| Literature DB >> 33092191 |
Steven D Hicks1, Robert P Olympia2, Cayce Onks3, Raymond Y Kim4, Kevin J Zhen1, Gregory Fedorchak5, Samantha DeVita5, Aakanksha Rangnekar5, Matthew Heller6, Hallie Zwibel6, Chuck Monteith7, Zofia Gagnon8, Callan D McLoughlin8, Jason Randall8, Miguel Madeira8, Thomas R Campbell9, Elise Fengler10, Michael N Dretsch11, Christopher Neville12, Frank A Middleton13.
Abstract
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 < 0.05) between former professional athletes and their peers. Two of these (miR-28-3p and miR-339-3p) demonstrated relationships (p < 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.Entities:
Keywords: biomarker; microRNA; mild traumatic brain injury; saliva; sports-related concussion
Mesh:
Substances:
Year: 2020 PMID: 33092191 PMCID: PMC7589940 DOI: 10.3390/ijms21207758
Source DB: PubMed Journal: Int J Mol Sci ISSN: 1422-0067 Impact factor: 5.923
Participant characteristics.
| Group 1, Mean (Range) | Group 2, Mean (Range) | |||||
|---|---|---|---|---|---|---|
| All ( | Football Athletes ( | Controls ( | All ( | Concussion ( | No Concussion ( | |
| Demographics | ||||||
| Male sex, No. (%) | 31 (100) | 13 (100) | 18 (100) | 208 (67) | 61 (76) | 147 (64) * |
| Age (years) | 73 (46–89) | 73 (66–78) | 72 (46–89) | 20 (7–39) | 21 (10–35) | 19 (7–39) * |
| White race, No. (%) | 14 (74) | 9 (69) | 5 (83) | 224 (82) | 67 (83) | 187 (82) |
| Medical history | ||||||
| BMI (kg/m2) | 28 (20–38) | 30 (25–38) | 26 (20–34) * | 24 (13–40) | 25 (13–39) | 24 (13–40) |
| ADHD, No. (%) | 0 (0) | 0 (0) | 0 (0) | 24 (8) | 8 (10) | 16 (7) |
| Anxiety, No. (%) | 0 (0) | 0 (0) | 0 (0) | 18 (5) | 4 (5) | 14 (6) |
| Depression, No. (%) | 0 (0) | 0 (0) | 0 (0) | 10 (3) | 4 (5) | 6 (3) |
| Diagnosed concussions, No. (%) | 5 (16) | 2 (16) | 3 (16) | 80 (26) | 80 (100) | 0 (0) * |
| No. diagnosed concussions | 0.3 (1–5) | 0.4 (1–5) | 0.2 (0–1) | 0.4 (0–7) | 1.5 (1–7) | 0 (0) * |
| Undiagnosed concussions, No. (%) | NA | 13 (100) | NA | NA | NA | NA |
| No. undiagnosed concussion | NA | 5 (1–25) | NA | NA | NA | NA |
| Time since last concussion (years) | NA | 45 (38–56) | NA | NA | 1 (0–7) | NA |
| Professional football career | NA | 13 (2) | NA | NA | NA | NA |
| PCSS burden | NA | 10 (3–21) | NA | 2 (0–22) | 2 (0–22) | 2 (0–19) |
| PCSS severity | NA | 19 (3–78) | NA | 4 (0–91) | 5 (0–91) | 3 (0–42) |
| Sample collection time (24 h clock) | 12 (8–18) | 13 (10–18) | 11 (8–13) | 14 (7–19) | 14 (7–18) | 12 (7–19) * |
* denotes p < 0.05 between groups on two-tailed Student’s t test. Abbreviations: not available/applicable (NA).
