Alzheimer DiseaseNCT06223438
Detecting an MCI and Amyloid Digital Neuro Signature(DNS) Using Altoida's Multimodal Digital Biomarkers.
An observational study of Alzheimer Disease and MCI, run by Altoida at 1 site in United States.
Trial summary
The purpose of this study is to establish multiple points of clinical validity for the Altoida digital biomarker assessment in individuals with a clinical diagnosis of cognitively normal (CN) and Mild Cognitive Impairment (MCI). Read more about this study
- Who is it for?
- Ages 50 years and olderHealthy volunteers can take part
- Conditions the study is for
- Alzheimer Disease, MCI
- How many people
- 614 participants
- Where
- 1 siteUnited States
- Study dates
- 12 Jan 2024to 10 Jan 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 50 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Participants will be identified as cognitively unimpaired or with MCI by a clinical diagnosis with defined adjudication criteria. For biomarker status determination, participants can be enrolled with a historical positive amyloid status assessment result through CSF analysis or amyloid-PET testing up to 18 months before the Altoida assessment. Historical amyloid negative data can be accepted up to 6-12 months before the Altoida assessment if MMSE>26. There will be at least 668 participants (334 CN; 334 MCI) (50+ years), enrolled globally across approximately six sites balanced for amyloid status (positive/negative). Underrepresented populations will be targeted with the goal of also maintaining an equal balance of males and females.
Who may be able to join
- Participants must provide written informed consent in the EC/IRB-approved informed consent form or have a Legally Authorized Representative (LAR) provide written consent on the participant's behalf;
- Male or female, 50+ years at the time of consent;
- Participants must be willing to comply with all study procedures as outlined in the informed consent;
- Fluency in the language of the tests used at the study site;
- At least four years of formal education (from primary school onwards);
- Adequate vision to complete the Altoida assessment and neuropsychological tests with or without corrective lenses;
Show all 9Show fewer
- Have undisturbed locomotion;
- Participants should have, when available, an amyloid status assessment result (positive or negative) through CSF analysis or amyloid-PET testing. Historical positive amyloid data is accepted up to 18 months before taking the Altoida assessment. Historical amyloid negative data can be accepted up to 6-12 months before the Altoida assessment if MMSE>26. If historical amyloid data is unavailable, determining amyloid status will be an optional component of the study protocol. The decision to include this assessment and the specific method employed will be collaboratively discussed and decided upon between the study sponsor and the respective study site;
- Optionally, participants might present, when available, a historical APOE, APP/PSEN1/2 genotype determination, and/or historical MRI/CT scan results relevant to clinical diagnosis.
Who cannot join
- ● Participants who have participated in a clinical trial longer than six months of any potential disease-modifying anti-amyloid AD treatment and remained active in the study for a duration of 6 or more months (i.e., continued receiving treatment);
- Participants who, in the opinion of the Site Principal Investigator, have serious or unstable medical conditions that would prohibit their completion of all study procedures and data collection or that would preclude their participation;
- Participants undergoing anticoagulant treatment or other blood dyscrasias, only if they need to undergo lumbar puncture for the assessment of amyloid pathology;
- Participants with a history of stroke or seizures within one year of study start;
- Participants with a history of chemotherapy within the past five years, or any type of malignancy or cancer that might interfere with the completion of the study, except for non-melanoma skin cancer or prostate cancer in situ;
- Participants with restrictions of performing physical activities or who are not ambulatory;
- Participants who have previously been enrolled in a study using the Altoida assessment
Trial locations and contacts
1 site in 1 country.
United States1 site
K2 Medical Research South Orlando
Orlando, Florida, 34711
About this study
The purpose of this study is to establish multiple points of clinical validity for the Altoida digital biomarker assessment in individuals with a clinical diagnosis of cognitively normal (CN) and Mild Cognitive Impairment (MCI). Participants will use the Altoida app and the de-identified sensor data captured by the device will be used to train specific machine-learning algorithms to recognize early symptoms of cognitive decline, such as MCI or MCI with likelihood of amyloid pathology, as measured by digital biomarkers (T1 - Visit 1). Participants will be invited for an additional visit to evaluate test-retest reliability (T1' - Visit 2). Optionally, an updated variation of the Altoida app will be tested over the course of two additional visits to ensure optimal digital assessment delivery based on best practices in neuropsychological testing, user experience design, and data collection integrity (T2 - Visit 3 and T2' - Visit 4).
