The health benefits of regular physical exercise are well documented, with evidence of association with significant improvements in survival and life quality and in inactivity-related diseases, such as obesity, dyslipidemia, hypertension, and cardiovascular disease (1). However, on another side of the same coin, extreme strenuous physical exercise has recently been linked with serious damage on organs and systems, ultimately affecting health (2). It is well established that physiological plasticity allows humans to adapt to regular and moderate-intensity physical exercise (3), although insights into mechanisms of adaptation to extreme exercise remain conspicuous. Only a few studies, in a small number of athletes, focused on the effects of cycling/running ultramarathon on blood and inflammatory markers (4–7). Repeated, strenuous, long-duration endurance efforts in the context of a 9-d cycling ultramarathon competition have not been physiologically characterized.
Strenuous exercise is known to induce intravascular hemolysis, thereby decreasing red blood cell mass, despite exercise-associated erythropoiesis up-regulation (8). Exercise-induced hemolysis has been described on senescent red blood cells due to decreased deformability and higher susceptibility to mechanical stress (6). Haptoglobin (Hp), an acute-phase inflammatory marker, increases in many inflammatory-associated conditions, such as in highly demanding extreme exercise, and binds to exercise-associated hemolysis-derived free hemoglobin. Upon binding of the hemoglobin–Hp complex with Hp-soluble receptor (CD163), it plays a critical role in preventing free hemoglobin–induced toxic effects, vascular dysfunction, and organ injury. This receptor–ligand complex works as a cellular triggering cascade upon binding and endocytosis, functioning as an immunomodulator (9). Hp is primarily synthesized in hepatocytes and leukocytes, although it is also produced by adipocytes and brain tissue in response to cytokine secretion (interleukins 6 and 1) (10). Hp is not recycled and its plasma levels recover only after 5 to 7 d, increasing with age (11). Genetic factors can account for Hp levels and may determine the severity of inflammatory response to extreme exercise. The HP gene has a common 1.7-kb copy number variant that influences the number of copies of a tandem two-exon segment containing a multimerization domain and determines Hp protein phenotype (12). The resulting phenotype is Hp circulating as a dimer (HP1 homozygous allele) or multimer (homozygous HP2) (13). Carrying HP1 homozygous allele has been associated with higher Hp circulating level and a more efficient antioxidant response (14). Moreover, heme oxygenase-1 (HMOX1) is responsible for degradation of heme to carbon monoxide and biliverdin and free ferrous iron (15). Iron is released by the action of HMOX1, stored as ferritin, and transported by transferrin to the bone marrow to be reused in hemoglobin synthesis (15). A functional genetic polymorphism of HMOX1 in locus −413T/A has been related to higher activity (16).
Here, we sought to analyze changes in blood cell counts and circulating inflammatory parameters, with a focus on mechanisms of red blood cells and hemoglobin catabolism in response to a 9-d cumulative strenuous effort cycling ultramarathon. Furthermore, we hypothesized that the regulatory mechanisms involved in hemoglobin degradation in response to extreme exercise might be dependent on genotypic variation in HP and HMOX1.
Participants in a cycling ultramarathon (TransPortugal) were contacted before the race for participation in this study. Of the 76 athletes who started the race, 55 accepted and were eligible for inclusion (25–60 yr of age, willing to donate blood samples before and in the end of the race). Participants were healthy amateur athletes from 20 nationalities (mainly Caucasians, 92.7%), with a mean age of 44.8 ± 7.1 yr (range, 27–56 yr). Blood was collected through venipuncture of the forearm and processed immediately before the start of the race (prerace) and immediately after the final course (postrace), as soon as they were finishing the race, between 5 and 15 min after completing the final course. Bioelectric impedance was only performed before the start of the race, because immediately after, the hydric level is expected to vary significantly, which will influence hemodilution and analyte concentration.
