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.
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.
Age younger than that stated in the inclusion criterion;
Inability to understand.
Single group
Digital solution for the prediction of the biological mechanisms of Alzheimer's disease through the analysis of risk factors
DeviceThe intervention consists of a mobile application ("app") for risk monitoring with gamified patient engagement, which is design to support remote health monitoring and participant adherence to the study. Participants will enter informative clinical variables every 3 months, including weight, height, age, systolic/diastolic blood pressure, and blood glucose levels. The app generates a qualitative risk assessment (low/medium/high) based on entered data, intended as a clinical monitoring support tool for the participating sites and not as a diagnostic tool that replaces medical evaluation and/or clinical judgment. Unlike standard data collection apps, the intervention provides continuous engagement incentives in the form of visual feedback and motivational messaging.
Conversion rate to dementia
Risk score that assesses the individual risk associated with predictors linked to (i) the development of AD and non-AD dementia, and (ii) biological biological changes consistent with AD or non-AD pathology.
Click on the lead sponsor to view all of their trials.
IRCCS Centro San Giovanni di Dio Fatebenefratelli
Lead sponsor
Asst Degli Spedali Civili Di Brescia
Collaborator
