Know more

Our use of cookies

Cookies are a set of data stored on a user’s device when the user browses a web site. The data is in a file containing an ID number, the name of the server which deposited it and, in some cases, an expiry date. We use cookies to record information about your visit, language of preference, and other parameters on the site in order to optimise your next visit and make the site even more useful to you.

To improve your experience, we use cookies to store certain browsing information and provide secure navigation, and to collect statistics with a view to improve the site’s features. For a complete list of the cookies we use, download “Ghostery”, a free plug-in for browsers which can detect, and, in some cases, block cookies.

Ghostery is available here for free:

You can also visit the CNIL web site for instructions on how to configure your browser to manage cookie storage on your device.

In the case of third-party advertising cookies, you can also visit the following site:, offered by digital advertising professionals within the European Digital Advertising Alliance (EDAA). From the site, you can deny or accept the cookies used by advertising professionals who are members.

It is also possible to block certain third-party cookies directly via publishers:

Cookie type

Means of blocking

Analytical and performance cookies

Google Analytics

Targeted advertising cookies


The following types of cookies may be used on our websites:

Mandatory cookies

Functional cookies

Social media and advertising cookies

These cookies are needed to ensure the proper functioning of the site and cannot be disabled. They help ensure a secure connection and the basic availability of our website.

These cookies allow us to analyse site use in order to measure and optimise performance. They allow us to store your sign-in information and display the different components of our website in a more coherent way.

These cookies are used by advertising agencies such as Google and by social media sites such as LinkedIn and Facebook. Among other things, they allow pages to be shared on social media, the posting of comments, and the publication (on our site or elsewhere) of ads that reflect your centres of interest.

Our EZPublish content management system (CMS) uses CAS and PHP session cookies and the New Relic cookie for monitoring purposes (IP, response times).

These cookies are deleted at the end of the browsing session (when you log off or close your browser window)

Our EZPublish content management system (CMS) uses the XiTi cookie to measure traffic. Our service provider is AT Internet. This company stores data (IPs, date and time of access, length of the visit and pages viewed) for six months.

Our EZPublish content management system (CMS) does not use this type of cookie.

For more information about the cookies we use, contact INRA’s Data Protection Officer by email at or by post at:

24, chemin de Borde Rouge –Auzeville – CS52627
31326 Castanet Tolosan CEDEX - France

Dernière mise à jour : Mai 2018

Menu Logo Principal logo Université Clermont Auvergne & associés

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.