Alzheimer's Disease Diagnostics Research Articles

"Advances in biomarker discovery and diagnostics for alzheimer's disease".

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by intracellular neurofibrillary tangles with tau protein and extracellular β-amyloid plaques. Early and accurate diagnosis is crucial for effective treatment and management. The purpose of this review is to investigate new technologies that improve diagnostic accuracy while looking at the current diagnostic criteria for AD, such as clinical evaluations, cognitive testing, and biomarker-based techniques. A thorough review of the literature was done in order to assess both conventional and contemporary diagnostic methods. Multimodal strategies integrating clinical, imaging, and biochemical evaluations were emphasised. The promise of current developments in biomarker discovery was also examined, including mass spectrometry and artificial intelligence. Current diagnostic approaches include cerebrospinal fluid (CSF) biomarkers, imaging tools (MRI, PET), cognitive tests, and new blood-based markers. Integrating these technologies into multimodal diagnostic procedures enhances diagnostic accuracy and distinguishes dementia from other conditions. New technologies that hold promise for improving biomarker identification and diagnostic reliability include mass spectrometry and artificial intelligence. Advancements in AD diagnostics underscore the need for accessible, minimally invasive, and cost-effective techniques to facilitate early detection and intervention. The integration of novel technologies with traditional methods may significantly enhance the accuracy and feasibility of AD diagnosis.

From pixels to prognosis: radiomics and AI in Alzheimer’s disease management

Alzheimer’s disease (AD), the leading cause of dementia, poses a growing global health challenge due to an aging population. Early and accurate diagnosis is essential for optimizing treatment and management, yet traditional diagnostic methods often fall short in addressing the complexity of AD pathology. Recent advancements in radiomics and artificial intelligence (AI) offer novel solutions by integrating quantitative imaging features and machine learning algorithms to enhance diagnostic and prognostic precision. This review explores the application of radiomics and AI in AD, focusing on key imaging modalities such as PET and MRI, as well as multimodal approaches combining structural and functional data. We discuss the potential of these technologies to identify disease-specific biomarkers, predict disease progression, and guide personalized interventions. Additionally, the review addresses critical challenges, including data standardization, model interpretability, and the integration of AI into clinical workflows. By highlighting current achievements and identifying future directions, this article underscores the transformative potential of AI-driven radiomics in reshaping AD diagnostics and care.

Diagnostic performance of plasma biomarkers for Alzheimer's disease using a fully automated platform: A real-world clinical study.

This study evaluated the diagnostic performance of plasma biomarkers for Alzheimer's disease (AD) using an automated platform. In a cohort of 74 consecutive patients, plasma p-Tau181 levels were significantly higher in AD compared to non-AD groups and showed correlation with cerebrospinal fluid biomarkers. Plasma p-Tau181 demonstrated high diagnostic accuracy for AD, with an area under the curve of 0.854. The findings suggest that plasma biomarkers, particularly p-Tau181, could improve the accessibility and efficiency of AD diagnostics in clinical settings, offering a less invasive and cost-effective alternative to traditional methods.

An Evolutionary Federated Learning Approach to Diagnose Alzheimer's Disease Under Uncertainty.

Background: Alzheimer's disease (AD) leads to severe cognitive impairment and functional decline in patients, and its exact cause remains unknown. Early diagnosis of AD is imperative to enable timely interventions that can slow the progression of the disease. This research tackles the complexity and uncertainty of AD by employing a multimodal approach that integrates medical imaging and demographic data. Methods: To scale this system to larger environments, such as hospital settings, and to ensure the sustainability, security, and privacy of sensitive data, this research employs both deep learning and federated learning frameworks. MRI images are pre-processed and fed into a convolutional neural network (CNN), which generates a prediction file. This prediction file is then combined with demographic data and distributed among clients for local training. Training is conducted both locally and globally using a belief rule base (BRB), which effectively integrates various data sources into a comprehensive diagnostic model. Results: The aggregated data values from local training are collected on a central server. Various aggregation methods are evaluated to assess the performance of the federated learning model, with results indicating that FedAvg outperforms other methods, achieving a global accuracy of 99.9%. Conclusions: The BRB effectively manages the uncertainty associated with AD data, providing a robust framework for integrating and analyzing diverse information. This research not only advances AD diagnostics by integrating multimodal data but also underscores the potential of federated learning for scalable, privacy-preserving healthcare solutions.

