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Detection of Alzheimer's Disease (AD)-Related Seeds for AD Diagnosis

Detection of Alzheimer's Disease (AD)-Related Seeds as Biomarkers for Accurate Diagnosis of AD(AD-seeds-detector)

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT04850053
Enrollment
1500
Registered
2021-04-20
Start date
2020-08-26
Completion date
2026-12-31
Last updated
2026-03-23

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Alzheimer's Disease

Keywords

Alzheimer's disease

Brief summary

The study will investigate the biomarkers of Aβ and Tau seeds in plasma detected by Alzheimer's disease (AD) related seeds quantitative detector (AD-seeds-detector), and their sensitivity and specificity in diagnosing AD, compared with those from age-matched cognitively normal controls, and those with other types of dementia. To perform a high throughput analysis of the amount of Aβ and Tau seeds, the investigators have developed an AD-seeds-detector, in which a fluorescence microplate reader was combined with an oscillating mixer or water-bath-type ultrasonicator.

Detailed description

Aβ and Tau seeds have the potential to serve as biomarkers for AD. The AD-seeds-detector could detect small quantities of Aβ and Tau seeds by taking advantage of their ability to nucleate and enhance aggregation, enabling a very high amplification of the signal. This study examines the effectiveness of using the AD-seeds-detector as a novel technique for discriminating AD from cognitively normal control and non-AD dementia by detecting small Aβ and Tau seeds in plasma. This will be an observational study aiming at using the AD-seeds-detector to detect minute amounts of Aβ and Tau seeds in plasma as novel biomarkers with high sensitivity and specificity for the accurate diagnosis of AD. To achieve this goal, the investigators will conduct two studies using the AD-seeds-detector to detect the Aβ and Tau seeds in the plasma samples. Study one: A single-center cohort that consists of well-characterized AD patients (n=150), cognitively normal controls (n=100) and non-AD dementia patients (n=50). Study two: A multi-center cohort with well-characterized AD patients (n=400), cognitively normal controls (n=400) and non-AD dementia patients (n=400).

Interventions

None listed

Sponsors

Capital Medical University
Lead SponsorOTHER
Shandong Provincial Hospital
CollaboratorOTHER_GOV
Xiangya Hospital of Central South University
CollaboratorOTHER
Zhejiang Provincial People's Hospital
CollaboratorOTHER
Beijing Geriatric Hospital
CollaboratorOTHER
Kaifeng Central Hospital
CollaboratorOTHER
The First Affiliated Hospital of Chongqing Medical Universty
CollaboratorUNKNOWN
Huashan Hospital
CollaboratorOTHER
First Affiliated Hospital Xi'an Jiaotong University
CollaboratorOTHER
West China Hospital
CollaboratorOTHER
Henan Provincial People's Hospital
CollaboratorOTHER

Study design

Observational model
CASE_CONTROL
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
55 Years to 75 Years
Healthy volunteers
Yes

Inclusion criteria

* Aged 55-75. Written informed consent obtained from participant or legal guardian prior to any study-related procedures. The diagnosis of AD is made using the National Institute on Aging and the Alzheimer's Association (NIA-AA) criteria. As for non-AD dementia, the McKeith criteria are used for DLB,the revised diagnostic criteria proposed by the International behavioral variant (bvFTD) Criteria Consortium for bvFTD,the Gorno-Tempini criteria for the semantic variant FTD or non-fluent aphasia, the Movement Disorder Society Task Force criteria for PDD, the vascular behavioral and cognitive disorders (Vas-Cog) criteria for VaD, the Armstrong's criteria for CBD, the CDC's diagnostic criteria for CJD, etc. In addition, normal cognition is supported by MMSE, CDR and other cognitive function scales.

Exclusion criteria

* Other medical or psychiatric illness. No one can serve as an informant. Refused to complete a cognitive test and provide biospecimen.

Design outcomes

Primary

MeasureTime frameDescription
The area under curve of the AD-seeds-detector for the accurate diagnosis of ADtwo yearsThe area under curve is used to show the ability of the AD-seeds-detector to diagnose AD. The value of area under curve is higher, then the ability of the AD-seeds-detector to diagnose AD is stronger.

Secondary

MeasureTime frameDescription
The sensitivitytwo yearsThe sensitivity is used to show the ability of the AD-seeds-detector to diagnose AD patients, and is represented by true positive/ (true positive +false negative).
The specificitytwo yearsThe specificity is used to show the ability of the AD-seeds-detector to avoid false AD patients and rule out AD patients, and is represented by true negative/ (false positive + true negative).
The positive predictive valuetwo yearsThe positive predictive value is used to show the ability of the AD-seeds-detector to correctly label AD patients who test positive, and is represented by true positive / (true positive + false positive)
The negative predictive valuetwo yearsThe negative predictive value is used to show the ability of the AD-seeds-detector to correctly label people who test negative, and is represented by true negative / (false negative + true negative)
Cellular toxcity of Aβ seeds proteintwo yearsToxcity of Aβ seeds protein on cells
Morphology and structure of Aβtwo yearsComparison of Morphology and structure of beta-amyloid protein between difference groups

Countries

China

Contacts

CONTACTJianping Jia, Doctor
jiajp@vip.126.com8610-83199449

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Mar 24, 2026