Does Muscle Mass Affect Running Times in Male Long-distance Master Runners?

Anamorelin for the Treatment of Cancer Anorexia-Cachexia Syndrome

PURPOSE OF REVIEW

The following review will highlight the development of anamorelin to treat cancer anorexia-cachexia syndrome (CACS) including the potential benefits, limitations, and future directions.

RECENT FINDINGS

Ghrelin, a 28-amino acid peptide hormone, is secreted by the stomach mucosa and regulates appetite, promotes lipogenesis, increases body weight, improves gastric motility, reduces catabolic wasting and inflammation. Several randomized, double-blind, placebo-controlled clinical trials evaluating anamorelin, a ghrelin agonist, for the treatment of CACS have reported improvement in appetite and body composition including both lean body and fat mass; however, most studies noted no improvement in physical function as assessed by measuring non-dominant hand-grip strength. Common adverse effects of anamorelin include the development of diabetes mellitus, hyperglycemia, and less frequently, hepatic abnormalities and cardiovascular events including conduction abnormalities, hypertension, and ischemic cardiomyopathy. Anamorelin has the potential to stimulate appetite, improve gastric movement, and may have anti-inflammatory effects on patients with CACS. In patients with cancer, studies involving anamorelin combined with other multimodal treatments including nutrition counseling (branched chain amino acids, omega 3 fatty acids, and other nutrients), exercise, treatment of hormonal abnormalities including hypogonadism and hypovitaminosis D, and anti-inflammatory agents are needed. Compliance with multimodality treatment has been a barrier and future studies may need to incorporate motivational counseling to promote adherence.

Differences in race history by distance of recreational endurance runners from The NURMI Study (Step 2)

Few studies were developed to understand the relationship between running characteristics and motivation. The purpose of this study was to assess the relationship between running event history, running experience, and best race performances in recreational distance runners. We used a web survey to obtain information regarding running experience, racing history, and periodization training routines/exercise habits, including weekly volumes and daily mileage and duration across periods and conditions. Associations between variables were conducted with the Chi-square test (χ2; nominal scale) and Wilcoxon test. Multiple linear regression analysis and multivariate linear regression were performed. Concerning the participants' motive for exercising, a significant difference was identified between the race distance subgroups (p < 0.001), where 58% of M/UM runners exercised for performance (n = 38) and 64% of HM runners (n = 57) and 57% of 10 km runners (n = 52) exercised for recreational purposes. A significant difference was found in the number of years of running completed without taking a break (p = 0.004), with marathoners/ultramarathoners reporting the most years. Runners competing in different race distances such as 10 km, half-marathon, marathon, and ultra-marathon presented differences in training background and habits according to the distance of preference.

Participation and performance characteristics in half-marathon run: a brief narrative review

Half-marathon (HM) is a running sport of increasing popularity in both sexes and in all age groups worldwide during the last years. Many studies have examined several aspects of HM, such as performance and participation trends, sex and age differences, physiological correlates, and training; however, no comprehensive review has ever been contacted to summarize the recently accumulated knowledge. Therefore, the aim of the present study was to review all previous research in this sport, focusing on participation and performance aspects. It was shown that HM runners had similar anthropometric and physiological characteristics as full-marathon runners which should be attributed to the affinity of these two races in terms of metabolic demands. Performance in HM was related with superior scores in aerobic capacity (maximal oxygen uptake, anaerobic threshold and running economy) and training characteristics (sport experience, weekly distance, training speed, frequency of sessions and long single endurance run distance), and lower scores in adiposity-related scores (e.g. body mass, body mass index, body fat percentage and skinfold thickness). Considering the popularity of HM race and the lack of many original studies (compared to FM race), this is an exciting field for scientific research with a large potential for practical applications, since the majority of HM runners are amateur runners in need of sex-, age- and performance-tailored exercise prescription.

Are Individual and Environmental Characteristics Associated With Running Performance in Female Runners of Different Age Categories?

The purpose of this study was to identify the individual and environmental predictors associated with performance in female runners of different ages. The sample comprised 440 female Brazilian runners, who answered an online questionnaire, that provided information regarding height, weight, age (categories: “young adult”, “adult”, “early middle-age”, and “older adults”), socioeconomic status, and training characteristics (frequency and volume per week, running pace, race event, and running club participation). Information about environmental variables was obtained from the official institutes and comprised the human development index (HDI), athletics events, athletic tracks, and female homicide. A linear regression model, clustered by state and performed by age groups, was computed. The sample presented a mean running pace of 5:57min/km, and a mean BMI of 23.51kg/m⁻². An increase in running pace and volume/week was observed with increasing age. In “young adults”, any of the variables were significantly associated with the performance. In “adult” group, only individual characteristics were statistically significantly related with the performance. In “early middle-age”, besides BMI (β=5.72; 95%CI=3.65–7.79) and training volume (β=−0.67; 95%CI=−1.07 − −0.27), the HDI was associated with the performance (β=−23.30; 95%CI=−44.11 − −2.49). In older adults, it was found an association between socioeconomic status (β=−19.47; 95%CI=−32.29 − −6.65), practice time (β=142.92; 95%CI=89.34–196.50), running event participation (β=−80.12; 95%CI=−114.35− −45.88), athletic events (β=33.44; 95%CI=15.16–51.72), and female homicide (β=−0.11; 95%CI=−0.17 − −0.05) with the performance, highlighting the influence of both individual and environmental characteristics. Information about the role of these constraints, and their relationships, in female runners’ performance, can be used to guide the development of projects/strategies aiming to increase their involvement in physical activities and sports practice, through the promotion of a more “friendly environment” to women, and providing support for decision-makers when suggesting/implementing public policies.

