Blood biomarkers are measureable characteristics that reflect a particular physiologic state (4) and, in the general population, are valuable in risk assessment and diagnosis of pathology as well as in determining effectiveness of treatment. In sports, they may be used to assess an athlete's overall health or to determine positive/negative adaptations to training/environmental stimuli (15,20). Blood biomarkers have been used to evaluate erythrocyte/hemoglobin status (1,17,18) and to determine muscle damage (5,6,7,8,12,14) in selected athletic populations. Traditionally, erythrocyte/hemoglobin status has been evaluated in endurance athletes because oxygen-carrying capacity is a key contributor to performance success. The prevalence of iron-deficiency anemia is believed to be higher in these endurance athletes, especially in female athletes (1,18), but a recent retrospective study of NCAA Division I male and female athletes participating in 15 different sports including cross country did not find a high prevalence of anemia in these athletic groups (17). However, they did find that 30.9% of female athletes were considered iron deficient without anemia (17). These data would suggest we need to re-evaluate how these biomarkers are altered in athletes participating in sports differing in their aerobic and anaerobic energy demands with an emphasis on how they change with training and competition. On the other hand, biomarkers of muscle damage such as creatine kinase (CK) and lactate dehydrogenase (LDH) have been more commonly assessed in contact sports such as football. To date, the literature has provided mixed results with peaks observed after preseason camps (10) vs. a gradual increase over the course of the season (10). Examining these biomarkers in endurance athletes has been limited but has merit due to the potential that intense training, poor nutrition, and overuse could impact these parameters (20).
Although using biomarkers to ensure maintenance of optimal hematology and minimize muscle damage across a competitive season has value, the interpretation of biomarker changes can be confounded by a number of factors. Currently, there are not commonly used reference values for blood biomarkers that are specific to elite athletes or individuals training at moderate altitudes, so clinically, these biomarkers are usually compared with the reference values of the general population. This can be problematic for multiple biomarkers where athletes may have different norms than the general population. An example of this is the assessment of CK values. The previous literature shows it is not uncommon for high-level athletes to have higher than normal values for muscle damage markers, especially during intense training bouts (16). Normal reference ranges for CK are ∼24–200 U·L−1 (11), whereas values much higher than these were recorded in athletic populations (2), and some researchers proposed reference values with upper bounds between 500 and 1,000 U·L−1 (16). In addition, previous studies showed fluctuations in hematologic parameters at a moderate altitude that may last up to 7 months (3). For athletes training at a moderate altitude, although competing or spending extended periods of time at lower altitudes, interpretation of blood biomarkers, specifically hematologic biomarkers, can be difficult.
This study used a retrospective approach to describe and compare the change in selected blood biomarkers in collegiate football and cross-country athletes across a competitive season. Specifically, we set out to describe seasonal changes in muscle damage markers, CK and LDH, as well as seasonal changes in oxygen-carrying capacity biomarkers including hemoglobin concentration, hematocrit (HCT), serum ferritin, red blood cell count (RBC), red cell distribution width (RDW), mean corpuscular volume (MCV), and mean corpuscular hemoglobin (MCH). Although a retrospective approach has limitations, our access to a unique data set that had been used for monitoring athlete health in 2 sports demanding different approaches to training and competition provided a unique opportunity to contribution to the literature and set the stage for future longitudinal approaches to evaluating blood biomarkers in collegiate athletes. In addition, this study assesses these biomarker changes in athletes training at a moderate altitude (1,655 m), which is novel to the assessment of biomarkers in an athletic population, and may have an impact on blood volume compartments affecting oxygen-carrying capacity and hydration status. As far as the authors are aware, no such descriptive longitudinal biomarker analysis has been published on athletes of multiple sports, especially at a moderate altitude. The importance of understanding these hematologic changes in an athletic population at a moderate altitude is of interest to those teams and athletes training or competing at a moderate altitude.
Experimental Approach to the Problem
As mentioned previously, this study had a unique opportunity to use a database of previously collected blood biomarkers that were regularly used to monitor the athlete's health and performance at the University of Colorado-Boulder, which is located at a moderate altitude of 1,655 m. The primary focus of this retrospective study was to assess and compare how selected blood biomarkers change in contact (football) and noncontact (cross-country) sports during the competition season. The secondary focus was to assess whether athletes training and living at a moderate altitude started within the normal reference range and whether they maintained values inside this reference range across the season.