Read the detailed description
Digital biomarkers are indicators of a person's health status as measured by a digital device. They are gaining traction in neurology research for their capacity to offer accurate, accessible, and continuous measures of cognitive performance that could enable earlier diagnoses. For individuals at risk of developing dementia, early and differential diagnosis is key to streamlining patient management, benefiting patients and healthcare systems alike.
While conventional neuropsychological assessments remain the gold standard for assessing cognitive and functional decline, these evaluations are lengthy (90-120 min), require a trained specialist, and are not free of bias and practice effects. In this context, digital biomarkers that enable the continuous and objective evaluation of multidimensional features assessing activities of daily living may have the potential to capture subtle changes in cognition and functional ability before the onset of cognitive decline.
The Altoida Digital Biomarker Platform enables an objective evaluation of an individual's cognitive and functional impairment. The Altoida platform consists of two parts: 1) a participant-facing assessment (tablet-based) and 2) a site-facing analytics and reporting web portal. The assessment evaluates cognitive and functional skills based on a series of motor and augmented reality (AR) tasks that mirror the engagement of the brain during activities of daily living (Figure 1). These activities include tapping and tracing shapes, as well as placing and finding virtual objects while faced with a distractor task. The assessment takes an average of 10 minutes (average cognitively normal) to 18 minutes (average MCI) to complete. The dashboard provides real-time analytics and integration of study data into clinical workflows. The platform is currently intended for investigational use only. It has not received FDA pre-marketing clearance or approval.
The platform evaluates multi-modal features, including micro-movements, speed, reaction times, and navigation trajectories, which are used to train specific machine-learning models, termed Digital Neuro Signature (DNS). Using machine learning, the digital biomarkers extracted by the Altoida assessment can be used to measure a patient's cognitive performance and to identify distinct clinical outcomes, such as MCI and MCI with likelihood of amyloid pathology in an ecological manner. The assessment also generates scores of specific brain domains of cognition defined by the DSM-V, such as learning and memory, executive function, complex attention, and perceptual-motor coordination derived from specific digital features scored with normative models (age and sex-adjusted). These are derived from specific digital features scored with normative models.
In previous studies, Altoida's digital biomarkers were found to be useful in detecting early cognitive decline and also in predicting progression to dementia. In recent years, the Altoida assessment has been used across several global research studies, confirming the ease of use, non-invasiveness, potential to identify cognitive impairment as well as correlations with neuropsychological assessments. Early clinical recognition of Alzheimer's disease (AD) is critical. There is currently no software-based tool approved by regulatory authorities to adjunctively diagnose individuals with MCI and amyloid positivity, which is a population with a greater likelihood of progressing to full AD dementia. Early and differential diagnosis could create opportunities for participation in clinical trials of disease-modifying therapies and assist drug developers with accelerating the enrollment of the right patients for the right therapies.
- Conditions
- Alzheimer Disease, MCI
- Keywords
- Augmented reality, Machine learning, MCI, Amyloid pathology, Alzheimer's disease, biomarkers, diagnosis
- Medical subject headings
- Alzheimer Disease, Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Cognitively normal (CN) | Participants must have an MMSE score of ≥26 and meet clinical criteria for cognitively normal based on National Institute of Aging (NIA) criteria verified in medical records or clinical assessment at first visit; ● Based on the judgment of the site PI, no evidence of functional decline based on the Functional Activities Questionnaire (FAQ) or equivalent assessment; | |
| Mild Cognitive Impairment (MCI) with known amyloid status. | Cognitive concern, reflecting a change in cognition reported by the participant, informant (family member, caregiver), or clinician; * Participants must have an MMSE score of ≥24 and meet clinical criteria for MCI based on National Institute of Aging (NIA) criteria and verified through medical records or clinical evaluation at first visit; * Based on the judgment of the site PI, minimal to mild functional impairment but with preservation of independence in functional abilities based on the Functional Activities Questionnaire (FAQ) or equivalent assessment; |
What is being measured
The primary outcome is the main question the study is designed to answer.
