Despite some advances, it remains largely unknown how the millions of variations in the human genome influence athletic performance (especially in endurance events), and no single genetic test can really predict sports talent. However, there is experimental evidence from animal research that selecting for even a simple characteristic such as running ability can produce comparatively large and rapid changes in performance. That such selection has not been specifically documented in humans is more evidence of the limits of physiology-archeology than of the unlikelihood of selection for physical abilities. Here, the authors argue that top Olympians are likely genetically gifted individuals who in addition have numerous contributors to the “complex trait” of being an athletic champion that may not necessarily depend on defined genetic variations.
Fabian Sanchis-Gomar, Helios Pareja-Galeano, Jose A. Rodriguez-Marroyo, Jos J. de Koning, Alejandro Lucia and Carl Foster
Carlos A. Muniesa, Zoraida Verde, Germán Diaz-Ureña, Catalina Santiago, Fernando Gutiérrez, Enrique Díaz, Félix Gómez-Gallego, Helios Pareja-Galeano, Luisa Soares-Miranda and Alejandro Lucia
Growing evidence suggests that regular moderate-intensity physical activity is associated with an attenuation of leukocyte telomere length (LTL) shortening. However, more controversy exists regarding higher exercise loads such as those imposed by elite-sport participation.
The authors investigated LTL differences between young elite athletes (n = 61, 54% men, age [mean ± SD] 27.2 ± 4.9 y) and healthy nonsmoker, physically inactive controls (n = 64, 52% men, 28.9 ± 6.3 y) using analysis of variance (ANOVA).
Elite athletes had, on average, higher LTL than control subjects, 0.89 ± 0.26 vs 0.78 ± 0.31, P = .013 for the group effect, with no significant sex (P = .995) or age effect (P = .114).
The results suggest that young elite athletes have longer telomeres than their inactive peers. Further research might assess the LTL of elite athletes of varying ages compared with both age-matched active and inactive individuals.
Alejandro Santos-Lozano, Ana M. Angulo, Pilar S. Collado, Fabian Sanchis-Gomar, Helios Pareja-Galeano, Carmen Fiuza-Luces, Alejandro Lucia and Nuria Garatachea
Most studies on aging and marathon have analyzed elite marathoners, yet the latter only represent a very small fraction of all marathon participants. In addition, analysis of variance or unpaired Student t tests are frequently used to compare mean performance times across age groups. In this report the authors propose an alternative methodology to determine the impact of aging on marathon performance in both nonelite and elite marathoners participating in the New York City Marathon. In all, 471,453 data points corresponding to 370,741 different runners over 13 race editions (1999–2011) were retrieved. Results showed that the effect of aging on marathon performance was overall comparable in both sexes, the effect of aging differed between the fastest and slowest runners in both sexes, and the magnitude of the sex differences was higher in the slowest runners than in the fastest ones. Current data suggest that the biological differences between sexes allow men to have better marathon performance across most of the human life span.
Adrián Hernández-Vicente, Alejandro Santos-Lozano, Carmen Mayolas-Pi, Gabriel Rodríguez-Romo, Helios Pareja-Galeano, Natalia Bustamante, Eva M. Gómez-Trullén, Alejandro Lucia and Nuria Garatachea
To objectively assess physical activity levels and sedentary behavior in a cohort of Spanish centenarians and their nonagenarian peers. Physical activity and sedentary behavior patterns were objectively measured by an ActiGraph GT3X accelerometer in centenarians (n = 18; 83% women; 100.8 ± 0.8 [100–103] years) and nonagenarians (n = 11; 91% women; 93.3 ± 2.5 [90–98] years). Centenarians showed less counts per minute (17.6 ± 7.1 vs. 46.1 ± 23.7, p = .003, d = 1.851) and steps per day (455 ± 237 vs. 1,249 ± 776, p = .007, d = 1.587) than nonagenarians. The daily number of sedentary breaks was also lower in the former (5.0 ± 1.5 vs. 6.7 ± 2.0, p = .019, d = 0.971). When observing time distribution, the most active day period in both groups was the morning, with a peak between 10:00 and 11:59. This data suggest that the decline in physical activity levels continues to worsen until the end of the human lifespan.