Iron is an essential mineral present in many foods and also available in dietary supplements. Iron is a component of the storage proteins hemoglobin (Hb), which transports oxygen, and myoglobin, which stores oxygen in working skeletal muscles and facilitates its transport to the mitochondria. Iron also enables erythropoiesis, the production of red blood cells (RBC). RBC supply oxygen to all organs and tissues in the body including skeletal muscle and the heart (1). The homeostasis of iron is modulated by several genes, one of which is the human homeostatic iron regulator protein (HFE). HFE is a nonclassic major histocompatibility protein located on the cell membrane that modulates intestinal iron uptake (2). The HFE protein, which is encoded by the HFE gene, binds to transferrin receptor 2 (TFR2), which upregulates the production of hepcidin. Hepcidin is a circulating peptide hormone that inhibits iron uptake by binding to and degrading the iron exporter ferroportin (2,3). The role of hepcidin is to regulate serum iron levels by preventing the release of iron from the duodenum into the blood stream. This is an important function because excessive amounts of iron in the bloodstream can lead to iron overload and cause a surge in the release of free radicals, which may cause oxidative stress and muscle damage (2).
A common single nucleotide polymorphism (SNP) in HFE, 845G>A (rs1800562), also referred to as C282Y, disrupts the formation of a disulfide bond in the HFE protein, causing it to aggregate intracellularly (2). This aggregation prevents HFE from binding to TFR2, ultimately decreasing hepcidin release (2). A decrease in hepcidin levels leads to impaired ferroportin degradation, enabling the transport of iron from the duodenum to the bloodstream. This may disrupt the tightly controlled regulation of serum iron levels, leading to excess iron in the bloodstream (2). The “A” allele of the C282Y polymorphism is associated with excess iron stores, such that being AA homozygous places one at a high risk of developing an iron overload condition known as hereditary hemochromatosis (HH) (4,5). The H63D polymorphism (rs1799945), H63D C>G, is another common SNP in HFE; however, its effects on iron status are not as pronounced. This polymorphism still results in the production of a stable complex with TFR2, but with a lower binding affinity. This also results in decreased hepcidin, but to a lesser degree compared with the rs1800562 SNP (6). The “G” allele of the H63D polymorphism is associated with excess iron stores, although to a lesser degree than the “A” allele of the C282Y polymorphism, and is associated with a medium risk of HH. Excess iron in the blood stream saturates transferrin iron uptake receptors, which may react with hydrogen or lipid peroxides and other reactive oxygen species . The production of reactive oxygen species may then lead to oxidative stress or tissue damage (2,7). C282Y has a greater penetrance than H63D; therefore, the C282Y polymorphism has a stronger influence on HH risk than the H63D variant.
HFE risk genotypes for iron overload are more prevalent in competitive athletes (8,9). Hermine et al. (8) reported that 41% of French elite athletes and 80% of athletes with medals in European/international competitions had at least one risk variant in HFE (including rs1800562 and rs1799945) compared with 27% of the French general population. Chicharro et al. (9) showed that 49.5% of athletes possessed an HFE SNP that increased their risk for HH, compared with 33.5% of the general population. The elite athlete population was composed of individuals from a diverse range of sports, suggesting that the HFE risk variants for iron overload confer a potential athletic performance advantage. After exercise-induced inflammation, hepcidin levels are upregulated preventing iron absorption (3). However, for athletes with medium or high HFE risk variants, hepcidin release may not be upregulated, which would increase iron bioavailability in the plasma (2,3,8). Higher serum iron levels may contribute to a rise in erythropoiesis because iron is necessary for the production of hemoglobin in RBC (10). The increased production of RBC and higher Hb levels may allow for enhanced oxygen delivery to skeletal muscle, enabling the maintenance of aerobic fitness, which is crucial for endurance athletes (3,11). The objective of this study was to determine whether HFE genotypes (rs1800562 and rs1799945) are associated with an athlete’s endurance performance as well as their maximal aerobic capacity.
