Are ‘Endurance’ Alleles ‘Survival’ Alleles? Insights from the ACTN3 R577X Polymorphism

Exercise phenotypes have played a key role for ensuring survival over human evolution. We speculated that some genetic variants that influence exercise phenotypes could be associated with exceptional survival (i.e. reaching ≥100years of age). Owing to its effects on muscle structure/function, a potential candidate is the Arg(R)577Ter(X) polymorphism (rs1815739) in ACTN3, the structural gene encoding the skeletal muscle protein α-actinin-3. We compared the ACTN3 R577X genotype/allele frequencies between the following groups of ethnically-matched (Spanish) individuals: centenarians (cases, n = 64; 57 female; age range: 100–108 years), young healthy controls (n = 283, 67 females, 216 males; 21±2 years), and humans who are at the two end-points of exercise capacity phenotypes, i.e. muscle endurance (50 male professional road cyclists) and muscle power (63 male jumpers/sprinters). Although there were no differences in genotype/allele frequencies between centenarians (RR:28.8%; RX:47.5%; XX:23.7%), and controls (RR:31.8%; RX:49.8%; XX:18.4%) or endurance athletes (RR:28.0%; RX:46%; XX:26.0%), we observed a significantly higher frequency of the X allele (P = 0.019) and XX genotype (P = 0.011) in centenarians compared with power athletes (RR:47.6%; RX:36.5%;XX:15.9%). Notably, the frequency of the null XX (α-actinin-3 deficient) genotype in centenarians was the highest ever reported in non-athletic Caucasian populations. In conclusion, despite there were no significant differences with the younger, control population, overall the ACTN3 genotype of centenarians resembles that of world-class elite endurance athletes and differs from that of elite power athletes. Our preliminary data would suggest a certain ‘survival’ advantage brought about by α-actinin-3 deficiency and the ‘endurance’/oxidative muscle phenotype that is commonly associated with this condition

The benefits of strength training on musculoskeletal system health: practical applications for interdisciplinary care

Global health organizations have provided recommendations regarding exercise for the general population. Strength training has been included in several position statements due to its multi-systemic benefits. In this narrative review, we examine the available literature, first explaining how specific mechanical loading is converted into positive cellular responses. Secondly, benefits related to specific musculoskeletal tissues are discussed, with practical applications and training programmes clearly outlined for both common musculoskeletal disorders and primary prevention strategies

An Emulator-Based Prediction of Dynamic Stiffness for Redundant Parallel Kinematic Mechanisms

The accuracy of a parallel kinematic mechanism (PKM) is directly related to its dynamic stiffness, which in turn is configuration dependent. For PKMs with kinematic redundancy, configurations with higher stiffness can be chosen during motion-trajectory planning for optimal performance. Herein, dynamic stiffness refers to the deformation of the mechanism structure, subject to dynamic loads of changing frequency. The stiffness-optimization problem has two computational constraints: (i) calculation of the dynamic stiffness of any considered PKM configuration, at a given task-space location, and (ii) searching for the PKM configuration with the highest stiffness at this location. Due to the lack of available analytical models, herein, the former subproblem is addressed via a novel effective emulator to provide a computationally efficient approximation of the high-dimensional dynamic-stiffness function suitable for optimization. The proposed method for emulator development identifies the mechanism\u27s structural modes in order to breakdown the high-dimensional stiffness function into multiple functions of lower dimension. Despite their computational efficiency, however, emulators approximating high-dimensional functions are often difficult to develop and implement due to the large amount of data required to train the emulator. Reducing the dimensionality of the approximation function would, thus, result in a smaller training data set. In turn, the smaller training data set can be obtained accurately via finite-element analysis (FEA). Moving least-squares (MLS) approximation is proposed herein to compute the low-dimensional functions for stiffness approximation. Via extensive simulations, some of which are described herein, it is demonstrated that the proposed emulator can predict the dynamic stiffness of a PKM at any given configuration with high accuracy and low computational expense, making it quite suitable for most high-precision applications. For example, our results show that the proposed methodology can choose configurations along given trajectories within a few percentage points of the optimal ones