Abstract
While ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) and glial fibrillary acidic protein (GFAP) have demonstrated clinical relevance in evaluating traumatic brain injury (TBI), less is known about how underlying pediatric neurological or psychiatric diagnoses may influence baseline biomarker levels. Plasma samples from a pediatric biobank were analyzed for UCH-L1 and GFAP concentrations. Eligible participants were <19 years of age and had a documented diagnosis of attention-deficit/hyperactivity disorder, anxiety, depression, or seizure disorder but no recent history of trauma, infectious illness, or other chronic medical conditions. Biomarker levels were compared (1) across diagnostic categories, (2) with a separate group of historic controls (n = 216), and (3) with adult thresholds used to guide head CT use in mild TBI (UCH-L1 400 pg/mL; GFAP 35 pg/mL). Among 88 participants (median age 15 years), UCH-L1 and GFAP values remained relatively stable across conditions. There were no significant differences between diagnostic groups or compared with controls. Approximately 6.8% of children had GFAP levels above the adult threshold, whereas <3% exceeded the UCH-L1 threshold. Underlying neuropsychiatric conditions do not appear to meaningfully modify baseline circulating GFAP or UCH-L1 levels in children, suggesting that these conditions may not preclude the use of these biomarkers in the evaluation of pediatric TBI.
Introduction
Ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) and glial fibrillary acidic protein (GFAP) have gained traction as blood-based indicators of brain injury, particularly following FDA approval for their use in adults with suspected traumatic brain injury (TBI). 1 These markers, which reflect astroglial and neuronal integrity, respectively, help predict the likelihood of intracranial injury in adults undergoing head computed tomography. 2 Their potential utility in pediatric care, however, remains under investigation, with several recent studies demonstrating age-dependent differences in normative biomarker profiles.3,4
In addition to age-dependent differences, biomarker profiles may be affected by various conditions such as depression 5 and seizure disorders, 6 although findings in adult populations have been inconsistent. 6 Even less is known in children about how common pre-existing neurological or psychiatric conditions—such as seizure disorders, attention-deficit/hyperactivity disorder (ADHD), depression, or anxiety—may affect biomarker baselines, independent of acute injury. Understanding how these pre-existing conditions influence GFAP and UCH-L1 levels is an essential first step in evaluating the potential utility of these biomarkers in managing TBI across a broad population of pediatric patients.
This study aimed to assess the baseline plasma concentrations of GFAP and UCH-L1 in children with select, common neuropsychiatric or neurological conditions, evaluating whether these levels differ from thresholds established for adults and whether variability exists across diagnostic categories. Here, we focused on four of the most common neuropsychiatric conditions affecting children: ADHD, anxiety, depression, and seizure disorders.
Methods
The study protocol received approval from the Institutional Review Board at Boston Children’s Hospital. Blood specimens were sourced from the Boston Children’s Hospital PrecisionLink Biobank, which is open to participation from all hospital patients. Enrollment in the Biobank requires informed consent, obtained by trained research staff. This consent authorizes: (1) extraction of clinical data from the electronic health record (EHR), including demographics, visit types and dates, diagnoses, clinical notes, laboratory orders and results, physical findings, medications, and procedures; (2) use of leftover clinical samples, such as blood and tissue, collected during routine care; and (3) optional collection of an additional research blood sample during a future clinical laboratory draw from which the plasma is derived. The biobank collection of venous blood samples is previously described. 3
Biobank samples are cataloged and accessible for de-identified review through a customized implementation of the STARLIMS laboratory information management system (version 10.06; Abbott Laboratories, Abbott Park, IL, USA). To select eligible participants for this study, the database was queried to identify individuals who met the following inclusion criteria:
Under 19 years of age No documented TBI or concussion within the past 12 months (ICD-10 codes) No emergency department visits or hospital admissions for trauma in the past year No evidence of active infection at the time of blood collection (ICD-10 codes) A diagnosis of ADHD, anxiety, depression, or seizure disorder (ICD-10 codes) Absence of other complex chronic conditions, as defined by ICD-10 coding
7
Using this query, a sample of 88 samples banked between February 2017 and May 2023 was identified. Coded samples were then shipped overnight on dry ice in a single batch.
A group of historic normal controls (n = 216, ages 11–18 years) from our recent study 3 was identified for comparison.
Samples underwent one freeze–thaw cycle, then were analyzed using the Alinity i TBI test, which is a panel of chemiluminescent microparticle immunoassays for the measurement of GFAP and UCH-L1 in plasma and serum. Two samples were not analyzed due to inadequate volume. The analytical measuring interval is 6.1–42 000.0 pg/mL for GFAP and 26.3–25 000.0 pg/mL for UCH-L1. The coefficient of variation is 2.8–5.3% for GFAP and 2.1–5.6% for UCH-L1. All samples were tested without dilution and in singlicate. Technicians performing biomarker measurements were blinded to clinical outcome data.
Simple descriptive statistics were used to describe the demographics of the study population. Sample distributions were descriptively compared to normative controls. Distributions of biomarkers were compared between groups using a Kruskal–Wallis test by ranks. In addition, cutoffs were set for GFAP and UCH-L1 of 35.0 and 400.0 pg/mL, respectively, derived from studies in the TBI literature relevant to CT-positive TBI in adults. 8 All analyses were performed using STATA (College Station, Texas). The anonymized patient data are not being publicly shared as they are being utilized for the development of a clinical trial.
