INTRODUCTION
Aging is often accompanied by a variety of chronic diseases, such as hypertension, diabetes, cardiovascular disease, and musculoskeletal disease, resulting in significant medical costs and social burdens.1) Aging is also associated with a decrease in both physical fitness, including aerobic endurance, muscle strength, and balance,2) and functional ability, which together increase the risk of falls and fractures,3) and ultimately lead to a poor quality of life.4) The age-related decrease in muscle mass and muscle strength (sarcopenia)5) is induced by causes such as a lack of physical activity and the loss of motor nerves.6)
Regular physical activity has been considered a key factor in successful aging7) by preventing age-related muscle loss8) and by increasing muscle strength.9) In a study analyzing the relationship between the amount of physical activity and body composition in middle-aged adults, higher physical activity level tend to show a lower body mass index (BMI) and body fat percentage.10) However, despite the demonstrated positive effect of physical activity on the physical function and body composition of older adults, some of studies so far have focused only on changes in the quality of life or body composition as a function of the amount of physical activity.11) Consequently, comprehensive information on physical function according to the physical activity level during aging is still lacking.
Muscle function and the ability to generate muscle strength are essentially determined by the contractile properties of muscle fibers.12,13) During aging, the loss of muscle strength and muscle function may be caused by a decrease in the contractile ability of muscle fibers. Thus, evaluating aging-related changes in the properties of single muscle fibers, excluding factors such as neuronal supply and the extracellular matrix, can contribute to the development of countermeasures to improve physical function during aging.
The contractile properties of the single muscle fibers of skeletal muscle differ in older adults versus young people. Both the specific force (SF), which is the standardized, Ca2+-activated maximal force (Po) per single muscle fiber cross-sectional area (CSA), and the maximal shortening velocity (Vo) are significantly lower in the type I and type II muscle fiber types of older adults.14,15) However, the contractile properties of single muscle fiber can be improved by strength training. Regular resistance exercise by older adults was shown to increase contractile function, including the Po, of single muscle fibers and, thereby, improve one-repetition maximum.16,17) The benefits of aerobic exercise training in older adults include increases in type I fiber Po, SF, and Vo.18) These findings indicate that, in older adults, the decrease in muscle function of the whole body, associated with aging, can be alleviated by improving the function at the muscle cell level through appropriate exercise intervention. However, strength training and aerobic training, which are generally recommended in older adults, may be difficult to perform and sustain. Instead, a frequent recommendation in older adults is to increase the total time and duration of exercise through simple physical activities, such as walking. However, this recommendation has not been accompanied by studies of the impact on the contractile properties of single muscle fibers.
In this work, we comprehensively examined the impact of the physical activity level on body composition, physical function, and muscle cell contractile properties of older adults. The self-reported the International Physical Activity Questionnaire (IPAQ) was used to estimate the amount of physical activity.19,20) Our aim was to assess whole-body function, isokinetic muscle strength, and muscle cell properties according to the usual level of physical activity, as determined using the IPAQ. We hypothesized that, in older adults, both systemic muscle function and the contractile properties of single muscle fibers would differ according to the level of physical activity.
MATERIALS AND METHODS
1. Participants
Twenty-one older men and women (71.1±3.7 years) living in Seongnam city in South Korea were recruited for the study. This study required 11 participants per group to achieve 85% power with an alpha of 0.05 and an effect size of 0.5. Patients receiving drugs or hormone therapy related to musculoskeletal function, or who had undergone surgery or hospitalization within the last 3 years were excluded from participation. Before study enrollment, the purpose and process of the study were explained to the participants and their consent was obtained. The study was carried out with the approval of the Institutional Review Board of Seoul National University Bundang Hospital (No. B-2002-594-004).
2. Physical Activity Assessment
The physical activity level of the study participants was evaluated using the IPAQ Short Form. IPAQ is an effective tool for investigating the level and type of physical activity. In this study, the frequency and duration of participation (min/hr usually spent per day) in four activity level domains (vigorous-intensity, moderate-intensity, walking, and sitting) over the past 7 days were queried using the IPAQ Short Form. Based on the IPAQ evaluation, the participants were classified into low physical activity (LPA) and high physical activity (HPA) groups.21) The LPA group consisted of 11 people (men=3, women=8) and the HPA group consisted of 10 people (men=3, women=7).