Label-Free Surface-Enhanced Raman Spectroscopy Detection of Amyloid Beta on Silver Nanostructured Substrates for Alzheimer's Diagnosis.

Alzheimer's disease (AD) is a public health concern, for which an early diagnosis is essential. Biomass-derived carbon fibres with surface-decorated silver nanoparticles (Ag@CFs) have been utilized as surface enhanced raman spectroscopy (SERS) sensor platformfor the detection of the amyloid beta Aβ (25-35) for the pre-diagnosis of Alzheimer's disease. Structural and morphological characterizations confirmed the distribution of plasmonic silver nanoparticles over the surface of carbon fibres. The SERS sensor performance of Ag@CFs was evaluated using rhodamine 6G, which showed an enhancement of the order of 106 which proved the effectiveness of the developed SERS sensor towards trace level detection of analyte. A range of amyloid beta concentrations, from 100uM to 10pM, have been analyzed as a proof-of concept for this study, showcasing the efficacy of the Ag@CFs based SERS sensor to detect trace level concentrations of amyloid beta, even as low as 10 pM. This investigation is a promising development in the field of AD diagnostics since it may turn out to be a non-invasive, economical and early diagnostic tool.

Blood‐based biomarkers of Alzheimer's disease: Standardization and comprehensiveness

AbstractPopulation aging is sweeping across the globe, resulting in a striking prevalence of Alzheimer's disease (AD) and dementia and a heavy economic burden. Given the time window of 10–20 years from pathological initiation to clinically detected cognitive impairment, early detection can significantly impact the prevention and control of AD. The invasiveness and high cost of cerebrospinal fluid biomarkers and positron emission tomography‐computed tomography imaging limit large‐scale disease screening. However, blood‐based biomarkers (BBMs) lack these disadvantages, shedding light on their usefulness in the large‐scale identification and prevention of AD. Prominent advancement has recently been made regarding BBMs of AD co‐pathology (amyloid β, tau protein, neurofilament light polypeptide, and glial fibrillary acidic protein) to improve their accuracy as clinical diagnostics of AD to a level comparable to that of canonical methods, facilitating the large‐scale clinical implementation of diagnostic tests with higher precision. To briefly summarize, the prospects of AD BBMs rely on standardization and comprehensiveness. Calibrating the sample collection procedure and clarifying the boundaries for indices and abnormalities are beneficial for constructing a canonical diagnostic assay. The comprehensive assembly of heterogeneous clinical evidence guarantees the accuracy of diagnosis and improves the workflow for early identification.

Electrochemical Analysis of Amyloid Plaques and ApoE4 with Chitosan-Coated Gold Nanostars for Alzheimer's Detection.

