Characterizing Factors Influencing Baseline Plasma Biomarkers for Sport-Related Concussion in Adolescents

Abstract

Developing objective measures to diagnose sport-related concussion (SRC) is a top priority, particularly in the pediatric context, given the vulnerability of the developing brain. While advances in SRC blood biomarkers are being made in adult populations, less data are available for adolescents. Clinical validation of blood biomarkers post-SRC will first require investigation in a healthy uninjured state. Further, rapid pubertal changes during adolescence may implicate possible interactions with circulating sex hormones and the menstrual cycle for females. This cross-sectional study aimed to characterize pre-injury plasma levels of glial fibrillary acidic protein (GFAP), neurofilament light (NF-L), ubiquitin C-terminal hydrolase-L1 (UCH-L1), total tau (T-tau), and phosphorylated tau-181 (P-tau-181), considering previous concussion, age, and sex in healthy adolescent sport participants. Possible associations with menstrual cycle phase and circulating sex hormone levels (i.e., progesterone, estradiol, testosterone) were also explored. Pre-injury blood samples were obtained from 149 healthy adolescents (48% female, ages 11-18) participating in a larger Surveillance in High Schools and Community Sports to Reduce Concussions and their Consequences (SHRed Concussions) multi-site longitudinal cohort study. Main outcomes were natural log (ln) transformed plasma GFAP, NF-L, UCH-L1, T-tau, and P-tau-181 concentrations, quantified on the Quanterix Simoa HD-X platform. Mixed-effects multi-variable linear regression was used to assess associations between biomarkers and self-reported previous concussion (yes/no), age (years), sex (male/female), objectively determined menstrual cycle phase (follicular/luteal), plasma progesterone, estradiol, and testosterone. Males had 19.8% lower UCH-L1 (β = -0.221, 95% confidence interval [CI; -0.396, -0.046]), 18.9% lower GFAP (β = -0.210, 95% CI [-0.352, -0.068]), and 21.8% higher P-tau-181 (β = 0.197, 95% CI [0.048, 0.346]) compared with females, adjusting for age and previous concussion. GFAP decreased 9.5% with each 1-year increase in age, adjusting for previous concussion and sex (β = -0.100, 95% CI [-0.152, -0.049]). No biomarkers were associated with a history of previous concussion. Exploratory investigations found no associations between biomarkers and menstrual cycle phase. Females displayed an age-adjusted negative association between T-tau and progesterone (β = -0.010, 95% CI [-0.018, -0.002]), whereas males had a negative age-adjusted association between UCH-L1 and testosterone (β = -0.020, 95% CI [-0.037, -0.002]). As such, age- and sex-specific reference intervals may be warranted for pediatric athlete populations prior to clinical validation of blood biomarkers for SRC. Additionally, hormonal associations highlight the need to consider puberty and development in adolescent studies. Overall, findings suggest these biomarkers are resilient to a history of previous concussion and menstrual cycle phase, supporting continued investigation in adolescent SRC.

Introduction

Pediatric sport-related concussion (SRC) is a major public health burden that impacts healthy adolescent development. Hospital visits for adolescents sustaining SRCs have increased over recent decades^1,2 and there is increasing concern for those suffering from prolonged symptoms that interfere with school and sport.^3

–6 Concussions are clinically diagnosed using subjective interpretations and self-reported symptoms. However, there may be risk of misdiagnosis in adolescents due to the diversity of available definitions of concussion/mild traumatic brain injury (mTBI), non-specific symptomology, or difficulties in symptom conceptualization, interpretation, and communication in adolescents.^7,8 Even with readily available international guidelines for concussion management, clinicians report having insufficient training, tools, and varied practice strategies in pediatric contexts.^9
-11 A misdiagnosed concussion risks many negative outcomes such as delayed recovery or an increased risk of a second concussion during a period of heightened brain vulnerability.¹²

Given these limitations in clinical practice and the lack of a gold standard definition for concussion, considerable efforts have been made to identify rapid objective fluid biomarker tests for diagnostics and prognostics. Substantial progress is being made towards clinical validation of fluid biomarkers including glial fibrillary acidic protein (GFAP), ubiquitin C-terminal hydrolase 1 (UCH-L1), neurofilament light (NF-L), total Tau (T-tau), and phosphorylated tau 181 (P-tau-181) in adult SRC.^13

–16 However, recent systematic reviews on fluid biomarkers of concussion have revealed a significant paucity of studies in pediatric populations.^17,18 This is cause for concern as it would be inappropriate to extrapolate adult biomarker results to those of the developing adolescent given the biopsychosocial differences between age groups. Adolescent athletes who sustain an SRC operate in a unique social environment, experience distinct life stressors,^5,19 and are undergoing puberty, implicating many hormonal and physiological changes in the brain. For these reasons, SRC fluid biomarker studies need to specifically target adolescent populations for clinical validation of the leading candidate biomarkers found in adults.

