Lipotype, founded in 2012, is the leading provider of lipidomics analysis services worldwide. Our mass spectrometry-based technology identifies and quantifies over 4200 lipid species, offering high-throughput, rapid quantitative analysis from minute sample amounts. Despite the emphasis on wellness products, the obesity pandemic continues to burden global health, with nearly 60% of the EU population and 42% of the US adult population classified as obese or overweight. This is linked to various non-communicable diseases, including type 2 diabetes and cardiovascular diseases, leading to escalating annual healthcare costs of 70B€ in the EU and reduced productivity. Lipotype addresses this issue with the Lipodoo test, a lipidomics-based blood test that measures metabolic health. Lipodoo leverages advanced mass spectrometry and machine learning algorithms to provide a comprehensive assessment of an individual's metabolic state. The test is designed for at-home use, where users can collect a dried blood spot sample and send it to Lipotype for analysis. The results are integrated with health apps, providing users with a personalized metabolic health score that is easily interpretable. This innovative approach offers a detailed snapshot of metabolic health and empowers individuals to make actionable interventions about their lifestyle. Lipodoo has the potential to revolutionize preventative healthcare by enabling early detection of obesity risk and interventions for a healthier lifestyle. By providing actionable insights into metabolic health, Lipodoo can help reduce the incidence of obesity-related diseases, improve lifestyle, and lower healthcare costs. As a B2B2C product, Lipodoo is expected to significantly impact individuals using health apps, public health, and academic research. Overall, Lipodoo offers significant advancement in metabolic health monitoring, with the potential to improve the healthspan of individuals globally and make a substantial impact on health.

Type 1 diabetes (T1D) is one of the main health challenges, with 6 million European citizens affected. Today, T1D accounts for a severe economic burden on healthcare and labour force. To bring advanced therapy in type T1D to patients, a scalable source of pancreatic islets for transplantation is needed. The objective of the ISLET project is to build and implement a new and innovative program for the production and marketing of human pluripotent stem cell (hPSC)-derived advanced therapy medicinal products (ATMPs) for treatment of EU citizens with T1D. To achieve this, ISLET gathers a constellation of experts to establish a transferable GMP-compliant manufacturing program based on improved and standardised protocols for generation and characterisation of future ATMPs. Furthermore, to make a product closer to the “golden standard” human pancreatic islet, ISLET will develop islet-like clusters composed of isolated hPSC-derived alpha and beta-like cells, and advance strategies for safe, up-scaled production and a quantitative go/no-go assessment of therapeutic quality. Specifically, to overcome the lack of robust qualitative and quantitative assays to assess islet function, ISLET will introduce a novel quality control concept for predicting the therapeutic efficacy by quantitative proteomics and lipidomics as part of the ATMP development chain - a concept that will be widely applicable. A commercial route for exploitation of HESC derived ATMPs for T1D treatment with EU will be developed. Finally, a professionally supported dual plan for public engagement in the fields of stem cell therapy and diabetes is rounding up the project.

LysoMod will innovate in the area of personalized medicine for disorders linked to lysosomal dysfunction. This will be achieved by implementing a collaborative staff-exchange program between highly complementary and multidisciplinary academic and non-academic partners with expertise in pharmacology, medicinal chemistry, cell biology, biochemistry, mouse and human genetics, transcriptomics, proteomics and lipidomics. Based on the critical role that lysosomes play in cells, a better understanding of lysosomal function will have a major impact on human health, fostering the development of new strategies to improve quality of life for people affected by a variety of diseases, ranging from lysosomal storage diseases (LSDs) to age-related neurodegenerative disorders. LysoMod’s specific objectives are: 1) to develop and further optimize existing therapies for LSDs; 2) to identify new targets for personalized therapies for LSDs; and 3) to investigate the cross-talk between lysosomal function, signalling pathways and gene expression regulation. The pioneer work of a participant in the consortium led to the development of a drug that is approved for clinical use. LysoMod will i) investigate the mechanisms of action of this and other drugs in lysosome-related disorder; ii) identify modifier genes involved in LSD pathology and test their potential as new targets for personalized therapeutic approaches; iii) identify candidate RNAs that can be targeted to enhance lysosomal function. The companies in the consortium will ensure a rapid transfer of new knowledge into applications for diagnostics and clinical trials. Prioritising lysosomal dysfunction as a highly relevant biomedical problem, the LysoMod consortium will implement a mentored staff-exchange program to provide young researchers with high-level training in innovative approaches for exploring biological systems, preparing the next generation of researchers for careers either in the private or public health sectors.