3. Anthropometry and Body Composition
Height, weight, and BMI were obtained through physical measurements. Skeletal muscle mass, body fat mass, and body fat percentage were measured by bioelectric impedance analysis (InBody S10; InBody, Seoul, Korea). Participants arrived at 4 pm, and measurement was begun after 10 minutes of rest. Before the measurement, participants were asked to remove jewelry such as necklaces, rings, and watches. In addition, fasting was maintained for 4 hours before the measurement, and drinking water and beverages was restricted from 1 hour before the measurement. The InBody device used in this study was a hand-to-foot bioelectrical impedance analysis that was measured while lying down. The subjects were laid down in an anatomical position, and electrodes were attached to both wrists and ankles. This position was held for the duration of the test. The whole procedures were performed by a skilled expert working in a hospital.
4. Functional Capacity and Isokinetic Muscle Strength
Whole-body functional capacity was evaluated by measuring grip strength together with 4-m walking, stair-climbing, and Time Up & Go (TUG) tests. Grip strength was measured using a digital dynamometer (Takei Scientific Instruments Co. Ltd., Niigata, Japan), with the angle of the second joint of the index finger at 90°. The grip strength of the dominant hand was measured twice, and the highest value was recorded. In the 4-m walking test, participants wore comfortable shoes and, with their toes behind the 4-m starting line, began walking at their normal speed, “as if walking down the street to a store.” Participants were given the command “Ready, 3, 2, 1, Go!” and set off. The examiner first demonstrated the test. The mean value obtained in two trials was used. For the stair climb test, participants wore comfortable shoes and climbed nine steps of about 20 cm height. The shorter time of the two tests was used. In the TUG test, participants sat on chairs, rose on command, walked 3 m, returned to the chair and sat down again. The test was then repeated. The time from when the participant rose from the chair and returned to it was measured, and the higher of two value was used. To evaluate isokinetic strength, the maximal muscle strength (isokinetic strength, 60°/sec) and isometric strength (30°) of the extensors and flexors of the dominant leg were measured using an isokinetic dynamometer (BTE PrimusRS; BTE Technologies, Hanover, MD, USA). For the isokinetic test, the average value was used from five consecutive trials, and for the isometric strength, the maximum value was used after three trials (30-second rest between trials).
5. Muscle Biopsy and Sampling
Muscle tissue was biopsied by a specialist affiliated with a Seoul National University Bundang Hospital located in Seongnam city. The subcutaneous and muscle areas of the vastus lateralis muscle were anesthetized using 2% lidocaine hydrochloride, and the muscle tissue was then safely biopsied using a modified Bergström needle (11750-06 and 11750-07; Dixons Surgical Instruments, Wickford, UK) and suction. The muscle sample was immediately transferred to a relaxing solution (2.5 M KCl, 0.1 M EGTA, 0.1 M CaCl2, 0.5 M Imidazole, 0.1 M MgCl2) and carefully separated into longitudinal sections (4°C). The separated muscle fiber bundle typically included 50–100 muscle fibers. The muscle fiber bundles were placed in a skinning solution (50% relaxing solution + 50% glycerol), incubated for 24 hours (4°C), and stored at -20°C.
6. Contractile Properties of Single Muscle Fibers
During the analysis, sarcomere length was maintained at 2.6 µm, confirmed by measurements at three different locations along a single muscle fiber. The fiber length was measured using a micrometer installed in a microscope. For muscle fiber CSA, the width and depth of four pairs of ovals along the length of the fiber were measured using an eyepiece micrometer, with the width measured from the optical plane, and depth using the image reflected from the prism directed toward the muscle fiber. All measurements were performed at 15°C. Po and Vo were measured in a slack test, according to the procedure described in previous studies.22) The muscle fiber was transferred to active solution (pCa 4.5); when the peak force was reached, a rapid (within 1–2 ms) shortening (7%–15%) occurred on one side of the fiber. The time until the tension was regenerated was then measured. The test was carried out at least five times, with different changes in the shortening length. The results were analyzed using the least-squares regression method. The slope of the regression line, representing the uniaxial length, was recorded as Vo. During the experiment, errors in muscle strength measurement due to baseline drift caused by external factors were avoided by calculating the maximal force of the muscle fiber as the difference between the maximal force in the active solution and the subsequent baseline force.