The Consequences of Training and Competition to the Musculoskeletal System in Ultramarathon Runners: A Narrative Review

Ultramarathons are becoming increasingly popular every year, leading to more and more publications focusing on athletes of these endurance events. This paper summarizes the current state of knowledge on the effects of ultramarathons on the motor system. Various studies have attempted to answer questions about negative and positive effects on the musculoskeletal system, common injuries, optimal strategies, and regeneration. Considering the increasing number of ultramarathon athletes, the discoveries may have practical applications for a multitude of experts in the field of sports medicine, as well as for the athletes themselves. Acute locomotor system changes in runners as assessed by locomotor biomarkers are reversible and may be asymptomatic or painful. Injuries suffered by runners largely allow them to finish the competition and are usually overlooked. Regeneration, including regular massage and the use of supporting techniques, allows for faster convalescence. This publication is meant to be a source of knowledge for people associated with this discipline.

To Be a Champion of the 24-h Ultramarathon Race. If Not the Heart ... Mosaic Theory?

This comprehensive case analysis aimed to identify the features enabling a runner to achieve championship in 24-h ultramarathon (UM) races. A 36-year-old, multiple medalist of the World Championships in 24-h running, was assessed before, one and 10 days after a 24-h run. Results of his extensive laboratory and cardiological diagnostics with transthoracic echocardiography (TTE) and a one-time cardiopulmonary exercise test (CPET) were analyzed. After 12 h of running (approximately 130 km), the athlete experienced an increasing pain in the right knee. His baseline clinical data were within the normal range. High physical efficiency in CPET (VO₂max 63 mL/kg/min) was similar to the average achieved by other ultramarathoners who had significantly worse results. Thus, we also performed genetic tests and assessed his psychological profile, body composition, and markers of physical and mental stress (serotonin, cortisol, epinephrine, prolactin, testosterone, and luteinizing hormone). The athlete had a mtDNA haplogroup H (HV0a1 subgroup, belonging to the HV cluster), characteristic of athletes with the highest endurance. Psychological studies have shown high and very high intensity of the properties of individual scales of the tools used mental resilience (62–100% depending on the scale), openness to experience (10th sten), coherence (10th sten), positive perfectionism (100%) and overall hope for success score (10th sten). The athlete himself considers the commitment and mental support of his team to be a significant factor of his success. Body composition assessment (%fat 13.9) and the level of stress markers were unremarkable. The tested athlete showed a number of features of the champions of ultramarathon runs, such as: inborn predispositions, mental traits, level of training, and resistance to pain. However, none of these features are reserved exclusively for “champions”. Team support’s participation cannot be underestimated. The factors that guarantee the success of this elite 24-h UM runner go far beyond physiological and psychological explanations. Further studies are needed to identify individual elements of the putative “mosaic theory of being a champion”.

Predictors of Athlete's Performance in Ultra-Endurance Mountain Races

Background: In previous studies, ultra-endurance performance has been associated with training and psychological variables. However, performance under extreme conditions is understudied, mainly due to difficulties in making field measures. Aim: The aim of this study was to analyze the role of training, hydration, nutrition, oral health status, and stress-related psychological factors in athletes’ performance in ultra-endurance mountain events. Methods: We analyzed the variables of race time and training, hydration state, nutrition, oral health status, and stress-related psychological factors in 448 ultra-endurance mountain race finishers divided into three groups according to race length (less than 45 km, 45–90 km, and greater than 90 km), using a questionnaire. Results: Higher performance in ultra-endurance mountain races was associated with better oral health status and higher accumulative altitude covered per week as well as higher positive accumulative change of altitude per week during training. In longer distance races, experience, a larger volume of training, and better hydration/nutrition prior to the competition were associated with better performance. Conclusions: Ultra-endurance mountain athletes competing in longer races (>90 km) have more experience and follow harder training schedules compared with athletes competing in shorter distances. In longer races, a larger fluid intake before the competition was the single best predictor of performance. For races between 45 and 90 km, training intensity and volume were key predictors of performance, and for races below 45 km, oral health status was a key predictor of performance. Psychological factors previously reported as ultra-endurance mountain race performance predictors were inconsistent or failed to predict the performance of athletes in the present research.

