Early Diagnosis Of Alzheimer's Disease Research Articles

Amplified fluorogenic immunoassay for early diagnosis and monitoring of Alzheimer’s disease from tear fluid

Accurate diagnosis of Alzheimer’s disease (AD) in its earliest stage can prevent the disease and delay the symptoms. Therefore, more sensitive, non-invasive, and simple screening tools are required for the early diagnosis and monitoring of AD. Here, we design a self-assembled nanoparticle-mediated amplified fluorogenic immunoassay (SNAFIA) consisting of magnetic and fluorophore-loaded polymeric nanoparticles. Using a discovery cohort of 21 subjects, proteomic analysis identifies adenylyl cyclase-associated protein 1 (CAP1) as a potential tear biomarker. The SNAFIA demonstrates a low detection limit (236 aM), good reliability (R2 = 0.991), and a wide analytical range (0.320–1000 fM) for CAP1 in tear fluid. Crucially, in the verification phase with 39 subjects, SNAFIA discriminates AD patients from healthy controls with 90% sensitivity and 100% specificity in under an hour. Utilizing tear fluid as a liquid biopsy, SNAFIA could potentially aid in long-term care planning, improve clinical trial efficiency, and accelerate therapeutic development for AD.

Health System Change for Alzheimer's Disease-Modifying Therapies in Canada: Beginning the Discussion.

Alzheimer's disease (AD) is a neurodegenerative disorder that accounts for 60%-70% of patients with dementia, and it is estimated that over one million Canadians will be living with dementia by 2030. Disease-modifying therapies (DMTs) targeting the underlying pathophysiology of AD are currently in development. Several models have demonstrated that the potential arrival of Alzheimer's DMTs will most likely overwhelm the already-constrained Canadian healthcare system. Canada does not have a strategy to address the extensive requirements of using DMTs, including providing an early diagnosis of AD, confirming DMT eligibility via amyloid biomarkers, and conducting ongoing treatment monitoring. Thus, a multidisciplinary group of experts involved in AD care in Canada gathered to review (1) the current barriers to diagnosis and management of AD; (2) how existing clinic models, including those used in multiple sclerosis (MS), could be applied to address key barriers in AD; and (3) how to design and implement optimal care pathways in the future. The actions outlined in this review will help clinicians and healthcare systems improve readiness to integrate the use of disease-modifying therapies in Alzheimer's disease, if such therapies are approved in Canada.

An Electrochemical Immunosensor with PEDOT: PSS/MWCNTs-COOH Nanocomposites as a Modified Working Electrode Material for Detecting Tau-441

The progression of Alzheimer’s disease (AD) is positively correlated with the phosphorylation damage of Tau-441 protein, which is the marker with the most potential for the early detection of AD. The low content of Tau-441 in human serum is a major difficulty for the realization of content detection. Herein, we prepared an electrochemical immunosensor modified with Poly(3,4-ethylene-dioxythiophene)-poly (styrene sulfonate) (PEDOT: PSS)/Carboxylated multi-walled carbon nanotube (MWCNTs-COOH) nanocomposites based on electrochemical immunoassay technology for the low-concentration detection of Tau-441. The immunosensor based on the nanocomposite can take advantage of the characteristics of conductive polymers to achieve electrical signal amplification and use MWCNTs-COOH to increase the contact area of the active site and bond with the Tau-441 antibodies on the electrode. The physicochemical and electrical properties of PEDOT: PSS/MWCNTs-COOH were studied by in situ characterization techniques and electrochemical characterization methods, indicating that the immunosensor has high selectivity and sensitivity to the Tau-441 immune reaction. Under optimized optimal conditions, the electrochemical immunosensor detected a range of concentrations of Tau-441 to obtain a low detection of limit (0.0074 ng mL−1) and demonstrated good detection performance through actual human serum sample testing experiments. Therefore, the study provides an effective reference value for the early diagnosis of AD.

