Early Diagnosis of SCD Based on Radiogenomics

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

800 participants

Study Classification

OBSERVATIONAL

Study Start Date

2021-01-01

Study Completion Date

2025-12-31

Brief Summary

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The incidence of AD dementia is increasing due to the aging population, putting a heavy burden on our society and economics. Exploring the mechanisms underlying SCD due to preclinical AD has scientific and clinical significance. However, it is challenging to construct and validate the preclinical diagnosis model of AD with fused multimodel information across culture/race. From the cooperation during the past five years, we have established cohorts by synchronized assessment, achieved consensus on SCD features extraction and made a breakthrough in the application of multiple parameter MRI with German collaborators. Therefore, in this project, SCD with and without amyloid pathology will be compared by clinical and cognitive data, genetics, blood and MRI biomarkers between the German and Chinese. Key features will be extracted and specific characteristics of SCD due to preclinical AD as well as risk factors for conversion between two countries will be clarified. Then the diagnosis model of preclinical AD in SCD will be established across culture/race based on radiogenomics, which will improve the current diagnostic system of AD. Through this project, the value of SCD in the etiologic, anatomical and quantitative diagnosis of preclinical AD will be identified to improve sensitivity and specificity of preclinical AD diagnosis in clinical practice.

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Detailed Description

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The overall prevalence of dementia worldwide is increasing, imposing a heavy burden on the public and health care systems. Subjective cognitive decline (SCD), characterized by a self-report of decline in cognitive function without objective impairment in neuropsychological assessments, is considered a high risk factor for AD. SCD with amyloidopathy is considered as a first symptomatic indicator of the preclinical AD (SCD due to preclinical AD). However, how to construct and validate the preclinical diagnosis model of AD with fused multimodel information across culture/race remain unclear.

In the present study, all SCD participants from Germany and China will be conducted amyloid PET scanning, and they will be classified into two groups (SCD with amyloid+ and SCD with amyloid-) based on whether there is the evidence of amyloid deposition. The investigators will compare the clinical information, genetics, blood and multiple parameter MRI data between the German and Chinese to evaluate the common and specific features from different culture/race. Then, key features associated with amyloid deposition will be extracted for the establisment of diagnosis model of SCD due to preclinical AD, which will improve the current diagnostic system of AD. After four-year follow-up, SCD will be classified into SCD converter group and SCD non-converter group. Risk factors for conversion to cognitive impairment and dementia will be further extracted as predicted biomarkers.

Through this project, the value of SCD in the etiologic, anatomical and quantitative diagnosis of preclinical AD will be identified to improve sensitivity and specificity of preclinical AD diagnosis in clinical practice.

Conditions

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Alzheimer Disease Subjective Cognitive Decline Neuroimaging Gene

Study Design

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Observational Model Type

CASE_CONTROL

Study Time Perspective

PROSPECTIVE

Study Groups

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SCD subjects with positive amyloid

In this study, the participants are from two research centers in China and Germany. All participants will conduct amyloid PET scanning, after which they are classified into two grous (SCD with amyloid+ and SCD with amyloid-). SCD subjects with positive amyloid show the evidence of amyloid deposition in brain. They have higher risk of conversion to mild cognitive impairment and dementia compared with SCD with negative amyloid. They are also considered as preclinical AD.

Multiple features extraction

Intervention Type DIAGNOSTIC_TEST

In the present study, the "gold standard" of preclinical AD is amyloid PET. SCD with positive amyloid is the target population for early AD intervention. The investigators aim to extract the diagnostic features from multiple parameter MRI, genetic, blood and clinical data using Max-Relevance and Min-Redundancy (mRMR) algorithm. Then, based on support vector machine (SVM), random forest (RF) and multi-kernel learning (MKL) classification methods, the investigators will construct predicted diagnostic model of preclinical AD.

SCD subjects with negative amyloid

In this study, the participants are from two research centers in China and Germany. All participants will conduct amyloid PET scanning, after which they are classified into two grous (SCD with amyloid+ and SCD with amyloid-). SCD subjects with negative amyloid do not show the evidence of amyloid deposition in brain. They have lower risk of conversion to mild cognitive impairment and dementia compared with SCD with positive amyloid.

Multiple features extraction

Intervention Type DIAGNOSTIC_TEST

In the present study, the "gold standard" of preclinical AD is amyloid PET. SCD with positive amyloid is the target population for early AD intervention. The investigators aim to extract the diagnostic features from multiple parameter MRI, genetic, blood and clinical data using Max-Relevance and Min-Redundancy (mRMR) algorithm. Then, based on support vector machine (SVM), random forest (RF) and multi-kernel learning (MKL) classification methods, the investigators will construct predicted diagnostic model of preclinical AD.

Interventions

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Multiple features extraction

In the present study, the "gold standard" of preclinical AD is amyloid PET. SCD with positive amyloid is the target population for early AD intervention. The investigators aim to extract the diagnostic features from multiple parameter MRI, genetic, blood and clinical data using Max-Relevance and Min-Redundancy (mRMR) algorithm. Then, based on support vector machine (SVM), random forest (RF) and multi-kernel learning (MKL) classification methods, the investigators will construct predicted diagnostic model of preclinical AD.

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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Inclusion Criteria

* 60-79 years old, right-handed and Mandarin-speaking subjects;
* self-experienced persistent decline in cognitive capacity in comparison with a previously normal status and unrelated to an acute event;
* normal age-, gender- and education-adjusted performance on standardised cognitive tests;
* concerns (worries) associated with memory complaint;
* failure to meet the criteria for MCI or dementia

Exclusion Criteria

* a history of stroke;
* major depression (Hamilton Depression Rating Scale score \> 24 points);
* other central nervous system diseases that may cause cognitive impairment, such as Parkinson's disease, tumors, encephalitis and epilepsy;
* cognitive impairment caused by traumatic brain injury;
* systemic diseases, such as thyroid dysfunction, syphilis and HIV;
* a history of psychosis or congenital mental growth retardation

Minimum Eligible Age

60 Years

Maximum Eligible Age

79 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No