An Analysis of Participation and Performance of 2067 100-km Ultra-Marathons Worldwide

This study aimed to analyze the number of successful finishers and the performance of the athletes in 100-km ultra-marathons worldwide. A total of 2067 100-km ultra-marathon races with 369,969 men and 69,668 women competing between 1960 and 2019 were analyzed, including the number of successful finishers, age, sex, and running speed. The results showed a strong increase in the number of running events as well as a strong increase in the number of participants in the 100-km ultra-marathons worldwide. The performance gap disappeared in athletes older than 60 years. Nevertheless, the running speed of athletes over 70 years has improved every decade. In contrast, the performance gap among the top three athletes remains persistent over all decades (F = 83.4, p < 0.001; _pη² = 0.039). The performance gap between the sexes is not significant in the youngest age groups (20–29 years) and the oldest age groups (>90 years) among recreational athletes and among top-three athletes over 70 years. In summary, especially for older athletes, a 100-km ultra-marathon competition shows an increasing number of opponents and a stronger performance challenge. This will certainly be of interest for coaches and athletes in the future, both from a scientific and sporting point of view.

Long distance running - Can bioprofiling predict success in endurance athletes?

The TransEuropeFootRace (TEFR) was one of the most extreme multistage competitions worldwide. The ultramarathon took the runners over a distance of 4487 km, from Bari, Italy, to the North Cape, Norway, in 64 days. The participating ultra-long-distance runners had to complete almost two marathons per day (~70 km). The race was accompanied by a research team analysing adaptations of different organ systems of the human body that were exposed to a chronic lack of regeneration time. Here, we analyzed runner's urine using mass spectrometric profiling of thousands of low-molecular weight compounds. The results indicated that pre-race molecular factors can predict finishers and separate them from nonfinishers already before the race. These observations were related to the training volume as finishers ran about twice as many kilometers per week before TEFR than nonfinishers, thus apparently achieving a higher performance level and resistance against overuse. While this hypothesis needs to be validated in future long-distance races, the bioprofiling experiments suggest that the competition readiness of the runners is measurable and might be adjustable.

Multidisciplinary Analysis of Differences Between Finisher and Non-finisher Ultra-Endurance Mountain Athletes

Ultra-endurance races are one of the most physically and psychologically demanding sports, depending performance on several elements. The aims of the present study were (i) to analyze differences in selected psychophysiological parameters between finisher and non-finisher ultra-endurance mountain athletes, and (ii) to analyze modifications in psychophysiological parameters before and after an ultra-endurance mountain event. Selected psychophysiological variables were assessed in 46 finishers and 24 non-finishers in two over 100 km ultra-endurance races were examined. We found how an ultra-endurance mountain race produced dehydration, a decrease in systolic blood pressure, weight and leg strength muscle values, as well as an increase in heart rate and rate of perceived exertion values. Finishers presented lower systolic blood pressure, weight, body mass index, half marathon time and fluid intake before competition day compared to non-finishers. In addition, body mass index, pre-race hydration, and performance in lower distance races were predictors of performance in these ultra-endurance mountain races.

The relationship between muscle mass and function in cancer cachexia: smoke and mirrors?

PURPOSE OF REVIEW

Randomized clinical trials of cancer cachexia interventions are based on the premise that an increase in the muscle mass of patients is associated with consequent improvements in muscle function, and ultimately, quality of life. However, recent trials that have succeeded in demonstrating increases in lean body mass have been unable to show associated increases in patient physical function. In this review, we examine the potential causes for this lack of association between muscle mass and function in cancer cachexia, paying particular attention to those factors that may be at play when using body composition analysis techniques involving cross-sectional imaging. Moreover, we propose a new population-specific model for the relationship between muscle mass and physical function in patients with cancer cachexia.

RECENT FINDINGS

The ROMANA 1 and 2 trials of anamorelin (a novel ghrelin agonist) and the POWER 1 and 2 trials of enobosarm (a selective androgen receptor modulator) were able to demonstrate improvements in patient lean body mass, but not the functional co-primary endpoints of handgrip strength and stair climb power, respectively. We report similar confirmatory findings in other studies, and describe potential reasons for these observations.

SUMMARY

The relationship between muscle mass and muscle function is complex and unlikely to be linear. Furthermore, the relationship is influenced by the techniques used to assess nutritional endpoints [e.g. computed tomography (CT)]; the nature of the chosen physical function outcome measures; and the sex and severity of the recruited cachectic patients. Such factors need to be considered when designing intervention trials for cancer cachexia with functional endpoints.

