Changes in Blood Biomarkers Across a Competitive Season in Collegiate Athletes Residing at a Moderate Altitude: a Retrospective Analysis

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1 University of Colorado, Boulder CU Scholar Integrative Physiology Graduate Theses & Dissertations Integrative Physiology Spring Changes in Blood Biomarkers Across a Competitive Season in Collegiate Athletes Residing at a Moderate Altitude: a Retrospective Analysis Kalee Lucy Morris University of Colorado at Boulder, kalee.morris@colorado.edu Follow this and additional works at: Part of the Physiology Commons Recommended Citation Morris, Kalee Lucy, "Changes in Blood Biomarkers Across a Competitive Season in Collegiate Athletes Residing at a Moderate Altitude: a Retrospective Analysis" (2017). Integrative Physiology Graduate Theses & Dissertations This Thesis is brought to you for free and open access by Integrative Physiology at CU Scholar. It has been accepted for inclusion in Integrative Physiology Graduate Theses & Dissertations by an authorized administrator of CU Scholar. For more information, please contact cuscholaradmin@colorado.edu.

2 CHANGES IN BLOOD BIOMARKERS ACROSS A COMPETITIVE SEASON IN COLLEGIATE ATHLETES RESIDING AT MODERATE ALTITUDE: A RETROSPECTIVE ANALYSIS by Kalee L. Morris University of Colorado Boulder A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirement for the degree of Master of Science Department of Integrative Physiology 2017

3 ii This thesis entitled: Seasonal changes in blood biomarkers in collegiate athletes residing at a moderate altitude: a retrospective analysis written by Kalee L. Morris has been approved for the Department of Integrative Physiology William C. Byrnes, Ph.D. Matthew McQueen, Ph.D. Kenneth P. Wright, Ph.D. Date The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline. IRB protocol #

4 iii Abstract Morris, Kalee L. (M.S., Integrative Physiology) Changes in blood biomarkers across a competitive season in collegiate athletes residing at a moderate altitude: a retrospective analysis Thesis directed by Associate Professor William C. Byrnes Blood biomarkers are measureable characteristics that reflect a particular physiologic state and in sports, have been used to assess an athlete s overall health or determine positive/negative adaptations to training/environmental stimuli. The present study aimed to describe the changes in blood biomarkers in collegiate football (FB) (n=31) and cross country (XC) (n=29; 16 female, 13 male) athletes across a competitive season. These athletes were training and living at a moderate altitude (1655 m). The study used a database of previously collected blood biomarkers, that were regularly used to monitor athlete s health and performance. All three groups (FB, MXC, FXC) had significant changes (p<0.05, tested by a linear mixed model) in both hematological biomarkers and muscle damage biomarkers. Although significant changes were observed, the means of the hematological blood biomarkers stayed within the normal reference ranges, and from a variance components analysis we observed that most of the variability was due to individual variability. Female XC had the most out of range hematology values (14%), which may be indicative of differences in hematological maintenance or pathology. Overall the hematological analyses suggest no significant decrements to oxygen carrying capacity across the season for FB, MXC, or FXC. Muscle damage biomarkers however, had means that were above the normal reference ranges, and the variance was mostly attributed to changes over time, rather than individual variability. These results could be high levels of muscle damage as a pathology in these athletes, or may indicate a need to consider new reference ranges for this population. This study provides evidence that both XC and FB athletes have changes in hematological and muscle damage markers across the season, of which may be due to effects of training, adaptations to moderate altitude, or nutrition. Further studies should assess these underlying mechanisms that cause the longitudinal changes in markers of hematology and muscle damage.

5 iv Acknowledgements First, I would like to thank my entire thesis committee for agreeing to review and provide guidance on my project. I would especially like to thank Dr. McQueen for providing me with a strong basis in statistics and epidemiology in my graduate career that will allow me to excel in my future. Being your teaching assistant this past year has given me confidence and skills I will carry with me forever. Additionally, I would like to thank Dr. Byrnes, for believing in me and developing my skills as a researcher and providing me with a passion for exercise physiology. The opportunity you have given me as a concurrent student in your lab has allowed me to think critically and begin my career as a scientist. Additionally, I would like to thank the Integrative Physiology department as a whole, for providing me with a solid foundation and passion for science. The knowledge I have learned at CU Boulder has challenged me to be well-rounded not only in academia, but in life. Also, the entire Applied Exercise Science Lab, especially Jim Peterman for being a wonderful mentor, and Jesse Goodrich, for helping me with all things stats related. I would like to thank the Sports Medicine Department for collaborating on this project and for collecting and de-identifying the data that was used. Last but not least, I would like to thank my family for always encouraging me to pursue my dreams, and Dylan for reminding me every day to enjoy life s journey.

