EarlyCause will identify and demonstrate causative mechanisms and molecular pathways linking early life stress (ELS) to depression and two of its main physical comorbidities, namely coronary heart disease and diabetes. The consortium will disentangle the complex biological contributions from four key interconnected domains linked to ELS, namely epigenetics, inflammation, neuroendocrine system, and microbiome. Furthermore, modifying effects of environmental factors such as sex/gender, socioeconomics, lifestyle and behavior will be quantified, thus uncovering potential intervention targets that may reverse the causative mechanisms and reduce the impact of ELS on multi-morbidity development in high-risk individuals. To achieve the goals of the project, this highly multi-disciplinary and experienced consortium will combine state-of-the-art and novel approaches from basic, pre-clinical and clinical research, including causal inference methods such as Mendelian randomisation, animal models of prenatal and postnatal stress, cellular models in various tissues, and integrative bioinformatics and machine learning methods. The consortium members will also enable access and exploitation of the largest set of European cohorts, comprising rich information on early stressors, biological and omics data, as well as depressive, cardiovascular and metabolic phenotypes. Generated data, tissue samples, experimental protocols and cell lines, as well as best practices, will be compiled and integrated into a new open-access research platform within ELIXIR to support future researchers in the emerging topics of ELS and multi-morbidity. Finally, the project will ensure the research, clinical and socioeconomic impacts are adequately quantified and translated to allow full exploitation of the identified biomarkers and innovation outputs, in particular in relation to new integrated care pathways taking into account ELS-induced multi-morbidity in clinical practice.

Neurodegenerative diseases are one of the most important contributors to morbidity and mortality in the elderly. In Europe, over 14 million people are currently living with dementia, at a cost of over 400 billion EUR annually. Comorbidities with these conditions are frequent and a major obstacle to optimal diagnosis and management. Recent advances in diagnostic technologies and the advent of disease-modifying therapies (DMT) for Alzheimer’s disease (AD), the most common aetiology of dementia, heralds the beginning of precision medicine in this disease area. PROMINENT will develop a digital platform for precision medicine that will remove barriers that currently exists for leveraging these technological advancements in the routine care of patients with neurodegenerative disorders and co-morbidities. The platform gives clinicians access to prediction models leveraging multimodal diagnostic data automatically derived from multiple sources (imaging repositories, medical records, mobile devices), helping them choose optimal care pathways and improving diagnostic precision. It will provide personalized, relevant and meaningful information on diagnosis and prognosis in a format understandable by patients and care partners. Further, it will support the introduction of new health technologies such as DMT for AD, by ensuring adherence to appropriate use guidelines and facilitating the prospective collection of data on real-world usage, safety and effectiveness. The expected impact of the project is to increase diagnostic accuracy and optimized use of existing and new treatment options. It will empower patients and caregivers by engaging them in more person-centric health care decisions, leading to improved adherence and patient experience. Ultimately this is expected to lead to cost-effective care, improved health outcomes and quality of life.

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.