Comparison of lower limb muscle strength between diabetic neuropathic and healthy subjects using OpenSim

Highlights

• Diabetic and control subjects’ muscle forces estimation with OpenSim models.

• Alterations in kinematics and kinetics, muscle forces and activation were observed.

• Muscles related to foot movements are stronger in the healthy population.

• Ankle rigidity and weaker dorsal-plantar flexor muscles in the diabetic population.

• This methodology can help planning training programs for increasing muscle strength.

Abstract

Diabetes neuropathy and vasculopathy are the two major complications of diabetes mellitus, leading to diabetic foot disease, of which the worst consequences are plantar ulcers and amputations. Motor impairments like joint stiffness and loss of balance are distinctive effects of diabetes and they have been extensively explored. However, while altered muscle function has been also assessed through experimentally measured surface electromyography, little is known about muscle forces. The objective of this study was to estimate muscle forces in subjects with diabetes and to use these data to identify differences with respect to a population of healthy subjects matched for age and BMI. This was obtained by generating musculoskeletal models of 10 diabetic and 10 control subjects in OpenSim starting from experimentally recorded data. Dynamic simulations of motion were run and hence muscle forces calculated. Student T test (p < 0.05) was used to compare joints kinematics, kinetics and muscle forces between the two populations. Significant changes were observed between lower limb muscle forces and activation of diabetic and healthy subjects, as well as between joints kinematics and kinetics. In particular muscles related to foot movements proved to be stronger in the healthy population. The typical ankle rigidity of the diabetic population was confirmed by a lower range of motion registered at the ankle plantar/flexion angle associated with weaker dorsal-plantar flexor muscles. The information provided by this methodology can help planning specific training programs aiming at augmenting muscle strength and joints mobility, and they can also improve the evaluation of the potential benefits.

Introduction

Diabetes mellitus is a chronic disease caused by deficiency in insulin production by the pancreas, or by the ineffectiveness of the insulin produced. This deficiency results in increased glucose concentrations in the blood, which in turn damage the blood vessels and nerves [1]. Approximately 382 million people suffer from diabetes mellitus worldwide, and this number may almost double by the year 2035 [2].

Diabetic peripheral neuropathy (DPN) is the most common complication of diabetes which affects up to 50% of people with diabetes [1]. DPN, together with peripheral arterial disease, is involved in the pathogenesis of the diabetic foot, which significantly limits mobility [1], [3], [4], [5].

In the last decade several research projects have investigated diabetic subjects’ foot biomechanics using instrumented 3D motion analysis. These studies have contributed to identify patients at higher risk for the development of ulcers and diabetic foot related complications [3], [4], [5].

DPN significantly decreases the ability to walk and causes alterations of foot posture and function. The result is an impaired balance and gait which increases the risk of falls [3], [4], [5]. Important alterations were demonstrated in hip, knee, ankle joints and trunk moment patterns over the entire stance phase of gait in diabetic subjects with DPN (DPNS) and without DPN [3], [4], [5]. In particular, the altered rollover process modifies the plantar pressure distribution to areas that have to bear higher pressures and are therefore more subjected to ulcer formation [6], [7].

Diabetes also accelerates age-related decreases in muscle mass [8], a condition related to insulin resistance. In this context some authors have reported lower limb muscle electromyographic abnormalities in subjects with and without DPN during gait [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20]. Furthermore also the muscle strength proved to be affected, and considering that it significantly contributes to lower limb joints loading during walking [13], [17], [18], specific trainings have been proposed for its improvement [17], [19]. In particular strengthening and stretching exercises were combined with functional training to ameliorate gait biomechanics and foot function [19]. Even though a comprehensive biomechanical evaluation should integrate muscle function during all the gait cycle, state of the art quantitative muscle strength evaluation relies on isokinetic dynamometry [13], [20] thus giving information on isometric, isotonic, isokinetic, concentric and eccentric muscle contractions in controlled movements.

In this context, the assessment of muscle strength during walking appears to be essential to understand the gait alterations related to the presence of diabetes, and this can be achieved by means of multi-body simulations based on musculoskeletal models (MSMs) [21], [22], [23]. These in the past decade, have proliferated in the biomechanics research community, thanks to the possibility to compute muscle forces starting from data experimentally acquired during gait analysis. Furthermore MSMs provide an estimate of kinematics, kinetic and muscle function during gait both in terms of activations and forces.

The aim of this study was to estimate muscle forces during gait in DPNS and to compare them with a population of healthy subjects matched for age and BMI. It was hypothesised that lower muscles forces would have been revealed in the DPNS when compared to CS, in particular on those muscles acting at the hip and ankle joints. The evaluation of the altered muscle forces distribution could lead to therapeutic interventions aiming at restoring the physiological muscle coordination around the ankle and other lower limb joints.