Abstract
und: Ultramarathon running represents an extreme physiological and metabolic challenge. Despite its growing popularity among recreational and competitive runners, evidence-based guidance for nutrition, energy balance, and recovery remains limited. Un- derstanding metabolic response and hormonal regulation during such events is crucial for improving athletes’ health and performance. Methods: This prospective observational study examined participants of the 2024 TorTour de Ruhr ® (100 km, 160.9 km, and 230 km). Pre- and post-race assessments included body composition, energy
and competitive runners, evidence-based guidance for nutrition, energy balance, and recovery remains limited. Un- derstanding metabolic response and hormonal regulation during such events is crucial for improving athletes’ health and performance. Methods: This prospective observational study examined participants of the 2024 TorTour de Ruhr ® (100 km, 160.9 km, and 230 km). Pre- and post-race assessments included body composition, energy intake and expenditure, metabolic and hormonal biomarkers (leptin, ghrelin, insulin, glucagon, irisin, creatine kinase muscle type (CKM), lactate dehydrogenase (LDH)), and continuous glucose moni- toring (CGM). Blood and saliva samples, bioimpedance analysis, and validated symptom questionnaires (General Assessment of Side Effects (GASE)) were used. Results: Of the 43 ultra runners(16 women, 27 men), 39 finished the race: 19 participants of the 100 km group, 8 of the 160.9 km group, and 16 of the 230 km group. Mean energy deficit was 6797 kcal (range: 417–18,364 kcal) with carbohydrate-dominant fueling (79%). Significant reductions in leptin and insulin and increases in ghrelin, glucagon, CKM, and LDH were observed, indicating disrupted energy homeostasis and muscle damage. The 230 km subgroup showed the greatest changes. Gastrointestinal and musculoskeletal symptoms increased post-race, aligning with biomarker patterns. Conclusions: Ultramarathon par- ticipation induces profound disturbances in metabolic and structural integrity, regardless of race distance. These findings underline the importance of developing individualized nutritional and recovery strategies and highlight the need for future research to investigate how energy deficit and macronutrient composition interact to influence metabolic strain and post-race recovery. Keywords:ultramarathon; sport nutrition; energy metabolism; muscular damage Nutrients2025,17, 3801 https://doi.org/10.3390/nu17233801
Nutrients2025,17, 3801 2 of 20 1. Introduction Ultra-endurance sports, including non-stop ultramarathons, have seen a substantial rise in popularity over the last decade, with thousands of athletes now competing in races that exceed the conventional marathon distance of 42.195 km [1]. These events, often ranging from 50 km to over 230 km and lasting more than 24 h, impose extreme physiological demands, challenging the limits of energy availability, muscle integrity, and metabolic and immune function [2]. Although ultramarathon runners are typically well-trained, numerous studies have shown that these events are associated with significant energy deficits, muscle catabolism, and systemic inflammatory responses, particularly when effective nutritional strategies are lacking [2,3]. During prolonged exertion, energy expenditure far exceeds the amount of energy that can realistically be consumed, resulting in sustained negative energy bal- ance [4]. As such, nutritional planning must go beyond in-race fueling to include pre-race carbohydrate loading, strategic protein timing, and post-race replenishment protocols. Still, evidence-based guidelines for macronutrient composition, optimal intake timing, and in-race tolerability remain lacking for ultra-endurance athletes [5,6]. This leads to inadequate and inconsistent nutrition strategies, resulting in substantial metabolic and inflammatory burden on the body in those athletes [7]. If frequently repeated over time, such large energy deficits may impair endocrine function, compromise bone health, and delay post-race recovery, potentially increasing the risk for long-term performance decline and injury [8]. Despite a growing body of literature, most existing studies focus on short-term out- comes, such as performance metrics or acute changes in body weight. Less attention has been given to the interplay between nutritional behavior and systemic stress, especially in relation to metabolic regulation and muscle damage signaling pathways during actual race conditions. Moreover, ultramarathons differ markedly in structure, duration, and elevation profile—characteristics that are difficult to replicate in laboratory settings. Consequently, findings from controlled laboratory studies often lack ecological validity and may not translate to real-world ultra-endurance performance contexts [9]. A further limitation of current research is the underrepresentation of endocrine mark- ers in the context of ultra-endurance. Hormones such as leptin and ghrelin play a crit- ical role in appetite regulation and energy
