There was a significant increase in body weight over time in all groups (P < 0.01). However, no significant differences between groups were observed for body weight over the total 24-wk period (P > 0.05) or over the concurrent 15-wk period (P > 0.05).
The purpose of this study was to determine whether the age at exposure to a running wheel influenced physical activity patterns in female C57Bl/6J mice later in life. Based on physical activity data in humans, it was hypothesized that the group exposed to a running wheel at the earliest age (7 wk) would maintain the highest level of running wheel activity throughout the duration of the study (3,25). However, our data did not support this hypothesis.
The tracking of physical activity from youth to adulthood in humans has allowed researchers to investigate whether one’s level of physical activity in childhood affects his or her level of physical activity in adulthood (3,24). These and other epidemiological studies suggest a positive relationship between physical activity levels in childhood and physical activity levels as adults (r = 0.05–0.53) (2,3,7,17,19,24,25). Most human studies suggest physical activity levels are influenced largely by behavioral or environmental factors, indicating that behavior patterns established early in life are maintained into adulthood. Therefore, one’s environment during youth appears to influence the amount of physical activity he or she performs as an adult (2,3,7,17,19,24,25).
The limited data available from human studies suggest a genetic influence on daily physical activity levels, especially in adulthood (21). These studies, most using monozygotic and dizygotic twins, have estimated the influence of genetics on physical activity levels to be 16–62% (10,13,18,19). Therefore, physical activity levels in adults appear to not only be influenced by the childhood environment, but also by genetics.
Animal studies have also indicated physical activity levels are influenced by genetics (6,14,15,26). Lerman et al. (14) observed inbred mouse strains over a 2-wk period and found that genetics account for 24–59% of the daily physical activity as performed on a running wheel. Similar results were found by Lightfoot et al. (15) in which heritability estimates for voluntary running wheel activity were 12–61% in inbred female mouse strains. Additionally, research has shown that the influence of genetics on daily activity changes throughout the first half of the life span (26). In a recent study by Turner et al. (26), the genetic influence on daily distance run on a running wheel among C57Bl/6J mice was approximately 34% at 11–13 wk of age. This genetic influence on daily distance run increased over the next 21 wk, where it peaked at 93% during ages 32–34 wk and began to drop thereafter. The average heritability estimate for daily distance run with female C57Bl/6J mice from age 11 to 36 wk was determined to be 61%. Thus, the influence of genetic factors during the first 9 months of life for female C57Bl/6J mice can vary depending on the age of assessment.
Collectively, human and animal studies indicate a significant genetic influence on daily physical activity levels (14,19,26). In the present study, when considering groups 1, 2, and 3, it appears that in C57Bl/6J mice, the age at running wheel exposure has little influence on levels of running wheel activity between ages 16 and 30 wk. The average weekly running patterns indicated that biological age rather than age at wheel exposure has a large influence on current running wheel activity, as evidenced by the similar activity levels throughout the duration of the study even though wheels were introduced at different ages (Figs. 1 and 2). Regardless of the age at which they received the wheel or the time (wk) they had been running before 16 wk of age, the amount of activity between 16 and 30 wk of age was the same.
Speculatively, it is possible that the genetic influence on physical activity was so strong that it overrode other intervening factors, such as age at wheel introduction when wheel introduction occurs before age 16 wk. Broad-sense heritability estimates of running wheel activity suggest the influence of genetics on daily distance and duration was 92–98% in groups 1, 2, and 3 for the 15-wk concurrent period (ages 16–30 wk). This suggests that these female C57Bl/6J mice were genetically predisposed to run a specified amount on a running wheel at any given age and that environmental influences on running wheel activity between 7 and 30 wk of age were minimal. The small variance within each group (groups 1, 2, and 3) also suggests a minimal environmental influence. This finding is supported by previous research indicating the genetic background of female inbred mice was largely responsible for the changes in running wheel activity observed over a 26-wk period (26). Our finding was further supported by current data in humans and C57Bl/6J mice (21,26). As mentioned earlier, the genetic influence on physical activity has been shown to be as high as 93% for C57Bl/6J mice (26). This finding suggests prior running wheel activity performed between ages 7 and 15 wk had little influence on running wheel activity between the ages of 16 and 30 wk in female C57Bl/6J mice when the wheel was introduced before 16 wk of age.
However, when group 4 running wheel activity data for the 15-wk concurrent period was included in the analysis, they had a higher level of activity compared with the other three groups. Group 4 had greater daily distance and duration compared with group 2 and a greater daily velocity compared with each of the other groups. There are several explanations for this difference. Whereas intrastrain variability in daily running wheel activity is typically low in inbred mouse strains (15,26), the SE for distance run for group 4 was more than twice as much as for any other group (e.g., 0.84 vs 0.35 km·d−1). This difference is largely due to one mouse in group 4 that had a daily distance and daily duration that were greater than 2 SD above the mean in all but 1 wk. Nonetheless, even when the most active mouse was removed from the data set as an outlier, significant differences remain between groups 2 and 4. When considering the data by week, it appears the activity from group 4 is decreasing and becoming more similar to the other groups by 30 wk of age.
It is also interesting to note that when each of the 20 mice was ranked for average daily distance, duration, and velocity over the entire 15-wk concurrent running period, the five mice in group 4 had four of the five highest averages for daily distance, four of the six highest averages for daily duration, and the highest five averages daily velocity.
A second explanation for the difference in group 4 is that the wheels were added to group 4 cages at 16 wk of age, which represents 16% of their life span. Perhaps there was some physiological change that occurred at this age that affected running wheel activity. If so, groups 1, 2, and 3 would have received the running wheel during one window (before 16 wk of age), whereas group 4 would have received the wheel during a separate window (after 16 wk of age). Sexual maturation is an unlikely factor because puberty occurs in female C57Bl/6J mice between the ages of 27 and 42 d depending on the measure used, which is well before 16 wk of age (1). We did not assess any physiological characteristics, other than body weight, to provide evidence concerning physiological changes during this age period.
In comparison with groups 1–3, which had heritability estimates of 92–98%, the broad-sense heritability estimates for running wheel activity in group 4 between 16 and 30 wk of age was 68–95%, suggesting a greater environmental influence on running wheel activity in group 4 compared with each of the other groups. Although there is no obvious explanation for the observed differences between group 4 and the other groups, we can speculate that perhaps the introduction of a running wheel after 15 wk of age resulted in greater environmental influence on running wheel activity in female C57Bl/6J mice, and thus greater variability. With increased variability resulting from later wheel introduction, it may be more difficult to predict physical activity levels later in life.
Given that groups 1–3 exhibited similar wheel-running patterns regardless of the age at wheel introduction, an alternate interpretation is that the genetic determinants of physical activity were not activated until the running wheel was introduced (the environment), regardless of the age of the mouse. In this context, the genes influencing running wheel activity may not be regulated by age, but rather by the onset of wheel activity. However, current data in our laboratory do not support this interpretation. A current study indicated running wheel activity was similar in two groups of mice between 11 and 14 wk of age regardless of the age at which the wheel was introduced, suggesting that introduction of the running wheel did not trigger a genetic predisposition toward activity (Jung, unpublished data, 2005).
In the current study, we attempted to standardize environmental factors known to influence the activity of mice, such as food composition, room temperature, and housing conditions (8,31). The mice all had identical housing conditions, were provided the same food ad libitum, and were all housed in the same room, so all were exposed to the same ambient environment. The only environmental manipulation we are aware of was the age at which a running wheel was accessible.
However, this study is not without limitations. The ages at wheel introduction were selected to ensure a critical period for running wheel activity was not missed, and these ages closely resemble those of studies with humans (24). However, it is possible the wheel introduction age gaps were too narrow to identify any significant differences. Additionally, the study only has implications up to 30% of the life span of the mice. Analyzing physical activity levels beyond 30% of the life span might provide important insight into genetic and environmental influences on physical activity in adult humans. Whereas the current design limits the ability to extrapolate to an adult population in whom hypokinetic disease are more common, the purpose of this study was to assess the influence of early running wheel activity on physical activity levels at between 20 and 30% of the life span. This age range was chosen, in part, because physical activity levels during adolescence and young adulthood are predictive of physical activity levels, cardiovascular disease, and cardiovascular disease risk factors in adulthood in humans (27,28). Additionally, it is not uncommon for cardiovascular disease to be diagnosed in humans by age 25 yr, indicating the importance of identifying factors that influence physical activity during adolescence and young adulthood (5,11). Finally, although the mouse model is useful, until multiple mouse strains are examined and the study duration is increased, it is difficult to transfer these results to humans.
Although a clear explanation is not evident in the current study, the increased activity level of group 4 cannot be discounted. If introduction of a running wheel at the age of 16 wk (vs 7, 10, or 13 wk, respectively) results in a greater amount of activity between 16 and 30 wk of age, one would expect a significant difference between group 4 and each of the other groups, rather than only group 2. Power analyses were 0.8 and 0.7 for distance and duration, respectively, suggesting more mice per group might have resulted in a greater difference between groups. However, finding a difference between group 4 and each of the groups likely would not change the conclusions. The variance for group 4 was greater across time compared with the other groups, which may have influenced the lack of significant differences between group 4 and the other groups. Because the variance in groups 1–3 was small compared with the variance of group 4, this suggests a greater genetic influence on running wheel activity when introduction of a wheel occurred at 13 wk of age or younger compared with introduction of the wheel at 16 wk of age. Thus, environmental influences may have had a greater effect on running wheel activity when the wheel was introduced at 16 wk of age compared with earlier introduction. To address this issue, future studies will increase the duration of the study period to determine the influence of early physical activity (e.g., 7–16 wk) on physical activity patterns later in life (i.e., later than 30 wk of age).
Groups 1, 2, and 3 displayed similar patterns of running wheel activity and small within-group variances during the concurrent 15-wk study period, suggestive of a strong genetic influence on physical activity, which is supported by the literature (14,15,26). It also appears, based on groups 1, 2, and 3, that the age at which activity begins does not influence activity level between 16 and 30 wk of age. The inclusion of group 4 indicates the absence of running wheel activity between the ages of 7 and 16 wk may result in greater amounts of running wheel activity in subsequent weeks. There are currently no data, in mice or humans, to support the idea that delaying the introduction of physical activity during childhood or adolescence actually increases physical activity later in life. Based on this study, it appears that physical activity between 16 and 30 wk of age in female C57Bl/6J mice was not influenced by prior running wheel activity. Thus, it appears, at least in female C57Bl/6J mice, that there was a genetically determined amount of running wheel activity that was performed between 16 and 30 wk of age, regardless of prior amount of running wheel activity or the age at which the running wheel was introduced. However, it appears that the introduction of a running wheel at 16 wk of age resulted in a greater within-group variance, suggesting a greater amount of environmental influence on running wheel activity between the ages of 16 and 30 wk.
1. Ahima, R. S., J. Dushay, S. N. Flier, D. Prabakaran, and J. S. Flier. Leptin accelerates the onset of puberty in normal female mice. J. Clin. Invest.
2. Barnekow-Bergkvist, M., G. Hedberg, U. Janlert, and E. Jansson. Physical activity pattern in men and women at the age of 16 and 34 and development of physical activity from adolescence to adulthood. Scand. J. Med. Sci. Sports.
3. Beunen, G. P., J. Lefevre, R. M. Philippaerts, et al. Adolescent correlates of adult physical activity: a 26-year follow-up. Med. Sci. Sports Exerc.
4. Centers for Disease Control. Risk factors and use of preventative services. In: Chronic Diseases and Their Risk Factors: The Nation’s Leading Causes of Death
. Atlanta: United States Government, 1999, pp. 39–79.
5. Cole, J. H., J. I. Miller, L. S. Sperling, and W. S. Weintraub. Long-term follow-up of coronary artery disease presenting in young adults. J. Am. Coll. Cardiol.
6. Festing, M. Wheel activity in 26 strains of mouse. Lab. Anim.
7. Glenmark, B., G. Hedberg, and E. Jansson. Prediction of physical activity level in adulthood by physical characteristics, physical performance and physical activity in adolescence: an 11-year follow-up study. Eur. J. Appl. Physiol. Occup. Physiol.
8. Gordon, C. J., P. Becker, and J. S. Ali. Behavioral thermoregulatory responses of single- and group-housed mice. Physiol. Behav.
9. Janz, K. F., J. D. Dawson, and L. T. Mahoney. Tracking physical fitness and physical activity from childhood to adolescence: the Muscatine study. Med. Sci. Sports Exerc.
