Concussion Biomarkers Assessed in Collegiate Student-Athletes (BASICS) I Normative study

Objective To describe variability in concussion biomarker concentrations collected from serum in a sample of healthy collegiate athletes, as well as report reliability metrics in a subsample of female athletes. Methods In this observational cohort study, beta-amyloid peptide 42 (A beta(42)), total tau, S100 calcium binding protein B (S100B), ubiquitin carboxy-terminal hydrolyzing enzyme L1 (UCH-L1), glial fibrillary acidic protein, microtubule associated protein 2, and 29,39-cyclic-nucleotide 39-phosphodiesterase (CNPase) serum concentrations were measured in 415 (61% male, 40% white, aged 19.0 +/- 1.2 years) nonconcussed collegiate athletes without recent exposure to head impacts. Standardized normative distributions are reported for each biomarker. We evaluated main effects (analyses of variance) of sex and race, reporting demographic-specific normative metrics when appropriate. In a subset of 31 female participants, test-retest reliability (Pearson r) and reliable change indices (80%, 90%, and 95% confidence intervals) across a 6- to 12-month interval are reported for A beta(42), total tau, S100B, and UCH-L1. Results Males exhibited higher UCH-L1 (p < 0.001, Cohen d = 0.75) and S100B (p < 0.001, d = 0.56) than females, while females had higher CNPase (p < 0.001, d = 0.43). Regarding race, black participants had higher baseline levels of UCH-L1 (p < 0.001, d = 0.61) and S100B (p < 0.001, d = 1.1) than white participants. Conversely, white participants had higher baseline levels of A beta(42) (p = 0.005, d = 0.28) and CNPase (p < 0.001, d = 0.46). Test-retest reliability was generally poor, ranging from -0.02 to 0.40, and A beta(42) significantly increased from time 1 to time 2. Conclusion Healthy collegiate athletes express concussion-related serum biomarkers in variable concentrations. Accounting for demographic factors such as sex and race is essential. Evidence suggested poor reliability for serum biomarkers; however, understanding how other factors influence biomarker expression, as well as knowledge of reliable change metrics, may improve clinical interpretation and future study designs.