The A-allele of the FTO Gene rs9939609 Polymorphism Is Associated With Decreased Proportion of Slow Oxidative Muscle Fibers and Over-represented in Heavier Athletes

Guilherme, JPLF, Egorova, ES, Semenova, EA, Kostryukova, ES, Kulemin, NA, Borisov, OV, Khabibova, SA, Larin, AK, Ospanova, EA, Pavlenko, AV, Lyubaeva, EV, Popov, DV, Lysenko, EA, Vepkhvadze, TF, Lednev, EM, Govorun, VM, Generozov, EV, Ahmetov, II, and Lancha Junior, AH. The A-allele of the FTO gene rs9939609 polymorphism is associated with decreased proportion of slow oxidative muscle fibers and over-represented in heavier athletes. J Strength Cond Res 33(3): 691-700, 2019-The purpose of this study was to explore the frequency of the FTO T > A (rs9939609) polymorphism in elite athletes from 2 cohorts (Brazil and Russia), as well as to find a relationship between FTO genotypes and muscle fiber composition. A total of 677 athletes and 652 nonathletes were evaluated in the Brazilian cohort, whereas a total of 920 athletes and 754 nonathletes were evaluated in the Russian cohort. It was found a trend for a lower frequency of A/A genotype in long-distance athletes compared with nonathletes (odds ratio [OR]: 0.65; p = 0.054). By contrast, it was found an increased frequency of the A-allele in Russian power athletes. The presence of the T/A + A/A genotypes rather than T/T increased the OR of being a Russian power athlete compared with matched nonathletes (OR: 1.45; p = 0.002). Different from that observed in combat sports athletes of lighter weight categories, the A-allele was also over-represented in combat sports athletes of heavier weight categories. The presence of the T/A + A/A genotypes rather than T/T increased the OR of being a combat sports athlete of heavier weight categories compared with nonathletes (OR: 1.79; p = 0.018). Regarding the muscle fibers, we found that carriers of the A/A genotype had less slow-twitch muscle fibers than T-allele carriers (p = 0.029). In conclusion, the A/A genotype of the FTO T > A polymorphism is under-represented in athletes more reliant on a lean phenotype and associated with decreased proportion of slow-twitch muscle fibers, while is over-represented in strength and heavier athletes.

Women Reduce the Performance Difference to Men with Increasing Age in Ultra-Marathon Running

Age and sex are well-known factors influencing ultra-marathon race performance. The fact that women in older age groups are able to achieve a similar performance as men has been documented in swimming. In ultra-marathon running, knowledge is still limited. The aim of this study was to analyze sex-specific performance in ultra-marathon running according to age and distance. All ultra-marathon races documented in the online database of the German Society for Ultra-Marathon from 1964 to 2017 for 50-mile races (i.e., 231,980 records from 91,665 finishers) and from 1953 to 2017 for 100-mile races (i.e., 107,445 records from 39,870 finishers) were analyzed. In 50-mile races, race times were 11.74 ± 1.95 h for men and 12.31 ± 1.69 h for women. In 100-mile races, race times were 26.6 ± 3.49 h for men and 27.47 ± 3.6 h for women. The sex differences decreased with older age and were smaller in 100-mile (4.41%) than in 50-mile races (9.13%). The overall age of peak performance was 33 years for both distances. In summary, women reduced the performance difference to men with advancing age, the relative difference being smaller in 100-mile compared to 50-mile races. These findings might aid coaches and ultra-marathon runners set long-term training goals considering their sex and age.

Strength Abilities in Men 50+ as an Effect of Long-Distance Run Training

The aim of this study was to evaluate the effect of long-term running training on muscle strength of men aged 50 and above. The study involved two groups of men aged 50 and above: physically active (E, n = 34) and inactive (NE, n = 20). Body composition was assessed with the electrical bioimpedance method. The isometric maximum voluntary contraction (MVC) was the main measurement. The value of muscle torque achieved by a group of synergists (operating in the given joint) during a short isometric contraction was evaluated. Ten groups of flexor and extensor muscles of the elbow, shoulder, hip, knee, and torso joints were measured. In addition, a 3 s measurement of grip strength of the right (F_r) and left (F_l) hand was taken using a hand dynamometer. The obtained values enabled to calculate the symmetry index (SI).

Men who had been running regularly were characterized by a significantly lower strength level (p < .05). Results in the NE group were determined to a great extent by significantly different body weights (p < .001) and a significantly higher body fat mass (FAT) content (p < .001). SI was statistically higher in the reference group (p < .05).

Long-distance run training reduces FAT while maintaining a high level of muscle strength. These studies indirectly confirm the effect of strengthening slow-twitch motor units in men aged 50 (Doherty & Brown 1993; Kanda & Hashizume 1989). In addition, stimulating the body through physical effort helps it also to maintain a high level of strength symmetry, which is a preventive factor in reducing the number of injuries.

Different Predictor Variables for Women and Men in Ultra-Marathon Running-The Wellington Urban Ultramarathon 2018

Ultra-marathon races are increasing in popularity. Women are now 20% of all finishers, and this number is growing. Predictors of performance have been examined rarely for women in ultra-marathon running. This study aimed to examine the predictors of performance for women and men in the 62 km Wellington Urban Ultramarathon 2018 (WUU2K) and create an equation to predict ultra-marathon race time. For women, volume of running during training per week (km) and personal best time (PBT) in 5 km, 10 km, and half-marathon (min) were all associated with race time. For men, age, body mass index (BMI), years running, running speed during training (min/km), marathon PBT, and 5 km PBT (min) were all associated with race time. For men, ultra-marathon race time might be predicted by the following equation: (r² = 0.44, adjusted r² = 0.35, SE = 78.15, degrees of freedom (df) = 18) ultra-marathon race time (min) = −30.85 ± 0.2352 × marathon PBT + 25.37 × 5 km PBT + 17.20 × running speed of training (min/km). For women, ultra-marathon race time might be predicted by the following equation: (r² = 0.83, adjusted r² = 0.75, SE = 42.53, df = 6) ultra-marathon race time (min) = −148.83 + 3.824 × (half-marathon PBT) + 9.76 × (10 km PBT) − 6.899 × (5 km PBT). This study should help women in their preparation for performance in ultra-marathon and adds to the bulk of knowledge for ultra-marathon preparation available to men.

