Saliva microRNA Biomarkers of Cumulative Concussion

Recurrent concussions increase risk for persistent post-concussion symptoms, and may lead to chronic neurocognitive deficits. Little is known about the molecular pathways that contribute to persistent concussion symptoms. We hypothesized that salivary measurement of microribonucleic acids (miRNAs), a class of epitranscriptional molecules implicated in concussion pathophysiology, would provide insights about the molecular cascade resulting from recurrent concussions. This hypothesis was tested in a case-control study involving 13 former professional football athletes with a history of recurrent concussion, and 18 age/sex-matched peers. Molecules of interest were further validated in a cross-sectional study of 310 younger individuals with a history of no concussion (n = 230), a single concussion (n = 56), or recurrent concussions (n = 24). There was no difference in neurocognitive performance between the former professional athletes and their peers, or among younger individuals with varying concussion exposures. However, younger individuals without prior concussion outperformed peers with prior concussion on three balance assessments. Twenty salivary miRNAs differed (adj. p \u3c 0.05) between former professional athletes and their peers. Two of these (miR-28-3p and miR-339-3p) demonstrated relationships (p \u3c 0.05) with the number of prior concussions reported by younger individuals. miR-28-3p and miR-339-5p may play a role in the pathophysiologic mechanism involved in cumulative concussion effects

Refinement of Saliva MicroRNA Biomarkers for Sports-Related Concussion

Purpose Recognizing sport-related concussion (SRC) is challenging and relies heavily on subjective symptom reports. An objective, biological marker could improve recognition and understanding of SRC. There is emerging evidence that salivary micro-ribonucleic acids (miRNAs) may serve as biomarkers of concussion; however, it remains unclear whether concussion-related miRNAs are impacted by exercise. We sought to determine whether 40 miRNAs previously implicated in concussion pathophysiology were affected by participation in a variety of contact and non-contact sports. Our goal was to refine a miRNA-based tool capable of identifying athletes with SRC without the confounding effects of exercise. Methods This case-control study harmonized data from concussed and non-concussed athletes recruited across 10 sites. Levels of salivary miRNAs within 455 samples from 314 individuals were measured with RNA sequencing. Within-subjects testing was used to identify and exclude miRNAs that changed with either: (a) a single episode of exercise (166 samples from 83 individuals) or (b) season-long participation in contact sports (212 samples from 106 individuals). The miRNAs that were not impacted by exercise were interrogated for SRC diagnostic utility using logistic regression (172 samples from 75 concussed and 97 non-concussed individuals). Results Two miRNAs (miR-532-5p, miR-182-5p) decreased (adjusted p \u3c 0.05) after a single episode of exercise, and 1 miRNA (miR-4510) increased only after contact sports participation. Twenty-three miRNAs changed at the end of a contact sports season. Two of these miRNAs (miR-26b-3p, miR-29c-3p) were associated (R \u3e 0.5; adjusted p \u3c 0.05) with the number of head impacts sustained in a single football practice. Among the 15 miRNAs not confounded by exercise or season-long contact sports participation, 11 demonstrated a significant difference (adjusted p \u3c 0.05) between concussed and non-concussed participants, and 6 displayed moderate ability (AUC \u3e 0.70) to identify concussion. A single ratio (miR-27a-5p/miR-30a-3p) displayed the highest accuracy (AUC = 0.810, sensitivity = 82.4%, specificity = 73.3%) for differentiating concussed and non-concussed participants. Accuracy did not differ between participants with SRC and non-SRC (z = 0.5, p = 0.60). Conclusion Salivary miRNA levels may accurately identify SRC when not confounded by exercise. Refinement of this approach in a large cohort of athletes could eventually lead to a non-invasive, sideline adjunct for SRC assessment

Diagnosing Mild Traumatic Brain Injury Using Saliva RNA Compared to Cognitive and Balance Testing

BACKGROUND: Early, accurate diagnosis of mild traumatic brain injury (mTBI) can improve clinical outcomes for patients, but mTBI remains difficult to diagnose because of reliance on subjective symptom reports. An objective biomarker could increase diagnostic accuracy and improve clinical outcomes. The aim of this study was to assess the ability of salivary noncoding RNA (ncRNA) to serve as a diagnostic adjunct to current clinical tools. We hypothesized that saliva ncRNA levels would demonstrate comparable accuracy for identifying mTBI as measures of symptom burden, neurocognition, and balance. METHODS: This case‐control study involved 538 individuals. Participants included 251 individuals with mTBI, enrolled ≤14 days postinjury, from 11 clinical sites. Saliva samples (n = 679) were collected at five time points (≤3, 4‐7, 8‐14, 15‐30, and 31‐60 days post‐mTBI). Levels of ncRNAs (microRNAs, small nucleolar RNAs, and piwi‐interacting RNAs) were quantified within each sample using RNA sequencing. The first sample from each mTBI participant was compared to saliva samples from 287 controls. Samples were divided into testing (n = 430; mTBI = 201 and control = 239) and training sets (n = 108; mTBI = 50 and control = 58). The test set was used to identify ncRNA diagnostic candidates and create a diagnostic model. Model accuracy was assessed in the naïve test set. RESULTS: A model utilizing seven ncRNA ratios, along with participant age and chronic headache status, differentiated mTBI and control participants with a cross‐validated area under the curve (AUC) of .857 in the training set (95% CI, .816‐.903) and .823 in the naïve test set. In a subset of participants (n = 321; mTBI = 176 and control = 145) assessed for symptom burden (Post‐Concussion Symptom Scale), as well as neurocognition and balance (ClearEdge System), these clinical measures yielded cross‐validated AUC of .835 (95% CI, .782‐.880) and .853 (95% CI, .803‐.899), respectively. A model employing symptom burden and four neurocognitive measures identified mTBI participants with similar AUC (.888; CI, .845‐.925) as symptom burden and four ncRNAs (.932; 95% CI, .890‐.965). CONCLUSION: Salivary ncRNA levels represent a noninvasive, biologic measure that can aid objective, accurate diagnosis of mTBI

