Biomarkers for the early diagnosis of Alzheimer’s disease: The challenge of XXI century

AD is the most common form of dementia among the aging population. The neuropathological alterations of AD are represented by the neurofibrillary tangles and extracellular amyloid plaques formation. These two hallmarks are routinely used as biomarkers for AD diagnosis and can allow the identification of the pathology in a late phase. The urgent need to develop probes for PET analysis that can be used in an early diagnosis of this disorder opened a new scenario in which new biomarkers involved in the first step of AD can be easily detected. Recently, an increasing number of studies indicated as new biomarkers P-gp, TLR4, MIR and free serum copper that are involved in the onset of AD. It has been extensively reported that a P-gp dysfunction in brain can be considered one of the causes of the ADaccumulation in brain parenchyma and that the up-regulation of inflammatory gene expression and inflammatory signaling due to MIR and TLR4 modulated the development and the progression of AD.

Novel Computer-Aided Diagnosis System for the Early Detection of Alzheimer’s Disease

Aging is a natural process that leads to debility,disease,and dependency.Alzheimer’s disease(AD)causes degeneration of the brain cells leading to cognitive decline and memory loss,as well as dependence on others to fulfill basic daily needs.AD is the major cause of dementia.Computer-aided diagnosis(CADx)tools aid medical practitioners in accurately identifying diseases such as AD in patients.This study aimed to develop a CADx tool for the early detection of AD using the Intelligent Water Drop(IWD)algorithm and the Random Forest(RF)classifier.The IWD algorithm an efficient feature selection method,was used to identify the most deterministic features of AD in the dataset.RF is an ensemble method that leverages multiple weak learners to classify a patient’s disease as either demented(DN)or cognitively normal(CN).The proposed tool also classifies patients as mild cognitive impairment(MCI)or CN.The dataset on which the performance of the proposed CADx was evaluated was sourced from the Alzheimer’s Disease Neuroimaging Initiative(ADNI).The RF ensemble method achieves 100%accuracy in identifying DN patients from CN patients.The classification accuracy for classifying patients as MCI or CN is 92%.This study emphasizes the significance of pre-processing prior to classification to improve the classification results of the proposed CADx tool.

Use of curcumin in diagnosis,prevention,and treatment of Alzheimer＇s disease

This review summarizes and describes the use of curcumin in diagnosis,prevention,and treatment of Alzheimer＇s disease.For diagnosis of Alzheimer＇s disease,amyloid-β and highly phosphorylated tau protein are the major biomarkers.Curcumin was developed as an early diagnostic probe based on its natural fluorescence and high binding affinity to amyloid-β.Because of its multi-target effects,curcumin has protective and preventive effects on many chronic diseases such as cerebrovascular disease,hypertension,and hyperlipidemia.For prevention and treatment of Alzheimer＇s disease,curcumin has been shown to effectively maintain the normal structure and function of cerebral vessels,mitochondria,and synapses,reduce risk factors for a variety of chronic diseases,and decrease the risk of Alzheimer＇s disease.The effect of curcumin on Alzheimer＇s disease involves multiple signaling pathways：anti-amyloid and metal iron chelating properties,antioxidation and anti-inflammatory activities.Indeed,there is a scientific basis for the rational application of curcumin in prevention and treatment of Alzheimer＇s disease.

Additive nanomanufacturing of lab-on-a-chip fluorescent peptide nanoparticle arrays for Alzheimer＇s disease diagnosis

This paper proposes an additive nanomanufacturing approach to fabricate a personalized lab-on-a-chip fluorescent peptide nanoparticles （f-PNPs） array for simultaneous multi-biomarker detection that can be used in Alzheimer＇s disease （AD） diagnosis. We will discuss optimization techniques for the additive nanomanufacturing process in terms of reliability, yield and manufacturing efficiency. One contribution of this paper lies in utilization of additive nanomanufacturing techniques to fabricate a patient-specific customize-designed lab-on-a-chip device for personalized AD diagnosis, which remains a major challenge for biomedical engineering. Through the integrated bio-design and bio-manufacturing process, doctor＇s check- up and computer-aided customized design are integrated into the lab-on-a-chip array for patient-specific AD diagnosis. In addition, f-PNPs with targeting moieties for personalized AD biomarkers will be self-assembled onto the customized lab-on-a- chip through the additive nanomanufacturing process, which has not been done before. Another contribution of this research is the personalized lab-on-a-chip f-PNPs array for AD diagnosis utilizing limited human blood. Blood-based AD assessment has been described as ＂the holy grail＂ of early AD detection. This research created the computer-aided design, fabrication through additive nanomanufacturing, and validation of the f-PNPs array for AD diagnosis. This is a highly interdisciplinary research contributing to nanotechnology, biomaterials, and biomedical engineering for neurodegenerative disease. The conceptual work is preliminary with intent to introduce novel techniques to the application. Large-scale manufacturing based on the proposed framework requires extensive validation and optimization.

