Diagnosis of Sport-related Concussion Using Urine Metabolites
Study Details
Study Description
Brief Summary
Millions of sport related concussions (SRC) occur yearly in the United States, and current diagnosis of concussion is based upon largely subjective clinical evaluations. The objective of this study is to determine whether urinary metabolites are significantly altered post SRC. Urine of 26 athletes will be analyzed pre-injury and after SRC by 1H NMR spectroscopy. Data will be analyzed using multivariate statistics, pairwise t-test, and metabolic pathway analysis. Variable Importance Analysis based on random Variable Combination (VIAVC) was used to select what features are present out of 224 features. Partial least squares discriminant analysis was performed leading to separation between pre-season and post-SRC groups. A Receiver Operator Curve (ROC) curve will be constructed to classify the features. Pathway topology analysis will also be completed to determine biological pathways are potentially affected following SRC.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
|
N/A |
Detailed Description
Background:
Sport concussion or sport-related mild traumatic brain injury (mTBI) affects patients across the spectrum of age, race and gender. It is a medical problem that if not treated appropriately can have devastating outcomes to patients and families. MTBI or concussion (terminology used interchangeably in this proposal) more recently has been a hot topic in the media with high profile politicians and athletes having career altering outcomes following a concussion. According to the Canadian Institute for Health Information, sports and recreational activities were the third leading cause of TBI admissions in Canadian hospitals in 2003-2004. In the United States, the Center for Disease Control and Prevention estimates that 1.6 to 3.8 million concussions occur in sports and recreational activities annually. In Calgary, approximately 10,000 patients were seen in the emergency departments with a head injury in 2013, 80% of which were diagnosed as mTBI and a vast majority of these were sport related (unpublished data).
Currently, there is no single "gold standard" assessment or diagnostic marker to objectively determine if a patient has suffered a sport concussion and if so, how long it will take to recover. As such, concussion remains a clinical diagnosis based on post-traumatic symptoms, physical signs, and impairment in cognitive function, which at best is done by a trained and experienced medical professional. But, this is primarily a qualitative, individualized approach to diagnosing and is typically clouded by a lack of objective clinical findings. Other assessment tools can aid a clinician in diagnosing a concussion such as the Sport Concussion Assessment Tool 3 (SCAT3) (Appendix 1), but unfortunately this has not been validated and was developed based on expert consensus. As well, computerized neuropsychological testing may help with the assessment of concussion recovery; however it is expensive, time consuming and has recently been criticized as lacking specificity and sensitivity for head injury.
Over the past 20 years researchers have studied biomarkers such as S100B and neuron-specific enolase in hopes of finding a quantitative approach to diagnosing and prognosticating concussion. This unfortunately, has met with much disappointment as none of these markers, in and of themselves, has adequate specificity or sensitivity to be used as a single biomarker in clinical practice.
Metabolomics is defined as "the quantitative measurement of the metabolic response of living systems to pathophysiological stimuli or genetic modification" and is based on analytical platforms such as proton nuclear magnetic resonance spectroscopy (1H NMR) and/or mass spectrometry. Metabolomic studies of clinically accessible bio-fluids, such as blood and urine, provides an intricate and relatively non-invasive snapshot into this response. To date, metabolomics has been shown to be a useful biomarker tool in amyotrophic lateral sclerosis, Alzheimer's disease, Parkinson's disease, neonatal hypoxic ischemic encephalopathy, brain cancer, multiple sclerosis, aging and neurodegenerative disease, epilepsy, stroke, severe traumatic brain injury and pediatric septic shock. To the best of our knowledge, no study has assessed quantitative metabolomic profiling as a diagnostic and prognosticating tool in sport concussion.
This pilot study would aid in the following:
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To determine the feasibility of testing metabolomics in the sport concussion population.
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The first study of its kind to use quantitative metabolic profiling to assist with the diagnosis of acute sport concussion.
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The first study of its kind to develop a metabolic "fingerprint" that may help prognosticate recovery in acute sport concussion.
Subjects will be asked to provide an mid-stream urine sample between 7-9 am at pre-injury and post-concussion. Urine aliquots will be obtained immediately by centrifuge and then stored at -80C until required for analysis. Once all samples are collected they will be batched and transported to the University of Lethbridge for analysis.
No samples for metabolomic analysis will undergo a previous freeze/thaw cycle. An aliquot of 450 μl will be removed from each of the urine samples and added to 250 μl of buffer solution. The buffer solution will be prepared using a potassium phosphate solution at 0.5M and pH 7.4 ± 0.04. This buffer solution also contains 10 mL of sodium azide which acts as an antimicrobial agent. The solvent for the buffer will 1 part D2O to 4 parts normal water and the D2O contains 0.05 weight percent of 3-(trimethylsilyl)propionic-2,2,3,3-d4 acid (TSP). The final concentration of TSP will be used as an internal chemical shift and quantification standard. The buffer/urine solution is then centrifuged at 10 000 rpm for five minutes to remove any particulate matter, large fats, or large proteins and 500 μl is pipetted into 5mm NMR tubes.