Association between skeletal muscle mass and cardiorespiratory fitness in community-dwelling elderly men

BACKGROUND

Sarcopenia reduces physical ability and cardiorespiratory fitness (CRF), leading to poor quality of life.

AIM

The aim of this study was to investigate the relationship between skeletal muscle mass and CRF in elderly men.

METHODS

We assessed 102 community-dwelling men over 60 years old. Appendicular skeletal muscle mass (ASM) was determined using bioelectrical impedance analysis, and the skeletal muscle mass index (SMI) was calculated as ASM divided by the square of height. Subjects with an SMI less than 7.0 kg/m² were included in the sarcopenic group, as recommended by the Asian Working Group for Sarcopenia. To investigate CRF parameters, a cardiopulmonary exercise test was performed using the Bruce protocol. CRF parameters were subdivided into aerobic capacity, cardiovascular response, and ventilatory response.

RESULTS

Of the 102 subjects, 15 (14.7%) were included in the sarcopenic group. There were significant correlations between SMI and peak oxygen consumption (VO_2peak) (r = 0.597, p < 0.001), and between SMI and VO_2peak/weight (r = 0.268, p = 0.024). Moreover, there were positive correlations between SMI and first ventilatory threshold (VT1) (r = 0.352, p = 0.008) and between SMI and VT1/weight (r = 0.189, p = 0.039). Additionally, peak oxygen pulse (O₂pulse_peak) was significantly correlated with SMI (r = 0.558, p < 0.001). VO_2peak, VO_2peak/weight and O₂pulse_peak showed significant differences between the sarcopenic and non-sarcopenic groups (p < 0.05, all). In multiple linear regression analyses, the factor related to VO_2peak was SMI (β = 0.473, p < 0.001) and that related to O₂pulse_peak was also SMI (β = 0.442, p < 0.001).

DISCUSSION AND CONCLUSIONS

This study demonstrated that skeletal muscle mass might be closely associated with CRF. Therefore, sarcopenia should be appropriately managed to improve an individual's CRF.

Body Mass Index in Master Athletes: Review of the Literature

Background

Masters athletes (MAs) have led a physically active lifestyle for an extended period of time or initiated exercise/sport in later life. Given the benefits of physical activity and exercise we investigated if body mass index (BMI), an indirect health indicator of obesity, was clinically superior in MAs as compared to controls or the general population.

Methods

Seven databases (Medline, PubMed, Scopus, Web of Science, CINAHL, PsycINFO, Cochrane) were electronically searched for studies on BMI (kg/m²) or as a percentage of BMI categories (underweight, normal, overweight, obesity) in MAs.

Results

Of the initial yield of 7,431 papers, 60 studies met our inclusion criteria and were used in this literature review. Studies identified were classified as: endurance sports (n = 14), runners (n = 14), mixed sports (n = 8), cyclists (n = 4), soccer (n = 4) swimmers (n = 3), non-specific (n = 3), orienteering (n = 2), World Masters Games (n = 2) and individual sports (n = 5). Where BMI was presented for the group of MAs the mean was 23.8 kg/m² (± 1.1) with a range from 20.8 kg/m² (endurance runners) to 27.3 kg/m² (soccer players), this was significantly lower (p < 0.001) than controls ( −9.5%, 26.13 ± 1.7 kg/m²). Where gender specific BMI was reported the mean for male MAs was 23.6 kg/m² (± 1.5) (range 22.4 kg/m² endurance to 26.4 kg/m² swimmers) and 22.4 kg/m² (± 1.2) for female MAs (range 20.8 kg/m² mixed to 24.7 kg/m² WMG).

Conclusion

In most, but not all studies the BMI of MAs was significantly lower than controls. A clinically superior BMI affords MAs reduced risk with regard to a number of cardiometabolic diseases, osteoarthritis and certain types of cancers.