6 v Chapter Table of Contents I. Introduction....1 II. Methods 5 III. Results..7 IV. Discussion..20 V. References..26 Appendix A. Literature Review B. Additional graphs..33

7 vi List of Tables Table 1. Cross country male (M) and female (F) blood biomarkers (means SD) over 5 time points across the year Football blood biomarkers (means SD) over four time point across the season Variance components for male cross country (M), female cross country (F), and football (FB). Bold text indicates greatest contributor to the overall variance. Fixed effects are the variability in the mean at each time point. Random Effects are individual variability.. 19

8 vii List of Figures Figure 1. Oxygen carrying capacity blood biomarkers in male and female XC. Bars to the right of the graphs indicate the normal reference ranges. (Black bars are the ranges for males, gray bars are ranges for females) Immune cell blood biomarkers in male and female XC. Bars to the right of the graphs indicate the normal reference ranges. (Black bars are the ranges for males, gray bars are ranges for females) Muscle damage blood biomarkers in male and female XC. Bars to the right of the graphs indicate the normal reference ranges. (Black bars are the ranges for males, gray bars are ranges for females) Oxygen carrying capacity blood biomarkers in FB. Bars to the right of the graphs indicate the normal reference ranges Immune cell blood biomarkers in FB. Bars to the right of the graphs indicate the normal reference ranges Muscle damage blood biomarkers in FB. Bars to the right of the graphs indicate the normal reference ranges

9 1 Introduction Blood biomarkers are measureable characteristics that reflect a particular physiologic state (Coriell institute 2016), 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 (Meyer and Meister 2011, San Millan 2013). Although limited, current literature does provide evidence that monitoring specific blood biomarkers can lead to insights in these areas. Blood hematology biomarker analysis has been utilized in the evaluation of both oxygen carrying capacity and immune function. Oxygen carrying capacity is a key determinant in optimal endurance exercise performance, and can be negatively impacted by factors like decreased erythropoiesis due to inadequate iron storage or excessive training. Inadequate iron storage leading to iron deficiency anemia, can impact multiple blood biomarkers including decreased hemoglobin concentration (Hb), decreased mean corpuscular hemoglobin concentration (MCH), decreased mean corpuscular volume (MCV) and decreased number of new red blood cells (Beard and Tobin 2000). The prevalence of iron deficiency anemia is thought to be higher in athletic populations, specifically endurance runners and female athletes (Rowland 2012, Beard and Tobin 2000), but comparisons to sports with lower aerobic demands such as football are lacking. Sports physiologists have also used these biomarkers to assess hematologic changes that may be indicative of a heavy training stimulus or overtraining (Celsing et al. 1987, Woodson 1984, Dressendorfer et al. 1981). Some studies have found these biomarkers are maintained across a competitive season (Heisterberg et al. 2013). Others have reported shifts in hematologic maintenance, such as decreasing hematocrit (Hct) values, of which they conclude are most likely due to plasma volume changes (Meyer and Meister 2011, Ostojic and Ahmetovic 2008), or shifts in blood volume components due to intense training and taper periods (Mujika et al. 1997). Blood hematology biomarker analysis may also give insight into immune function. For athletes specifically, training may impact these immune biomarkers. Previous studies have shown moderate physical activity can have a positive effect on immune function (Gleeson 2007, Shephard and Shek 1994), whereas prolonged intense physical activity

