Abstract
derate endurance exercise leads to an improvement in cardiovascular performance, stress resilience, and blood function. However, the in uence of chronic endurance exercise over several hours or days is still largely unclear. We examined the in uence of a non-stop160.9/230 km ultramarathon on body composition, stress/cardiac response, and nutrition parameters. Blood samples were drawn before (pre) and after the race (post) and analyzed for ghrelin, insulin, irisin, glucagon, cortisol, kynurenine, neopterin, and total antioxidant capacity. Additional measurements included heart function by echocardiography, nutrition questionnaires, and body impedance analyses. Of the 28 included ultra-runners (7f/21m), 16 participants dropped out during the race.
ultramarathon on body composition, stress/cardiac response, and nutrition parameters. Blood samples were drawn before (pre) and after the race (post) and analyzed for ghrelin, insulin, irisin, glucagon, cortisol, kynurenine, neopterin, and total antioxidant capacity. Additional measurements included heart function by echocardiography, nutrition questionnaires, and body impedance analyses. Of the 28 included ultra-runners (7f/21m), 16 participants dropped out during the race. The remaining 12 nishers (2f/10m) showed depletion of antioxidative capacities and increased in ammation/stress (neopterin/cortisol), while energy metabolism (insulin/glucagon/ghrelin) remained unchanged despite a high negative energy balance. Free fat mass, protein, and mineral content decreased and echocardiography revealed a lower stroke volume, left end diastolic volume, and ejection fraction post race. Optimizing nutrition (high-density protein-rich diet) during the race may attenuate the observed catabolic and in ammatory effects induced by ultramarathon running. As a rapidly growing discipline, new strategies for health prevention and extensive monitoring are needed to optimize the athletes' performance. Keywords: ultramarathon; in ammation; body composition; heart function; exercise stress; en- durance exercise 1. Introduction Although humanity has evolutionarily developed to be a race of two-leg runners that can sustain running potentially for days [1], the impact of long distance runs on the human body is still largely unknown. An ultramarathon is a modern type of long- lasting competition and includes all distances above 42.195 km, the traditional marathon distance [2]. In recent years, this discipline has enjoyed growing popularity and has come into focus of exercise research [3]. Although regular endurance exercise at moderate intensity is accepted as natural medicine for longevity and health risks' reduction [4,5], it remains unknown whether an ultramarathonwhich usually takes at least 6 h from start to nishand its high training volumes may present health risks [6,7]. Strenuous long-term endurance exercise in training and/or competition induces changes in metabolic demands and may cause long-lasting disturbances in the individual Metabolites2022,12, 1138.
Metabolites2022,12, 1138 2 of 14 hormonal pro le [8]. These disturbances can contribute to an increased risk of cardiovascu- lar, respiratory, musculoskeletal, renal, immunological, gastrointestinal, or neurological injuries [8,9]. Even the heart seems to be transiently affected, with up-to-now no de nite conclusions on the immediate health outcomes [10,11]. Further risk factors, depending on the environmental conditions, include uid/electrolyte disturbances, central nervous sys- tem, and gastrointestinal system problems, as well as dehydration and exercise-associated muscle disturbances [12]. Besides the obvious orthopaedical demands of an ultramarathon, including muscu- loskeletal injuries [13], the stress-induced damages on the molecular and cellular level are equally challenging to the athletes' health and regeneration. In ammatory processes can be observed after a competition with an increase in pro-in ammatory cytokines [14] and oxidative stress [15], which can last for several days or months, along with reduced anti-oxidative capacity [16]. Even the gut microbiota seems to be negatively affected after an ultramarathon competition, which may compromise the athletes' immune func- tion [17]. The assessment of the stress response on the molecular level is thus of the utmost importance to reduce the health risk during participation in ultramarathons, but is still un- derrepresented in current studies. Monitoring the depletion of anti-oxidative capacities, the changes in stress markers like cortisol or the measurement of immunological risk markers like neopterin or kynurenine may improve the present gaps in molecular adaptations. A further consideration for ultramarathon performance capacity is the balance of energy expenditure and consumption. Studies revealed a runner's energy de cit of about 7000 kcal(or 30 MJ) per day, despite a high amount of food and (energized) water in- take[18,19] . This de cit might subsequently put health at risk and increase the risk of injury; especially in multistage races, e.g., through a continuous catabolic state [6,9,20]. Nutritional strategies during the race might vary and depend on personal experience rather than of cial recommendations, partly owing to limited scienti c evidence or in- suf cient transfer from the scienti c into the broad running community [9]. Knowing that gastrointestinal symptoms are one of the most common problems experienced dur-
