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Record W4322616805 · doi:10.1089/neu.2022.0501

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

2023· article· en· W4322616805 on OpenAlexaff
Jason Tabor, Linden C. Penner, Jennifer Cooper, Mohammad Ghodsi, Jean‐Michel Galarneau, Douglas D. Fraser, Carolyn A. Emery, Cheryl L. Wellington, Chantel T. Debert

Bibliographic record

VenueJournal of Neurotrauma · 2023
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of British ColumbiaHotchkiss Brain InstituteWestern UniversityAlberta Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsConcussionContext (archaeology)MedicineTestosterone (patch)Menstrual cycleCohortHormoneInternal medicineFollicular phaseBiomarkerPoison controlPhysiologyEndocrinologyOncologyInjury preventionBiologyMedical emergency

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.128
GPT teacher head0.375
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2023
Admission routes1
Has abstractyes

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