Referral Patterns From Primary Care Clinicians to Specialists Among People With Cognitive Impairment Disorders, including Alzheimer’s Disease

AbstractBackgroundA timely and accurate diagnosis of Alzheimer’s disease (AD) can optimize treatment outcomes; however, few people are referred to a specialist for reasons that are not well understood. The objective of this study was to evaluate referral to a specialist by a primary care clinician (PCC) or other healthcare provider (HCP) among people diagnosed with cognitive impairment disorders, including AD (CIDiAD).MethodThis retrospective cohort study analyzed claims data from the Optum de‐identified Normative Health Information database between 1/1/2016‐31/12/2021 (study period). Adults were required to have ≥2 ICD‐9/10‐CM diagnosis codes for AD from any provider during the study period. The index event was defined as the first CIDiAD diagnosis (Table 1) from a PCC or other HCP before or on the date of their diagnosis during the identification period (1/1/2017‐31/12/2020) (Table 2). Continuous medical enrollment was required in the pre‐ and postindex periods. No diagnoses of CIDiAD in the pre‐index period were allowed. People with a post‐index diagnosis of AD from a specialist comprised the referral cohort. Time to specialist referral was assessed, and characteristics of people who were or were not referred were summarized.ResultA total of 48,525 people with a CIDiAD diagnosis from a PCC or other HCP were included; of these, 13,074 (26.9%) were referred to a specialist. The overall median (IQR) time to first specialist visit was 10.0 (3.2‐20.7) months. Time to first specialist visit was longer for referrals from PCCs compared with those from other HCPs (10.8 [3.8‐21.6] vs 9.2 [2.8‐19.9] months, respectively; P<0.0001). No notable differences in sex, age, race and ethnicity, or geographic region were observed between people who were referred and those who were not (Table 3).ConclusionThe majority of people with CIDiAD were not referred to a specialist. Among people who were referred to a specialist, time to first visit was long. Approval of disease‐modifying therapies may accelerate referral to a specialist and shorten referral times as early diagnosis of AD becomes more important. Further analyses will inform geographic differences in specialist referral patterns.

Developing Topics.

This study aims to detect direct cost savings with early detection of Alzheimer's disease (AD) and related dementias. We hypothesize that earlier diagnosis of AD can reduce direct healthcare costs and unnecessary utilization. A retrospective case-control study is conducted using Optum Market Clarity Claims/EHR linked dataset on Medicare Advantage population from Jan 1 2011 to Dec 31 2020. Based on whether the patient was diagnosed with prior Mild Cognitive Impairment (MCI) before progressing to an AD diagnosis per ICD-9/10 cm diagnosis codes, AD patients are categorized into early vs. late diagnosis (for those who were identified in the MCI stage vs not). Both groups are compared on annual all-cause healthcare costs and frequency of encounters longitudinally spanning across 6 years pre-index period and 3 years post-index with AD diagnosis date as the index date. To understand the impact of early diagnosis, we estimated total cost, medical service cost and drug cost post-index within the first 3 years by risk adjustment on baseline demographics, comorbidities, procedures, lab tests, and visits prior to AD diagnosis using propensity score weighting. Early-diagnosed patients (N = 421) have a lower utilization rate of inpatient skilled nursing facility, nursing home and hospitalization than late-diagnosed patients (N = 2817) after being diagnosed with AD. We also observed average medical service cost savings in all 3 years post-index, ranging from $320 to $2556 per patient per year. Per cost allocation analysis, medical service cost savings are observed in inpatient, emergency room, inpatient SNF, urgent care, nursing home/assisted living, and observation unit. Risk adjusted cost savings with statistical significance are observed in year 3 post-index. The mean cost difference between late and early diagnoses: total cost ($15472.11), medical service ($15013.7) and medication cost ($1601.61). The study highlights opportunities for earlier diagnosis and suggests long-term healthcare savings for AD patients who have been diagnosed at MCI stage compared to patients who were directly diagnosed with AD. Further research is needed to drill down into the driver of cost savings and whether it is attributable to disease management of MCI or other complications.

