Amyloid Prediction in Early Stage Alzheimer's Disease Through Speech Phenotyping (AMYPRED-US)

A Study to Evaluate the Ability of Speech- and Language-based Digital Biomarkers to Detect and Characterise Prodromal and Preclinical Alzheimer's Disease in a Clinical Setting

The primary objective of the study is to evaluate whether a set of algorithms analysing acoustic and linguistic patterns of speech can detect amyloid-specific cognitive impairment in early stage Alzheimer's disease, as measured by the AUC of the receiver operating characteristic (ROC) curve of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) Arms. Secondary objectives include (1) evaluating whether similar algorithms can detect amyloid-specific cognitive impairment in the cognitively normal (CN) and MCI Arms respectively, as measured on binary classifier performance; (2) whether they can detect MCI, as measured on binary classifier performance (AUC, sensitivity, specificity, Cohen's kappa), and the agreement between the PACC5 composite and the corresponding regression model predicting it in all Arms pooled (Wilcoxon signed-rank test, CIA); (3) evaluating variables that can impact performance of such algorithms of covariates from the speaker (age, gender, education level) and environment (measures of acoustic quality).

Study Overview

• Status: Completed
• Conditions: Alzheimer Disease; Mild Cognitive Impairment; Prodromal Alzheimer's Disease; Alzheimer's Disease (Incl Subtypes); Preclinical Alzheimer's Disease

• Study Type: Observational
• Enrollment (Actual): 67

Contacts and Locations

Study Locations

• United States
  • California
    • Santa Ana, California, United States, 92705
      • Syrentis Clinical Research

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 50 years to 85 years (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Participants will be identified primarily through data-base searches of the Investigator sites.

Description

Inclusion Criteria:

• Amyloid status must be known, based on an amyloid PET scan or CSF amyloid test, no older than 30 months at the time of consent for Arm 2 and Arm 4 participants (amyloid negative Arms).
• Amyloid status must be known, based on an amyloid PET scan or CSF amyloid test, no older than 60 months at the time of consent for Arm 1 and Arm 3 (amyloid positive Arms).
• Subjects must be aged 50-85 (inclusive).
• Subjects must have MMSE scores of 23-30 (inclusive) based on a test not older than 1 month at the time of the visit.
• Date of diagnosis (if applicable) maximum of five years prior to consent.
• Subjects' first language must be English.
• Willing to participate in a study investigating speech and dementia.
• Availability of a person ('caregiver') who in the investigator's judgment has frequent and sufficient in-person contact with the participant, and is able to provide accurate information regarding the participant's cognitive and functional abilities. This is most likely met when living with a caregiver.
• Able to provide valid informed consent.
• Able to use, or has a caregiver who is able to use a smartphone device.
• Has access to a smartphone device running an operation system of Android 6 or above; or iOS 10 or above.

If taking part in the study through virtual visits, the following inclusion criteria also applies:

• Able to use, or has a caregiver who is able to use a personal computer, notebook or tablet.
• Has access to a personal computing device of that is:
• Running an operating system of macOS X with macOS 10.9 or later; or Windows 7 or above; or Ubuntu 12.04 or higher; or
• Have access to one of following internet browser software Internet Explorer version 11 or above; or Microsoft Edge version 12 or above; or Firefox version 27 or above; or Google Chrome version 30 or above; or Safari version 7 or above; capable of audio and video recording; and able to connect to the internet.

Exclusion Criteria:

• Clinically significant unstable psychiatric illness in 6 months.
• Diagnosis of General Anxiety Disorder.
• Current, or history within the past 2 years of major depressive disorder diagnosis (according to DSM-5 criteria); or psychiatric symptoms that, in the opinion of the investigator, could interfere with study procedures.
• History or presence of stroke within the past 2 years.
• Documented history of transient ischemic attack or unexplained loss of consciousness within the last 12 months.
• The participant is using drugs to treat symptoms related to AD, and the doses of these drugs were not stable for at least 8 weeks prior to consent.

Study Plan

How is the study designed?