10 2 may have a negative effect (Gleeson 2007). Nieman et al. (1994) have suggested the relationship between immune function (risk of upper respiratory infection) and exercise follows a J-shaped curve. For athletes, it may be possible to have these beneficial effects of exercise on the immune system during moderate intensity stretches of the season, whereas more intense periods may be accompanied by a strain on immune function. Through immune biomarker analysis, studies using athletes have found increases in immune biomarkers such as immunoglobulins and white blood cells, at the beginning and the end of a season, as compared to midseason. (Heisterberg et al. 2013). Monitoring for muscle damage via blood biomarkers is another common practice in the field of sports medicine and performance. Models of exercise induced muscle injury have utilized the presence of muscle specific enzymes in blood to indicate the presence of muscle damage. These models suggest that through eccentric muscle contraction, intense training and competition, and/or poor nutrition, myofibril disruption occurs leading to microtears within the muscle and allowing specific enzymes to leak in to the blood (San Milan 2013). Exercise physiologists can then use these enzymes, most commonly creatine kinase (CK) and lactate dehydrogenase (LDH) as biomarkers of muscle damage (Clarkson and Ebbening 1988; McNeil and Khakee 1992, Sorichter et al. 1995, Clarkson & Hubal 2002, Brancaccio et al. 2007). Although these experimental models and the use of enzymes to measure an induced muscle injury are well established in a laboratory setting, there has been little research on how these enzymes change in response to longer term training stimuli in high level athletes. Previous muscle damage biomarker literature in athletes has focused mainly on contact sports, and the pattern of how these enzymes change across a competitive season have been inconsistent. For football players, Kraemer et al. (2013) found a trend for increasing muscle damage across the season, and found that the number of plays for an individual was a predictor of amount of muscle damage later in the season, whereas earlier in the season it was not. These results may imply increasing muscle damage with increased training duration. Other studies have found muscle damage values that peak during pre-season camps and remain steady for the duration of the season (Hoffman et al. 2012), implying a possible protective repeated bout effect. It is also unclear whether fluctuations in muscle damage biomarkers are similar when resulting from contact muscle damage, such as a

11 3 block or hit in football, and continuous overuse damage, such as that we would expect to see in cross country. Although employing biomarkers to maintain 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, so clinically, these biomarkers are usually compared to the reference values of the public population. This can be problematic for multiple biomarkers where athletes may differ from the general population. A good example of this is the assessment of CK values. Previous literature has shown it is not uncommon for high level athletes to have higher than normal values for muscle damage markers, especially during intense training bouts (Mougios 2007). Normal reference ranges for CK are ~ U/L (Labcorp 2017), while values much higher than these have been recorded in athletic populations (Brancaccio 2007), and some researchers have proposed reference values with upper bounds between U/L (Mougios 2007). Thus, if an athlete has a high CK test result, it may be difficult for a clinician to distinguish between pathology and an adaptation to a high training stimulus. Additionally, sport to sport variation and fluctuation of blood biomarkers is also unknown. One prior study compared hematological blood biomarkers between multiple male sports at one time point in the season (Biancotti et al. 1992), and found slight differences, specifically lower haptoglobin, between endurance runners and other sports including cross-country skiing, tennis and fencing, swimming, cycling, rowing, and soccer. The authors are not aware of any studies that have compared endurance sport and contact sport athletes blood biomarkers at multiple time points across a season. We are also unaware of any studies that have assessed sex differences in blood biomarkers in collegiate athletes of the same sport across a season, despite the previous literature suggesting sex differences in muscle damage markers resulting from similar training stimuli (Kendall 2002), and hematology, especially oxygen carrying capacity (Murphy 2014, Lewis et al. 1986). Finally, previous studies have shown fluctuations in hematology at moderate altitude, that may last up to seven months (Brothers et al. 2007). Because of this, for athletes training at moderate altitude, while competing or spending extended

12 4 periods of time at lower altitudes, interpretation of blood biomarkers, specifically hematology biomarkers, can be difficult. The present study aimed to describe the change in blood biomarkers in collegiate football and cross country athletes across a competitive season. Specifically, we set out to describe changes in muscle damage markers, creatine kinase and lactate dehydrogenase, changes in oxygen carrying capacity biomarkers including hemoglobin concentration, hematocrit, serum ferritin, red blood cell count, red cell distribution width, mean corpuscular volume, and mean corpuscular hemoglobin, and immune function biomarkers such as white blood cell count, platelets, neutrophils, and monocytes. Additionally, this study assesses these biomarker changes in athletes training at a moderate altitude (1655 m), which is novel to the assessment of biomarkers in an athletic population, and may have an impact on hematology, specifically oxygen carrying capacity, and hydration status. The importance of understanding these hematologic changes in an athletic population at altitude is of interest to numerous teams and athletes training or competing at moderate altitude locations such as Colorado, Wyoming, California, Montana, New Mexico, Arizona, and Utah. This study had a unique opportunity to use a database of previously collected blood biomarkers, that were regularly used to monitor athlete s health and performance, at the University of Colorado, Boulder which is located at a moderate altitude of 1655m. The primary focus of this retrospective study was to assess how blood biomarkers change within each individual team, during the competition season. The secondary focus was to assess if athletes started within the normal reference range, and if they maintained values inside this reference range across the season.