training and reinforcing a specific ML algorithm
Attainment of ROC-area under the curve (AUC) of atleast 0.75-0.80 for the identification of MCI vs CN
Measured: 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Jan 2024
- Study start12 Jan 2024
- Primary completion15 Dec 2024
- Study completion10 Jan 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (7)Papers the sponsor has linked to this study.
- Other trials for Alzheimer DiseaseStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 614 participants (actual)
Sponsor and investigators
- Lead sponsor
- Altoida
- Sponsor type
- Industry (pharma, biotech, devices)
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06223438
- Sponsor's protocol ID
- MCI-Amyloid-DNS-GT-001
- Status last verified
- Feb 2025
- First submitted
- 12 Jan 2024
- Last updated
- 19 Feb 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Ohman F, Hassenstab J, Berron D, Scholl M, Papp KV. Current advances in digital cognitive assessment for preclinical Alzheimer's disease. Alzheimers Dement (Amst). 2021 Jul 20;13(1):e12217. doi: 10.1002/dad2.12217. eCollection 2021.
PubMed 34295959Harms RL, Ferrari A, Meier IB, Martinkova J, Santus E, Marino N, Cirillo D, Mellino S, Catuara Solarz S, Tarnanas I, Szoeke C, Hort J, Valencia A, Ferretti MT, Seixas A, Santuccione Chadha A. Digital biomarkers and sex impacts in Alzheimer's disease management - potential utility for innovative 3P medicine approach. EPMA J. 2022 Jun 6;13(2):299-313. doi: 10.1007/s13167-022-00284-3. eCollection 2022 Jun.
PubMed 35719134Buegler M, Harms R, Balasa M, Meier IB, Exarchos T, Rai L, Boyle R, Tort A, Kozori M, Lazarou E, Rampini M, Cavaliere C, Vlamos P, Tsolaki M, Babiloni C, Soricelli A, Frisoni G, Sanchez-Valle R, Whelan R, Merlo-Pich E, Tarnanas I. Digital biomarker-based individualized prognosis for people at risk of dementia. Alzheimers Dement (Amst). 2020 Aug 19;12(1):e12073. doi: 10.1002/dad2.12073. eCollection 2020.
PubMed 32832589Jack CR Jr, Wiste HJ, Vemuri P, Weigand SD, Senjem ML, Zeng G, Bernstein MA, Gunter JL, Pankratz VS, Aisen PS, Weiner MW, Petersen RC, Shaw LM, Trojanowski JQ, Knopman DS; Alzheimer's Disease Neuroimaging Initiative. Brain beta-amyloid measures and magnetic resonance imaging atrophy both predict time-to-progression from mild cognitive impairment to Alzheimer's disease. Brain. 2010 Nov;133(11):3336-48. doi: 10.1093/brain/awq277. Epub 2010 Oct 8.
PubMed 20935035Hu Y, Kirmess KM, Meyer MR, Rabinovici GD, Gatsonis C, Siegel BA, Whitmer RA, Apgar C, Hanna L, Kanekiyo M, Kaplow J, Koyama A, Verbel D, Holubasch MS, Knapik SS, Connor J, Contois JH, Jackson EN, Harpstrite SE, Bateman RJ, Holtzman DM, Verghese PB, Fogelman I, Braunstein JB, Yarasheski KE, West T. Assessment of a Plasma Amyloid Probability Score to Estimate Amyloid Positron Emission Tomography Findings Among Adults With Cognitive Impairment. JAMA Netw Open. 2022 Apr 1;5(4):e228392. doi: 10.1001/jamanetworkopen.2022.8392.