TransPortugal is a 1150-km off-road cycling race across forest tracks, gravel roads, and steep single tracks on cliff tops across Portugal, from north to south in 9 d (from Bragança to Sagres). It is formally subscribed in the Portuguese Cycling Federation and is characterized by a high level of technical requirements and by adverse conditions of routes, through everyday course (shortest 95 km and longest 162 km), with a total of 24,500-m vertical climbing. Temperature and humidity data were obtained for each course of TransPortugal from registries of the Portuguese Institute of the Sea and the Atmosphere. According with the rules of TransPortugal, disqualification occurs when participants withdraw or achieve three uncompleted courses.
This study was approved by the Scientific and Ethic Committees of the Lisbon Academic Medical Centre (Faculty of Medicine, University of Lisbon) and was conducted in agreement with the Declaration of Helsinki. All athletes gave their written informed consent.
Weight and height were measured according to standardized procedures previously described (17). Body mass index (BMI) was calculated using the following formula: weight (kg)/height2 (m2).
Body composition estimates (fat mass percent and fat-free mass percent) were determined through bioelectrical impedance analysis (BIA) using a portable tetrapolar bioelectrical system (Quantum X; RJL Systems Inc., Clinton Township, MI).
Hemogram and biochemical determinations
Venous blood was collected into EDTA tubes, separated and kept at 4°C until further processing. Routine hematological cell count (cell counter cytometer; Beckman-Coulter) and biochemical measurements (C-reactive protein (CRP) was determined by turbidimetry, whereas total cholesterol, HDL, and LDL fractions were measured by using automated enzymatic assays; ABX Diagnostic) were analyzed at a certified clinical laboratory, Joaquim Chaves Laboratory. Serum Hp was determined by nephelometry, whereas CD163 level was measured by using precoated enzyme-linked immunosorbent assay (Quantikine human CD163; R&D Systems, Abingdon, United Kingdom).
Determination of genetic polymorphisms
The polymorphism in HP (HP 1.1, HP 2.1, and HP 2.2) was determined in plasma using polyacrylamide gel electrophoresis, and its presence was detected by peroxidase activity in the complex Hp–hemoglobin over the color using as substrate o-dianisidine, as previously reported (18).
Genomic DNA was isolated through a nonenzymatic method adapted from a previously described method (19). A single-nucleotide polymorphism in HMOX1 gene, a substitution A > T located at the position −413 in the promoter region (rs2071746), was determined by using a two-tube multiplex polymerase chain reaction (PCR). Briefly, the region of interest was amplified using an allele-specific PCR in a 25-μL reaction: 10 mM of each primer (direct A: 5′FAM-TGATGTTGCCCACCAGGCTA3´, direct T: 5′FAM-TGATGTTGCCCACCAGGCTT3′ and reverse: 5′GGAGCAGTCATATGACCCTTGGG3′), 100 ng of genomic DNA, 0.2 mM nucleotides, DreamTaq Green with 10 mM dNTP, 1.5 mM MgCl2, and 1 U Taq polymerase (Thermo Scientific). PCR conditions were an initial denaturation step at 94°C for 5 min, followed by 28 cycles of amplification (94°C for 30 s, 61°C for 30 s, 72°C for 40 s), and a final extension step of 72°C for 10 min. The resulting fragment was a 290-bp band, resolved by electrophoresis in a 3% agarose gel stained with ethidium bromide (10 μg·mL−1) for 60 min at 80 V. Quality control procedures for genotyping included double sampling of 10% of the samples to assess reliability and the use of negative controls to step-away false positives. Two authors obtained the results independently, and the ambiguous results were reanalyzed.
Departure from normality was tested using Shapiro–Wilk test. Descriptive statistics are presented as mean ± SD or median (interquartile range). Comparison between central tendency values was done using independent-measures t-test and factorial ANOVA or Mann–Whitney and Kruskal–Wallis tests, accordingly. Correlation between continuous variables was conducted by Spearman correlation coefficients and linear regression analyses (with equation determination).
To examine the effect of time to complete the race, athletes in physiological variables were divided into tertiles (fast, ≤3715 min; intermediate, 3715–4030 min; and slowest, ≥4030 min).