As part of established routine monitoring, 91 football players (FB) from a single Division I team had laboratory test results drawn over the course of a season. Of these, 31 players were present for at least 3 of 4 lab draws and were included in the analyses. These 31 players represented a cross section of positions on offense and defense but did not include athletes listed as kickers. Specifically, our sample distribution by the position was defensive backs/wide receivers represented 26%, running backs/linebackers/tight ends represented 35%, defensive/offensive linemen represented 35%, and quarterback represented 4%. Forty-six cross-country runners (25 men and 21 women) from the same institution had the same laboratory test results drawn over the course of the season. Of these, 29 runners (13 men—MXC and 16 women—FXC) were present for at least 3 of 5 lab draws and were included in the analyses. Laboratories focusing on blood biomarkers of hematology and muscle damage were collected at each time point and included a complete blood count, serum ferritin, CK, and LDH. Laboratory test results were drawn under standard conditions and under the control of the sports medicine staff. A physician-directed standardized iron supplementation protocol was used for all athletes who showed indication or risk of anemia, at the institution during the time period in which this study took place, and all athletes were instructed to maintain an iron-rich diet. As far as the researchers are aware, none of the FB athletes was iron supplemented. By contrast, almost all XC athletes were supplemented, most with an over-the-counter oral iron supplementation (Hemaplex). In addition, 1 man underwent 2 iron infusion procedures, 1 man had an iron glycimate (Xymogen), 1 woman had ferrous sulfate liquid elixir supplementation, and 1 woman had a prescription iron supplement (Multigen Plus). There was only 1 recorded MXC athlete who was unable to have iron supplementation because of another medical condition and 1 additional individual who was not supplemented.
Approval was obtained from the University of Colorado Institutional Review Board for this retrospective study. Written informed consent was not required because the previously collected results were deidentified before proceeding with data analyses by a designated individual, and the study was determined to not involve human subjects as determined by Department of Health and Human Services and Federal Drug Administration regulations.
For FB, laboratory test results were collected at 4 time points: preseason (early August—FT1), following fall training camp (late August—FT2), mid-season (October—FT3), and late-season (November—FT4). Five collection times occurred for XC: cross-country preseason (early August—CT1), cross-country mid-season (October—CT2), indoor track season (January—CT3), outdoor track season (March—CT4), and cross-country preseason of the following year (August year 2—CT5). All laboratory test results were drawn in the morning after an overnight fast and in the case of cross-country athletes, after a day off from running.
FB, MXC, and FXC were each analyzed in separate groups using the same process. Extreme outlier values (±3 inner quartile range [IQR]) were removed from all analyses (IQR, or inner 50% of the data found by Q1–Q3). To account for the missing data points in the analysis, a linear mixed model (LMM, package lmer4) was used to evaluate whether hematological parameters changed systematically over time. Assumptions of the LMM were assessed using a combination of q-q plots and residuals plots. For variables that violated any of the assumptions of the LMM, further analyses were performed to help substantiate the results. This analysis consisted of only using athletes with all data points present and combining male and female cross-country athletes into 1 analysis. This technique allowed for variables to pass the assumptions of the LMM that previously failed. However, for all variables, similar trends were observed between the initial analysis and this secondary analysis.
A variance components model was used to determine the amount of variability due to fixed (i.e., variability in the mean at each time point) and random (i.e., between subject variability at any given time point) effects across time, for all variables.
Athletes were compared to the normal lab values provided by the laboratory performing the blood biomarker analyses. To compare the 3 groups (FB, MXC, and FXC), a Welch 2-sample T test using the overall mean including all individuals at every time point for each group was used. Finally, using the overall mean for each group, we calculated percent change at each time point to additionally assess the magnitude each group was changing across the season. Statistical analysis was performed in R Studio version 3.3.1 (19), and significance for all analysis was set at p ≤ 0.05.
The variance components models for all 3 groups (FB, MXC, and FXC) showed a high amount of between-subject variability (random effects) in most variables (Table 1). This indicates that in these biomarkers, the variance is mostly attributed to between subject variability as opposed to variability in the mean over time. Creatine kinase and LDH for all 3 groups were the only variables that had a higher percentage of the variability in the model that was attributed to changes across time (fixed effects).
