The project, in which two biotechnology companies and the Institute of Integrative Systems Biology (I2Sysbio) of the University of Valencia Science Park (PCUV) are participating, has obtained a grant of more than 800,000 euros from the MICINN and from the EU NextGeneration European funds. SARCOTECH will develop a sarcopenia prediction model by integrating genome, microbiome and other biochemical biomarkers analysis
The new project, under the acronym SARCOTECH, focuses on the systematic investigation of risk factors involved in sarcopenia and the implementation of omics technologies, bioinformatics and information and communication technologies to develop a rapid and low-cost solution for the early detection of sarcopenia. Its development involves the participation of
Sabartech and
Dawako Medtech, two biotechnology companies, and the Evolutionary Genetics-Symbiosis Group, a consolidated group and joint research unit of the
Institute of Integrative Systems Biology (I2Sysbio), established at the facilities of the
University of Valencia Science Park (PCUV). Both PCUV companies have experience in the study of sarcopenia, with a previous cooperation funded by the Valencian Innovation Agency (AVI) in the
iSARC-GENETICS project.
Also participating in the consortium are the FISABIO research group (FISABIO), led by professor and researcher Andrés Moya, and the Cardiovascular and Nutritional Epidemiology of Aging group (GCNEA) of the Autonomous University of Madrid (UAM) and the Consortium for Biomedical Research in Epidemiology and Public Health Network (CIBERESP), led by Fernando Rodríguez Artalejo. The FISABIO group is dedicated to the study of the human and animal microbiome and has a team of experts in genomics, metagenomics and bioinformatics, and the UAM group has extensive research experience in the field of gerontology and geroscience, carrying out the study and follow-up of elderly patients for the study of geriatric diseases and syndromes for several years, with the ENRICA-Seniors cohort, one of the most important cohorts of patients of this type at the international level.
The new project, under the acronym SARCOTECH, focuses on the systematic investigation of risk factors involved in sarcopenia and the implementation of omics technologies, bioinformatics and information and communication technologies to develop a rapid and low-cost solution for the early detection of sarcopenia
Currently, Sabartech, FISABIO and UAM have two other projects in execution, the
FRAYLTECH project funded by MICINN in the 2022 call for Strategic projects (PLEC2022-009352) and the LEGTECH project funded by ISCIII and CDTi in the 2022 ISCIII-CDTi joint action (IDI-20230068]), for the study of Fragility and functional impairment of the lower extremities (DFEI), respectively.
Machine learning to predict
The novelty of the proposal is to combine multiple risk factors for sarcopenia, such as genetics, microbiota, clinical and biochemical biomarkers of oxidation, inflammation and diabetes among others, as well as lifestyle parameters into a machine learning algorithm capable of predicting the onset and severity of this geriatric syndrome. This algorithm will be integrated into a Multiscale Risk Calculator that will provide professionals with an easily accessible and user-friendly diagnostic tool to assess Sarcopenia Risk in their patients and family members.
The consortium met last Wednesday for the first virtual meeting to plan the implementation of the project, which will run from December 1, 2023 to the end of November 2026. In addition, the participants also aim to generate the first databases based on all these layers of information generated and collected for Sarcopenia in order to improve the current state of the art associated with age-associated pathologies and conditions, which could be made available to the sarcopenia research community. With this new project, the consortium positions itself as a reference in R&D in the field of geroscience and gerotherapy.
Additional information
PROJECT REFERENCE: CPP2022-009718
PROJECT TITLE: Development of a sarcopenia prediction model by integrating genome, microbiome and other biochemical biomarker analyses.
With the support of: