Effect of Player Position on Serum Biomarkers during Participation in a Season of Collegiate Football

Abstract

This prospective cohort study examined the relationship between a panel of four serum proteomic biomarkers (glial fibrillary acidic protein [GFAP], ubiquitin C-terminal hydrolase-L1 [UCH-L1], total Tau, and neurofilament light chain polypeptide [NF-L]) in 52 players from two different cohorts of male collegiate student football athletes from two different competitive seasons of Division I National Collegiate Athletic Association Football Bowl Subdivision. This study evaluated changes in biomarker concentrations (as indicators of brain injury) over the course of the playing season (pre- and post-season) and also assessed biomarker concentrations by player position using two different published classification systems. Player positions were divided into: 1) speed (quarterbacks, running backs, halfbacks, fullbacks, wide receivers, tight ends, defensive backs, safety, and linebackers) versus non-speed (offensive and defensive linemen), and 2) “Profile 1” (low frequency/high strain magnitudes positions including quarterbacks, wide receivers, and defensive backs), “Profile 2” (mid-range impact frequency and strain positions including linebackers, running backs, and tight ends), and “Profile 3” (high frequency/low strains positions including defensive and offensive linemen). There were significant increases in GFAP 39.3 to 45.6 pg/mL and NF-L 3.5 to 5.4 pg/mL over the course of the season (p < 0.001) despite only five players being diagnosed with concussion. UCH-L1 decreased significantly, and Tau was not significantly different. In both the pre- and post-season blood samples Tau and NF-L concentrations were significantly higher in speed versus non-speed positions. Concentrations of GFAP, Tau, and NF-L increased incrementally from “Profile 3,” to “Profile 2” to “Profile 1” in the post-season. UCH-L1 did not. GFAP increased (by Profiles 3, 2, 1) from 42.4 to 49.6 to 78.2, respectively (p = 0.051). Tau increased from 0.37 to 0.61 to 0.67, respectively (p = 0.024). NF-L increased from 3.5 to 4.9 to 8.2, respectively (p < 0.001). Although GFAP and Tau showed similar patterns of elevations by profile in the pre-season samples they were not statistically significant. Only NF-L showed significant differences between profiles 2.7 to 3.1 to 4.2 in the pre-season (p = 0.042). GFAP, Tau, and NF-L concentrations were significantly associated with different playing positions with the highest concentrations in speed and “Profile 1” positions and the lowest concentrations were in non-speed and “Profile 3” positions. Blood-based biomarkers (GFAP, Tau, NF-L) provide an additional layer of injury quantification that could contribute to a better understanding of the risks of playing different positions.

Introduction

Nearly 8 million students who currently participate in high school athletics and the more than 480,000 who compete as National Collegiate Athletic Association (NCAA) athletes¹ in the United States are at risk for concussive and subconcussive injuries.² Numerous studies have documented that both clinically diagnosed concussive and repetitive subconcussive injuries induce similar changes in brain structure and functions.^3,4 The additive effect of subconcussive impacts has the potential for long-term deleterious effects on brain function and neurodegeneration in select individuals.^5
-7 These concerns are leading researchers to assess concussion risk and to estimate contact exposure.

It appears that the location, frequency, and magnitude of head impacts in football differ by position, with specific player positions at higher risk of concussion and long-term sequelae.^7

–11 In a study of the effects of playing position in former college and professional athletes, white matter integrity, functional neural recruitment, and concussion history depended on career duration and playing position.⁹ Recent studies have stratified players into speed and non-speed positions, with speed players building up momentum prior to tackling or being tackled, and non-speed players engaging other players before significant momentum can be generated. Therefore, speed players encompass quarterback, running back, halfback, fullback, wide receiver, tight end, defensive back, safety, and linebacker positions and non-speed players include all defensive and offensive linemen.¹¹

