Age-Related Slowing of Contractile Properties Differs Between Power, Endurance, and Nonathletes: A Tensiomyographic Assessment

Age-Related Slowing of Contractile Properties Differs Between Power, Endurance, and Nonathletes:... Abstract Although master athletes maintain high levels of physical activity, they also suffer from an age-related decline in skeletal muscle function. There are indications of disproportional age- and physical inactivity-induced muscle wasting between muscles. Tensiomyography is a noninvasive tool that has been used to study the effects of a variety of sports on the contraction time (Tc) in different skeletal muscles. The aim of this cross-sectional study was to assess age-related changes in the Tc of the vastus lateralis, gastrocnemius medialis, and biceps femoris muscles with Tensiomyography in older nonathletes (age = 62.1 ± 12.7 years; NMALES = 133; NFEMALES = 246), and power (age = 56.9 ± 13.5 years; NMALES = 100; NFEMALES = 78) and endurance master athletes (age = 56.5 ± 14.5 years; NMALES = 76; NFEMALES = 73). We found an age-related slowing in all muscles, irrespective of discipline, where endurance master athletes had the longest and power master athletes had the shortest Tc. The longer Tc in endurance master athletes than in nonathletes suggests that regular endurance sport activity aggravates slowing of skeletal muscles during aging. Skeletal muscle, TMG, Contraction time, Master athletes, MHC In nonathletes muscle mass shows a progressive decline of as much as 1% to 1.5% per year after about the age of 50 (1). The age-related loss of muscle mass is a consequence of loss and atrophy of muscle fibers (2–4). Since many of the age-related changes in skeletal muscle are similar to those induced by disuse (5) it is likely that the decrease in physical activity in old age (6,7) is a major contributor to the muscle wasting during aging. Master athletes, however, maintain high levels of physical activity (8–10) and suffer from fewer morbidities (11), thereby providing a unique human research model to disentangle the effects of disuse and comorbidities from aging per se. Master athletes are athletes older than 35 years who train for and participate in athletic competitions. Their training regimes range from 6 to over 20 hours per week (12) and they may well work at the limits of physiology (10). Even though their athletic performance exceeds that in age-matched nonathletes, also master athletes suffer from an age-related decline in muscle force and power (2,13,14), ventilatory function (15), and maximal oxygen consumption (16). In line with these decreases in physiological parameters, jump, sprint, and endurance performance decline with age (13,17), something seen even at the individual level (18), and after the age of 70 it even appears to be an exponential decline (19). This age-related decline in performance is not only caused by a loss of muscle mass and force generating capacity, but also by slowing of movement as a consequence of slower muscle contractile properties and increased tendon compliance (13). Part of the slowing of skeletal muscle is attributable to a preferential atrophy of fast type II fibers (3,4,20,21), which produce at least five times as much power as slow type I fibers (22). Single-fiber studies have also provided evidence of an age-related decrease in shortening velocity independent of fiber type switching in fibers expressing types I and IIa myosin heavy chain (MHC) isoforms (23,24). Thus, the loss of muscle mass, changes in fiber type composition, and slowing of muscle fibers may all contribute to the decline in anaerobic performance. Tensiomyography (TMG) is a noninvasive tool to assess the contractile properties (eg, contraction time [Tc]) of a muscle and can be used to estimate the percentage of type I MHC at least in the vastus lateralis (VL) muscle (25). We have recently shown in a large cohort of adolescents that regular sport exercise decreased Tc in the nonpostural biceps femoris (BF), but not in the postural VL (26). In addition, we observed that 8 weeks of plyometric training in young athletes results in an 8%–26% shorter Tc in five lower limb skeletal muscles that explained ~30% of the improvement in explosive power (27). This suggests that Tc is indeed related to muscle contractile properties and secondly that regular exercise may modulate the effects of aging on skeletal muscle properties. Previously, we found in older people that a lower performance in the timed-up-and-go and 6-minute walk tests was to a significant extent related to a lower maximal shortening velocity of the muscle (28). A longer Tc in older people may likewise have similar implications for functions of daily life. Yet, despite numerous studies reporting TMG results in children, adults, and athletes, there is only one study (29) that investigated the impact of aging on the TMG-derived velocity of contraction. They reported that the oldest group had the lowest velocity of contraction, but it is unclear whether this was due to a lower Tc and/or higher Dm. Therefore, the aim of this study was to assess age-related changes in VL, BF, and gastrocnemius medialis (GM) muscle contractile properties with TMG in male and female older nonathletes, and master endurance and power athletes in a cross-sectional study. Methods Participants Altogether 706 participants (311 men; aged 35–90 years) were recruited. Master athletes were recruited during: (i) European Veteran Athletics Championships in Ljubljana 2008 (EVACS; N = 253); (ii) European Master Games in Lignano 2011 (EMG; N = 56); and (iii) Slovenian Championships in Master Athletics Ljubljana 2015 (VAD; N = 18). From the 176 male master athletes, 40 were sprinters (≤400 m), 38 were throwers, 22 were jumpers, 57 runners (≥800 m), and 19 competed in walking events. From the 151 female master athletes, 29 were sprinters (≤400 m), 29 were throwers, 20 were jumpers, 51 runners (≥800 m), and 22 competed in walking events. All master athletes had competed for more than 3 years in master championships. Data of nonathletes were obtained from mass measurements during two international projects led by the Institute for Kinesiology Research at the Science and Research Centre Koper: (i) Physical Activity and Nutrition for Great Ageing 2013–2015 (PANGEA; N = 259); and (ii) Active and Quality Ageing in Home Environment 2015–2016 (A-Qu-A; N = 120). Nonathletes were not members of any sport clubs and did not compete in sport championships. Participants were excluded if they suffered from major skeletal, muscular, or nervous disorders. Basic anthropometric data and age, sex, and sport group distribution are presented in Table 1. The study was reviewed and approved by the Republic of Slovenia National Medical Ethics Committee (EVACS, PANGEA, and AQUA), Italian Ethics Committee at regional level (EMG), and Institutional Ethics Committee of the University of Primorska (VAD). Data collection conformed to the standards set by the Declaration of Helsinki (2002) and its amendments. All participants were fully informed of any risks and benefits associated with the study, and written informed consent was obtained from each participant prior to data collection. Table 1. Anthropometric Data of Participants, Grouped by Age, Sex, and Sport   Nonathletes  Power Athletes  Endurance Athletes  Men  Women  Men  Women  Men  Women  N  133  246  100  78  76  73  N35–44  31  28  32  26  20  27  N45–64  45  68  33  28  25  22  N65–90  57  150  35  24  31  24  Age/years  57.8 ± 14.0  63.4 ± 11.4  57.5 ± 14.1  55.5 ± 12.2  56.8 ± 14.7  56.2 ± 14.6  Age35–49/years  42.0 ± 7.4  41.3 ± 5.2  40.8 ± 4.5  41.4 ± 5.1  40.0 ± 4.1  41.4 ± 3.7  Age50–64/years  57.2 ± 4.6  60.3 ± 4.8  58.8 ± 5.3  56.4 ± 4.5  56.5 ± 5.3  56.7 ± 4.9  Age65–90/years  72.4 ± 4.7  71.8 ± 5.1  71.6 ± 8.0  70.6 ± 2.5  71.8 ± 5.1  73.9 ± 4.8  Body height/m  1.78 ± 0.07  1.62 ± 0.07  1.78 ± 0.08  1.69 ± 0.07  1.78 ± 0.07  1.66 ± 0.07  Body height35–49/m  1.81 ± 0.07  1.68 ± 0.08  1.81 ± 0.07  1.73 ± 0.06  1.81 ± 0.07  1.70 ± 0.09  Body height50–64/m  1.79 ± 0.07  1.63 ± 0.06  1.78 ± 0.05  1.67 ± 0.08  1.75 ± 0.08  1.66 ± 0.05  Body height65–90/m  1.74 ± 0.06  1.61 ± 0.06  1.73 ± 0.08  1.67 ± 0.06  1.73 ± 0.06  1.59 ± 0.04  Body mass/kg  85 ± 12  70 ± 12  79 ± 13  64 ± 6  72 ± 12  60 ± 9  Body mass35–49/kg  86 ± 12  66 ± 10  83 ± 16  64 ± 6  75 ± 9  63 ± 8  Body mass50–64/kg  87 ± 13  70 ± 13  79 ± 13  63 ± 8  72 ± 16  59 ± 10  Body mass65–90/kg  83 ± 11  70 ± 11  72 ± 12  65 ± 11  64 ± 6  55 ± 8  BMI/kg/m2  26.6 ± 3.4  26.2 ± 4.4  24.9 ± 2.9  22.4 ± 3.0  22.9 ± 3.7  21.7 ± 2.8  BMI35–49/kg/m2  26.1 ± 3.4  23.6 ± 3.3  25.5 ± 3.6  21.3 ± 2.5  23.1 ± 2.9  21.9 ± 2.9  BMI50–64/kg/m2  26.3 ± 3.3  25.9 ± 4.6  24.9 ± 2.6  22.6 ± 3.1  23.4 ± 4.2  21.5 ± 3.0  BMI65–90/kg/m2  27.2 ± 3.1  27.0 ± 4.3  24.1 ± 2.1  23.1 ± 3.2  21.6 ± 1.7  21.9 ± 2.5    Nonathletes  Power Athletes  Endurance Athletes  Men  Women  Men  Women  Men  Women  N  133  246  100  78  76  73  N35–44  31  28  32  26  20  27  N45–64  