races, e.g., through a continuous catabolic state [6,9,20]. Nutritional strategies during the race might vary and depend on personal experience rather than of cial recommendations, partly owing to limited scienti c evidence or in- suf cient transfer from the scienti c into the broad running community [9]. Knowing that gastrointestinal symptoms are one of the most common problems experienced dur- ing ultramarathon races [6,18], optimization of nutritional strategies for energy refueling with concomitant maintaining good gastric tolerance and high nutrient density intake is a challenging task. Furthermore, hunger signaling is reduced during and shortly after en- durance exercise, which can intensify reduced energy intake [21,22], and studies examining energy related hormonal responses or e.g., gluconeogenesis, during ultra-endurance events are missing. Despite that, studies suggest that especially well-trained competitors may compensate for the long-lasting physiological load and stress through an improved cardiovascular system [23,24], cognitive abilities [25], and lipid pro le [26]. These seemingly contradicting observations of the health risks and potential bene ts of ultramarathon training and com- petition require investigations regarding the impact of long-lasting exercise characterized by relatively low intensities. The TorTour de Ruhr ® , which is the focus of the present investigation, is a non-stop ultramarathon that takes place every two years in Germany and includes the distances 100 km, 160.9 km, and 230 km. The start nish of the 230/160.9 km race is Winterberg Duisburg and ArnsbergDuisburg, respectively. It is one of the most physiologically and psychologically demanding runs in Germany that requires optimal performance capacities. This study aimed to assess body composition, antioxidant status and cardiac/metabolic stress, and energy parameters before (pre) and after the race (post) to gain new insights into the effects of this extreme endurance race regarding molecular mechanisms and interactions that might help to improve performance capacity and to protect the health of the participants.
Metabolites2022,12, 1138 3 of 14 2. Materials and Methods 2.1. Entry Eligibility TorTour de Ruhr ® participation required a medical sports examination that had been conducted less than 6 months before the race and that con rmed the physical resilience of the athlete. Inclusion criteria for the study participation were as follows: male/female; endurance athlete and 160.9 km or 230 km participant of the TorTour de Ruhr ® 2022; no previous injuries; and the ability to understand the study procedure and to give informed consent. Exclusion criteria included the following: participants of the 100 km (owing to or- ganizational restrictions); nicotine consumption; diseases of the intestine; blood clotting disorders or intake of blood-thinning medications; acute or chronic vascular (blood ow) disorders; cardiovascular, metabolic, or autoimmune diseases; and non-consenting subjects. All subjects received information about the study content and the use of the data and provided written consent. The study was conducted in compliance with the Declaration of Helsinki. The study was approved by the ethics committee of German Sports University Cologne (25/2020). All measures included pre and post assessments. Pre-race measurements were carried out the evening (230 km) or two hours (160.9) before the start at the organizer's race brie ng to determine basal resting values, while post-race measurements were performed immediately at the nish line at the nishers' arrival (Supplemental File S1, Figure S1). The study team was divided into groups taking (i) body composition, (ii) echocardiographic and (iii) laboratory data, and (iv) blood sampling to minimize examination time pre and post. Blood sampling as well as echocardiography was performed by the study physician with an MD degree in internal medicine and sports medicine as well as a special expertise in cardiology. Blood samples were taken pre and post from thevena mediana cubitiand anticoagulated using ethylenediaminetetraacetic acid (EDTA) as anticoagulant. Blood samples were stored at 4 C until transportation to the analysis facility. All samples were transported in a time frame of a maximum of four hours and either measured immediately in the laboratory or stored at 80 C until further analysis. 2.2. Anthropometry and Body Composition Anthropometric measurements
from thevena mediana cubitiand anticoagulated using ethylenediaminetetraacetic acid (EDTA) as anticoagulant. Blood samples were stored at 4 C until transportation to the analysis facility. All samples were transported in a time frame of a maximum of four hours and either measured immediately in the laboratory or stored at 80 C until further analysis. 2.2. Anthropometry and Body Composition Anthropometric measurements included height, body mass, and body composition. Height was measured without shoes, in light clothing, with a standardized scale. For measuring body mass and body composition, a bio-impedance scale (InBody 770, InBody Europe B.V., Eschborn, Germany) was used. 2.3. Study Group Characteristics In total, 28 ultramarathon runners (7f/21m) were initially included. Participants had already completed 62 ultramarathons on average. Twelve participants dropped out during the race. From the remaining 16 nishers (4f/12m), 4 did not nish within the de ned period (cut-off time: 38 h/230 km and 30 h/160.9 km). Data of a total of 12 (2f/10m) participants were nally analyzed. Detailed anthropometric characteristics of the runners are presented in Table.
