Alzheimer's disease and related disorders caregiver's acceptance of a web-based structured written emotional expression intervention

2015 ◽

pp. 1-4

Author(s):

D. Polhamus ◽

J. Kang ◽

J. Rogers ◽

M. Gastonguay

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Statistical Power ◽

Operating Characteristics ◽

Longitudinal Models ◽

Exclusion Criteria ◽

Web Based ◽

Correct Dose ◽

Time Simulation ◽

Simulation Based

Clinical trials for Alzheimer’s Disease (AD) are necessarily designed in the presence of substantial quantitative uncertainty. Certain important aspects of this uncertainty can be mitigated by developing longitudinal models for AD progression and by using these models to simulate virtual trials and estimate operating characteristics (such as statistical power, the probability of stopping at an interim analysis, the probability of identifying the correct dose, etc.) as a function of candidate design features, such as inclusion / exclusion criteria. In this brief report we describe the development and deployment of a customized software solution that allows such simulation-based results to be generated “on the fly” in the context of a drug development team meeting. This solution leverages a number of recent practical advances in statistical and scientific computing that could be much more broadly leveraged to assure more quantitatively grounded trial designs in Alzheimer’s Disease.

Download Full-text

RECRUITMENT INTO THE ALZHEIMER PREVENTION TRIALS (APT) WEBSTUDY FOR A TRIAL-READY COHORT FOR PRECLINICAL AND PRODROMAL ALZHEIMER’S DISEASE (TRCPAD)

Journal of Prevention of Alzheimer's Disease ◽

10.14283/jpad.2020.46 ◽

2020 ◽

pp. 1-7

Author(s):

S. Walter ◽

T.B. Clanton ◽

O.G. Langford ◽

M.S. Rafii ◽

E.J. Shaffer ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Effective Means ◽

Web Based ◽

The Us ◽

Study Team ◽

Prodromal Alzheimer’S Disease ◽

Prevention Trials ◽

Study Participants ◽

The Impact

BACKGROUND: The Alzheimer Prevention Trials (APT) Webstudy is the first stage in establishing a Trial-ready Cohort for Preclinical and Prodromal Alzheimer’s disease (TRC-PAD). This paper describes recruitment approaches for the APT Webstudy. Objectives: To remotely enroll a cohort of individuals into a web-based longitudinal observational study. Participants are followed quarterly with brief cognitive and functional assessments, and referred to Sites for in-clinic testing and biomarker confirmation prior to enrolling in the Trial-ready Cohort (TRC). Design: Participants are referred to the APT Webstudy from existing registries of individuals interested in brain health and Alzheimer’s disease research, as well as through central and site recruitment efforts. The study team utilizes Urchin Tracking Modules (UTM) codes to better understand the impact of electronic recruitment methods. Setting: A remotely enrolled online study. Participants: Volunteers who are at least 50 years old and interested in Alzheimer’s research. Measurements: Demographics and recruitment source of participant where measured by UTM. Results: 30,650 participants consented to the APT Webstudy as of April 2020, with 69.7% resulting from referrals from online registries. Emails sent by the registry to participants were the most effective means of recruitment. Participants are distributed across the US, and the demographics of the APT Webstudy reflect the referral registries, with 73.1% female, 85.0% highly educated, and 92.5% Caucasian. Conclusions: We have demonstrated the feasibility of enrolling a remote web-based study utilizing existing registries as a primary referral source. The next priority of the study team is to engage in recruitment initiatives that will improve the diversity of the cohort, towards the goal of clinical trials that better represent the US population.

Download Full-text

Increasing Diagnostic Accuracy of Mild Cognitive Impairment due to Alzheimer’s Disease by User-Independent, Web-Based Whole-Brain Volumetry

Journal of Alzheimer s Disease ◽

10.3233/jad-180532 ◽

2018 ◽

Vol 65 (4) ◽

pp. 1459-1467 ◽

Cited By ~ 1

Author(s):

Dennis M. Hedderich ◽

Judith E. Spiro ◽

Oliver Goldhardt ◽

Johannes Kaesmacher ◽

Benedikt Wiestler ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Diagnostic Accuracy ◽

Whole Brain ◽

Web Based ◽

Brain Volumetry

Download Full-text

MicroRNA-Seq Data Analysis Pipeline to Identify Blood Biomarkers for Alzheimer's Disease from Public Data

Biomarker Insights ◽

10.4137/bmi.s25132 ◽

2015 ◽

Vol 10 ◽

pp. BMI.S25132 ◽

Cited By ~ 68

Author(s):

