Personalised blueprint intestinal health
The miGut-Health consortium aims to develop a personalised blueprint of intestinal health to predict and prevent inflammatory bowel disease. The overall goal is to deliver interdisciplinary solutions (molecular, nutritional, eHealth and patient engagement/empowerment level) for health promotion and disease prevention that would enable active patient engagement in health and self-care management.
Early Interception of Inflammatory-mediated Type 2 Diabetes
The overall concept of INTERCEPT-T2D is to establish whether an inflammatory-mediated profile contributes to the onset of Type 2 Diabetes (T2D) complications, thus enabling the identification of patients most at risk of complications and the design of personalised prevention measures. The project will bring a new and clinically relevant dimension in T2D care considering diagnosis inflammatory parameters that are of importance for the transition to T2D-related complications.
CArdiovascularREsolution of INflammation to promote HEALTH
Chronic inflammation is a critical residual risk for the health to cardiovascular diseases transition with limited possibilities to stop it without causing immunosuppression. To tackle this issue, CARE-IN-HEALTH proposes to identify the resolution of inflammation to develop new prevention strategies to lower inflammation with a retained immune defense against infections to sustainably stay healthy.
Imminent Disease Prediction and Prevention at the Environment Host Interface
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. The identification of clinical and omics-derived biomarkers will – by employing AI algorithms – yield a personalised risk / resilience score of chronic inflammation and thus a better prediction of an individual’s risk of transition towards disease.
Advanced PeRsOnalized Therapies for Osteoarthritis – Tackling inflammation to improve patient outcomes
The PROTO consortium strives to implement new evidence- and patient-centered treatment strategies for early- and pre-disease stages in osteoarthritis (OA) patients. The project intends to prevent health to disease transition by restoring physiological movement and reducing joint inflammation.
Glycans as master triggers of health to intestinal inflammation transition
GlycanTrigger proposes a thorough and innovative approach to better understand the health to chronic inflammation transition occurring in patients with Crohn´s disease that will be translated in improved disease prediction and prevention. The project will address how changes in glycosylation of the gut mucosa act as a primary event that dysregulates not only local mechanisms but also systemic mechanisms.
Biomarkers affecting the transition from cardiovascular disease to lung cancer – towards stratified interception.
Based on multiple OMIC- approaches, PREVALUNG EU will conduct a prospective study in cardiovascular disease (CVD) tobacco consumers. This will allow to unveil four drivers of early carcinogenesis leading to actionable biomarkers that can be harnessed to pioneer personalised interceptive measures.
Physics informed machine learning-based prediction and reversion of impaired fasting glucose management
The aim of PRAESIIDIUM is to develop a prototype tool for the real-time prediction of the prediabetic risk based on a series of patient-specific mathematical models that simulate metabolism, pancreas hormone production, microbiome metabolites, inflammatory process and immune system response. The prediction algorithm will be based on a “physics-informed machine learning” approach.
An Artificially Intelligent Diagnostic Assistant for gastric inflammation
AIDA aims (i) to help researchers to understand the mechanisms that trigger gastric oncogenesis, (ii) to help clinicians diagnose precancerous inflammation at the earliest possible stage, (iii) to select personalised therapeutic strategies for treatment and follow-up, and (iv) to make personalised recommendations for monitoring patient health status, thus contributing to gastric cancer prevention.
Discovering chronic inflammation biomarkers that define key stages in the Healthy-to-NASH (non-alcoholic steatohepatitis) transition to inform early prevention and treatment strategies
Halt-RONIN aims to uncover the early triggers of Non-alcoholic fatty liver disease (NAFLD) initiation and complex mechanistic drivers of disease progression by implementing a systems biology approach with integrative disease modelling resulting in opportunities for the improvement of the existing detection methods, providing a blueprint to inform personalized intervention strategies and drug discovery for NAFLD.
Inflammation in human early life – targeting impacts on life-course health
INITIALISE focuses on the development of the human immune system in early life, which impacts the risks of several diseases later in life, particularly immune-mediated diseases such as allergies, asthma, and autoimmunity. With the help of intersecting multiple cohorts and existing biobanks, applying state-of the art technologies for exposure analyses and immune system investigation, the project will try to understand the environmental factors shaping human immune systems early in life, their mechanisms of action, and impact on life-course health.
Psoriatic arthritis inflammation explained through multi-spurce data analysis guiding a novel personalised digital care ecosystem
PROLEPSIS aspires to shed light upon the health to Psoriatic Arthritis (PsA) transition with a comprehensive multiscale/multifactorial PsA model employing novel trustworthy AI-based analysis of multisource and heterogenous data. The project aims to identify key drivers of PsA onset and supports personalised models for PsA risk/progression prediction and monitoring as well as associated inflammation detection and severity assessment.
