scholarly journals A plasma protein classifier for predicting amyloid burden for preclinical Alzheimer’s disease

2019 ◽  
Vol 5 (2) ◽  
pp. eaau7220 ◽  
Author(s):  
Nicholas J. Ashton ◽  
Alejo J. Nevado-Holgado ◽  
Imelda S. Barber ◽  
Steven Lynham ◽  
Veer Gupta ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
High Resolution Mass Spectrometry ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Feature Model ◽  
Therapeutic Trials ◽  
Positron Emission ◽  
Proteomic Study ◽  
Peptide Fractionation

A blood-based assessment of preclinical disease would have huge potential in the enrichment of participants for Alzheimer’s disease (AD) therapeutic trials. In this study, cognitively unimpaired individuals from the AIBL and KARVIAH cohorts were defined as Aβ negative or Aβ positive by positron emission tomography. Nontargeted proteomic analysis that incorporated peptide fractionation and high-resolution mass spectrometry quantified relative protein abundances in plasma samples from all participants. A protein classifier model was trained to predict Aβ-positive participants using feature selection and machine learning in AIBL and independently assessed in KARVIAH. A 12-feature model for predicting Aβ-positive participants was established and demonstrated high accuracy (testing area under the receiver operator characteristic curve = 0.891, sensitivity = 0.78, and specificity = 0.77). This extensive plasma proteomic study has unbiasedly highlighted putative and novel candidates for AD pathology that should be further validated with automated methodologies.

scholarly journals A blood-based diagnostic test incorporating plasma Aβ42/40 ratio, ApoE proteotype, and age accurately identifies brain amyloid status: findings from a multi cohort validity analysis

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Tim West ◽  
Kristopher M. Kirmess ◽  
Matthew R. Meyer ◽  
Mary S. Holubasch ◽  
Stephanie S. Knapik ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
High Resolution Mass Spectrometry ◽  
Clinical Performance ◽  
Characteristic Curve ◽  
Youden Index ◽  
Amyloid Pet ◽  
Drug Trials ◽  
Site Specific

Abstract Background The development of blood-based biomarker tests that are accurate and robust for Alzheimer’s disease (AD) pathology have the potential to aid clinical diagnosis and facilitate enrollment in AD drug trials. We developed a high-resolution mass spectrometry (MS)-based test that quantifies plasma Aβ42 and Aβ40 concentrations and identifies the ApoE proteotype. We evaluated robustness, clinical performance, and commercial viability of this MS biomarker assay for distinguishing brain amyloid status. Methods We used the novel MS assay to analyze 414 plasma samples that were collected, processed, and stored using site-specific protocols, from six independent US cohorts. We used receiver operating characteristic curve (ROC) analyses to assess assay performance and accuracy for predicting amyloid status (positive, negative, and standard uptake value ratio; SUVR). After plasma analysis, sites shared brain amyloid status, defined using diverse, site-specific methods and cutoff values; amyloid PET imaging using various tracers or CSF Aβ42/40 ratio. Results Plasma Aβ42/40 ratio was significantly (p < 0.001) lower in the amyloid positive vs. negative participants in each cohort. The area under the ROC curve (AUC-ROC) was 0.81 (95% CI = 0.77–0.85) and the percent agreement between plasma Aβ42/40 and amyloid positivity was 75% at the optimal (Youden index) cutoff value. The AUC-ROC (0.86; 95% CI = 0.82–0.90) and accuracy (81%) for the plasma Aβ42/40 ratio improved after controlling for cohort heterogeneity. The AUC-ROC (0.90; 95% CI = 0.87–0.93) and accuracy (86%) improved further when Aβ42/40, ApoE4 copy number and participant age were included in the model. Conclusions This mass spectrometry-based plasma biomarker test: has strong diagnostic performance; can accurately distinguish brain amyloid positive from amyloid negative individuals; may aid in the diagnostic evaluation process for Alzheimer’s disease; and may enhance the efficiency of enrolling participants into Alzheimer’s disease drug trials.