difficult to replicate in laboratory settings. Consequently, findings from controlled laboratory studies often lack ecological validity and may not translate to real-world ultra-endurance performance contexts [9]. A further limitation of current research is the underrepresentation of endocrine mark- ers in the context of ultra-endurance. Hormones such as leptin and ghrelin play a crit- ical role in appetite regulation and energy homeostasis, and their fluctuations during prolonged physical activity may provide insight into central fatigue, fuel prioritization, and post-exercise recovery [10,11]. However, real-world data on these hormones during ultra-endurance races remain scarce. Additionally, the high interindividual variability in physiological and biochemical responses suggests that outcomes are likely influenced by a combination of innate physiological traits (e.g., body composition, hormonal baseline levels) and training-induced reactions (e.g., mitochondrial efficiency, substrate flexibility) [12]. Additionally, recent studies have begun to explore the use of continuous glucose monitoring (CGM) systems to assess real-time glycemic responses during ultra-endurance events [13–15]. Although initial data suggest that CGM can capture dynamic fluctuations in interstitial glucose linked to carbohydrate intake and pacing strategy, its application in non- diabetic, ultramarathon settings remains limited and warrants further investigation [16]. Therefore, we monitored interstitial glucose profiles during the race to explore real-time glycemic patterns in relation to distance and nutritional intake. In our previous study during the TorTour de Ruhr ® 2022—one of the longest non-stop ultramarathons in Europe—we demonstrated significant alterations in body composition, inflammatory and metabolic markers, and cardiac stress among athletes who completed the 160.9 km and 230 km distances [17]. However, that study was limited by its focus on the longer distances, the small sample size and a lack of integrated dietary analysis. Building
Nutrients2025,17, 3801 3 of 20 on those findings, the present study aimed to broaden the analytical scope by including all three race distances of the TorTour de Ruhr ® 2024 (100 km, 160.9 km, and 230 km) and by incorporating detailed in-race nutritional tracking alongside pre- and post-race biomarker assessments. The goal was to improve how in-race nutritional strategies relate to metabolic and endocrine responses and to help close the existing knowledge gap regarding real-world physiological stress in ultra-endurance competition. By integrating metabolic, biochemical, and behavioral data under field condi- tions, this study aims to advance our understanding of how athletes respond to pro- longed exertion—and how targeted nutrition strategies may help mitigate its adverse physiological consequences. The specific aims of this study were to characterize energy expenditure and nutritional intake across different race distances, with a particular focus on macronutrient distribution and overall energy balance. Furthermore, we sought to analyze biochemical markers of muscle damage, including creatine kinase muscle type (CKM) and lactate dehydrogenase (LDH), using pre- and post-race blood and saliva samples. Endocrine regulation of appetite and energy homeostasis was assessed by quantifying circulating leptin and ghrelin con- centrations, thereby enabling evaluation of their role in post-race recovery and metabolic response. Finally, we aimed to assess the relationship between nutritional strategies during competition and physiological stress responses, thereby providing a potential basis for developing evidence-based nutritional recommendations for ultra-endurance athletes. 2. Methods 2.1. Entry Eligibility TorTour de Ruhr ® participation required a medical sports examination that had been conducted less than six months before the race to confirm the physical resilience of the athlete for successful completion of the competition. Additionally, the participants had to provide proof of previous marathon and ultramarathon experience. The registration for the event is by invitation only to ensure entry eligibility. Inclusion criteria for study participation were as follows: male and female endurance athletes with a minimum age of 18 years, participating in one of the three distances (100 km, 160.9 kmor230 km) of the TorTour de Ruhr ® 2024; no previous severe injuries; and the ability to understand the study procedure and
is by invitation only to ensure entry eligibility. Inclusion criteria for study participation were as follows: male and female endurance athletes with a minimum age of 18 years, participating in one of the three distances (100 km, 160.9 kmor230 km) of the TorTour de Ruhr ® 2024; no previous severe injuries; and the ability to understand the study procedure and to give informed consent. Exclusion criteria included the following: nicotine consumption; diseases of the intestine; blood clotting disorders or intake of blood-thinning medications; acute or chronic vascular (blood flow) 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 the German Sports University Cologne (protocol code: 012/2024, date of approval: 21 February 2024). 2.2. Sample Collection All measures included pre- and post-assessments. Pre-race measurements were car- ried out in the evening (230 km) or in the time frame of two hours (100 km and 160.9 km) before the start at the organizer’s race briefing to determine basal resting values, while post-race measurements were performed immediately at the finish line at the finishers’ arrival. The study team was divided into groups responsible for (i) body composition, (ii) laboratory data, and (iii) blood sampling to minimize examination time pre and post. Blood samples were taken pre and post from thevena mediana cubitiand anticoagulated using ethylenediaminetetraacetic acid (EDTA) as anticoagulant. Samples were instantly