Metabolites2022,12, 1138 4 of 14 Table 1. Anthropometric data, nish time, and nutrition details of the study participants. All data are presented as mean standard deviation. Anthropometry Age [years] Height [cm] Body Mass [kg] Body Mass Index [kg/m 2 ] 160.9 km 230 km 160.9 km 230 km 160.9 km 230 km 160.9 km 230 km 50.20 (7.98) 50.14 (9.74) 174.80 (4.62) 177.43 (8.74) 71.14 (4.18) 71.89 (11.30) 23.30 (1.29) 22.79 (2.93) Finish time [hours] 160.9 km 22.56 (3.31) 230 km 32.28 (2.95) Race Nutrition Energy expenditure [kcal] Energy intake [kcal] Macronutrient distribution (%) 160.9 km 230 km 160.9 km 230 km Carbohydrates Fat Protein Alcohol 11,203.78 (1793.33) 15,977.52 (2351.22) 5379.10 (1460.69) 6695.85 (3748.86) 64.6 (17.5) 24.4 (16.4) 8.8 (4.1) 2.2 (2.4) 2.4. Environmental Conditions Blood sampling of the 230 km race was scheduled for 3 June 2022, between 6 and 8 p.m., while blood sampling of the 160.9 km race was scheduled for 4 June 2022, between 4 and 5:30 p.m. The start of the 230 km race was scheduled for 4 June 2022, at 8 a.m., at 13.8 C with a humidity of 71.6% and 14.4 km/h west-wind. The start of the 160.9 km race was scheduled for 4 June 2022, at 6 p.m.; the temperature was 23.1 C, humidity 50%, and wind 7.2 km/h south-west. The nish was 5 June between 10:30 a.m. and 8:00 p.m.; the mean temperature during participants' arrival was 21 C, with a humidity of 83%, occasional rain, and 18 km/h south-west wind. 2.5. Energy Intake and Expenditure Participants received a questionnaire and were asked to note the time of food in- take and a detailed description of the respective meal/component. Brie y, the amount of food/snack (in grams/household measurements, i.e., one handful, one bowl, cup, mug, and so on), the fat content for dairy products, the respective brand, all beverages (quantity and brand of beverage, if applicable), and the mixing ratio for homemade drinks should be indicated. Documentation was performed by the mandatory athletes' team crew or retrospectively by the athlete himself/herself. In addition, the questionnaire included questions regarding gender, age, number
one bowl, cup, mug, and so on), the fat content for dairy products, the respective brand, all beverages (quantity and brand of beverage, if applicable), and the mixing ratio for homemade drinks should be indicated. Documentation was performed by the mandatory athletes' team crew or retrospectively by the athlete himself/herself. In addition, the questionnaire included questions regarding gender, age, number of ultramarathons, type/amount/number of supplements, specialized nutritional habits (e.g., vegan/vegetarian), experienced gastroin- testinal symptoms related to an ultramarathon, and products that are repeatedly/always used for food during ultramarathons. For evaluation of the total energy intake and the respective contribution of pro- tein/fat/carbohydrates, EBIS Pro software (EBIS Pro 3.02,, accessed on 1 November 2022) was used. The energy expenditure calculation was based on the heart frequency determined by the respective running wearable (Garmin International, Olathe, KS, USA) during the race and the respective energy expenditure data on the Garmin Connect online platform. 2.6. Energy Metabolism and Stress Response Ghrelin (#BMS2192, Thermo Fisher, MA, USA), insulin (ab100578, Abcam, Berlin, Ger- many), irisin (EK-067-29, Phoenix Pharmaceuticals, Burlingame, CA, USA), glucagon (#EHGCG, Thermo Fisher, MA, USA), cortisol (#15642299, Fisher Scienti c, Reinach,