Age‐related relative comorbidity burden of mild cognitive impairment: A US database study

AbstractBackgroundAs novel therapies emerge, timely detection and diagnosis of early Alzheimer’s disease (AD) become increasingly relevant. While primary care would be the most suitable setting for early detection, diagnosis rates of mild cognitive impairment (MCI) are low and systematic screening of asymptomatic individuals is currently not recommended. We investigated whether a predictive algorithm based on established AD risk factors can help to identify individuals at elevated risk of having undiagnosed MCI, in particular in younger age cohorts.MethodOur retrospective study used MarketScan insurance claims data to identify individuals aged ≥50 years with MCI (but without dementia) and assemble controls without MCI/dementia diagnoses matched on age, sex, and geographic region. Medical conditions previously identified as AD risk factors in the literature were considered MCI risk factors if their prevalence in MCI individuals was statistically significantly higher than in controls. Logistic regression and extreme gradient boosting (XGBoost; a machine learning method) were applied to discriminate MCI from control individuals based on area under the curve (AUC). Analyses were conducted for the overall cohort and by age group (50‐64, 65‐79, and ≥80 years).ResultTwenty‐five medical conditions had statistically higher frequency in the MCI cohort (n = 5,185) compared to controls (n = 15,555). The top MCI risk factors (with odds ratio for the 50‐64, 65‐79, and ≥80 years subgroups, respectively) were: depression (4.4, 3.1, 1.9); stroke/transient ischemic attack, TIA (6.4, 3.0, 2.1); obstructive sleep apnea, OSA (3.6, 2.5, 1.7); hyperlipidemia (1.7, 1.5, 1.4); and hypertension (1.6, 1.5, 1.5) (all p‐values <0.0001). The logistic regression models for identifying MCI individuals reached AUCs of 0.75, 0.69, and 0.66, for the 3 age groups, respectively. The XGBoost approach reached AUC = 0.94 (training set) and AUC = 0.78 (test set). Each approach was most accurate when discriminating MCI for the youngest age group.ConclusionOur two modeling approaches were able to predict MCI with acceptable accuracy. The predictions were more accurate in the 50‐64 age group than in the older groups, suggesting that the use of prediction models for triage in primary care will play a greater role in younger age groups.

Routes to standardisation for tau protein measurement in CSF

AbstractBackgroundCSF biomarkers are the target of several commercially available in vitro diagnostic (IVD) kits allowing an early diagnosis of Alzheimer’s disease (AD) and eventually the monitoring of treatment effects in clinical trials. Tau hyper‐phosphorylation is one of the main hallmarks of AD and its measurement provides higher accuracy to stratify patients at an early‐stage.Due to the rapid advancements in proteomics, standardisation of measurements can be achieved through the development of reference measurement procedures (RMPs) and certified reference materials (CRMs).MethodSI‐traceable protein and peptide primary calibrators were developed for t‐tau and p‐tau: mass fraction was determined by amino acid analysis and purity was assessed by LC‐MS at high resolution. CSF pools at different tau concentration were used for the development and validation of LC‐IDMS candidate RMP and their commutability was assessed.ResultAn LC‐IDMS candidate reference method was develop and validated in CSF for t‐tau using a SI‐traceable candidate protein primary calibrator. Three different CSF pools (low, medium, high) were successfully quantified with a relative expanded uncertainty below 10%. The commutability of 13 matrix‐based CRMs was assessed involving 8 immunoassays from 5 IVD providers by measuring 40 single CSF donations.According to the AT(N) classification, high levels of p‐tau(181) are highly correlated to tau pathology and commercially available immunoassays are able to target this specific epitope. We conducted a feasibility study on the development of a candidate reference method for p‐tau(181) in CSF relying on a SI‐traceable peptide primary calibrator and an antibody‐free sample preparation workflow.New assays targeting p‐tau(217) in CSF and plasma were also developed, making it necessary the organisation of standardisation initiatives. We sourced and characterised multiple phosphopeptide primary calibrators as well as a GSK3beta‐phosphorylated protein primary calibrator for the development of an LC‐MS RMP including an immunoprecipitation step in the sample preparation workflow.Conclusiontau is considered as a priority for the IFCC WG on NDD and the development of SI traceable RMPs and CRMs is essential to underpin a worldwide standardisation of results. This will help IVD providers to address the traceability requirements of the IVDR thus facilitating regulatory approvals of the kits.