Jun-ichi Satoh ◽

Yoshihiro Kino ◽

Shumpei Niida

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Data Analysis ◽

Small Rna ◽

Small Rna Sequencing ◽

Rna Seq ◽

Analysis Pipeline ◽

Web Based ◽

Public Data ◽

Data Analysis Pipeline

Background Alzheimer's disease (AD) is the most common cause of dementia with no curative therapy currently available. Establishment of sensitive and non-invasive biomarkers that promote an early diagnosis of AD is crucial for the effective administration of disease-modifying drugs. MicroRNAs (miRNAs) mediate posttranscriptional repression of numerous target genes. Aberrant regulation of miRNA expression is implicated in AD pathogenesis, and circulating miRNAs serve as potential biomarkers for AD. However, data analysis of numerous AD-specific miRNAs derived from small RNA-sequencing (RNA-Seq) is most often laborious. Methods To identify circulating miRNA biomarkers for AD, we reanalyzed a publicly available small RNA-Seq dataset, composed of blood samples derived from 48 AD patients and 22 normal control (NC) subjects, by a simple web-based miRNA data analysis pipeline that combines omiRas and DIANA miRPath. Results By using omiRas, we identified 27 miRNAs expressed differentially between both groups, including upregulation in AD of miR-26b-3p, miR-28–3p, miR-30c-5p, miR-30d-5p, miR-148b-5p, miR-151a-3p, miR-186–5p, miR-425–5p, miR-550a-5p, miR-1468, miR-4781–3p, miR-5001–3p, and miR-6513–3p and downregulation in AD of let-7a-5p, let-7e-5p, let-7f-5p, let-7g-5p, miR-15a-5p, miR-17–3p, miR-29b-3p, miR-98–5p, miR-144–5p, miR-148a-3p, miR-502–3p, miR-660–5p, miR-1294, and miR-3200–3p. DIANA miRPath indicated that miRNA-regulated pathways potentially down– regulated in AD are linked with neuronal synaptic functions, while those upregulated in AD are implicated in cell survival and cellular communication. Conclusions The simple web-based miRNA data analysis pipeline helps us to effortlessly identify candidates for miRNA biomarkers and pathways of AD from the complex small RNA–Seq data.

Download Full-text

PREDICTING AMYLOID BURDEN TO ACCELERATE RECRUITMENT OF SECONDARY PREVENTION CLINICAL TRIALS

Journal of Prevention of Alzheimer's Disease ◽

10.14283/jpad.2020.44 ◽

2020 ◽

pp. 1-6

Author(s):

O. Langford ◽

R. Raman ◽

R.A. Sperling ◽

J. Cummings ◽

C.-K. Sun ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Clinical Trials ◽

Secondary Prevention ◽

Demographic Variables ◽

Amyloid Burden ◽

Cross Sectional ◽

Web Based ◽

Predictive Algorithms ◽

Remote Setting

BACKGROUND: Screening to identify individuals with elevated brain amyloid (Aβ+) for clinical trials in Preclinical Alzheimer’s Disease (PAD), such as the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s disease (A4) trial, is slow and costly. The Trial-Ready Cohort in Preclinical/Prodromal Alzheimer’s Disease (TRC-PAD) aims to accelerate and reduce costs of AD trial recruitment by maintaining a web-based registry of potential trial participants, and using predictive algorithms to assess their likelihood of suitability for PAD trials. OBJECTIVES: Here we describe how algorithms used to predict amyloid burden within TRC-PAD project were derived using screening data from the A4 trial. DESIGN: We apply machine learning techniques to predict amyloid positivity. Demographic variables, APOE genotype, and measures of cognition and function are considered as predictors. Model data were derived from the A4 trial. SETTING: TRC-PAD data are collected from web-based and in-person assessments and are used to predict the risk of elevated amyloid and assess eligibility for AD trials. PARTICIPANTS: Pre-randomization, cross-sectional data from the ongoing A4 trial are used to develop statistical models. MEASUREMENTS: Models use a range of cognitive tests and subjective memory assessments, along with demographic variables. Amyloid positivity in A4 was confirmed using positron emission tomography (PET). RESULTS: The A4 trial screened N=4,486 participants, of which N=1323 (29%) were classified as Aβ+ (SUVR ≥ 1.15). The Area under the Receiver Operating Characteristic curves for these models ranged from 0.60 (95% CI 0.56 to 0.64) for a web-based battery without APOE to 0.74 (95% CI 0.70 to 0.78) for an in-person battery. The number needed to screen to identify an Aβ+ individual is reduced from 3.39 in A4 to 2.62 in the remote setting without APOE, and 1.61 in the remote setting with APOE. CONCLUSIONS: Predictive algorithms in a web-based registry can improve the efficiency of screening in future secondary prevention trials. APOE status contributes most to predictive accuracy with cross-sectional data. Blood-based assays of amyloid will likely improve the prediction of amyloid PET positivity.

Download Full-text