Monitoring the progression of Alzheimer's disease (AD) is crucial for mitigating dementia symptoms, alleviating pain, and improving mobility. Traditionally, AD biomarkers like amyloid plaques are predominantly identified in cerebrospinal fluid (CSF) due to their concentrated presence. However, detecting these markers in blood is hindered by the blood-brain barrier (BBB), resulting in lower concentrations. To address this challenge and identify pertinent AD biomarkers-specifically amyloid plaques and apolipoprotein E4 (ApoE4)-in blood plasma, we propose an innovative approach. This involves enhancing a screen-printed carbon electrode (SPCE) with an immobilization matrix comprising gold nanostars (AuNSs) coated with chitosan. Morphological and electrical analyses confirmed superior dispersion and conductivity with 0.5% chitosan, supported by UV-Vis spectroscopy, cyclic voltammetry, and Nyquist plots. Subsequent clinical assays measured electrical responses to quantify amyloid-β 42 (Aβ42) (15.63-1000 pg/mL) and APoE4 levels (0.41 to 40 ng/mL) in human blood plasma samples. Differential pulse voltammetry (DPV) responses exhibited peak currents proportional to biomarker concentrations, demonstrating high linear correlations (0.985 for Aβ42 and 0.919 for APoE4) with minimal error bars. Cross-reactivity tests with mixed solutions of amyloid-β 40 (Aβ40), Aβ42, and ApoE4 indicated minimal interference between biomarkers (<3% variation), further confirming the high specificity of the developed sensor. Validation studies demonstrated a strong concurrence with the gold-standard enzyme-linked immunosorbent assay (ELISA), while interference tests indicated a minimal variation in peak currents. This improved device presents promising potential as a point-of-care system, offering a less invasive, cost-effective, and simplified approach to detecting and tracking the progression of AD. The substantial surface binding area further supports the efficacy of our method, offering a promising avenue for advancing AD diagnostics.

Blood-Based Biomarkers in Alzheimer's Disease: Advancing Non-Invasive Diagnostics and Prognostics.

Alzheimer's disease (AD), the most prevalent form of dementia, is expected to rise dramatically in incidence due to the global population aging. Traditional diagnostic approaches, such as cerebrospinal fluid analysis and positron emission tomography, are expensive and invasive, limiting their routine clinical use. Recent advances in blood-based biomarkers, including amyloid-beta, phosphorylated tau, and neurofilament light, offer promising non-invasive alternatives for early AD detection and disease monitoring. This review synthesizes current research on these blood-based biomarkers, highlighting their potential to track AD pathology and enhance diagnostic accuracy. Furthermore, this review uniquely integrates recent findings on protein-protein interaction networks and microRNA pathways, exploring novel combinations of proteomic, genomic, and epigenomic biomarkers that provide new insights into AD's molecular mechanisms. Additionally, we discuss the integration of these biomarkers with advanced neuroimaging techniques, emphasizing their potential to revolutionize AD diagnostics. Although large-scale validation is still needed, these biomarkers represent a critical advancement toward more accessible, cost-effective, and early diagnostic tools for AD.

Differentiation of Alzheimer's disease from other neurodegenerative disorders using chemiluminescence immunoassays measuring cerebrospinal fluid biomarkers.

Prior research identified four neurochemical cerebrospinal fluid (CSF) biomarkers, Aβ1-42, Aβ1-40, tTau, and pTau(181), as core diagnostic markers for Alzheimer's disease (AD). Determination of AD biomarkers using immunoassays can support differential diagnosis of AD vs. several neuropsychiatric disorders, which is important because the respective treatment regimens differ. Results of biomarker determination can be classified according to the Amyloid/Tau/Neurodegeneration (ATN) system into profiles. Less is known about the clinical performance of chemiluminescence immunoassays (ChLIA) measuring specific biomarkers in CSF samples from patients suffering from neuropsychiatric impairments with various underlying causes. Chemiluminescence immunoassays (ChLIAs, EUROIMMUN) were used to determine Beta-Amyloid (1-40), Beta-Amyloid (1-42), Total-Tau, and pTau(181) concentrations in precharacterized cerebrospinal fluid (CSF) samples from 219 AD patients, 74 patients with mild cognitive impairment (MCI), and 220 disease control (DC) patients. 83.0% of AD patients had ATN profiles consistent with AD, whereas 85.5% of DC patients and 77.0% of MCI patients had profiles inconsistent with AD. AD patients showed significantly lower amyloid ratio Aβ1-42/Aβ1-40 (mean: 0.07) and significantly higher concentrations of tTau (mean: 901.6 pg/ml) and pTau(181) (mean: 129 pg/ml) compared to DC and MCI patients (all p values < 0.0071). The ChLIAs effectively determined specific biomarkers and can support differential diagnostics of AD. Their quality was demonstrated in samples from 513 patients with cognitive impairments, representing a realistic mix of underlying causes for seeking treatment at a memory clinic.

