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EPIDEMIOLOGY

Performance Trends and Cardiac Biomarkers in a 30-km Cross-Country Race, 1993–2007

AAGAARD, PHILIP; SAHLÉN, ANDERS; BRAUNSCHWEIG, FRIEDER

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Medicine & Science in Sports & Exercise: May 2012 - Volume 44 - Issue 5 - p 894-899
doi: 10.1249/MSS.0b013e31823cd051
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Abstract

Long-distance running has increased in popularity among individuals of all ages, probably resulting from an increased awareness of the multiple health benefits associated with regular exercise (21). Today, participation in long-distance races forms part of an athletic lifestyle for millions of individuals around the world, and a growing number of long-distance races are held every year. In 2007, the 10 largest marathon races alone had more than 250,000 finishers (1).

Regular moderate exercise is recommended to improve health (7). Moreover, endurance race participants live longer compared with the general population (24), which may be due to regular physical training, lifestyle, genetics, or selection bias (6). Conversely, endurance events are associated with a momentary increase of medical complications (11) including trauma, hypovolemic collapse, hypoglycemia, and, in the worst case, myocardial infarction and sudden cardiac death (14,15).

To reduce the risk of adverse outcomes during sports, participants should be well prepared in terms of appropriate physical fitness (3), which has been shown to predict cardiac events and survival in healthy individuals (9). Furthermore, possible underlying cardiovascular conditions should be identified beforehand to enable appropriate treatment and exercise prescription (3,12). In the context of endurance sports, as in the general population, the risk for cardiovascular events increases with age (25) and male gender (27).

The objective of the first part of this study was to test our hypothesis that the increase in race participants during recent years is associated with changes in average fitness, as measured by run time. As a model, we chose the 30-km Lidingöloppet, the world’s largest cross-country race, held annually in Sweden. The second part of the study assessed possible associations between fitness, as measured by run time, and markers of cardiovascular risk in a large group of middle-aged and senior male race participants.

METHODS

The Lidingöloppet is held annually on the last weekend of September in the hilly terrain on the island of Lidingö, outside Stockholm. We analyzed an electronic database (www.lidingoloppet.se) containing an individual race identification number, the date of birth, the gender, and the run time of all runners participating in the Lidingöloppet 30 km between 1993 and 2007. During this period, the racetrack remained unchanged. For 2003–2007, the database also provided the number of previously completed races in the Lidingöloppet for any given runner (since 1965). Run time was automatically measured using a radiofrequency identification chip system (Neptron AB, Danderyd, Sweden).

To investigate longitudinal trends in performance, runners were separated into groups on the basis of gender (M = male and F = female) and age (20 was used for runners age 20–29 yr, 30 was used for runners age 30–39 yr, 40 was used for runners age 40–49 yr, 50 was used for runners age 50–59 yr, and 60 was used for runners age >60 yr). Thus, M20 comprises all male runners age 20–29 yr and so on.

These groups were analyzed for absolute and relative changes in participation as well as for changes in mean, median, top and bottom quartile, and 5th and 95th percentile run times. In addition, the top 50 runners of each male age group were analyzed. These analyses were not performed in F60 because there were few (<50) finishers in this female age group during the first years of the study.

In the 2003, 2006, 2008, and 2010 races, male registered runners aged 45 or above were asked to participate in a biochemical study. Height and body weight (Exclusive; EKS International, Wittisheim, France) were measured before and after the race, and body mass index (BMI = weight in kilograms / [height in meters]2) was calculated.

Blood specimens for hemoglobin, creatinine, high-sensitivity C-reactive protein (hsCRP), N-terminal pro–brain natriuretic peptide (NT-proBNP) and troponin T (TnT) (both Elecsys®; Roche Diagnostics, Bromma, Sweden) were taken 1–2 h before the race and within 45 min after the race, kept on ice, and centrifuged within 4 h. In a subset of runners, blood samples were also taken 24 h after the race. NT-proBNP >194 ng·L−1 and TnT ≥0.03 μg·L−1 were considered abnormal, as per the manufacturer’s advice. The detection threshold for this TnT assay was 0.01 μg·L−1.