Physiology and Pathophysiology in Ultra-Marathon Running

In this overview, we summarize the findings of the literature with regards to physiology and pathophysiology of ultra-marathon running. The number of ultra-marathon races and the number of official finishers considerably increased in the last decades especially due to the increased number of female and age-group runners. A typical ultra-marathoner is male, married, well-educated, and ~45 years old. Female ultra-marathoners account for ~20% of the total number of finishers. Ultra-marathoners are older and have a larger weekly training volume, but run more slowly during training compared to marathoners. Previous experience (e.g., number of finishes in ultra-marathon races and personal best marathon time) is the most important predictor variable for a successful ultra-marathon performance followed by specific anthropometric (e.g., low body mass index, BMI, and low body fat) and training (e.g., high volume and running speed during training) characteristics. Women are slower than men, but the sex difference in performance decreased in recent years to ~10–20% depending upon the length of the ultra-marathon. The fastest ultra-marathon race times are generally achieved at the age of 35–45 years or older for both women and men, and the age of peak performance increases with increasing race distance or duration. An ultra-marathon leads to an energy deficit resulting in a reduction of both body fat and skeletal muscle mass. An ultra-marathon in combination with other risk factors, such as extreme weather conditions (either heat or cold) or the country where the race is held, can lead to exercise-associated hyponatremia. An ultra-marathon can also lead to changes in biomarkers indicating a pathological process in specific organs or organ systems such as skeletal muscles, heart, liver, kidney, immune and endocrine system. These changes are usually temporary, depending on intensity and duration of the performance, and usually normalize after the race. In longer ultra-marathons, ~50–60% of the participants experience musculoskeletal problems. The most common injuries in ultra-marathoners involve the lower limb, such as the ankle and the knee. An ultra-marathon can lead to an increase in creatine-kinase to values of 100,000–200,000 U/l depending upon the fitness level of the athlete and the length of the race. Furthermore, an ultra-marathon can lead to changes in the heart as shown by changes in cardiac biomarkers, electro- and echocardiography. Ultra-marathoners often suffer from digestive problems and gastrointestinal bleeding after an ultra-marathon is not uncommon. Liver enzymes can also considerably increase during an ultra-marathon. An ultra-marathon often leads to a temporary reduction in renal function. Ultra-marathoners often suffer from upper respiratory infections after an ultra-marathon. Considering the increased number of participants in ultra-marathons, the findings of the present review would have practical applications for a large number of sports scientists and sports medicine practitioners working in this field.

The age of the best ultramarathon performance - the case of the "Comrades Marathon"

The aim of the present study was to determine the age of the fastest running speed in 202,370 runners (34,090 women and 168,280 men) competing in the "Comrades Marathon" between 1994 and 2015 using non-linear regression analysis (second order polynomial function). When all runners were considered in 1-year age intervals, the fastest running speed (9.61 ± 1.65 km/h) was achieved at the age of 29.89 years in men, whereas women achieved it at the age of 35.96 years 8.60 ± 1.10 km/h. When the fastest runners were considered in 1-year intervals, the fastest running speed (16.65 km/h) was achieved in men at the age of 36.38 years. For the fastest women, the age of the fastest running speed (13.89 km/h) was 32.75 years. To summarize, for all runners, men achieved the best ultramarathon performance ~6 years earlier than women. When the fastest runners were considered, however, men achieved the best performance ~4 years later than women.

Do women reduce the gap to men in ultra-marathon running?

The aim of the present study was to examine sex differences across years in performance of runners in ultra-marathons lasting from 6 h to 10 days (i.e. 6, 12, 24, 48, 72, 144, and 240 h). Data of 32,187 finishers competing between 1975 and 2013 with 93,109 finishes were analysed using multiple linear regression analyses. With increasing age, the sex gap for all race durations increased. Across calendar years, the gap between women and men decreased in 6, 72, 144 and 240 h, but increased in 24 and 48 h. The men-to-women ratio differed among age groups, where a higher ratio was observed in the older age groups, and this relationship varied by distance. In all durations of ultra-marathon, the participation of women and men varied by age (p < 0.001), indicating a relatively low participation of women in the older age groups. In summary, between 1975 and 2013, women were able to reduce the gap to men for most of timed ultra-marathons and for those age groups where they had relatively high participation.

Performance differences between sexes in 50-mile to 3,100-mile ultramarathons

Anecdotal reports have assumed that women would be able to outrun men in long-distance running. The aim of this study was to test this assumption by investigating the changes in performance difference between sexes in the best ultramarathoners in 50-mile, 100-mile, 200-mile, 1,000-mile, and 3,100-mile events held worldwide between 1971 and 2012. The sex differences in running speed for the fastest runners ever were analyzed using one-way analysis of variance with subsequent Tukey–Kramer posthoc analysis. Changes in sex difference in running speed of the annual fastest were analyzed using linear and nonlinear regression analyses, correlation analyses, and mixed-effects regression analyses. The fastest men ever were faster than the fastest women ever in 50-mile (17.5%), 100-mile (17.4%), 200-mile (9.7%), 1,000-mile (20.2%), and 3,100-mile (18.6%) events. For the ten fastest finishers ever, men were faster than women in 50-mile (17.1%±1.9%), 100-mile (19.2%±1.5%), and 1,000-mile (16.7%±1.6%) events. No correlation existed between sex difference and running speed for the fastest ever (r²=0.0039, P=0.91) and the ten fastest ever (r²=0.15, P=0.74) for all distances. For the annual fastest, the sex difference in running speed decreased linearly in 50-mile events from 14.6% to 8.9%, remained unchanged in 100-mile (18.0%±8.4%) and 1,000-mile (13.7%±9.1%) events, and increased in 3,100-mile events from 12.5% to 16.9%. For the annual ten fastest runners, the performance difference between sexes decreased linearly in 50-mile events from 31.6%±3.6% to 8.9%±1.8% and in 100-mile events from 26.0%±4.4% to 24.7%±0.9%. To summarize, the fastest men were ~17%–20% faster than the fastest women for all distances from 50 miles to 3,100 miles. The linear decrease in sex difference for 50-mile and 100-mile events may suggest that women are reducing the sex gap for these distances.