45  68  33  28  25  22  N65–90  57  150  35  24  31  24  Age/years  57.8 ± 14.0  63.4 ± 11.4  57.5 ± 14.1  55.5 ± 12.2  56.8 ± 14.7  56.2 ± 14.6  Age35–49/years  42.0 ± 7.4  41.3 ± 5.2  40.8 ± 4.5  41.4 ± 5.1  40.0 ± 4.1  41.4 ± 3.7  Age50–64/years  57.2 ± 4.6  60.3 ± 4.8  58.8 ± 5.3  56.4 ± 4.5  56.5 ± 5.3  56.7 ± 4.9  Age65–90/years  72.4 ± 4.7  71.8 ± 5.1  71.6 ± 8.0  70.6 ± 2.5  71.8 ± 5.1  73.9 ± 4.8  Body height/m  1.78 ± 0.07  1.62 ± 0.07  1.78 ± 0.08  1.69 ± 0.07  1.78 ± 0.07  1.66 ± 0.07  Body height35–49/m  1.81 ± 0.07  1.68 ± 0.08  1.81 ± 0.07  1.73 ± 0.06  1.81 ± 0.07  1.70 ± 0.09  Body height50–64/m  1.79 ± 0.07  1.63 ± 0.06  1.78 ± 0.05  1.67 ± 0.08  1.75 ± 0.08  1.66 ± 0.05  Body height65–90/m  1.74 ± 0.06  1.61 ± 0.06  1.73 ± 0.08  1.67 ± 0.06  1.73 ± 0.06  1.59 ± 0.04  Body mass/kg  85 ± 12  70 ± 12  79 ± 13  64 ± 6  72 ± 12  60 ± 9  Body mass35–49/kg  86 ± 12  66 ± 10  83 ± 16  64 ± 6  75 ± 9  63 ± 8  Body mass50–64/kg  87 ± 13  70 ± 13  79 ± 13  63 ± 8  72 ± 16  59 ± 10  Body mass65–90/kg  83 ± 11  70 ± 11  72 ± 12  65 ± 11  64 ± 6  55 ± 8  BMI/kg/m2  26.6 ± 3.4  26.2 ± 4.4  24.9 ± 2.9  22.4 ± 3.0  22.9 ± 3.7  21.7 ± 2.8  BMI35–49/kg/m2  26.1 ± 3.4  23.6 ± 3.3  25.5 ± 3.6  21.3 ± 2.5  23.1 ± 2.9  21.9 ± 2.9  BMI50–64/kg/m2  26.3 ± 3.3  25.9 ± 4.6  24.9 ± 2.6  22.6 ± 3.1  23.4 ± 4.2  21.5 ± 3.0  BMI65–90/kg/m2  27.2 ± 3.1  27.0 ± 4.3  24.1 ± 2.1  23.1 ± 3.2  21.6 ± 1.7  21.9 ± 2.5  Note: BMI = Body mass index; indexes 35–49, 50–64, 65–90 represent the age range in an age group. For statistical analysis, see Results. View Large Table 1. Anthropometric Data of Participants, Grouped by Age, Sex, and Sport   Nonathletes  Power Athletes  Endurance Athletes  Men  Women  Men  Women  Men  Women  N  133  246  100  78  76  73  N35–44  31  28  32  26  20  27  N45–64  45  68  33  28  25  22  N65–90  57  150  35  24  31  24  Age/years  57.8 ± 14.0  63.4 ± 11.4  57.5 ± 14.1  55.5 ± 12.2  56.8 ± 14.7  56.2 ± 14.6  Age35–49/years  42.0 ± 7.4  41.3 ± 5.2  40.8 ± 4.5  41.4 ± 5.1  40.0 ± 4.1  41.4 ± 3.7  Age50–64/years  57.2 ± 4.6  60.3 ± 4.8  58.8 ± 5.3  56.4 ± 4.5  56.5 ± 5.3  56.7 ± 4.9  Age65–90/years  72.4 ± 4.7  71.8 ± 5.1  71.6 ± 8.0  70.6 ± 2.5  71.8 ± 5.1  73.9 ± 4.8  Body height/m  1.78 ± 0.07  1.62 ± 0.07  1.78 ± 0.08  1.69 ± 0.07  1.78 ± 0.07  1.66 ± 0.07  Body height35–49/m  1.81 ± 0.07  1.68 ± 0.08  1.81 ± 0.07  1.73 ± 0.06  1.81 ± 0.07  1.70 ± 0.09  Body height50–64/m  1.79 ± 0.07  1.63 ± 0.06  1.78 ± 0.05  1.67 ± 0.08  1.75 ± 0.08  1.66 ± 0.05  Body height65–90/m  1.74 ± 0.06  1.61 ± 0.06  1.73 ± 0.08  1.67 ± 0.06  1.73 ± 0.06  1.59 ± 0.04  Body mass/kg  85 ± 12  70 ± 12  79 ± 13  64 ± 6  72 ± 12  60 ± 9  Body mass35–49/kg  86 ± 12  66 ± 10  83 ± 16  64 ± 6  75 ± 9  63 ± 8  Body mass50–64/kg  87 ± 13  70 ± 13  79 ± 13  63 ± 8  72 ± 16  59 ± 10  Body mass65–90/kg  83 ± 11  70 ± 11  72 ± 12  65 ± 11  64 ± 6  55 ± 8  BMI/kg/m2  26.6 ± 3.4  26.2 ± 4.4  24.9 ± 2.9  22.4 ± 3.0  22.9 ± 3.7  21.7 ± 2.8  BMI35–49/kg/m2  26.1 ± 3.4  23.6 ± 3.3  25.5 ± 3.6  21.3 ± 2.5  23.1 ± 2.9  21.9 ± 2.9  BMI50–64/kg/m2  26.3 ± 3.3  25.9 ± 4.6  24.9 ± 2.6  22.6 ± 3.1  23.4 ± 4.2  21.5 ± 3.0  BMI65–90/kg/m2  27.2 ± 3.1  27.0 ± 4.3  24.1 ± 2.1  23.1 ± 3.2  21.6 ± 1.7  21.9 ± 2.5    Nonathletes  Power Athletes  Endurance Athletes  Men  Women  Men  Women  Men  Women  N  133  246  100  78  76  73  N35–44  31  28  32  26  20  27  N45–64  45  68  33  28  25  22  N65–90  57  150  35  24  31  24  Age/years  57.8 ± 14.0  63.4 ± 11.4  57.5 ± 14.1  55.5 ± 12.2  56.8 ± 14.7  56.2 ± 14.6  Age35–49/years  42.0 ± 7.4  41.3 ± 5.2  40.8 ± 4.5  41.4 ± 5.1  40.0 ± 4.1  41.4 ± 3.7  Age50–64/years  57.2 ± 4.6  60.3 ± 4.8  58.8 ± 5.3  56.4 ± 4.5  56.5 ± 5.3  56.7 ± 4.9  Age65–90/years  72.4 ± 4.7  71.8 ± 5.1  71.6 ± 8.0  70.6 ± 2.5  71.8 ± 5.1  73.9 ± 4.8  Body height/m  1.78 ± 0.07  1.62 ± 0.07  1.78 ± 0.08  1.69 ± 0.07  1.78 ± 0.07  1.66 ± 0.07  Body height35–49/m  1.81 ± 0.07  1.68 ± 0.08  1.81 ± 0.07  1.73 ± 0.06  1.81 ± 0.07  1.70 ± 0.09  Body height50–64/m  1.79 ± 0.07  1.63 ± 0.06  1.78 ± 0.05  1.67 ± 0.08  1.75 ± 0.08  1.66 ± 0.05  Body height65–90/m  1.74 ± 0.06  1.61 ± 0.06  1.73 ± 0.08  1.67 ± 0.06  1.73 ± 0.06  1.59 ± 0.04  Body mass/kg  85 ± 12  70 ± 12  79 ± 13  64 ± 6  72 ± 12  60 ± 9  Body mass35–49/kg  86 ± 12  66 ± 10  83 ± 16  64 ± 6  75 ± 9  63 ± 8  Body mass50–64/kg  87 ± 13  70 ± 13  79 ± 13  63 ± 8  72 ± 16  59 ± 10  Body mass65–90/kg  83 ± 11  70 ± 11  72 ± 12  65 ± 11  64 ± 6  55 ± 8  BMI/kg/m2  26.6 ± 3.4  26.2 ± 4.4  24.9 ± 2.9  22.4 ± 3.0  22.9 ± 3.7  21.7 ± 2.8  BMI35–49/kg/m2  26.1 ± 3.4  23.6 ± 3.3  25.5 ± 3.6  21.3 ± 2.5  23.1 ± 2.9  21.9 ± 2.9  BMI50–64/kg/m2  26.3 ± 3.3  25.9 ± 4.6  24.9 ± 2.6  22.6 ± 3.1  23.4 ± 4.2  21.5 ± 3.0  BMI65–90/kg/m2  27.2 ± 3.1  27.0 ± 4.3  24.1 ± 2.1  23.1 ± 3.2  21.6 ± 1.7  21.9 ± 2.5  Note: BMI = Body mass index; indexes 35–49, 50–64, 65–90 represent the age range in an age group. For statistical analysis, see Results. View Large Research Design The study was cross-sectional and participants were divided by sex, sport, and age. We defined three sport groups: nonathletes (NA), power athletes (PowA: sprints ≤ 400 m, jumps and throws), and endurance athletes (EndA: runs ≥ 800 m; walking). We defined three age groups: 35–49 years, 50–64 years, and ≥65 years, where the oldest participant was 90 years old. Anthropometric Measurements The participants were asked to abstain from intense physical activity for 24 hours (NA) or competition (PowA and EndA) before the assessment. After determination of body height and body mass (Seca Instruments Ltd., Hamburg, Germany), TMG was performed. TMG TMG was used to assess skeletal muscle contractile properties in the VL, BF, and GM muscles. All measurements were performed during electrically-evoked isometric contractions. For VL TMG, participants were in a supine position with the knee angle at 300 flexion (where 00 represents a fully extended knee joint). For the BF they were in a prone position with the knee at 50 flexion and for the GM they were in a prone position with the ankle in neutral position as previously reported (25,30). Foam pads were used to support the joints. A single 1-ms maximal monophasic electrical impulse was used to elicit a twitch that caused the muscle belly to oscillate. These oscillations were recorded using a sensitive digital displacement sensor (TMG-BMC, Ljubljana, Slovenia) that was placed on the surface of the skin over the mid belly of the muscle of interest. If needed, the measuring point and electrode positions were adjusted to obtain maximal Dm of the muscle belly. Initially, the stimulation amplitude was set just above the threshold and then gradually increased until the Dm of the radial twitch displacement increased no further. From two maximal twitch responses, Tc was calculated and the average used for further analysis. Tc was defined (Figure 1) as the time for the amplitude to increase from 10% to 90% of Dm (25). Figure 1. View largeDownload slide Typical tensiomyographic responses of the biceps femoris of a nonathlete, power athlete and endurance athlete (above) and of a nonathlete in different age groups (below). Tc: contraction time defined as the time from 10% to 90% of the maximal displacement amplitude (Dm). Figure 1. View largeDownload slide Typical tensiomyographic responses of the biceps femoris of a nonathlete, power athlete and endurance athlete (above) and of a nonathlete in different age groups (below). Tc: contraction time defined as the time from 10% to 90% of the maximal displacement amplitude (Dm). Muscle Biopsies In a subsample of master athletes (N = 17; 4 EndA + 13 PowA) we have taken VL muscle biopsies after TMG assessment and determined the MHC composition as described previously (25). Briefly, VL muscle biopsies were obtained with a conchotome after skin sterilization and local anesthesia (2% lidocaine) and sterile conditions. The sample (~100 mg) was taken at 40% from the distance of the knee joint (0%) and the spina iliaca anterior superior (100%). The samples were placed on cork perpendicular to the long axis of the fibers, frozen in liquid nitrogen with vigorous shaking and stored at −80°C until analysis. For MHC determination, 10-μm cross-sections were cut in a cryostat, collected in a centrifuge tube with Laemli sample buffer (three sections in 100 μL) and boiled for 2 minutes to denature the proteins. Twelve-microliter samples were loaded on SDS poly-acrylamide gels run at 15°C, 120 V for 27 hours. The stacking gel contained 4% acrylamide and the separating gel had 7% acrylamide with both containing 30% glycerol. After the run, the gels were stained with the Silver Stain Plus kit following the instructions of the manufacturer (Biorad Laboratories, UK). Bands (Figure 2) were identified based on the migration distance, and relative quantities determined with Quantity One software (Biorad Laboratories). Figure 2. View largeDownload slide Representative SDS-PAGE gel to determine the myosin heavy chain composition of the vastus lateralis muscle from master