The study complied with the Declaration of Helsinki and was approved by the regional ethical review board. Participants in the biochemical study provided written informed consent.

The data are presented as mean ± SD except for TnT, which is given as median (interquartile range (IQR)). All statistical analyses were performed using PASW Statistics 18 (SPSS, Inc., Chicago, IL). Normality was analyzed by the Kolmogorov–Smirnov test. Correlations were tested with Pearson r or Spearman ρ (TnT). The independent t-test or Mann–Whitney U test (TnT) was used to analyze differences between groups.

Multiple linear regression analysis was used to examine predictors of run time. Independent variables included were those that correlated significantly with run time in the univariate analysis (except hsCRP at day 1 because of the small sample size). Interactions were evaluated with the forced entry method. To allow for comparison between independent variables, the coefficients are presented as standardized β weight. Data from repeated measures were analyzed using the Friedman ANOVA. A two-tailed P value < 0.05 was considered statistically significant.

RESULTS

Between 1993 and 2007, a total of 149,104 subjects started in the Lidingöloppet, and 126,370 (84.7%) finished the race. Of these, 1762 (1.4%) were excluded from analysis because of lack of information about their age. Thus, 124,608 runners (108,649 males and 15,959 females) with complete data were included. No subgroup analysis was performed in F60 because of the small sample size (n = 158).

From 1993 to 2007, race participation increased by 56% with a 1076% rise in female (from 2.7% to 19.8% of participants) and a 29% rise in male participation. Numbers increased in all male age groups except in M20, with the largest percent increase observed in M60 (271%). Contrary to men, participation among younger females (F20) increased the most. As a result, mean age increased from 36.4 ± 9.9 to 38.2 ± 9.9 yr in males (P < 0.001) but decreased from 37.4 ± 10.3 to 36.0 ± 9.5 yr in females (P < 0.001).

After randomly selecting one single participation for each athlete starting between 2003 and 2007 (n = 28,385, 22,205 male and 6180 female runners), there was an even split between first-time runners (48.4%) and those with at least one previous participation in the Lidingöloppet (51.6%). Males and females had previously participated 2.3 ± 3.3 and 1.4 ± 2.0 times, respectively (P < 0.001).

Overall, men and women finished the race after 177 ± 32 and 200 ± 32 min, respectively (P < 0.001). Notably, from 1993 to 2007, mean run time gradually increased after a strong linear relationship. In men, the overall increase was 20 min (12%) with an average time increase of 98.8 s·yr−1 (r = 0.98), whereas female run times increased 23 min (13%) or 84.4 s·yr−1 (r = 0.93). Deteriorating performance was seen in all age groups (Figs. 1A, B). Moreover, run times also increased in the 5th, 25th, median, 75th, and 95th percentiles in all age groups of both genders. For example, in M60, run times increased for the mean (12%), top quartile (10%), and fifth percentile (5%). Analyzing only the top 50 male runners in each age group, run time increased in M20 to M50 but decreased in M60 (P for trend <0.01 for all).

F1-17
FIGURE 1:
Performance as measured by mean run time in different male (A) and female (B) age groups in the Lidingöloppet 30-km cross-country race, 1993–2007. For definition of age groups, see “Methods” section.

In a pooled analysis of all runners from 1993 to 2007 (only one participation per individual, n = 64,456, 54,311 males and 10,145 females), run times in both genders remained stable from age 20 to age 47 (average annual increase of 0.1% in male and 0.0% in female runners). After age 47, run times increased with age in both genders but at a slower rate in female runners.