We will develop tools and knowledge to support physicians in accurately managing Long COVID syndrome (LCS) which has a significant impact on sufferers as well as their surroundings. Although much is now known regarding appropriate clinical management of acute COVID-19, very little is known about clinical manifestations, risk factors and underlying mechanisms for development of the highly heterogenous LCS. In this project, we aim to understand and mechanisms of LCS by combining front-line expertise from the fields of clinical medicine, virology, metabolism and immunology. We will study the pathogenesis of LCS by conducting geographically diverse cohort and registry studies, by conducting mechanistic studies, by using novel high-throughput methods for biomarker analysis, and by conducting interventional and follow-up studies on LCS patients. We will combine results from clinical and mechanistic studies to identify molecular and physiological parameters and/or pathways to decipher the mechanisms underlying LCS. We will exploit the high-throughput omics technologies to identify the predisposing factors and biomarkers that lead to the development of LCS. We will collect data from the cohort, mechanistic, biomarker and interventional studies and use these to validate the predictive artificial intelligence algorithms and to produce information and gain understanding on the combination of factors that lead to certain clustering of patients into different groups with specific symptoms. A machine learning and AI-informed Long Covid Prediction Support (LCPS) tool will be developed for the use of clinicians to predict the LCS and its possible clinical manifestations in patients. It will also help in the choice of personalized treatments for LCS patients. Additionally, an interactive graphic user interface infographic will also be available to clinicians and patients; this will communicate novel and understandable information about LCS and recommendations for patient management.

Chronic obstructive pulmonary disease (COPD) and cystic fibrosis (CF) are two very debilitating non-communicable diseases that are of particular interest to consider in parallel in a human exposome study. Their roots are opposite: COPD is currently considered to be mainly related to the external exposome, while factors outside of the exposome play a major role in CF. However, COPD and CF share common characteristics such as high phenotypic variability of unknown origin, which prevents good therapeutic efficacy. It is therefore clear that the overall picture must be supplemented by taking into account additional components of the exposome than those currently considered in COPD and CF. Thus, the overall objective of the REMEDIA project is to extend the understanding of the contribution of the exposome, taken as a complex set of different components, to COPD and CF diseases. We will exploit data from existing cohorts and population registries in order to create a unified global database gathering phenotype and exposome information; we will develop a flexible individual sensor device combining environmental and biomarker toolkits; and use a versatile atmospheric simulation chamber to simulate the health effects of complex exposomes. We will use machine learning supervised analyses and causal inference models to identify relevant risk factors; and econometric and cost-effectiveness models to assess the costs, performance and cost-effectiveness of a selection of prevention strategies. The results will be used to develop guidelines to better predict disease risks and constitute the elements of the REMEDIA toolbox (global unified database, sensor device, versatile atmospheric simulation chamber, machine learning supervised analyses, causal inference model, Pan-European multi-criteria risk assessment tool, econometric models, cost-effectiveness models, new guidelines and recommendations). Deciphering the impact of environmental components throughout life on the phenotypic variability of COPD and CF could represent a major breakthrough in reducing morbidity and mortality associated with these two non-curable diseases and would lead to the identification of modifiable risk factors on which preventive action could be implemented. REMEDIA will be part of the European Human Exposome Network established between the 9 projects funded within the Human Exposome programme call H2020-SC1-BHC-2018-2020.