Automated AI-based volume measurement of the medial temporal lobe in early diagnosis of Alzheimer's disease

Automated AI-based volume measurement of the medial temporal lobe in early diagnosis of Alzheimer's disease

Plasma biomarker and neuroimaging changes in a Chinese community aging cohort: The Greater‐Bay‐Area Healthy Aging Brain Study (GHABS)

AbstractBackgroundTo investigate the alternations of β‐amyloid (Aβ), tau, neurodegeneration, and cognition in different stages of Alzheimer’s disease (AD) in the Chinese community aging population is important for early diagnosis and intervention of AD in China.MethodWe recruited 422 Greater‐Bay‐Area Healthy Aging Brain Study (GHABS) participants from the community and completed cognitive assessments, including 100 cognitively unimpaired (CU), 189 subjective cognitive decline (SCD), 82 mild cognitive impairment (MCI) and 51 AD dementia individuals. Among them, 343 individuals had plasma Aβ42/Aβ40, p‐Tau181, neurofilament light (NfL), and glial fibrillary acidic protein (GFAP) data measured by the Simoa platform. Furthermore, 159 and 96 individuals had concurrent MRI and 18F‐D3‐FSP Aβ PET scans, respectively. Generalized linear models were used to compare plasma biomarkers, AD typical cortical FSP Aβ standard uptake value ratio (SUVR), residual hippocampal volume (rHCV), and cognitive scores between different clinical and A/T (A: plasma Aβ42/Aβ40, T: plasma p‐Tau181) groups, controlling for age and sex. Multiple comparison corrections were conducted (Benjamini‐Hochberg, p<0.05). The association of plasma Aβ42/Aβ40 and p‐Tau181 with plasma NfL, GFAP, Aβ PET, rHCV, MoCa, and SCD‐Q were also investigated, including age, sex, and diagnosis as the covariates.ResultSCD individuals had higher plasma p‐Tau181 (standardized β (βstd) = 0.251[95% confidence interval (ci): 0.031, 0.472], p = 0.038) and GFAP (βstd = 0.259[95% ci: 0.035, 0.483], p = 0.023) than the CU individuals (Figure 1). Compared to the A‐/T‐ group, A+/T+ individuals had more (p<0.001) abnormal changes in all the plasma biomarkers, Aβ PET, rHCV, and MoCa, and A‐/T+ individuals had more hippocampal atrophy (p = 0.012) and marginal higher plasma NfL (p = 0.074) (Figure 2). Continuously, lower plasma Aβ42/Aβ40 was only related to higher plasma GFAP (p<0.001), whereas plasma p‐Tau181showed significant association with plasma NfL (p<0.001), plasma GFAP (p = 0.026), Aβ PET (p = 0.028), and MoCA (p = 0.023) (Figure 3).ConclusionThis study summarizes the characteristics of plasma biomarkers and brain imaging of AD and provides novel insights into the alternations of Aβ, tau, neurodegeneration, neuroinflammation, and cognition in different stages of AD in Southern China’s aging population. These findings are critical for using plasma biomarkers and neuroimaging to detect early AD changes in China.

Altered cortical and subcortical morphometric features and asymmetries in the subjective cognitive decline and mild cognitive impairment.

