectively distinguishes between MCI and AD.