More recently, Karton and colleagues employed a novel method for quantifying repetitive head impacts using a biomechanical measurement method that incorporated frequency of impact, tissue strain magnitude and time interval between impacts to measure exposures specific to professional football player field positions.¹⁰ They evaluated 3439 head impacts for eight player positions during 32 regular seasons and divided player positions into three profiles. “Profile 1” positions were exposed to low impact frequency of proportionately higher strain magnitudes within 25- to 30-min time intervals and included quarterbacks, wide receivers, and defensive backs. “Profile 2” consisted of all levels of magnitude of mid-range impact frequency and intervals (approximately 13 min) and included linebackers, running backs and tight ends. “Profile 3” positions experienced the highest frequency of head impacts within short time intervals of (approximately 6 to 7 min) of predominately low strain magnitude and included offensive and defensive lines.¹⁰ Tight ends had some overlap between Profiles 2 and 3. Overall, there is growing evidence suggesting differential vulnerability of football players to head acceleration events a function of player position, but neural underpinning of this effect is poorly understood.

Like other blood tests used in medicine, a blood test for traumatic brain injury (TBI) has the potential to provide invaluable information about the severity or magnitude of injury to the brain. Glial fibrillary acidic protein (GFAP) is a protein found in astroglial skeleton of both white and gray brain matter and has been used as a histological marker for glial cells. Ubiquitin C-terminal hydrolase-L1 (UCH-L1) is protein in neurons that is involved in the addition and removal of ubiquitin from proteins that are destined for metabolism and has been used as a histological marker for neurons.¹² Tau is an intracellular, microtubule-associated protein that is enriched in axons and is involved with axonal microtubule assembly and axoplasmic transport.¹³ Neurofilament light chain polypeptide (NF-L) is principally found in axons and is part of the neuron cytoskeleton that functions to provide structural support.¹⁴ Previous studies have shown that these blood-based biomarkers of TBI are associated with sports-related concussion, subconcussive injuries, and neurocognitive functioning.^15

–20

This prospective cohort study examined the relationship between a panel of four serum proteomic biomarkers (GFAP, UCH-L1, total Tau, and NF-L) in two different cohorts of male collegiate student football athletes from two competitive seasons in a Division I NCAA Football Bowl Subdivision. This study evaluated changes in biomarker concentrations (as indicators of brain injury) over the course of the playing season (pre- and post-season) and also assessed biomarker concentrations by player position using two different stratification methods previously published by Karton and colleagues and by Lehman.^10,11

Methods

Study population

This prospective cohort study enrolled two different cohorts of male collegiate student football athletes from the Pennsylvania State University from two distinct competitive seasons in a Division I NCAA Football Bowl Subdivision. This study was approved by the Pennsylvania State University Institutional Review Board and informed consent was obtained from all participants prior to enrollment.

Study procedures

All participants completed a comprehensive pre-season interview which included demographic information, medical and concussion history (self-report), and history of learning disabilities. Pre-season blood draws were completed within 1 week before the athletic season began (prior to any pre-season contact practices or competitions) and the postseason blood draw within 1 week after the last game of the regular season. None of the athletes were recovering from or were diagnosed with a concussion in the 9 months prior to the pre-season evaluation. A blood sample of 5 mL was placed in a serum separator tube and allowed to clot at room temperature before being centrifuged. The serum was placed in bar-coded aliquot containers and stored at -70°C until transport to a central laboratory where samples were analyzed in batches. Lab personnel conducting the biomarker analysis were blinded to the clinical data. All athletic trainers, physicians, and research personnel were blinded to the serum biomarker results.