Change in plasma volume (ΔPV) between before and after exercise was calculated using the Dill and Costill equation (20,21): ΔPV (%) = 100 × [(HbPre/HbPost) × (100 − HtcPost)/(100 − HtcPre) − 1] (where Hb is hemoglobin; Htc, hematocrit; Pre; prerace; Post, postrace). Postrace analytes levels were corrected for hemoconcentration [Postrace corrected = Postrace uncorrected × (1 + ΔPV(%)/100)]. The difference between measurements for each variable from before-to-after race (gain) was calculated for each participant [Δ% = 100 × (Postracecorrected − Prerace/Prerace)].
One-way ANCOVA was used to determine the relation of HP phenotypes to Hp levels and that of HMOX1 genotypes to bilirubin levels while controlling for relevant variables from previous analyses.
Statistical analyses were conducted using SPSS version 24.0 (SPSS Inc., Chicago, IL) and GraphPad Prism package (GraphPad Prism 6; Software Inc., La Jolla, CA). Significance was attributed if P < 0.05.
At departure, the TransPortugal race started with 76 athletes, from which 55 accepted to participate in current study. Five of these participants lack data for postrace because of incomplete (n = 1) and accidents (n = 3) during the course, and one athlete refused to participate. Mean temperature and humidity conditions during the event were 17.0°C ± 2.0°C (14.9°C–18.7°C) and 54.7% ± 6.2% (50.5%–59.5%), respectively. Mean temperature and humidity of each course are depicted in Fig. 1.
Rest and postexercise data, as well as the gain (percent of variation) adjusted for plasma volume variation, are depicted in Table 1 for body composition, hematological, and inflammatory markers. Anthropometric data, for weight and height, were collected before and after the race (BMI decreased by approximately 4%), whereas waist, fat mass, and free fat mass measurements were performed only at rest before the race, representing the baseline body composition profile of participants (mean body fat, 16.7% ± 0.5%; Table 1). Both in the beginning and at the end of the cycling ultramarathon, hematological cell counts were within normal clinical reference values. Nevertheless, peripheral blood cell counts demonstrated important variations in association with this extreme effort. Total leukocytes increased due to neutrophil and monocyte count (by 70% and 61%, respectively; P = 0.0001 and P < 0.0001; Table 1). Erythrocyte count and hemoglobin concentration increased significantly by 2.1% and 3.4%, respectively (P < 0.0001; Table 1). Inflammatory parameters were also modified in response to exhaustive exercise, CRP increased nearly 13,000% (P < 0.0001), whereas Hp and its soluble receptor CD163 behave differently with strenuous exercise. Hp increased by 72.2% (P < 0.0001) and, although without statistical significance, CD163 varied 1.2% from pre- to postrace (P = 0.710; Table 1). Circulating levels of hepatic transaminases were significantly increased (by 181.2% for alanine aminotransferase (ALT) and 138% for aspartate aminotransferase (AST), P < 0.0001). Total bilirubin and cholesterol levels were comparable between baseline and postrace measurements.
Of all the athletes who adhered to this study with concomitant predata and postdata (n = 50), 37 completed the nine courses of TransPortugal. Therefore, athletes were then separated into two groups (those who completed all nine courses (n = 38 at baseline and n = 37 in the end of the race) in one group and the remaining in another group (n = 16 at start and n = 13 in the end)) and compared for hematological and biochemical parameters in both moments of the race and in relation to the magnitude of effect for each parameter (Δ%; Table 2). Significant differences in erythrocyte and lymphocyte cell count were found between groups. At the end of this strenuous cycling, ultramarathon athletes who completed all nine courses had higher leukocyte and neutrophil count (P = 0.022), neutrophils (P = 0.018), erythrocyte count (P = 0.004), and superior concentration of hemoglobin (P = 0.004), cholesterol (P = 0.001), and total bilirubin (P = 0.002) compared with noncompleters. Completers of all courses of the ultramarathon presented with significantly lower levels of the inflammatory markers CRP and Hp compared with participants who have not completed all courses of the race (P = 0.048 and P = 0.007, respectively; Table 2). Moreover, the group including completers compared with noncompleters evidenced superior gain of leukocyte (P = 0.013) and neutrophil (P = 0.004) count, and total cholesterol (P = 0.023) and total bilirubin (P = 0.010) levels. The effect of this cycling ultramarathon on red blood cell count was more pronouncedly increased in completers compared with noncompleters, for erythrocyte count (P = 0.015), hemoglobin concentration (P = 0.015), and red cell distribution width (RDW; P = 0.007). Conversely, Hp increased less for completers compared with noncompleters (P = 0.014) (Table 2).