To better understand whether these biomarkers have diagnostic potential in pediatric populations, they must first be examined in healthy pediatric participants. Potential confounders may influence biomarker levels and should be considered prior to reporting reference ranges with diagnostic cut-offs. Therefore, the primary objective of this study was to characterize the influence of previous concussion, age, and sex on plasma levels of leading brain injury biomarkers including GFAP, UCH-L1, NF-L, T-tau, and P-tau-181 in healthy adolescent athletes.

An exploratory objective to this study was to investigate possible hormonal influences on blood SRC biomarkers. Limited research has been done to examine how sex hormones may confer changes in protein expression on circulating levels of central nervous system biomarkers. In addition to the brain as a site of active steroid biosynthesis, the non-polar structure of steroid-derived sex hormones (e.g., progesterone, estradiol, testosterone) enable them to freely diffuse through the blood–brain barrier and into various cells of the central nervous system, potentially playing important roles in the transcription of brain injury biomarkers or other neuroinflammatory processes.²⁰ Following concussion, the menstrual cycle may be disrupted and recovery may be phase-dependent.^21,22 Phase-specific (i.e., follicular, luteal) concentrations of estradiol and progesterone may be responsible for these effects.²² The hypothalamic-pituitary-gonadal axes have been shown to be impaired following moderate and severe TBI and recent studies suggest this can also occur following SRC.^23

–26 SRC-induced alterations of sex hormones may occur; however, the influence of these hormones on candidate biomarkers of SRC has not been investigated. Given that hormone levels change throughout puberty and the menstrual cycle, we explored possible associations between plasma levels of GFAP, UCH-L1, NF-L, T-tau, and P-tau-181 and menstrual cycle phase and plasma concentrations of progesterone, estradiol, and testosterone in a healthy pediatric athlete population.

Methods

Participants

This cross-sectional study investigated a sub-cohort of adolescent sport participants enrolled in the broader pan-Canadian Surveillance in High Schools and Community Sports to Reduce Concussions and their Consequences (SHRed Concussions) study. SHRed Concussions is a longitudinal cohort study including Canadian adolescents participating in at least one high-risk concussion sport (e.g., football, ice hockey, rugby), collecting clinical, psychosocial, and biomarker measurements to inform SRC prevention, detection, diagnosis, prognosis, and recovery. Participants in this sub-cohort study included healthy male and female adolescent athletes, ages 11-18. Adolescent participants were recruited from high school and community sports in Calgary, Alberta, Edmonton, Alberta, and London, Ontario. Written consent was obtained from all participants (mature minor consent ≥14 years) or athlete parent consent and adolescent assent (< 14 years). Exclusion criteria for this study included a previous medical history of systemic disease (e.g., heart disease, cancer) or severe and/or chronic neurological conditions (e.g., stroke, severe TBI) in addition to a history of bone fracture or surgery in the past year interfering with anticipated sport participation. Demographic information collected for this study included age, sex, sport, medical history, medication use, and concussion history. Concussion history was self-reported using a pre-season baseline questionnaire with the question: “Have you ever had a concussion (either diagnosed or not) or been ‘knocked out’ or had your ‘bell rung?’” Total number and date of each injury was collected. Study approval was granted by the University of Calgary conjoint research ethics board (Ethics ID: REB18-2107).

Blood collection

Plasma samples were collected from healthy study participants at pre-season by standard venipuncture of the antecubital fossa by certified phlebotomists and study research nurses using K2-EDTA Becton Dickinson (BD) vacutainer blood collection tubes. Blood samples were centrifuged upon collection at 1300 g for 10 min at room temperature. Plasma supernatant was aliquoted and immediately frozen at -80°C until analysis. Processing and aliquoting was completed within 2 h of collection.

Biomarker analysis

Plasma samples were analyzed using the Quanterix Simoa HD-X^® analyzer by an investigator blinded to all participant data. The Quanterix Neurology-4 Plex B assay (103670) was used to measure GFAP, UCH-L1, NF-L, and T-tau. The Quanterix P-tau-181 assay (103714) was used to measure P-tau-181. Participants were randomized into five runs, each of which included an eight-point standard curve for the Neurology-4 Plex B assay and a 7-point standard curve for the P-tau-181 assay. Assays were performed using the manufacturers protocols and all plasma samples were diluted on board at a 4-fold dilution and run in duplicate. To track accuracy and inter-assay reliability, two manufacturer provided controls and three internally provided plasma controls were included in each run. The mean intra-assay coefficient of variance (CV) for each biomarker was: 8.77 % (GFAP), 25.70% (UCH-L1), 11.50% (NF-L), 10.20% (T-tau), and 6.55% (P-tau-181). The inter-assay CV was between 3.58 – 7.37% for all analytes. The limit of detection (LOD; pg/mL) for each biomarker was: 1.18 (GFAP), 2.43 (UCH-L1), 0.0962 (NF-L), 0.0371 (T-tau), and 0.028 (P-tau-181). Lower and upper levels of quantification (LLOQ and ULOQ; pg/mL) for each biomarker were: 9.38 – 38352.00 (GFAP), 9.38 – 36032.00 (UCH-L1), 0.50 – 1464.00 (NF-L), 0.125 – 335.60 (T-tau), and 0.338 – 326.80 (P-tau-181).

