As the world's population increases in age, the number of people living with dementia grows. Dementia has long been considered to be neither preventable nor treatable, but while the underlying illnesses are not curable, today we know that the disease course might be modifiable with good preventive interventions at an early time point. The Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) showed a positive effect after a 2-year intervention targeting several lifestyles and vascular risk factors simultaneously. LETHE will go beyond and provide a data-driven risk factor prediction model for older individuals at risk of cognitive decline building upon big data analysis of cross-sectional observational and longitudinal intervention datasets from 4 clinical centers in Europe including the 11- years analysis of FINGER. LETHE will establish novel digital biomarkers, for early detection of risk factors, based on unobtrusive ICT-based passive and active monitoring. The aim is to establish a digital-enabled intervention for cognitive decline prevention based on the evolution of a successful protocol (FINGER) evolving into an ICT based preventive lifestyle intervention through individualized profiling, personalized recommendations, feedback and support (FINGER 2.0), well targeted on a population stratified by cost-effective biological biomarkers. The LETHE solution will be tested in a feasibility study validating the achieved improvements. A successful LETHE project could lead to a more personalized risk factor prevention for persons with beginning cognitive decline, thereby empowering people to an active and healthy lifestyle. Expansions of prevention trials on large scale by an automatized roll out of a multimodal intervention approach, reaching out to large populations, could save future costs on expensive traditional interventions and confer benefits for the wider society.

Heart failure (HF) is a prominent chronic disease, despite efforts in improving its prevention, diagnosis and treatment, represents the leading cause of disability and premature death throughout the world, while being is a heavy burden on health systems due to the multiple, prolonged hospitalizations required for patients suffering from it and the related management costs. Nevertheless, studies have shown that routine patient-physician or patient-nurse communication following discharge can result in significant reduction of hospitalizations. The pertinent collected patient data may include simple yet important information (e.g. patient weight), as well as previously difficult to obtain information such as routine ECG, ICD/CRT-D interrogation and pulmonary arterial pressure monitoring, which are now within reach due to technological progress. Motivated by the above, RETENTION aims to develop and deliver an innovative platform supporting enhanced clinical monitoring and interventions aimed at improving the clinical management of patients with chronic HF, reducing their mortality and hospitalisation rates, and improving their quality of life, safety, and well-being. The RETENTION platform will support clinical decision making and evidenced based personalised interventions for HF patients by: (a) continually monitoring and collecting medical, clinical, physiological, behavioural, psychosocial, and real-world data for such patients, (b) analysing these data using innovative model-driven big data analytics, statistical, artificial intelligence and machine learning techniques, (c) detecting patterns in the HF disease progression and the quality of life of patients, (d) cross checking and validating them against the clinical literature, and (e) offering transparent, explainable and verifiable decision making capabilities that leverage the evidence produced by the underlying data analysis and augment clinical studies targeting HF and other CVDs.

DORIAN GRAY project investigates the link between cardiovascular disease (CVD) and Mild Cognitive Impairment (MCI) in the ageing population. Its goal is to uncover underlying mechanisms to promote healthy, independent, and active living while reducing disease burden. Aligned with Horizon Europe’s Strategic Plan on NCDs prevention, the project focuses on two core objectives: designing a multidomain intervention to delay CVD-related MCI progression and developing digital solutions to enhance its adherence. By leveraging large datasets that include omics, brain imaging, lifestyle behaviors, and socio-economic data, the project aims to develop a trustworthy AI tool for predicting MCI. The AI tool will be upgraded into a state-of-the-art framework for modelling and simulation (digital twin), integrating real-world data collected in clinical studies from smartwatches and mobile devices. Including these digital biomarkers enable the early detection of disease and enhance precision in healthcare. The DIGITAL TWIN will inform and update a personalized multidomain intervention using a novel avatar-based coaching exergame, which will serve dual purposes: as a cognitive enhancement tool with structured physical-cognitive tasks and as a lifestyle intervention promoting positive behaviours experienced in the virtual environment. Feasibility will be tested in a pilot randomized controlled trials involving patients with MCI and heart failure, and in an implementation study with patients having MCI and cardiovascular risk factors. To achieve his aim DORIAN GRAY brings together a diverse team of experts, including clinicians, psychologists, sports scientists, computers engineers, AI specialists, stakeholders, entrepreneurs, and academics in ethics, law, and theoretical philosophy. Patients and citizens actively engage in shaping and guiding project decisions. DORIAN GRAY is projected to prevent up to 2 million dementia cases over 27 years, leading to a cost reduction of 485 billion.

DIOPTRA aims to introduce a front-line screening tool that will consider risk factors and protein biomarkers for pinpointing individuals at a high risk for colorectal cancer (CRC) incidence. Tissue & blood samples will be examined towards a discriminative set of prognostic proteins that are detectable via standard bloodwork and can indicate a need for further evaluation (i.e. colonoscopy). Other data (e.g. medical, behavioural) will also be considered as potential risk factors. Artificial intelligence (AI) will be leveraged for assessing prognostic power, while personalised behavioural change will be promoted based on modifiable risk factors. Given the low citizen participation on CRC screening across EU, DIOPTRA seeks to broaden the evaluated population, boosting participation rates and bypassing age screening thresholds. This action is part of the Cancer Mission cluster of projects on ‘Prevention, including Screening’.

During the last decades, overweight & obesity rates have reached epidemic proportions across EU. Prevalence in childhood is particularly alarming, resulting in psychological impacts & increased risks for NCDs. However, although obesity-related research has provided interesting results, several issues arise due to the lack of meta-reviews & the nature of data collected. To address this, Bio-Streams aims on supporting the optimal use (and re-use) of health data (e.g. biological, demographic, epigenetic, etc.) to generate metadata/knowledge and provide new evidences, methodologies & tools for: a) creating and deploying the Bio-Streams Biobank that will act as a scientific platform for research in obesity and better diagnostic and therapeutic approaches;b) understanding the transition from metabolically healthy to unhealthy, preventing under age obesity; c) designing better strategies to educate & empower young citizens for weight self-management; d) coordinating authorities & policy makers to develop cross-sectoral solutions for health promotion & under age obesity prevention. Specifically, Bio-Streams delivers a dedicated under age obesity biobank providing real-world health data (including biospecimens, anthropometrics, behavioural and cost data) from retrospective and prospective sources, while taking into account efficient data harmonisation and standardisation principles, transforming data valorisation towards under age obesity prevention and future research. Moreover, the Bio-Streams framework for data handling, provides robust and transparent methodologies for operational procedures (data infrastructures), analysis and reporting (via meta-reviews and AI-based Apps/components, evaluated via 12 multi-site pilots (7 clinical and 5 schools)), citizen awareness and lifestyle alteration, delivering obesity prevention guidelines, knowledge generation while fostering considerable opportunities for regional and national health authorities/policymakers.