athletes. Lane 1-3, 5, 7-11 contain all three myosin heavy chain isoforms (from top to bottom IIx, IIa, I) and lane 4 and 6 only contain type I and IIa myosin heavy chain isoforms. Figure 2. View largeDownload slide Representative SDS-PAGE gel to determine the myosin heavy chain composition of the vastus lateralis muscle from master athletes. Lane 1-3, 5, 7-11 contain all three myosin heavy chain isoforms (from top to bottom IIx, IIa, I) and lane 4 and 6 only contain type I and IIa myosin heavy chain isoforms. Statistics SPSS (IBM, USA) software was used for all statistical analyses. All data in text and tables are presented as mean ± SD, whereas in figures standard errors were used. Visual inspection and the Shapiro-Wilk test indicated that all data were normally distributed. Sphericity (homogeneity of covariance) was verified by the Mauchly’s test. When the assumption of sphericity was not met, the significance of the F-ratios was adjusted according to the Greenhouse-Geisser procedure. Main effects were studied with a General Linear Model repeated-measures analysis of variance with muscle (VL, GM, and BF) as within factor, and three between factors: sport group (three levels: NA, PowA, EndA), sex (man or woman), and age (three levels: 35–49 years, 50–64 years, and ≥65 years). Where significant effects were found for sport or age effects or two-way interactions (three-way interactions were excluded from the analysis), post-hoc analysis with Bonferroni corrections was used to locate the differences between sport and age groups. We used the Pearson correlation coefficient to assess associations between age and Tc in each muscle and sex. We also performed stepwise multiple regression analysis of Tc with age, sex, muscle, and sport as predictors, with entry criteria at p ≤ .05 and removal criteria at p ≥ .1. In a subsample of 17 master athletes who also gave a VL muscle biopsy, we performed Pearson correlation analysis to assess to what extent VL Tc is affected by the MHC-I content of the muscle, age and sport. Statistical significance was accepted at p values less than .05. Additionally, the effect size for dependent variables was assessed as partial eta-squared (ɳ2). Results Table 1 shows the anthropometric data. We found that PowA were the tallest (p = .005), and that they and NA had a higher body mass than EndA (p < .001). NA had a higher body mass index than PowA and EndA (p < .001). Women were smaller (p < .001), lighter (p < .001), and had a lower body mass index (p = .001) than men. Tc was positively correlated with age, irrespective of muscle and sport group (Figure 3). This correlation was weakest in VL (even not significant for female NA), and strongest in BF for PowA and EndA, where EndA had the strongest correlation in GM. Figure 3. View largeDownload slide Pearson correlation (r) between age and contraction time (Tc) for men (left) and women (right) in each muscle and sport group. Figure 3. View largeDownload slide Pearson correlation (r) between age and contraction time (Tc) for men (left) and women (right) in each muscle and sport group. A multiple regression analysis (Table 2) confirmed an overall correlation of R = .690 (p < .001) with 47.6% of the variance in Tc explained by Age, BF, VL, and athletic discipline as predictors, but not sex. The largest contributors (part correlations) to the explained variance in Tc are BF and Age. The multiple regression prediction model is as follows:  Tc=11.754×BF+.193×Age−3.292×An−3.185×VL+3.142×Ae+18.098 where Tc is a muscle contraction time (in ms), BF is the biceps femoris muscle (0 or 1), VL is the vastus lateralis muscle (0 or 1), Age is an age (in years), An is Power athletes (0 or 1), and Ae is Endurance athletes (0 or 1). Table 2. Multiple Linear Regression Analysis of Age, Sex (removed by regression), Sport (aerobic and anaerobic), and Muscle (biceps femoris and vastus lateralis) as Predictors of Tensiomyographic Contraction Time Predictors  Unstandardized Coefficients  Standardized Coefficients  p  Part r  VIF  Constant  18.098 ms  .907  <.001      Biceps femoris  11.754 ms  .421  <.001  .453  1.371  Age  0.193 ms/years  .013  <.001  .236  1.050  Anaerobic  -3.292 ms  .426  <.001  -.125  1.088  Vastus lateralis  -3.185 ms  .419  <.001  -.123  1.372  Aerobic  3.142 ms  .509  <.001  .100  1.069  Predictors  Unstandardized Coefficients  Standardized Coefficients  p  Part r  VIF  Constant  18.098 ms  .907  <.001      Biceps femoris  11.754 ms  .421  <.001  .453  1.371  Age  0.193 ms/years  .013  <.001  .236  1.050  Anaerobic  -3.292 ms  .426  <.001  -.125  1.088  Vastus lateralis  -3.185 ms  .419  <.001  -.123  1.372  Aerobic  3.142 ms  .509  <.001  .100  1.069  Note: Part r: Part correlation of each predictor to contraction time when controlling for effects of other predictors. VIF = Variance inflation factor. View Large Table 2. Multiple Linear Regression Analysis of Age, Sex (removed by regression), Sport (aerobic and anaerobic), and Muscle (biceps femoris and vastus lateralis) as Predictors of Tensiomyographic Contraction Time Predictors  Unstandardized Coefficients  Standardized Coefficients  p  Part r  VIF  Constant  18.098 ms  .907  <.001      Biceps femoris  11.754 ms  .421  <.001  .453  1.371  Age  0.193 ms/years  .013  <.001  .236  1.050  Anaerobic  -3.292 ms  .426  <.001  -.125  1.088  Vastus lateralis  -3.185 ms  .419  <.001  -.123  1.372  Aerobic  3.142 ms  .509  <.001  .100  1.069  Predictors  Unstandardized Coefficients  Standardized Coefficients  p  Part r  VIF  Constant  18.098 ms  .907  <.001      Biceps femoris  11.754 ms  .421  <.001  .453  1.371  Age  0.193 ms/years  .013  <.001  .236  1.050  Anaerobic  -3.292 ms  .426  <.001  -.125  1.088  Vastus lateralis  -3.185 ms  .419  <.001  -.123  1.372  Aerobic  3.142 ms  .509  <.001  .100  1.069  Note: Part r: Part correlation of each predictor to contraction time when controlling for effects of other predictors. VIF = Variance inflation factor. View Large In a subsample of master athletes (NPOW = 13 + NEND = 4), we assessed the MHC composition of the VL (Table 3) and found a significant bi-variate correlation between Tc and MHC-I (r = .864; p < .001), whereas there was no significant correlation between age and Tc (p = .309) or MHC-I (p = .719). A correlation was found also between VL Tc and MHC-IIa (r = −.760; p < .001) but not between VL Tc and MHC-IIx (r = −.326; p = .201). Table 3. MHC Composition of the Vastus lateralis Muscle for a Subsample of Master Athletes (master athlete’s data taken from ref. (25))   Total N = 17  Power Athletes N = 13  Endurance Athletes N = 4  MHC-I/%  35.6 ± 18.4  31.7 ± 16.3  48.1 ± 21.5  MHC-II/%  64.5 ± 18.4  68.3 ± 16.3  52.0 ± 21.5  MHC-IIa/%  44.7 ± 14.4  45.2 ± 12.4  43.3 ± 22.0  MHC-IIx/%  19.7 ± 15.1  23.1 ± 15.0  8.7 ± 10.3    Total N = 17  Power Athletes N = 13  Endurance Athletes N = 4  MHC-I/%  35.6 ± 18.4  31.7 ± 16.3  48.1 ± 21.5  MHC-II/%  64.5 ± 18.4  68.3 ± 16.3  52.0 ± 21.5  MHC-IIa/%  44.7 ± 14.4  45.2 ± 12.4  43.3 ± 22.0  MHC-IIx/%  19.7 ± 15.1  23.1 ± 15.0  8.7 ± 10.3  Note: MHC = Myosin heavy chain. View Large Table 3. MHC Composition of the Vastus lateralis Muscle for a Subsample of Master Athletes (master athlete’s data taken from ref. (25))   Total N = 17  Power Athletes N = 13  Endurance Athletes N = 4  MHC-I/%  35.6 ± 18.4  31.7 ± 16.3  48.1 ± 21.5  MHC-II/%  64.5 ± 18.4  68.3 ± 16.3  52.0 ± 21.5  MHC-IIa/%  44.7 ± 14.4  45.2 ± 12.4  43.3 ± 22.0  MHC-IIx/%  19.7 ± 15.1  23.1 ± 15.0  8.7 ± 10.3    Total N = 17  Power Athletes N = 13  Endurance Athletes N = 4  MHC-I/%  35.6 ± 18.4  31.7 ± 16.3  48.1 ± 21.5  MHC-II/%  64.5 ± 18.4  68.3 ± 16.3  52.0 ± 21.5  MHC-IIa/%  44.7 ± 14.4  45.2 ± 12.4  43.3 ± 22.0  MHC-IIx/%  19.7 ± 15.1  23.1 ± 15.0  8.7 ± 10.3  Note: MHC = Myosin heavy chain. View Large After dividing the participants into three age groups we found significant effects for muscle (p < .001; η2 = .395), sport (p < .001; η2 = .133), age (p < .001; η2 = .153), and sport × age (p < .001; η2 = .036), muscle × sex (p = .048; η2 = .005), muscle × sport (p < .001; η2 = .033), muscle × age (p < .001; η2 = .043) interactions. As there were interactions between muscle with sport, age, and sex, we performed subsequent post-hoc analysis of variances first for the nonathletes only and then for each muscle separately (Figure 4). Figure 4. View largeDownload slide Contraction time (Tc) differences in three skeletal muscles between athletes and nonathletes in different age groups for men (left column graphs) and women (right column graphs). a, b, c…significantly different from nonathletes at p < .05; p < .01, and p < .001, respectively; i, j, k… significantly different from previous age group at p < .05; p < .01, and p < .001, respectively; G. … Gastrocnemius. Figure 4. View largeDownload slide Contraction time (Tc) differences in three skeletal muscles between athletes and nonathletes in different age groups for men (left column graphs) and women (right column graphs). a, b, c…significantly different from nonathletes