Micro-doppler radar to evaluate risk for musculoskeletal injury: Protocol for a case-control study with gold standard comparison.

BackgroundBeyond causing significant morbidity and cost, musculoskeletal injuries (MSKI) are among the most common reasons for primary care visits. A validated injury risk assessment tool for MSKI is conspicuously absent from current care. While motion capture (MC) systems are the current gold standard for assessing human motion, their disadvantages include large size, non-portability, high cost, and limited spatial resolution. As an alternative we introduce the Micro Doppler Radar (MDR); in contrast with MC, it is small, portable, inexpensive, and has superior spatial resolution capabilities. While Phase 1 testing has confirmed that MDR can identify individuals at high risk for MSKI, Phase 2 testing is still needed. Our aims are to 1) Use MDR technology and MC to identify individuals at high-risk for MSKI 2) Evaluate whether MDR has diagnostic accuracy superior to MC 3) Develop MDR algorithms that enhance accuracy and enable automation.Methods and findingsA case control study will compare the movement patterns of 125 ACL reconstruction patients to 125 healthy controls. This study was reviewed and approved by the Pennsylvania State University Human Research Protection Program (HRPP) on May 18, 2022, and the IRB approval number is STUDY00020118. The ACL group is used as a model for a "high risk" population as up to 24% will have a repeat surgery within 2 years. An 8-camera Motion Analysis MC system with Cortex 8 software to collect MC data. Components for the radar technology will be purchased, assembled, and packaged. A micro-doppler signature projection algorithm will determine correct classification of ACL versus healthy control. Our previously tested algorithm for processing the MDR data will be used to identify the two groups. Discrimination, sensitivity and specificity will be calculated to compare the accuracy of MDR to MC in identifying the two groups.ConclusionsWe describe the rationale and methodology of a case-control study using novel MDR technology to detect individuals at high-risk for MSKI. We expect this novel approach to exhibit superior accuracy than the current gold standard. Future translational studies will determine utility in the context of clinical primary care

Inclusion and exclusion criteria.

BackgroundBeyond causing significant morbidity and cost, musculoskeletal injuries (MSKI) are among the most common reasons for primary care visits. A validated injury risk assessment tool for MSKI is conspicuously absent from current care. While motion capture (MC) systems are the current gold standard for assessing human motion, their disadvantages include large size, non-portability, high cost, and limited spatial resolution. As an alternative we introduce the Micro Doppler Radar (MDR); in contrast with MC, it is small, portable, inexpensive, and has superior spatial resolution capabilities. While Phase 1 testing has confirmed that MDR can identify individuals at high risk for MSKI, Phase 2 testing is still needed. Our aims are to 1) Use MDR technology and MC to identify individuals at high-risk for MSKI 2) Evaluate whether MDR has diagnostic accuracy superior to MC 3) Develop MDR algorithms that enhance accuracy and enable automation.Methods and findingsA case control study will compare the movement patterns of 125 ACL reconstruction patients to 125 healthy controls. This study was reviewed and approved by the Pennsylvania State University Human Research Protection Program (HRPP) on May 18, 2022, and the IRB approval number is STUDY00020118. The ACL group is used as a model for a “high risk” population as up to 24% will have a repeat surgery within 2 years. An 8-camera Motion Analysis MC system with Cortex 8 software to collect MC data. Components for the radar technology will be purchased, assembled, and packaged. A micro-doppler signature projection algorithm will determine correct classification of ACL versus healthy control. Our previously tested algorithm for processing the MDR data will be used to identify the two groups. Discrimination, sensitivity and specificity will be calculated to compare the accuracy of MDR to MC in identifying the two groups.ConclusionsWe describe the rationale and methodology of a case-control study using novel MDR technology to detect individuals at high-risk for MSKI. We expect this novel approach to exhibit superior accuracy than the current gold standard. Future translational studies will determine utility in the context of clinical primary care.</div