Plasma progesterone, estradiol, and testosterone levels were determined using homogeneous competitive immunoassays measured by electrochemiluminescence (ECLIA; Roche Diagnostics, Rotkreuz, Switzerland) by Alberta Precision Laboratories (APL), Calgary, AB, Canada. Plasma hormone assays were performed in singlicate and no estimates of variability were performed. APL internal studies using healthy age and sex-matched individuals provided sex-specific normal hormone reference ranges. Plasma progesterone was considered normal if reported between 0.0-0.5 nmol/L (male), 0.0-2.8 nmol/L (female, follicular phase), 5.0-76.0 nmol/L (female, luteal phase). Normal plasma estradiol ranges were 0.0-160.0 pmol/L (male), 90.0-700.0 pmol/L (female, follicular phase), and 150.0-950.0 pmol/L (female, luteal phase). Lastly, normal reference ranges for plasma testosterone were 4.0-27.0 nmol/L (males) and 0.0-1.8 nmol/L nmol (females). Menstrual cycle phase was objectively determined by APL plasma progesterone cut-offs where levels <5.0 nmol/L classified female participants in the follicular phase, and progesterone levels ≥5.0 nmol/L classified as luteal phase.

Statistical analysis

Participant characteristics were described using frequency for previous concussions and means and standard deviations for age and time since last concussion. Protein biomarker data were natural log transformed to reduce skewness and better approximate normal distributions for statistical modeling. Mixed-effects multi-variable linear regression was used to examine the influence of previous concussion (yes/no), age (years), and sex (male/female) on biomarker levels, allowing for the use of both replicates of each biomarker assayed for each individual participant in the model to optimize point-estimate precision. Previous concussion was dichotomized between participants with no self-reported history of concussion and those reporting ≥1 previous concussion.

Separate mixed multi-variable linear regression models were used to explore the influence of menstrual cycle phase (females only; dichotomized as follicular and luteal phase), and individual sex hormones (progesterone, estradiol, testosterone) on biomarker levels, with each model controlling for age (total of seven models). Separate models were used for males and females for each hormone given that production, cyclicity, receptor expression, and overall function of each sex hormone differ considerably between the sexes.^27,28 For each model, 95% confidence intervals were constructed using bootstrapping methods to account for small sample sizes and increase estimate precision. Biomarker levels below the LOD were excluded from statistical analyses. Females self-reporting oral hormonal birth control use were excluded from menstrual cycle analyses. Statistical analysis was performed using STATA (v 16.0) software with an a priori alpha level of 0.05. Regression beta-coefficients and 95% confidence intervals are reported for all estimates of association where appropriate.

Results

Participant characteristics

A total of 4502 participants were included in the SHRed Concussions study (September 2019-November 2021), of which 149 healthy participants provided blood samples for this study. The study sample consisted of 78 males (mean age 15.74 ± 1.17) and 71 females (mean age 15.74 ± 1.64). Overall, 34% of participants reported a history of one or more previous concussions. Blood samples were taken from adolescents participating in high concussion risk sports. Twenty-one participants reported the following medications at time of blood draw: non-steroidal anti-inflammatory drugs (n = 10), acetaminophen (n = 1), Ventolin/antihistamines (n = 5), Symbicort (n = 2), oral hormonal birth control (n = 12), insulin (n = 2), and Accutane (n = 1). Seven (four male, three female) were excluded from relevant statistical tests based on missing data for history of previous concussion. For female participants, menstrual cycle phase (determined by plasma progesterone cut-offs) identified n = 53 in the follicular phase and n = 18 in the luteal phase. See Table 1 for a summary of participant characteristics along with untransformed raw plasma biomarker levels and hormone concentrations.

Table 1.

Participant Characteristics

	Male (n = 78)	Female (n = 71)
Age (mean, SD)	15.74 ± 1.17	15.74 ± 1.64
Previous concussion (yes/no) n (%) Missing n (%)	35 (45%) 4 (5%)	16 (23%) 3 (4%)
Number of previous concussions
1	24 (31%)	11 (16%)
2	8 (10%)	2 (3%)
3	3 (4%)	2 (3%)
4+	0	1 (1%)
Time since last concussion (months, mean, SD)	23.30 ± 22.14	23.33 ± 19.73
Raw biomarker levels (mean, SD)
T-Tau (pg/mL)	7.27 ± 5.29	7.03 ± 6.39
NF-L (pg/mL)	5.83 ± 8.49	4.54 ± 3.63
UCH-L1 (pg/mL)	14.20 ± 6.43	20.12 ± 18.19
GFAP (pg/mL)	54.44 ± 20.60	69.48 ± 46.26
P-Tau 181 (pg/mL)	2.31 ± 1.77	1.97 ± 2.23
Testosterone (nmol/L)	13.47 ± 5.67	1.19 ± 2.60
Estradiol (pmol/L)	84.76 ± 32.68	Follicular: 236.89 ± 235.04Luteal: 435.93 ± 301.70
Progesterone (nmol/L)	0.44 ± 0.28	Follicular: 0.872 ± 1.09Luteal: 22.04 ± 14.97
Menstrual cycle phase
Follicular n (%)		53 (75%)
Luteal n (%)		18 (25%)

SD, standard deviation; T-tau, total tau; NF-L, neurofilament light; UCH-L1, ubiquitin C-terminal hydrolase-L1; GFAP, glial fibrillary acidic protein; P-tau-181, phosphorylated tau-181.