The Welch 2-sample T-test analyses showed significant differences between the overall averages of a number of different variables. FB was higher than MXC in average CK and ferritin, whereas FB was lower than MXC in LDH, MCV, and MCH. No differences in Hct, Hb, or RBC were found between MXC and FB (Table 3). There were also sex differences between XC, with MXC having higher CK, LDH, Hct, Hb, RBC, and lower MCV. There were no sex differences in ferritin, MCH, or RDW (Table 2).
For MXC, 22 of 494 (4%) hematology marker samples were outside the normal reference range. This included 14% of the ferritin samples being below the normal reference range, from 5 different individuals across all time points. In addition, 6% of the Hct samples were high, 5% of the RDW samples were low, 4% of the RBC samples were high, and 2% of the MCV samples were high, compared with the normal reference range. There were 3 individuals who had multiple different hematology marker samples outside the reference range, and ultimately, 10 of the 13 men who were analyzed had at least 1 hematology biomarker outside of the normal reference range.
For FXC, 80 of 578 (14%) hematology marker samples were outside the normal reference range. This included 37% of RDW samples being low from 11 individuals and 26% of MCV samples being high from 6 individuals across every time point in the season. In addition, 12% of the Hct samples were high, 11% of the Hb samples were high, 11% of the MCH samples were high, 7% of the RBC samples were high, and 6% of the ferritin samples were low, compared with the normal reference range. Fourteen of the 16 individuals who were analyzed had at least 1 hematology biomarker outside the normal reference range, and 11 of these individuals had multiple different hematological biomarkers that were outside the range.
For both MXC and FXC, ferritin and RBC had no significant changes across the year. In addition, for women, Hb and MCH had no significant changes across the year. All other variables showed significant changes (p < 0.05) (Table 2 and Figure 1). The overall average of Hct was 47.78 for men and 43.78 for women. Hct was significantly higher at CT2 (M: +3.85%, F: +2.64%) when compared with all other time points for men and most other time points for women (CT1 and CT5). Hb showed a similar trend in men with an overall average of 16.26, and CT2 (M: +2.92%) was significantly higher than most other time points (CT3, CT4, CT5). The mean corpuscular volume had an overall average of 90.61 for men and 93.55 for women and was lowest at CT1 (M: −1.62%, F: −1.84%) from most time points in men (CT2, CT4, and CT5) and from all time points in women. Males' MCH was highest at CT1 (M: +1.0%) compared with all other time points with an overall average of 30.89. Men had the highest RDW at CT2 (M: +1.75%) with an overall average of 12.87 and women at both CT2 and CT3 (F: +1.39%, +1.09%) with an overall average of 12.57, but RDW was only higher at these time points compared with CT5.
For cross-country men, 66 of 99 (66%) muscle damage marker samples were outside the normal reference range. Forty-six percent of the CK samples from 10 individuals across all 5 time points were higher than the normal reference range. In addition, 89% of the LDH samples were high compared with the normal reference range. All samples at CT2, CT3, and CT4 were above the reference range, and all but one was high at CT5.
For cross-country women, 91 of 130 (73%) muscle damage marker samples were outside the normal reference range. 64% of the CK samples from 13 individuals across all 5 time points were higher than the normal reference range. Ninety-two percent of the LDH samples were high compared with the normal reference range. All samples at CT2, CT3, CT4, and CT5 were above the normal reference range.
Creatine kinase and LDH significantly changed across the year (p < 0.05) (Table 2 and Figure 2). Creatine kinase had an overall average of 270.35 for men and 171.85 for women and was significantly higher at CT4 (M: +41.88%, F: +21.26%) compared with all other time points for men and for most other time points (CT3 and CT5) for women. Women also had a significantly lower CK at CT2 (F: −33.93%) compared with all other time points. Overall average LDH was 502.23 for men and 425.29 for women, and for both sexes, LDH was lowest at CT1 (M: −55.29% F: −52.04%) compared with all other time points.
For football players, 40 of 814 (5%) hematology marker samples were outside the normal reference range. This included 12% of RBC samples being high from 7 individuals across all 4 time points. In addition, 8% of Hb samples were high, 4% of MCH samples were low, 3% of MCH samples were high, 6% of HCT samples were high, 3% of MCV samples were low, and 2% of ferritin samples were low, compared with the normal reference range. There were 11 individuals of the 31 total individuals with hematology samples outside the normal reference range.