This project used data collected from a previous study on the effects of caffeine, genetics, and endurance performance (12). Ethics approval was obtained from the University of Toronto Institutional Review Board, and the study was registered with clinicaltrials.gov (NCT 02109783). All subjects provided written informed consent and were told that they could terminate their participation at any point. One hundred and thirteen competitive male athletes were recruited from a variety of sports, including endurance-type (e.g., triathlon, cross-country skiing, cycling), power-type (e.g., boxing, volleyball, weightlifting), and combination or mixed sports (e.g., soccer, football, swimming). Athletes were required to be training and/or competing for ≥8 h·wk−1, for 9 out of 12 months per year for at least 3 yr in their primary sport. Three athletes dropped out because of a sports-related injury, two because of school or work demands, two because of objection to abstaining from caffeine, and one because of relocation. The remaining 100 athletes had a mean ± SD age of 25 ± 4 yr and body mass of 81.0 ± 13.1 kg.
Athletes completed four visits, each ~90–120 min and approximately 1 wk apart, in the exercise laboratory at the Goldring Centre for High Performance Sport at the University of Toronto. Anthropometric measurements were obtained, and the athletes performed a maximal aerobic capacity test (V˙O2peak) and completed a general health and sport history questionnaire during their first visit. Saliva samples were also obtained for genotyping during the first visit using the Oragene ON-500 kit following standard procedures (DNA Genotek, Ottawa, Ontario, Canada) (13). The athletes were asked to maintain regular sleeping and eating habits, avoid strenuous activity 48 h before each visit, and abstain from caffeine 1 wk before the first visit and for the remainder of data collection (4 wk total). To ensure dietary consistency, the athletes were instructed to replicate the same meal they consumed before the first visit for the subsequent visits. During visits 2–4, the athletes were randomly assigned to 0, 2, or 4 mg of caffeine per kg of body mass (mg·kg−1).
Parameters of assessment
After receiving their randomly assigned dose of caffeine, the athletes completed questionnaires for 25 min, followed by a 7-min dynamic warm-up routine that began with light cycling. After completing their warm-up, the athletes performed four physical tests, which consisted of a vertical jump, handgrip, Wingate, and 10-km cycling time trial (TT). Only the results of the 10-km cycling TT are reported here.
Height and total % body fat were measured, and details have been previously described (12).
Maximal exercise test (V˙O2peak)
Maximal aerobic capacity was measured through the V˙O2peak test. The test began at a work rate of 50 W on a mechanically weighted and braked cycle ergometer (Monark Ergomedic 839E; Monark Exercise AB, Vansbro, Sweden), with load increases of 50 W each minute for the first 2 min, then 25 W each minute thereafter until volitional exhaustion. A portable metabolic system was used to measure gas exchange (Cortex Metamax 3B; CORTEX Biophysik GmbH, Leipzig, Germany). Maximal oxygen uptake (V˙O2peak) was defined as the highest 1 min oxygen uptake value obtained during the test. V˙O2peak power (W) was calculated by measuring power output (W) at V˙O2peak. End power Wpower was calculated using the power output (W) at volitional fatigue.
The 10-km cycling TT was the last of the four exercises that the athletes preformed. The athletes began the 10-km cycling TT when blood lactate levels dropped below 2.5 mmol·L−1 (post–Wingate test), measured by a finger prick test and analyzed by Lactate Scout 4, from the prior Wingate test. An Ergomedic 839 E stationary bike was set to a constant resistance or power output, and each subject cycled 10 km at the specified resistance (W). Resistance was set to 65% Wpower for all subjects based on calculations from the V˙O2peak test, equivalent to 65%–69% V˙O2peak. The on-board computer automatically controlled the degree of resistance through application of various braking forces on the belt. The speed was calculated based on the cadence of pedaling (rpm); a faster cadence would result in a faster speed. A 10-km TT requires 1667 rotations (6 m per rotation). Different cadences will result in different completion times for this trial. The subjects were unable to see their time, speed, and heart rate but were able to see distance traveled. Water was made available ad libitum. Heart rate was monitored throughout the trial using a heart rate monitor. The subjects determined their RPE using Borg’s rating scale (score ratings from 6 [no exertion] to 20 [extremely difficult]) at 5- and 9-km marks.