What is the age for the fastest ultra-marathon performance in time-limited races from 6 h to 10 days?

Recent findings suggested that the age of peak ultra-marathon performance seemed to increase with increasing race distance. The present study investigated the age of peak ultra-marathon performance for runners competing in time-limited ultra-marathons held from 6 to 240 h (i.e. 10 days) during 1975-2013. Age and running performance in 20,238 (21%) female and 76,888 (79%) male finishes (6,863 women and 24,725 men, 22 and 78%, respectively) were analysed using mixed-effects regression analyses. The annual number of finishes increased for both women and men in all races. About one half of the finishers completed at least one race and the other half completed more than one race. Most of the finishes were achieved in the fourth decade of life. The age of the best ultra-marathon performance increased with increasing race duration, also when only one or at least five successful finishes were considered. The lowest age of peak ultra-marathon performance was in 6 h (33.7 years, 95% CI 32.5-34.9 years) and the highest in 48 h (46.8 years, 95% CI 46.1-47.5). With increasing number of finishes, the athletes improved performance. Across years, performance decreased, the age of peak performance increased, and the age of peak ultra-marathon performance increased with increasing number of finishes. In summary, the age of peak ultra-marathon performance increased and performance decreased in time-limited ultra-marathons. The age of peak ultra-marathon performance increased with increasing race duration and with increasing number of finishes. These athletes improved race performance with increasing number of finishes.

Relationship of anthropometric and training characteristics with race performance in endurance and ultra-endurance athletes

A variety of anthropometric and training characteristics have been identified as predictor variables for race performance in endurance and ultra-endurance athletes. Anthropometric characteristics such as skin-fold thicknesses, body fat, circumferences and length of limbs, body mass, body height, and body mass index were bi-variately related to race performance in endurance athletes such as swimmers in pools and in open water, in road and mountain bike cyclists, and in runners and triathletes over different distances. Additionally, training variables such as volume and speed were also bi-variately associated with race performance. Multi-variate regression analyses including anthropometric and training characteristics reduced the predictor variables mainly to body fat and speed during training units. Further multi-variate regression analyses including additionally the aspects of previous experience such as personal best times showed that mainly previous best time in shorter races were the most important predictors for ultra-endurance race times. Ultra-endurance athletes seemed to prepare differently for their races compared to endurance athletes where ultra-endurance athletes invested more time in training and completed more training kilometers at lower speed compared to endurance athletes. In conclusion, the most important predictor variables for ultra-endurance athletes were a fast personal best time in shorter races, a low body fat and a high speed during training units.

Runners in their forties dominate ultra-marathons from 50 to 3,100 miles

OBJECTIVES

This study investigated performance trends and the age of peak running speed in ultra-marathons from 50 to 3,100 miles.

METHODS

The running speed and age of the fastest competitors in 50-, 100-, 200-, 1,000- and 3,100-mile events held worldwide from 1971 to 2012 were analyzed using single- and multi-level regression analyses.

RESULTS

The number of events and competitors increased exponentially in 50- and 100-mile events. For the annual fastest runners, women improved in 50-mile events, but not men. In 100-mile events, both women and men improved their performance. In 1,000-mile events, men became slower. For the annual top ten runners, women improved in 50- and 100-mile events, whereas the performance of men remained unchanged in 50- and 3,100-mile events but improved in 100-mile events. The age of the annual fastest runners was approximately 35 years for both women and men in 50-mile events and approximately 35 years for women in 100-mile events. For men, the age of the annual fastest runners in 100-mile events was higher at 38 years. For the annual fastest runners of 1,000-mile events, the women were approximately 43 years of age, whereas for men, the age increased to 48 years of age. For the annual fastest runners of 3,100-mile events, the age in women decreased to 35 years and was approximately 39 years in men.

CONCLUSION

The running speed of the fastest competitors increased for both women and men in 100-mile events but only for women in 50-mile events. The age of peak running speed increased in men with increasing race distance to approximately 45 years in 1,000-mile events, whereas it decreased to approximately 39 years in 3,100-mile events. In women, the upper age of peak running speed increased to approximately 51 years in 3,100-mile events.

Will women outrun men in ultra-marathon road races from 50 km to 1,000 km?