Nutrients2025,17, 3801 4 of 20 centrifuged at 2000×gat 4 ◦ C; the respective supernatant was aliquoted into 2 mL cry- otubes and stored at−20 ◦ 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. 2.3. 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 bioimpedance scale (InBody 770, InBody Europe B.V., Eschborn, Germany) was used. 2.4. Environmental Conditions Environmental conditions, including pre-testing time points, race starts, and respective weather parameters, are summarized in Table. Table 1.Pre-testing schedule, race start, and environmental conditions across the three distances of the TorTour de Ruhr 2024. Variables 100 km 160.9 km 230 km Pre-testing 19 May 2024 2–3 a.m. 18 May 2024 4–5:30 p.m. 17 May 2024 6–8 p.m. Start of race 19 May 2024 4 a.m. 18 May 2024 6 p.m. 18 May 2024 8 a.m. Start conditions 7.4 ◦ C 80% humidity 14.7 km/h south wind 21.5 ◦ C 66% humidity 7.8 km/h west wind 9.2 ◦ C 85% humidity 10.5 km/h west wind End of race 19 May 2024 10:30 a.m.–9:00 p.m.; Finish conditions Mean 14 ◦ C, 83% humidity, heavy rain at times, 14 km/h west wind Notes: Values indicate the timing of pre-race assessments, race start, and environmental conditions for each race distance. Pre-testing time points refer to baseline measurements conducted prior to race initiation. Start and finish conditions include ambient temperature (degrees Celsius, ◦ C), relative humidity (percentage, %), and wind speed/direction (kilometers per hour, km/h). 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. Briefly, 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
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. Briefly, 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 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, the FDDB app (Food Database GmbH, Bremen, Germany) was used. Although FDDB relies on standardized nutrient databases, previous evaluations have shown that such tools provide acceptable accuracy for field-based dietary assessment. The energy expenditure calculation was based on the heart rate 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. While
Nutrients2025,17, 3801 5 of 20 wearable-derived estimations of caloric expenditure may show individual variability, heart- rate–based algorithms from Garmin devices have demonstrated reasonable accuracy during prolonged endurance exercise and are widely used in research. Nevertheless, the provided data should be interpreted as approximations rather than precise measurements. 2.6. Energy Metabolism and Muscle Damage Biomarkers for muscle damage—LDH (Merck, Darmstadt, Germany) and CKM (Abcam, Cambridge, UK)—as well as for energy metabolism—leptin, ghrelin (Meso Scale Discovery, Rockville, MD, USA), glucagon-like peptide 1 (GLP-1) (Thermo Fisher Sci- entific, Waltham, MA, USA), irisin (Phoenix Pharmaceuticals, Burlingame, CA, USA), insulin (Abcam, Cambridge, UK) and glucagon (Thermo Fisher Scientific, Waltham, MA, USA)—were analyzed in the plasma fraction using enzyme-linked immunosorbent assays (ELISA) or multiplex electrochemiluminescence (ECL) immunoassays according to the manufacturers’ instructions. Tissue glucose was measured with a subcutaneous glucose sensor (FreeStyle Libre 3, analyzed by App-based program LibreView, Abbot Diabetes Care, Alameda, CA, USA). As the number of available devices was limited, sensors were allocated to participants on a voluntary basis and applied successively until the supply was exhausted, resulting in continuous glucose monitoring being conducted in a subgroup of the study cohort, including 10 participants in the 100 km group, 4 in the 160.9 km group, and 3 in the 230 km group. 2.7. General Assessment of Side Effects (GASE) To assess participants’ perceived physical and psychological complaints related to ultra-endurance exertion, we used the General Assessment of Side Effects (GASE) ques- tionnaire [18]. The GASE is a validated self-report instrument originally developed to systematically capture the frequency and intensity of common somatic symptoms and side effects that may occur during medical or physiological stress. Participants were asked to rate the severity of 24 predefined symptoms (e.g., headache, nausea, muscle pain, dizzi- ness, fatigue, gastrointestinal discomfort) on a four-point Likert scale ranging from 0 (not present) to 3 (severe). Additionally, three free-text fields were provided for reporting further complaints not covered by the predefined items. The GASE was administered immediately before (30 to 45 min) and within 15 min after the race to capture acute subjective side effects related to the prolonged physical effort