For football, all hematology variables, besides MCH, changed significantly across the season (p < 0.05) (Table 3). HCT overall average was 46.95 and was significantly lower at FT2 (−2.10%) compared to all other time points, and Hb overall average was 16.04 and significantly lower at both FT2 (−2.46%) and FT3 (−0.58%) compared to FT1 and FT4. Ferritin overall average was 112.58 and increased across the season and was significantly higher at both FT3 (+26.49%) and FT4 (+44.57%) compared to FT1 and FT2. Red blood cell count was lower at FT2 (−2.48%) and FT3 (−0.40%) compared to FT1 and FT4, with an overall average of 5.40. The mean corpuscular volume overall average was 87.26 and was lowest at FT1 (−0.76%) compared with all other time points and highest at FT3 (+0.84%) compared with all other time points.
For football players, 144 of 207 (70%) muscle damage marker samples were outside the normal reference range. Eighty-two percent of CK samples were higher than the normal reference range and were spread across every time point. All but one individual had at least 1 CK value higher than the normal reference range. In addition, 58% of the LDH samples were above the normal reference range, from 28 of the 31 individuals. All 31 individuals had at least 1 sample above the normal reference range for one (CK or LDH) of the muscle damage biomarkers.
Both CK and LDH changed significantly over the season for FB (p < 0.05) (Table 3 and Figure 2). Creatine kinase was significantly lower at FT1 (−45.40%) compared with all other time points, with an overall average of 456.36. Lactate dehydrogenase had an overall average of 253.86 and was significantly higher at FB2 (+21.05%) compared with all other time points.
MXC, FXC, and FB athletes had significant changes in several hematological blood biomarkers across monitored time points, but all means remained within the normal range. In addition, the variance components model showed that most of the variability in the hematology biomarkers was due to differences between subjects at each given time point, as opposed to differences between the means at any given time point. These results suggest XC and FB athletes' training and living at a moderate altitude maintain oxygen-carrying capacity over their competitive seasons. These findings are similar to those reported by Parks et al. (17), indicating that the incidence of anemia is low across a wide range of sports and does not seem to differ by an athletic event. However, changes in serum ferritin relative to Hb and Hct within and across the athletic populations studied suggest the need for additional research to determine their clinical/applied significance. For FB, the large (44.6%) increase in serum ferritin warrants further examination to determine whether the changes are related to altered erythropoiesis, diet, altitude, or training.
Although we observed no significant change in ferritin levels for male or female XC athletes over the year, most of these athletes were at the low end of the normal reference range with no difference between sexes, despite most of the athletes being iron-supplemented throughout the year of competition. Interestingly, all athletes had higher than normal values for Hct and Hb. Surprisingly, there was no difference in Hct and Hb between MXC and FB athletes, although there was a significant difference in ferritin between these groups. These findings suggest we need to reconsider the role of serum ferritin levels in maintaining red blood cell homeostasis in athletic populations living and training at a moderate altitude.
For XC athletes, one explanation for our observation of low serum ferritin values and maintained levels of Hb and Hct could be an increase in red blood cell turnover, which has been supported in the literature in endurance athletes and individuals living at an altitude (5,9,12,14). In addition to the altitude, hemolysis has been found to be increased in endurance runners due to repetitive footstrikes on hard running surfaces (8) resulting in an increase in erythropoiesis and iron turnover (6,12). This increase in red blood cell turnover may be exacerbated in the FXC athletes, who demonstrated a greater percentage of hematological values outside the normal reference range when compared with the male athletes. The demands of XC as a sport, changes in red blood cell turnover, exposure to a moderate altitude, menstruation/amenorrhea (for FXC), or poor dietary habits could all influence these biomarkers in XC athletes. Future studies should investigate these stimuli and the contribution they make to the hematological profiles of XC athletes training at a moderate altitude.
Unlike the changes we observed in the hematological blood biomarkers, many of the time point means for the 2 serum muscle damage markers (CK and LDH) were outside the normal reference ranges, and as seen from the variance components models, most of the variability in these markers were attributed to changes over time. In addition, fluctuations from the overall mean in these markers for each group were much higher than those observed in the hematological variables, with some reaching over 55%. In agreement with the previous literature (16), we found a large proportion of the muscle damage biomarker values were higher than the normal reference range for both FB and XC. If elevated serum CK and LDH were in fact indicative of muscle damage, we would expect to see at least some decreases in training ability and performance, but based on team performance and training staff reports, these parameters were not adversely affected. Although serum CK and LDH levels have been used to indicate muscle damage in much of the previous literature, there are also other possible mechanisms for the trends and high values of these markers in our populations.