13 5 Methods: Subjects As part of established routine monitoring, 91 football players from a single Division I team had labs drawn over the course of a season. Of these, 31 players were present for at least 3 of 4 lab draws and included in analysis. 46 cross country runners (21 female, 25 male) from the same institution had the same labs drawn over the course of the season. Of these, 29 runners (16 female, 13 male) were present for at least 3 of 5 lab draws and included in analysis. Labs focusing on blood biomarkers of hematology and muscle damage which were collected at each time point included a complete blood count (CBC), ferritin, creatine kinase (CK), and lactate dehydrogenase (LDH). Approval was obtained from the University of Colorado IRB for this retrospective study. Written informed consent was not required as the previously collected results were de-identified prior to proceeding with data analysis by a designated individual, and the study was determined to not involve human subjects as determined by DHHS and FDA regulations. Iron Deficiency Prevention Protocol A standardized iron supplementation protocol was used for all athletes at the institution during the time period in which this study took place. The threshold value of ferritin for initiation of iron supplementation is less than 50 mcg/dl for males and less than 40 mcg/dl for females. All athletes are instructed to maintain an iron rich diet. If athletes fall below the threshold values they are initiated on elemental iron ranging from mg/day as determined by the Sports Dietitian. Labs are rechecked at 12 weeks to assess responsiveness, and doses are adjusted as needed. If athletes fall below 40 mcg/dl for males and 30 mcg/dl for females, medical doctor evaluation and treatment is sought. Blood Draws For football players labs were collected at 4 times points: pre-season (August 1- FT1), following fall training camp (August 21- FT2), mid-season (October 16 - FT3), and late-season (November 10 - FT4). 5 collection times occurred for cross country runners: cross country preseason (August1 - CT1), cross country mid-season (October - CT2), spring preseason (January - CT3),

14 6 spring midseason (March - CT4), and cross country preseason of the following year (August2 - CT5). All labs were drawn in the morning after an overnight fast, and in the case of cross country runners after a day of rest. Athletes who were present for at least 3 collection times were included in the analysis. Statistics Football players, male runners, and female runners were each analyzed in separate groups using the same process. Extreme outlier values ( 3 IQR) were removed from all analyses. 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 analysis using a separate LMM was used to verify results. This analysis consisted of only using athletes with all data points present, and combining male and female cross country athletes into one analysis. This technique allowed for variables to pass the assumptions of the LMM that had 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. individual variability) effects across time, for all variables. Finally, using reference ranges for normal lab values, the percentage of values outside of the reference range for each variable was determined. Statistical analysis was performed in R version (R Foundation for Statistical Computing, Vienna, Austria) and significance for all analysis was set at p < 0.05.

15 7 Results: XC: Hematology: For MXC 22 out of 494 (4%) hematology marker collections were outside the normal reference range (reference ranges can be found in Appendix B). This included 14% of the ferritin collections being below the normal reference range, from 5 different individuals across all time points. Additionally, 7% of the PLT collections were low, 6% of the Hct collections were high, 5% of the RDW collections were low, 4% of the RBC collections were high, 2% of the MCV collections were high, and 2% of the WBC collections were high, compared to the normal reference range. There were 3 individuals that had multiple different hematology marker collections outside the reference range, and ultimately 10 out of the 13 males who were analyzed had at least one hematology biomarker that was outside of the normal reference range. For FXC, 80 out of 578 (14%) hematology marker collections were outside the normal reference range. This included 37% of RDW collections being low from 11 individuals, and 26% of MCV collection being high from 6 individuals across every time point in the season. Additionally, 12% of the Hct collections were high, 11% of the Hb collections were high, 11% of the MCH collections were high, 7% of the RBC collections were high, 6% of the ferritin collections were low and 1% of the WBC collections were high, compared to the normal reference range. 14 out of the 16 individuals who were analyzed had at least one 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. Additionally, for females Hb, MCH, and WBC had no significant changes across the year. All other variables showed significant changes (p<0.05) (Table 1, Figure 1, Figure 2). Hct was significantly higher at CT2 (M: , F: ) compared to all other time points for males, and most other time points for females (CT1 and CT5). Hb showed a similar trend in males, with CT2 (M: ) being significantly higher than most other time points (CT3,CT4,CT5). MCV was lowest at CT1 (M: F: ) from most time points

