Amyloid Prediction in Early Stage Alzheimer's Disease Through Speech Phenotyping - FUTURE Extension (FUTURE-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 - AMYPRED-US FUTURE Extension Study.

The primary objective of the study is to evaluate whether a set of algorithms analysing acoustic and linguistic patterns of speech, can predict change in Preclinical Alzheimer's Clinical Composite with semantic processing (PACC5) between baseline and +12 month follow up across all four Arms, as measured by the coefficient of individual agreement (CIA) between the change in PACC5 and the corresponding regression model, trained on baseline speech data to predict it. Secondary objectives include (1) evaluating whether similar algorithms can predict change in PACC5 between baseline and +12 month follow up in the cognitively normal (CN) and MCI populations separately; (2) evaluating whether similar algorithms trained to regress against PACC5 scores at baseline, still regress significantly against PACC5 scores at +12 month follow-up, as measured by the coefficient of individual agreement (CIA) between the PACC5 composite at +12 months and the regression model, trained on baseline speech data to predict PACC5 scores at baseline; (3) evaluating whether similar algorithms can classify converters vs non-converters in the cognitively normal Arms (Arm 3 + 4), and fast vs slow decliners in the MCI Arms (Arm 1 + 2), as measured by the Area Under the Curve (AUC) of the receiver operating characteristic curve, sensitivity, specificity and Cohen's kappa of the corresponding binary classifiers. Secondary objectives include the objectives above, but using time points of +24 months and +36 months; and finally to evaluate whether the model performance for the objectives and outcomes above improved if the model has access to speech data at 1 week, 1 month, and 3 month timepoints.

Study Overview

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

• Study Type: Observational
• Enrollment (Actual): 42

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

• Sampling Method: Non-Probability Sample

Study Population

Participants will be identified from participants of the AMYPRED-US study.

Description

Inclusion Criteria:

• Subjects are fully eligible for and have completed the AMYPRED-US (Amyloid Prediction in early stage Alzheimer's disease from acoustic and linguistic patterns of speech) study.

(See https://clinicaltrials.gov/ct2/show/NCT04928976)

- Subject consents to take part in FUTURE extension study.

Exclusion Criteria:

• Subject hasn't completed the full visit day in the AMYPRED-US study.

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
The agreement between the change in the PACC5 composite between baseline and +12 months and the corresponding regression model, trained on baseline speech data, predicting in all four Arms, as measured by the coefficient of individual agreement (CIA).	12 months

Secondary Outcome Measures

Outcome Measure	Time Frame
The agreement between the change in the PACC5 composite between baseline and +24 months and the corresponding regression model, trained on baseline speech data, predicting in all four Arms, as measured by the coefficient of individual agreement (CIA).	24 months
The agreement between the change in the PACC5 composite between baseline and +36 months and the corresponding regression model, trained on baseline speech data, predicting in all four Arms, as measured by the coefficient of individual agreement (CIA).	36 months
The agreement between the change in the PACC5 composite between baseline and +12 months and the corresponding regression model, trained on baseline speech data, to predict it in the CN Arms (Arms 3 and 4), as measured by the CIA.	12 months
The agreement between the change in the PACC5 composite between baseline and +24 months and the corresponding regression model, trained on baseline speech data, to predict it in the CN Arms (Arms 3 and 4), as measured by the CIA.	24 months
The agreement between the change in the PACC5 composite between baseline and +36 months and the corresponding regression model, trained on baseline speech data, predicting it in the MCI Arms (Arms 1 and 2), as measured by the CIA.	36 months
The agreement between the PACC5 composite at +12 months and the corresponding regression model, trained on baseline speech data, predicting in all four Arms based on +12 month speech data, as measured by the coefficient of individual agreement (CIA).	12 months
The agreement between the PACC5 composite at +24 months and the corresponding regression model, trained on baseline speech data, predicting in all four Arms based on +12 month speech data, as measured by the coefficient of individual agreement (CIA).	24 months
The agreement between the PACC5 composite at +36 months and the corresponding regression model, trained on baseline speech data, predicting in all four Arms based on +12 month speech data, as measured by the coefficient of individual agreement (CIA).	36 months
The agreement between the PACC5 composite and the corresponding regression model, trained on baseline speech data and +12 month speech data, as measured by the coefficient of individual agreement (CIA).	12 months
The agreement between the PACC5 composite and the corresponding regression model, trained on baseline speech data and +24 month speech data, as measured by the coefficient of individual agreement (CIA).	24 months
The agreement between the PACC5 composite and the corresponding regression model, trained on baseline speech data and +36 month speech data, as measured by the coefficient of individual agreement (CIA).	36 months
The AUC of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +12 months.	12 months
The AUC of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +24 months.	24 months
The AUC of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +36 months.	36 months
The sensitivity of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +12 months.	12 months
The sensitivity of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +24 months.	24 months
The sensitivity of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +36 months.	36 months
The specificity of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +12 months.	12 months
The specificity of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +24 months.	24 months
The specificity of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +36 months.	36 months
The Cohen's kappa of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +12 months.	12 months
The Cohen's kappa of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +24 months.	24 months
The Cohen's kappa of the binary classifier distinguishing between converters vs non-converters in the cognitively normal (CN) Arms (Arms 3 and 4); converters defined as having a CDR Global score of 0.5 or more at +36 months.	36 months
The AUC of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +12 months.	12 months
The AUC of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +24 months.	24 months
The AUC of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +36 months.	36 months
The sensitivity of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +12 months.	12 months
The sensitivity of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +24 months.	24 months
The sensitivity of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +36 months.	36 months
The specificity of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +12 months.	12 months
The specificity of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +24 months.	24 months
The specificity of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +36 months.	36 months
The Cohen's kappa of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +12 months.	12 months
The Cohen's kappa of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +24 months.	24 months
The Cohen's kappa of the binary classifier distinguishing between fast decliners vs slow decliners in the MCI Arms (Arms 1 and 2) at +36 months.	36 months

Collaborators and Investigators

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

Study record dates

Study Major Dates

• Study Start (Actual): January 21, 2021
• Primary Completion (Actual): November 21, 2022
• Study Completion (Actual): May 28, 2024

Study Registration Dates

• First Submitted: June 17, 2021
• First Submitted That Met QC Criteria: June 30, 2021
• First Posted (Actual): July 6, 2021

Study Record Updates

• Last Update Posted (Actual): June 14, 2024
• Last Update Submitted That Met QC Criteria: June 12, 2024
• Last Verified: June 1, 2024

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-0110-1

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