Diagnostics of Alzheimer’s disease using fluorescent nanodiamond-based spin-enhanced lateral flow immunoassay

Fluorescent nanodiamond (FND) has been regarded as a superior alternative reporter for lateral flow immunoassays. The negatively charged nitrogen-vacancy (NV−) center in FND is a point defect with unique magneto-optical properties, giving outstanding characteristics to detect biomarkers of diseases, including Alzheimer’s disease (AD). AD is the most common form of dementia, which might ultimately result in complete brain failure and, eventually, death. The two most commonly used techniques to detect brain changes caused by AD are brain imaging and lumbar puncture. However, brain imaging and lumbar puncture are costly and time-consuming. Thus, we have developed Spin-Enhanced Lateral Flow Immunoassay (SELFIA) for in vitro AD diagnostics utilizing the electron spin properties of the NV−-centers in FND to achieve background-free ultrasensitive detection. To detect the pTau protein (a critical AD biomarker), we first conjugated FND to anti-pTau antibodies through non-covalent conjugation. We employed a sandwich assay to enhance sensitivity and specificity by immobilizing capture antibodies on the membrane so that antibody-antigen–antibody immunocomplexes would be formed. Our FND-based SELFIA only requires approximately 30 min to reach a detection limit of 7 pg/mL, where the threshold for positivity was 101.95 pg/mL for cerebrospinal fluid pTau217. This is the first study that utilized FND-based SELFIA for AD detection. The current SELFIA platform is rapid, easy to use, and affordable. This study not only represents a significant innovation but also opens new avenues for early diagnosis and treatment strategies.

Using Deep Learning Techniques as an Attempt to Create the Most Cost-Effective Screening Tool for Cognitive Decline.

This study aimed to use deep learning (DL) to develop a cost-effective and accessible screening tool to improve the detection of cognitive decline, a precursor of Alzheimer's disease (AD). This study integrating a comprehensive battery of neuropsychological tests adjusted for individual demographic variables such as age, sex, and education level. A total of 2,863 subjects with subjective cognitive complaints who underwent a comprehensive neuropsychological assessment were included. A random forest classifier was used to discern the most predictive test combinations to distinguish between dementia and nondementia cases. The model was trained and validated on this dataset, focusing on feature importance to determine the cognitive tests that were most indicative of decline. Subjects had a mean age of 72.68 years and an average education level of 7.62 years. The DL model achieved an accuracy of 82.42% and an area under the curve of 0.816, effectively classifying dementia. Feature importance analysis identified significant tests across cognitive domains: attention was gauged by the Trail Making Test Part B, language by the Boston Naming Test, memory by the Rey Complex Figure Test delayed recall, visuospatial skills by the Rey Complex Figure Test copy score, and frontal function by the Stroop Test Word reading time. This study showed the potential of DL to improve AD diagnostics, suggesting that a wide range of cognitive assessments could yield a more accurate diagnosis than traditional methods. This research establishes a foundation for future broader studies, which could substantiate the approach and further refine the screening tool.

Rapid Alzheimer's Disease Diagnosis Using Advanced Artificial Intelligence Algorithms

Alzheimer's disease (AD) is a leading cause of dementia, predominantly impacting the elderly and characterized by progressive cognitive decline. Early and precise detection is critical for effective management and improved patient outcomes. Traditional diagnostic methods such as neuroimaging and cerebrospinal fluid analysis are often invasive, expensive, and time- consuming. Advances in artificial intelligence (AI) and machine learning (ML) provide promising alternatives that are non-invasive, efficient, and cost-effective. This study explores the application of various ML algorithms to predict Alzheimer's disease. The methodology involved data preprocessing and feature selection using the Spearman algorithm to enhance computational efficiency and model performance. We evaluated k-Nearest Neighbors (k-NN), Naive Bayes (NB), Decision Trees (DT), and Ensemble methods. Results indicate that the Ensemble method achieved a predictive accuracy of 94.07% using only 13 features. These results demonstrate the potential of ML algorithms in revolutionizing AD diagnostics, offering scalable and accurate solutions for early detection.