at p < .05; p < .01, and p < .001, respectively; i, j, k… significantly different from previous age group at p < .05; p < .01, and p < .001, respectively; G. … Gastrocnemius. Looking at the NA only, we found the lowest Tc in VL (26.1 ± 4.2 ms), the longest in BF (43.1 ± 11.3 ms) and that of the GM (30.0 ± 7.7 ms) in between the two. In the VL, there were effects of age (p < .001; η2 = .039), sex (p < .001; η2 = .017), sport (p < .001; η2 = .065), and sport × age (p = .003; η2 = .023), and sex × age (p = .072; η2 = .008) interactions. The interactions indicated that the effects of age differed between sports and sexes. The sex × age interaction was reflected by an age-related increase in Tc in men only (p = .002). The sport × age interaction was reflected by the shorter Tc in PowA than NA at all ages in men only (p = .004). In both male PowA and NA Tc increased from 35–49 to 50–64 years by 8.7% (p = .004) and 10.5% (p < .001), respectively. The Tc was higher in 65–90-year-old EndA than 50–64-year-old EndA (p = .015) and EndA had a longer Tc than NA (p = .008). In the GM, there were effects of age (p < .001; η2 = .066) and sport (p < .001; η2 = .039), and a sport × age interaction (p = .009; η2 = .022). The latter indicated that the effect of age differed between the different sports. Although in all groups Tc was longer at 65–90 years in comparison to 35–49 years (10.2%; p = .017), PowA had shorter Tc than NA at 35–49 years (−9.7%; p = .014), and EndA had longer Tc at 65–90 years than NA (22.6%; p < .001). In the BF, there were effects of age (p < .001; η2 = .129) and sport (p < .001; η2 = .099), and sport × age (p = .086; η2 = .012) and sex × sport (p = .087; η2 = .007) interactions. The interactions indicated that the effects of sport differed between sports, and between men and women. Tc was consistently increased with increasing age in PowA, but PowA always had a shorter Tc than NA in both men and women. However, the relative difference of Tc between NA and PowA was decreasing with increasing age (in men: from 20.5% to 12.3%; in women from 27.4% to 11.3%). In EndA, Tc was higher than in NA at 35–49 years (only in men: 22.5%; p = .001), then stabilized at 50–64 years and increased again at 65–90 years (in men: 33.2%; p < .001, in women: 26.5%; p < .001), when EndA had higher Tc than NA (only in men: 20.5%; p < .001). We found that body mass and body mass index explained at best 1% of the variance (data not shown). Discussion In the present cross-sectional study, we used TMG to noninvasively assess the contractile properties of leg skeletal muscles in master endurance and power athletes, and age-matched nonathletes. The main observation of the present study is the age-related slowing of all muscles. Endurance athletes had slower muscles than age-matched nonathletes, whereas power athletes had the fastest contractile properties, irrespective of sex. For all muscles, an age × sport interaction was found, indicating that the effects of age differ between sports. Given that endurance athletes had slower contractile properties than nonathletes, this suggests that regular long-distance running accelerates the age-related slowing of skeletal muscle. TMG TMG was developed to noninvasively measure skeletal muscle contractile properties (31). We have previously shown in a population of 20–83-year olds that Tc predicted 77% of the variance of the proportion of type I MHC in the VL (25), which was confirmed by our results obtained in a sub group of 17 master athletes (75%). Although this regression model has not been validated in other muscles, there are no obvious reasons to believe that it would not apply to other muscles, albeit with different regression coefficients. Here we show that Tc was lowest in the VL (26.1 ± 4.2 ms), then in GM (30.0 ± 7.7 ms) and longest in BF (43.1 ± 11.3 ms), corresponding with the reported differences in type I proportions between these muscles (50%, 54%–63%, and 67%, respectively) (32,33). Sex and Sport-related Differences in Contractile Properties We did not observe significant differences between men and women in the contractile properties of the VL, BF, and GM. This corresponds with the similar fiber type distribution in men and women (21,34,35). Power athletes had a lower Tc than endurance and nonathletes, particularly in the nonpostural BF, and to a lesser extent in the postural VL and GM. The shorter Tc in power athletes is most likely attributable to a higher shortening velocity in both type I (75%) and type IIa (45%) fibers, as reported after 12 weeks of resistance exercise in older men (36). Interestingly, the same authors (37) demonstrated that the same program in women induced a decrease in the shortening velocity of type I fibers, with no change in that of type IIa fibers. We did not observe a significant sex × sport interaction for VL, but such an interaction for the BF was reflected by a longer Tc in male, rather than female, endurance than nonathletes, whereas female endurance and nonathletes had a similar Tc. We have no explanation for these differences, but the effects of resistance exercise on single fiber contractile properties are not unequivocal, with at least one study showing no change in single fiber contractile properties in young men (38). Age-related Slowing In the present study, we observed an age-related slowing of the muscle contractile properties, confirming the general observation of an age-related slowing of muscle in both nonathletes and master athletes (13,39). This may be attributable to a preferential loss of type II fibers (40). That, however, is not an unequivocal observation as other studies did not see such a change (4,21,41) and one longitudinal study even reported an increased proportion of type II fibers over a 12-year period (24). Even if type II fibers are preserved, their contribution to the contractile properties of the muscle may decrease if they undergo a larger age-related atrophy than type I fibers. Indeed, some studies reported a protection of type I fibers as compared with type II fibers (4,21,40,42), but again, others have reported that type I and II fibers atrophy similarly with age (41,43). The discrepancy between studies could be attributed to several factors: (i) pooling of muscle fiber phenotypes; (ii) different muscles investigated; (iii) small study samples; and (iv) by necessity when using biopsies, a relatively small amount of muscle tissue being analyzed. Age-related slowing may not only be due to fiber type shifts, but also slowing of fibers. In fact, it has been reported in both rodents (44) and humans (43,45) that particularly type I and IIa fibers are exhibiting an age-related slowing independent of shifts in myosin heavy and light chain isoform composition. Such an age-related slowing maybe caused by glycation of the myosin molecule (46) that may have a mitochondrial origin (47). Given that endurance athletes have a larger proportion of type I and IIa fibers than power and nonathletes (14), we expected the slowing to be more pronounced in the endurance than power and nonathletes, something we did indeed observe. Although this may to some extent, as in aging, be related to glycation of myosin in these fibers, such an effect will be attenuated, but probably not entirely abolished, in endurance athletes, who have a higher insulin sensitivity than nonathletes (48). The role of myosin glycation is admittedly speculative, but is an interesting avenue of investigation. If this pathway is indeed involved then consumption of antiglycating agents (49) may alleviate some of the age-related slowing, particularly in endurance athletes. Conclusion and Perspective TMG revealed that the age-related slowing of muscle contractile properties occurs particularly in endurance athletes. Here we suggest that this may be related to their high proportion of type I and IIa fibers that have been reported to exhibit an age-related slowing independent of shifts in myosin heavy and light chain composition. Funding This work was supported by the Cross-border Cooperation Program Slovenia–Italy 2007–2013 with European Regional Development Fund (PANGEA – Physical Activity and Nutrition for Quality Ageing, grant number 042-2/2009–18/052012); by the Slovenian Research Agency (L5-5550 – Development of Non-invasive Marker for Muscle Atrophy, grant number 1000-15-1988); by the Norwegian Financial Mechanism and the Republic of Slovenia Government Office for Development and European Cohesion (A-Qu-A – Active and Quality Ageing in Home Environment, grant number 4300–472/2014); and by the Slovenian Sport Foundation (The Effect of Regular Sport Exercise on Muscle Composition in Master Athletes, grant number RR-14–419). Conflict of Interest None reported. Acknowledgments The authors acknowledge the excellent assistance of the entire staff of the European Veteran Athletics Championships (Ljubljana, Slovenia). 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Age-Related Slowing of Contractile Properties Differs Between Power, Endurance, and Nonathletes: A Tensiomyographic Assessment

