The ForceLoss study aims to develop personalised modeling and simulation procedures to enable the differential diagnosis for the loss of muscle force, namely dynapenia. The primary causes of dynapenia can be identified in a diffuse or selective sarcopenia, a lack of activation (inhibition), or suboptimal motor control. Each of these causes requires different interventions, but a reliable differential diagnosis is currently impossible. While biomedical instruments and tools can provide valuable information, it is often left to the experience of the single clinican to integrate such information into a complete diagnostic picture. An accurate diagnosis for dynapenia is important for a number of pathologies, including neurological diseases, age-related frailty, diabetes, and orthopaedic conditions.

The hypothesis is that the use of mechanistic, subject-specific models (digital twins) to simulate a maximal isometric knee extension task, informed by experimental measures may be employed to conduct a robust differential diagnosis for dynapenia.

In this study, on patients candidate for knee arthroplasty, the investigators will expand (i) the experimental protocol previously developed and tested on healthy volunteers with a measure of involuntary muscle contraction (superimposed neuromuscular electrical stimulation, SNMES), a hand-grip test, measures of bio-impedance and clinical questionnaires, and (ii) the modeling and simulation framework to include one additional step (to check for muscle inhibition).

Medical imaging, electromyography (EMG) and dynamometry data will be collected and combined to inform a digital twin of each participant. Biomechanical computer simulations of a Maximal Voluntary Isometric Contraction (MVIC) task will then be performed. Comparing the models' estimates to in vivo dynamometry measurements and EMG data, the investigators will test one by one the three possible causes of dynapenia, and, through a process of hypothesis falsification will exclude those that do not explain the observed loss of muscle force.

Inclusion Criteria:

• Diagnosis of Primary Arthrosis at the knee (according to the American College of Rheumatology criteria), subjects elected for tota knee arthroplasty
• Body Mass Index between 18.5 and 30 kg/m2
• Health status (according to the American Society of Anesthesiology classification) equal to 1 or 2
• Suspected systemic sarcopenia due to aging or localized sarcopenia due to disuse

Exclusion Criteria:

• Neurological, rheumatic or tumoral diseases
• Inguinal or abdominal hernia
• Diabetes
• Severe Hypertension (Level 3)
• Severe Cardio-pulmonary insufficiency
• Diagnosis of Osteonecrosis in the lower limb joints
• Pathologies or physical conditions incompatible with the use of magnetic resonance imaging and electrostimulation (i.e., active and passive implanted biomedical devices, epilepsy, severe venous insufficiency in the lower limbs)
• Previous interventions or traumas to the joints of the lower limb

We plan to share a database of experimental data representative of a population of patients elected for total knee arthroplasty. The dataset will include torques profiles and EMG data recorded during the MVIC test, the torque profiles recorded while delivering the electrical stimulation, and may include processed Magnetic Resonance Imaging data (segmentations of lower limb bones bones and muscles). All data will be irreversibly anonymized.

The (anonymized) dataset will be made available to the wider biomechanical community upon study completion

Not yet defined