Abstract Objective The purpose of this study was to determine whether the ε4 allele of the APOE gene influences neurocognitive outcome following sports-related concussion. It was hypothesized that participants with an ε4 allele would show poorer neurocognitive performance and greater neurocognitive variability than those without an ε4 allele. Method Participants included 57 concussed collegiate athletes (77.2% male) who participated in a concussion management program at a large university. All athletes underwent a comprehensive neuropsychological assessment and provided a DNA sample for determination of their APOE genotype. The test battery included measures sensitive to concussion, covering the broad domains of learning and memory, attention, processing speed, and executive functions. Results The sample was divided into ε4 + (n = 20) and ε4 – (n = 37) groups. No significant differences were found between athletes with and without an ε4 allele when examining mean neurocognitive standardized scores (all p > .05; d = 0.16–0.18). However, athletes with an ε4 allele were more likely to show a greater number of impaired neurocognitive scores post-injury compared to athletes without an ε4 allele, χ2(1, N = 57) = 3.96, p = < .05, φ = 0.26. Additionally, athletes with an ε4 allele demonstrated greater neurocognitive variability than athletes without an ε4 allele, t(55) = −2.04, p < .05, d = 0.53. Conclusions This research furthers our understanding of how genetic factors uniquely contribute to neurocognitive performance differences following concussion. Our findings suggest a possible relationship between the ε4 allele and post-concussion impairment, as well as between the ε4 allele and neurocognitive performance variability, suggesting that the ε4 genotype may be a risk factor for less efficient cognitive processing in concussed athletes. APOE gene, Genetics, Mild traumatic brain injury, Neuropsychological tests, Performance variability, Sports-related concussion Introduction Despite decades of clinical practice and research related to concussion, or mild traumatic brain injury (TBI), the ability to reliably diagnose concussive injuries, manage ensuing sequelae, and predict course of recovery following brain injury remains challenging. Diagnosis and prediction of outcome is hampered by yet unexplained heterogeneity in clinical presentation and recovery trajectory (Iverson, Brooks, Collins, & Lovell, 2006; McCrory et al., 2013). Prior research has focused on identifying both risk (Lange et al., 2013; Lau, Kontos, Collins, Mucha, & Lovell, 2011; McCrea et al., 2013; Ponsford et al., 2012), and protective factors (McCauley, Boake, Levin, Contant, & Song, 2001) that may influence outcome from concussion/TBI. Accumulating evidence suggests that genetic variations likely have an influence on the pathogenesis and outcome of neurological conditions such as brain injury (Wilson & Montgomery, 2007). In particular, the APOE gene has been associated with increased risk for neurological conditions such as Alzheimer’s disease (Bartzokis et al., 2006; Corder et al., 1993; Petersen et al., 1995) and has been linked to poor outcome following TBI (Samatovicz, 2000; Teasdale, Nicoll, Murray, & Fiddes, 1997). APOE is a lipoprotein present in plasma and cerebrospinal fluid (CSF); within CSF, APOE plays a crucial role in the maintenance of neuronal membranes and neurotransmission (Dardiotis et al., 2010; Wilson & Montgomery, 2007). When the brain suffers an injury, APOE production increases and the encoded protein is involved in neuronal repair and plasticity (Finnoff, Jelsing & Smith, 2011). The APOE gene is polymorphic, comprised of three alleles (ε2, ε3, and ε4), for a total of six gene combinations (three heterozygous and three homozygous phenotypes). Each allele differentially influences the neuronal restoration process, and the ε4 allele is thought to be less effective at promoting repair and neuritic growth than the ε2 and ε3 alleles (Finnoff et al., 2011; Mahley, Weisgraber, & Huang, 2006; Silver, McAllister, & Yudofsky, 2011); thus, the ε4 allele has been posited as a “risk factor” gene. A number of studies have begun to explore the relationship between the APOE ε4 allele and global outcome following TBI. Mixed findings have resulted, with some reports concluding that the ε4 allele is associated with worse outcome (Chiang, Chang, & Hu, 2003; Teasdale, Murray, & Nicoll, 2005) and longer recovery rates following TBI (Alexander et al., 2007), and other studies showing no significant relationships between the ε4 allele and gross outcome post-TBI (Millar, Nicoll, Thornhill, Murray, & Teasdale, 2003; Willemse-van Son, Ribbers, Hop, Van Duijn & Stam, 2008). Among the studies that have examined more specific post-injury sequelae—including performance on neurocognitive testing—results have also been mixed. Studies that have found evidence in support of a deleterious effect of ε4 on outcome include an investigation of moderate to severe patients 6 months post-injury (Ariza et al., 2006), a study of military veterans