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Abstract

Abstract Although master athletes maintain high levels of physical activity, they also suffer from an age-related decline in skeletal muscle function. There are indications of disproportional age- and physical inactivity-induced muscle wasting between muscles. Tensiomyography is a noninvasive tool that has been used to study the effects of a variety of sports on the contraction time (Tc) in different skeletal muscles. The aim of this cross-sectional study was to assess age-related changes in the Tc of the vastus lateralis, gastrocnemius medialis, and biceps femoris muscles with Tensiomyography in older nonathletes (age = 62.1 ± 12.7 years; NMALES = 133; NFEMALES = 246), and power (age = 56.9 ± 13.5 years; NMALES = 100; NFEMALES = 78) and endurance master athletes (age = 56.5 ± 14.5 years; NMALES = 76; NFEMALES = 73). We found an age-related slowing in all muscles, irrespective of discipline, where endurance master athletes had the longest and power master athletes had the shortest Tc. The longer Tc in endurance master athletes than in nonathletes suggests that regular endurance sport activity aggravates slowing of skeletal muscles during aging. Skeletal muscle, TMG, Contraction time, Master athletes, MHC In nonathletes muscle mass shows a progressive decline of as much as 1% to 1.5% per year after about the age of 50 (1). The age-related loss of muscle mass is a consequence of loss and atrophy of muscle fibers (2–4). Since many of the age-related changes in skeletal muscle are similar to those induced by disuse (5) it is likely that the decrease in physical activity in old age (6,7) is a major contributor to the muscle wasting during aging. Master athletes, however, maintain high levels of physical activity (8–10) and suffer from fewer morbidities (11), thereby providing a unique human research model to disentangle the effects of disuse and comorbidities from aging per se. Master athletes are athletes older than 35 years who train for and participate in athletic competitions. Their training regimes range from 6 to over 20 hours per week (12) and they may well work at the limits of physiology (10). Even though their athletic performance exceeds that in age-matched nonathletes, also master athletes suffer from an age-related decline in muscle force and power (2,13,14), ventilatory function (15), and maximal oxygen consumption (16). In line with these decreases in physiological parameters, jump, sprint, and endurance performance decline with age (13,17), something seen even at the individual level (18), and after the age of 70 it even appears to be an exponential decline (19). This age-related decline in performance is not only caused by a loss of muscle mass and force generating capacity, but also by slowing of movement as a consequence of slower muscle contractile properties and increased tendon compliance (13). Part of the slowing of skeletal muscle is attributable to a preferential atrophy of fast type II fibers (3,4,20,21), which produce at least five times as much power as slow type I fibers (22). Single-fiber studies have also provided evidence of an age-related decrease in shortening velocity independent of fiber type switching in fibers expressing types I and IIa myosin heavy chain (MHC) isoforms (23,24). Thus, the loss of muscle mass, changes in fiber type composition, and slowing of muscle fibers may all contribute to the decline in anaerobic performance. Tensiomyography (TMG) is a noninvasive tool to assess the contractile properties (eg, contraction time [Tc]) of a muscle and can be used to estimate the percentage of type I MHC at least in the vastus lateralis (VL) muscle (25). We have recently shown in a large cohort of adolescents that regular sport exercise decreased Tc in the nonpostural biceps femoris (BF), but not in the postural VL (26). In addition, we observed that 8 weeks of plyometric training in young athletes results in an 8%–26% shorter Tc in five lower limb skeletal muscles that explained ~30% of the improvement in explosive power (27). This suggests that Tc is indeed related to muscle contractile properties and secondly that regular exercise may modulate the effects of aging on skeletal muscle properties. Previously, we found in older people that a lower performance in the timed-up-and-go and 6-minute walk tests was to a significant extent related to a lower maximal shortening velocity of the muscle (28). A longer Tc in older people may likewise have similar implications for functions of daily life. Yet, despite numerous studies reporting TMG results in children, adults, and athletes, there is only one study (29) that investigated the impact of aging on the TMG-derived velocity of contraction. They reported that the oldest group had the lowest velocity of contraction, but it is unclear whether this was due to a lower Tc and/or higher Dm. Therefore, the aim of this study was to assess age-related changes in VL, BF, and gastrocnemius medialis (GM) muscle contractile properties with TMG in male and female older nonathletes, and master endurance and power athletes in a cross-sectional study. Methods Participants Altogether 706 participants (311 men; aged 35–90 years) were recruited. Master athletes were recruited during: (i) European Veteran Athletics Championships in Ljubljana 2008 (EVACS; N = 253); (ii) European Master Games in Lignano 2011 (EMG; N = 56); and (iii) Slovenian Championships in Master Athletics Ljubljana 2015 (VAD; N = 18). From the 176 male master athletes, 40 were sprinters (≤400 m), 38 were throwers, 22 were jumpers, 57 runners (≥800 m), and 19 competed in walking events. From the 151 female master athletes, 29 were sprinters (≤400 m), 29 were throwers, 20 were jumpers, 51 runners (≥800 m), and 22 competed in walking events. All master athletes had competed for more than 3 years in master championships. Data of nonathletes were obtained from mass measurements during two international projects led by the Institute for Kinesiology Research at the Science and Research Centre Koper: (i) Physical Activity and Nutrition for Great Ageing 2013–2015 (PANGEA; N = 259); and (ii) Active and Quality Ageing in Home Environment 2015–2016 (A-Qu-A; N = 120). Nonathletes were not members of any sport clubs and did not compete in sport championships. Participants were excluded if they suffered from major skeletal, muscular, or nervous disorders. Basic anthropometric data and age, sex, and sport group distribution are presented in Table 1. The study was reviewed and approved by the Republic of Slovenia National Medical Ethics Committee (EVACS, PANGEA, and AQUA), Italian Ethics Committee at regional level (EMG), and Institutional Ethics Committee of the University of Primorska (VAD). Data collection conformed to the standards set by the Declaration of Helsinki (2002) and its amendments. All participants were fully informed of any risks and benefits associated with the study, and written informed consent was obtained from each participant prior to data collection. Table 1. Anthropometric Data of Participants, Grouped by Age, Sex, and Sport   Nonathletes  Power Athletes  Endurance Athletes  Men  Women  Men  Women  Men  Women  N  133  246  100  78  76  73  N35–44  31  28  32  26  20  27  N45–64  45  68  33  28  25  22  N65–90  57  150  35  24  31  24  Age/years  57.8 ± 14.0  63.4 ± 11.4  57.5 ± 14.1  55.5 ± 12.2  56.8 ± 14.7  56.2 ± 14.6  Age35–49/years  42.0 ± 7.4  41.3 ± 5.2  40.8 ± 4.5  41.4 ± 5.1  40.0 ± 4.1  41.4 ± 3.7  Age50–64/years  57.2 ± 4.6  60.3 ± 4.8  58.8 ± 5.3  56.4 ± 4.5  56.5 ± 5.3  56.7 ± 4.9  Age65–90/years  72.4 ± 4.7  71.8 ± 5.1  71.6 ± 8.0  70.6 ± 2.5  71.8 ± 5.1  73.9 ± 4.8  Body height/m  1.78 ± 0.07  1.62 ± 0.07  1.78 ± 0.08  1.69 ± 0.07  1.78 ± 0.07  1.66 ± 0.07  Body height35–49/m  1.81 ± 0.07  1.68 ± 0.08  1.81 ± 0.07  1.73 ± 0.06  1.81 ± 0.07  1.70 ± 0.09  Body height50–64/m  1.79 ± 0.07  1.63 ± 0.06  1.78 ± 0.05  1.67 ± 0.08  1.75 ± 0.08  1.66 ± 0.05  Body height65–90/m  1.74 ± 0.06  1.61 ± 0.06  1.73 ± 0.08  1.67 ± 0.06  1.73 ± 0.06  1.59 ± 0.04  Body mass/kg  85 ± 12  70 ± 12  79 ± 13  64 ± 6  72 ± 12  60 ± 9  Body mass35–49/kg  86 ± 12  66 ± 10  83 ± 16  64 ± 6  75 ± 9  63 ± 8  Body mass50–64/kg  87 ± 13  70 ± 13  79 ± 13  63 ± 8  72 ± 16  59 ± 10  Body mass65–90/kg  83 ± 11  70 ± 11  72 ± 12  65 ± 11  64 ± 6  55 ± 8  BMI/kg/m2  26.6 ± 3.4  26.2 ± 4.4  24.9 ± 2.9  22.4 ± 3.0  22.9 ± 3.7  21.7 ± 2.8  BMI35–49/kg/m2  26.1 ± 3.4  23.6 ± 3.3  25.5 ± 3.6  21.3 ± 2.5  23.1 ± 2.9  21.9 ± 2.9  BMI50–64/kg/m2  26.3 ± 3.3  25.9 ± 4.6  24.9 ± 2.6  22.6 ± 3.1  23.4 ± 4.2  21.5 ± 3.0  BMI65–90/kg/m2  27.2 ± 3.1  27.0 ± 4.3  24.1 ± 2.1  23.1 ± 3.2  21.6 ± 1.7  21.9 ± 2.5    Nonathletes  Power Athletes  Endurance Athletes  Men  Women  Men  Women  Men  Women  N  133  246  100  78  76  73  N35–44  31  28  32  26  20  27  N45–64  45  68  33  28  25  22  N65–90  57  150  35  24  31  24  Age/years  57.8 ± 14.0  63.4 ± 11.4  57.5 ± 14.1  