7. Single Muscle Fiber Type Analysis
After measuring the contractile properties of single muscle fibers, the fiber was cut it to a length of about 2–3 mm and placed in a microtube containing 15 μL of SDS sample buffer and stored at -20°C until silver staining. The muscle fiber type was determined using sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE; Bio-Rad’s Mini-PROTEAN Tetra Cell electrophoresis system) with 8% separating gel (40% acryl amide, 1.5 M tris pH 8.8, 10% SDS, 60% glycerol, tetramethylethylenediamine [TEMED], 10% ammonium persulfate solution [APS]) and 4% stacking gel (40% acrylamide, 0.5 M tris pH 6.8, 10% SDS, 60% glycerol, TEMED, 10% APS) were electrophoresed at 140 V for 5 hours and 20 minutes. In addition, during electrophoresis, standards for three subtypes of human muscle obtained from existing samples were developed together to confirm the subtypes of the samples. After electrophoresis, the gel was immersed in a fixing solution (acetic acid), washed 2–3 times with distilled water, and then the gel was immersed in glutaldehyde solution (10%) for 45 minutes to shake. After washing 4 times for 15 minutes with distilled water, stain with silver staining solution (0.09 M NaOH, 28% NH4OH, 1.14 M AgNO3) for 10 minutes while shaking. Then, it was washed 3 times with distilled water for 1 minute and 30 seconds each. After immersing the gel in a developing solution (47.6 mM citric acid, 37% formalde hyde) and shaking it, when a band capable of distinguishing muscle fiber types was confirmed on the surface of the gel, storage solution (5% acetic acid) was added and then stored. The gel was stained with instant blue solution and carefully moved to ChemiDoc XRS Image System (Bio-Rad). The fiber type in each sample was analyzed using Quantity One 1-D Analysis Software (Bio-Rad).
8. Statistical Analysis
The data were statistically analyzed using the statistical software STATA, version 12.0 (STATA Corp, College Station, TX, USA). Variables were descriptively expressed as the mean±standard deviation. Based on the normal distribution of the data and the differences between groups (LPA vs. HPA), a linear mixed regression model was used, as it allowed correlations between multiple fibers per person (random effects component of the model) to be evaluated. The model assumes a mean measurement per person (normal distribution between fibers), and that each follows a normal distribution around the mean value. Statistical significance was set at p<0.05. Correlations between physical activity and the contractile properties of single muscle fiber were evaluated using Pearson two-tailed correlation analysis. A p-value <0.05 was considered to indicate statistical significance.
RESULTS
1. IPAQ Scores
The differences in the IPAQ scores between the LPA and HPA groups were not significant, with the exception of the walking activity level and the total physical activity level, both of which were significantly higher in the HPA group than in the LPA group (p=0. 044 and p=0.001, respectively). There was no significant difference in the sitting score and the results of the IPAQ for the two groups are presented in Table 1.
2. Body Composition
Height, body weight, BMI, skeletal muscle mass, lower extremity skeletal muscle mass, body fat mass, and body fat percentage did not significantly differ between the LPA and HPA groups (Table 2).
3. Functional Capacity and Lower Limb Muscle Strength
In the LPA and HPA groups, the grip strength test, stair climbing test, and TUG test, as measures of whole-body function, were not significantly different, but the 4-m walking velocity was significantly higher in the HPA group than in the LPA group (p=0.006) (Fig. 1B).
4. Contractile Properties of Single Muscle Fibers
A total of 447 muscle fibers were analyzed. The average was 20.4 fibers per person (type I=14, type I/IIa=0.2, type IIa=5.1, type IIa/IIx=1.1). There were no significant differences between the LPA and HPA groups with respect to muscle fiber CSA, a measure of the size of a single muscle fiber. The differences in peak force and SF were also not significant, which suggested that the physical activity level does not affect muscle strength at the cellular level. By contrast, the Vo of a single muscle fiber was significantly (36.2%; p=0.003) higher in the HPA group than in the LPA group (Fig. 2).
5. Single Muscle Fiber Types and Contractile Properties according to Fiber Type
The two groups did not significantly differ in the distribution ratio of single muscle fiber MHC (myosin heavy chain) types, which suggested that the physical activity level does not affect the fiber type distribution (Table 3). In this study, MHC I/IIa, MHC IIa/IIx and MHC IIx fibers were excluded from the analysis since the number of fibers was not enough to show statistical results. There was no significant difference in the CSA, Po, and SF in both MHC I and MHC IIa. On the contrary, Vo was significantly higher in the HPA group for MHC I (p=0.001) fibers but not in the LPA group (Fig. 3).
