IMMEDIATE aims to investigate and explore the diet-microbiome-immunometabolism-axis as a sensor for health-to-disease transition and evaluate strategies to maintain an individual’s well-being. Chronic inflammation is the major root of most diseases. Understanding of this process preceding organ dysfunction or damage and identification of biomarkers in the pre-symptomatic stage and risk but also resilience factors of health-to-disease transition will enable targeted and personalized interventions to prevent irreversible organ damage. Metabolites of gut microbiota are key messengers between diet, microbiota and host, maintaining the balance of pro- and anti-inflammation. Our study takes advantage of cutting-edge omics technologies available within the IMMEDIATE consortium in conjunction with available clinical data and biospecimens from ongoing observational studies, enrolling “healthy” subjects and individuals in the pre-disease stage but with largely distinct environmental and dietary modulators, including a cohort of kidney transplant recipients in whom renal function has been “reset to baseline”. The identification of clinical and omics-derived biomarkers will – by employing AI algorithms - yield a personalized risk / resilience score of chronic inflammation and thus a better prediction of an individual’s risk of transition towards disease. A proof-of-concept intervention study with the anti-inflammatory microbe Akkermansia muciniphila will be conducted to test whether deflections of the microbiome-metabolite-immune axis can be reverted on the biomarker level but also with respect to clinical outcomes and overall well-being. Mobile apps developed by the IMMEDIATE consortium in collaboration with patient organizations tracking numerous lifestyle-related measures and providing guidance and feedback on these aspects will empower individuals to adopt and integrate these knowledge-based health interventions into their own lives, hereby self-managing their own health.

The ForMAT project addresses limitations in forensic DNA profiling, particularly in criminal cases, disaster victim identification (DVI), and legal age assessment for asylum claimants. Traditional DNA methods struggle with unsolved cases, mass disasters, and legal age estimation. ForMAT focuses on DNA methylation (DNAm) as a solution, using epigenetic markers to estimate age and lifestyles, as well to predict tissue type. This approach enhances criminal investigations by narrowing suspect profiles and expedites missing person identification in mass disasters, improving the return of remains to families. In asylum cases, DNAm offers a radiation-free alternative for assessing age, reducing reliance on X-rays. ForMAT builds on the VISAGE project, aiming to advance these tools from Technology Readiness Level (TRL) 5 to TRL 7. By developing new DNAm kits and a bioinformatic applicaiton including forensic prediction models, ForMAT enables law enforcement to estimate age, to infer lifestyles and to identify the tissue-of origin from DNA samples. Kits include a DNAm somatic kit, as well as a DNAm germ-line kit for criminal cases, a DNAm DVI kit for guiding in the identification process of human remains, and a DNAm legal age kit for asylum assessments. The integration of Next Generation Sequencing (NGS) technologies, including second- and third-generation sequencing, enhances forensic workflows and ensures high accuracy even with degraded samples. The ForMAT consortium , consisting of 11 EU institutions, leverages expertise across countries and disciplines to bring these innovations to real-world use. Members include leading universities, police bodies, and forensic institutes, committed to scaling up, testing, and validating these tools across Europe. Their involvement in international forensic boards ensures widespread dissemination, aiming for broad adoption of ForMAT’s epigenetic molecular tools in forensic science, ultimately enhancing investigative practices and public safety.

The EuCARE project gathers a comprehensive multidisciplinary team of clinicians, virologists, epidemiologists, statisticians and top experts in artificial intelligence to unveil the impact of SARS-CoV-2 variants on key sectors of public health, as addressed by the call. The specific activities include: 1. The assessment of natural and artificial immunity to the different viral variants in health care workers with the aim of defining the cross-immunization patterns and the risk of vaccine escape, informing vaccination strategies for the general population; 2. The analysis of the clinical course and long-term follow-up of hospitalized COVID-19 patients to derive the role of different viral variants in the outcome of the infection, including post-acute sequelae of SARS-CoV-2 infection; 3. The definition of the best strategies to control the spread of different viral variants in schools, by comparing the outcome of diverse containment and prevention measures in relation to the prevalence and dynamics of the variants. To ensure a suitable representativeness of the different variants, vaccines and preventive measures, EuCARE has secured the appropriate cohorts from diverse geographic areas including European countries, Kenya, Mexico, Russia and Vietnam, and will consolidate or expand interactions with other cohorts. To deal with complex interactions among many variables, including large dimensional parameters, EuCARE harnesses the power of artificial intelligence to define the role of viral variants and inform clinical guidelines and prevention measures. In the longer-term, EuCARE is committed to maintain active cooperation beyond the duration of the project with a dedicated task in the project. The solid IT and ethics infrastructure and the harmonised research procedures will make the cohort and laboratory network rapidly available to tackle newly emerging infectious diseases, thus contributing to pandemic preparedness on a global scale.

Pancreatic cancer (PC) has the lowest survival rate of all cancers in Europe, with no early detection strategies available. The IMAGene project will develop, implement and test a comprehensive Cancer Risk Prediction Algorithm (CRPA) to predict PC in high-risk (HR) asymptomatic subjects; it will investigate the potential for DNA methylation biomarkers to improve currently available risk indexes, and validate the feasibility of using liquid biopsies for early detection of cancer in such HR individuals. A sample of 200 healthy first-degree relatives of PC patients will be recruited, and their epidemiological factors related to PC risks assessed through initial interviews. Subjects will receive medical and psychological visits, and will undergo screening for germline mutations, DNA methylation profiling plus Whole-body MRI at baseline. Biostatistical analysis of data will be performed to develop algorithms able to extract risk profiles from biological and imaging data. All subjects will undergo epigenetic follow-ups plus radiological exam at 1 year after baseline. Participants’ lifestyle, epidemiological and psycho-decisional assessment will be performed through a monitoring process over the 3 years of the project. HR subjects’ lifestyle data will be correlated with DNA methylation profiles. All data collected will feed the supervised machine learning CRPA. Assuming the risk assessment through CRPA and DNA methylation profiling allows a two or three-fold enrichment in early detection of suspicious cancer in HR individuals (compared to the detection rate of pancreatic cysts with malignant potential observed in non-stratified asymptomatic population, 9.3%), we expect a detection rate of suspicious lesions of 20-25% in the selected population. A cost-utility and a detailed ethical analysis will be conducted. The IMAGene project will adopt a transnational, multi-level, multidisciplinary and multi-methodological approach to achieve its aims.