Presymptomatic Diagnosis and Gene Therapy for Alzheimer’s Disease: Genomic, Therapeutic, and Ethical Aspects—A Systematic Review

Over the past three decades, genomic and epigenetic sciences have identified more than 70 genes involved in the molecular pathophysiology of Alzheimer’s disease (AD). DNA methylation, abnormal histone and chromatin regulation and the action of various miRNAs induce AD. The identification of mutated genes has paved the way for the development of diagnostic kits and the initiation of gene therapy trials. However, despite major advances in neuroscience research, there is yet no suitable treatment for AD. Therefore, the early diagnosis of this neurodegenerative disease raises several ethical questions, including the balance between the principle of non-maleficence and the principle of beneficence. The aims of this research were to present the genomic and ethical aspects of AD, and to highlight the ethical principles involved in its presymptomatic diagnosis and therapy. A systematic review of the literature in PubMed, Google Scholar and Science Direct was carried out to outline the genomic aspects and ethical principles relating not only to the presymptomatic diagnosis of AD, but also to its gene therapy. A total of 16 publications were selected. AD is a multifactorial disease that can be genetically classified into Sporadic Alzheimer’s Disease and Familial Alzheimer’s Disease based on family history. Gene therapy targeting specific disease-causing genes is a promising therapeutic strategy. Advancements in artificial intelligence applications may enable the prediction of AD onset several years in advance. While early diagnosis of AD may empower patients with full decision competence for early decision-making, it also carries implications for the patient’s family members, who are at risk of developing the disease, potentially becoming a source of confusion or anxiety. AD has a significant impact on the life of individuals at risk and their families. Given the absence of disease modifying therapy, genetic screening and early diagnosis for this condition raise ethical issues that must be carefully considered in the context of fundamental bioethical principles, including autonomy, beneficence, non-maleficence, and justice.

Using dementia rating scales in the diagnosis of Alzheimer’s disease

Objective： To study the significance of dementia rating scales in the diagnosis of Alzheimer＇ s disease（AD）. Methods： Probable AD patients（118 cases） diagnosed according to NINCDS-ADRDA criteria and the normal controls（100 cases） were examined with a battery of neuropsychological tests and the dementia severity of AD patients was determined with clinical dementia rating （CDR）. Changed neuropsychological characteristics of different AD dementia severities were analyzed. The discriminant analysis and ROC curve analysis were perfomed to analyze the specificity, the sensitivity, and the general accuracy of various dementia rating scales in the diagnosis of AD, and the area under the ROC curve. Results： The total cognition function in mild （CDR = 1 ）, moderate（CDR = 2） and severe stages（CDR=3） of AD had an obvious trend of continuous decline, with the MMSE values 17.44±2.64, 13.90±4.32, and 5.50 ± 3.90 respectively. The trend of decline of the verbal fluency function in AD was same as that of total cognition function. The visuospatial function was reduced in early stage of AD （CDR = 1 ） and completely lost in moderate and severe AD. Delay memory function began to show decline in the early stage of AD, and the decline turned apparent in moderate and severe AD. Immediate memory function showed unchanged in early stage of AD, while showed decline in moderate AD, and the decline became very quick in severe AD. The impairment of daily living ability and social activity function developed with the severity degree of AD. But the decline of social activity function was very quick in moderate stage of AD. In general, the leading scale to diagnose AD was FOM, followed by RVR, POD, MMSE, BD,ADL and DS. When MMSE was combined with one or more of FOM, RVR, BD, DS, the general accuracy in distinguishing AD from the normal controls was improved. Conclusion： Neuropsychological test is useful in the diagnosis of AD, especially in the early stage. The validity is improved when dementia rating scales are combined correctly.