and active duty service members 1–2 months post-TBI (Crawford et al., 2002), and a study of mild-moderate TBI patients at 3 and 6 weeks post-injury (Liberman, Stewart, Wesnes, & Troncoso, 2002). Furthermore, another study evaluated within-person change by comparing participants’ baseline and post-injury neurocognitive scores and found that ε4 carriers showed significant cognitive decline, whereas non-carriers did not demonstrate significant changes between pre- and post-injury assessments (Sundström et al., 2004). In contrast, other investigators have failed to observe a negative effect of the ε4 allele on neurocognitive outcome. For example, one study evaluating active duty service members with mild to moderate TBI found that ε4+ participants scored better than ε4– participants on some measures of memory, attention, and executive functioning administered 1–2 months post-injury (Han et al., 2007). In another study examining the relationship between APOE and cognitive outcome in children with mild TBI, Moran and colleagues (2009) reported better performance on a measure of constructional skill in those with the ε4 allele compared to those without the ε4 allele, but no other significant differences were observed on other measures of cognitive performance. Within the sports-concussion literature, some authors have examined associations between APOE genotype and likelihood of sustaining a concussion (Kristman et al., 2008; Terrell et al., 2008; Tierney et al., 2010). However, the role of APOE genotype on outcomes following a concussion has received less attention. To our knowledge, only one published study has examined the relationship between the APOE gene and neurocognitive performance in athletes. Kutner, Erlanger, Tsai, Jordan, and Relkin (2000) assessed a group of professional football players of varying ages with and without the ε4 allele, and found that athletes with the ε4 allele performed more poorly on neurocognitive tests than did athletes without the allele. Review of the above studies highlights the range of efforts that have examined the association between the APOE gene and neurocognitive outcome following concussion. Although heterogeneous samples (i.e., adult and child, military and civilian/athletes, and differing injury severities) have been studied thus far—which may certainly play a role in the inconsistent findings that have been reported—another factor to consider is that the majority of the studies conducted to date have utilized measures of central tendency (i.e., mean scores) to examine neurocognitive differences between APOE groups. Relying solely on measures of central tendency may result in investigators missing associations that may be manifested in performance variability within and between cognitive tests, or at an individual level in terms of impaired scores. With these considerations in mind, the main objective of our study was to determine how the ε4 allele of the APOE gene influences neurocognitive outcome following concussion. We examined neurocognitive performance in a sample of concussed athletes, divided into two groups based on the presence or absence of the ε4 allele. Additionally, we used several methods to determine whether there were differences between the allele groups including examining: (1) mean neurocognitive standardized scores; (2) total number of impaired test scores; and (3) variability in neurocognitive standardized scores. We hypothesized that concussed athletes with an ε4 allele would show poorer neurocognitive performance and greater variability than concussed athletes without an ε4 allele. Method Participants and Procedures Participants were collegiate athletes who were prospectively enrolled in a university-based sports concussion management program. All participants were diagnosed with a concussion, or mild TBI, by team physicians using criteria established by the Mild TBI Committee of the Head Injury Interdisciplinary Special Interest Group (1993) and Ruff, Iverson, Barth, Bush, and Broshek (2009). Briefly, these guidelines state that at least one of the following must be present: (1) loss of consciousness (≤30 min); (2) memory loss for events before or after the injury event (<24 hr); or (3) any alternation in mental status at the time of injury. Post-concussion referrals are made as soon as possible following the injury event; however, in some cases, referrals may be delayed for several days or weeks due to other clinical concerns. The following athletic teams routinely participate in concussion testing: Football, Wrestling, Men’s and Women’s Basketball, Men’s and Women’s Lacrosse, Men’s and Women’s Soccer, and Men’s and Women’s Ice Hockey. Athletes were included in the present study if they met the following inclusion criteria: (1) sustained a concussion according to the definition provided above; (2) underwent post-concussion neuropsychological testing as soon as clinically indicated following concussion but no greater than 6 months post-injury; (3) performed adequately on measures of performance validity; and (4) provided