This study aimed to evaluate morphological changes in cortical and subcortical regions and their asymmetrical differences in individuals with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). These morphological changes may provide valuable insights into the early diagnosis and treatment of Alzheimer's disease (AD). We conducted structural MRI scans on a cohort comprising 62 SCD patients, 97 MCI patients, and 70 age-, sex-, and years of education-matched healthy controls (HC). Using Freesurfer, we quantified surface area, thickness, the local gyrification index (LGI) of cortical regions, and the volume of subcortical nuclei. Asymmetry measures were also calculated. Additionally, we explored the correlation between morphological changes and clinical variables related to cognitive decline. Compared to HC, patients with MCI exhibited predominantly left-sided surface morphological changes in various brain regions, including the transverse temporal gyrus, superior temporal gyrus, insula, and pars opercularis. SCD patients showed relatively minor surface morphological changes, primarily in the insula and pars triangularis. Furthermore, MCI patients demonstrated reduced volumes in the anterior-superior region of the right hypothalamus, the fimbria of the bilateral hippocampus, and the anterior region of the left thalamus. These observed morphological changes were significantly associated with clinical ratings of cognitive decline. The findings of this study suggest that cortical and subcortical morphometric changes may contribute to cognitive impairment in MCI, while compensatory mechanisms may be at play in SCD to preserve cognitive function. These insights have the potential to aid in the early diagnosis and treatment of AD.

Tau pathway‐based gene analysis in the Alzheimer’s disease continuum identifies CLU and FYN associated with tau deposition on PET in a Korean Cohort

AbstractBackgroundAbnormal tau protein aggregation and spreading in the brain is closely linked to cognitive impairment and neurodegeneration in Alzheimer’s disease (AD). The role of genetic variation in brain tau aggregation has not been fully elucidated. Here, we performed pathway‐based candidate gene association analysis to identify novel genes associated with brain tau deposition on PET in a well‐characterized Korean AD continuum Cohort.MethodWe analyzed data for 145 older adults (69 cognitively normal (CN), 38 mild cognitive impairment (MCI), and 38 AD) from the Korean Brain Aging Study for the Early Diagnosis and Prediction of AD (KBASE). We selected 15 candidate genes related to both tau pathways and AD, curated from existing pathway databases, literature mining, and genome‐wide association study findings. We performed gene‐based association analysis of tau deposition measured by 18F‐AV‐1451 PET scans using PLINK. In addition, we performed whole brain voxel‐wise association analysis of identified single nucleotide polymorphisms (SNPs) to investigate their topographic distributions of differences in tau deposition across the whole brain using SPM12. We examined the interaction of the identified SNPs with APOE ε4 carrier status.ResultGene‐based analysis identified CLU and FYN as significantly associated with tau deposition after adjusting for multiple testing. The most significant signals originated from rs149413552 in CLU and rs57650567 in FYN. A voxel‐wise analysis for the two SNPs showed that individuals carrying at least one minor allele demonstrated greater tau throughout the temporal, parietal, and frontal lobes than individuals having no minor alleles (Fig. 1). The two SNPs were associated with greater cortical atrophy of AD‐signature regions and worse global cognition. rs149413552 in CLU was also associated with longitudinal decline in global cognition and immediate verbal memory over two years. APOE ε4 and the two SNPs appeared to exert additive effects on tau levels (Fig. 2). An interaction between rs57650567 in FYN and APOE ε4 carrier status was observed (Fig. 2)Conclusion CLU and FYN may play a specific role in tau pathophysiology. These risk genes may prove useful for biomarker and therapeutic development. Larger and multiethnic studies are needed to confirm these findings and to determine generalizability across populations.