The average speed to complete TransPortugal varied between 120 m·min−1 for the fastest and 390 m·min−1 for the last athlete. Thirty-seven athletes completed all nine courses and were included into tertiles according to the time to complete the race, although no effect was found in the time to complete the ultramarathon for the analyzed variables (data not shown).
Genotype frequencies were analyzed in known functional polymorphisms at HP (HP 1.1, 0.24; HP 2.1, 0.42; and HP 2.2, 0.34) and HMOX1 genes (A homozygous, 0.11; heterozygous, 0.55; and T, homozygous, 0.34). The analyzed polymorphisms were not associated with having completed the ultramarathon or with the time to complete the race (data not shown). Noteworthy, there were significant genotype–phenotype interactions, considering both all athletes and only the completer group (Figs. 2 and 3, respectively). We present results using recessive and dominant models, respectively, for HMOX1 and HP genetic variants. The HMOX1 AA genotype was associated with higher prerace leukocyte count for both the overall group and only in completers. Notably, in the group of athletes that completed nine courses, the HMOX1 homozygous A carriers presented higher lymphocyte counts and Hp levels at both pre- and postrace (Fig. 3). When all athletes were included in the analysis, the AA genotype was only associated with higher lymphocyte counts in the prerace (Fig. 2).
For HP polymorphism, we found that athletes carrying the 2.2 phenotype had increased gain in neutrophils and lower Hp levels in both pre- and postrace moments (Figs. 2 and 3). The gain % in hemoglobin and cholesterol levels was increased only when all athletes were analyzed.
Analysis of covariance showed significant effect of HP genetic variants on Hp postrace circulating levels (F = 13.017, P = 0.001), controlling for the variation in hemoglobin, age, and time of performance. For Hp gain (Δ%) as a continuous dependent variable in ANCOVA analysis, the effect of HP genetic variants remained significant (F = 5.103, P = 0.029).
Cumulative strenuous physical exercise, such as TransPortugal cycling ultramarathon, is representative of ultimate extreme exercise, which can expose the organism to significant stress and expand the boundaries of adaptive and plastic human physiological capacity. Here, the exercise was performed for extended periods of time, for 9 consecutive days with minimal rest, and with underlying cumulative fatigue effects. Beyond the local and systemic trauma, individuals were subjected to adverse weather conditions and terrain. Athletes participating in the TransPortugal cycling ultramarathon were nonprofessionals; therefore, although they exercise frequently, it is unlikely to reach the level of physiological and metabolic preparedness that elite athletes are supposed to have. Therefore, the TransPortugal characteristics and nonprofessional profile of participants render our study unique, reflecting the effects of cumulative long-term repetitive exhaustive exercise in hematological and biochemical parameters of inflammation, while accounting for genetic variation. A particular interest was dedicated to the regulation of hemoglobin catabolism.
