Rapid Universal Early Screening for Alzheimer's Disease and Related Dementia via Pattern Discovery in Diagnostic History

2021 ◽  
Author(s):  
Dmytro Onishchenko ◽  
Sam Searle ◽  
Kenneth Rockwood ◽  
James Mastrianni ◽  
Ishanu Chattopadhyay
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Pattern Discovery ◽  
Early Screening

scholarly journals The Role of Salivary Biomarkers in the Early Diagnosis of Alzheimer’s Disease and Parkinson’s Disease

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 371
Author(s):  
Patrycja Pawlik ◽  
Katarzyna Błochowiak
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Early Diagnosis ◽  
Neurodegenerative Diseases ◽  
Diagnostic Tool ◽  
Clinical Symptoms ◽  
Early Screening ◽  
Salivary Biomarkers

Many neurodegenerative diseases present with progressive neuronal degeneration, which can lead to cognitive and motor impairment. Early screening and diagnosis of neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD) are necessary to begin treatment before the onset of clinical symptoms and slow down the progression of the disease. Biomarkers have shown great potential as a diagnostic tool in the early diagnosis of many diseases, including AD and PD. However, screening for these biomarkers usually includes invasive, complex and expensive methods such as cerebrospinal fluid (CSF) sampling through a lumbar puncture. Researchers are continuously seeking to find a simpler and more reliable diagnostic tool that would be less invasive than CSF sampling. Saliva has been studied as a potential biological fluid that could be used in the diagnosis and early screening of neurodegenerative diseases. This review aims to provide an insight into the current literature concerning salivary biomarkers used in the diagnosis of AD and PD. The most commonly studied salivary biomarkers in AD are β-amyloid1-42/1-40 and TAU protein, as well as α-synuclein and protein deglycase (DJ-1) in PD. Studies continue to be conducted on this subject and researchers are attempting to find correlations between specific biomarkers and early clinical symptoms, which could be key in creating new treatments for patients before the onset of symptoms.

scholarly journals High-Throughput Metabolomics for Discovering Potential Biomarkers and Identifying Metabolic Mechanisms in Aging and Alzheimer’s Disease

2020 ◽  
Author(s):  
Kun Xie ◽  
Qi Qin ◽  
Zhiping Long ◽  
Yihui Yang ◽  
Chenghai Peng ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Stearic Acid ◽  
Natural Aging ◽  
Aging Population ◽  
Plasma Metabolites ◽  
Pathway Enrichment Analysis ◽  
Proline Metabolism ◽  
Early Screening ◽  
Potential Biomarkers

Abstract Background and Aims: Alzheimer’s disease (AD) is an aging-related neurodegenerative disease. The current diagnosis of AD may fail to identify a substantial number of asymptomatic individuals who will progress to AD. We aimed to investigate the metabolic mechanisms of aging and AD and to identify potential biomarkers for the early screening of AD in a natural aging population.Methods: To analyse the plasma metabolites related to aging, we conducted an untargeted metabolomics analysis using ultra-high-performance liquid chromatography/quadrupole time-of-flight mass spectrometry in a two-stage cross-sectional study. Spearman’s correlation analysis and random forest were applied to model the relationship between age and each metabolite. Moreover, systematic reviews of metabolomics studies of AD in the PubMed, Cochrane and Embase databases were searched to extract the differential metabolites and altered pathways from original studies. Pathway enrichment analysis was conducted using Mummichog.Results: In total, 669 metabolites were significantly altered with aging, and thirteen pathways were enriched and correlated with aging. Five metabolites (palmitic acid, stearic acid, linoleic acid, glutamine, and oleic acid) were identified as potential biomarkers for AD based on a systematic review. Arginine and histidine were considered candidate monitoring markers of disease progression in the mild cognitive impairment (MCI) population. Moreover, three pathways (purine metabolism, arginine and proline metabolism, and the TCA cycle) were shared between aging and AD. Arginine and proline metabolism play a key role in the progression from CN to MCI and to AD in the natural aging population. Three metabolites, 16-a-hydroxypregnenolone, stearic acid and PC (16:0/22:5(4Z,7Z,10Z,13Z,16Z)), were finally proposed as potential markers of AD in the natural aging population.Conclusion: The underlying mechanism shared between aging and AD and the potential biomarkers for AD diagnosis were proposed based on multistep comparative analysis.

scholarly journals High-Throughput Metabolomics for Discovering Potential Biomarkers and Identifying Metabolic Mechanisms in Aging and Alzheimer’s Disease

2021 ◽  
Vol 9 ◽  
Author(s):  
Kun Xie ◽  
Qi Qin ◽  
Zhiping Long ◽  
Yihui Yang ◽  
Chenghai Peng ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Natural Aging ◽  
Aging Population ◽  
Plasma Metabolites ◽  
Pathway Enrichment Analysis ◽  
Proline Metabolism ◽  
Early Screening ◽  
Cross Sectional ◽  
Potential Biomarkers