Association of previous concussion, age, and sex with biomarker levels

Results from mixed-effects multi-variable regression models examining associations between natural log-transformed plasma biomarker levels and previous concussion, age, and sex are reported in Table 2. GFAP and UCH-L1 data from one participant were extreme values (GFAP = 21369.24 pg/mL; UCH-L1 = 1720.89 pg/mL) thus were excluded from analyses. Three samples had one of two replicates that had UCH-L1 values below the LOD and were excluded from analyses. Twenty-seven samples had UCH-L1 concentrations below the LLOQ. A sensitivity analysis excluding these values found the same associations to be statistically significant as those reported in Table 2 (Supplementary Table S1). None of the five plasma biomarkers showed any significant associations with a history of one or more previous concussions after controlling for age and sex (Fig. 1). GFAP was significantly associated with age after adjusting for history of concussion (HOC) and sex (β = -0.100, 95% confidence interval [CI; -0.152, -0.049], p < 0.001). For clinical interpretation, GFAP decreased an estimated 9.5% with each 1-year increase in age (Fig. 2). Lastly, compared with females (total group), males displayed 21.8% higher P-tau-181 (β = 0.197, 95% CI [0.048, 0.346], p = 0.006), 19.8% lower UCH-L1 (β = -0.221, 95% CI [-0.396, -0.046], p = 0.017), and 18.9% lower GFAP (β = -0.210, 95% CI [-0.352, -0.068], p = 0.005) after controlling for the other variables in the model (Fig. 3).

FIG. 1.

Scatterplot of plasma levels of total tau (T-tau), neurofilament light (NF-L), ubiquitin C-terminal hydrolase-L1 (UCH-L1), glial fibrillary acidic protein (GFAP), and phosphorylated tau-181 (P-tau-181; ln pg/mL) in participants with no history of concussion and participants with ≥1 previous concussions. Error bars represent mean and standard deviation for each biomarker. Findings from mixed-effects multi-variable modeling indicated no significant influence of previous concussions on any plasma biomarker levels after adjusting for age and sex.

FIG. 2.

Scatterplot of plasma levels of total tau (T-tau), neurofilament light (NF-L), ubiquitin C-terminal hydrolase-L1 (UCH-L1), glial fibrillary acidic protein (GFAP) and phosphorylated tau-181 (P-tau-181; ln pg/mL) across all ages included in the study. Mixed-effects multi-variable modeling revealed a significant negative association between plasma GFAP and age whereby a 1-year increase in age was associated with a 9.5% decrease in GFAP adjusting for previous concussion and sex. No significant associations were found between age and any other plasma biomarker after adjusting for previous concussion and sex.

FIG. 3.

Comparison between male and female plasma levels of total tau (T-tau), neurofilament light (NF-L), ubiquitin C-terminal hydrolase-L1 (UCH-L1), glial fibrillary acidic protein (GFAP), and phosphorylated tau-181 (P-tau-181; ln pg/mL). Error bars represent mean and standard deviation of each biomarker. Mixed-effects multi-variable modeling revealed significant associations (*) between sex and plasma biomarker levels in 3/5 biomarkers controlling for age and history of previous concussion. Males had 21.8% higher P-tau-181, but 19.8% lower UCH-L1 and 18.9% lower GFAP compared with females. No significant associations were found between sex and plasma levels of T-Tau or NF-L.

Table 2.

Beta-Coefficients and 95% Confidence Intervals from Mixed Multi-Variable Regression Modeling of Baseline Biomarkers

Biomarker (in pg/mL)	History of previous concussion (Yes/No)	Age	Sex
T-Tau	0.081 (-0.162, 0.324)	0.010 (-0.084, 0.105)	0.060 (-0.177, 0.298)
NF-L	-0.043 (-0.213, 0.128)	-0.033 (-0.112, 0.046)	0.133 (-0.041, 0.306)
UCH-L1	-0.060 (-0.238, 0.118)	0.006 (-0.066, 0.077)	-0.221 (-0.396, -0.046)^*
GFAP	-0.073 (-0.202, 0.056)	-0.100 (-0.152, -0.049)^*	-0.210 (-0.352, -0.068)^*
P-tau-181	-0.029 (-0.176, 0.119)	-0.062 (-0.146, 0.021)	0.197 (0.048, 0.346)^*

Statistically significant association at p < 0.05.

T-tau, total tau; NF-L, neurofilament light; UCH-L1, ubiquitin C-terminal hydrolase-L1; GFAP, glial fibrillary acidic protein; P-tau-181, phosphorylated tau-181.