Gastrointestinal polyposis with esophageal polyposis is useful for early diagnosis of Cowden's disease

Cowden's disease, one of the several hamartoma syndromes, is characterized by hyperplastic lesions and hamartomas distributed in the whole body. About thirty percent of patients with Cowden's disease have been reported to be complicated by malignant tumors. Based on the criteria of the International Cowden Consortium, this disease is mainly diagnosed as trichilemmoma of the face and oral mucosal papillomatosis. However, Cowden's disease patients themselves often do not recognize trichilemmoma of the face and oral mucosal papillomatosis. We report a case of Cowden's disease in a 33-year-old female patient who was diagnosed based on the characteristic findings at gastrointestinal endoscopy. Clinically, the patient was aware of having bloody stools. Multiple polyps found endoscopically in the esophagus, stomach, ileum, colon and rectum showed histopathologically hamartomatous changes and epithelial hyperplasia. Physical examination revealed oral papillomatosis and facial trichilemmomas. A germline mutation in exon 8 of the phosphatase and tensin homolog deleted on chromosome ten (PTEN) gene was found in this case. It was a point mutation of C to T at codon 1003 (CGA→TGA, arginine→stop codon). The characteristic findings on gastrointestinal endoscopy led us to a diagnosis of Cowden's disease. It has been reported that gastrointestinal polyposis with esophageal polyposis is found in about 85.7% of Japanese patients with Cowden's disease. The characteristic findings on gastrointestinal endoscopy can be a useful diagnostic clue to Cowden's disease.

Partial improvement in performance of patients with severe Alzheimer's disease at an early stage of fornix deep brain stimulation

Deep brain stimulation is a therapy for Alzheimer＇s disease（AD） that has previously been used for mainly mild to moderate cases. This study provides the first evidence of early alterations in performance induced by stimulation targeted at the fornix in severe AD patients. The performance of the five cases enrolled in this study was scored with specialized assessments including the Mini-Mental State Examination and Clinical Dementia Rating, both before and at an early stage after deep brain stimulation. The burden of caregivers was also evaluated using the Zarit Caregiver Burden Interview. As a whole, the cognitive performance of patients remained stable or improved to varying degrees, and caregiver burden was decreased. Individually, an improved mental state or social performance was observed in three patients, and one of these three patients showed remarkable improvement in long-term memory. The conditions of another patient deteriorated because of inappropriate antipsychotic medications that were administered by his caregivers. Taken together, deep brain stimulation was capable of improving some cognitive aspects in patients with severe AD, and of ameliorating their emotional and social performance, at least at an early stage. However, long-term effects induced by deep brain stimulation in patients with severe AD need to be further validated. More research should focus on clarifying the mechanism of deep brain stimulation. This study was registered with ClinicalTrials.gov（NCT03115814） on April 14, 2017.

Deficit of pursuit ocular movements in early Alzheimer's disease

Previous studies have demonstrated that advanced Alzheimer＇s disease（AD）patients have deficiency of eye movements.However,there have been no reports on eye movement in the early stages of AD.The aim of this study was to evaluate pursuit ocular movements（POM）provided by a vision-based non-intrusive eye tracker in patients with early AD.POM values were significantly lower in AD patients than in normal controls（P 〈 0.01）.In AD patients,POM values were not closely correlated with the Mini-Mental State Examination scores（P = 0.3）.There was no significant difference in POM values among patients treated with or without anticholinesterase therapy.We used a vision-based method,for non-intrusive eye tracking,which can be proposed as a possible tool for supporting the diagnosis of early AD.

Magnetic resonance imaging markers for early diagnosis of Parkinson's disease

Parkinson＇s disease （PD） is a neurodegenerative disorder characterized by selective and progressive degeneration, as well as loss of dopaminergic neurons in the substantia nigra. In PD, approximately 60-70% of nigrostriatal neurons are degenerated and 80% of content of the striatal dopamine is reduced before the diagnosis can be established according to widely accepted clinical diagnostic criteria. This condition describes a stage of disease called ＂prodromal＂, where non-motor symptoms, such as olfactory dysfunction, constipation, rapid eye movement behaviour disorder, depression, precede motor sign of PD. Detection of prodromal phase of PD is becoming an important goal for determining the prognosis and choosing a suitable treatment strategy. In this review, we present some non-invasive instrumental approaches that could be useful to identify patients in the prodromal phase of PD or in an early clinical phase, when the first motor symptoms begin to be apparent. Conventional magnetic resonance imaging （MRI） and advanced MRI techniques, such as magnetic resonance spectroscopy imaging, diffusion-weighted and diffusion tensor imaging and functional MRI, are useful to differentiate early PD with initial motor symptoms from atypical parkinsonian disorders, thus, making easier early diagnosis. Functional MRI and diffusion tensor imaging techniques can show abnormalities in the olfactory system in prodromal PD.