scholarly journals Temporal Dynamics of Beta-amyloid Accumulation in Aging and Alzheimer’s Disease

Neurology ◽  
2021 ◽  
pp. 10.1212/WNL.0000000000011524
Author(s):  
William J Jagust ◽  
Susan M Landau ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Incidence Rate ◽  
Time Course ◽  
Temporal Dynamics ◽  
Therapeutic Trials ◽  
Positron Emission ◽  
Two Samples ◽  
Clinically Significant ◽  
Pet Scans

Objective:We performed this study to understand the time course of β-amyloid (Aβ) deposition in the brain, which is crucial for planning therapeutic trials of Aβ lowering therapies in Alzheimer’s disease (AD).Methods:Two samples of participants from the Alzheimer’s disease neuroimaging initiative were studied with [18F]Florbetapir (FBP) Aβ positron emission tomography (PET) and followed for up to 9 years. Sample A included 475 cognitively normal (CN) older people and those with mild cognitive impairment (MCI) and AD, and sample B included 220 CN Aβ- individuals. We examined the trajectory of FBP over time in sample A, and the incidence rate of conversion from negative to positive Aβ PET scans in sample B.Results:The relationship between time and brain Aβ was sigmoidal, taking 6.4 years to transition from amyloid negative to positive and another 13.9 years to the onset of MCI. Aβ deposition rates began to slow only 3.8 years after reaching the positivity threshold. The incidence rate for scan positivity was 38/1000 person-years, and factors associated with conversion were age, baseline FBP, and being a female Apolipoprotein E4 carrier. Among CN Aβ- individuals, FBP slopes were associated with rates of memory decline and brain tau measured with [18F]Flortaucipir PET 5 years after baseline.Conclusions:Lowering brain Aβ must be accomplished early in the evolution of Alzheimer’s disease. Transitions of PET scans from Aβ- to Aβ+ should be predictable, and it is reasonable to expect that lowering rates of Aβ even in early stages could produce clinically significant benefits.

scholarly journals Applications of Neuroimaging to Disease-Modification Trials in Alzheimer’s Disease

2009 ◽  
Vol 21 (1-2) ◽  
pp. 129-136 ◽  
Author(s):  
Adam S. Fleisher ◽  
Michael Donohue ◽  
Kewei Chen ◽  
James B. Brewer ◽  
Paul S. Aisen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Drug Development ◽  
Imaging Techniques ◽  
Surrogate Endpoint ◽  
Treatment Development ◽  
Therapeutic Trials ◽  
Positron Emission ◽  
Clinical Measures ◽  
Significant Disease

Critical to development of new therapies for Alzheimer’s disease (AD) is the ability to detect clinical or pathological change over time. Clinical outcome measures typically used in therapeutic trials have unfortunately proven to be relatively variable and somewhat insensitive to change in this slowly progressive disease. For this reason, development of surrogate biomarkers that identify significant disease-associated brain changes are necessary to expedite treatment development in AD. Since AD pathology is present in the brain many years prior to clinical manifestation, ideally we want to develop biomarkers of disease that identify abnormal brain structure or function even prior to cognitive decline. Magnetic resonance imaging, fluorodeoxyglucose positron emission tomography, new amyloid imaging techniques, and spinal fluid markers of AD all have great potential to provide surrogate endpoint measures for AD pathology. The Alzheimer’s disease neuroimaging initiative (ADNI) was developed for the distinct purpose of evaluating surrogate biomarkers for drug development in AD. Recent evidence from ADNI demonstrates that imaging may provide more sensitive, and earlier, measures of disease progression than traditional clinical measures for powering clinical drug trials in Alzheimer's disease. This review discusses recently presented data from the ADNI dataset, and the importance of imaging in the future of drug development in AD.