PubMed 35446396Alcolea D, Pegueroles J, Munoz L, Camacho V, Lopez-Mora D, Fernandez-Leon A, Le Bastard N, Huyck E, Nadal A, Olmedo V, Sampedro F, Montal V, Vilaplana E, Clarimon J, Blesa R, Fortea J, Lleo A. Agreement of amyloid PET and CSF biomarkers for Alzheimer's disease on Lumipulse. Ann Clin Transl Neurol. 2019 Sep;6(9):1815-1824. doi: 10.1002/acn3.50873. Epub 2019 Aug 28.
PubMed 31464088Fowler CJ, Stoops E, Rainey-Smith SR, Vanmechelen E, Vanbrabant J, Dewit N, Mauroo K, Maruff P, Rowe CC, Fripp J, Li QX, Bourgeat P, Collins SJ, Martins RN, Masters CL, Doecke JD. Plasma p-tau181/Abeta1-42 ratio predicts Abeta-PET status and correlates with CSF-p-tau181/Abeta1-42 and future cognitive decline. Alzheimers Dement (Amst). 2022 Nov 25;14(1):e12375. doi: 10.1002/dad2.12375. eCollection 2022.
PubMed 36447478
Study at a glance
From the registry record, last updated by the sponsor on 19 Feb 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Detecting an MCI and Amyloid Digital Neuro Signature(DNS) Using Altoida's Multimodal Digital Biomarkers.
- ClinicalTrials.gov ID
- NCT06223438
- Acronym / sponsor ID
- MCI-Amyloid-DNS-GT-001
- Conditions
- Alzheimer Disease, MCI
- Keywords
- Augmented reality, Machine learning, MCI, Amyloid pathology, Alzheimer's disease, biomarkers, diagnosis
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 614 participants (actual)
- Lead sponsor
- AltoidaIndustry (pharma, biotech, devices)
- Study start
- 12 Jan 2024 (actual)
- Primary completion
- 15 Dec 2024 (actual)
- Study completion
- 10 Jan 2025 (actual)
- First posted
- 25 Jan 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The purpose of this study is to establish multiple points of clinical validity for the Altoida digital biomarker assessment in individuals with a clinical diagnosis of cognitively normal (CN) and Mild Cognitive Impairment (MCI). Participants will use the Altoida app and the de-identified sensor data captured by the device will be used to train specific machine-learning algorithms to recognize early symptoms of cognitive decline, such as MCI or MCI with likelihood of amyloid pathology, as measured by digital biomarkers (T1 - Visit 1). Participants will be invited for an additional visit to evaluate test-retest reliability (T1' - Visit 2). Optionally, an updated variation of the Altoida app will be tested over the course of two additional visits to ensure optimal digital assessment delivery based on best practices in neuropsychological testing, user experience design, and data collection integrity (T2 - Visit 3 and T2' - Visit 4).
Detailed description
Digital biomarkers are indicators of a person's health status as measured by a digital device. They are gaining traction in neurology research for their capacity to offer accurate, accessible, and continuous measures of cognitive performance that could enable earlier diagnoses. For individuals at risk of developing dementia, early and differential diagnosis is key to streamlining patient management, benefiting patients and healthcare systems alike.
While conventional neuropsychological assessments remain the gold standard for assessing cognitive and functional decline, these evaluations are lengthy (90-120 min), require a trained specialist, and are not free of bias and practice effects. In this context, digital biomarkers that enable the continuous and objective evaluation of multidimensional features assessing activities of daily living may have the potential to capture subtle changes in cognition and functional ability before the onset of cognitive decline.
The Altoida Digital Biomarker Platform enables an objective evaluation of an individual's cognitive and functional impairment. The Altoida platform consists of two parts: 1) a participant-facing assessment (tablet-based) and 2) a site-facing analytics and reporting web portal. The assessment evaluates cognitive and functional skills based on a series of motor and augmented reality (AR) tasks that mirror the engagement of the brain during activities of daily living (Figure 1). These activities include tapping and tracing shapes, as well as placing and finding virtual objects while faced with a distractor task. The assessment takes an average of 10 minutes (average cognitively normal) to 18 minutes (average MCI) to complete. The dashboard provides real-time analytics and integration of study data into clinical workflows. The platform is currently intended for investigational use only. It has not received FDA pre-marketing clearance or approval.