10. Kaprio, J. M., M. Koskenvuo, and S. Sarna. Cigarette smoking, use of alcohol and leisure-time activity among same-sex adult male twins. In: Progress in Clinical and Biological Research, Twin Research 3: Epidemiological and Clinical Studies
, L. Gedda, P. Parisi, and W. E. Nance (Eds.) New York: Liss, 1981, pp. 37–46.
11. Klein, L. W., J. B. Agarwal, M. B. Herlich, T. M. Leary, and R. H. Helfant. Prognosis of symptomatic coronary artery disease in young adults aged 40 years or less. Am. J. Cardiol.
12. Kohl, H., and K. E. Hobbs. Development of physical activity behaviors among children and adolescents. Pediatrics
13. Lauderdale, D. S., R. Fabsitz, J. M. Meyer, P. Sholinsky, V. Ramakrishnan, and J. Goldberg. Familial determinants of moderate and intense physical activity: a twin study. Med. Sci. Sports Exerc.
14. Lerman, I., B. C. Harrison, K. Freeman, et al. Genetic variability in forced and voluntary endurance exercise performance in seven inbred mouse strains. J. Appl. Physiol.
15. Lightfoot, J. T., M. J. Turner, M. Daves, A. Vordermark, and S. R. Kleeberger. Genetic influence on daily running wheel activity level. Physiol. Genomics
16. Malina, R. Adherence to physical activity from childhood to adulthood: a perspective from tracking studies. Quest
17. Malina, R. Tracking of physical activity and physical fitness across the lifespan. Res. Q. Exerc. Sport
18. Perusse, L. A.Tremblay, C. LeBlanc, and C. Bouchard. Genetic and environmental influences on habitual physical activity and exercise participation. Am. J. Epidemiol.
19. Simonen, R. L., T. Videman, J. Kaprio, E. Levalaht, and M. C. Battie. Factors associated with exercise lifestyle—a study of monozygotic twins. Int. J. Sports Med.
20. Storer, J. B. Longevity and gross pathology at death in 22 inbred strains of mice. J. Gerontol.
21. Stubbe, J. H., D. I. Boomsma, and E. J. De Geus. Sports participation during adolescence: a shift from environment to genetic factors. Med. Sci. Sports Exerc.
22. Swallow, J. G., T. Garland, Jr., P. A. Carter, W. Z. Zhan, and G. C. Sieck, Effects of voluntary activity and genetic selection on aerobic capacity in house mice (Mus domesticus
). J. Appl. Physiol.
23. Telama, R., X. Yang, L. Laasko, and J. Viikari. Physical activity in childhood and adolescence as predictor of physical activity in young adulthood. Am. J. Prev. Med.
24. Telama, R., X. Yang, J. Viikari, I. Valimaki, O. Wanne, and O. Raitakari. Physical activity from childhood to adulthood: a 21-year tracking study. Am. J. Prev. Med.
25. Trudeau, F., L. Laurencelle, and R. J. Shephard. Tracking of physical activity from childhood to adulthood. Med. Sci. Sports Exerc.
26. Turner, M. J., S. R. Kleeberger, and J. T. Lightfoot. Genetic influence on age-related changes in daily wheel running activity. Physiol. Genomics
27. Twisk, J. W., H. C. Kemper, and W. Van Mechlen. Tracking of activity and fitness and the relationship with cardiovascular disease risk factors. Med. Sci. Sports Exerc.
28. Twisk, J. W., H. C. Kemper, W. Van Mechlen, and G. B. Post. Which lifestyle parameters discriminate high- from low-risk participants for coronary disease risk factors. Longitudinal analysis covering adolescence and young adulthood. J. Cardiovasc. Risk
29. U.S. Department Health and Human Services. Healthy People 2010.
Washington, DC: U.S. Government Printing Office, 2000, pp. 26–27.
30. U.S. Department Health and Human Services. Physical Activity and Health: A Report of the Surgeon General.
Atlanta: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, 1996, pp. 146–150
31. Yashiro, M., and S. Kimura. Effect of voluntary exercise on physiological function and feeding behavior of mice on 20% casein diet or 10% casein diet. J. Nutr. Sci. Vitaminol. (Tokyo).
Keywords:©2006The American College of Sports Medicine
TRACKING; PHYSICAL ACTIVITY; BIOLOGICAL INFLUENCE; C57BL/6J; CRITICAL PERIOD