16 8 Table 1: Cross country male (M) and female (F) Blood Biomarkers (means SD) over 5 time points across the year. CT1 CT2 CT3 CT4 CT5 ferritin (ng/ml) RBC (x10e6/ul) M F M F Hb (g/dl) HCT (%) M F M F $& * * $& * MCV (fl) MCH (pg) RDW (%) WBC (x10e3/ul) PLT (x10e6/ul) NEU (%) CK (U/L) LDH (IU/L) M F #& $ #$& M F #$& # M F M * $+ F M F M #$ F #$& M F M #$& #$& & & $ * * * # * & * #$ * $& $ & # $ F * significant difference from T1, # significant difference from T2, $ significant difference from T3, & significant difference from T4, + significant difference from T5, (p<0.05)

17 9 Figure 1: Oxygen carrying capacity blood biomarkers in male and female XC. Bars to the right of the graphs indicate the normal reference ranges (Black bars are the ranges for males, gray bars are ranges for females). * significant difference from T1, # significant difference from T2, $ significant difference from T3, & significant difference from T4, + significant difference from T5

18 10 Figure 2: Immune cell blood biomarkers in male and female XC. Bars to the right of the graphs indicate the normal reference ranges (Black bars are the ranges for males, gray bars are ranges for females). * significant difference from T1, # significant difference from T2, $ significant difference from T3, & significant difference from T4, + significant difference from T5

19 11 in males (CT2,CT4,CT5) and from all time points in females, and males MCH was highest at CT1 (M: ) compared to all other time points. Males had the highest RDW at CT2 (M: ), and females at both CT2 and CT3 (F: ), but RDW was only higher at these time points compared to CT5. WBC for males was highest at CT2 (M: ) compared to most other time points (CT1,CT3,CT5). PLT were highest at CT3 and CT4 (M: , , F: , ) for both males and females. Changes in NEUT across the year were similar in both sexes, with both being significantly higher at CT1 (M: , F: ) from all time points besides CT4 for males, and from all time points for females. Muscle Damage: For cross country males, 66 out of 99 (66%) muscle damage marker collections were outside the normal reference range. 46% of the CK collections from 10 individuals across all five time points, were higher than the normal reference range. Additionally, 89% of the LDH collections were high compared to the normal reference range. All collections at CT2, CT3, and CT4 were above the reference range, and all but one were high at CT5. For cross country females, 91 out of 130 (73%) muscle damage marker collections were outside the normal reference range. 64% of the CK collections from 13 individuals across all five time points, were higher than the normal reference range. 92% of the LDH collections, were high compared to the normal reference range. All collections at CT2, CT3, CT4, and CT5 were above the normal reference range. CK and LDH significantly changed across the year (p<0.05) (Table 1, Figure 3). CK was significantly higher at CT4 (M: F: ) compared to all other time points for males, and for most other time points (CT3 and CT5) for females. Females also had a significantly lower CK at CT2 (F: ) compared to all other time points. For both males and females LDH was lowest at CT1 (M: , F: ) compared to all other time points.

20 12 Figure 3: Muscle damage blood biomarkers in male and female XC. Bars to the right of the graphs indicate the normal reference ranges (Black bars are the ranges for males, gray bars are ranges for females). * significant difference from T1, # significant difference from T2, $ significant difference from T3, & significant difference from T4, + significant difference from T5