Advances in Early Prevention of Alzheimer’s Disease in the Aged

Alzheimer’s disease (AD) is a progressive neurodegenerative disease, is one of the leading causes of death in the aging population, and has become a serious public problem endangering the physical and mental health of the elderly. The disease not only affects the health of the elderly and the quality of life in their later years, but also brings heavy financial and emotional burden to the family and society. The specific mechanism of AD is still unclear, and the current treatments focus on improving mental function and slowing down memory loss. The early diagnosis of AD is difficult and there is no cure for it, so the early prevention is particularly important. This review will introduce the major treatment, potential risk factors and summarize the relevant research about early preventions of AD, such as regular physical sports, reasonable nutritional diet, certain social activities and so on, and expect to provide a new direction for the improvement of AD.

The role of small vessel disease in development of Alzheimer's disease

Classically Alzheimer＇s disease and vascular dementia have been considered as two different entities, with their own clinical criteria, but relatively recent epidemiological and clinicopathological studies suggest an overlap between them sharing not only most of the risk factors and some clinical aspects but also pathophysiological mechanisms. Cerebrovascular lesions, especially small vessel disease （lacunar infarcts, white matter hyperintensities and microbleeds）, may magnify the effects of mild Alzheimer＇s disease pathology and promote the progression of cognitive decline and may also be a precursor of neuronal damage and dementia. ＂Vascular hypothesis＂ of Alzheimer＇s disease would open a window for new approaches and treatments.

An abnormal resting-state functional brain network indicates progression towards Alzheimer's disease

Brain structure and cognitive function change in the temporal lobe, hippocampus, and prefrontal cortex of patients with mild cognitive impairment and Alzheimer＇s disease, and brain network-connection strength, network efficiency, and nodal attributes are abnormal. However, existing research has only analyzed the differences between these patients and normal controls. In this study, we constructed brain networks using resting-state functional MRI data that was extracted from four populations （nor- mal controls, patients with early mild cognitive impairment, patients with late mild cognitive impairment, and patients with Alzheimer＇s disease） using the Alzheimer＇s Disease Neuroimaging Initiative data set. The aim was to analyze the characteristics of resting-state functional neural networks, and to observe mild cognitive impairment at different stages before the transformation to Alzheimer＇s disease. Results showed that as cognitive deficits increased across the four groups, the shortest path in the rest- ing-state functional network gradually increased, while clustering coefficients gradually decreased. This evidence indicates that dementia is associated with a decline of brain network efficiency. In addi- tion, the changes in functional networks revealed the progressive deterioration of network function across brain regions from healthy elderly adults to those with mild cognitive impairment and AIz- heimer＇s disease. The alterations of node attributes in brain regions may reflect the cognitive functions in brain regions, and we speculate that early impairments in memory, hearing, and language function can eventually lead to diffuse brain injury and other cognitive impairments.

Advanced diffusion magnetic resonance imaging in patients with Alzheimer’s and Parkinson’s diseases

The prevalence of neurodegenerative diseases is increasing as human longevity increases. The objective biomarkers that enable the staging and early diagnosis of neurodegenerative diseases are eagerly anticipated. It has recently become possible to determine pathological changes in the brain without autopsy with the advancement of diffusion magnetic resonance imaging techniques. Diffusion magnetic resonance imaging is a robust tool used to evaluate brain microstructural complexity and integrity, axonal order, density, and myelination via the micron-scale displacement of water molecules diffusing in tissues. Diffusion tensor imaging, a type of diffusion magnetic resonance imaging technique is widely utilized in clinical and research settings;however, it has several limitations. To overcome these limitations, cutting-edge diffusion magnetic resonance imaging techniques, such as diffusional kurtosis imaging, neurite orientation dispersion and density imaging, and free water imaging, have been recently proposed and applied to evaluate the pathology of neurodegenerative diseases. This review focused on the main applications, findings, and future directions of advanced diffusion magnetic resonance imaging techniques in patients with Alzheimer's and Parkinson's diseases, the first and second most common neurodegenerative diseases, respectively.