Association of menstrual cycle and sex hormones with biomarker levels

Table 3 shows the results of mixed-effects multi-variable regression models (seven separate models for males and females and each predictor variable adjusting for age) concerning the influence of menstrual cycle (follicular vs luteal phase) and sex hormones (progesterone [nmol], estradiol [pmol], and testosterone [nmol]) on plasma biomarker levels (ln pg/mL). In female participants, none of the protein biomarkers were significantly associated with menstrual cycle phase. However, there was a negative association between T-tau and progesterone levels, whereby T-tau decreased by 1.0% for each 1 nmol/L increase in progesterone (β = -0.010, 95% CI [-0.018, -0.002], p = 0.019). In male participants, a negative association was found between UCH-L1 and testosterone whereby UCH-L1 levels decreased by 2.0% for each 1 nmol/L increase in testosterone (β = -0.020, 95% CI [-0.037, -0.002], p = 0.029). A sensitivity analysis excluding the 27 UCH-L1 samples below LLOQ found no significance in the association with testosterone (Supplementary Table S2).

Table 3.

Beta-Coefficients and 95% Confidence Intervals of Associations between Menstrual Cycle, Individual Sex Hormones, and Baseline Biomarkers

Biomarker (ln pg/mL)	Menstrual cycle (follicular/luteal)		Progesterone		Estradiol		Testosterone
Biomarker (ln pg/mL)	Female	Male	Female	Male	Female	Male	Female	Male
T-tau	-0.295 (-0.637, 0.046)	-	-0.010 (-0.018, -0.002)^*	-0.339 (-0.862, 0.184)	-0.0001 (-0.0009, 0.0008)	0.002 (-0.003, 0.007)	-0.058 (-0.271, 0.155)	-0.015 (-0.039, 0.008)
NF-L	0.123 (-0.295, 0.542)	-	0.009 (-0.011, 0.026)	-0.164 (-0.475, 0.147)	0.0004 (-0.0002, 0.0009)	0.004 (0.001, 0.010)	0.013 (-0.107, 0.133)	0.031 (-0.002, 0.063)
UCH-L1	0.138 (-0.397, 0.674)	-	0.010 (-0.009, 0.028)	-0.067 (-0.417, 0.282)	0.0004 (-0.0004, 0.0012)	-0.002 (-0.005, 0.002)	-0.007 (-0.304, 0.290)	-0.020 (-0.037, -0.002)^*
GFAP	0.121 (-0.213, 0.455)	-	0.008 (-0.003, 0.019)	-0.171 (-0.502, 0.159)	0.0003 (-0.0001, 0.0007)	-0.001 (-0.003, 0.002)	-0.010 (-0.168, 0.148)	0.001 (-0.013, 0.015)
P-tau-181	0.034 (-0.363, 0.432)	-	0.009 (-0.009, 0.027)	-0.277 (-0.571, 0.017)	0.0003 (-0.0003, 0.0008)	0.001 (-0.002, 0.005)	-0.004 (-0.209, 0.202)	-0.010 (-0.022, 0.002)

Statistically significant association at p < 0.05.

T-tau, total tau; NF-L, neurofilament light; UCH-L1, ubiquitin C-terminal hydrolase-L1; GFAP, glial fibrillary acidic protein; P-tau-181, phosphorylated tau-181.

Discussion

This study aimed to characterize protein biomarkers of SRC in healthy adolescent athletes regarding multiple participant characteristics that require consideration prior to the development of reference ranges in an active adolescent population. A history of one or more previous concussions did not influence any of the five protein biomarkers examined. Notably, all biomarkers except for T-tau and NF-L displayed significant associations with sex, while GFAP was the only biomarkers associated with age. No association was found between biomarker concentrations and menstrual cycle phase in female participants based on hormone analyses. However, exploratory models considering the influence of individual sex hormones did reveal a possible link between progesterone and T-tau in females, and testosterone and UCH-L1 in males. In summary, these findings suggest age- and sex-specific reference intervals may be required prior to clinical validation of these protein biomarkers in pediatric athletes, with special considerations of puberty and development effects on candidate protein biomarkers.

The influence of previous concussion on biomarker levels

In recent years, many studies have examined the potential diagnostic utility of GFAP, UCH-L1, NF-L, T-tau, and P-tau-181 in the context of SRC.^13,15 Most of these studies have been performed in adult SRC, with relatively few studies specifically examining pediatric populations.¹⁸ The field of SRC fluid biomarkers remains in its infancy due to many factors embodying a theme of heterogeneity in the context of injury mechanisms, individual medical histories, complex pathological processes, and a lack of methodological standardization across clinical studies.¹⁷ Due to the inherent variation surrounding these potential SRC biomarkers, it is imperative that the field explores variables contributing to this complexity to better inform the production of clinically useful reference ranges.