European Alliance of Associations for Rheumatology
The European Alliance of Associations for Rheumatology, EULAR, is the organisation which represents the people with arthritis/rheumatism, health professionals in rheumatology (HPR) and scientific societies of rheumatology of all the European nations.
Digital Health Uptake
Digital Health Uptake (DHU) is an EU-funded project under the Digital Europe Programme. The aim is to facilitate the alignment of policies, strategies, instruments and activities to advance the uptake of digital health solutions and services in Europe.
LongCOVID – decision support for prediction and management of LCS
The EU-funded Long Covid project will develop tools to support physicians in accurately managing Long COVID syndrome (LCS). Currently, very little is known about clinical manifestations, risk factors and underlying mechanisms. The project will fill this knowledge gap by combining front-line expertise from the fields of clinical medicine, virology, metabolism and immunology. Also, a machine learning and AI-informed Long prediction tool will be developed to predict the LCS and its possible clinical manifestations in patients.
Smart Health Innovation & Future Technologies Hub
Technological advances such as artificial intelligence and big data are set to transform healthcare in a positive way. These smart health technologies usually employ sensors to obtain information that is transmitted and processed using cloud computing. However, their uptake is often impeded by poor awareness and literacy. To address these limitations, the EU-funded SHIFT-HUB project proposes to establish a patient-driven approach for creating and adopting smart health solutions. The project will set up a pan-European innovation hub, bringing together various stakeholders to collaborate on the future development of smart health innovations. It will create a network of healthcare organisations that will implement selected smart health applications after they have been tested by patients and online users.
The global network for chidren with arthritis and autoinflammatory conditions
ENCA is the global network for children with arthritis and autoinflammatory conditions. This network is led for and by parents and young people living with rheumatic, musculoskeletal and autoinflammatory diseases. ENCA is the ‘The parent’s organisation subcommittee’ of the Paediatric Rheumatology European Society (PReS), and we represent patients, parents and patient organisations.
The EU-funded project SQUEEZE utilizes models from data science, clinical trials, translational, and behavioural science to define the best use of biomarkers and medicines for rheumatoid arthritis. SQUEEZE results will improve rheumatologists’ ability to select the disease modifying antirheumatic drugs (DMARD) with the highest likelihood to fit the patient’s immune and clinical profile, optimise the dose and route of existing DMARDs, and help design an innovative model of care focusing on the patient´s preferences and needs to increase adherence to prescribed medications and satisfaction with treatment.
Stratification of Rheumatoid Arthritis: CompuTational models to personalise mAnagement strategies for difFIcult-to-Treat disease
The STRATA-FIT consortium sets out to develop and validate computational models to identify and stratify difficult-to-treat rheumatoid arthritis (D2T RA) patients into clinically relevant phenotypes using real world clinical data. Subsequently, STRATA-FIT execute a pilot study with a clinical decision aid based on their models to assess the effectiveness of personalised treatment strategies. In parallel STRAT-FIT will develop a computational model to identify early RA patients at risk of developing D2T RA. By doing so, not only will they provide better treatment for patients with D2T RA, but also work towards its prevention in early RA patients. STRATA-FIT will establish a unique European Learning Healthcare System, using a privacy-proof, state-of-the-art federated learning infrastructure in which patients with, or at risk of D2T RA are identified, stratified and treated in a personalised manner.
Stratification of patients with musculoskeletal symptoms using advanced integrative data modelling
Early disease stratification is important to ensure appropriate care of patients with musculoskeletal symptoms. The EU-funded SPIDeRR project aims to streamline early rheumatic diseases diagnosis. The innovative approach will identify disease groups amongst similar-symptom patients by integrating all relevant data dimensions from every healthcare level. Application of machine learning techniques from the omics field to clinical patient data will result in new pipelines for translational science. The project objective is to deliver three clinical models: a symptom checker for patients; a support tool for healthcare providers, guiding additional examination and referrals; and a patient comparison network for optimisation of diagnostic groups and treatment decisions.
Helsinki University Hospital (HUS)
Helsinki, Finland
ENDOTARGET Project Coordinator
Kari Eklund
kari.eklund@hus.fi
ENDOTARGET Deputy Project Coordinator
Gonçalo Barreto
goncalo.barreto@helsinki.fi
ENDOTARGET Project Manager
Ana Valkama
ana.valkama@hus.fi
Steinbeis Europa Zentrum (SEZ)
Stuttgart, Germany
Lena Schleicher
lena.schleicher@steinbeis-europa.de