Biochemistry and BMI were analyzed in 249 subjects (n = 96 in 2003, n = 36 in 2006, n = 26 in 2008, and n = 91 in 2010). Baseline characteristics are shown in Table 1. After the race, there was a significant increase in levels of NT-proBNP from 70 ± 154 to 215 ± 323 ng·L−1 (P < 0.001), and TnT increased from ≤0.01 (IQR = ≤0.01–0.03) to 0.03 μg·L−1 (IQR = ≤0.01–0.08, P < 0.001). An inflammatory response (hsCRP) was not seen until day 1 after the race (1.2 ± 1.5 vs 10.3 ± 5.6 μmol·L−1, P < 0.001) and correlated with baseline levels of hsCRP (r = 0.66, P < 0.001). HsCRP on day 1 after the race also correlated with run time (r = 0.32, P < 0.05) but not with other variables such as age, BMI, or number of previous races.

T1-17
TABLE 1:
Baseline characteristics.

Similarly, a race-induced fall in hemoglobin was not evident until day 1 after the race (146 ± 9 vs 137 ± 8 g·dL−1, P < 0.01). Creatinine increased immediately after the race (85 ± 11 vs 123 ± 23 μmol·L−1, P < 0.001) but normalized on day 1.

Multiple linear regression analysis, including variables that correlated significantly with run time in the univariate analysis (BMI, age, NT-proBNP, and previous number of races), was used to examine associations with run time (Table 2). BMI (β = 0.406) was the strongest independent predictor of performance followed by age (β = 0.400), previous race participation (β = −0.199), and prerace level of NT-proBNP (β = 0.105).

T2-17
TABLE 2:
Predictors of run time.

DISCUSSION

We analyzed performance characteristics of more than 120,000 runners in a 30-km cross-country race during a period of 15 yr. Whereas annual participation increased, with the largest percentage increase in female and elderly male runners, mean run times gradually deteriorated in all age and gender groups. In a subset of male runners age ≥45 yr, run time independently correlated with not only BMI, age, and fewer previous race participations but, notably, also with baseline values of the heart failure marker NT-proBNP. We did not study biomarkers in female participants, and therefore, a possible association between biomarkers and run time could not be assessed in female participants. Taken together, however, the aforementioned findings provide a picture of a growing number of participants less well prepared for the biophysical challenge of long-distance running.

The increasing popularity, in particular among older male athletes, and shifting gender distribution are consistent with observations from marathon races (8,10). Data available during the last 5 yr of our study show that first-time and repeat participants evenly contributed to the increasing number of race participants.

Run time correlates well with other performance measures such as V˙O2max and speed at lactate threshold and is a good predictor of fitness level (2). In our study, performance deteriorated in all analyzed run time percentiles, including the top 5%, suggesting that today’s runners are generally less well trained compared with those starting 15 yr ago. Although not documented in this study, lifestyle changes (sedentary behavior, increasing body weight) may also have contributed to increasing run times. In fact, in our subanalysis of 249 male runners, BMI was the strongest independent predictor of running time.

Run times only improved in the top 50 runners of men age ≥60 yr likely reflecting the previously unexplored potential and growing competition in this former “minority group.” However, our data clearly show that improved performance in the top older runners, as also described by Jokl et al. (8) in participants of the New York Marathon 1983–1999, is not representative for the expanding group of race participants as a whole.

Average run time was preserved in both genders until age 47, followed by an accelerated decline, which may largely reflect that oxygen uptake at the anaerobic threshold and V˙O2max can be held at top levels only up to the age of 45–49 yr in both sexes (4,10,22).

Interestingly, gender differences in race time decreased with increasing age, pointing toward a smaller age-related decline in V˙O2max in females compared with males (28), but may also indicate that older women still have a higher threshold to participate and only register for the race if they are really well prepared.