The saliva samples collected during visit 1 were then genotyped for a panel of genes, including rs1800562 (C282Y) and rs1799945 (H63D) in the HFE gene using the Sequenom MassArray platform (13). An algorithm based on the two HFE alleles was used to group participants into a low, medium, or high risk for iron overload, which can be found in Table 1. The algorithm was determined by the combined genotypes for the C282Y and H63D polymorphisms, whereby the “A” allele of C282Y and the “G” allele of H63D were considered the risk alleles (5). Because the C282Y polymorphism has greater penetrance than the H63D variant, being AA homozygous for C282Y means that one is at high risk for HH irrespective of their H63D genotype. Having one “G” allele for H63D while possessing one “A” allele for C282Y or carrying two copies of the “G” allele for H63D without possessing an “A” allele for C282Y results in an individual’s allocation to the medium-risk group for iron overload. All other combined genotypes were considered low risk.
TABLE 1 -
Hemochromatosis risk classified based on HFE
Data were analyzed using RStudio (version 1.2.1335). Descriptive data (height, body mass, age, % body fat, V˙O2peak, and dietary caffeine or caffeine used for sport and sport type distribution) were compared between genotypes using ANOVA or between sport types using a chi-square analysis. Allele frequencies of individual SNP were reported. Only performance at the 0-mg·kg−1 caffeine dose was considered in the present study. The outcome variable was time to completion for the 10-km cycling TT, where a lower time indicates a better performance. ANCOVA was conducted to determine the effect of the HFE risk groups, individual SNP, and sport types on the 10-km cycling TT. If a significant effect was observed (P ≤ 0.05), post hoc analyses using Tukey’s HSD were performed. HFE risk groups were classified based on the algorithm, between the genotypes of each of the individual SNP. Covariates included % body fat, visit number, V˙O2peak (mL·kg−1⋅min−1), and sport type. For statistical analyses, % body fat was coded as a categorical variable (low, <10%; medium, 10%–11%; high, >11%). Visit number was a categorical variable with three levels (visit 1, visit 2, and visit 3). V˙O2peak (mL·kg−1⋅min−1) was coded as a categorical variable with three levels (low, <43 mL·kg−1⋅min−1; medium, 43–55 mL·kg−1⋅min−1; high, >55 mL·kg−1⋅min−1). Sport type was coded as a categorical variable with the following three levels: endurance athletes (e.g., triathlon, cross-country skiing, cycling, marathon running), power athletes (e.g., boxing, volleyball, Olympic weightlifting), and mixed athletes (e.g., soccer, football, swimming). The participants received the placebo dose in random order on one of the three visits. Therefore, visit was added as a covariate to account for a possible learning effect. The secondary outcome variable was V˙O2peak (mL·kg−1⋅min−1), where a larger value indicates greater aerobic fitness, and the covariates adjusted for included sport type and % body fat. ANCOVA was used to determine the effect of the HFE risk groups, individual SNP, and sport types on V˙O2peak. If a significant effect was observed (P ≤ 0.05), post hoc analyses using Tukey’s HSD were performed. Because there was only one athlete in the high-risk group, athletes were stratified based on hemochromatosis risk in two groups: medium/high risk or low risk.
Of the 100 subjects, 89% (n = 89) were characterized as low risk and 11% (n = 11) were considered medium/high risk. Descriptive characteristics of both risk groups are shown in Table 2. Athletes in the medium- or high-risk group had significantly higher V˙O2peak (mL·kg−1⋅min−1 and L·min−1) measures compared with athletes in the low-risk group. There were no significant differences across the genotypes for body mass, height, age, body fat percent, or percent distribution of sport type. The distribution by sport type of all participants was as follows: 40% endurance, 43% power, and 17% mixed. For the rs1799945 SNP, 81 participants were CC, 17 were GC, and 2 were GG. For the rs1800562 SNP, 90 participants were GG, 9 were AG, and 1 was AA.
TABLE 2 -
Descriptive characteristics of participants by iron overload
||Low Iron Overload Risk (n = 89)
||Medium/High Iron Overload Risk (n = 11)
||81.3 ± 13.3
||79.3 ± 9.6
||178 ± 6.8
||178 ± 7.1
||24.4 ± 4.4
||26.1 ± 3.8
|Body fat (%)
||14.5 ± 4.6
||12.3 ± 4.2
|Sport type, n (%)
aP values were derived using ANOVA, or for sport type by using chi-square.
bMean ± SD (all values).
