Identification of Neuroinflammation and Neuroimaging Biomarkers Using Artificial Intelligence to Understand Dementia Risk in Older Adults and Improve Prevention A Study of Multidomain Training Effects in Mild Cognitive Impairment and Subjective Cognitive Decline

What this Trial Is About

This research aims to identify neuroinflammation and neuroimaging biomarkers in older adults aged 65 to 89 years at risk of dementia, including those with Mild Cognitive Impairment (MCI) and Subjective Cognitive Decline (SCD). By applying advanced data-driven artificial intelligence clustering techniques to brain imaging and inflammatory data, the study seeks to better understand the heterogeneity of cognitive decline and to develop personalized early interventions for subjects with high risk of progression to dementia. Participants include 35 MCI individuals in a training group, 35 MCI individuals in a placebo group, and 75 SCD subjects. The intervention involves a 7-month multidomain training program combining cognitive exercises, physical aerobic training, and music therapy. Cognitive training consists of 8 cycles of 18 sessions, focusing on multiple cognitive functions and increasing in complexity. Physical training includes aerobic exercise on a cycle ergometer with gradually increasing duration, followed by strength and flexibility exercises. Some participants will receive training while others will be observed in their natural history. Throughout the study, cognitive assessments will be conducted at multiple time points up to 28 months depending on the group. Brain MRI scans assessing structure and cerebral blood flow (CBF) will be performed at baseline and follow-up visits. Blood samples will be collected to analyze immune cell populations and inflammatory markers. Outcomes measured include cognitive performance, brain imaging changes, and inflammatory markers. The study also includes data analysis using clustering methods to identify subgroups and patterns related to cognitive decline and risk of dementia.

Identification of Neuroinflammation and Neuroimaging Biomarkers Through Data Driven Artificial Intelligence Techniques for Unraveling the Heterogeneity of Aged Subjects at Risk of Dementia and to Better Inform Prevention Strategies