The platform evaluates multi-modal features, including micro-movements, speed, reaction times, and navigation trajectories, which are used to train specific machine-learning models, termed Digital Neuro Signature (DNS). Using machine learning, the digital biomarkers extracted by the Altoida assessment can be used to measure a patient's cognitive performance and to identify distinct clinical outcomes, such as MCI and MCI with likelihood of amyloid pathology in an ecological manner. The assessment also generates scores of specific brain domains of cognition defined by the DSM-V, such as learning and memory, executive function, complex attention, and perceptual-motor coordination derived from specific digital features scored with normative models (age and sex-adjusted). These are derived from specific digital features scored with normative models.
In previous studies, Altoida's digital biomarkers were found to be useful in detecting early cognitive decline and also in predicting progression to dementia. In recent years, the Altoida assessment has been used across several global research studies, confirming the ease of use, non-invasiveness, potential to identify cognitive impairment as well as correlations with neuropsychological assessments. Early clinical recognition of Alzheimer's disease (AD) is critical. There is currently no software-based tool approved by regulatory authorities to adjunctively diagnose individuals with MCI and amyloid positivity, which is a population with a greater likelihood of progressing to full AD dementia. Early and differential diagnosis could create opportunities for participation in clinical trials of disease-modifying therapies and assist drug developers with accelerating the enrollment of the right patients for the right therapies.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Cognitively normal (CN) | – | –Participants must have an MMSE score of ≥26 and meet clinical criteria for cognitively normal based on National Institute of Aging (NIA) criteria verified in medical records or clinical assessment at first visit; ● Based on the judgment of the site PI, no evidence of functional decline based on the Functional Activities Questionnaire (FAQ) or equivalent assessment; |
| Mild Cognitive Impairment (MCI) with known amyloid status. | – | –Cognitive concern, reflecting a change in cognition reported by the participant, informant (family member, caregiver), or clinician; * Participants must have an MMSE score of ≥24 and meet clinical criteria for MCI based on National Institute of Aging (NIA) criteria and verified through medical records or clinical evaluation at first visit; * Based on the judgment of the site PI, minimal to mild functional impairment but with preservation of independence in functional abilities based on the Functional Activities Questionnaire (FAQ) or equivalent assessment; |
Who may qualify
- Ages
- 50 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Participants will be identified as cognitively unimpaired or with MCI by a clinical diagnosis with defined adjudication criteria. For biomarker status determination, participants can be enrolled with a historical positive amyloid status assessment result through CSF analysis or amyloid-PET testing up to 18 months before the Altoida assessment. Historical amyloid negative data can be accepted up to 6-12 months before the Altoida assessment if MMSE>26. There will be at least 668 participants (334 CN; 334 MCI) (50+ years), enrolled globally across approximately six sites balanced for amyloid status (positive/negative). Underrepresented populations will be targeted with the goal of also maintaining an equal balance of males and females.
Key inclusion criteria (9 listed)
- Participants must provide written informed consent in the EC/IRB-approved informed consent form or have a Legally Authorized Representative (LAR) provide written consent on the participant's behalf;
Key exclusion criteria (7 listed)
- ● Participants who have participated in a clinical trial longer than six months of any potential disease-modifying anti-amyloid AD treatment and remained active in the study for a duration of 6 or more months (i.e., continued receiving treatment);
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
training and reinforcing a specific ML algorithm
Time frame: 6 months
Attainment of ROC-area under the curve (AUC) of atleast 0.75-0.80 for the identification of MCI vs CN
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 Feb 2025.
- Background: PubMed 34295959Ohman F, Hassenstab J, Berron D, Scholl M, Papp KV. Current advances in digital cognitive assessment for preclinical Alzheimer's disease. Alzheimers Dement (Amst). 2021 Jul 20;13(1):e12217. doi: 10.1002/dad2.12217. eCollection 2021.