Figure 1 shows the average 10-km TT times for all subjects (n = 100). There was a significant difference in the 10-km TT performance between the low and the medium/high-risk groups (P = 0.05). Individuals in the medium- or high-risk group outperformed those with the low-risk genotypes by ~8% (1.3 min) (17.0 ± 0.8 vs 18.3 ± 0.3 min, P = 0.05).
We then examined whether the rs179945 SNP alone was associated with the 10-km TT performance but found no differences between genotypes (P = 0.5). However, a significant association was observed between the rs1800562 SNP and the 10-km TT performance (P = 0.02). Post hoc analyses showed that the AA genotype outperformed the GG genotype on the 10-km cycling TT by ~5 min (P = 0.05).
The effect of sport type was significant on TT performance (P = 3.7 × 10−5); therefore, post hoc analyses using Tukey’s HSD were used to compare TT performance between sport types. Post hoc analyses revealed that endurance and mixed athletes outperformed power athletes (P = 0.00006 and P = 0.007, respectively).
Maximal oxygen uptake (V˙O2peak)
Figure 2 shows the maximal oxygen uptake, i.e., V˙O2peak, for all subjects (n = 100). There was a significant difference in V˙O2peak between the low and the medium- or high-risk groups (P = 0.003). Individuals in the medium- or high-risk group had a higher V˙O2peak (54.6 ± 3.2 mL·kg−1⋅min−1) compared with individuals in the low-risk group (46.7 ± 1.0 mL·kg−1⋅min−1).
We then examined whether the rs179945 SNP alone was associated with V˙O2peak but found no differences between genotypes (P = 0.7). However, a significant difference in V˙O2peak was observed between the three genotypes (P = 0.001) of the rs1800562 variant. Post hoc analyses showed that the AA genotype had a larger V˙O2peak by ~19.4 mL·kg−1⋅min−1 compared with the GG genotype (P = 0.05). Those with the AG genotype had a larger V˙O2peak compared with those with the GG genotype by 8.58 mL·kg−1⋅min−1 (P = 0.01).
The effect of sport type was significant on V˙O2peak (P = 7.5 × 10−5). Post hoc analyses showed that endurance and mixed athletes had greater V˙O2peak than power athletes (P = 0.0002 and P = 0.004, respectively).
The current study is the first to examine the association between HFE genotype and endurance performance in competitive male athletes. We also investigated the association between HFE genotype and aerobic capacity (V˙O2peak). Our results indicate that HFE risk genotypes are associated with improved endurance performance in a 10-km cycling TT. This is consistent with previous studies that have shown that the iron overload risk genotypes are more common in elite endurance athletes compared with the general population (9). Notably, a recent meta-analysis reporting on 586 athletes of various ethnicities corroborated the higher prevalence of H63D polymorphism in endurance athletes compared with the general population, regardless of one’s ethnicity (14). However, the meta-analysis had only considered the allelic effect of H63D genotype (14). The present study is the first to examine the association between an individual’s iron overload risk, as determined by the compound allelic effect of the HFE genotypes C282Y and H63D, and endurance performance.
Our results also indicate that HFE risk genotypes are associated with higher V˙O2peak. Specifically, our results showed that athletes with the medium- or high-risk HFE genotypes have an approximately 17% higher V˙O2peak compared with athletes possessing the low-risk HFE genotypes. Examining each of the two SNP separately revealed that the effects were driven primarily by the C282Y (rs1800562) SNP. There was no significant difference in TT performance or V˙O2peak between the genotypes of the H63D variant. This is inconsistent with a recent study that demonstrates the “G” allele of H63D is associated with improved aerobic capacity (14). This may be due to the risk variant of C282Y having greater penetrance than the risk variant of H63D (5). It is also possible that the association between H63D and aerobic capacity is only observed in higher level competitive athletes, such as Olympic athletes (14). Endurance athletes and mixed athletes outperformed power athletes on the TT and had a greater aerobic capacity. This suggests that endurance athletes performed better because of more extensive training within this domain compared with other sport types; however, the significant association between HFE genotype and 10-km TT performance as well as V˙O2peak was still present after controlling for sport type.