Evaluating p-tau217 and p-tau231 as Biomarkers for Early Diagnosis and Differentiation of Alzheimer's Disease: A Narrative Review.

The escalating prevalence of Alzheimer's disease (AD) highlights the urgent need to develop reliable biomarkers for early diagnosis and intervention. AD is characterized by the pathological accumulation of amyloid-beta plaques and tau neurofibrillary tangles. Phosphorylated tau (p-tau) proteins, particularly p-tau217 and p-tau231, have been identified as promising biomarker candidates to differentiate the disease progression from preclinical stages. This narrative review is devoted to a critical evaluation of the diagnostic accuracy, sensitivity, and specificity of p-tau217 and p-tau231 levels in the detection of AD, measured in plasma, serum, and cerebrospinal fluid, compared to established biomarkers. Additionally, the efficacy of these markers in distinguishing AD from other neurodegenerative disorders is examined. The significant advances offered by p-tau217 and p-tau231 in AD diagnostics are highlighted, demonstrating their unique utility in early detection and differential diagnosis. This comprehensive analysis not only confirms the excellent diagnostic capabilities of these markers, but also deepens the understanding of the molecular dynamics of AD, contributing to the broader scientific discourse on neurodegenerative diseases. This review is aimed to provide key information for researchers and clinicians across disciplines, filling interdisciplinary gaps and highlighting the role of p-tau proteins in revolutionizing AD research and clinical practice.

Multicenter Experience with Good Manufacturing Practice Production of [11C]PiB for Amyloid Positron Emission Tomography Imaging.

Alzheimer's disease (AD) is a neurodegenerative disorder with increasing global prevalence and accounts for over half of all dementia cases. Early diagnosis is paramount for not only the management of the disease, but also for the development of new AD treatments. The current golden standard for diagnosis is performed by positron emission tomography (PET) scans with the tracer [11C]Pittsburg Compound B ([11C]PiB), which targets amyloid beta protein (Aβ) that builds up as plaques in the brain of AD patients. The increasing demand for AD diagnostics is in turn expected to drive an increase in [11C]PiB-PET scans and the setup of new [11C]PiB production lines at PET centers globally. Here, we present the [11C]PiB production setups, experiences, and use from four Danish PET facilities and discuss the challenges and potential pitfalls of [11C]PiB production. We report on the [11C]PiB production performed with the 6-OH-BTA-0 precursor dissolved in either dry acetone or 2-butanone and by using either [11C]CO2 or [11C]CH4 as 11C- precursors on three different commercial synthesis modules: TracerLab FX C Pro, ScanSys, or TracerMaker. It was found that the [11C]CO2 method gives the highest radioactive yield (1.5 to 3.2 GBq vs. 0.8 ± 0.3 GBq), while the highest molar activity (98.0 ± 61.4 GBq/μmol vs. 21.2 to 95.6 GBq/μmol) was achieved using [11C]CH4. [11C]PiB production with [11C]CO2 on a TracerLab FX C Pro offered the most desirable results, with the highest yield of 3.17 ± 1.20 GBq and good molar activity of 95.6 ± 44.2 GBq/μmol. Moreover, all reported methods produced [11C]PiB in quantities suitable for clinical applications, thus providing a foundation for other PET facilities seeking to establish their own [11C]PiB production.

Impact of aging on copper isotopic composition in the murine brain.