For SRC fluid biomarkers to offer clinical diagnostic utility for sideline point-of-care or primary care, it would ideally be minimally influenced by patient characteristics or an individual's medical history to be generalizable to all athlete populations. In the current work with pediatric participants, we demonstrate that all five biomarkers (GFAP, UCH-L1, NF-L, T-tau, and P-tau-181) seemed robust to a history of one or more previous concussions (Fig. 1; Table 2). Considering prior concussion is associated with increased recurrent concussion rates,²⁹ it is favorable that these biomarkers do not display any association with a previous concussion at healthy pre-injury levels. However, the protein biomarker values in this study are very low compared with levels reported post-concussion across various time points,^{13

-16,30

–33} and this finding would need to be replicated in a larger sample size with more statistical power to detect smaller effect sizes. Notably, our results are consistent with adult data from a recent study of Australian rules football players that found no differences in the same five biomarkers between athletes with and without a history of concussion.³⁴

These findings also rest on the assumption that players were fully recovered from prior concussions, but there is growing evidence that physiological recovery may extend beyond clinical recovery metrics.^16,35 Our sample had a large range of time since last concussion (range: 1-70 months) and it was not possible to infer from the data collected which participants may have ongoing pathophysiology. We also had few participants with >1 previous concussions and cannot rule out the possibility of any cumulative effects of multiple concussions on these biomarkers. Future biomarker studies may benefit from multi-modal approaches to investigating physiological processes in healthy athletes with a history of concussion.

Sex differences and the female menstrual cycle

Multiple systematic reviews including adult and pediatric SRC studies have concluded a lack of female representation exists in the field of fluid biomarker research.^17,18 This is a crucial problem given that sexual dimorphisms exist on multiple fronts surrounding SRC. Clinically, females are more likely to report greater numbers of symptoms and symptom severity, and have been shown to have increased recovery time compared with males.^36
-38 Biologically, female brains are structurally and developmentally different than males,³⁹ and the menstrual cycle adds a further layer of complexity to many physiological responses to injury.⁴⁰ Given these differences, variation due to sex in any biomarker should be examined at pre-injury levels. Results from our study found sex differences in 3/5 protein biomarkers examined. After controlling for age and previous history of concussion, males displayed higher P-tau-181 (21.8%), yet lower UCH-L1 (19.8%) and GFAP (18.9%) compared with females (Fig. 3; Table 2).

There is a lack of literature examining sex differences in biomarkers at either pre-injury or post-concussion levels (particularly due to low female representation in SRC biomarker studies).¹⁷ Further, comparing biomarker levels across studies that may vary substantially in collection, processing, and analytical methodologies becomes increasingly difficult without widely adopted standardized study protocols.⁴¹ As such, future large-scaled, multisite studies should endeavor to explore the influence of sex on these exploratory biomarkers, particularly in adolescents. Interestingly, no associations between T-tau and sex were observed. A recent study found sex differences in T-tau immediately post-SRC and throughout recovery, where females displayed higher T-tau concentrations over time compared with males.⁴² The absence of sex effects observed in the current study may be due to participants being far enough out from the last reported concussion whereby T-tau levels would have returned to baseline.

The menstrual cycle may play a critical role in injury pathology and clinical recovery post-SRC. Of the limited number of studies examining the influence of the menstrual cycle, there is evidence to suggest the phase in which a female sustains a concussion may affect outcomes.²² Further, research by Snook and colleagues found that adolescent and young woman who have sustained an SRC had increased risk of abnormal menstrual patterns at 6 months post-injury.²¹ It has been postulated that cyclical levels of estradiol and/or progesterone may be key to fully understanding the variation in pathological responses post-SRC in females, perhaps due to the anti-inflammatory properties of both hormones.^43,44 To our knowledge, no studies have examined the relationship between the SRC biomarkers analyzed in this study and menstrual cycle phase in healthy adolescent populations.

Using plasma progesterone levels, this study objectively determined menstrual cycle phase for female participants. Results showed no difference in pre-injury biomarker levels between females in the follicular versus the luteal phase in this sample (Fig. 4; Table 3). These findings are encouraging as they suggest that these biomarkers may have widespread clinical utility in all females regardless of menstrual cycle phase. However, these results should be interpreted with caution as more females were determined to be in the follicular phase in our sample. These findings should be replicated in a larger sample size, ideally with a better ratio of participants in follicular vs luteal phases of the menstrual cycle. Future studies would benefit by investigating menstrual cycle in relation to these biomarkers at post-injury level concentrations as well.

FIG. 4.

Comparison of plasma levels of total tau (T-tau), neurofilament light (NF-L), ubiquitin C-terminal hydrolase-L1 (UCH-L1), glial fibrillary acidic protein (GFAP), and phosphorylated tau-181 (P-tau-181; ln pg/mL) in female samples stratified by menstrual cycle phase (Follicular, n = 44; Luteal, n = 15). Error bars represent mean and standard deviation of each biomarker. Mixed multi-variable modeling found no significant associations between any biomarker and menstrual cycle phase after controlling for age.