Outcome measures

The primary outcome measure was the change in biomarker concentrations over the course of a season. The secondary outcome measure was the association between protein biomarkers and playing positions using two different published classification systems. Per Lehman and colleagues, player positions were divided into either speed versus non-speed positions or three profiles. Speed players included quarterbacks, running backs, halfbacks, fullbacks, wide receivers, tight ends, defensive backs, safety, and linebackers. Non-speed players included all defensive and offensive linemen.¹¹

Additionally, player positions were divided into three profiles recently developed by Karton and colleagues in a study that used a biomechanical measurement method that incorporated frequency of impact, tissue strain magnitude and time interval between impacts to measure exposures.¹⁰ In this study, “Profile 1” positions were exposed to low impact frequency of proportionately higher strain magnitudes (quarterbacks, wide receivers, and defensive backs). “Profile 2” consisted of all levels of magnitude of mid-range impact frequency and intervals (linebackers, running backs and tight ends). “Profile 3” positions experienced the highest frequency of head impacts within short time intervals (offensive and defensive lines).¹⁰

Biomarker analysis

Serum samples were analyzed using the Simoa (single molecule array) Neurology 4-plex assay kit (Quanterix, Lexington, MA) for simultaneous measurement of GFAP, UCH-L1, total Tau, and NF-L on the HDX Analyzer. Assays were batched to minimize variability, with each batch run with appropriate standards and controls to ensure reliability. The laboratory was blinded to the clinical data. All samples were analyzed in duplicate. None of the intra-assay coefficients of variance exceeded 20%. Samples with concentrations below the level of detection were excluded from analysis, this included one pre-season UCH-L1 value and three post-season UCH-L1 values. The average coefficient of variation of measurement of GFAP, UCH-L1, Tau and NF-L were 7.7%, 10.2%, 6.3%, and 5.8%, respectively. Limit of detection of GFAP was 0.211 pg/mL, UCH-L1 was 1.05 pg/mL, Tau was 0.0146 pg/mL, and NF-L was 0.038 pg/mL.

Statistical analysis

Descriptive statistics with medians, proportions and interquartile ranges were used to describe the data. For statistical analysis, biomarker levels were treated as continuous data and measured in pg/mL. Data were assessed for equality of variance and distribution using the Shapiro-Wilk test. Logarithmic transformations were conducted on non-normally distributed data. Concentrations of all four biomarkers had to be transformed. Group comparisons were performed using the Fisher's exact test, analysis of variance, and independent sample t-tests with variance consideration. Correlational analysis used Pearson's correlation and Spearman's rank correlation. All analyses were performed using the statistical software package SPSS 27.0 (IBM Corporation^®).

Results

There were 52 male collegiate student football athletes included in the study. Mean age of the enrolled athletes was 21 years (with a range from 19 to 24) with a mean height and weight of 75 inches and 265 pounds, respectively (Table 1). The average number of years of football experience was 11 years, and 40% had previously experienced a concussion. There were 32 athletes in non-speed positions and 20 athletes in speed positions. There were no significant differences in baseline characteristics between the two groups except in weight. Players in the non-speed positions were significantly heavier than players in the speed positions (291 vs. 224 lbs; p < 0.001; Table 1). Five total players were diagnosed with a concussion (9.4%), two players from the first cohort and three players in the second cohort (Table 1). There were no significant differences in concussion incidence in the speed (n = 2; 9.5%) and non-speed (n = 3; 9.4%) players (p = 0.99) and no significant difference in between “Profile 1” (n = 1; 17%), “Profile 2” (n = 1; 7%) and “Profile 3” (n = 3; 9%; p = 0.78).

Table 1.

Characteristics of Athletes Included in the Study in Speed and Non-Speed Positions

	Non-speed positionsn = 20[95% CI]	Speed positionsn = 32[95% CI]	TotalN = 52[95% CI]	p Value
Age Range	21 [20-21] 19-24	21 [20-21] 18-24	21 [20-21] 18-24	0.570
Height (inches)	76 [74-77]	74 [72-76]	75 [74-76]	0.099
Weight (lbs)	291 [282-300]	224 [213-235]	265 [254-278]	<0.001
Years playing football	11 [10-13]	12 [10-13]	11 [10-12]	0.759
Previous concussions	13 (41%)	8 (40%)	21 (40%)	0.598
History of ADHD	8 (25%)	2 (10%)	10 (19%)	0.283
Player positions Offense Defense	14 (44%) 18 (56%)	11 (55%) 9 (45%)	25 (48%) 27 (52%)	0.570
Concussion during season	3 (9.4%)	2 (9.5%)	5 (9.4%)	0.999

ADHD, attention-deficit hyperactivity disorder.