As recently observed on ultramarathon runners (7), also mountain cycling ultramarathon with a long-term strenuous profile conveys considerable effect on whole body due to frequent striking on hard surfaces, especially on the lower limbs, spine, and arms, capable of inducing hemolysis. The exercise-associated intravascular hemolysis, mainly of senescent red blood cells, seems to be related to mechanical stress in red blood cells passing through capillaries in contracting muscles and load surfaces (22). Accordingly, it was reported that one course of consecutive, strenuous exercise caused anemia (23), albeit the effect of highly demanding effort repeatedly performed for 9 consecutive days with minor rest and cumulative fatigue on anemia and hemoglobin catabolism markers is largely unknown. The exercise profile of TransPortugal has shown a significant increase in RDW, whereas decreases were observed for erythrocyte count and hemoglobin levels. We hypothesize that the repetitive and cumulative nature of the exercise demanded in this race might contribute toward increased hemolysis and catabolism of hemoglobin, allowing for neither erythropoiesis to replenish RBC nor RBC aging due to race’s long duration. This decreased hemoglobin concentration and erythrocyte count, taken together with an increase of Hp in circulation after the TransPortugal race, likely reflects the adaptive buffering mechanism of Hp to sequester toxic free hemoglobin released with erythrocyte damage. Indeed, hemolysis was more pronounced in athletes exposed to increased level of stress (8). Concordantly, the effects of the cycling ultramarathon on erythrocyte count, hemoglobin, and RDW were noticeable in completers of the nine courses, who underwent higher loads of exercise, compared with noncompleters. Persistently demanding exercise seems to be also associated with extravascular hemolysis by modification of the oxidative environment in the spleen and liver due to free iron released from hemolysis (24). In fact, despite AST nonspecificity and the exercise-associated muscle damage, with subsequent releasing of the enzyme to the bloodstream, the participants in the TransPortugal race presented concomitantly with elevated ALT and gamma-glutamyltransferase (GGT) activities after the race, pinpointing toward the liver as the primary source of these enzymes (23,25), thus suggesting at least minor hepatic stress.
The soluble CD163 protein is a macrophage-specific receptor for Hp–hemoglobin complexes, known to protect from tissue damage by scavenging oxidative stress–induced by-products of hemoglobin (26,27). Only one small study (n = 8) on runners has revealed increased sCD163 levels after strenuous exercise such as marathon or half-marathon (28). In a 9-d cycling ultramarathon, we observed a nonsignificant sCD163 decrease from pre- to postrace, despite increased serum Hp. Hp–hemoglobin complex receptor, sCD163, is usually released to peripheral blood to mediate the innate immune response of appropriating hemoglobin-bound iron. The unaltered sCD163, together with higher Hp levels and decreased hemoglobin at the end of this cumulative effort, might reflect counter-regulatory and protective effects to the inflammatory, oxidative, and hemolytic effects of strenuous exercise, likely mediated by recruiting monocyte–macrophages toward the damaged skeletal muscle. In agreement, other reports make reference to an increased scavenger function of Hp–hemoglobin complex by macrophage populations in association with decreased availability of sCD163 (29), because of endocytic internalization and slow recycling. The differences in sCD163 that we observed in comparison with Niemelä et al. (28) likely reveal the cumulative effect of highly demanding physical exercise.
Conversely, heme released from shattered red blood cells is metabolized into iron, biliverdin (later to bilirubin), and carbon monoxide by HMOX1. Produced bilirubin stimulates endothelial activation of the nuclear factor–erythroid 2 p45-related factor 2 (30). This molecule, a cellular sensor of oxidative stress, further contributes to adaptation to hemolysis by up-regulating the repair and degradation of damaged macromolecules, and by modulating intermediary metabolism such as that resulting from metabolism in demanding exercises (31). Bilirubin levels, which also depend on heme metabolization by HMOX1, were not altered in all athletes after this demanding ultramarathon, suggesting that hemoglobin catabolism might be preferentially done through the macrophage Hp–CD163 clearing pathway. However, we cannot exclude the formation of heme-hemopexin complexes that signal through CD91, ultimately up-regulating interleukin-10 expression, which is a potent inducer of HMOX1 (32), being possibly associated with the significant increased postrace levels and gain percentage of bilirubin in completers.
