Proteomic Discovery of Noninvasive Biomarkers Associated With Sport-Related Concussions

Non-invasive fluid biomarkers in the diagnosis of mild traumatic brain injury (mTBI): a systematic review

BACKGROUND

Despite approximately 55.9 million annual mild traumatic brain injuries (mTBIs) worldwide, the accurate diagnosis of mTBI continues to challenge clinicians due to symptom ambiguity, reliance on subjective report and presentation variability. Non-invasive fluid biomarkers of mTBI offer a biological measure to diagnose and monitor mTBI without the need for blood draws or neuroimaging. The objective of this study is to systematically review the utility of such biomarkers to diagnose mTBI and predict disease progression.

METHODS

A systematic review performed in PubMed, Scopus, Cochrane and Web of Science followed by a manual search of references without a specified timeframe. Search strings were generated and run (27 June 2022) by a research librarian. Studies were included if they: (1) included human mTBI subjects, (2) assessed utility of a non-invasive biomarker and (3) published in English. Exclusion criteria were (1) non-mTBI subjects, (2) mTBI not assessed separately from moderate/severe TBI, (3) required intracranial haemorrhage or (4) solely assesses genetic susceptibility to mTBI.

RESULTS

A total of 29 studies from 27 subject populations (1268 mTBI subjects) passed the inclusion and exclusion criteria. Twelve biomarkers were studied. Salivary RNAs, including microRNA, were assessed in 11 studies. Cortisol and melatonin were assessed in four and three studies, respectively. Eight salivary and two urinary biomarkers contained diagnostic or disease monitoring capability.

DISCUSSION

This systematic review identified several salivary and urinary biomarkers that demonstrate the potential to be used as a diagnostic, prognostic and monitoring tool for mTBI. Further research should examine miRNA-based models for diagnostic and predictive utility in patients with mTBI.

PROSPERO REGISTRATION NUMBER

CRD42022329293.

Proteomic discovery of prognostic protein biomarkers for persisting problems after mild traumatic brain injury

Some individuals with mild traumatic brain injury (mTBI), also known as concussion, have neuropsychiatric and physical problems that last longer than a few months. Symptoms following mTBI are not only impacted by the kind and severity of the injury but also by the post-injury experience and the individual's responses to it, making the persistence of mTBI particularly difficult to predict. We aimed to identify prognostic blood-based protein biomarkers predicting 6-month outcomes, in light of the clinical course after the injury, in a longitudinal mTBI cohort (N = 42). Among 420 target proteins quantified by multiple-reaction monitoring-mass spectrometry assays of blood samples, 31, 43, and 15 proteins were significantly associated with the poor recovery of neuropsychological symptoms at < 72 h, 1 week, and 1 month after the injury, respectively. Sequential associations among clinical assessments (depressive symptoms and cognitive function) affecting the 6-month outcomes were evaluated. Then, candidate biomarker proteins indirectly affecting the outcome via neuropsychological symptoms were identified. Using the identified proteins, prognostic models that can predict the 6-month outcome of mTBI were developed. These protein biomarkers established in the context of the clinical course of mTBI may have potential for clinical application.

Role of biomarkers and emerging technologies in defining and assessing neurobiological recovery after sport-related concussion: a systematic review

OBJECTIVE

Determine the role of fluid-based biomarkers, advanced neuroimaging, genetic testing and emerging technologies in defining and assessing neurobiological recovery after sport-related concussion (SRC).

DESIGN

Systematic review.

DATA SOURCES

Searches of seven databases from 1 January 2001 through 24 March 2022 using keywords and index terms relevant to concussion, sports and neurobiological recovery. Separate reviews were conducted for studies involving neuroimaging, fluid biomarkers, genetic testing and emerging technologies. A standardised method and data extraction tool was used to document the study design, population, methodology and results. Reviewers also rated the risk of bias and quality of each study.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

Studies were included if they: (1) were published in English; (2) represented original research; (3) involved human research; (4) pertained only to SRC; (5) included data involving neuroimaging (including electrophysiological testing), fluid biomarkers or genetic testing or other advanced technologies used to assess neurobiological recovery after SRC; (6) had a minimum of one data collection point within 6 months post-SRC; and (7) contained a minimum sample size of 10 participants.

RESULTS

A total of 205 studies met inclusion criteria, including 81 neuroimaging, 50 fluid biomarkers, 5 genetic testing, 73 advanced technologies studies (4 studies overlapped two separate domains). Numerous studies have demonstrated the ability of neuroimaging and fluid-based biomarkers to detect the acute effects of concussion and to track neurobiological recovery after injury. Recent studies have also reported on the diagnostic and prognostic performance of emerging technologies in the assessment of SRC. In sum, the available evidence reinforces the theory that physiological recovery may persist beyond clinical recovery after SRC. The potential role of genetic testing remains unclear based on limited research.

CONCLUSIONS

Advanced neuroimaging, fluid-based biomarkers, genetic testing and emerging technologies are valuable research tools for the study of SRC, but there is not sufficient evidence to recommend their use in clinical practice.

PROSPERO REGISTRATION NUMBER

CRD42020164558.

Molecular Big Data in Sports Sciences: State-of-Art and Future Prospects of OMICS-Based Sports Sciences

Together with environment and experience (that is to say, diet and training), the biological and genetic make-up of an athlete plays a major role in exercise physiology. Sports genomics has shown, indeed, that some DNA single nucleotide polymorphisms (SNPs) can be associated with athlete performance and level (such as elite/world-class athletic status), having an impact on physical activity behavior, endurance, strength, power, speed, flexibility, energetic expenditure, neuromuscular coordination, metabolic and cardio-respiratory fitness, among others, as well as with psychological traits. Athletic phenotype is complex and depends on the combination of different traits and characteristics: as such, it requires a “complex science,” like that of metadata and multi-OMICS profiles. Several projects and trials (like ELITE, GAMES, Gene SMART, GENESIS, and POWERGENE) are aimed at discovering genomics-based biomarkers with an adequate predictive power. Sports genomics could enable to optimize and maximize physical performance, as well as it could predict the risk of sports-related injuries. Exercise has a profound impact on proteome too. Proteomics can assess both from a qualitative and quantitative point of view the modifications induced by training. Recently, scholars have assessed the epigenetics changes in athletes. Summarizing, the different omics specialties seem to converge in a unique approach, termed sportomics or athlomics and defined as a “holistic and top-down,” “non-hypothesis-driven research on an individual’s metabolite changes during sports and exercise” (the Athlome Project Consortium and the Santorini Declaration) Not only sportomics includes metabonomics/metabolomics, but relying on the athlete’s biological passport or profile, it would enable the systematic study of sports-induced changes and effects at any level (genome, transcriptome, proteome, etc.). However, the wealth of data is so huge and massive and heterogenous that new computational algorithms and protocols are needed, more computational power is required as well as new strategies for properly and effectively combining and integrating data.