It has been assumed that women would be able to outrun men in ultra-marathon running. The present study investigated the sex differences in running speed in ultra-marathons held worldwide from 50 km to 1,000 km. Changes in running speeds and the sex differences in running speeds in the annual fastest finishers in 50 km, 100 km, 200 km and 1,000 km events held worldwide from 1969–2012 were analysed using linear, non-linear and multi-level regression analyses. For the annual fastest and the annual ten fastest finishers, running speeds increased non-linearly in 50 km and 100 km, but not in 200 km and 1,000 km where running speeds remained unchanged for the annual fastest. The sex differences decreased non-linearly in 50 km and 100 km, but not in 200 and 1,000 km where the sex difference remained unchanged for the annual fastest. For the fastest women and men ever, the sex difference in running speed was lowest in 100 km (5.0%) and highest in 50 km (15.4%). For the ten fastest women and men ever, the sex difference was lowest in 100 km (10.0 ± 3.0%) and highest in 200 km (27.3 ± 5.7%). For both the fastest (r² = 0.003, p = 0.82) and the ten fastest finishers ever (r² = 0.34, p = 0.41) in 50 km, 100 km, 200 km and 1,000 km, we found no correlation between sex difference in performance and running speed. To summarize, the sex differences in running speeds decreased non-linearly in 50 km and 100 km but remained unchanged in 200 km and 1,000 km, and the sex differences in running speeds showed no change with increasing length of the race distance. These findings suggest that it is very unlikely that women will ever outrun men in ultra-marathons held from 50 km to 100 km.

Increase in finishers and improvement of performance of masters runners in the Marathon des Sables

Aim

The aim of the study was to examine finisher and performance trends of ultrarunners in the Marathon des Sables, the world’s largest multistage ultramarathon.

Methods

The age and running speed was analyzed for 6945 finishes of 909 women and 6036 men between 2003 and 2012 at the Marathon des Sables covering about 240 km in the Moroccan desert.

Results

The number of finishes increased significantly for both women and men from 2003–2012. The annual number of finishes increased in age groups: 30–34 years (r² = 0.50; P = 0.021), 45–49 years (r² = 0.81; P = 0.0004), and 50–54 years (r² = 0.46; P = 0.029) for women and in all age groups older than 35 years for men (35–39 years: r² = 0.64, P = 0.0054; 40–44 years: r² = 0.67, P = 0.0036; 45–49 years: r² = 0.77, P = 0.0007; 50–54 years: r² = 0.72, P = 0.0018; 55–59 years: r² = 0.42, P = 0.041; and 60–64 years: r² = 0.67, P = 0.0038). The fastest running speed was achieved by runners in the age group of 35–39 years for both sexes. The mean age of overall finishers was 41.0 ± 9.1 years for women and 41.3 ± 9.5 years for men. For men, running speed improved for athletes in the age group of 35–39 years (r² = 0.44; P = 0.036) and of 40–44 years (r² = 0.51; P = 0.019), while it decreased for athletes in the age group of 30–34 years (r² = 0.66, P = 0.0039). For women, running speed remained stable during the study period for athletes in all age groups.

Conclusion

These data suggest that the number of finishers of masters runners older than 40 years increased for both sexes at the Marathon des Sables, as has been previously observed for single-stage ultramarathons. In contrast to women, men aged 35 to 44 years improved running speed during the study period. Future studies are needed to investigate the reasons for the growing numbers of masters athletes in endurance sports and their improvement in performance.

Reduced performance difference between sexes in master mountain and city marathon running

Background

The performance in master marathoners has been investigated in flat city marathons but not in mountain marathons. This study examined changes in the sex differences in performance across time in female and male master runners competing in a mountain marathon compared to a flat city marathon.

Methods

The association between age and performance of finishers in the Jungfrau Marathon, Switzerland, with 1830 meter changes in altitude and a flat city marathon (Lausanne Marathon), Switzerland, were analyzed from 2000 to 2011.

Results

In both events, athletes in the 35–44 years age group showed the highest number of finishers. In the mountain marathon, the number of female master runners aged > 35 years increased in contrast to female finishers aged < 35 years, while the number of male finishers was unchanged in all age groups. In the city marathon, the number of female finishers was unchanged while the number of male finishers in the age groups for 25–34-year-olds and 35–44-year-olds decreased. In female marathoners, performance improved in athletes aged 35–44 and 55–64 years in the city marathon. Male marathoners improved race time in age group 45–54 years in both the city marathon and the mountain marathon. Female master runners reduced the sex difference in performance in the 45–54-year age group in both competitions and in the 35–44-year age group in the mountain marathon. The sex difference in performance decreased in the 35–44-year age group from 19.1% ± 4.7% to 16.6% ± 1.9% in the mountain marathon (r² = 0.39, P = 0.03). In age groups 45–54 years, the sex difference decreased from 23.4% ± 1.9% to 15.9% ± 6.1% in the mountain marathon (r² = 0.39, P < 0.01) and from 34.7% ± 4.6% to 11.8% ± 6.2% in the city marathon (r² = 0.39, P < 0.01).

Conclusion

These findings suggest that female master runners aged 35–54 years reduced sex differences in their performance in both mountain and city marathon running.