Enhancing Parkinson’s Disease Diagnosis Accuracy Through Speech Signal Algorithm Modeling

Parkinson’s disease(PD),one of whose symptoms is dysphonia,is a prevalent neurodegenerative disease.The use of outdated diagnosis techniques,which yield inaccurate and unreliable results,continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical field.To solve this issue,the study proposes using machine learning and deep learning models to analyze processed speech signals of patients’voice recordings.Datasets of these processed speech signals were obtained and experimented on by random forest and logistic regression classifiers.Results were highly successful,with 90%accuracy produced by the random forest classifier and 81.5%by the logistic regression classifier.Furthermore,a deep neural network was implemented to investigate if such variation in method could add to the findings.It proved to be effective,as the neural network yielded an accuracy of nearly 92%.Such results suggest that it is possible to accurately diagnose early-stage PD through merely testing patients’voices.This research calls for a revolutionary diagnostic approach in decision support systems,and is the first step in a market-wide implementation of healthcare software dedicated to the aid of clinicians in early diagnosis of PD.

Detection of Different Stages of Alzheimer’s Disease Using CNN Classifier

Alzheimer’s disease(AD)is a neurodevelopmental impairment that results in a person’s behavior,thinking,and memory loss.Themost common symptoms ofADare losingmemory and early aging.In addition to these,there are several serious impacts ofAD.However,the impact ofADcanbemitigatedby early-stagedetection though it cannot be cured permanently.Early-stage detection is the most challenging task for controlling and mitigating the impact of AD.The study proposes a predictive model to detect AD in the initial phase based on machine learning and a deep learning approach to address the issue.To build a predictive model,open-source data was collected where five stages of images of AD were available as Cognitive Normal(CN),Early Mild Cognitive Impairment(EMCI),Mild Cognitive Impairment(MCI),Late Mild Cognitive Impairment(LMCI),and AD.Every stage of AD is considered as a class,and then the dataset was divided into three parts binary class,three class,and five class.In this research,we applied different preprocessing steps with augmentation techniques to efficiently identifyAD.It integrates a random oversampling technique to handle the imbalance problem from target classes,mitigating the model overfitting and biases.Then three machine learning classifiers,such as random forest(RF),K-Nearest neighbor(KNN),and support vector machine(SVM),and two deep learning methods,such as convolutional neuronal network(CNN)and artificial neural network(ANN)were applied on these datasets.After analyzing the performance of the used models and the datasets,it is found that CNN with binary class outperformed 88.20%accuracy.The result of the study indicates that the model is highly potential to detect AD in the initial phase.

Regional patterns of atrophy on MRI in Alzheimer’s disease: Neuropsychological features and progression rates in the ADNI cohort

Background: Discrete clinical and pathological subtypes of Alzheimer’s disease (AD) with variable presentations and rates of progression are well known. These subtypes may have specific patterns of regional brain atrophy, which are identifiable on MRI scans. Methods: To examine distinct regions which had distinct underlying patterns of cortical atrophy, factor analytic techniques applied to structural MRI volumetric data from cognitively normal (CN) (n = 202), amnestic mild cognitive impairment (aMCI) (n = 333) or mild AD (n = 146) subjects, in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database was applied. This revealed the existence of two neocortical (NeoC-1 and NeoC-2), and a limbic cluster of atrophic brain regions. The frequency and clinical correlates of these regional patterns of atrophy were evaluated among the three diagnostic groups, and the rates of progression from aMCI to AD, over 24 months were evaluated. Results: Discernable patterns of regional atrophy were observed in about 29% of CN, 55% of aMCI and 83% of AD subjects. Heterogeneity in clinical presentation and APOE ε4 frequency were associated with regional patterns of atrophy on MRI scans. The most rapid progression rates to dementia among aMCI subjects (n = 224), over a 24-month period, were in those with NeoC-1 regional impairment (68.2%), followed by the Limbic regional impairment (48.8%). The same pattern of results was observed when only aMCI amyloid positive subjects were examined. Conclusions: The neuroimaging results closely parallel findings described recently among AD patients with the hippocampal sparing and limbic subtypes of AD neuropathology at autopsy. We conclude that NeoC-1, Limbic and other patterns of MRI atrophy may be useful markers for predicting the rate of progression of aMCI to AD and could have utility selecting individuals at higher risk for progression in clinical trials.

