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Dernière mise à jour : Mai 2018

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Human Nutrition Unit

Zone de texte éditable et éditée et rééditée

Molecular, phenotypic or behavioral signatures of sarcopenia.

Molecular, phenotypic or behavioral signatures of sarcopenia.
Characterization of molecular, phenotypic or behavioral signatures of sarcopenia to facilitate its diagnosis and management.

The complex and multifactorial pathophysiology of sarcopenia is a major obstacle to identify a single specific biomarker that would be easily available and would reflect both muscle mass and function. It is nevertheless essential for an early diagnosis, and to target and monitor the treatments and their effectiveness. The development of a multidimensional approach, including molecular, phenotypic or behavioral signatures could help to stratify the risk and the management of sarcopenia.

Our team is developing a new field regarding the phenotyping of sarcopenia and the modelization of “dysmobility” in human by the characterization of biomarkers, for the prediction of sarcopenia or cachexia during aging and chronic diseases. To these aims, we are combining state of the art approaches (proteomic, lipidomic, metabolomic) and biological/clinical/functional/behavioral phenotyping of patients. The main goal is to identify the changes and mechanisms accounting for the inter-individual variability in elderly subjects of identical chronological age but having different muscle mass and function ("BioAge" project).  

Another objective concerns populations with chronic diseases and reduced mobility, such as cancer cachexia, rheumatic diseases, obesity or cardiac dysfunction. Circulating metabolites, proteins, FA (ω6 / ω3 ratio), gut microbiota or complex lipids analyzed in different matrix (plasma, blood cells, urine, stools) will be correlated to data collected on the platform of evaluation of the mobility (physical capacity, body composition, spontaneous physical activity and quality of life. "Mobipath" project). In addition to the constitution of large phenotyped cohorts, spontaneous data of sedentary time and physical activity, food choices and emotional responses will be collected in free-living conditions using the WellBeNet application developed by the human nutrition unit. From molecular, phenotypic or behavioral signatures, prediction models of healthy state versus ill-health state (overweight, inflammation, sarcopenia) will be developed for the diagnosis and stratification of the risk using machine learning algorithms. Longitudinal follow-up of existing cohorts (PROOF, OBESAR) will allow assessment of the risk evolution and of countermeasure effectiveness.