Identify Compounds' Target Against Alzheimer's Disease Based on In-Silico Approach

2019 ◽  
Vol 16 (3) ◽  
pp. 193-208 ◽  
Author(s):  
Yan Hu ◽  
Guangya Zhou ◽  
Chi Zhang ◽  
Mengying Zhang ◽  
Qin Chen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Small Molecule ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Learner Model ◽  
Mao B ◽  
Number Of Patients ◽  
Sar Model ◽  
Set Up

Background: Alzheimer's disease swept every corner of the globe and the number of patients worldwide has been rising. At present, there are as many as 30 million people with Alzheimer's disease in the world, and it is expected to exceed 80 million people by 2050. Consequently, the study of Alzheimer’s drugs has become one of the most popular medical topics. Methods: In this study, in order to build a predicting model for Alzheimer’s drugs and targets, the attribute discriminators CfsSubsetEval, ConsistencySubsetEval and FilteredSubsetEval are combined with search methods such as BestFirst, GeneticSearch and Greedystepwise to filter the molecular descriptors. Then the machine learning algorithms such as BayesNet, SVM, KNN and C4.5 are used to construct the 2D-Structure Activity Relationship(2D-SAR) model. Its modeling results are utilized for Receiver Operating Characteristic curve(ROC) analysis. Results: The prediction rates of correctness using Randomforest for AChE, BChE, MAO-B, BACE1, Tau protein and Non-inhibitor are 77.0%, 79.1%, 100.0%, 94.2%, 93.2% and 94.9%, respectively, which are overwhelming as compared to those of BayesNet, BP, SVM, KNN, AdaBoost and C4.5. Conclusion: In this paper, we conclude that Random Forest is the best learner model for the prediction of Alzheimer’s drugs and targets. Besides, we set up an online server to predict whether a small molecule is the inhibitor of Alzheimer's target at http://47.106.158.30:8080/AD/. Furthermore, it can distinguish the target protein of a small molecule.

Easy Screening for Mild Alzheimer's Disease and Mild Cognitive Impairment from Elderly Speech

2018 ◽  
Vol 15 (2) ◽  
pp. 104-110 ◽  
Author(s):  
Shohei Kato ◽  
Akira Homma ◽  
Takuto Sakuma
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Characteristic Curve ◽  
Linear Regression Analysis ◽  
Speech Sound ◽  
The Elderly ◽  
Speech Sounds ◽  
Mel Frequency Cepstral Coefficients

Objective: This study presents a novel approach for early detection of cognitive impairment in the elderly. The approach incorporates the use of speech sound analysis, multivariate statistics, and data-mining techniques. We have developed a speech prosody-based cognitive impairment rating (SPCIR) that can distinguish between cognitively normal controls and elderly people with mild Alzheimer's disease (mAD) or mild cognitive impairment (MCI) using prosodic signals extracted from elderly speech while administering a questionnaire. Two hundred and seventy-three Japanese subjects (73 males and 200 females between the ages of 65 and 96) participated in this study. The authors collected speech sounds from segments of dialogue during a revised Hasegawa's dementia scale (HDS-R) examination and talking about topics related to hometown, childhood, and school. The segments correspond to speech sounds from answers to questions regarding birthdate (T1), the name of the subject's elementary school (T2), time orientation (Q2), and repetition of three-digit numbers backward (Q6). As many prosodic features as possible were extracted from each of the speech sounds, including fundamental frequency, formant, and intensity features and mel-frequency cepstral coefficients. They were refined using principal component analysis and/or feature selection. The authors calculated an SPCIR using multiple linear regression analysis. Conclusion: In addition, this study proposes a binary discrimination model of SPCIR using multivariate logistic regression and model selection with receiver operating characteristic curve analysis and reports on the sensitivity and specificity of SPCIR for diagnosis (control vs. MCI/mAD). The study also reports discriminative performances well, thereby suggesting that the proposed approach might be an effective tool for screening the elderly for mAD and MCI.

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