a DNA sample via a buccal (cheek) swab that was successfully analyzed for their APOE genotype. The Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) Impulse Control Composite (ICC) and the Computerized Assessment of Response Bias (CARB) were used as indicators of performance validity. Adequate effort was defined as athletes scoring ≤30 on the ImPACT ICC (Lovell, 2012) and performance of ≥89% on the CARB (Allen, Conder, Green, & Cox, 1999). In order to be included in the study, athletes must have passed both measures of effort. The 6-month time frame was selected because we were interested in determining the relationship between the APOE gene and relatively short-term outcomes following concussion while maintaining an adequate sample size. Applying these criteria resulted in a final sample of N = 57. Approval for the present study was obtained through the university’s Institutional Review Board and all participants signed an informed consent form. Laboratory Procedures Buccal samples were used to collect participants’ DNA and were analyzed for two single nucleotide polymorphisms (SNPs) in the APOE gene (SNPs APOE112 and APOE158, rs429358 and rs7412, respectively). DNA extraction was performed according to procedures described by Freeman and colleagues (2003), and the procedures described by Christensen and colleagues (2008) and Ingelsson and colleagues (2003) were used to define the different genotypes. Measures The neurocognitive test battery was designed to include measures that are sensitive to concussion, covering the broad domains of learning and memory, attention, processing speed, and executive functions. The following measures were administered to all participants: the Brief Visuospatial Memory Test-Revised (BVMT-R; Benedict, 1997); the Hopkins Verbal Learning Test-Revised (HVLT-R; Brandt & Benedict, 2001); the Symbol-Digit Modalities Test (SDMT; Smith, 1991); the Vigil/W Continuous Performance Test (Cegalis & Cegalis, 1994); a modified version of the Digit Span Test from the WAIS-III (Wechsler, 1997); the Comprehensive Trail-Making Test (CTMT; Reynolds, 2002); the PSU Cancellation Test (Echemendia & Julian, 2001); and the Stroop Color-Word Test (SCWT; Trenerry, Crosson, DeBoe, & Leber, 1989). The ImPACT (Lovell, Collins, Podell, Powell, & Maroon, 2000) computer program was also administered. The ImPACT is comprised of six unique modules that evaluate a variety of cognitive domains, including memory, visual scanning, attention, and speed of processing/reaction time. Five composite scores are generated from the ImPACT, one of which is the ICC, an indicator of effort (Lovell, 2012). The CARB was also administered to measure effort (Allen, Conder, Green, & Cox, 1999). Finally, in order to assess pre-morbid functioning, the Wechsler Test of Adult Reading (WTAR; The Psychological Corporation, 2001) was administered. Approach to Data Analysis The Statistical Package for the Social Sciences (SPSS), Version 22, was used to conduct all analyses. Data Transformations To examine post-concussion neurocognitive performance, all neurocognitive test indices of interest were converted from raw scores to standard scores using gender-specific means and standard deviations based on a baseline normative sample (Merritt et al., 2016). Standard score units were selected as the metric of choice because many neuropsychological tests commonly apply this criterion for test comparison. The following post-concussion neurocognitive variables were transformed from raw to standard scores: BVMT-R Total Immediate Recall, BVMT-R Delayed Recall, HVLT-R Total Immediate Recall, HVLT-R Delayed Recall, SDMT Total Correct, SDMT Incidental Memory, Vigil Average Delay, Digit Span Forward, Digit Span Backward, CTMT 1 Time, CTMT 2 Time, PSU Cancellation Test Total Correct, Stroop Word Time, Stroop Color-Word Time, ImPACT Verbal Memory Composite, ImPACT Visual Memory Composite, ImPACT Visuomotor Speed Composite, and ImPACT Reaction Time Composite (for a total of 18 variables). Finally, all standard scores were calculated so that higher values reflect better cognitive performance. Examining Mean Neurocognitive Standardized Scores After the neurocognitive variables of interest were converted to standard scores, domain-specific neurocognitive composite scores were calculated. A theory-driven approach was used to derive the domain-specific composites. Each individual cognitive test index listed above was assigned to a composite index that most closely matches the cognitive domain that it purports to measure. A memory composite was derived, as was a composite representing executive functioning, attention, and processing speed (subsequently referred to as the “executive functioning composite”). After the individual variables were assigned to the appropriate cognitive domain, the internal consistency of these theoretically-derived composite scores was evaluated using Cronbach’s alpha. Concussed athletes were then divided into two groups based on APOE genotype status (ε4+ allele, ε4– allele), and independent samples t-tests were