Alzheimer’s disease (AD) is an aging-related neurodegenerative disease. We aimed to investigate the metabolic mechanisms of aging and AD and to identify potential biomarkers for the early screening of AD in a natural aging population. To analyze the plasma metabolites related to aging, we conducted an untargeted metabolomics analysis using ultra-high-performance liquid chromatography/quadrupole time-of-flight mass spectrometry in a two-stage cross-sectional study. Spearman’s correlation analysis and random forest were applied to model the relationship between age and each metabolite. Moreover, a systematic review of metabolomics studies of AD in the PubMed, Cochrane and Embase databases were searched to extract the differential metabolites and altered pathways from original studies. Pathway enrichment analysis was conducted using Mummichog. In total, 669 metabolites were significantly altered with aging, and 12 pathways were enriched and correlated with aging. Three pathways (purine metabolism, arginine and proline metabolism, and the TCA cycle) were shared between aging and AD. Arginine and proline metabolism play a key role in the progression from healthy to mild cognitive impairment and to AD in the natural aging population. Three metabolites, 16-a-hydroxypregnenolone, stearic acid and PC[16:0/22:5(4Z,7Z,10Z,13Z,16Z)] were finally proposed as potential markers of AD in the natural aging population. The underlying mechanism shared between aging and AD and the potential biomarkers for AD diagnosis were proposed based on multistep comparative analysis.

scholarly journals Risk of Alzheimer’s Disease and Related Dementia Among Adults With Congenital and Acquired Disabilities

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 228-228
Author(s):  
Elham Mahmoudi ◽  
Paul Lin ◽  
Neil Kamdar ◽  
Anam Khan ◽  
Mark Peterson
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cox Regression ◽  
Late Onset ◽  
Early Screening ◽  
Age Related ◽  
Hazard Ratios ◽  
Cord Injury ◽  
Race Ethnicity ◽  
Rehabilitative Services

Abstract Objective Adults with congenital (cerebral palsy or spina bifida (CP/SB)) or acquired disabilities (spinal cord injury (SCI) or multiple sclerosis (MS)) have higher incidence of age-related health conditions. There is a gap in the literature about the risk of dementia among adults living with these disabilities. This study aimed to examine time to incidence of Alzheimer’s disease and related dementia (ADRD) among these disability cohorts. Method: Using national private payer claims data from 2007-2017, we identified adults (45+) with diagnosis of CP/SB (n=7,226), SCI (n=6,083), and MS (n=6,025). Adults without disability diagnosis were included as controls. Using age, sex, race/ethnicity, cardiometabolic, psychologic, and musculoskeletal chronic conditions, and socioeconomic variables, we propensity score matched persons with and without disabilities. Incidence of ADRD was compared at 4-years. Cox Regression was used to estimate adjusted hazard ratios (aHR) for incident early and late onset ADRD. Results Incidence of early and late onset ADRD were substantially higher among people with disabilities compared to their non-disabled counterparts. Adults with CP, SCI, and MS had higher risk for early [CP/SB: aHR= 3.35 (95% CI: 2.18-5.14); SCI: aHR=1.93 (95% CI:1.06-3.51); and MS: aHR=4.49 (95% CI:2.62-7.69)] and late [CP: aHR=1.68 (95% CI:1.38-2.03); SCI: aHR: 1.77 (95% CI:1.55-2.02); and MS: aHR=1.26 (95% CI:1.04, 1.54)] onset ADRD. Conclusions Risk of ADRD was higher among adults with CP/SB, SCI, and MS compared to their matched cohort without disability. Investment in early screening and use of therapeutic or rehabilitative services that may help preserving cognitive function among these patient cohorts is warranted.

scholarly journals Advances in the development paradigm of biosample‐based biosensors for early ultrasensitive detection of alzheimer’s disease

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Hem Prakash Karki ◽  
Yeongseok Jang ◽  
Jinmu Jung ◽  
Jonghyun Oh
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Diagnosis ◽  
Future Trend ◽  
Ultrasensitive Detection ◽  
Sample Collection ◽  
Early Screening ◽  
Detection Techniques ◽  
Non Invasive ◽  
Home Based

AbstractThis review highlights current developments, challenges, and future directions for the use of invasive and noninvasive biosample-based small biosensors for early diagnosis of Alzheimer’s disease (AD) with biomarkers to incite a conceptual idea from a broad number of readers in this field. We provide the most promising concept about biosensors on the basis of detection scale (from femto to micro) using invasive and noninvasive biosamples such as cerebrospinal fluid (CSF), blood, urine, sweat, and tear. It also summarizes sensor types and detailed analyzing techniques for ultrasensitive detection of multiple target biomarkers (i.e., amyloid beta (Aβ) peptide, tau protein, Acetylcholine (Ach), microRNA137, etc.) of AD in terms of detection ranges and limit of detections (LODs). As the most significant disadvantage of CSF and blood-based detection of AD is associated with the invasiveness of sample collection which limits future strategy with home-based early screening of AD, we extensively reviewed the future trend of new noninvasive detection techniques (such as optical screening and bio-imaging process). To overcome the limitation of non-invasive biosamples with low concentrations of AD biomarkers, current efforts to enhance the sensitivity of biosensors and discover new types of biomarkers using non-invasive body fluids are presented. We also introduced future trends facing an infection point in early diagnosis of AD with simultaneous emergence of addressable innovative technologies.

