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Record W7117119733 · doi:10.1002/alz70856_096745

The Inflammatory Signature of RHI in Athletes at Risk of CTE

2025· article· en· W7117119733 on OpenAlexaff
Carmela M. Tartaglia, C. H. Tator, Lian L. Troncoso, Chloe Anastassiadis, Simrika Thapa, Mozhgan Khodadadi

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsOntario Brain InstituteToronto Western HospitalOccupational Cancer Research CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsAthletesConcussionInflammationNeurologyNeurodegenerationSports medicine

Abstract

fetched live from OpenAlex

Abstract Background There is accumulating evidence that repetitive head impact (RHI) in former contact sports athletes is a risk factor for developing chronic traumatic encephalopathy (CTE) and other neurodegenerative diseases. The pathological link between RHI and neurodegeneration remains unknown. There is increasing evidence that inflammation is one of the physiological responses to RHI 1,2 . Previous studies have demonstrated elevated inflammatory cytokines in blood immediately and years after a concussion 2,3 . The aim of this study was to compare the inflammatory profiles between former professional contact sports athletes with RHI (ExPro) and patients with Alzheimer's disease (AD) and Healthy controls (HC). Method We used a multiplex proximity extension assay (PEA) technology using Olink Explore Inflammation I and II panel to quantify 737 inflammatory proteins in the cerebrospinal fluid (CSF) of 16 ExPro (mean age=60.9+/‐11), 24 AD (mean age 69.1+/‐9), and 6 HC (mean age=57.0+/‐11). We also evaluated CSF Neurofilament Light (NfL) using Single Molecule Array (SIMOA). An ANCOVA (covariate age) was used to compare inflammatory markers between groups and Pearson correlation was used to evaluate relationship of inflammatory markers with NfL. Result There were 87 neuroinflammatory proteins that were significantly different between ExPro, AD and HC. These markers span immune activation, oxidative stress, vascular inflammation, extracellular matrix remodelling, and neuroinflammation. SCRN1 and MDH1 remained significant after FDR correction. SCRN1 is predominantly involved in immune regulation, vesicular trafficking, and stress response while MDH1 is central to metabolic pathways (TCA cycle, redox balance) and plays a role in oxidative stress and metabolic reprogramming. In the ExPro and AD, NfL was correlated with 105 inflammatory markers but after FDR correction was positively correlated with TBCA, LAIR1, KMT1A/CKMT1B, MILR1, ADAMTS1 and GLOD4. These markers play important roles in inflammation, immune regulation, metabolism, and tissue homeostasis. Conclusion Our results provide evidence that distinct inflammatory profiles exist between former contact sports athletes with RHI, patients with AD and HC. As well, we show a relationship between markers of inflammation and neurodegeneration. Identifying specific inflammatory markers relevant to RHI could support the development of targeted therapies to halt neurodegeneration in RHI. 1. Neurology 93(5), e497(2019); 2. Front Neurol 4, 18(2013); 3. Concussion 2(1), CNC30(2017).

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)0.000
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.025
GPT teacher head0.306
Teacher spread0.281 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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