Collectively, there were significant increases in GFAP and NF-L serum concentrations over the course of the season for all players under study (Table 2). GFAP increased from a median of 39.3 (interquartile range [IQR] 31.7-56.3) to 45.6 (IQR 35.2-63.3; p < 0.001). Similarly, NF-L increased from 3.5 (IQR 3.0-4.6) to 5.4 (IQR 3.7-7.0; p < 0.001). UCH-L1 levels decreased significantly (p = 0.047), and Tau levels were not significantly different (Table 2). There were no significant differences in post-season biomarker concentrations between players who were and were not diagnosed with concussion during the season (Table 3). Similarly, concussions previously incurred before the current season did not correlate with concentrations of pre-season biomarkers.

Table 2.

Comparison of Biomarker Concentrations Pre- and Post-Season

	Pre-seasonn = 52median (IQR)	Post-seasonn = 52median (IQR)	p Value
GFAP (pg/mL)	39.3 (31.7-56.3)	45.6 (35.2-63.3)	< 0.001
UCH-L1 (pg/mL)	6.9 (3.1-12.7)	5.0 (3.0-9.8)	0.047
Total Tau (pg/mL)	0.82 (0.57-0.98)	0.67 (0.49-0.97)	0.105
NF-L (pg/mL)	3.5 (3.0-4.6)	5.4 (3.7-7.0)	< 0.001

GFAP, glial fibrillary acidic protein; UCH-L1, ubiquitin C-terminal hydrolase-L1; NF-L, neurofilament light chain polypeptide.

Table 3.

Comparison of Post-Season Biomarker Concentrations between Players Who Were and Were Not Diagnosed with Concussion during the Season

	No concussion during seasonn = 47median (IQR)	Concussion during seasonn = 5median (IQR)	p Value
Post-season GFAP (pg/mL)	43.8 (35.0-63.4)	47.4 (37.3-76.4)	0.687
Post-season UCH-L1 (pg/mL)	4.8 (3.0-10.6)	4.7 (2.2-5.7)	0.417
Post-season Total Tau (pg/mL)	0.66 (0.47-0.97)	0.67 (0.54-1.29)	0.310
Post-season NF-L (pg/mL)	5.3 (3.6-6.9)	5.9 (5.6-9.0)	0.197

GFAP, glial fibrillary acidic protein; UCH-L1, ubiquitin C-terminal hydrolase-L1; NF-L, neurofilament light chain polypeptide.

When biomarker concentrations were compared in speed and non-speed position players,¹¹ there were significant differences in median levels of Tau and NF-L between the two groups (Fig. 1). The differences were evident in both pre- and post-season serum samples. Median pre-season Tau concentrations were 0.63 (0.50-0.94) in non-speed players, compared with 0.91 (0.67-1.02) in speed players (p = 0.032). Median post-season Tau concentrations were 0.53 (0.37-0.87) in non-speed players and 0.87 (0.62-1.07) in speed players (p = 0.011). Similarly, median pre-season NF-L concentrations were 3.4 (2.7-4.1) in non-speed players compared with 4.3 (3.3-5.1) in speed players (p = 0.033). Even more pronounced differences were seen in post-season NF-L concentrations with speed players having significantly higher concentrations than non-speed players 6.7 (5.3-8.7) versus 4.7 (3.5-5.7), respectively (p = 0.004). GFAP concentrations were also higher in speed versus non-speed position players, but the differences were not statistically significant. Differences were not evident for UCH-L1.

FIG. 1.

