Frontotemporal dementia (FTD) has a debilitating effect on patients and their caregivers and leads to substantial economic costs. 15-30% of patients have familial FTD caused by known pathogenetic mutations. For the other 70-85% of patients, termed sporadic FTD, diagnosis is slow (~3.6 years) with frequent misdiagnosis due to clinical, genetic and molecular heterogeneity. Thus, there is great need for biomarkers for early diagnosis of sporadic FTD and its pathological subtypes. In PREDICTFTD, we will validate a set of biomarkers and create a diagnostic tool for early diagnosis of familial and sporadic FTD, which will facilitate tailored support and symptomatic treatments and care. We will apply several new approaches to achieve this: 1) we combine 11 geographically diverse cohorts of sporadic and familial FTD with retrospective and prospective longitudinal liquid biopsy samples and extensive clinical and behavioural data; 2) we are the first to use multimodal clinical and liquid biomarker data to train an AI-algorithm as a diagnostic tool for quick and early clinical FTD diagnosis; and 3) we implement a novel robust two-stage strategy for biomarker and AI algorithm validation, where phase I validates biomarkers and algorithms on a cohort of genetic and autopsied cases and phase II assesses biomarker value for diagnosis of sporadic FTD and at-risk pre-symptomatic mutation carriers. We will apply this two-stage validation strategy to address three critical clinical challenges: i) To distinguish sporadic FTD from (non-) neurodegenerative disorders that show significant clinical/symptomatic overlap, ii) To robustly detect FTD pathological subtypes in sporadic FTD and iii) pre-symptomatic identification of FTD onset. Thus, PREDICTFTD will transform FTD diagnosis, offering potential for early disease confirmation, guiding treatment decisions, facilitating patient recruitment for clinical trials, guidance of patients and caregivers, and enabling preventive measures.

Rheumatic diseases (RDs) affect more than 40% of Europe's population and cause significant disability, pain, reduced lifespan and a very high economic burden. In this project, we will explore the role of chronic systemic inflammation caused by intestinal microbiota derived immunologically active compounds, as a driver in the transition from health to disease, with a special focus on three RDs; osteoarthritis (OA), rheumatoid arthritis (RA), and spondylarthritis (SpA). We aim to explore the relationship between gut microbiota, intestinal permeability, and endotoxemia. We aim to understand their role as drivers of disease onset and disease activity in RA, SpA and OA, as well as targets of preventive and therapeutic approaches. We will study the events leading from health to disease onset by i) taking advantage of geographically diverse large cohorts of people with available blood and faeces samples, ii) search for novel risk biomarkers for RA, SpA, and OA by using high-throughput OMICS-based analyses iii) conducting targeted clinical studies, iv) performing in vitro mechanistic studies to explore the gut-joint axis using tissue explant cultures and organ-on-chip models v) conducting interventional proof of concept studies of diet, faecal transplantation and a gut permeability decreasing drug in RA and SpA patients, vi) exploring in vitro new potential drugs or nutraceuticals to cope with endotoxemia effects on target tissues. By combining all these results, machine learning and AI-informed rheumatic disease prediction tool will be developed for clinicians to help them identify patients with increased risk of developing the target diseases. It will thus assist in the choice of personalized blueprint intervention to reduce the risk of these diseases and disease activity in RA and SpA and to slow down the progression of OA.