Because older male athletes are of particular interest in the context of endurance exercise and risk for cardiovascular events (27), we performed a biochemical substudy in 249 male runners age 45 or above. Notably, increased run time was independently associated with higher prerace levels of the cardiac biomarker NT-proBNP, whereas other biochemical baseline variables did not correlate with run time. In agreement with a previous study (20), we found markedly elevated levels of the cardiac biomarkers NT-proBNP and TnT after the race.

Clinically, NT-proBNP is used as a marker of cardiac dysfunction and is a strong predictor of cardiovascular events, even at levels well below the diagnostic threshold for heart failure (26). Our group has previously shown that the level of NT-proBNP present before the race strongly predicts its increase during the race (18), suggesting that differences in baseline values reflect how well the heart is able to cope with the stress of endurance running. Furthermore, baseline NT-proBNP correlates with postrace echocardiographic findings of cardiac fatigue (18) and exercise-related electrophysiological changes of ventricular repolarization (17) and may assist in identifying runners with severe underlying cardiac pathology (16).

We also measured postrace levels of TnT, a marker used clinically to diagnose myocardial infarction. Unlike NT-proBNP, TnT elevation, typically observed during a small time window after endurance exercise (13), was not related to run time. Previous studies have not been able to explain the phenomenon of postexertional TnT elevation, although it has been speculated that it may originate from the cytosol of the cardiac myocyte rather than from damaged contractile elements (19).

Further biochemical changes after the race included a fall in hemoglobin, which was masked by dehydration immediately after the race but appeared on day 1 after the race after rehydration. Falling hemoglobin after endurance running has been attributed to intravascular mechanical destruction (23). The inflammatory marker hsCRP also showed a delayed response to endurance exercise on day 1 after the race that was associated with run time, suggesting that different levels of fitness and exercise exposure may play a role in the degree of postrace inflammation.

Our findings of an increasing number of runners with gradually decreasing fitness and the associations observed with NT-proBNP levels seem unfortunate given the risk for exercise-related cardiovascular events, particularly among older male athletes (25). Therefore, we believe our data emphasize the importance of ensuring appropriate cardiovascular fitness and cardiovascular health (12) and support the screening recommendations of a recent European expert consensus (3). However, it is important to note that the overall risk of endurance race participation is very low, supported by the fact that there were no fatal events reported in the Lidingöloppet 30 km during the time of this study. Still, better knowledge about the recommended dose of exercise, particularly in advanced age, is warranted to ensure that sports are performed in the most beneficial and safe fashion.

We acknowledge several limitations of our study. First, our study is limited by the small number of baseline variables available from the database. Second, when analyzing run times, we have not accounted for weather, a factor that can affect race performance (5). However, because the race has been held on the same weekend and at the same location every year of the study, we believe that weather factors are of limited importance in explaining run time changes. Finally, the study is limited by only having analyzed associations between biomarkers and run time in male runners. Future studies should investigate a possible relationship between biomarkers and run time in female athletes.

CONCLUSIONS

Although there is overwhelming evidence for the general health benefits of regular moderate exercise, more research is warranted to better understand the net health effects associated with participation in endurance events, especially in older male runners who carry a higher cardiovascular risk. In the present study, increased race participation was accompanied by a gradually decreasing level of fitness among participants in all age and gender groups during the 15-yr study period. Moreover, in a group of male runners age ≥45 yr, poorer race performance correlated with higher baseline levels of the cardiac biomarker NT-proBNP. These findings support the usefulness of preparticipation evaluations to ensure appropriate fitness and cardiovascular health among endurance race participants.

This work was supported by grants from the Swedish Heart-Lung Foundation, Stockholm; the Folksam Foundation, Stockholm; and the Swedish Centre for Sports Research, Stockholm.

The authors have no conflict of interest to declare.

The results of the present study do not constitute endorsement by the American College of Sports Medicine.

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Keywords:

SUDDEN CARDIAC DEATH; EXERCISE; MASTER ATHLETES; AGING; NT-PROBNP; TROPONIN

©2012The American College of Sports Medicine