Our findings are consistent with the important role that iron metabolism plays in endurance performance (15). The key function of iron is to facilitate oxygen transport in RBC and tissues via Hb (16). Some studies suggest that exercise training may stimulate erythropoiesis, which decreases the age of one’s RBC (17,18). This will ultimately increase one’s metabolic activity because of an increased amount of young RBC (18). 2,3-Diphosphoglycerate (2,3-DPG) is an intermediate glycolysis product that is produced in RBC. Increased metabolic activity due to exercise training may drive the long-term elevation of the Hb allosteric effector 2,3-DPG (18–21). Increased levels of 2,3-DPG will decrease Hb-O2 affinity, promoting oxygen unloading and availability in the lungs and other tissues around the body (22). In addition to Hb-O2 affinity, oxygen transport capacity is affected by one’s blood volume of Hb. An increased amount of Hb leads to increased quantities of oxygen being transported to the tissues (18), which has been associated with improved athletic performance (23). A strong association exists between total Hbmass and maximal oxygen uptake (V˙O2max) (21). Oxygen carrying capacity, which is associated with total Hbmass (18), has been linked with variable outcomes in endurance performance (24). Increased levels of Hb in the blood have been associated with improved oxygen carrying capacity and, by extension, one’s maximal aerobic capacity (23). Accordingly, improved oxygen uptake and aerobic capacity can confer a potential performance advantage to athletes by improving oxygen delivery to the muscle.
Athletes are at a greater risk of possessing low iron levels compared with the general population because of increased training and competition demands (25–27), which exacerbate iron loss through means of heavy sweating, increased blood loss in the gastrointestinal tract and urine (from effect, jostling of organs), and the accelerated breakdown of RBC due to exercise (28). Endurance athletes are at a higher risk compared with athletes from other sports (29–31), potentially because of increased foot-strike hemolysis among other factors. Iron deficiency can negatively affect athletic performance because of reduced oxygen delivery to the skeletal muscle, reduced blood pH, and accelerated depletion of muscle glycogen stores (32). Normal iron levels also enable the maintenance of redox balance in the muscle and production of mitochondrial energy, both of which are crucial to athletic performance (33). One study suggested that endurance athletes are likely to possess lower Hb levels than normal because of low iron levels, potentially due to foot-strike hemolysis (34,35). Hence, athletes are likely to consume iron supplements as an aid to improve maximal oxygen uptake (7,17). However, this may worsen an athlete’s performance as excess iron can lead to increased oxidative stress, including muscle tissue damage (36).
The risk variants C282Y and H63D may have significantly reduced binding affinity to TFR2, which may downregulate hepcidin release leading to increased iron levels in the bloodstream (4). Iron levels on the higher end of the normal range may confer a potential performance advantage because of improved oxygen carrying capacity mediated through hematological parameters such as Hb (7,37). Greater bioavailability of serum iron may lead to increased erythropoiesis that could increase an athlete’s oxygen carrying capacity and maximal oxygen uptake because of elevated Hb (2). Furthermore, increased erythropoiesis can also improve an athlete’s recovery between intermittent high-intensity exercise and muscle regeneration by decreasing muscle fatigue (8). Increased Hb has been associated with the HFE risk variant genotypes, which may explain the potential physiological differences compared with those without the risk variants (37,38).
The results of the present study suggest that athletes may benefit from regularly monitoring their iron status and consider supplements to optimize iron stores under the guidance of dietitians or other health professionals (2). When monitoring and optimizing iron status, athletes, their trainers, and health providers should be mindful of their HFE genotype. Collectively, our results also demonstrate that a medium/high risk of HH determined by an individual’s HFE (rs1800562 and rs1799945) genotype is associated with better endurance performance. However, the effect of HFE risk genotypes remains unknown for other measures of performance, such as anaerobic power-based type exercise.