Metabolites2022,12, 1138 5 of 14 Switzerland), neopterin (#RE59321, IBL International, Hamburg, Germany), and total antioxidative capacity (TAC, measurement of the combination of both small molecule antioxidants and proteins by Cu 2+ reduction) (ab65329 Abcam, Berlin, Germany) were analyzed in the plasma fraction using respective enzyme-linked immunosorbent assays (ELISA) according to the manufactures' instruction. Plasma kynurenine concentrations were determined by spectrometry using an estab- lished protocol [27]. Brie y, serum samples were deproteinized with acetic acid trichloride and following deproteinization. Kynurenine reacts under the use of 4-dimethylamino- benzaldehyde into a yellow product, which can be measured spectrometrically at 492 nm and quanti ed with concomitant preparation of a stand with known concentrations. Tissue glucose was measured with an intracutaneous glucose sensor (Free style libre 3, analyzed by App-based program LibreView, Abbot Diabetes Care, Alameda, CA, USA). 2.7. Echocardiography Echocardiographic examinations were performed using an CX50 ultrasound system with a phased-array probe S5-1 (Philips GmbH, Hamburg, Germany). The images were analyzed of ine using TomTec post-processing software (2D Cardiac Performance Analysis, TomTec Imaging Systems, Unterschleissheim, Germany). Measurements were made in the apical four-, three-, and two-chamber views and in the parasternal short and long axes. The following parameters were collected: left ventricular ejection fraction (EF by biplane LV planimetry by the modi ed Simpson's rule and EF two-dimensional by Teichholz), end-diastolic volume (EDV), end-systolic volume (ESV), and stroke volume (SV). The wall thicknesses of the interventricular septum and left ventricular free wall in diastole (IVSd/LPWd) and systole (IVSs/LPWs) as well as the left ventricular diameter in diastole (LVIDd) and systole (LVIDs) and left atrial size (in mm) and diameter of the aortic root were determined. Diastolic function was characterized by maximum velocities of E and A waves (VmaxE, VmaxA), E/A ratio, E/E'lateral ratio (VmaxE and maximum myocardial velocities (E'lateral) of the lateral mitral annulus), E/E'medial ratio (VmaxE and maximum myocardial velocities (E'medial) of the basal mitral annulus), and deceleration time (dec time). Right ventricular diameter diastolic (RVIDd), right ventricular function (tricuspid annular plane systolic excursion (TAPSE), and maximum velocity across the tricuspid valve (TVmax) and maximum pressure gradient (TVmax PG) were determined.
ratio (VmaxE and maximum myocardial velocities (E'lateral) of the lateral mitral annulus), E/E'medial ratio (VmaxE and maximum myocardial velocities (E'medial) of the basal mitral annulus), and deceleration time (dec time). Right ventricular diameter diastolic (RVIDd), right ventricular function (tricuspid annular plane systolic excursion (TAPSE), and maximum velocity across the tricuspid valve (TVmax) and maximum pressure gradient (TVmax PG) were determined. The endocardial contour was manually adjusted. Images that did not capture all myocardial segments suf ciently well and participants from whom only one examination (pre or post) was available were excluded from the analysis. To avoid interrater variability, the echocardiographic analysis was performed by one investigator. 2.8. Statistics Data analysis was performed using GraphPad Prism (GraphPad Prism 9.4; San Diego, CA, USA). To improve data analysis and statistical power, data of both 160.9 and230 km participants were analyzed in one dataset. Subgroup analysis was performed for descrip- tive statistics (anthropometry, energy expenditure/intake, and nish time). All data were tested on Gaussian distribution using the KolmogorovSmirnov normality test. One-tailed t-testing was used comparing pre and post for all normally distributed data. Otherwise, a Wilcoxon matched-pairs signed rank test was used to determine the statistical signi - cance of differences between pre and post. If not other stated, all data are presented as mean standard deviation. Statistical signi cance was established atp 0.05. 3. Results 3.1. Race Nutrition In this study, 36% of the participants reported the regular intake of dietary supple- ments (mainly micronutrients/vitamins, sports refresher, or protein powder), 46% did consume special supplements, and 18% provided no information. Mean heart frequency during the race was 120 10 beats per minute. Mean energy de cit was calculated to