Boxplot comparing of median serum levels of four protein biomarkers [GFAP], ubiquitin C-terminal hydrolase-L1 [UCH-L1], total Tau, and neurofilament light chain polypeptide [NF-L]) by player speed position. Boxplots represent medians with interquartile ranges. (A) Pre-season serum GFAP concentrations in non-speed vs speed positions; (B) Post-season serum GFAP concentrations in non-speed vs speed positions; (C) Pre-season serum UCH-L1 concentrations in non-speed vs speed positions; (D) Post-season serum UCH-L1 concentrations in non-speed vs speed positions; (E) Pre-season serum Tau concentrations in non-speed vs speed positions; (F) Post-season serum Tau concentrations in non-speed vs speed positions; (G) Pre-season serum NF-L concentrations in non-speed vs speed positions; (H) Post-season serum NF-L concentrations in non-speed vs speed positions.

Using the profiles created by Karton and colleagues, players were divided into three profiles based on their positions: n = 5 in “Profile 1,” n = 15 in “Profile 2,” and n = 32 in “Profile 3.”¹⁰ In the post-season, concentrations of GFAP, Tau, and NF-L increased incrementally from “Profile 3” to “Profile 2” to “Profile 1” (Fig. 2). GFAP increased from 42.4 to 49.6 to 78.2 pg/mL, respectively (p = 0.051). Tau increased from 0.37 to 0.61 to 0.67 pg/mL, respectively (p = 0.024). NF-L increased from 3.5 to 4.9 to 8.2 pg/mL, respectively (p < 0.001). Although GFAP and Tau showed similar patterns of elevations by profile in the pre-season, they were not statistically significant. In the pre-season, GFAP concentrations changed from 37.7 to 37.2 to 61.4 pg/mL, respectively (p = 0.063) and Tau concentrations increased from 0.50 to 0.63 to 0.73 pg/mL, respectively (p = 0.093). Only NF-L serum concentrations showed statistically significant differences by profile, 2.7 to 3.1 to 4.2 pg/mL, respectively, in the pre-season (p = 0.042). Differences were not evident for UCH-L1.

FIG. 2.

Boxplot comparing of median serum levels of four protein biomarkers [GFAP], ubiquitin C-terminal hydrolase-L1 [UCH-L1], total Tau, and neurofilament light chain polypeptide [NF-L]) by player position profile. Boxplots represent medians with interquartile ranges. (A) Pre-season serum GFAP concentrations in three distinct player position profiles; (B) Post-season serum GFAP concentrations in three distinct player position profiles; (C) Pre-season serum UCH-L1 concentrations in three distinct player position profiles; (D) Post-season serum UCH-L1 concentrations in three distinct player position profiles; (E) Pre-season serum Tau concentrations in three distinct player position profiles; (F) Post-season serum Tau concentrations in three distinct player position profiles; (G) Pre-season serum NF-L concentrations in three distinct player position profiles; (H) Post-season serum NF-L concentrations in three distinct player position profiles.

Changes in biomarker concentrations from pre- to post-season in speed versus non-speed players as well as among the three different profiles are presented in Table 4 and in Supplementary Figure S1 and Supplementary Figure S2. GFAP and NF-L demonstrated large increases in speed players Compared with non-speed players over the season. GFAP increased 31% (speed) versus 12% (non-speed) and NF-L increased 87% (speed) versus 45% (non-speed). A similar pattern emerged for the three profiles. GFAP increased 44% in “Profile 1” players, 27% in “Profile 2” players, and 12% in “Profile 3” players. The largest increases were seen in NF-L with rises of 165%, 62%, and 45% in Profiles 1 through 3, respectively. UCH-L1 and Tau did not follow these patterns. These changes were not statistically significant.

Table 4.