21 13 FB: Hematology: For football players 40, out of 814 (5%) hematology marker collections were outside the normal reference range. This included 12% of RBC collections being high from 7 individuals, across all four time points. Additionally, 8% of Hb collections were high, 4% of MCH collections were low and 3% of MCH collections were high, 6% of HCT collections were high, 3% of MCV collections were low, 2% of ferritin collections were low, and 1% of WBC collections were high, compared to the normal reference range. There were 11 individuals out of the 31 total individuals that had hematology collections outside the normal reference range. For football, all hematology variables besides MCH and WBC, changed significantly across the season. (p<0.05) (Table 2, Figure 4, Figure 5). HCT was significantly lower at FT2 ( ) compared to all other time points, and Hb was significantly lower at both FT2 ( ) and FT3 ( ) compared to FT1 ( ) and FT4 ( ). Ferritin increased across the season and was significantly higher at both FT3 ( ) and FT4 ( ) compared to FT1 ( ) and FT2 ( ). RBC was lower at FT2 ( ) and FT3 ( ) compared to FT1 ( ) and FT4 ( ). MCV was lowest at FT1 ( ) compared to all other time points, and highest at FT3 ( ) compared to all other time points. PLT were highest at FT2 ( ) compared to FT1 ( ) and FT3 ( ). MONO was significantly higher at FT4 ( ) than FT2 ( ). Muscle Damage: For football players 144 out of 207 (70%) muscle damage marker collections were outside the normal reference range. 82% of CK collections were higher than the normal reference range, and were spread across every time point. All but one individual had at least one CK value that was higher than the normal reference range. Additionally, 58% of the LDH collections were above the normal reference range, from 28 out of the 31 individuals. All 31 individuals had at least 1 collection that was above the normal reference range for one (CK or LDH) of the muscle damage biomarkers.

22 14 Table 2: Football Blood Biomarkers (means SD) over four time point across the season FT1 FT2 FT3 FT4 Ferritin (ng/ml) * # * #$ RBC (x10e6/ul) * $& * & Hb (g/dl) * & * & HCT (%) * $& MCV (fl) #$ #& MCH (pg) WBC (x10e3/ul) PLT (x10e6/ul) * $ * MONO (%) # CK (U/L) #$& & LDH (IU/L) * $& & * significant difference from T1, # significant difference from T2, $ significant difference from T3, & significant difference from T4, (p<0.05)

23 15 Figure 4: Oxygen carrying capacity blood biomarkers in FB. Bars to the right of the graphs indicate the normal reference ranges. * significant difference from T1, # significant difference from T2, $ significant difference from T3, & significant difference from T4

24 16 Figure 5: Immune cell blood biomarkers in FB. Bars to the right of the graphs indicate reference ranges from the general population. * significant difference from T1, # significant difference from T2, $ significant difference from T3, & significant difference from T4

25 17 Both CK and LDH changed significantly over the season for FB (p<0.05) (Figure 6). CK was significantly lower at FT1 ( ) compared to all other time points. LDH was significantly higher at FB2 ( ) compared to all other time points. The variance components models for all three groups (FB, MXC,FXC) showed a high amount of individual variance (random effects) in most variables (Table 3). This indicates that in these variables, the changes observed are greatly influenced by individual variability. MXC NEU and CK, as well as all three of the groups LDH, were the only variables that had a higher percentage of the variability of the model that was attributed to changes across time (fixed effects).

26 18 Figure 6: Muscle damage blood biomarkers in FB #$& & *$& & * significant difference from T1, # significant difference from T2, $ significant difference from T3, & significant difference from T4

27 19 Table 3: Variance components for male cross country (M), female cross country (F), and football (FB). Bold text indicates greatest contributor to the overall variance. Fixed effects are the variability in the mean at each time point. Random Effects are individual variability. Ferritin (ng/ml) RBC (x10e6/ul) Hb (g/dl) HCT (%) MCV (fl) MCH (pg) RDW (%) WBC (x10e3/ul) PLT (x10e6/ul) NEU (%) MONO (%) CK (U/L) LDH (IU/L) Fixed Effects (%) (across time) M 3 F 5 FB 30 M 5 F 5 FB 8 M 14 F 4 FB 11 M 20 F 8 FB 10 M 14 F 6 FB 2 M 6 F 2 FB 0 M 6 F 5 FB - M 14 F 5 FB 18 M 5 F 3 FB 4 M 29 F 21 - FB M - F - FB 6 M 38 F 21 FB 24 M 67 F FB Random Effects (%) (for subjects) Total (%)