Age and sex hormones

Given brain development throughout adolescence, we explored whether these commonly studied biomarkers of SRC may change with age. The only biomarker found to be associated with age was GFAP, where each 1-year increase was associated with a 9.5% decline in GFAP concentrations. This finding is important given the rising potential for GFAP as an indicator of TBI severity over recent years, and an increased focus specifically in the field of concussion detection, diagnosis, and long-term outcomes.^13,45 Interestingly, we did not observe any significant associations between age and the other four biomarkers examined. This may be due to our limited sample size to detect smaller age effects in healthy samples, or perhaps these biomarkers would reveal more of a relationship with a larger age range, as older individuals show age-dependency of GFAP and NFL concentrations.⁴⁶ Little work has been done in the SRC literature concerning the effects of age on these biomarkers given that most studies are done in collegiate-age adults.¹⁷ However, age-related differences in diagnostic accuracy for GFAP, P-tau, and T-tau have been demonstrated for detecting intracranial trauma (positive findings on computed tomography) for complicated mTBI.⁴⁷ Should GFAP remain a major focus for concussion detection, age-specific reference intervals will likely be required for pediatric populations.

This study also explored potential associations between sex hormones and protein biomarkers of SRC. As mentioned in the methods, we conducted separate analyses for males and females given the differential roles sex hormones play.^28,48 In females, the only significant association concerned T-tau, where levels decreased by 1.0% for every 1 nmol/L increase in progesterone (Table 3). This small decrease may not be clinically relevant at such low values of T-tau (female mean 7.03 ± 6.39 pg/mL); however, it could be relevant at post-concussion levels. For illustrative purposes, using our female sample mean and the normative range of plasma progesterone provided by APL (0.0 – 76.0 nmol/L), this could result in concentrations of approximately 3.31 pg/mL at peak progesterone levels during the luteal phase of the menstrual cycle after compounding the percentage decrease. While it is unknown whether this could be a clinically relevant change, we speculate it may be important to consider at post-concussion levels as one study reported female athletes with SRC having median plasma T-tau levels (detected by Simoa) ranging from a peak of 10.78 pg/mL post-injury to 6.70 pg/mL at 7 days post-SRC.⁴² Hormonal contraceptive use may affect this relationship, as it has been reported to influence SRC outcomes in females⁴⁹; however, since our sample only had 12 participants reporting birth control use, we were unable to evaluate this variable.

In males, we found a negative association between plasma UCH-L1 and testosterone (2.0% UCH-L1 decrease per 1 nmol/L increase of testosterone). Our male sample had a mean UCH-L1 value of 14.20 ± 6.43 pg/mL and if we similarly compound the decrease over a normative testosterone range (4.0 – 27.0 nmol/L), UCH-L1 concentrations could hypothetically reach 8.92 pg/mL at peak testosterone levels. Like T-tau, this relationship between testosterone and UCH-L1 may be amplified at post-concussion levels.

McCrea and colleagues reported peak UCH-L1 levels within 4 h post-SRC in a large sample of collegiate athletes to be 27.64 pg/mL (Simoa). A similar calculation would have UCH-L1 values fall below their sample's reported baseline levels (18.88 pg/mL).¹³ However, testosterone ranges differ in collegiate athletes and this association requires investigation in older age groups. Future studies may consider conducting hormone level-specific analyses of SRC biomarker data given that hormone levels at the time of sampling (baseline and post-injury time-points) possibly influence protein expression and therefore biomarker levels measured in circulation. Progesterone, estrogen, and androgen receptors are expressed widely throughout the body (including the central nervous system), and freely diffuse into cells to regulate gene expression.⁵⁰ Moreover, this relationship may be further complicated by normal hormone changes due to puberty,⁵¹ or abnormal hormone changes in conditions such as obesity,⁵² relative energy deficiency in sport,⁵³ or exogenous hormone administration (e.g., contraceptives, hormone therapy, anabolic steroids). Overall, sex hormones may be a factor that can influence these brain injury biomarkers requiring consideration in future SRC studies.

Strengths and limitations

A major strength to this study includes a sample with even male and female representation across multiple high-risk concussion sports. We were also able to objectively determine menstrual cycle phase rather than rely on self-report measures that may introduce bias. This allowed us to interrogate the possible influence of the menstrual cycle on SRC biomarkers, which to our knowledge has not been adequately investigated to date, particularly in adolescents.

This study also has limitations. As we were investigating biomarker levels in healthy active individuals, we may have remained underpowered to statistically detect smaller effect sizes with respect to age or previous concussion. Further, we did not collect data on possible sub-concussive repetitive head impacts which have been associated with altered levels of SRC fluid biomarkers.^54
-56 Our measures of previous concussion also were based on self-report, which could subject our findings to recall bias. This includes the possible effect of the time since previous concussion that cannot be ruled out. However, we speculate our sample is expected to be minimally affected given the average time since concussion was approximately 2 years prior to data collection (Table 1). Future studies using both medical records and self-report would be needed. As well, using a validated concussion tool such as the brain injury screening questionnaire (BISQ) could be utilized for more rigorous evaluation of concussion history.

