Digital tool to predict Alzheimer's-related changes in people with memory problems
Tests a digital tool that predicts Alzheimer's-related changes in people with memory complaints.
Plain-language title generated from the registry record
Registry title: Digital Solutions for Predicting the Biological Mechanisms of Alzheimer's Disease Through the Analysis of Risk Factors
Key facts
- Where
- Brescia, ItalySee the 2 sites
Where in Italy
- ASST Spedali Civili di BresciaBrescia, ItalyTaking people
- IRCCS Istituto Centro San Giovanni di Dio FatebenefratelliBrescia, ItalyTaking people
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- IRCCS Centro San Giovanni di Dio Fatebenefratelli
- Trial code
- NCT07731191
- Record updated
- 28 July 2026
- Original description
Population ageing is one of the main factors responsible for the global increase in the prevalence of dementia. Recent evidence suggests that modifiable risk factors, such as cardiovascular disease and lifestyle, may increase the risk of developing dementia and contribute to its progression. Furthermore, the use of non-invasive plasma biomarkers enables the identification of individuals with neurodegenerative diseases, even in the prodromal stage. However, the relationship between the cumulative burden of risk factors and plasma biomarkers is still poorly understood.Read full original descriptionShow less
Development of Digital Solutions for the Prediction of the Biological Mechanisms of Alzheimer's Disease Through the Analysis of Risk Factors (PrevAI)
Population ageing is one of the main factors responsible for the global increase in the prevalence of dementia. Recent evidence suggests that modifiable risk factors, such as cardiovascular disease and lifestyle, may increase the risk of developing dementia and contribute to its progression. Furthermore, the use of non-invasive plasma biomarkers enables the identification of individuals with neurodegenerative diseases, even in the prodromal stage. However, the relationship between the cumulative burden of risk factors and plasma biomarkers is still poorly understood.
The main objective of this study is to identify and estimate the risk associated with modifiable and non-modifiable predictors (risk factors) linked to the development of Alzheimer's disease (AD) and non-AD dementia, as well as biological alterations consistent with AD or non-AD, through the development of a predictive tool based on Artificial Intelligence algorithms (Machine Learning model). The study also aims to provide a range of technological tools (an app for active patient monitoring and a web platform for clinicians) that could improve risk stratification and the personalisation of care pathways.
The study is divided into two different phases. Firstly, a retrospective phase is conducted in order to construct a predictive model for the risk of dementia and biological alterations consistent with AD. Secondly, a prospective phase is performed for the validation of the predictive model.
Inclusion: Male or female subjects aged more than 18 years at the time of signing the informed consent form; Subjects with MCI or SCD who, at the time of their first visit, did not have a clinical diagnosis of dementia (MMSE ≥ 24); Smartphone user.
Exclusion: Age younger than that stated in the inclusion criterion; Inability to understand.