55.5 ± 12.2  56.8 ± 14.7  56.2 ± 14.6  Age35–49/years  42.0 ± 7.4  41.3 ± 5.2  40.8 ± 4.5  41.4 ± 5.1  40.0 ± 4.1  41.4 ± 3.7  Age50–64/years  57.2 ± 4.6  60.3 ± 4.8  58.8 ± 5.3  56.4 ± 4.5  56.5 ± 5.3  56.7 ± 4.9  Age65–90/years  72.4 ± 4.7  71.8 ± 5.1  71.6 ± 8.0  70.6 ± 2.5  71.8 ± 5.1  73.9 ± 4.8  Body height/m  1.78 ± 0.07  1.62 ± 0.07  1.78 ± 0.08  1.69 ± 0.07  1.78 ± 0.07  1.66 ± 0.07  Body height35–49/m  1.81 ± 0.07  1.68 ± 0.08  1.81 ± 0.07  1.73 ± 0.06  1.81 ± 0.07  1.70 ± 0.09  Body height50–64/m  1.79 ± 0.07  1.63 ± 0.06  1.78 ± 0.05  1.67 ± 0.08  1.75 ± 0.08  1.66 ± 0.05  Body height65–90/m  1.74 ± 0.06  1.61 ± 0.06  1.73 ± 0.08  1.67 ± 0.06  1.73 ± 0.06  1.59 ± 0.04  Body mass/kg  85 ± 12  70 ± 12  79 ± 13  64 ± 6  72 ± 12  60 ± 9  Body mass35–49/kg  86 ± 12  66 ± 10  83 ± 16  64 ± 6  75 ± 9  63 ± 8  Body mass50–64/kg  87 ± 13  70 ± 13  79 ± 13  63 ± 8  72 ± 16  59 ± 10  Body mass65–90/kg  83 ± 11  70 ± 11  72 ± 12  65 ± 11  64 ± 6  55 ± 8  BMI/kg/m2  26.6 ± 3.4  26.2 ± 4.4  24.9 ± 2.9  22.4 ± 3.0  22.9 ± 3.7  21.7 ± 2.8  BMI35–49/kg/m2  26.1 ± 3.4  23.6 ± 3.3  25.5 ± 3.6  21.3 ± 2.5  23.1 ± 2.9  21.9 ± 2.9  BMI50–64/kg/m2  26.3 ± 3.3  25.9 ± 4.6  24.9 ± 2.6  22.6 ± 3.1  23.4 ± 4.2  21.5 ± 3.0  BMI65–90/kg/m2  27.2 ± 3.1  27.0 ± 4.3  24.1 ± 2.1  23.1 ± 3.2  21.6 ± 1.7  21.9 ± 2.5  Note: BMI = Body mass index; indexes 35–49, 50–64, 65–90 represent the age range in an age group. For statistical analysis, see Results. View Large Table 1. Anthropometric Data of Participants, Grouped by Age, Sex, and Sport   Nonathletes  Power Athletes  Endurance Athletes  Men  Women  Men  Women  Men  Women  N  133  246  100  78  76  73  N35–44  31  28  32  26  20  27  N45–64  45  68  33  28  25  22  N65–90  57  150  35  24  31  24  Age/years  57.8 ± 14.0  63.4 ± 11.4  57.5 ± 14.1  55.5 ± 12.2  56.8 ± 14.7  56.2 ± 14.6  Age35–49/years  42.0 ± 7.4  41.3 ± 5.2  40.8 ± 4.5  41.4 ± 5.1  40.0 ± 4.1  41.4 ± 3.7  Age50–64/years  57.2 ± 4.6  60.3 ± 4.8  58.8 ± 5.3  56.4 ± 4.5  56.5 ± 5.3  56.7 ± 4.9  Age65–90/years  72.4 ± 4.7  71.8 ± 5.1  71.6 ± 8.0  70.6 ± 2.5  71.8 ± 5.1  73.9 ± 4.8  Body height/m  1.78 ± 0.07  1.62 ± 0.07  1.78 ± 0.08  1.69 ± 0.07  1.78 ± 0.07  1.66 ± 0.07  Body height35–49/m  1.81 ± 0.07  1.68 ± 0.08  1.81 ± 0.07  1.73 ± 0.06  1.81 ± 0.07  1.70 ± 0.09  Body height50–64/m  1.79 ± 0.07  1.63 ± 0.06  1.78 ± 0.05  1.67 ± 0.08  1.75 ± 0.08  1.66 ± 0.05  Body height65–90/m  1.74 ± 0.06  1.61 ± 0.06  1.73 ± 0.08  1.67 ± 0.06  1.73 ± 0.06  1.59 ± 0.04  Body mass/kg  85 ± 12  70 ± 12  79 ± 13  64 ± 6  72 ± 12  60 ± 9  Body mass35–49/kg  86 ± 12  66 ± 10  83 ± 16  64 ± 6  75 ± 9  63 ± 8  Body mass50–64/kg  87 ± 13  70 ± 13  79 ± 13  63 ± 8  72 ± 16  59 ± 10  Body mass65–90/kg  83 ± 11  70 ± 11  72 ± 12  65 ± 11  64 ± 6  55 ± 8  BMI/kg/m2  26.6 ± 3.4  26.2 ± 4.4  24.9 ± 2.9  22.4 ± 3.0  22.9 ± 3.7  21.7 ± 2.8  BMI35–49/kg/m2  26.1 ± 3.4  23.6 ± 3.3  25.5 ± 3.6  21.3 ± 2.5  23.1 ± 2.9  21.9 ± 2.9  BMI50–64/kg/m2  26.3 ± 3.3  25.9 ± 4.6  24.9 ± 2.6  22.6 ± 3.1  23.4 ± 4.2  21.5 ± 3.0  BMI65–90/kg/m2  27.2 ± 3.1  27.0 ± 4.3  24.1 ± 2.1  23.1 ± 3.2  21.6 ± 1.7  21.9 ± 2.5    Nonathletes  Power Athletes  Endurance Athletes  Men  Women  Men  Women  Men  Women  N  133  246  100  78  76  73  N35–44  31  28  32  26  20  27  N45–64  45  68  33  28  25  22  N65–90  57  150  35  24  31  24  Age/years  57.8 ± 14.0  63.4 ± 11.4  57.5 ± 14.1  55.5 ± 12.2  56.8 ± 14.7  56.2 ± 14.6  Age35–49/years  42.0 ± 7.4  41.3 ± 5.2  40.8 ± 4.5  41.4 ± 5.1  40.0 ± 4.1  41.4 ± 3.7  Age50–64/years  57.2 ± 4.6  60.3 ± 4.8  58.8 ± 5.3  56.4 ± 4.5  56.5 ± 5.3  56.7 ± 4.9  Age65–90/years  72.4 ± 4.7  71.8 ± 5.1  71.6 ± 8.0  70.6 ± 2.5  71.8 ± 5.1  73.9 ± 4.8  Body height/m  1.78 ± 0.07  1.62 ± 0.07  1.78 ± 0.08  1.69 ± 0.07  1.78 ± 0.07  1.66 ± 0.07  Body height35–49/m  1.81 ± 0.07  1.68 ± 0.08  1.81 ± 0.07  1.73 ± 0.06  1.81 ± 0.07  1.70 ± 0.09  Body height50–64/m  1.79 ± 0.07  1.63 ± 0.06  1.78 ± 0.05  1.67 ± 0.08  1.75 ± 0.08  1.66 ± 0.05  Body height65–90/m  1.74 ± 0.06  1.61 ± 0.06  1.73 ± 0.08  1.67 ± 0.06  1.73 ± 0.06  1.59 ± 0.04  Body mass/kg  85 ± 12  70 ± 12  79 ± 13  64 ± 6  72 ± 12  60 ± 9  Body mass35–49/kg  86 ± 12  66 ± 10  83 ± 16  64 ± 6  75 ± 9  63 ± 8  Body mass50–64/kg  87 ± 13  70 ± 13  79 ± 13  63 ± 8  72 ± 16  59 ± 10  Body mass65–90/kg  83 ± 11  70 ± 11  72 ± 12  65 ± 11  64 ± 6  55 ± 8  BMI/kg/m2  26.6 ± 3.4  26.2 ± 4.4  24.9 ± 2.9  22.4 ± 3.0  22.9 ± 3.7  21.7 ± 2.8  BMI35–49/kg/m2  26.1 ± 3.4  23.6 ± 3.3  25.5 ± 3.6  21.3 ± 2.5  23.1 ± 2.9  21.9 ± 2.9  BMI50–64/kg/m2  26.3 ± 3.3  25.9 ± 4.6  24.9 ± 2.6  22.6 ± 3.1  23.4 ± 4.2  21.5 ± 3.0  BMI65–90/kg/m2  27.2 ± 3.1  27.0 ± 4.3  24.1 ± 2.1  23.1 ± 3.2  21.6 ± 1.7  21.9 ± 2.5  Note: BMI = Body mass index; indexes 35–49, 50–64, 65–90 represent the age range in an age group. For statistical analysis, see Results. View Large Research Design The study was cross-sectional and participants were divided by sex, sport, and age. We defined three sport groups: nonathletes (NA), power athletes (PowA: sprints ≤ 400 m, jumps and throws), and endurance athletes (EndA: runs ≥ 800 m; walking). We defined three age groups: 35–49 years, 50–64 years, and ≥65 years, where the oldest participant was 90 years old. Anthropometric Measurements The participants were asked to abstain from intense physical activity for 24 hours (NA) or competition (PowA and EndA) before the assessment. After determination of body height and body mass (Seca Instruments Ltd., Hamburg, Germany), TMG was performed. TMG TMG was used to assess skeletal muscle contractile properties in the VL, BF, and GM muscles. All measurements were performed during electrically-evoked isometric contractions. For VL TMG, participants were in a supine position with the knee angle at 300 flexion (where 00 represents a fully extended knee joint). For the BF they were in a prone position with the knee at 50 flexion and for the GM they were in a prone position with the ankle in neutral position as previously reported (25,30). Foam pads were used to support the joints. A single 1-ms maximal monophasic electrical impulse was used to elicit a twitch that caused the muscle belly to oscillate. These oscillations were recorded using a sensitive digital displacement sensor (TMG-BMC, Ljubljana, Slovenia) that was placed on the surface of the skin over the mid belly of the muscle of interest. If needed, the measuring point and electrode positions were adjusted to obtain maximal Dm of the muscle belly. Initially, the stimulation amplitude was set just above the threshold and then gradually increased until the Dm of the radial twitch displacement increased no further. From two maximal twitch responses, Tc was calculated and the average used for further analysis. Tc was defined (Figure 1) as the time for the amplitude to increase from 10% to 90% of Dm (25). Figure 1. View largeDownload slide Typical tensiomyographic responses of the biceps femoris of a nonathlete, power athlete and endurance athlete (above) and of a nonathlete in different age groups (below). Tc: contraction time defined as the time from 10% to 90% of the maximal displacement amplitude (Dm). Figure 1. View largeDownload slide Typical tensiomyographic responses of the biceps femoris of a nonathlete, power athlete and endurance athlete (above) and of a nonathlete in different age groups (below). Tc: contraction time defined as the time from 10% to 90% of the maximal displacement amplitude (Dm). Muscle Biopsies In a subsample of master athletes (N = 17; 4 EndA + 13 PowA) we have taken VL muscle biopsies after TMG assessment and determined the MHC composition as described previously (25). Briefly, VL muscle biopsies were obtained with a conchotome after skin sterilization and local anesthesia (2% lidocaine) and sterile conditions. The sample (~100 mg) was taken at 40% from the distance of the knee joint (0%) and the spina iliaca anterior superior (100%). The samples were placed on cork perpendicular to the long axis of the fibers, frozen in liquid nitrogen with vigorous shaking and stored at −80°C until analysis. For MHC determination, 10-μm cross-sections were cut in a cryostat, collected in a centrifuge tube with Laemli sample buffer (three sections in 100 μL) and boiled for 2 minutes to denature the proteins. Twelve-microliter samples were loaded on SDS poly-acrylamide gels run at 15°C, 120 V for 27 hours. The stacking gel contained 4% acrylamide and the separating gel had 7% acrylamide with both containing 30% glycerol. After the run, the gels were stained with the Silver Stain Plus kit following the instructions of the manufacturer (Biorad Laboratories, UK). Bands (Figure 2) were identified based on the migration distance, and relative quantities determined with Quantity One software (Biorad Laboratories). Figure 2. View largeDownload slide Representative SDS-PAGE gel to determine the myosin heavy chain composition of the vastus lateralis muscle from master athletes. Lane 1-3, 5, 7-11 contain all three myosin heavy chain isoforms (from top to bottom IIx, IIa, I) and