Are EPB41 and alpha-synuclein diagnostic biomarkers of sport-related concussion? Findings from the NCAA and Department of Defense CARE Consortium.


Journal

Journal of sport and health science
ISSN: 2213-2961
Titre abrégé: J Sport Health Sci
Pays: China
ID NLM: 101606001

Informations de publication

Date de publication:
05 2023
Historique:
received: 12 02 2022
revised: 15 07 2022
accepted: 08 10 2022
medline: 17 5 2023
pubmed: 21 11 2022
entrez: 20 11 2022
Statut: ppublish

Résumé

Current protein biomarkers are only moderately predictive at identifying individuals with mild traumatic brain injury or concussion. Therefore, more accurate diagnostic markers are needed for sport-related concussion. This was a multicenter, prospective, case-control study of athletes who provided blood samples and were diagnosed with a concussion or were a matched non-concussed control within the National Collegiate Athletic Association-Department of Defense Concussion Assessment, Research, and Education Consortium conducted between 2015 and 2019. The blood was collected within 48 h of injury to identify protein abnormalities at the acute and subacute timepoints. Athletes with concussion were divided into 6 h post-injury (0-6 h post-injury) and after 6 h post-injury (7-48 h post-injury) groups. We applied a highly multiplexed proteomic technique that used a DNA aptamers assay to target 1305 proteins in plasma samples from athletes with and without sport-related concussion. A total of 140 athletes with concussion (79.3% males; aged 18.71 ± 1.10 years, mean ± SD) and 21 non-concussed athletes (76.2% males; 19.14 ± 1.10 years) were included in this study. We identified 338 plasma proteins that significantly differed in abundance (319 upregulated and 19 downregulated) in concussed athletes compared to non-concussed athletes. The top 20 most differentially abundant proteins discriminated concussed athletes from non-concussed athletes with an area under the curve (AUC) of 0.954 (95% confidence interval: 0.922‒0.986). Specifically, after 6 h of injury, the individual AUC of plasma erythrocyte membrane protein band 4.1 (EPB41) and alpha-synuclein (SNCA) were 0.956 and 0.875, respectively. The combination of EPB41 and SNCA provided the best AUC (1.000), which suggests this combination of candidate plasma biomarkers is the best for diagnosing concussion in athletes after 6 h of injury. Our data suggest that proteomic profiling may provide novel diagnostic protein markers and that a combination of EPB41 and SNCA is the most predictive biomarker of concussion after 6 h of injury.

Sections du résumé

BACKGROUND
Current protein biomarkers are only moderately predictive at identifying individuals with mild traumatic brain injury or concussion. Therefore, more accurate diagnostic markers are needed for sport-related concussion.
METHODS
This was a multicenter, prospective, case-control study of athletes who provided blood samples and were diagnosed with a concussion or were a matched non-concussed control within the National Collegiate Athletic Association-Department of Defense Concussion Assessment, Research, and Education Consortium conducted between 2015 and 2019. The blood was collected within 48 h of injury to identify protein abnormalities at the acute and subacute timepoints. Athletes with concussion were divided into 6 h post-injury (0-6 h post-injury) and after 6 h post-injury (7-48 h post-injury) groups. We applied a highly multiplexed proteomic technique that used a DNA aptamers assay to target 1305 proteins in plasma samples from athletes with and without sport-related concussion.
RESULTS
A total of 140 athletes with concussion (79.3% males; aged 18.71 ± 1.10 years, mean ± SD) and 21 non-concussed athletes (76.2% males; 19.14 ± 1.10 years) were included in this study. We identified 338 plasma proteins that significantly differed in abundance (319 upregulated and 19 downregulated) in concussed athletes compared to non-concussed athletes. The top 20 most differentially abundant proteins discriminated concussed athletes from non-concussed athletes with an area under the curve (AUC) of 0.954 (95% confidence interval: 0.922‒0.986). Specifically, after 6 h of injury, the individual AUC of plasma erythrocyte membrane protein band 4.1 (EPB41) and alpha-synuclein (SNCA) were 0.956 and 0.875, respectively. The combination of EPB41 and SNCA provided the best AUC (1.000), which suggests this combination of candidate plasma biomarkers is the best for diagnosing concussion in athletes after 6 h of injury.
CONCLUSION
Our data suggest that proteomic profiling may provide novel diagnostic protein markers and that a combination of EPB41 and SNCA is the most predictive biomarker of concussion after 6 h of injury.

Identifiants

pubmed: 36403906
pii: S2095-2546(22)00114-4
doi: 10.1016/j.jshs.2022.11.007
pmc: PMC10199139
pii:
doi:

Substances chimiques

erythrocyte membrane band 4.1 protein 0
alpha-Synuclein 0
Biomarkers 0

Types de publication

Multicenter Study Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

379-387

Informations de copyright

Copyright © 2023. Production and hosting by Elsevier B.V.

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Auteurs

Rany Vorn (R)

Johns Hopkins School of Nursing and Medicine, Baltimore, MD 21205, USA; National Institutes of Health, Bethesda, MD 20892, USA.

Christina Devoto (C)

National Institutes of Health, Bethesda, MD 20892, USA.

Timothy B Meier (TB)

Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI 53226, USA.

Chen Lai (C)

National Institutes of Health, Bethesda, MD 20892, USA.

Sijung Yun (S)

Predictiv Care, Inc., Mountain View, CA 94086, USA.

Steven P Broglio (SP)

Michigan Concussion Center, University of Michigan, Ann Arbor, MI 48109, USA.

Sara Mithani (S)

National Institutes of Health, Bethesda, MD 20892, USA.

Thomas W McAllister (TW)

Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN 46202, USA.

Christopher C Giza (CC)

Departments of Pediatrics and Neurosurgery, University of California at Los Angeles (UCLA), Los Angeles, CA 90024, USA.

Hyung-Suk Kim (HS)

National Institutes of Health, Bethesda, MD 20892, USA.

Daniel Huber (D)

Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI 53226, USA.

Jaroslaw Harezlak (J)

Department of Epidemiology and Biostatistics School of Public Health - Bloomington, Indiana University, Bloomington, IN 47405, USA.

Kenneth L Cameron (KL)

John A. Feagin Sports Medicine Fellowship, Keller Army Community Hospital, West Point, NY 10996, USA.

Gerald McGinty (G)

United States Air Force Academy, Colorado Springs, CO 80840, USA.

Jonathan Jackson (J)

United States Air Force Academy, Colorado Springs, CO 80840, USA.

Kevin M Guskiewicz (KM)

Mathew Gfeller Center, Department of Exercise and Sport Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA.

Jason P Mihalik (JP)

Mathew Gfeller Center, Department of Exercise and Sport Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA.

Alison Brooks (A)

Department of Orthopedics, Division of Sports Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, WI 53705, USA.

Stefan Duma (S)

Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA 24061, USA.

Steven Rowson (S)

Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA 24061, USA.

Lindsay D Nelson (LD)

Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI 53226, USA.

Paul Pasquina (P)

Center for Neuroscience & Regenerative Medicine, Uniformed Services University, Bethesda, MD 20814, USA.

Michael A McCrea (MA)

Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI 53226, USA.

Jessica M Gill (JM)

Johns Hopkins School of Nursing and Medicine, Baltimore, MD 21205, USA. Electronic address: jessicagill@jhu.edu.

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