The CircRNAs for Diagnostic, Prognostic, and Therapy in Alzheimer’s Disease

The structure and biological function of circular RNAs (circRNAs) in regulating gene expression in a cell is far from known. CircRNAs are unique molecules that contain potent regulatory elements. CircRNAs actively interact with miRNAs (sponging), affecting their regulation and functions. In addition, circRNAs have roles in transcriptional regulation, splicing, and peptide synthesis. With all these properties, circRNAs could play an essential role in diseases, especially Alzheimer’s. Their role in early diagnosis, previous to present symptoms, prognosis associated with neuropathological AD of specific circRNAs, and one of their primary functions is to act as a sponge for miRNAs, which could be a starting point for future gene therapy. This review aims to summarize the current knowledge of these exciting molecules and their potential use as new markers for AD risk. This article will focus on circRNAs deregulated in Alzheimer’s.

FDG and Amyloid PET in Cognitively Normal Individuals at Risk for Late-Onset Alzheimer’s Disease

Having a parent affected by late-onset Alzheimer’s disease (AD) is a major risk factor for cognitively normal (NL) individuals. This study explores the potential of PET with 18F-FDG and the amyloid-β (Aβ) tracer 11C-Pittsburgh Compound B (PiB) for detection of individual risk in NL adults with AD-parents. Methods: FDG- and PiB-PET was performed in 119 young to late-middle aged NL individuals including 80 NL with positive family history of AD (FH+) and 39 NL with negative family history of any dementia (FH-). The FH+ group included 50 subjects with maternal (FHm) and 30 with paternal family history (FHp). Individual FDG and PiB scans were Z scored on a voxel-wise basis relative to modality-specific reference databases using automated procedures and rated as positive or negative (+/-) for AD-typical abnormalities using predefined criteria. To determine the effect of age, the cohort was separated into younger (49 ± 9 y) and older (68 ± 5 y) groups relative to the median age (60 y). Results: Among individuals of age >60 y, as compared to controls, NL FH+ showed a higher frequency of FDG+ scans vs. FH- (53% vs. 6% p < 0.003), and a trend for PiB+ scans (27% vs. 11%;p = 0.19). This effect was observed for both FHm and FHp groups. Among individuals of age ≤60 y, NL FHm showed a higher frequency of FDG+ scans (29%) compared to FH- (5%, p = 0.04) and a trend compared to FHp (11%) (p = 0.07), while the distribution of PiB+ scans was not different between groups. In both age cohorts, FDG+ scans were more frequent than PiB+ scans among NL FH+, especially FHm (p < 0.03). FDG-PET was a significant predictor of FH+ status. Classification according to PiB status was significantly less successful. Conclusions: Automated analysis of FDG- and PiB-PET demonstrates higher rates of abnormalities in at-risk FH+ vs FH-subjects, indicating potentially ongoing early AD-pathology in this population. The frequency of metabolic abnormalities was higher than that of Aβ pathology in the younger cohort, suggesting that neuronal dysfunction may precede major aggregated Aβ burden in young NL FH+. Longitudinal follow-up is required to determine if the observed abnormalities predict future AD.

Progress on early diagnosing Alzheimer’s disease

Alzheimer’s disease(AD)is a progressive neurodegenerative disorder that affects both cognition and non-cognition functions.The disease follows a continuum,starting with preclinical stages,progressing to mild cognitive and behavioral impairment,ultimately leading to dementia.Early detection of AD is crucial for better diagnosis and more effective treatment.However,the current AD diagnostic tests of biomarkers using cerebrospinal fluid and/or brain imaging are invasive or expensive,and mostly are still not able to detect early disease state.Consequently,there is an urgent need to develop new diagnostic techniques with higher sensitivity and specificity during the preclinical stages of AD.Various non-cognitive manifestations,including behavioral abnormalities,sleep disturbances,sensory dysfunctions,and physical changes,have been observed in the preclinical AD stage before occurrence of notable cognitive decline.Recent research advances have identified several biofluid biomarkers as early indicators of AD.This review focuses on these non-cognitive changes and newly discovered biomarkers in AD,specifically addressing the preclinical stages of the disease.Furthermore,it is of importance to explore the potential for developing a predictive system or network to forecast disease onset and progression at the early stage of AD.