Number of groups / cohorts

4

Cohorts and Interventions

Group / Cohort
Arm 2: MCI amyloid negative; Non-AD Mild Cognitive Impairment (MCI); Negative amyloid PET or amyloid CSF status.; MMSE 23-30 (inclusive)
Arm 3: CN amyloid positive; Absence of a diagnosis of cognitive disorder and/or subjectively reported cognitive decline; Positive amyloid PET or amyloid CSF status.; MMSE 26-30 (inclusive)
Arm 4: CN amyloid negative; Absence of a diagnosis of cognitive disorder and/or subjectively reported cognitive decline; Negative amyloid PET or amyloid CSF status.; MMSE 26-30 (inclusive)
Arm 1: MCI amyloid positive; Meet the National Institute of Aging - Alzheimer's Association (NIA-AA) core clinical criteria (2011) for MCI due to Alzheimer's; Positive amyloid PET or amyloid CSF status.; MMSE 23-30 (inclusive)

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Time Frame
Area under the curve (AUC) of the receiver operating characteristic (ROC) curve of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) Arms using speech recordings as input.	baseline

Secondary Outcome Measures

Outcome Measure	Time Frame
The sensitivity of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) Arms.	baseline
The specificity of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) Arms.	baseline
The Cohen's kappa of the binary classifier distinguishing between amyloid positive (Arms 1 and 3) and amyloid negative (Arms 2 and 4) Arms.	baseline
The sensitivity of the binary classifier distinguishing between amyloid positive cognitively normal (CN) (Arm 3) and amyloid negative cognitively normal (CN) (Arm 4) Arms.	baseline
The specificity of the binary classifier distinguishing between amyloid positive cognitively normal (CN) (Arm 3) and amyloid negative cognitively normal (CN) (Arm 4) Arms.	baseline
The Cohen's kappa of the binary classifier distinguishing between amyloid positive cognitively normal (CN) (Arm 3) and amyloid negative cognitively normal (CN) (Arm 4) Arms.	baseline
The AUC of the binary classifier distinguishing between amyloid positive cognitively normal (CN) (Arm 3) and amyloid negative cognitively normal (CN) (Arm 4) Arms.	baseline
The sensitivity of the binary classifier distinguishing between amyloid positive MCI (Arm 1) and amyloid negative MCI (Arm 2) Arms.	baseline
The specificity of the binary classifier distinguishing between amyloid positive MCI (Arm 1) and amyloid negative MCI (Arm 2) Arms.	baseline
The Cohen's kappa of the binary classifier distinguishing between amyloid positive MCI (Arm 1) and amyloid negative MCI (Arm 2) Arms.	baseline
The AUC of the binary classifier distinguishing between amyloid positive MCI (Arm 1) and amyloid negative MCI (Arm 2) Arms.	baseline
The sensitivity of the binary classifier distinguishing between the MCI (Arms 1 and 2) and the CN (Arms 3 and 4) Arms.	baseline
The specificity of the binary classifier distinguishing between the MCI (Arms 1 and 2) and the CN (Arms 3 and 4) Arms.	baseline
The Cohen's kappa of the binary classifier distinguishing between the MCI (Arms 1 and 2) and the CN (Arms 3 and 4) Arms.	baseline
The AUC of the binary classifier distinguishing between the MCI (Arms 1 and 2) and the CN (Arms 3 and 4) Arms.	baseline
For each classifier/regressor in outcome 1-16, the correlation between the AUC/CIA and each age group, gender and speech-to-reverberation modulation energy ratio group, as measured by the Kendall rank correlation coefficient.	baseline
The agreement between the PACC5 composite and the corresponding regression model predicting it in all four Arms, as measured by the coefficient of individual agreement (CIA).	baseline

Collaborators and Investigators

• Sponsor: Novoic Limited
• Investigators: Principal Investigator: Emil Fristed, MSc, Novoic Limited

Study record dates

Study Major Dates

• Study Start (Actual): January 22, 2021
• Primary Completion (Actual): July 30, 2021
• Study Completion (Actual): July 30, 2021

Study Registration Dates

• First Submitted: May 20, 2021
• First Submitted That Met QC Criteria: June 14, 2021
• First Posted (Actual): June 16, 2021

Study Record Updates

• Last Update Posted (Actual): September 5, 2021
• Last Update Submitted That Met QC Criteria: September 3, 2021
• Last Verified: June 1, 2021

More Information

Terms related to this study

• Keywords: Language; Alzheimer's disease; Mild Cognitive Impairment; Artificial Intelligence; Speech; Machine Learning; Amyloid; Preclinical Alzheimer's disease; Prodromal Alzheimer's disease; Normal Cognition; Acoustic; Linguistic
• Additional Relevant MeSH Terms: Mental Disorders; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Neurocognitive Disorders; Neurodegenerative Diseases; Dementia; Tauopathies; Cognition Disorders; Alzheimer Disease; Cognitive Dysfunction
• Other Study ID Numbers: NOV-0110

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No