Change in Biomarker Concentration (Mean and Percent Difference) over the Season by Player Position

	Non-Speedn = 32(95% CI)	Speedn = 20(95% CI)	Profile 1n = 5(95% CI)	Profile 2n = 15(95% CI)	Profile 3n = 32(95% CI)
GFAP
% Change	12% (5-19)	31% (2-60)	44% (-77-165)	27% (0.04-54)	12% (5-19)
Mean change (pg/mL)	5.1 (2.3-7.8)	13.1 (-1.7-27.9)	29.3 (-47.2-106)	7.8 (1.0-14.6)	5.1 (2.3-7.8)
UCH-L1
% Change	58% (-42-157)	-3% (-43-37)	8% (-99-115)	-7% (-56-42)	57% (-42-157)
Mean change (pg/mL)	-1.6 (-3.7-0.5)	-1.1 (-3.6-1.5)	0.7 (-6.6-8.1)	-1.8 (-4.7-1.1)	-1.6 (-3.7-0.5)
Total Tau
% Change	-8% (-19-3)	-1 (-14-12)	4% (-23-32)	-3% (-20-13)	-8% (-19-3)
Mean change (pg/mL)	-0.09 (-0.16- -0.02)	-0.01 (-0.13- 0.12)	0.05 (-0.24-0.34)	-0.02 (-0.18-0.13)	-0.09 (-0.16- -0.01)
NF-L
% Change	45% (25-65)	87% (26-149)	165% (-135-464)	62% (27-97)	45% (25-65)
Mean change (pg/mL)	1.3 (0.08-1.9)	3.3 (0.9-5.8)	6.8 (-5.1-18.6)	2.2 (1.0-3.4)	1.3 (0.8-1.9)

GFAP, glial fibrillary acidic protein; UCH-L1, ubiquitin C-terminal hydrolase-L1; NF-L, neurofilament light chain polypeptide.

Discussion

This is among the first studies, to our knowledge, to assess the association between player position in football and proteomic biomarker concentrations in the pre- and post-season. This study evaluated a panel of four proteomic biomarkers of brain injury (GFAP, UCH-L1, Tau, and NF-L) in two distinct cohorts of players in two competitive seasons of Division I NCAA Football. Compared with pre-season, post-season levels of GFAP and NF-L1 increased significantly. Only five players were diagnosed with concussions during the two football seasons and there were no significant differences in post-season biomarker concentrations between players who were and were not diagnosed with concussions. Therefore, head impact exposures that did not result in concussion were significant enough to raise biomarker levels, suggesting cellular disruption from non-concussive impacts.

In samples collected in the pre-season (prior to any contact practices), all four biomarkers were above known baseline levels of uninjured control patients that have been analyzed on the same assay platform.²¹ Pre-season elevations in biomarkers have been observed in other studies in collegiate football players.^15,22 Considering the players in our cohort had an average of 11 years playing experience and 41% had previously reported concussions, pre-season biomarker elevations could suggest residual circulating biomarkers from prior concussive and subconcussive impacts over their playing career. Subacute (weeks) and chronic (months to years) elevations in GFAP and NF-L concentrations have been found in patients following mild to moderate TBI compared with uninjured control patients with more variability in the pattern of elevations with Tau and UCH-L1.^23,24 Chronic elevations of Tau tend to occur in severe TBI patients and UCH-L1 does not have a consistent pattern of elevation either by severity of injury or time.²³

A novel aspect of this study was the evaluation of the effects of player position on TBI biomarkers. We observed significant higher levels of Tau and NF-L in speed positions such as quarterbacks, running backs, halfbacks, fullbacks, wide receivers, tight ends, defensive backs, safeties, and linebackers, compared with non-speed players like defensive and offensive linemen. Speed players build considerable momentum prior to tackling or being tackled. In contrast non-speed players usually engage other players soon after the football is snapped, reducing momentum prior to contact. NF-L and Tau as measures of axonal injury are much higher in those speed layers, perhaps indicating higher “shear” forces to axons. Axons, however, are not injured in isolation and injuries to the astroglia (GFAP) and neuronal bodies (UCH-L1) also occur.

This is consistent with the three-tier “brain strain exposure” profiles in which post-season GFAP, Tau, and NF-L concentrations were highest in “Profile 1” players (quarterbacks, wide receivers, and defensive backs) who had higher strain magnitudes impacts. Even though they occurred less frequently, the “higher strain magnitude” impact players had the highest levels of GFAP, Tau and NF-L. Impact strain had more of an effect on the biomarkers than the frequency of impacts and intervals between impacts.