lane 4 and 6 only contain type I and IIa myosin heavy chain isoforms. Figure 2. View largeDownload slide Representative SDS-PAGE gel to determine the myosin heavy chain composition of the vastus lateralis muscle from master athletes. Lane 1-3, 5, 7-11 contain all three myosin heavy chain isoforms (from top to bottom IIx, IIa, I) and lane 4 and 6 only contain type I and IIa myosin heavy chain isoforms. Statistics SPSS (IBM, USA) software was used for all statistical analyses. All data in text and tables are presented as mean ± SD, whereas in figures standard errors were used. Visual inspection and the Shapiro-Wilk test indicated that all data were normally distributed. Sphericity (homogeneity of covariance) was verified by the Mauchly’s test. When the assumption of sphericity was not met, the significance of the F-ratios was adjusted according to the Greenhouse-Geisser procedure. Main effects were studied with a General Linear Model repeated-measures analysis of variance with muscle (VL, GM, and BF) as within factor, and three between factors: sport group (three levels: NA, PowA, EndA), sex (man or woman), and age (three levels: 35–49 years, 50–64 years, and ≥65 years). Where significant effects were found for sport or age effects or two-way interactions (three-way interactions were excluded from the analysis), post-hoc analysis with Bonferroni corrections was used to locate the differences between sport and age groups. We used the Pearson correlation coefficient to assess associations between age and Tc in each muscle and sex. We also performed stepwise multiple regression analysis of Tc with age, sex, muscle, and sport as predictors, with entry criteria at p ≤ .05 and removal criteria at p ≥ .1. In a subsample of 17 master athletes who also gave a VL muscle biopsy, we performed Pearson correlation analysis to assess to what extent VL Tc is affected by the MHC-I content of the muscle, age and sport. Statistical significance was accepted at p values less than .05. Additionally, the effect size for dependent variables was assessed as partial eta-squared (ɳ2). Results Table 1 shows the anthropometric data. We found that PowA were the tallest (p = .005), and that they and NA had a higher body mass than EndA (p < .001). NA had a higher body mass index than PowA and EndA (p < .001). Women were smaller (p < .001), lighter (p < .001), and had a lower body mass index (p = .001) than men. Tc was positively correlated with age, irrespective of muscle and sport group (Figure 3). This correlation was weakest in VL (even not significant for female NA), and strongest in BF for PowA and EndA, where EndA had the strongest correlation in GM. Figure 3. View largeDownload slide Pearson correlation (r) between age and contraction time (Tc) for men (left) and women (right) in each muscle and sport group. Figure 3. View largeDownload slide Pearson correlation (r) between age and contraction time (Tc) for men (left) and women (right) in each muscle and sport group. A multiple regression analysis (Table 2) confirmed an overall correlation of R = .690 (p < .001) with 47.6% of the variance in Tc explained by Age, BF, VL, and athletic discipline as predictors, but not sex. The largest contributors (part correlations) to the explained variance in Tc are BF and Age. The multiple regression prediction model is as follows:  Tc=11.754×BF+.193×Age−3.292×An−3.185×VL+3.142×Ae+18.098 where Tc is a muscle contraction time (in ms), BF is the biceps femoris muscle (0 or 1), VL is the vastus lateralis muscle (0 or 1), Age is an age (in years), An is Power athletes (0 or 1), and Ae is Endurance athletes (0 or 1). Table 2. Multiple Linear Regression Analysis of Age, Sex (removed by regression), Sport (aerobic and anaerobic), and Muscle (biceps femoris and vastus lateralis) as Predictors of Tensiomyographic Contraction Time Predictors  Unstandardized Coefficients  Standardized Coefficients  p  Part r  VIF  Constant  18.098 ms  .907  <.001      Biceps femoris  11.754 ms  .421  <.001  .453  1.371  Age  0.193 ms/years  .013  <.001  .236  1.050  Anaerobic  -3.292 ms  .426  <.001  -.125  1.088  Vastus lateralis  -3.185 ms  .419  <.001  -.123  1.372  Aerobic  3.142 ms  .509  <.001  .100  1.069  Predictors  Unstandardized Coefficients  Standardized Coefficients  p  Part r  VIF  Constant  18.098 ms  .907  <.001      Biceps femoris  11.754 ms  .421  <.001  .453  1.371  Age  0.193 ms/years  .013  <.001  .236  1.050  Anaerobic  -3.292 ms  .426  <.001  -.125  1.088  Vastus lateralis  -3.185 ms  .419  <.001  -.123  1.372  Aerobic  3.142 ms  .509  <.001  .100  1.069  Note: Part r: Part correlation of each predictor to contraction time when controlling for effects of other predictors. VIF = Variance inflation factor. View Large Table 2. Multiple Linear Regression Analysis of Age, Sex (removed by regression), Sport (aerobic and anaerobic), and Muscle (biceps femoris and vastus lateralis) as Predictors of Tensiomyographic Contraction Time Predictors  Unstandardized Coefficients  Standardized Coefficients  p  Part r  VIF  Constant  18.098 ms  .907  <.001      Biceps femoris  11.754 ms  .421  <.001  .453  1.371  Age  0.193 ms/years  .013  <.001  .236  1.050  Anaerobic  -3.292 ms  .426  <.001  -.125  1.088  Vastus lateralis  -3.185 ms  .419  <.001  -.123  1.372  Aerobic  3.142 ms  .509  <.001  .100  1.069  Predictors  Unstandardized Coefficients  Standardized Coefficients  p  Part r  VIF  Constant  18.098 ms  .907  <.001      Biceps femoris  11.754 ms  .421  <.001  .453  1.371  Age  0.193 ms/years  .013  <.001  .236  1.050  Anaerobic  -3.292 ms  .426  <.001  -.125  1.088  Vastus lateralis  -3.185 ms  .419  <.001  -.123  1.372  Aerobic  3.142 ms  .509  <.001  .100  1.069  Note: Part r: Part correlation of each predictor to contraction time when controlling for effects of other predictors. VIF = Variance inflation factor. View Large In a subsample of master athletes (NPOW = 13 + NEND = 4), we assessed the MHC composition of the VL (Table 3) and found a significant bi-variate correlation between Tc and MHC-I (r = .864; p < .001), whereas there was no significant correlation between age and Tc (p = .309) or MHC-I (p = .719). A correlation was found also between VL Tc and MHC-IIa (r = −.760; p < .001) but not between VL Tc and MHC-IIx (r = −.326; p = .201). Table 3. MHC Composition of the Vastus lateralis Muscle for a Subsample of Master Athletes (master athlete’s data taken from ref. (25))   Total N = 17  Power Athletes N = 13  Endurance Athletes N = 4  MHC-I/%  35.6 ± 18.4  31.7 ± 16.3  48.1 ± 21.5  MHC-II/%  64.5 ± 18.4  68.3 ± 16.3  52.0 ± 21.5  MHC-IIa/%  44.7 ± 14.4  45.2 ± 12.4  43.3 ± 22.0  MHC-IIx/%  19.7 ± 15.1  23.1 ± 15.0  8.7 ± 10.3    Total N = 17  Power Athletes N = 13  Endurance Athletes N = 4  MHC-I/%  35.6 ± 18.4  31.7 ± 16.3  48.1 ± 21.5  MHC-II/%  64.5 ± 18.4  68.3 ± 16.3  52.0 ± 21.5  MHC-IIa/%  44.7 ± 14.4  45.2 ± 12.4  43.3 ± 22.0  MHC-IIx/%  19.7 ± 15.1  23.1 ± 15.0  8.7 ± 10.3  Note: MHC = Myosin heavy chain. View Large Table 3. MHC Composition of the Vastus lateralis Muscle for a Subsample of Master Athletes (master athlete’s data taken from ref. (25))   Total N = 17  Power Athletes N = 13  Endurance Athletes N = 4  MHC-I/%  35.6 ± 18.4  31.7 ± 16.3  48.1 ± 21.5  MHC-II/%  64.5 ± 18.4  68.3 ± 16.3  52.0 ± 21.5  MHC-IIa/%  44.7 ± 14.4  45.2 ± 12.4  43.3 ± 22.0  MHC-IIx/%  19.7 ± 15.1  23.1 ± 15.0  8.7 ± 10.3    Total N = 17  Power Athletes N = 13  Endurance Athletes N = 4  MHC-I/%  35.6 ± 18.4  31.7 ± 16.3  48.1 ± 21.5  MHC-II/%  64.5 ± 18.4  68.3 ± 16.3  52.0 ± 21.5  MHC-IIa/%  44.7 ± 14.4  45.2 ± 12.4  43.3 ± 22.0  MHC-IIx/%  19.7 ± 15.1  23.1 ± 15.0  8.7 ± 10.3  Note: MHC = Myosin heavy chain. View Large After dividing the participants into three age groups we found significant effects for muscle (p < .001; η2 = .395), sport (p < .001; η2 = .133), age (p < .001; η2 = .153), and sport × age (p < .001; η2 = .036), muscle × sex (p = .048; η2 = .005), muscle × sport (p < .001; η2 = .033), muscle × age (p < .001; η2 = .043) interactions. As there were interactions between muscle with sport, age, and sex, we performed subsequent post-hoc analysis of variances first for the nonathletes only and then for each muscle separately (Figure 4). Figure 4. View largeDownload slide Contraction time (Tc) differences in three skeletal muscles between athletes and nonathletes in different age groups for men (left column graphs) and women (right column graphs). a, b, c…significantly different from nonathletes at p < .05; p < .01, and p < .001, respectively; i, j, k… significantly different from previous age group at p < .05; p < .01, and p < .001, respectively; G. … Gastrocnemius. Figure 4. View largeDownload slide Contraction time (Tc) differences in three skeletal muscles between athletes and nonathletes in different age groups for men (left column graphs) and women (right column graphs). a, b, c…significantly different from nonathletes at p < .05; p < .01, and p < .001, respectively; i, j, k… significantly different from previous age group