Early Diagnosis of SCD Based on Radiogenomics
Study Details
Study Description
Brief Summary
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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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Detailed Description
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.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| 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. |
Diagnostic Test: 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.
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| 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. |
Diagnostic Test: 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.
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Outcome Measures
Primary Outcome Measures
- Biomarkers associated with amyloid deposition [Four years]
SCD with the evidence of brain amyloid deposition is considered as preclinical AD. The investigators aim to extract multiple features involving neuroimaging, gene sequencing, blood, and clinical data to identify individuals with amyloid+ from SCD persons. These features associated with amyloid deposition are valuable biomarkers for early diagnosis of AD.
Secondary Outcome Measures
- Predicted biomarkers associated with conversion to cognitive impairment [Four years]
Individuals with SCD will be followed for four years. The investigators aim to characterize those who convert to mild cognitive impairment or dementia during the follow-up, and further find the predicted factors associated with the progression of AD.
Eligibility Criteria
Criteria
Inclusion Criteria:
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60-79 years old, right-handed and Mandarin-speaking subjects;
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self-experienced persistent decline in cognitive capacity in comparison with a previously normal status and unrelated to an acute event;
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normal age-, gender- and education-adjusted performance on standardised cognitive tests;
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concerns (worries) associated with memory complaint;
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failure to meet the criteria for MCI or dementia
Exclusion Criteria:
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a history of stroke;
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major depression (Hamilton Depression Rating Scale score > 24 points);
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other central nervous system diseases that may cause cognitive impairment, such as Parkinson's disease, tumors, encephalitis and epilepsy;
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cognitive impairment caused by traumatic brain injury;
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systemic diseases, such as thyroid dysfunction, syphilis and HIV;
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a history of psychosis or congenital mental growth retardation
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Department of Neurolgy, Xuanwu Hospital of Capital Medical University | Beijing | Beijing | China | 100053 |
Sponsors and Collaborators
- XuanwuH 2
- University of Cologne
Investigators
- Principal Investigator: Ying Han, PhD, Xuanwu Hospital of Capital Medical University
- Principal Investigator: Jessen Frank, PhD, University of Cologne
Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- HanYingsc5