All NMR spectra will be obtained on a Bruker AVANCE III HD 700 MHz spectrometer (Bruker BioSpin Ltd., Milton, ON, Canada) equipped with a high-throughput sample changer platform, a triple resonance (TBO) 5mm probe and TopSpin 3.2 software. Each sample will be acquired using the standard Bruker 1D pre-saturation pulse sequence (noesygppr1d) with a mixing time of 10 ms, an acquisition time of 5 seconds, a recycle delay of 4 seconds and a total of 512 scans. Each sample will then be processed with a line broadening of 0.3 Hz.
After initial processing in TopSpin 3.2, all spectra will be loaded into matNMR (24), correctly referenced by setting the TSP shift to 0 ppm, and exported into the Matlab ® workspace for further processing. The spectra will then be binned using a dynamic adaptive binning (DAB) method that has been coded for the Matlab environment. This DAB method deals with any movement in spectral lines from spectra to spectra, which can be caused by slight variation in pH from sample to sample (pH = 7.4 ± 0.04), and ensures that peaks from the same metabolite are not broken apart into different bins. After spectral binning, the spectra will be normalized, with water and urea excluded from the normalization, and Pareto scaled (26). The normalized and scaled spectra will then be analyzed using principal component analysis (PCA) and Partial Least Squared Discriminant Analysis (PLS-DA). The loadings plot of these two methods will be used for identifying the outlying bins. The chemical shift range associated with these outlying bins will then be compared to an established metabolite library in order to determine which metabolites have variation across comparison groups (concussion and pre-injury). Eretic tool in the TopSpin 3.2 software will be used to determine the quantitative changes in the concentration of these metabolites. This quantification of the concentration is based on the known internal concentration of TSP in our NMR samples. A Mann Whitney U test for metabolic analysis and a Fisher exact test for the categorical data will be performed.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Other: Pre-injury Each participant will act as their own control. Pre-injury samples were collected. |
Other: urine metabolomics
We aim to determine of a specific urine metabolomic biomarkers can diagnose concussion.
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| Active Comparator: Post-injury Post-injury samples were collected at the same time and within 72 hours of injury. |
Other: urine metabolomics
We aim to determine of a specific urine metabolomic biomarkers can diagnose concussion.
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Outcome Measures
Primary Outcome Measures
- Urine metabolite profile [Samples were taken within 72 hours of injury]
224 urine metabolites were analyzed pre- and post sport related concussion to determine which metabolites were altered post injury.
Eligibility Criteria
Criteria
Inclusion Criteria:
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age greater than 18 years
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diagnosis of concussion determined by the specialists (sports medicine physician or physiatrist)
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concussion sustained within 72 hours of being seen.
Exclusion Criteria:
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multiple injuries sustained coinciding with the concussive head injury
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past medical history of neurological pathology such as seizures, CNS tumors or neurodegenerative disorders.
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Chantel T Debert | Calgary | Alberta | Canada | T2N2T9 |
Sponsors and Collaborators
- University of Calgary
- University of Lethbridge
Investigators
- Principal Investigator: Chantel T Debert, MD MSc, University of Calgary
Study Documents (Full-Text)
None provided.More Information
Publications
- Anderson, P.E.; Mahle, M.A.; Doom, T.E.; Reo, N.V.; DelRaso, N.J.; Raymer, M.L. Dynamic Adaptive binning: an improved quantification technique for NMR spectroscopic data. Metabolomics. 2011, 7, 179-190.
- Canadian Institute for Health Information. Head Injuries in Canada: A Decade of Change (1994-1995 to 2003-2004). Available at http://secure.cihi.ca/cihiweb/products/ntr_head_injuries_20 06_e.pdf
- Craig A, Cloarec O, Holmes E, Nicholson JK, Lindon JC. Scaling and normalization effects in NMR spectroscopic metabonomic data sets. Anal Chem. 2006 Apr 1;78(7):2262-7.
- Dieterle F, Ross A, Schlotterbeck G, Senn H. Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Anal Chem. 2006 Jul 1;78(13):4281-90.
- Eriksson L, Johansson E, Kettaneh-Wold N, Trygg J, Wikström C, Wold S. Multi- and megavariate data analysis part I: basic principles and applications. Umeå, Sweden: Umetrics AB;2006. p. 425.
- Lichtenstein JD, Moser RS, Schatz P. Age and test setting affect the prevalence of invalid baseline scores on neurocognitive tests. Am J Sports Med. 2014 Feb;42(2):479-84. doi: 10.1177/0363546513509225. Epub 2013 Nov 15.
- Lindon JC, Holmes E, Bollard ME, Stanley EG, Nicholson JK. Metabonomics technologies and their applications in physiological monitoring, drug safety assessment and disease diagnosis. Biomarkers. 2004 Jan-Feb;9(1):1-31. Review.
- Nicholson JK, Lindon JC, Holmes E. 'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica. 1999 Nov;29(11):1181-9. Review.
- Papa L, Ramia MM, Edwards D, Johnson BD, Slobounov SM. Systematic review of clinical studies examining biomarkers of brain injury in athletes after sports-related concussion. J Neurotrauma. 2015 May 15;32(10):661-73. doi: 10.1089/neu.2014.3655. Epub 2015 Jan 23. Review.
- van Beek JD. matNMR: a flexible toolbox for processing, analyzing and visualizing magnetic resonance data in Matlab. J Magn Reson. 2007 Jul;187(1):19-26. Epub 2007 Apr 7.
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