gastrointestinal discomfort) on a four-point Likert scale ranging from 0 (not present) to 3 (severe). Additionally, three free-text fields were provided for reporting further complaints not covered by the predefined items. The GASE was administered immediately before (30 to 45 min) and within 15 min after the race to capture acute subjective side effects related to the prolonged physical effort of the race. For analysis, symptom scores were evaluated as total sum scores, with descriptive and comparative statistics stratified by race distance. Furthermore, a single-item descriptive analysis was performed for specific items regarding food intake and processing, as well as items for muscular discomfort and exhaustion. 2.8. Statistics Data analysis was performed using GraphPad Prism (GraphPad Prism 10.5; San Diego, CA, USA). First, for determining the general effect of an ultramarathon on target variables, data from participants of all distances (100 km, 160.9 km and 230 km) were analyzed in one dataset. Secondly, subgroup analysis by race distance was performed for descriptive statistics (anthropometry, energy expenditure/intake, and finish time) and for all other measures. All data were tested on Gaussian distribution using the Kolmogorov–Smirnov normality test. Depending on the target variable and hypothesis, one- or two-tailed t- tests were used comparing pre and post for all normally distributed data. Otherwise, a Wilcoxon matched-pairs signed rank test was used to determine the statistical significance of differences between pre and post. Multiple comparison corrections were consistently applied across all biomarker analyses. Differences between the 100 vs. 160.9 vs. 230 km
Nutrients2025,17, 3801 6 of 20 participants were determined using one-way ANOVA followed by Holm-Šídák’s multiple comparisons test (normally distributed data) or with Kruskal–Wallis test followed by Dunn’s multiple comparisons test (not normally distributed data). If not otherwise stated, all data are presented as mean±standard deviation. Statistical significance was established atp≤0.05. 3. Results 3.1. Study Group Characteristics In total, 43 ultramarathon runners (16 f/27 m) were included. Participants had already completed 37 ultramarathons on average. Four participants dropped out during the race. The remaining 39 runners (13 f/26 m) all finished within the defined period (cut-off time: 37 h/230 km, 27 h/160.9 km and 17 h/100 km). Data of all finishers were used for analysis. Detailed pre-race anthropometric characteristics of the runners from the different distances are presented in Table. Additionally, post-race body mass was recorded; however, reliable bioimpedance body composition assessment could not be performed for all participants due to environmental conditions and the exhausted physical state of the runners at the finish line (for details, see Supplementary Table S1). Table 2.Anthropometric data and finish time of the study participants subdivided into each race distance. The anthropometric data presented was collected pre-race. All data are presented as mean±standard deviation (SD). Variables 100 km 160.9 km 230 km Finisher (Drop-outs) n = 18 (n = 1) n = 7 (n = 1) n = 14 (n = 2) Sex Female: n = 10 Male: n = 9 Female: n = 1 Male: n = 7 Female: n = 5 Male: n = 11 Age [years] 51.6 ±9.1 48.0 ±7.7 47.2 ±7.7 Body mass [kg] 70.6 ±8.5 74.8 ±7.7 71.2 ±12.7 Body fat mass [kg] 15.3 ±4.2 13.6 ±4.6 10.8 ±4.5 Whole Body Phase Angle 50 kHz [ ◦ ] 5.4±0.6 5.9 ±0.7 5.7 ±0.6 Finish time [h] 14.3 ±1.8 22.5 ±2.1 32.5 ±2.9 Running distance per week [km] 59.3±22.6 72.5 ±36.1 85.3 ±14.4 Mean finished marathons 49.3±52.7 46.6 ±36.7 54.7 ±55.1 Mean finished ultramarathons 27.7±37.4 32.8 ±28.2 49.4 ±48.6 Notes: The anthropometric data presented was collected pre-race. All data are presented as mean±standard devi- ation (SD). Finishers (drop-outs) are reported as
±0.7 5.7 ±0.6 Finish time [h] 14.3 ±1.8 22.5 ±2.1 32.5 ±2.9 Running distance per week [km] 59.3±22.6 72.5 ±36.1 85.3 ±14.4 Mean finished marathons 49.3±52.7 46.6 ±36.7 54.7 ±55.1 Mean finished ultramarathons 27.7±37.4 32.8 ±28.2 49.4 ±48.6 Notes: The anthropometric data presented was collected pre-race. All data are presented as mean±standard devi- ation (SD). Finishers (drop-outs) are reported as absolute numbers. Age (years); body mass (kilograms, kg); body fat mass (kilograms, kg); whole body phase angle at 50 kHz (degrees, ◦ ); finish time (hours, h); running distance per week (kilometers, km). Numbers of finished marathons and ultramarathons reflect lifetime participation. 3.2. Energy Intake and Expenditure Mean total energy intake increased with race distance; however, it remained consis- tently below the estimated energy expenditure across all groups. Consequently, participants did not meet the caloric demands of the race, with those competing in 230 km exhibiting proportionally greater energy deficits compared to 160.9 km and 100 km athletes. Detailed data from the energy intake and expenditure, as well as fluid intake of the runners from the different distances, are presented in Table.
Description
The study investigates metabolic and muscular challenges faced by ultramarathon runners.