Biological & Fonctional Signatures for Muscle Failures, Aged People & Personalized Physical Activity

Biological & Fonctional Signatures for Muscle Failures, Aged People & Personalized Physical Activity : BioFaSt MAPPA

Physical activity is one of the most effective therapeutic interventions for frailty, sarcopenia or dynapenia. The benefits of physical activity processes are already widely documented. Typically, researchers want to understand the average response to an intervention to determine its overall effectiveness. However, sports trainers have understood it for a long time, the response of an athlete or a patient to training is very variable and the standard deviations present in all the scientific studies on the subject confirm it. There is therefore inter-individual variation in the response to exercise, with some subjects showing much greater improvements than others. Thus, one can wonder if this "non-response" following training is specific to the training modality. The effects of physical exercise on the body depend mainly on its type, intensity and duration. Thus, from a practical point of view, the most important perspective is the prediction and, ultimately, the individual optimization of management through physical activity.

Main objective: To determine the effects of personalized physical activity management on the biological signatures of muscle failure.

Secondary objectives:

• Propose a support system through adapted and individualized physical activity, based on physical, biological and psycho-social indicators
• Identify the biological signatures (metabolomics, epigenetic) of muscle failure in the elderly.

Phenotype responders and non-responders in training

900 seniors (300 per group) APAP group: adapted and personalized physical activity APA group: adapted physical activity Control group: no physical activity

Total duration of the study 5 years, duration of participation for a patient 3 months; inclusion period: 57 months

Improvement of muscle failure in its broad definition (Strength, power, speed and lean mass)

) General data: description of characteristics with frequencies and percentage for categorical variables and with mean and standard deviation for quantitative variables.

ii) Analysis of the primary endpoint: Machine Learning (or supervised classification) and signaling pathway prediction approaches for statistical analysis and data interpretation.

iii) Analysis of secondary judgment criteria: approaches in Machine Learning (or supervised classification) and signaling pathway prediction for statistical analysis and interpretation of data with comparison of quantitative variables by Pearson correlation and categorical variables by t or chi 2 test depending on the case.

Adapt and individualize physical activity support for healthy or frail elderly people

Inclusion Criteria:

• Age greater than or equal to 60 years
• Affiliated patient or beneficiary of a social security scheme
• Patient having signed a prior informed consent.

Exclusion criteria

• Neurocognitive disorder preventing the expression of informed consent
• Patients protected by law under guardianship or curatorship, or who cannot participate in a clinical study under Article L. 1121-16 of the French Public Health Code
• Presence of a physical or cognitive pathology preventing the performance of the adapted physical activity protocol over 3 months.