[Progress on prognostic assessment methods for mild cognitive impairment]

Area of Science:

• Neurology
• Biomarkers
• Neuroimaging

Background:

• Mild cognitive impairment (MCI) is a precursor to dementia, necessitating precise prognostic assessment due to its heterogeneity.
• Single indicators have limited predictive power for MCI progression; stratified assessment based on subtypes is crucial.
• Clinical indicators like sleep, physical function, and psychiatric symptoms, alongside neuropsychological tests, offer insights but can be influenced by external factors.

Purpose of the Study:

• To outline a multimodal, stepwise approach for accurate prognostic assessment of mild cognitive impairment (MCI).
• To integrate diverse indicators, including clinical data, biomarkers, and neuroimaging, for enhanced MCI prediction.
• To establish a framework for individualized prognostic management and early intervention strategies for MCI.

Main Methods:

• Utilized core cerebrospinal fluid (CSF) biomarkers (Aβ42, P-tau181, T-tau) and plasma biomarkers (P-tau217, GFAP, Aβ42/Aβ40 ratio) for predictive assessment.
• Employed multimodal neuroimaging techniques, including structural MRI, functional MRI, and PET scans, to visualize pathological changes.
• Developed intelligent prediction models integrating multimodal data for improved prognostic performance, from risk stratification to dynamic prediction.

Main Results:

• Core CSF and plasma biomarkers show high correlation with Alzheimer's pathology and accurately predict MCI prognosis.
• Multimodal neuroimaging provides a comprehensive link between molecular events and clinical phenotypes.
• Intelligent prediction models significantly enhance the fusion predictive performance of multimodal data for MCI prognosis.

Conclusions:

• MCI prognostic assessment is shifting towards an integrated, stepwise approach combining clinical data, neuropsychological tests, and blood biomarkers.
• Structural MRI, CSF analysis, and PET imaging are introduced for precise diagnosis and in complex cases.
• Future research should focus on dynamic monitoring and modifiable risk factors for individualized MCI management and early intervention.