Metabolites2022,12, 1138 6 of 14 be around 6000 kcal (160.9 km) and 9000 kcal (230 km), respectively. The detailed energy expenditure and food intake as well as the macronutrient distribution are presented in Table. Energy-providing components of the diet during the race consisted of 8.8 4.1% protein, 24.4 16.4% fat, 64.6 17.5% carbohydrates, and 2.2 2.4% alcohol. 3.2. Body Composition Body fat mass (p= 0.0685), free fat mass (p= 0.0277), and skeletal muscle mass (p= 0.0678) were reduced post-race. While there was only a trend for decreased total protein mass post-race (p= 0.0739), mineral content was signi cantly lower after the race (p= 0.0425) (FigureAE).Metabolites 2022, 12, x FOR PEER REVIEW 6 of 14 Table 1. Energy-providing components of the diet during the race consisted of 8.8 ± 4.1% protein, 24.4 ± 16.4% fat, 64.6 ± 17.5% carbohydrates, and 2.2 ± 2.4% alcohol. 3.2. Body Composition Body fat mass (p = 0.0685), free fat mass (p = 0.0277), and skeletal muscle mass (p = 0.0678) were reduced post-race. While there was only a trend for decreased total protein mass post-race (p = 0.0739), mineral content was significantly lower after the race (p = 0.0425) (Figure 1A–E). Pre Post 0 5 10 15 20 25 30 Body fat mass [kg] 0.0685 (A) Pre Post 0 10 40 50 60 70 80 Free Fat Mass [kg] ✱ (B) Pre Post 0 10 25 30 35 40 45 Skeletal Muscle Mass [kg] 0.0678 (C) Pre Post 0 1 8 9 10 11 12 13 14 15 Protein mass ]kg] 0.0739(D) Pre Post 0.0 1.0 3.0 3.5 4.0 4.5 5.0 Minerals [kg] ✱ (E) 59.7760.81 11.8812.02 4.0974.190 10.8111.66 33.8034.25 Figure 1. Body composition variables related to tissue distribution and micronutrients of the 160.9/230 km finishers before (pre) and after (post) the race (full dataset obtained for n = 9). The decrease in (A) body fat mass, (C) skeletal muscle mass, and (D) protein mass was on the verge of statistical significance. (B) Free fat mass and (E) mineral content were significantly decreased post- race. Individual changes as well as mean number values are presented.
finishers before (pre) and after (post) the race (full dataset obtained for n = 9). The decrease in (A) body fat mass, (C) skeletal muscle mass, and (D) protein mass was on the verge of statistical significance. (B) Free fat mass and (E) mineral content were significantly decreased post- race. Individual changes as well as mean number values are presented. * p ≤ 0.05. 3.3. Energy Metabolism Energy-related and nutrition-dependent variables insulin (p = 0.1433), glucagon (p = 0.0549), and ghrelin (p = 0.2983) were not significantly different from pre to post, but showed highly individual variability. Irisin decreased post-race (p = 0.0324) (Figure 2A– D). Despite infrequent and various energy intake, tissue glucose concentrations deter- mined in a subgroup of participants (n = 4) never reached values below (70 mg/dL) or above (180 mg/dL) reference values (Supplementary File S1, Figure S2). 3.4. Stress Response Figure 1. Body composition variables related to tissue distribution and micronutrients of the 160.9/230 km nishers before (pre) and after (post) the race (full dataset obtained forn= 9). The decrease in (A) body fat mass, (C) skeletal muscle mass, and (D) protein mass was on the verge of statistical signi cance. (B) Free fat mass and (E) mineral content were signi cantly decreased post-race. Individual changes as well as mean number values are presented. *p 0.05. 3.3. Energy Metabolism Energy-related and nutrition-dependent variables insulin (p= 0.1433), glucagon (p= 0.0549), and ghrelin (p= 0.2983) were not signi cantly different from pre to post, but showed highly individual variability. Irisin decreased post-race (p= 0.0324) (FigureAD). Despite infrequent and various energy intake, tissue glucose concentrations deter- mined in a subgroup of participants (n= 4) never reached values below (70 mg/dL) or above (180 mg/dL) reference values (Supplementary File S1, Figure S2).
Description
This study examines the metabolic effects of a non-stop ultramarathon on body composition and inflammation.