Sarcopenia Physical Activity and Metabolomic

NCT ID: NCT05199207

Last Updated: 2022-07-13

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 60 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2022-01-11
• Study Completion Date: 2022-07-05

Brief Summary

Muscle failure (sarcopenia or dynapenia) is a factor of frailty and therefore, ultimately, of loss of autonomy in the elderly. Currently, no biomarker of muscle failure has a high sensitivity, specificity and positive predictive value. Several results, although preliminary, suggest that metabolomics could facilitate the early identification of frail patients, allowing the implementation of primary prevention strategies. Untargeted high-resolution metabolomics analysis would identify discriminative biomarkers and biological mechanisms associated with frailty. Finally, the hypothesis that metabolic signatures can be identified as risk factors for the development of age-related dynapenia should be tested in a longitudinal design.

Detailed Description

Sarcopenia is defined as decreased muscle strength and low muscle quantity or quality. Screening and management of sarcopenia was modified in early 2019 by the European Working Group on Sarcopenia in Older People (EWGSOP) with the creation of the F-A-C-S (Find-Assess-Confirm-Severity) protocol. The search for sarcopenia (Find) is done during the interrogation of the patient expressing symptoms that may be related to the loss of muscle mass, such as falls, asthenia, weight loss, decreased walking speed, or difficulty getting up from a chair. A simple self-report questionnaire (SARC-F) has been created to facilitate screening. Clinical suspicion of sarcopenia requires the performance of a functional assessment (Assess), using for example grip strength.and the chair lift test to look for decreased muscle strength. A pathological result already allows the suspicion of sarcopenia and the introduction of secondary prophylactic measures. Diagnostic confirmation of sarcopenia (Confirm) can be obtained by demonstrating a decrease in muscle mass by one of four validated techniques: magnetic resonance imaging (MRI), computed tomography (CT), dual-energy X-ray absorptiometry (DXA) (Buckinx et al., 2018), or bioimpedancemetry (Rossi et al., 2014). Sarcopenia is considered severe (Severity) if there is a decrease in overall physical performance objectified by physical tests such as the Time Up and Go Test, walking speed, or the Short Physical Performance Battery (SPPB) test.

The development and validation of a single biomarker could be a simple and cost-effective way to diagnose and monitor individuals with sarcopenia. Potential biomarkers could include markers of neuromuscular junction, muscle protein turnover, behaviorally mediated pathways, inflammation-mediated pathways, redox-related factors, and hormones or other anabolic factors (Curcio et al., 2016). However, due to the complex pathophysiology of sarcopenia, it is unlikely that a single biomarker can identify the disease in the heterogeneous population of young and old. Instead, the development of a panel of biomarkers should be considered, including potential serum markers and tissue markers. Implementing a multidimensional methodology for modeling these pathways could provide a means to stratify risk for sarcopenia, facilitate identification of worsening of the condition, and track treatment efficacy.

In the context of physical frailty and sarcopenia, the study of dynamic metabolic responses to stressors and the characterization of the biochemical pathways involved are particularly relevant, as this condition is closely associated with metabolic disorders. Disturbances in protein and amino acid metabolism may contribute substantially to the pathophysiology of sarcopenia.

The hypothesis that metabolic signatures can be identified as risk factors for the development of age-related sarcopenia needs to be tested in a longitudinal design.

The main objective is to Identify metabolomic signatures of muscle failure in the elderly.

Conditions

Sarcopenia

Study Design

• Allocation Method: NON_RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: BASIC_SCIENCE
• Blinding Strategy: NONE

Study Groups

Sarcopenic patient

Patient with sarcopenic criteria (SARC-F Score, muscle strengh value, appendicular lean mass value)

Group Type: EXPERIMENTAL

Physical activity programm

Intervention Type: BEHAVIORAL

adapted physical activity during 3 month

Non sarcopenic patient

Patient without sarcopenic criteria (SARC-F Score, muscle strengh value, appendicular lean mass value)

Group Type: ACTIVE_COMPARATOR

Physical activity programm

Intervention Type: BEHAVIORAL

adapted physical activity during 3 month

Interventions

Physical activity programm

adapted physical activity during 3 month

Intervention Type: BEHAVIORAL

Eligibility Criteria

Inclusion Criteria

• Age greater than or equal to 65 years;
• Patient affiliated or beneficiary of a social security plan;
• Patient having signed a prior informed consent

Exclusion Criteria

• presence of a physical or cognitive pathology preventing the performance of the physical activity protocol during 3 months
• Patient with legal protection

• Minimum Eligible Age: 65 Months
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Centre Hospitalier Universitaire de Nice

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Emeline MICHEL, MD

Role: PRINCIPAL_INVESTIGATOR

Nice University Hospital

Locations

CHU de Nice

Nice, , France

Countries

France

Other Identifiers

20-AOI-11

Identifier Type: -

Identifier Source: org_study_id