Despite the potential for improvements in athletic performance in those who possess the HFE risk variants, some studies have suggested otherwise (11,39). One study showed that male adolescents with one H63D risk variant (n = 7) possessed lower aerobic capacity compared with those without the risk variants (n = 6) (39). However, there are several limitations, such as a small sample size and different sport modalities being assessed, which could have influenced this result. Another study determined that the H63D risk variant did not predict performance at the 2008 Kona Ironman championship triathlon (11). However, performance could have been influenced by many uncontrolled variables, including ambient temperature, precompetition nutrition, the presence of overtraining syndrome, and familiarity with the racing distances. Some limitations include that this study population consisted solely of competitive male athletes, so future trials should examine if HFE risk variants predict endurance performance and aerobic fitness in recreationally active males and females and competitive female athletes. Another limitation is the small number of subjects with the rare genotypes of each SNP associated with medium or high risk.
In summary, we found that individuals possessing the medium- or high-risk HFE genotypes (rs1800562 and rs1799945) outperform those with the low-risk genotypes in a 10-km cycling TT. Furthermore, individuals in the medium- or high-risk group possess a greater V˙O2peak compared with those with a low risk for HH. The results of our study highlight the importance of monitoring and optimizing an athlete’s iron status.
The authors thank all participants in the study as well as Alicia Jarosz, Peter Radonic, Nicholas Wojcik, and Angela Solomon for their help with data collection. This study was funded by the Canadian Institute of Health Research, the Canadian Foundation for Dietetic Research, Nutrigenomix Inc., the Coca-Cola Company, and Mitacs. Results of the present study do not constitute endorsement by the American College of Sports Medicine. Results are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation. A.E.-S. is the Founder and holds shares in Nutrigenomix Inc. B.G-B is Director of Research and Development at Nutrigenomix Inc., and N.G. serves on the Scientific Advisory Board of Nutrigenomix Inc.
1. Anker SD, Comin Colet J, Filippatos G, et al. Ferric carboxymaltose in patients with heart failure and iron deficiency. N Engl J Med
2. Hollerer I, Bachmann A, Muckenthaler MU. Pathophysiological consequences and benefits of HFE
mutations: 20 years of research. Haematologica
3. McKay AKA, Pyne DB, Burke LM, Peeling P. Iron metabolism: interactions with energy and carbohydrate availability. Nutrients
4. Porto G, Brissot P, Swinkels DW, et al. EMQN best practice guidelines for the molecular genetic diagnosis of hereditary hemochromatosis (HH). Eur J Hum Genet
5. Allen KJ, Gurrin LC, Constantine CC, et al. Iron-overload–related disease in HFE
hereditary hemochromatosis. N Engl J Med
6. Hanson EH. HFE gene and hereditary hemochromatosis: a HuGE review. Am J Epidemiol
7. Guest NS, Horne J, Vanderhout SM, El-Sohemy A. Sport nutrigenomics: personalized nutrition for athletic performance. Front Nutr
8. Hermine O, Dine G, Genty V, et al. Eighty percent of French sport winners in Olympic, World and Europeans competitions have mutations in the hemochromatosis HFE gene. Biochimie
9. Chicharro JL. Mutations in the hereditary haemochromatosis gene HFE in professional endurance athletes. Br J Sports Med
10. Brutsaert TD, Hernandez-Cordero S, Rivera J, Viola T, Hughes G, Haas JD. Iron supplementation improves progressive fatigue resistance during dynamic knee extensor exercise in iron-depleted, nonanemic women. Am J Clin Nutr
11. Grealy R, Herruer J, Smith CLE, Hiller D, Haseler LJ, Griffiths LR. Evaluation of a 7-gene genetic profile for athletic endurance phenotype in ironman championship triathletes. Roemer K, editor. Plos one
12. Guest N, Corey P, Vescovi J, El-Sohemy A. Caffeine, CYP1A2 genotype, and endurance performance in athletes. Med Sci Sports Exerc
. 2018;50(8, 8):1570.