MiRNAs peripheral biomarkers for early diagnosis of AD in Latinamerican population

AbstractBackgroundTimely diagnosis of Alzheimer’s disease (AD) is a critical first step in clinical care treatment. However, the availability of diagnostic tools for these conditions is either unavailable or unaffordable, especially in Latin American and the Caribbean (LAC) countries. In this scenario, microRNAs (miRNA) have recently emerged as promising cost‐effective and noninvasive biomarkers since they can be readily detected in different biofluids such as plasma bound to protein or inside exosomes. Our aim is to identify new blood biomarkers to calculate the risk to develop AD in the early stages using machine learning based on exosomal miRNAs detection and different cognitive domains.MethodmiRNAs were extracted from plasma circulating exosomes samples of Subjective Cognitive Complaint subjects belonging to the GERO cohort in two different times (T1 = baseline and T2 = 18 months, n = 50). miRNAs were sequencing to identify the expression levels. Dysregulated miRNAs were analyzed and correlated with neuropsychological evaluations to create machine learning algorithms. miRNAs obtained from algorithms were validated in a new population of subjects at both stages (T1 and T2, n = 50), using qRT‐PCR and correlation analysis with neuropsychological tests.ResultFor algorithm development, we classified subjects into stationary and progressive impairment. Algorithms were trained using the sequenced miRNAs, neuropsychological tests and neuroimaging. As possible predictors of progression of cognitive impairment, the best performing algorithm was Random forest using only miRNAs data. In a new group of subjects, three miRNAs selected by the algorithm and four miRNAs obtained from the literature were validated and the ROC curves were constructed. The combination of these miRNAs (algorithm and literature miRNAs), were the best predictor of the progression of cognitive impairment.ConclusionThis study could be used in the initial phases of the diagnostic process of AD because it is minimally invasive, low cost and accessible to a large number of patients in Latin American and the Caribbean (LAC) countries.

AMSF: attention-based multi-view slice fusion for early diagnosis of Alzheimer's disease.

Alzheimer's disease (AD) is an irreversible neurodegenerative disease with a high prevalence in the elderly population over 65 years of age. Intervention in the early stages of AD is of great significance to alleviate the symptoms. Recent advances in deep learning have shown extreme advantages in computer-aided diagnosis of AD. However, most studies only focus on extracting features from slices in specific directions or whole brain images, ignoring the complementarity between features from different angles. To overcome the above problem, attention-based multi-view slice fusion (AMSF) is proposed for accurate early diagnosis of AD. It adopts the fusion of three-dimensional (3D) global features with multi-view 2D slice features by using an attention mechanism to guide the fusion of slice features for each view, to generate a comprehensive representation of the MRI images for classification. The experiments on the public dataset demonstrate that AMSF achieves 94.3% accuracy with 1.6-7.1% higher than other previous promising methods. It indicates that the better solution for AD early diagnosis depends not only on the large scale of the dataset but also on the organic combination of feature construction strategy and deep neural networks.

Neuropsychological features evaluation of data related to Alzheimer’s disease progression using feature selection

BACKGROUND: Alzheimer’s Disease (AD) is normally assessed in clinical settings using neuropsychological tests and medical procedures such as neuroimaging techniques: Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) among others. The latter procedures are expensive and unavailable in most nations, so early diagnosis of AD does not occur, which heavily increases the subsequent treatment costs for the patients. AIMS: This research aims to evaluate cognitive features related to dementia progression based on neuropsychological tests’ data that are related to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) We utilise data related to two neuropsychological tests including the Clinical Dementia Rating Scale Sum of Boxes (CDR-SOB), and Mini-Mental State Examination (MMSE), to assess advancement of the AD. METHODS: To achieve the aim, we develop a data process called Neuropsychological Feature Assessment via Feature Selection (NFAFS) to identify impactful features using Information Gain (IG) and Pearson Correlation to assess class-feature and feature-feature correlations Later we will model a minimal subset of neuropsychological features using machine learning techniques to derive classification models. RESULTS AND IMPLICATIONS: Results obtained show key cognitive features of the MMSE are Time Orientation, Recall and Complex Attention, since they correlate with the progression class being ranked high in results of the feature selection techniques. For the CDR-SOB features, and aside from the memory feature it was difficult to identify other specific features that are signs of the dementia progression Clinicians can use specific features in a digital knowledge base to pay more attention to specific cognitive deficits related to Recall, Orientation and Complex Attention during dementia clinical evaluations in order to seek possible signs of the disease progression early.