Functional measures of balance, neurocognition, and olfaction.
| Group 1, Mean (SD) | Group 2, Mean (SD) | |||||
|---|---|---|---|---|---|---|
| All ( | Football Athletes ( | Controls ( | All ( | Concussion ( | No Concussion ( | |
| Balance | ||||||
| TLEO | 72 (14) | 83 (5) | 63 (12) * | 85 (3) | 85 (3) | 85 (3) |
| TLEC | 69 (12) | 77 (9) | 63 (11) * | 84 (4) | 84 (4) | 84 (3) |
| TSEO | 65 (18) | 65 (22) | 64 (12) | 84 (4) | 83 (5) | 85 (4) * |
| TSEC | 48 (24) | 48 (26) | 48 (21) | 82 (8) | 79 (9) | 82 (7) * |
| TLEOFP | 61 (20) | 69 (18) | 54 (19) | 86 (7) | 85 (7) | 86 (6) |
| Neurocognition | ||||||
| SRT1 | 156 (40) | 159 (41) | 155 (39) | 191 (24) | 188 (22) | 192 (25) |
| SRT2 | 152 (36) | 148 (39) | 155 (32) | 186 (23) | 185 (22) | 186 (23) |
| PRT | 79 (13) | 76 (10) | 81 (14) | 100 (13) | 99 (12) | 100 (13) |
| GNG | 94 (15) | 91 (12) | 96 (17) | 121 (13) | 121 (13) | 121 (13) |
| Olfaction | ||||||
| BSIT | 69 (23) | 63 (23) | 82 (17) | NA | NA | NA |
Note that balance, neurocognition, and olfaction assessments were available for only a subset of participants in Groups 1 and/or 2. * denotes p < 0.05 between groups on two-tailed Student’s t test. Note that higher scores indicate superior performance on respective tasks. Abbreviations: not available (NA); two-legs, eyes open (TLEO); two-legs, eyes closed (TLEC); tandem stance, eyes open (TSEO); tandem stance, eyes closed (TSEC); two-legs, eyes open on foam pad (TLEOFP); spontaneous reaction time, trial 1 (SRT1); spontaneous reaction time, trial 2 (SRT2); procedural reaction time (PRT); go-no-go (GNG); Brief Smell Identification Test (BSIT).
Figure 1Salivary miRNA profiles differentiate former professional football athletes from peers. Partial least squares discriminant analysis (PLSDA) was applied to individual salivary miRNA profiles (A), balance performance measures (B), and neurocognitive scores (C) among professional athletes in the National Football League (NFL; n = 13; green) and control participants (CTRL; n = 18; red). The two-dimensional PLSDA plot based on saliva miRNA levels achieved complete separation of groups, while accounting for 27.5% of variance in the miRNA data.
Figure 2Twenty salivary miRNAs differ between former professional football athletes and peers. The heatmap displays salivary levels of 20 miRNAs with significant differences (adj p < 0.05) in salivary expression between former professional football athletes (n = 13; green) and control participants (n = 18; red). Hierarchical clustering of both participants and miRNAs is based on a Ward clustering algorithm with a Euclidean distance measure. V statistics and adjusted p values on non-parametric analysis of variance are presented for each miRNA.
Figure 3Three miRNA candidates differ among individuals with prior concussion. A non-parametric analysis of variance comparing levels of the 20 miRNA candidates identified in former professional football athletes, identified three miRNAs (miR-339-3p, miR-361-5p, and miR-28-3p) nominal differences (p < 0.05; adjusted p < 0.15) between individuals with prior concussion, and those without prior concussion. Red denotes increased expression, while blue denotes decreased expression. Notably, for 2/3 miRNAs (miR-339-3p and miR-361-5p), expression levels among individuals with prior concussion mirrored differences observed in former professional football athletes.
Physiologic targets of candidate miRNAs.
| KEGG Pathway | #Genes | #miRNAs | |
|---|---|---|---|
| Adherens junction | 3.01 × 10−17 | 20 | 3 |
| ECM-receptor interaction | 2.5 × 10−07 | 9 | 3 |
| Bacterial invasion of epithelial cells | 4.3 × 10−06 | 16 | 3 |
| Hippo signaling pathway | 9.6 × 10−06 | 15 | 3 |
| Protein processing in endoplasmic reticulum | 6.2 × 10−05 | 25 | 3 |
| Proteoglycans in cancer | 4.2 × 10−4 | 24 | 3 |
| Lysine degradation | 9.3 × 10−4 | 7 | 2 |
| Cell cycle | 4.8 × 10−3 | 19 | 3 |
Abbreviations: Kyoto Encyclopedia of Genes and Genomes (KEGG).