- Background: PubMed 35719134Harms RL, Ferrari A, Meier IB, Martinkova J, Santus E, Marino N, Cirillo D, Mellino S, Catuara Solarz S, Tarnanas I, Szoeke C, Hort J, Valencia A, Ferretti MT, Seixas A, Santuccione Chadha A. Digital biomarkers and sex impacts in Alzheimer's disease management - potential utility for innovative 3P medicine approach. EPMA J. 2022 Jun 6;13(2):299-313. doi: 10.1007/s13167-022-00284-3. eCollection 2022 Jun.
- Background: PubMed 32832589Buegler M, Harms R, Balasa M, Meier IB, Exarchos T, Rai L, Boyle R, Tort A, Kozori M, Lazarou E, Rampini M, Cavaliere C, Vlamos P, Tsolaki M, Babiloni C, Soricelli A, Frisoni G, Sanchez-Valle R, Whelan R, Merlo-Pich E, Tarnanas I. Digital biomarker-based individualized prognosis for people at risk of dementia. Alzheimers Dement (Amst). 2020 Aug 19;12(1):e12073. doi: 10.1002/dad2.12073. eCollection 2020.
- Background: PubMed 20935035Jack CR Jr, Wiste HJ, Vemuri P, Weigand SD, Senjem ML, Zeng G, Bernstein MA, Gunter JL, Pankratz VS, Aisen PS, Weiner MW, Petersen RC, Shaw LM, Trojanowski JQ, Knopman DS; Alzheimer's Disease Neuroimaging Initiative. Brain beta-amyloid measures and magnetic resonance imaging atrophy both predict time-to-progression from mild cognitive impairment to Alzheimer's disease. Brain. 2010 Nov;133(11):3336-48. doi: 10.1093/brain/awq277. Epub 2010 Oct 8.
- Background: PubMed 35446396Hu Y, Kirmess KM, Meyer MR, Rabinovici GD, Gatsonis C, Siegel BA, Whitmer RA, Apgar C, Hanna L, Kanekiyo M, Kaplow J, Koyama A, Verbel D, Holubasch MS, Knapik SS, Connor J, Contois JH, Jackson EN, Harpstrite SE, Bateman RJ, Holtzman DM, Verghese PB, Fogelman I, Braunstein JB, Yarasheski KE, West T. Assessment of a Plasma Amyloid Probability Score to Estimate Amyloid Positron Emission Tomography Findings Among Adults With Cognitive Impairment. JAMA Netw Open. 2022 Apr 1;5(4):e228392. doi: 10.1001/jamanetworkopen.2022.8392.
- Background: PubMed 31464088Alcolea D, Pegueroles J, Munoz L, Camacho V, Lopez-Mora D, Fernandez-Leon A, Le Bastard N, Huyck E, Nadal A, Olmedo V, Sampedro F, Montal V, Vilaplana E, Clarimon J, Blesa R, Fortea J, Lleo A. Agreement of amyloid PET and CSF biomarkers for Alzheimer's disease on Lumipulse. Ann Clin Transl Neurol. 2019 Sep;6(9):1815-1824. doi: 10.1002/acn3.50873. Epub 2019 Aug 28.
- Background: PubMed 36447478Fowler CJ, Stoops E, Rainey-Smith SR, Vanmechelen E, Vanbrabant J, Dewit N, Mauroo K, Maruff P, Rowe CC, Fripp J, Li QX, Bourgeat P, Collins SJ, Martins RN, Masters CL, Doecke JD. Plasma p-tau181/Abeta1-42 ratio predicts Abeta-PET status and correlates with CSF-p-tau181/Abeta1-42 and future cognitive decline. Alzheimers Dement (Amst). 2022 Nov 25;14(1):e12375. doi: 10.1002/dad2.12375. eCollection 2022.
- Related InfoLink provided by the sponsor.
- Related InfoLink provided by the sponsor.
- Related InfoLink provided by the sponsor.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Alzheimer Disease and MCI, run by Altoida at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 9 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 19 Feb 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
training and reinforcing a specific ML algorithm (time frame: 6 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06223438, which also lists 7 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 19 Feb 2025.