Recent Research Progress in Fluorescent Probes for Detection of Amyloid-β In Vivo.

Alzheimer's disease (AD) is a neurodegenerative disease. Due to its complex pathological mechanism, its etiology is not yet clear. As one of the main pathological markers of AD, amyloid-β (Aβ) plays an important role in the development of AD. The deposition of Aβ is not only related to the degeneration of neurons, but also can activate a series of pathological events, including the activation of astrocytes and microglia, the breakdown of the blood-brain barrier, and the change in microcirculation, which is the main cause of brain lesions and death in AD patients. Therefore, the development of efficient and reliable Aβ-specific probes is crucial for the early diagnosis and treatment of AD. This paper focuses on reviewing the application of small-molecule fluorescent probes in Aβ imaging in vivo in recent years. These probes efficiently map the presence of Aβ in vivo, providing a pathway for the early diagnosis of AD and providing enlightenment for the design of Aβ-specific probes in the future.

Trajectories of CSF and plasma biomarkers across Alzheimer's disease continuum: Disease staging by NF-L, p-tau181, and GFAP

CSF-to-plasma transition will open new avenues for molecular phenotyping of Alzheimer's disease (AD). Here we evaluated a panel of AD biomarkers in matched CSF and plasma samples across the AD continuum, from preclinical AD to dementia. The aims were to: 1) compare diagnostic performance of the two biofluids, 2) evaluate trajectories of the biomarkers along AD progression. We analyzed CSF and plasma Aβ42/40, p-tau181, p-tau231, t-tau, NF-L, GFAP, UCHL-1 and CSF SNAP-25 in a cohort (n = 173) of preclinical AD, MCI-AD, AD dementia, frontotemporal dementia patients, and controls. We found a significant correlation between CSF and plasma levels of Aβ42/40, p-tau181, p-tau231, NF-L, and GFAP, while no CSF-plasma correlation was observed for t-tau and UCHL-1. Next to the core CSF biomarkers (Aβ42/40, p-tau181, t-tau), those providing the best discrimination between controls and preclinical AD were CSF p-tau231 and SNAP-25 and plasma Aβ42/40, p-tau231, and GFAP. Among plasma biomarkers, we found Aβ42/Aβ40, GFAP, and p-tau231 to show the largest rate of change at the CSF biomarker-defined cut-offs for amyloidosis and tauopathy. Finally, we identified GFAP, NF-L, and p-tau181 as the biomarkers most significantly associated with disease progression in both CSF and plasma. We suggest that a well-standardized and validated panel of selected plasma markers can facilitate early AD diagnosis, even at the asymptomatic disease stage. We propose that both CSF and plasma measurement of NF-L, p-tau181, and GFAP may play a significant role in disease staging and monitoring.

Early detection of Alzheimer's disease by measuring amyloid beta-42 concentration in human serum based on liquid crystals

Amyloid beta (Aβ) protein is one of main biomarkers for the diagnosis of Alzheimer's disease (AD). The detection of Aβ in the blood leads to the identification of people prone to AD years before the appearance of the disease. Since the concentration of Aβ in the blood is very low, it is still challenging to detect Aβ using a sensitive, simple and rapid sensor. Herein, we fabricate a new type of liquid crystal-based biosensor to detect Aβ. This sensor is based on the interaction between the negative DNA aptamer and the cationic surfactant called Cetyltrimethylammonium bromide (CTAB). The response time of this setup is about a few minutes and has a good specificity for Aβ in Human Serum (HS). The specific aptamer provided high sensitivity for the sensor with a limit of detection of Aβ as low as 100 pg/ml. The fabricated sensor is inexpensive, easy manufactured and rapid. The proposed system can be a promising strategy for early diagnosis of AD and prevention of side effects of this disease. © 2017 Elsevier Inc. All rights reserved.