28 20 Discussion: Hematology Despite differences in training and competitive demands, both XC and FB had significant changes in some hematological blood biomarkers, but their trends across the season were different. Although all three groups had significant changes, we can see from the variance components models that most of the variability in the hematology biomarkers were due to individual variability. Additionally, the means remained within the normal reference ranges. These results indicated neither the XC or FB players training and living at moderate altitude had systematic changes that would decrease oxygen carrying capacity or immune function over the season. Although the mean stayed within the normal reference range for all variables across time, there was a significant amount of individual values that fell outside this range. Reference values are made on a 95% confidence interval for the public population (American Association of Clinical Chemistry 2017), meaning you may expect 5% of healthy individuals to fall outside this range (2.5% above and 2.5% below). For FB and MXC we saw 5% and 4% hematology values out of range respectively, which may be slightly higher than we would expect in the general population. For FXC we saw 14% of values out of range, which is much higher than we would expect. This may indicate that the females in this population have more hematological pathologies than the males and the general population. Conversely, since there doesn t seem to be major pathologies present, it may indicate new reference ranges should be explored for these females. The demands of XC as a sport, exposure to a moderate altitude, menstruation/amenorrhea, or poor dietary habits could all influence these biomarkers in FXC athletes. Future studies should investigate these stimuli and the contribution they make to the hematological differences of FXC compared to males training at a moderate altitude. We observed no significant change in ferritin levels for male or female XC athletes over the year, although it should be noted that the majority of these athletes are at the low end of the normal reference range, despite most of them taking oral iron supplements throughout the year of competition. Interestingly though, these athletes Hct and Hb are on the higher end of normal, for

29 21 both males and females, indicating these low levels of ferritin are not causing an anemic pathology. Our athletes as a whole, displayed much more variability in hematological markers across the season compared to those observed in previous studies (Ostojic and Ahmetovic 2008, Meyer and Meister 2011, Heisterberg et al. 2013). One mechanism for this may be that these athletes are undergoing stressors from both the lifestyle of a high-level athlete (similar those of other studies) but also have the additional stressor of adaptations to altitude. Together it seems, these stimuli affect red blood cell hematology more so than they would individually. Although our study is limited in the ability to assume a causal relationship between stimuli and blood biomarker outcomes due to its retrospective nature, we would like to propose potential mechanisms that could be correlated to our findings and should be explored in the future assessments of athletes similar to our population. These possible mechanisms include a combination of altitude acclimation, intense training stimuli, and diet s effect on hematological changes that occur in these collegiate athletes at moderate altitude. For FB we saw a significant increase in ferritin across the season, most likely attributed to changes in diet, since none of these athletes were being iron supplemented. Despite this prominent increase in ferritin, we observed a decrease in RBC, Hb, and Hct at FT2 and FT3 (after fall training camp and the midseason time point). The occurrence of these changes despite adequate iron storage may be indicative of exercise induced hemodilution, due to increased plasma volume resulting from the activation of the renin-aldosterone axis from a heavy training stimulus. Hemodilution occurs due to this increase in plasma volume, causing Hct, Hb and RBC values to decline, without a true decline in Hb and RBC. Exercise induced hemodilution is well established in endurance athletes following intense training (Gillen et al. 1991, Banfi 2011), but the presence of it in contact sport athletes has not been explored. These changes could also be an indication of an adaptation to altitude, since FT1 was higher in all three parameters than FT2 and FT3 (At FT1 we may expect a decrease in plasma volume with arrival to altitude (Schmidt 2002), which would artificially increase these variables).

30 22 Male XC runners had significantly higher Hb (females showed a trend) and HCT (both males and females) at CT2, despite a maintained RBC and ferritin across the year. Additionally, at this time point there was an increase in MCV and slight increase in RDW. The combination of these parameters are most likely indicative of an increased red blood turn over (production/erythropoiesis and destruction/hemolysis), compared to the other time points. Other studies have also reported an increase in red blood cell turnover in endurance athletes, and individuals at altitude (Klausen et al. 1991, Ehn et al. 1980, Latunde-Dada et al. 2012, Mairbaurl 2013). Haemolysis has been found to be increased specifically in the feet of endurance runners during high training periods, due to the frequent striking on hard surfaces (Janakiraman et al. 2011). Others have also proposed that as a response to this increase in hemolysis, an increase in erythropoiesis and iron absorption may occur (Frazer et al. 2004, Latunde-Dada et al. 2004). Additonally, increases in erythropoiesis have been found to occur at altitude (Schmidt 1991, Klausen et al. 1991) Therefor, this change in hematological maintenance could be due to acclimation effect of moderate altitude and/or could be a result from intense training during this period (these athletes are preparing for the Pac-12 championships at the end of October and NCAA championships in mid November). Despite all other trends in hematology being drastically different between the two sports, MCV was lowest at the first time point for both. Interestingly, these time points correspond to when individuals would be returning to school. A significantly lower MCV indicates a larger proportion of small old red blood cells (Bosch 1992, Waugh 1992). This time point also corresponds to the lowest level of training for both groups that were studied, so we may expect the lowest level of erythropoiesis due to their de-trained state. In terms of immune markers both male and female XC had similar trends, but these changes, as well as the ones in FB, did not seem to follow major patterns that would indicate issues with immune function across the season. PLT were higher for both male and female XC at CT3 and CT4 (the end of the season), but the literature on changes in PLT due to exercise have been unconclusive previously (El-Sayed et al. 2005), so it is difficult to interpret the implication of these changes. NEU was higher at CT1 for both male and female XC, which may indicate the stress on the immune system of arriving at altitude, training with the team again, and/or beginning school.

