Personalized aging pattern for early risk detection and prevention of cognitive impairment and dementia in cognitively healthy individuals

The project aims to improve dementia prevention by developing and validating a personalized medicine methodology for the detection of early signs of cognitive decline and markers enabling early and personalized multidomain interventions.

Curative drugs for dementia and Alzheimer’s disease (AD) are not yet widely available, but a multidomain lifestyle intervention (FINGER randomized controlled trial (RCT)) showed improved cognition in older adults with elevated risk for dementia.

Data from FINGER and other multidomain lifestyle RCTs have emphasized that intervention effectiveness depends on accurate identification of risk profiles.

This project will enable a personalized preventive approach by 1)Developing methods to predict cognitive decline based on clinical data and differentiate between healthy individuals at higher risk for mild cognitive impairment and AD vs. those who remain healthy; 2)Test the methodology in ongoing RCTs for dementia prevention.

Instead of a standard machine learning approach, we propose an innovative concept of personalized aging pattern rooted in data from healthy individuals.

Specific deviations from the personalized aging pattern will be detected as specific risk factors for the onset of cognitive decline and dementia.

The consortium will utilize multiple data resources, including a large observational healthy-aging study (Vallecas) and 3 prevention RCTs (FINGER, MIND-ADmini, MET-FINGER) based on the successful FINGER multidomain intervention model