Immunoinflammation is a physiologically required process that is associated with physical exercise and contributes to repair exercise-related damaged tissues (33), when homeostasis is achievable. A long-term exhaustive and repetitive exercise implicates a cumulative load with potential effect on immunoinflammatory regulatory processes. The TransPortugal cycling ultramarathon represents such a demanding exercise type. We found that total leukocyte count in peripheral blood increased because of neutrophil and monocyte differentials, whereas the lymphocyte subset decreased significantly. These data are in agreement with established effects of strenuous exercise on leukocyte subsets after exercise (34). In addition, the group of athletes who completed all nine courses presented with higher increase of neutrophil count, which might reflect the excess load they were subjected to, in relation to noncompleters. Therefore, in these exhaustive challenges, there seems to be a correlation between the intensity of performance and the circulating white line cells, suggesting the early mobilization of these cells in response to exercise. Notably, there was no derangement in leukocyte response and mobilization, even after a 9-d repetitive strenuous effort, where a cumulative depletion was plausible. In fact, acute bouts of intensive exercise induce a proinflammatory response with transient lymphocytopenia and neutrophilia mediated through enhanced apoptosis (35), whereas regular exercise training has been shown to enhance immune function. Given that participants in TransPortugal had been training for several months before the race, despite the fact that they are nonprofessionals, our findings suggest that this physically demanding and cumulative exercise has minor immunological derangements for leukocytes.
Genetic studies on HP phenotype and HMOX1 genotypes using analysis of covariance demonstrated that HP influences Hp variation during highly demanding physical exercise, although no effect was observed for HMOX1. These findings suggest that besides physical exertion, a genetic-dependent modulatory effect influences Hp levels, thereby concurring for the resulting levels of the exercise-associated inflammatory marker Hp. The activation of neutrophils during exercise generates reactive oxygen species as well as the recruitment of monocytes and macrophages (36). The HP 2.2 phenotype was associated with elevated neutrophil levels reflecting a lower capacity to mitigate the potential local and systemic damage by reactive oxygen species production. Concordant with other studies, we noted that known hematological biomarkers such as neutrophils and high-sensitivity CRP increased at the immediate postrace time point after the ultramarathon (37). The genotype–phenotype association studies corroborate the role of HP genetic polymorphism inducing a more efficient antioxidant response and consequently a better performance. The functional genetic polymorphism in HMOX1 at locus −413T > A has been associated with cardioprotection during submaximal exercise (38). The effect of this variant on enzyme activity and immune function in TransPortugal race participants was determined using genotype–phenotype analyses. According to these analyses, we found that HMOX1 AT genotype might be associated with an attempted protection from persistent damage in the bone marrow (15). Indeed, the role of HMOX1 affects the response of white line cells throughout the process where iron was release, stored as ferritin, and transported by transferrin to the bone marrow to be reused in hemoglobin synthesis.
In this study, we have not controlled for water and sodium ingestion, which is known to influence postexercise plasma volume, therefore accounting for a potential limitation. Nevertheless, adjustments undertaken on postrace concentrations were only minimally modified, suggesting that there was no significant variation from pre- to postrace plasma volume, confirming a good hydration of athletes throughout the ultramarathon.
Findings from current research add to our understanding on how humans handle toxicities from produced metabolites during highly demanding cumulative physical exercise. Results presented here pinpoint a complex regulatory model for controlling exercise-induced damage. Although we present findings from a unique human exercise model in a larger sample of cycling ultramarathon participants, other inflammatory markers will certainly add mechanistic insight for regulation of complexity associated with endurance exercises. These results should be taken into account when analyzing the cumulative effects of extreme physical exercise. Further studies focusing on exercise-associated hemoglobin catabolism pathways in response to other types of exercise are warranted.
The authors acknowledge financial support from the Instituto de Investigação Científica Bento da Rocha Cabral, Liberty Seguros and Clinical Chemistry Laboratory of Grupo Dr. Joaquim Chaves. The Superior Technician of the Diagnostic and Therapeutic Areas of Genetics Laboratory of Faculty of Medicine, Conceição Afonso, by the contribution for sample collection and haptoglobin phenotype determination.
The study is presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation. The results of the present study do not constitute endorsement by the American College of Sports Medicine.
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Keywords:© 2018 American College of Sports Medicine
LONG-TERM EXHAUSTIVE EXERCISE; ACUTE-PHASE PROTEINS; HEMOGLOBIN CATABOLISM; METABOLISM