神経画像および認知評価を用いた認知症リスク予測

Dementia affects over 55 million people globally, with numbers expected to double in the coming decades. Early detection is critical, yet traditional risk assessments relying on age, family history, and basic cognitive tests often fall short. This study explores whether combining structural brain imaging with brief cognitive assessments can more accurately predict dementia risk.

Using data from 312 older adults enrolled in the KU Alzheimer's Disease Center cohort, researchers evaluated two modeling approaches: one based on a single clinic visit and another using longitudinal data across multiple visits. Participants underwent cognitive testing and MRI scans, including measures of hippocampal volume, gray matter, and Alzheimer's disease signature regions. Depressive symptoms were also assessed using the Geriatric Depression Scale (GDS).

Results showed that models incorporating neuroimaging significantly outperformed those using demographics or cognitive scores alone. The best-performing model combined imaging and cognitive data, achieving 77.6% accuracy in predicting dementia status. Longitudinal models further improved prediction by capturing changes over time, with imaging features contributing most to explained variance. Key predictors included reduced hippocampal volume, lower gray matter, and higher GDS scores. These findings align with known patterns of neurodegeneration and suggest that depression may interact with brain changes to influence dementia risk.

Importantly, the study demonstrates that a compact, multimodal approaching standard MRI scans with brief cognitive tests-can generate individualized risk profiles suitable for clinical use. This method offers a scalable path to early intervention, trial enrollment, and personalized care planning. Future work will focus on validating these models in more diverse populations and integrating fluid biomarkers to enhance precision. Ultimately, this research supports the development of practical tools for forecasting dementia risk and advancing preventive strategies in aging populations.