Establish Diagnostic and Prognostic Models for Preclinical AD Patients Based on Multimodal MRI, Behavioral, Genetic, and Plasma Biomarkers
- Conditions
- Alzheimer Disease, Late OnsetMild Cognitive ImpairmentSubjective Cognitive Decline
- Interventions
- Other: Multimodal magnetic resonance imaging scanning, behavioral, genetic and plasma biomarker testing
- Registration Number
- NCT06561906
- Lead Sponsor
- The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
- Brief Summary
To establish the diagnostic and prognostic models that could help the preclinical identification of subjects at higher risk of clinical progression to mild cognitive impairment and dementia based on combined features of baseline demographic, cognitive, behavioral, multimodal MRI, genetic, and plasma data.
- Detailed Description
Alzheimer's disease (AD) is a global concern. Due to the lack of effective therapeutic methods targeting late-stage AD patients, it is critical to investigate brain alterations in the preclinical stage to pave the way for early diagnosis and intervention. Structural and functional magnetic resonance imaging (MRI) has been proven to be an effective and non-invasive approach to explore the neural mechanisms underlying neurological disorders. Genetic factors such as apolipoprotein E and plasma biomarkers play important roles in AD development and progression. However, the interaction effects of risk genes and different pathologic pathways implicated in the pathogenesis of AD remain unclear. Furthermore, the diagnostic and prognostic models that could predict future cognitive decline or clinical progression based on objective features derived from baseline demographic, cognitive, behavioral, multimodal MRI, genetic, and plasma data need to be further explored.
We aim to investigate the neural basis underlying early cognitive deficits using structural and functional MRI data combined with novel analytical methods such as dynamic functional connectivity, surface-based morphometry, graph theory, multilayer network, functional-structural coupling, hidden Markov model, and connectome gradient mapping. Secondly, to explore the interaction effects of risk genes, which may help a better illustration of different biological pathways implicated in the pathogenesis of Alzheimer's disease. Thirdly, to investigate the divergent and dynamic abnormalities of multimodal imaging markers across different stages of Alzheimer's disease and their associations with plasma biomarkers, which may enhance our understanding of the neuropathological mechanisms. Fourthly, to provide scientific evidence on the potential targets for early intervention of neurodegenerative diseases. Lastly, to establish the diagnostic and prognostic models that could help the preclinical identification of subjects at higher risk of clinical progression to mild cognitive impairment and dementia based on combined features of baseline multimodal biomarkers. These studies may help a better understanding of the neural and biological basis underlying AD and pave the way for early diagnosis and intervention.
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- All
- Target Recruitment
- 1000
- The inclusion criteria were 50-79 years old and having 8 or more years of education.
- Participants with a history of stroke, other neurological disorders that could lead to cognitive impairment (Parkinson's disease, encephalitis, epilepsy, brain tumors, etc.), severe anxiety or depression, and contraindications for magnetic resonance imaging (MRI) were not enrolled.
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description Subjective cognitive decline Multimodal magnetic resonance imaging scanning, behavioral, genetic and plasma biomarker testing Subjects who complain of memory decline within the last 5 years and express worries associated with memory decline and do not meet the diagnostic criteria for MCI are defined as subjective cognitive decline (SCD). Alzheimer's disease dementia Multimodal magnetic resonance imaging scanning, behavioral, genetic and plasma biomarker testing Patients are diagnosed as AD dementia by an experienced neurologist based on MMSE and CSF/PET biomarker evidence. Normal control Multimodal magnetic resonance imaging scanning, behavioral, genetic and plasma biomarker testing Subjects without memory complaints and associated worries and do not meet the diagnostic criteria for mild cognitive impairment (MCI) are recruited as normal control (NC). Mild cognitive impairment Multimodal magnetic resonance imaging scanning, behavioral, genetic and plasma biomarker testing Participants are considered MCI patients with scores \>1 standard deviation (SD) below the normative means in both subtests within one cognitive domain or \>1 SD below the normative means in three single tests in three different domains.
- Primary Outcome Measures
Name Time Method the area under the curve of the classification analysis between progressors and nonprogressors Baseline, Year 1, Year 2, Year 3 We'll measure the area under the curve of the ROC curves based on combined features of baseline demographic, cognitive, behavioral, multimodal MRI, genetic, and plasma data in discriminating those convert to MCI or AD (progressors) from those do not convert (nonprogressors)
- Secondary Outcome Measures
Name Time Method mediation effects of MRI on the associations between gene/plasma biomarker and cognition/behavior Baseline We'll explore whether MRI features could act as mediators between genetic factors and cognition or behavior, as well as between plasma biomarkers and cognition or behavior.
Trial Locations
- Locations (1)
Nanjing Drum Tower Hospital
🇨🇳Nanjing, Jiangsu, China