First, we would note that serum LDH and CK for FB and XC athletes did not follow similar patterns, suggesting the 2 biomarkers may be indicative of different pathologies or physiology in these populations. For FB, we saw a significant increase in CK across the season, while LDH peaked at FT2 and returned to initial values. In contrast for XC, CK was highest at only 1 time point at the end of the season and LDH was lowest at the beginning. There could be multiple mechanisms responsible for these differences including tissue specificity and appearance/clearance rate differences of these 2 enzymes. Aside from its common use as an indirect muscle damage marker, the previous literature suggested LDH also increases with hemolysis (21), which as mentioned previously may be occurring in XC athletes due to repetitive footstrikes on hard running surfaces (8). This finding could provide an additional explanation for the different trends of CK and LDH, as well as the higher levels of LDH in XC athletes than in the FB athletes in this population.
Other factors influencing the appearance and clearance rate of these enzymes may also explain the high values observed. One animal study suggested increasing the lymphatic pump through muscle movement can increase serum enzyme levels without any indication of muscle damage (13). Increasing lymphatic flow through the high level of muscle activity in these athletes could explain the high serum muscle damage markers.
This study provides evidence that the occurrence of high serum enzymes such as CK and LDH may not be indicative of muscle damage pathology in this population and should be further investigated in terms of its specificity to muscle damage and the normal physiologic range for high-level athletes. Increased hemolysis and lymphatic flow are just 2 possible explanations that could explain the presence of high values in serum proteins in the absence of significant muscle damage pathology and should be investigated further in future studies.
To illustrate the need for longitudinal studies to establish the significance of our observed alterations in blood biomarkers in collegiate athletes training and competing at moderate altitude, Table 4 provides blood biomarker values for a small sample of nonvarsity college students residing at a moderate altitude. These students are part of an ongoing protocol and were recruited from a large enrollment nonmajor undergraduate class focusing on nutrition and health. When compared with male varsity athletes, male control values for LDH, CK, and ferritin were not different, but male athletes had significantly higher values for Hb and HCT. When compared with female varsity athletes, female control values for LDH, CK, Hb, and HCT were not different, but female controls had significantly lower ferritin values. These data suggest the need for additional research to determine how moderate altitude exposure influences blood biomarkers independent of training.
Overall, our retrospective data analyses provide unique insight into hematological and muscle damage blood biomarkers in collegiate athletes living and competing at a moderate altitude. We observed changes in hematological biomarkers in all 3 groups of athletes, but the means remained within the normal reference range, and there was no indication of systematic decreases in oxygen-carrying capacity. Future studies should explore the mechanisms for both the individual and systematic variability in these parameters over time and determine whether changes within the normal ranges are related to normal fluctuations or training/performance.
In addition, there were significantly higher values of markers traditionally used to assess muscle damage in all 3 groups (even in their preseason values), compared with the normal reference ranges. However, when compared with nonvarsity college students residing at a moderate altitude, no differences were observed. Future studies should explore whether these values are indicative of muscle or other tissue damage pathology or whether the high values may be explained by an increased enzymatic flux associated with training and competition as well as adaptations associated with the altitude.
Owing to the retrospective nature of our study, we were only able to use concentration measurements without knowing how changes in plasma volume influenced these measures. There are well-established changes in plasma volume due to both training and altitude, which could potentially confound our interpretation of changes in blood biomarkers between athletic groups as well as across the competitive season. For example, our data revealed that Hb did not discriminate between MXC and FB athletes or between FXC and controls. However, a more appropriate comparison might be to measure total Hb mass and express the parameter relative to body mass. Such an analysis would likely reveal that MXC athletes have significantly higher values when compared with FB athletes and that FXC athletes have significantly higher values than female controls. Future studies should consider taking this approach.
The biomarkers in this study are novel in both the extended time frame that they were taken and the moderate altitude condition. The researchers believe the data provide strong descriptive evidence that the use of biomarker monitoring in this population can be useful in health and performance maintenance of these athletes but that researchers and clinicians should be aware of the confounding variables associated with these measurements. These data provide a comparative basis for future investigation into the best tools for ensuring optimal athlete health and performance across a season. It also highlights that such investigations need to take a comprehensive approach to examining iron status especially in the athletic and nonathletic female populations.
Pac-12 Student-Athlete Health & Well Being Grant Program.