31 23 FB had a significant increase in PLT at FT2, after the preseason training camp which could be related to the stress of the camp. Muscle Damage Unlike the changes we observed in the hematological blood biomarkers, many of the means for the two 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 agreement with the previous literature (Mougios 2007), we found a large proportion of the muscle damage biomarker values were higher than the normal reference range for both FB and XC. As hypothesized, both XC and FB have elevated muscle damage biomarkers compared with the general population, suggesting either a) these athletes have an enormous amount of pathological muscle damage that is occurring or b) that we may need a new set of normal lab values for athletes during periods of regular training and competition. As far as the authors are aware, there was no systematic decreases in performance due to soreness or other indicators of excessive muscle damage, so it is unlikely these high values are indicative of a muscle damage pathology. Interestingly, the trends in LDH and CK for both FB and XC did not follow similar patterns, suggesting the two biomarkers may be indicative of different pathologies/muscle damage present in this population, or could be a result of differences in appearance and clearance rate. Again, due to the nature of this retrospective analysis, we are not claiming causal relationships for these changes over time, but would like to propose possible mechanisms for future studies to investigate. For FB, we saw a significant increase in CK across the season, while LDH peaked at FT2. The differences in the trends of CK and LDH could be a result of numerous different mechanisms. First, CK is only found in muscle tissue, whereas LDH is found in almost all cells of the body (American Association of Clinical Chemistry 2017). Thus, an increase in LDH at FT2 could be indicative of overall bodily tissue damage due to the high volume and intensity of the preseason training camp during August. The increase in CK over the season could be due to a number of

32 24 factors including increased release of CK in to the blood stream, or a decreased clearance rate, both of which could result in changes of serum CK over time (Baird et al. 2012). A study from Kraemer et al. (2013) found play number was a significant contributor to muscle damage later in the season but not early in the season. This may suggest that the increase in CK over the season may not be due to increased muscle damage, but decreasing ability of the body to respond to this damage. For XC, there seemed to be differences in magnitude of CK at each time point between sex, which aligns with previous literature that females often have lower CK values than males (Baird et al. 2012, Komulainen 1999, Amelink et al. 1990), which has been associated with differences in lean muscle mass (Kraemer and Rogol, 2005). For CK male and females XC values followed similar trends, they had their lowest value at CT2, and their highest value at CT4, although the significance of these changes were different depending on sex. CT4 is towards the end of their competitive season, which may be why we saw increases in CK at this point, similar to FB. For both males and females, LDH was lowest at CT1. This could be due to the low level of training at this time point compared to the others. One animal study showed a lower LDH activity with endurance training at altitude that may be due to an increase in type two muscle fibers (Bigard et al. 1991), but these mechanisms are not yet well understood. Interestingly, XC recorded higher values of LDH than FB across the season. Some previous literature has suggested LDH increases with hemolysis (Yucel and Dalva 1992), but no previous studies have confirmed this occurrence in endurance athletes. Overall, our retrospective data analysis provides unique insight in to hematological and muscle damage blood biomarkers in collegiate athletes living and competing at a moderate altitude. We observed changes in hematological biomarkers in all three 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. For XC, we saw significant increases in Hct and Hb despite no change in ferritin and RBC across the season. These changes, along with other markers of oxygen carrying capacity, suggest an increased rate of red blood cell turnover compared to the general population. These changes also suggest XC athletes in this population are able to adapt to changes in altitude and training and maintain optimal oxygen

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