Multimodal fusion diagnosis of Alzheimer’s disease based on FDG-PET generation

Alzheimer’s disease (AD) is a central nervous system disease that mainly appears in the aged. Early diagnosis of AD is valuable in delaying the progression of the disease. With the development of medical imaging technology, various medical images such as structural Magnetic Resonance Imaging (sMRI) and Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) can obtain the structural and functional lesions of the brain to assist in diagnosing diseases. However, FDG-PET is usually incomplete due to radiation and high costs. Most existing methods exclude missing modal subjects, which is remarkably one-sided. Meanwhile, how to extract the features of different levels of multimodal fusion is still a challenge. To solve these issues, we propose a Consistent Manifold Projection Generative Adversarial Network (CMPGAN) for FDG-PET generation and a Multilevel Multimodal Fusion Diagnosis Network (MMFDN) for diagnosing AD. First, we propose a CMPGAN model to project the distribution onto low-dimensional manifolds through consistent manifold projection, and present a distribution distance metric to optimize the model. Our proposed model can avoid problems of mode collapse and gradient disappearance. Then, we construct a multiscale feature-level feature extraction network based on our proposed radial medley unit and a voxel-level feature extraction network based on a harmonic voxel fusion matrix. The fusion of the two parts obtains the final diagnosis result. Experimental results indicate that our proposed method performs better than state-of-the-art methods in FDG-PET generation and AD diagnosis. Our approach also has the significance of guiding clinicians in diagnosing diseases.

Research progress on biomarkers and detection methods for Alzheimer's disease diagnosis in vitro

Alzheimer's disease (AD) is a neurodegenerative disease with insidious onset, posing a serious threat to human physical and mental health. The cognitive impairments caused by AD are generally diffuse and overlap symptomatically with other neurodegenerative diseases. Moreover, the symptoms of AD are often covert, leading to missed opportunities for optimal treatment after diagnosis. Therefore, early diagnosis of AD is crucial. In vitro diagnostic biomarkers not only contribute to the early clinical diagnosis of AD but also aid in further understanding the disease's pathogenesis, predicting disease progression, and observing the effects of novel candidate therapeutic drugs in clinical trials. Currently, although there are numerous biomarkers associated with AD diagnosis, the complex nature of AD pathogenesis, limitations of individual biomarkers, and constraints of clinical detection methods have hindered the development of efficient, cost-effective, and convenient diagnostic methods and standards. This article provides an overview of the research progress on in vitro diagnostic biomarkers and detection methods related to AD in recent years.

Amyloid-β mediates intestinal dysfunction and enteric neurons loss in Alzheimer's disease transgenic mouse.

Alzheimer's disease (AD) is traditionally considered as a brain disorder featured by amyloid-β (Aβ) deposition. The current study on whether pathological changes of AD extend to the enteric nervous system (ENS) is still in its infancy. In this study, we found enteric Aβ deposition, intestinal dysfunction, and colonic inflammation in the young APP/PS1 mice. Moreover, these mice exhibited cholinergic and nitrergic signaling pathways damages and enteric neuronal loss. Our data show that Aβ42 treatment remarkably affected the gene expression of cultured myenteric neurons and the spontaneous contraction of intestinal smooth muscles. The intra-colon administration of Aβ42 induced ENS dysfunction, brain gliosis, and β-amyloidosis-like changes in the wild-type mice. Our results suggest that ENS mirrors the neuropathology observed in AD brains, and intestinal pathological changes may represent the prodromal events, which contribute to brain pathology in AD. In summary, our findings provide new opportunities for AD early diagnosis and prevention.