Aging is the main risk factor for Alzheimer's disease (AD). AD is linked to alterations in metal homeostasis and changes in stable metal isotopic composition can occur, possibly allowing the latter to serve as relevant biomarkers for potential AD diagnosis. Copper stable isotopes are used to investigate changes in Cu homeostasis associated with various diseases. Prior work has shown that in AD mouse models, the accumulation of 63Cu in the brain is associated with the disease's progression. However, our understanding of how the normal aging process influences the brain's isotopic composition of copper remains limited. In order to determine the utility and predictive power of Cu isotopes in AD diagnostics, we aim-in this study-to develop a baseline trajectory of Cu isotopic composition in the normally aging mouse brain. We determined the copper concentration and isotopic composition in brains of 30 healthy mice (WT) ranging in age from 6 to 12 mo, and further incorporate prior data obtained for 3-mo-old healthy mice; this range approximately equates to 20-50yr in human equivalency. A significant 65Cu enrichment has been observed in the 12-mo-old mice compared to the youngest group, concomitant with an increase in Cu concentration with age. Meanwhile, literature data for brains of AD mice display an enrichment in 63Cu isotope compared to WT. It is acutely important that this baseline enrichment in 65Cu is fully constrained and normalized against if any coherent diagnostic observations regarding 63Cu enrichment as a biomarker for AD are to be developed.

Blood-based biomarkers in Alzheimer's disease- moving towards a new era of diagnostics.

Alzheimer's disease (AD), a primary cause of dementia globally, is traditionally diagnosed via cerebrospinal fluid (CSF) measures and positron emission tomography (PET). The invasiveness, cost, and limited accessibility of these methods have led to exploring blood-based biomarkers as a promising alternative for AD diagnosis and monitoring. Recent advancements in sensitive immunoassays have identified potential blood-based biomarkers, such as Aβ42/Aβ40 ratios and phosphorylated tau (p-tau) species. This paper briefly evaluates the clinical utility and reliability of these biomarkers across various AD stages, highlighting challenges like refining plasma Aβ42/Aβ40 assays and enhancing the precision of p-tau, particularly p-tau181, p-tau217, and p-tau231. The discussion also covers other plasma biomarkers like neurofilament light (NfL), glial fibrillary acidic protein (GFAP), and synaptic biomarkers, assessing their significance in AD diagnostics. The need for ongoing research and development of robust assays to match the performance of CSF and PET biomarkers is underscored. In summary, blood-based biomarkers are increasingly crucial in AD diagnosis, follow-up, prognostication, treatment response evaluation, and population screening, particularly in primary care settings. These developments are set to revolutionize AD diagnostics, offering earlier and more accessible detection and management options.

Alzheimer's Disease: Models and Molecular Mechanisms Informing Disease and Treatments.

Alzheimer's Disease (AD) is a complex neurodegenerative disease resulting in progressive loss of memory, language and motor abilities caused by cortical and hippocampal degeneration. This review captures the landscape of understanding of AD pathology, diagnostics, and current therapies. Two major mechanisms direct AD pathology: (1) accumulation of amyloid β (Aβ) plaque and (2) tau-derived neurofibrillary tangles (NFT). The most common variants in the Aβ pathway in APP, PSEN1, and PSEN2 are largely responsible for early-onset AD (EOAD), while MAPT, APOE, TREM2 and ABCA7 have a modifying effect on late-onset AD (LOAD). More recent studies implicate chaperone proteins and Aβ degrading proteins in AD. Several tests, such as cognitive function, brain imaging, and cerebral spinal fluid (CSF) and blood tests, are used for AD diagnosis. Additionally, several biomarkers seem to have a unique AD specific combination of expression and could potentially be used in improved, less invasive diagnostics. In addition to genetic perturbations, environmental influences, such as altered gut microbiome signatures, affect AD. Effective AD treatments have been challenging to develop. Currently, there are several FDA approved drugs (cholinesterase inhibitors, Aß-targeting antibodies and an NMDA antagonist) that could mitigate AD rate of decline and symptoms of distress.