7. Relationship between the Physical Activity Level and Vo and SF
The total physical activity level correlated with the SF and the Vo of a single muscle fiber, which implied that the higher the physical activity level, the higher the Vo (r2=0.691, p=0.002) (Fig. 4A). However, there was no correlation between the physical activity level and SF (r2=0.102, p=0.696) (Fig. 4B).
DISCUSSION
This study divided participants into LPA and HPA groups, as determined using the IPAQ, to investigate the effect of physical activity level on body composition, muscle function and strength, and the contractile properties of single muscle fibers in older adults. The results showed that the physical activity level in older adults did not have a significant effect on their body weight, BMI, muscle mass, body fat mass, or body fat percentage, but it did have a positive effect on isometric and isokinetic muscle strength. These findings suggest that, in older adults, the level of physical activity is related to functional aspects, such as muscle strength, rather than body composition aspects, such as muscle mass. In particular, the level of physical activity was associated with Vo at the muscle cell level (especially in type I fibers). Moreover, the impact on functional aspects was evident both at the whole-body and the cellular levels.
The IPAQ is an efficient, easy-to-use tool to survey the physical activity levels of adults, including older adults. To enhance the reliability of the IPAQ, we ensured that the elderly participants had sufficient time and a comfortable environment to complete the survey. The purpose of the survey and the meaning of each item were clearly explained to aid their understanding. When needed, the researcher provided further clarification and recorded responses through interviews while maintaining a neutral attitude. In a previous study using IPAQ, the total amount of physical activity per week in 60- to 80-year-olds was 3,160.56–4,503.43 MET-min.23) Similarly, for the participants in this study, the value was 3,165.50 MET-min per week. There was a remarkable difference of total physical activity level between groups (380.91 min/wk vs. 1,158.33 min/wk). In addition, the HPA group had significantly higher IPAQ scores for walking activity and total activity, but there was no difference between the two groups in vigorous and moderate activity (Table 1). This suggests that the level of physical activity in older adults is more likely to be determined by the amount of light activity, such as walking, rather than moderate- or high-intensity activity. Nonetheless, sedentary behavior is a risk factor threatening the health of older adults, regardless of the amount of physical activity,24) and an association between increased sedentary time and cardiovascular disease and premature death has been reported.25) In this study, the sitting score was 283.64 min/wk in the LPA group and 200 min/wk in the HPA group (Table 1). While the difference was not statistically significant, previous studies have shown that sitting for more >4 hours (240 minutes) per day is a risk factor for a decline in muscle strength, walking speed, agility, flexibility, and aerobic endurance.26)
The amount of inactivity time increases with increasing age and that of moderate or higher intensity activity gradually decreases,27) both of which contribute to changes in the body composition of older adults. Based on the results of an IPAQ survey of adults 40–69 years of age and comparative analyses of body fat percentage and BMI, among people with the same BMI, those who are more active will have a lower body fat percentage.10) A study of the body composition of young college students according to the amount of physical activity, determined using the IPAQ, showed that the higher the level of physical activity, the lower the body fat percentage regardless of sex, but there was no relationship with BMI.28) A study targeting older adults examined the average number of steps and MET during walking for 14 days, determined using an activity meter device. The body fat mass in the women who walked >4,000 steps was lower than that of women who walked <4,000 steps on average, whereas there was no difference in the body composition, including muscle mass and body fat mass, in men.29) Similar to previous studies, in this study, the BMI and muscle mass of the LPA and HPA groups did not significantly differ. While the body fat percentage was 10.6% higher in the LPA group than in the HPA group, the difference was not significant. These results can in part be explained by the different age groups of the participants, the different methods used to measure the amount of physical activity, and the small size of the study population.
In contrast to body composition, we found significant differences between the two groups in terms of whole-body function and muscle function of the lower extremities. Grip strength is an easy and simple measure of muscle strength that can be applied in both younger and older adults. While grip strength has been shown to decrease gradually with age,30) in our study there was no difference in grip strength according to the amount of physical activity in the same age group, as previously reported.29) Furthermore, in the stair climbing and TUG tests, which measure systemic function in older adults, there were also no differences between the LPA and HPA groups (Fig. 1A, 1C, 1D). However, walking speed during the 4-m walking test was 17.9% higher in the HPA group, indicating a functional difference according to the amount of physical activity (Fig. 1B). In addition, similar to our study results, a previous report showed that the time spent on physical activity correlated positively with walking speed.31) In older adults, walking speed is a key vitality indicator that reflects overall health and function.32,33) In addition to an increased risk of falls,34) slow walking is associated with an impairment of daily living,35) hospitalization,36) and death.37,38) Another possible reason for the difference between the LPA and HPA groups in walking velocity only during the whole-body function test is that the walking score in the IPAQ was significantly higher in the HPA group.