at p < .05; p < .01, and p < .001, respectively; G. … Gastrocnemius. Looking at the NA only, we found the lowest Tc in VL (26.1 ± 4.2 ms), the longest in BF (43.1 ± 11.3 ms) and that of the GM (30.0 ± 7.7 ms) in between the two. In the VL, there were effects of age (p < .001; η2 = .039), sex (p < .001; η2 = .017), sport (p < .001; η2 = .065), and sport × age (p = .003; η2 = .023), and sex × age (p = .072; η2 = .008) interactions. The interactions indicated that the effects of age differed between sports and sexes. The sex × age interaction was reflected by an age-related increase in Tc in men only (p = .002). The sport × age interaction was reflected by the shorter Tc in PowA than NA at all ages in men only (p = .004). In both male PowA and NA Tc increased from 35–49 to 50–64 years by 8.7% (p = .004) and 10.5% (p < .001), respectively. The Tc was higher in 65–90-year-old EndA than 50–64-year-old EndA (p = .015) and EndA had a longer Tc than NA (p = .008). In the GM, there were effects of age (p < .001; η2 = .066) and sport (p < .001; η2 = .039), and a sport × age interaction (p = .009; η2 = .022). The latter indicated that the effect of age differed between the different sports. Although in all groups Tc was longer at 65–90 years in comparison to 35–49 years (10.2%; p = .017), PowA had shorter Tc than NA at 35–49 years (−9.7%; p = .014), and EndA had longer Tc at 65–90 years than NA (22.6%; p < .001). In the BF, there were effects of age (p < .001; η2 = .129) and sport (p < .001; η2 = .099), and sport × age (p = .086; η2 = .012) and sex × sport (p = .087; η2 = .007) interactions. The interactions indicated that the effects of sport differed between sports, and between men and women. Tc was consistently increased with increasing age in PowA, but PowA always had a shorter Tc than NA in both men and women. However, the relative difference of Tc between NA and PowA was decreasing with increasing age (in men: from 20.5% to 12.3%; in women from 27.4% to 11.3%). In EndA, Tc was higher than in NA at 35–49 years (only in men: 22.5%; p = .001), then stabilized at 50–64 years and increased again at 65–90 years (in men: 33.2%; p < .001, in women: 26.5%; p < .001), when EndA had higher Tc than NA (only in men: 20.5%; p < .001). We found that body mass and body mass index explained at best 1% of the variance (data not shown). Discussion In the present cross-sectional study, we used TMG to noninvasively assess the contractile properties of leg skeletal muscles in master endurance and power athletes, and age-matched nonathletes. The main observation of the present study is the age-related slowing of all muscles. Endurance athletes had slower muscles than age-matched nonathletes, whereas power athletes had the fastest contractile properties, irrespective of sex. For all muscles, an age × sport interaction was found, indicating that the effects of age differ between sports. Given that endurance athletes had slower contractile properties than nonathletes, this suggests that regular long-distance running accelerates the age-related slowing of skeletal muscle. TMG TMG was developed to noninvasively measure skeletal muscle contractile properties (31). We have previously shown in a population of 20–83-year olds that Tc predicted 77% of the variance of the proportion of type I MHC in the VL (25), which was confirmed by our results obtained in a sub group of 17 master athletes (75%). Although this regression model has not been validated in other muscles, there are no obvious reasons to believe that it would not apply to other muscles, albeit with different regression coefficients. Here we show that Tc was lowest in the VL (26.1 ± 4.2 ms), then in GM (30.0 ± 7.7 ms) and longest in BF (43.1 ± 11.3 ms), corresponding with the reported differences in type I proportions between these muscles (50%, 54%–63%, and 67%, respectively) (32,33). Sex and Sport-related Differences in Contractile Properties We did not observe significant differences between men and women in the contractile properties of the VL, BF, and GM. This corresponds with the similar fiber type distribution in men and women (21,34,35). Power athletes had a lower Tc than endurance and nonathletes, particularly in the nonpostural BF, and to a lesser extent in the postural VL and GM. The shorter Tc in power athletes is most likely attributable to a higher shortening velocity in both type I (75%) and type IIa (45%) fibers, as reported after 12 weeks of resistance exercise in older men (36). Interestingly, the same authors (37) demonstrated that the same program in women induced a decrease in the shortening velocity of type I fibers, with no change in that of type IIa fibers. We did not observe a significant sex × sport interaction for VL, but such an interaction for the BF was reflected by a longer Tc in male, rather than female, endurance than nonathletes, whereas female endurance and nonathletes had a similar Tc. We have no explanation for these differences, but the effects of resistance exercise on single fiber contractile properties are not unequivocal, with at least one study showing no change in single fiber contractile properties in young men (38). Age-related Slowing In the present study, we observed an age-related slowing of the muscle contractile properties, confirming the general observation of an age-related slowing of muscle in both nonathletes and master athletes (13,39). This may be attributable to a preferential loss of type II fibers (40). That, however, is not an unequivocal observation as other studies did not see such a change (4,21,41) and one longitudinal study even reported an increased proportion of type II fibers over a 12-year period (24). Even if type II fibers are preserved, their contribution to the contractile properties of the muscle may decrease if they undergo a larger age-related atrophy than type I fibers. Indeed, some studies reported a protection of type I fibers as compared with type II fibers (4,21,40,42), but again, others have reported that type I and II fibers atrophy similarly with age (41,43). The discrepancy between studies could be attributed to several factors: (i) pooling of muscle fiber phenotypes; (ii) different muscles investigated; (iii) small study samples; and (iv) by necessity when using biopsies, a relatively small amount of muscle tissue being analyzed. Age-related slowing may not only be due to fiber type shifts, but also slowing of fibers. In fact, it has been reported in both rodents (44) and humans (43,45) that particularly type I and IIa fibers are exhibiting an age-related slowing independent of shifts in myosin heavy and light chain isoform composition. Such an age-related slowing maybe caused by glycation of the myosin molecule (46) that may have a mitochondrial origin (47). Given that endurance athletes have a larger proportion of type I and IIa fibers than power and nonathletes (14), we expected the slowing to be more pronounced in the endurance than power and nonathletes, something we did indeed observe. Although this may to some extent, as in aging, be related to glycation of myosin in these fibers, such an effect will be attenuated, but probably not entirely abolished, in endurance athletes, who have a higher insulin sensitivity than nonathletes (48). The role of myosin glycation is admittedly speculative, but is an interesting avenue of investigation. If this pathway is indeed involved then consumption of antiglycating agents (49) may alleviate some of the age-related slowing, particularly in endurance athletes. Conclusion and Perspective TMG revealed that the age-related slowing of muscle contractile properties occurs particularly in endurance athletes. Here we suggest that this may be related to their high proportion of type I and IIa fibers that have been reported to exhibit an age-related slowing independent of shifts in myosin heavy and light chain composition. Funding This work was supported by the Cross-border Cooperation Program Slovenia–Italy 2007–2013 with European Regional Development Fund (PANGEA – Physical Activity and Nutrition for Quality Ageing, grant number 042-2/2009–18/052012); by the Slovenian Research Agency (L5-5550 – Development of Non-invasive Marker for Muscle Atrophy, grant number 1000-15-1988); by the Norwegian Financial Mechanism and the Republic of Slovenia Government Office for Development and European Cohesion (A-Qu-A – Active and Quality Ageing in Home Environment, grant number 4300–472/2014); and by the Slovenian Sport Foundation (The Effect of Regular Sport Exercise on Muscle Composition in Master Athletes, grant number RR-14–419). Conflict of Interest None reported. Acknowledgments The authors acknowledge the excellent assistance of the entire staff of the European Veteran Athletics Championships (Ljubljana, Slovenia). 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The Journals of Gerontology Series A: Biomedical Sciences and Medical SciencesOxford University Press

Published: Apr 12, 2018

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