Implementation of an ultra-sensitive microwell-based electrochemical sensor for the detection of Alzheimer’s disease

Alzheimer's Disease (AD) is one of the most common neurodegenerative disorders in elderly people. It is diagnosed by detecting amyloid beta (Aβ) protein in cerebrospinal fluid (CSF) obtained by lumbar puncture or through expensive positron emission tomography (PET) imaging. Although blood-based diagnosis of AD offers a less invasive and cost-effective alternative, the quantification of Aβ is technically challenging due to its low abundance in peripheral blood. To address this, we developed a compact yet highly sensitive microwell-based electrochemical sensor with a densely packed microelectrode array (20 by 20) for enhancing sensitivity. Employing microwells on the working and counter electrodes minimized the leakage current from the metallic conductors into the assay medium, refining the signal fidelity. We achieved a detection limit <10 fg/mL for Aβ by elevating the signal-to-noise ratio, thus capable of AD biomarker quantification. Moreover, the microwell structure maintained the performance irrespective of variations in bead number, indicative of the sensor's robustness. The sensor's efficacy was validated through the analysis of Aβ concentrations in plasma samples from 96 subjects, revealing a significant distinction between AD patients and healthy controls with an area under the receiver operating characteristic curve (AUC) of 0.85. Consequently, our novel microwell-based electrochemical biosensor represents a highly sensitive platform for detecting scant blood-based biomarkers, including Aβ, offering substantial potential for advancing AD diagnostics.

Comparison of Diagnostic Routines for Suspected Alzheimer’s Disease Patients in US–American and German Primary Care

Aim: Thorough diagnostics are a prerequisite for the optimal treatment of Alzheimer's disease (AD). Biomarker-based diagnostics are standard in academia, data on practitioners' diagnostic workups is scarce. Materials &amp; methods: Surveys in German and US healthcare providers (HCP) were conducted regarding diagnostics in presumed AD patients. A subsample of 153 German and 88 US professionals was analyzed in detail. Results: Fewer German physicians conduct AD diagnostics themselves compared with US colleagues (67% vs 99%; p &lt; 0.0001). German doctors more often order diagnostics at other institutions (65% vs 45%; p &lt; 0.005). No significant differences were found regarding the type of diagnostics ordered at other institutions. Conclusion: Diagnostic routines for suspected AD patients differ between German and US–American healthcare providers.

Functional Nanomaterials for the Diagnosis of Alzheimer's Disease: Recent Progress and Future Perspectives.

Alzheimer's disease (AD) is one of the main causes of dementia worldwide, whereby neuronal death or malfunction leads to cognitive impairment in the elderly population. AD is highly prevalent, with increased projections over the next few decades. Yet current diagnostic methods for AD occur only after the presentation of clinical symptoms. Evidence in the literature points to potential mechanisms of AD induction beginning before clinical symptoms start to present, such as the formation of amyloid beta (Aβ) extracellular plaques and neurofibrillary tangles (NFTs). Biomarkers of AD, including Aβ 40, Aβ 42, and tau protein, amongst others, show promise for early AD diagnosis. Additional progress is made in the application of biosensing modalities to measure and detect significant changes in these AD biomarkers within patient samples, such as cerebral spinal fluid (CSF) and blood, serum, or plasma. Herein, a comprehensive review of the emerging nano-biomaterial approaches to develop biosensors for AD biomarkers' detection is provided. Advances, challenges, and potential of electrochemical, optical, and colorimetric biosensors, focusing on nanoparticle-based (metallic, magnetic, quantum dots) and nanostructure-based biomaterials are discussed. Finally, the criteria for incorporating these emerging nano-biomaterials in clinical settings are presented and assessed, as they hold great potential for enhancing early-onset AD diagnostics.