Epoch-based Entropy for Early Screening of Alzheimer’s Disease

2015 ◽  
Vol 25 (08) ◽  
pp. 1550032 ◽  
Author(s):  
N. Houmani ◽  
G. Dreyfus ◽  
F. B. Vialatte
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Healthy Subjects ◽  
Local Density ◽  
Early Stage ◽  
Sampling Frequency ◽  
Entropy Measure ◽  
Early Screening ◽  
Complexity Measures ◽  
Spatial Level

In this paper, we introduce a novel entropy measure, termed epoch-based entropy. This measure quantifies disorder of EEG signals both at the time level and spatial level, using local density estimation by a Hidden Markov Model on inter-channel stationary epochs. The investigation is led on a multi-centric EEG database recorded from patients at an early stage of Alzheimer’s disease (AD) and age-matched healthy subjects. We investigate the classification performances of this method, its robustness to noise, and its sensitivity to sampling frequency and to variations of hyperparameters. The measure is compared to two alternative complexity measures, Shannon’s entropy and correlation dimension. The classification accuracies for the discrimination of AD patients from healthy subjects were estimated using a linear classifier designed on a development dataset, and subsequently tested on an independent test set. Epoch-based entropy reached a classification accuracy of 83% on the test dataset (specificity = 83.3%, sensitivity = 82.3%), outperforming the two other complexity measures. Furthermore, it was shown to be more stable to hyperparameter variations, and less sensitive to noise and sampling frequency disturbances than the other two complexity measures.

scholarly journals Advances in computerized MRI‐based biomarkers in Alzheimer’s disease

2021 ◽  
Vol 7 (1) ◽  
pp. 26-43
Author(s):  
Raymond Wong ◽  
Yishan Luo ◽  
Vincent Chung-tong Mok ◽  
Lin Shi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
State Of The Art ◽  
Lewy Bodies ◽  
Clinical Settings ◽  
Early Screening ◽  
Future Studies ◽  
Magnetic Resonance Imaging Mri ◽  
Aided Diagnosis

The use of neuroimaging examinations is crucial in Alzheimer’s disease (AD), in both research and clinical settings. Over the years, magnetic resonance imaging (MRI)–based computer‐aided diagnosis has been shown to be helpful for early screening and predicting cognitive decline. Meanwhile, an increasing number of studies have adopted machine learning for the classification of AD, with promising results. In this review article, we focus on computerized MRI‐based biomarkers of AD by reviewing representative studies that used computerized techniques to identify AD patients and predict cognitive progression. We categorized these studies based on the following applications: (1) identifying AD from normal control; (2) identifying AD from other dementia types, including vascular dementia, dementia with Lewy bodies, and frontotemporal dementia; and (3) predicting conversion from NC to mild cognitive impairment (MCI) and from MCI to AD. This systematic review could act as a state‐of‐the‐art overview of this emerging field as well as a basis for designing future studies.

Pathophysiology of Alzheimer’s disease

2017 ◽  
Author(s):  
Shelley J. Allen
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Essential Role ◽  
Amyloid Plaques ◽  
Early Screening ◽  
Risk Genes ◽  
Risk Gene ◽  
Sporadic Cases ◽  
Synaptic Changes

We now know that the onset of the pathological processes leading to Alzheimer’s disease (AD) may be 15–20 years before symptoms appear. This focuses attention on synaptic changes and the early role of tau, and less on the hallmark amyloid plaques (Aβ‎) and neurofibrillary tau tangles. Sensitive biomarkers to allow early screening will be essential. Familial autosomal AD is the result of mutations in one of three genes (APP, PSEN1, or PSEN2), each directly related to increased Aβ‎, and informs pathological mechanisms in common sporadic cases, but are also subject to influence by many risk genes and environmental factors. The essential role of apolipoprotein E in neuronal repair and Aβ‎ clearance provides a therapeutic target but also a challenge in carriers of the risk gene APOE4. Current treatments are symptomatic, derived from neurotransmitter deficits seen; particularly cholinergic, but emerging data suggest alternative targets which may prove more productive.

The Role of Thyroid Function in Alzheimer’s Disease

2021 ◽  
pp. 1-10
Author(s):  
Feifei Ge ◽  
Donglin Zhu ◽  
Minjie Tian ◽  
Jingping Shi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Thyroid Function ◽  
Growth And Development ◽  
Thyroid Dysfunction ◽  
Intervention Strategies ◽  
Early Screening ◽  
Regulation Of Metabolism ◽  
The Central Nervous System

The thyroid gland is crucial for the regulation of metabolism, growth, and development of various tissues, organs, systems, including the central nervous system. Recent studies have implicated the role of thyroid dysfunction in the etiology of Alzheimer’s disease (AD), while AD leads to a significant increase in the prevalence of thyroid dysfunction. In this review, we have analyzed the role of thyroid function in the pathophysiology of AD as well as its biomarkers. The present review aims to provide encouraging targets for early screening of AD risk factors and intervention strategies.

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