13. Josse AR, Da Costa LA, Campos H, El-Sohemy A. Associations between polymorphisms in the AHR and CYP1A1-CYP1A2 gene regions and habitual caffeine consumption. Am J Clin Nutr
14. Semenova EA, Miyamoto-Mikami E, Akimov EB, et al. The association of HFE gene H63D polymorphism with endurance athlete status and aerobic capacity: novel findings and a meta-analysis. Eur J Appl Physiol
15. Abbaspour N, Hurrell R, Kelishadi R. Review on iron and its importance for human health. J Res Med Sci Off J Isfahan Univ Med Sci
16. Volpe SL. Iron and athletic performance. ACSMs Health Fit J
17. Marengo-Rowe AJ. Structure–function relations of human hemoglobins. Bayl Univ Med Cent Proc
18. Mairbäurl H. Red blood cells in sports: effects of exercise and training on oxygen supply by red blood cells. Front Physiol
19. Böning D, Schweigart U, Tibes U, Hemmer B. Influences of exercise and endurance training on the oxygen dissociation curve of blood under in vivo and in vitro conditions. Eur J Appl Physiol
20. Braumann KM, Boning D, Trost F. Bohr effect and slope of the oxygen dissociation curve after physical training. J Appl Physiol
21. Schmidt W, Prommer N. Impact of alterations in total hemoglobin mass on V˙O2max
. Exerc Sport Sci Rev
22. Bauer C. Antagonistic influence of CO2
and 2,3 diphosphoglycerate on the Bohr effect of human haemoglobin. Life Sci
23. Berglund B, Hemmingson P. Effect of reinfusion of autologous blood on exercise performance in cross-country skiers. Int J Sports Med
24. Calbet JAL, Lundby C, Koskolou M, Boushel R. Importance of hemoglobin concentration to exercise: acute manipulations. Respir Physiol Neurobiol
25. Fallon KE. Screening for haematological and iron-related abnormalities in elite athletes-analysis of 576 cases. J Sci Med Sport
26. Malczewska J, Szczepańska B, Stupnicki R, Sendecki W. The assessment of frequency of iron deficiency in athletes from the transferrin receptor-ferritin index. Int J Sport Nutr Exerc Metab
27. Parks RB, Hetzel SJ, Brooks MA. Iron deficiency and anemia among collegiate athletes: a retrospective chart review. Med Sci Sports Exerc
28. Sekulic D, Tahiraj E, Maric D, Olujic D, Bianco A, Zaletel P. What drives athletes toward dietary supplement use: objective knowledge or self-perceived competence? Cross-sectional analysis of professional team-sport players from Southeastern Europe during the competitive season. J Int Soc Sports Nutr
29. Rietjens GJ, Kuipers H, Hartgens F, Keizer HA. Red blood cell profile of elite Olympic distance triathletes. A three-year follow-up. Int J Sports Med
30. Lukaski HC. Vitamin and mineral status: effects on physical performance. Nutr Burbank Los Angel Cty Calif
31. Sinclair LM, Hinton PS. Prevalence of iron deficiency with and without anemia in recreationally active men and women. J Am Diet Assoc
32. Sim M, Garvican-Lewis LA, Cox GR, et al. Iron considerations for the athlete: a narrative review. Eur J Appl Physiol
33. Buratti P, Gammella E, Rybinska I, Cairo G, Recalcati S. Recent advances in iron metabolism: relevance for health, exercise, and performance. Med Sci Sports Exerc
34. Magnusson B, Hallberg L, Rossander L, Swolin B. Iron metabolism and “sports anemia.” II. A hematological comparison of elite runners and control subjects. Acta Med Scand
35. Watts E. Athletes’ anaemia. A review of possible causes and guidelines on investigation. Br J Sports Med
36. Recalcati S, Minotti G, Cairo G. Iron regulatory proteins: from molecular mechanisms to drug development. Antioxid Redox Signal
37. Astle WJ, Elding H, Jiang T, et al. The allelic landscape of human blood cell trait variation and links to common complex disease. Cell
38. Mast AE, Lee T-H, Schlumpf KS, et al. The impact of HFE mutations on haemoglobin and iron status in individuals experiencing repeated iron loss through blood donation*: HFE mutations in blood donors. Br J Haematol
39. Luszczyk M, Kaczorowska-Hac B, Milosz E, et al. Reduction of skeletal muscle power in adolescent males carrying H63D mutation in the HFE
gene. Biomed Res Int