Mild cognitive impairment (MCI), a key prodromal stage of dementia, requires precise prognostic assessment to help delay disease progression. Given the high heterogeneity of MCI, any single indicator has limited predictive efficacy. Different clinical subtypes of MCI—such as amnestic MCI, non-amnestic MCI, and subjective cognitive decline—exhibit fundamental differences in pathological mechanisms and outcomes, forming the basis for stratified prognostic assessment. Abnormal sleep duration, physical functional decline, and psychiatric symptoms (depression, anxiety, apathy) are all indicative of cognitive decline risk to varying degrees and can serve as clinical observational indicators for evaluating MCI prognosis. Neuropsychological assessment scales and instrumental activities of daily living (IADL) measures together characterize patients' cognitive impairments, but their results are susceptible to educational and cultural influences. Core cerebrospinal fluid (CSF) biomarkers—including amyloid-β42 (Aβ42), phosphorylated tau (p-tau)181, and total tau (t-tau)—are highly correlated with the core pathology of Alzheimer's disease (AD) and can accurately predict MCI prognosis. Plasma biomarkers such as p-tau217, neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), and the Aβ42/Aβ40 ratio are suitable for screening and follow-up; combining CSF and plasma biomarkers enhances predictive performance. Serum markers like Klotho and insulin-like growth factor-1 (IGF-1) lack specificity, and their independent predictive value requires further validation. Multimodal neuroimaging, including structural MRI, functional MRI (revealing network compensation and decompensation), and positron emission tomography (PET) showing molecular pathological changes (Aβ and tau deposition), can form a complete chain of evidence linking molecular events to clinical phenotypes. Intelligent prediction models, ranging from basic risk stratification and static integrated models to longitudinal dynamic prediction, significantly improve the integration and predictive performance of multimodal data. Consequently, the prognostic assessment of MCI is moving away from a single modality toward a stepwise integrated approach: primary screening adopts the combination of "MCI subtype characteristics + MoCA core subtests (focusing on delayed recall and executive function) + plasma p-tau217 and GFAP"; the precise diagnostic phase adds structural MRI to assess hippocampal atrophy; for difficult cases and research settings, CSF testing, Aβ-PET, and Tau-PET are further introduced. Future research should focus on constructing dynamic monitoring frameworks and deepening mechanistic exploration of modifiable risk factors, thereby advancing individualized prognostic management and early intervention strategies for MCI.
LIN et al. (2026) studied this question.