Additionally, the %CV for NF-L, T-tau, and UCH-L1 were all >10%, a potential source of variation in our results. However, our multi-level modeling approach used both biomarker replicates to factor in the variation found between duplicate samples. Replication of these findings should also consider the variation inherent across analytical platforms that may influence the generalizability of biomarker assay results. We also did not collect information on exercise immediately before sampling, as this has been shown to influence change in biomarkers like tau.⁵⁷

Lastly, we were unable to assess potential biomarker changes specific to clinical pubertal stages. Future studies may consider Tanner Staging or the Puberty Development Scale, depending on clinical setting, to properly interrogate how pubertal stages may influence biomarkers of SRC.⁵⁸ Large, well-powered studies will be needed to further investigate the effects of variables of known importance in pediatric SRC.

Conclusions

Overall, our results show that SRC-related blood biomarkers in adolescents must consider multiple possible confounding variables prior to clinical validation. Our findings suggest the five biomarkers investigated seem resilient to history of previous concussion and menstrual cycle phase (encouraging characteristics for any potential diagnostic biomarker of SRC). These results should be replicated in a larger sample. Further, we found plasma GFAP to be negatively associated with age, and GFAP, UCH-L1, and P-tau-181 to be influenced by sex, calling for age- and sex-specific reference intervals of these biomarkers in adolescents. Lastly, exploratory analyses revealed significant associations between individual biomarkers and sex hormones in male and female participants, warranting future investigations into the effects of puberty and endocrine function on the expression of these biomarkers in both healthy and post-SRC adolescents.

Footnotes

Transparency,Rigor,and Reproducibility Summary

This study was not formally registered as it was part of a longitudinal cohort study. The analysis plan was not formally pre-registered. The larger SHRed Concussions multi-year longitudinal cohort study planned for approximately 6000 participants/year over the course of the study. The sub-cohort that provided blood samples for this study included 149 adolescent participants from a total of 4502 recruited for study years 1 and 2 (September 2019-November 2021). Sample were analyzed in January 2022. No a priori power calculations were performed as this was an exploratory study to potentially inform future analyses. Three samples had one UCH-L1 assay replicate with levels below the LOD that were excluded from analyses (replicate 2 was retained). Four samples had one of two replicates fail in analysis for GFAP, UCH-L1, NF-L, and T-tau, and one extra sample had one replicate fail for only UCH-L1 (replicate 2 retained for each sample). One sample failed completely on the Neurology-4-plex assay but was successful on the P-tau-181 assay. All hormones were successfully analyzed. Human participants were blinded to results of the plasma biomarker measurements. Investigators blinded to relevant participant characteristics performed all biomarker measurements, analyses, and quality control decisions. Plasma samples were labeled using codes linked to subject identification information. Plasma samples were acquired from Calgary, Edmonton, and London study sites. Plasma was acquired via standard venipuncture into K2-EDTA BD vacutainer tubes, centrifuged immediately at 1300 g for 10 min at room temperature, aliquoted into cryovials, and frozen at -80°C until analysis. Samples underwent one freeze-thaw cycle for protein biomarker analysis. Samples underwent one (Calgary) or two (Edmonton, London) freeze-thaw cycles for hormone analysis. for details on protein biomarker and hormone analyses. Analytical reagents and equipment used for biomarker analyses are available from commercial sources. Key inclusion criteria and primary clinical variables are established standards in the field (previous concussion history, age, and sex). Mixed-effects multi-variable linear regression using log-transformed protein biomarker concentrations was used with bootstrapping methods (1000 repetitions) to help account for small sample sizes and possible violations of model assumptions in stratified models. Estimates and 95% confidence intervals are reported in the abstract for primary outcomes and main text for all outcomes. Replication studies by the study group are ongoing. Data from this study are not available in a public archive. There is no analytic code associated with this study. All plasma samples used to conduct the study were obtained by investigators and may be used for future research, though limited quantities remain. The authors agree to provide the full content of the manuscript on request by contacting Jason Tabor.

Acknowledgments

The Sport Injury Prevention Research Centre is one of the International Research Centres for Prevention of Injury and Protection of Athlete Health supported by the International Olympic Committee. We acknowledge the support of SHRed Concussions (National Football League Play Smart Play Safe Program) and Integrated Concussion Research Program (University of Calgary). We acknowledge the support of the SHRed research team and the SHRed participants for their engagement in this study. J.B. Tabor is supported by a Canadian Institutes of Health Research Banting Doctoral Award. C.A. Emery holds a Canada Research Chair (Tier 1) in Concussion.

Authors' Contributions

All authors contributed to the writing and development of this manuscript. Authors JBT, CAE, CLW, and CTD were involved in study conceptualization and design. Authors JBT, LCP, JGC, and MG had major roles in acquisition of data. Authors JBT, JMG, DDF, CAE, CLW, and CTD were involved in analysis and interpretation of the data. Authors JBT, LCP, JMG, DDF, CAE, CLW, and CTD were involved in writing, revision, and finalizing the manuscript.

Funding Information

This work is aligned with the Surveillance in High Schools and Community Sports to Reduce Concussions and Their Consequences study, which is supported by the National Football League's Play Smart Play Safe Program).

Author Disclosure Statement

No competing financial interests exist.

Supplementary Material

Supplementary Table S1

Supplementary Table S2

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