These incremental changes in biomarkers from highest to lowest strain magnitude occurred not only after the season but were evident in player samples even before the season began. It was unexpected considering there had been no practices or football hitting activity. Pre-season GFAP, Tau, and NF-L demonstrated these similar changes by player position profile but only NF-L was statistically significant. This could be a function of the modest sample size or a function of the different temporal profiles of each biomarker.^23,25,26 For instance, serum NF-L can remain elevated in mild TBI for up to 5 years after injury compared with controls.²³ This supports our hypothesis that elevations in circulating biomarker concentrations prior to season onset could reflect prior cumulative head impact exposures including players without prior history of concussions. Visible signs or symptoms of neurological dysfunction may not develop despite those impacts having the potential for neurological injury.^3,6,27 This includes studies in children aged 8 to 13 years who play football²⁸ as well as those who play high school football.²⁹

Another important consideration is that there may be differences in the mechanism of injury between player positions, including player's helmet impact location.³⁰ For instance, offensive and defensive lineman may have a greater proportion of impacts to the front of the helmet.⁷ Lowest strains consistently occur in impacts to the crown and forehead.³⁰ Other players may have impact locations that contribute to greater cervical strain. The cervical spine is particularly susceptible to injury because of axial loading forces to the head with the neck in flexion or extension.^31,32

These results support exploring these biomarkers as surrogates of subconcussive damage as they relate to magnitude, location, and frequency of head impacts. Biomarkers provide an additional layer of injury quantification that could contribute to a better understanding of the risks of playing different positions. Moreover, this type of biomarker-positional analysis could be used to investigate game rule changes, player safety measures, on-field behavior, return-to-play decisions, and enhancements to equipment.³³

Limitations

While these data are encouraging, there are limitations to this study. Athletes enrolled over two non-consecutive seasons represents a small sample of athletes. The players, however, are representative of many players in this Division of the Football Bowl Subdivision of the NCAA. Only five players were diagnosed with concussion in the study population during the season. Additionally, there were only five players in “Profile 1” positions. Future studies should include a larger sample of players.

Since non-speed players traditionally weigh more than speed players, our study is limited by the lack of information on weight's impact on the studied baseline biomarkers in non-athletes. We also did not assess the impact of exertion, sleep and supplement use on biomarker levels. Moreover, we did not report neurocognitive data or imaging data on the players and did not correlate biomarker levels with these measures.

We used previously published categorizations of player position and did not report actual accelerometer data from the players. Additionally, we have no information pertaining to helmet standardization, nor did we factor the type of helmet the players were using during the study. Not all collegiate programs utilize the same brand of helmet, and it would be interesting to study the elevation of biomarkers from sub-concussive impacts between different helmet technologies.

Conclusion

Serum concentrations of GFAP and NF-L increased significantly over the course of the season despite few players being diagnosed with concussion. Moreover, post-season GFAP, Tau, and NF-L concentrations were significantly associated with different playing position profiles based on magnitude, location, and frequency of head impacts. The highest concentrations of post-season GFAP, Tau, and NF-L were in speed and “Profile 1” positions (quarterbacks, wide receivers, and defensive backs) and the lowest concentrations of GFAP, Tau, and NF-L were in non-speed or “Profile 3” positions (defensive and offensive linemen). Blood-based biomarkers provide an additional layer of injury quantification that could contribute to a better understanding of the risks of playing different positions.

Footnotes

Funding Information

This study was supported in part by Award Number R01NS057676 (Papa, PI) from the National Institute of Neurological Disorders and Stroke. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Neurological Disorders and Stroke or the National Institutes of Health. The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

This study was supported in part by NCAA Grant #106031 “Effects of cumulative head acceleration events (HAE) over a single athletic season on brain functional and structural integrity.” The content is solely the responsibility of the authors and does not necessarily represent the official views of the NCAA. The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Author Disclosure Statement

No competing financial interests exist.

Supplementary Material

Supplementary Figure S1

Supplementary Figure S2

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