Results
The final sample included 88 children (median age: 15 years; IQR: 12–16 years, range: 11–18 years), with 50 (57%) females. Diagnostic categories included ADHD (n = 20), anxiety (n = 20), depression (n = 24), and seizure disorder (n = 29). Comorbid diagnoses were not mutually exclusive. Demographics of control samples (n = 216) were as follows: median age 15 years (Interquartile range [IQR]: 13–17 years, range: 11–18 years) with 121 (56%) females.
Figure 1 depicts the distribution of biomarkers across the different diagnostic groups as well as controls. Notably, 6 of 88 children (6.8%) with an underlying condition had GFAP levels above the adult diagnostic threshold, 4 of whom had a diagnosed seizure disorder (Fig. 1B). In contrast, only 2 of 88 children (2.3%) with an underlying condition had UCH-L1 concentrations above the 400 pg/mL cutoff (Fig. 1A). In the control population, 8 of 216 (3.7%) and 3 of 216 (1.4%) had elevations above GFAP and UCH-L1 cutoffs, respectively. No significant group differences in biomarker levels were observed for GFAP (χ2[3] = 7.897, p = 0.10) or UCH-L1 (χ2[3] = 2.819, p = 0.59).
Distribution of UCH-L1
Discussion
In this study of children with common neuropsychiatric conditions, including ADHD, anxiety, depression, and seizure disorders, we found no significant differences in plasma levels of UCH-L1 or GFAP across diagnostic groups. Although a small subset of children with seizure disorders exhibited GFAP concentrations above adult TBI thresholds, overall biomarker variability was limited, and group comparisons did not reveal statistically meaningful differences.
While previous studies in adults have suggested possible associations between GFAP and conditions such as depression or epilepsy, the current pediatric data do not demonstrate strong parallels. Instead, our findings suggest that underlying neuropsychiatric diagnoses may not substantially alter baseline GFAP or UCH-L1 levels in pediatric populations. This is particularly relevant for the use of these biomarkers in the clinical assessment of TBI, where elevated values might otherwise be misattributed to pre-existing neurological or psychiatric conditions. Our data indicate that, with few exceptions, these common conditions are unlikely to confound biomarker interpretation.
This study has several limitations. Diagnoses were based on ICD-10 coding within the EHR, which may miss subclinical or undocumented comorbidities, and are unable to ascertain whether or not the diagnoses are active (or actively being treated). The sample size, while sufficient to detect large differences, may have been underpowered to identify subtle effects. In addition, sample collection followed biobank protocols, which may differ from research-specific processing pipelines. Finally, in the case of entities such as seizure disorder, the seizure type, frequency, and proximity to a recent seizure may substantially affect the biomarker profiles.
Despite these limitations, our findings support the potential applicability of GFAP and UCH-L1 in the evaluation of pediatric TBI, even among children with common neurodevelopmental and psychiatric conditions. Larger, prospective studies are needed to confirm these observations and further refine the role of biomarkers in diverse pediatric populations.
Data Availability Statement
The data underlying this study are not publicly available due to restrictions related to participant consent and institutional policies governing the PrecisionLink Biobank. De-identified data may be available from the corresponding author upon reasonable request and with appropriate institutional approvals.
Authors’ Contributions
R.M.: Conceptualization, methodology, data acquisition, data curation, data analysis, original draft preparation, writing—reviewing and editing. E.B.: Conceptualization, data acquisition, data curation, writing—reviewing and editing. J.K.: Conceptualization, data acquisition, data curation, writing—reviewing and editing. C.L.: Master writing—reviewing and editing. D.C.: Writing—reviewing and editing. M.B.: Writing—reviewing and editing. D.T.: Writing—reviewing and editing. A.R.: Writing—reviewing and editing. G.M.: Writing—reviewing and editing.
Footnotes
Author Disclosure Statement
E.B., J.K., D.C., and A.R. report no disclosures relevant to the article.
Funding Information
R.M. reports funding from Abbott Laboratories, the National Football League,the Department of Defense, and the National Institutes of Health. E.B. reports no disclosures relevant to the manuscript. J.K. reports no disclosures relevant to the manuscript. C.L.M. reports funding from the Centers for Disease Control, the National Institutesof Health, the Department of Defense, Pennsylvania Department of Health, American Medical Society for Sports Medicine, Children’s Hospital of Philadelphia Frontier Program, Chuck Noll Foundation for Brain Injury Research, Toyota Way Forward Fund. D.C. reports no disclosures relevant to the manuscript. M.B. reports funding from the Centers for Disease Control, the National Institutes of Health, EMSC-Network Development Demonstration Project, and the Health Resources & Services Administration. D.T. has reports funding from the Centers for Disease Control, the National Institutes of Health, Clinical and Translational Science Institute of Southeastern Wisconsin,and the Children’s (Wisconsin) Research Institute. A.R. reports no disclosures relevant to the manuscript. A.F. reports funding from Pediatric Pandemic Network. G.M. reports funding from Abbott Laboratories, US DoD, NIH, the NFL, Neuro Trauma Sciences LLC and One Mind outside the submitted work.