Cardiorespiratory diseases and stroke are among the top four leading causes of death in the EU in prehospital settings. Urgent care services are in huge need of novel Point of care (POC) computing technologies that can specifically detect patient condition and enable Hospital information system (HIS) with real-time data for triaging patients for right care. Our goal in POC4TRIAGE is to develop robust and accurate POC technologies, from POC testing (devices) to POC systems (platform) that is capable of fast diagnosis and efficient transfer of data to HIS. We will develop and clinically validate four rapid (<10 min) easy-to-use, compact, cost- and energy-efficient POC devices with Edge AI computing models, to be used in ambulance & emergency room settings. POC4TRIAGE devices include a multimodal patch for real-time monitoring of cardiorespiratory data, novel sub-hairline non-invasive EEG based head caps for rapid stroke diagnosis, including detection of large vessel occlusion stroke, and a handheld, rapid immunodetector to diagnose stroke with clinical utility for various conditions. These devices integrate into a new Device Hospital Connectivity Platform (DHCP) that visualizes data, uses AI from multiple devices to triage and seamlessly integrates with hospital systems and clinical workflows. The POC devices and DHCP will be clinically validated. POC4TRIAGE brings together some of Europe's leading POC device developers, medical professionals and clinicians, patient representatives, ethics experts, data scientists, and health economists. POC4TRIAGE will shorten the time to treatment and improve clinical outcome. POC4TRIAGE has potential to revolutionize healthcare delivery, making it more accessible and efficient, traceable, and interpretable for patients and providers alike. As the POC device and computation market is growing fast, the new POC devices, real-time data analysis, and secure computing have potential for major economic impact.

280M of people worldwide suffers from major depressive disorders (MDD). Although a well-populated therapeutic landscape of anti-depre280M of people worldwide suffers from major depressive disorders (MDD). Although a well- populated therapeutic landscape of anti-depressants, the number of patients in remission is particularly low with not more than 6% of the patients who benefit from the current therapeutic journey. OPADE objective is to identify key biomarkers that support the decision-making process of the healthcare providers. The project focuses on the microbiota – brain -axis which plays a major role in mental health and in particular MDD. Through clinical investigations, the consortium partners will study the combination between genetics, epigenetics, microbiome and inflammatory networks to: - Establish patient profiles to predict and optimise the efficacy of the antidepressants prescribed with an increase in the remission rate and reduction of impairment of real-life functioning, - Establish the possible correlation between neuroinflammatory indices, target indicators of the microbiome, metabolomics, immune-profile linked, epigenomic, enzymatic algorithms, - Evaluate molecular and non-molecular biomarkers that may represent predictive indices of recurrence - Discover new molecular targets for a personalised approach, - Improve the diagnostic accuracy for primary prevention, - Evaluate retrospectively, using accurate anamnesis, the onset of depressive symptoms in adolescence. - Establish how much and to what extent do blood biomarkers correlate with other specific biomarkers 350 patients between 14 and 50 years will be recruited in 4 EU and international countries for 24 months. Real-time EEG and patient cognitive assessment will be collected with blood, stool and saliva samples. Results and analysis will be used to train the AI / ML predictive tool, the main outcome of the project. A patient empowerment tool will be deployed over the project duration.

GEMMA will be the first project to combine a multi-omic approach with robust environmental data to exploit the analysis of the composition and function of the microbiome for personalized treatment and, ultimately, disease interception in infants at risk of Autistic Spectrum Disorders (ASD) . The project will provide solid mechanistic evidence of the disease onset and progression in relation to dynamic changes in abnormal gut microbiota causing epigenetic modifications controlling gut barrier and immune functions, based on the in-depth evaluation of 600 infants at risk observed from birth and followed over time. These data will be integrated with pre-clinical studies to mechanistically link human microbiota composition/function with clinical outcome through humanized murine models transplanted with stools obtained from the ASD proband patient of recruited families. The project will support novel personalized prediction (personalized treatment) and disease interception (prevention) approaches that attempt to modulate gut microbiota to re-establish/maintain immune homeostasis. The biomarkers identified in this project will contribute to a better understanding of the pathogenesis of ASD in at-risk children and the possibility to manipulate the microbiota through pre/pro/symbiotic administration for prevention and treatment, a complete paradigm shift in ASD pathogenesis and early intervention. The identification of specific ASD metabolic phenotypes will further aid to define biomarkers that can be used as diagnostic tools and patient stratification models for other conditions in which the interplay between genome, microbiome and metabolic profile has been suspected or proved. Finally, the project will collect biospecimens from a cohort of 600 infants as risk of ASD observed from birth, generating a unique biobank of 16,000+ blood, stool, urine and saliva samples prospectively collected that can be exploited in future multiomic studies.