In the HPA group, both the isometric and the isokinetic strength of the knee extensors of the lower extremities were significantly higher than in the LPA group, reflecting the difference in the amount of physical activity (Fig. 1E, 1F). In older adults, lower extremity muscle strength is an indicator of frailty, as it is involved in functional aspects such as walking speed.39) Our study provides further proof of the importance of physical activity level in the development and maintenance of lower extremity muscle function in older adults. In particular, that the main contributing factor to the difference in the amount of physical activity between the two groups was walking, not vigorous or moderate activity, is noteworthy.
Muscle function can be described as muscle quality, which is defined as the amount of muscle strength and power per unit of muscle. Muscle quality is an important biomarker of muscle health, as reductions in the functional ability of muscle, such as during aging and disease, can lead to a decrease in muscle strength.40,41) In fact, during aging the decrease in muscle strength is faster than the decrease in muscle mass (4% vs. 1% per year). Thus, in-depth studies of changes in muscle cells and the neurological and metabolic aspects of muscle strength due to aging are required.42) Previous studies have reported that the age-related changes in muscle function at the muscle cellular level43,44) can be improved through exercise interventions.16,18) However, our study is the first to report differences in muscle function at the cellular level according to the habitual physical activity level, without any intervention. We also examined the function of a single muscle fiber at the cellular level. An average of 20 single muscle fibers were analyzed per person, for a total of 447 fibers (LPA=219, HPA=228). We found no difference in fiber CSA, peak force, and SF (normalized to fiber CSA) between the LPA group and the HPA group. These findings did not correspond with those observed in whole-body muscle strength. While the contractile properties of single muscle fibers are known to influence whole-body muscle strength, it is difficult to fully explain the function of the entire muscle by this alone.45) It is considered that the nervous system, the extracellular matrix, and the interactions between them play a significant and complex role in determining whole-body muscle strength.46-48) On the other hand, Vo was 36.2% higher in the latter (Fig. 2D). Vo is a determinant of muscle power (force×velocity) of single muscle fiber.49) Therefore, the increase in Vo achieved with greater physical activity may play a positive role in muscle cell function. Furthermore, considering that whole-body function may be affected by changes at the cellular level, the Vo at the single muscle fiber level may impact the 4-m walking velocity. In our study, this pattern was limited to type I fiber types (Fig. 3D), suggesting that the speed function of type I fibers improves in response to higher levels of physical activity, such as a faster walking time. This hypothesis is supported by the finding that the IPAQ total score correlated positively with the Vo of a single muscle fiber (Fig. 4). The absence of a relationship between the peak force or SF of a single muscle fiber and isometric and isokinetic measurements (Fig. 2A, 2B, 2C) suggests the further involvement of nerves and the extracellular matrix, neither of which were examined in our study but should be included in future research.
Taken together, the results of this study showed that a higher level of physical activity (without exercise intervention) affects functional aspects, such as walking speed and lower extremity muscle strength, rather than body composition, including muscle mass and body fat percentage, in older adults. This difference in whole-body function can be tracked to the muscle cell level. Therfore, if participating in structured exercise, such as resistance training, is not feasible for any reason, increasing overall physical activity levels, including walking, is considered an effective alternative for maintaining or improving muscle function.
While this study is the first to examine comprehensively the effect of the amount of physical activity in older adults from the whole body to the cellular level, several limitations should be pointed out. First, although IPAQ has high reliability, the amount of physical activity could have been more objectively investigated using instruments such as an accelerometer or a pedometer. Second, as our study required a muscle biopsy for muscle cell research, a large number of study participants could not be recruited.
In conclusion, increasing the amount of physical activity in older adults (≥150–300 min/wk) may play a positive role in maintaining or improving muscle function at the whole body and cellular levels, but without an effect on body composition. The effect of the amount of physical activity was shown to be independent of the amount of sitting time. Based on these findings, we recommend that older adults actively engage in physical activity to prevent or mitigate the decline in physical function associated with aging, even if they do not participate in regular exercise routines.
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