used to determine whether there were group mean differences on the neurocognitive composite variables. Examining Total Number of Impaired Test Scores “Impaired” scores were defined as any standard score that is below 78 (i.e., more than 1.5 SD below the mean). A 1.5 SD threshold was applied because this is a commonly used metric to establish clinically significant change or impairment (Iverson & Brooks, 2011). The number of scores across the neurocognitive test battery (possible range: 0–18) that fell below the designated impairment level (i.e., >1.5 SD below the mean) was counted for each participant and descriptive statistics were run on this variable. “Impaired” and “not impaired” groups were created using a cutoff of 3 (Arnett et al., 2014; Iverson, 2011); athletes who demonstrated impairments on 3 or more test indices were categorized into the “impaired” group, and athletes who demonstrated impairments on fewer than 3 test indices were categorized into the “not impaired” group. Chi-square analysis was used to determine whether there were significant differences between athletes with and without the ε4 allele who fell in the impaired group at the >1.5 SD threshold. Examining Variability in Neurocognitive Standardized Scores Neurocognitive performance variability was assessed utilizing a within-person, across-neuropsychological domain approach, and two variability indices were calculated: (1) an average standard deviation (ASD) score across the entire neurocognitive test battery (Rabinowitz & Arnett, 2013), and (2) a range score, or “maximum discrepancy” (MD) score (Schretlen, Munro, Anthony, & Pearlson, 2003). To derive the ASD score, descriptive statistics were run on all of the individual neurocognitive test variables and the mean of all standard deviations was computed. The MD score was calculated by taking the difference between each athlete’s highest and lowest standard scores across the test battery; higher scores represent greater intraindividual variability. To determine whether the ε4 allele of the APOE gene influences neurocognitive performance variability following concussion, independent samples t-tests were used to compare the two variability indices across the ε4 allele groups. Additionally, “high” and “low” variability groups were created for the ASD and MD scores using the mean of each variable as the cutoff value; athletes who scored at or above the mean were categorized into the “high” variability group, and athletes who scored less than the mean were categorized into the “low” variability group. Chi-square analyses were used to determine whether there were significant differences between athletes with and without the ε4 allele who fell in the high variability groups. Results Demographic Characteristics The sample (N = 57) was predominantly male (77.2%) and had completed, on average, 13.7 years (SD = 1.3) of education. The following athletic teams are represented in the sample: Football (n = 16, 28.1%), Basketball (n = 11, 19.3%), Lacrosse (n = 8, 14.0%), Rugby (n = 7, 12.3%), Hockey (n = 7, 12.3%), Soccer (n = 4, 7.1%), Wresting (n = 2, 3.5%), and Other (n = 2, 3.5%). The average time from injury to assessment was 14.1 days (SD = 24.0, Mdn = 5 days; Mode = 2 days; range = 0–150 days), and 73.7% of the athletes were tested within two weeks following their concussion. At the time of the post-concussion evaluation, no athletes had returned to play. APOE genotyping results for the sample were as follows: ε2/ε2 (n = 1, 1.8%), ε2/ε3 (n = 2, 3.5%), ε2/ε4 (n = 2, 3.5%), ε3/ε3 (n = 34, 59.6%), ε3/ε4 (n = 15, 26.3%), and ε4/ε4 (n = 3, 5.3%). The sample was divided into two groups based on ε4 allele status: 20 athletes (35.1%) were ε4+ and 37 athletes (64.9%) were ε4–. Descriptive statistics, including basic demographic and injury severity variables, are presented in Table 1 by allele group. No significant differences between the two groups were found on any of the demographic (age, sex, education, ethnicity, concussion history, and history of attention-deficit/hyperactivity disorder or learning disability) or injury severity variables (days tested post-injury, loss of consciousness, retrograde amnesia, and anterograde amnesia) examined. Additionally, athletes’ pre-morbid functioning, as assessed by the WTAR FSIQ, was equivalent between allele groups. Table 1. Concussed athletes: sample characteristics by ε4 allele group Variables ε4+ allele group (N = 20) ε4– allele group (N = 37) pa M SD M SD Age 20.30 1.42 20.27 1.47 .941 Years of education 13.95 1.47 13.59 1.17 .321 WTAR FSIQ 105.45 5.84 106.32 7.98 .672 Days tested post-injury 15.35 33.47 13.49 17.43 .783 N % N % pb Sex Male 16 80.0 28 75.7 .710 Female 4 20.0 9 24.3 Ethnicity Caucasian 14 70.0 27 73.0 .812 Other 6 30.0 10 27.0 Concussion historyc 0 6 30.0 12 32.4 .982 1 9 45.0 16 43.2 2 or more 5 25.0 9 24.3 History of ADHD/LD Yes 3 15.0 1 2.7 .083 No 17 85.0 36 97.3 Loss of consciousness Yes 3 15.0 3 8.1 .418 No 17 85.0 34 91.9 Retrograde amnesia Yes 3 15.0 5 13.5 .877 No 17 85.0 32 86.5 Anterograde amnesia Yes 5 25.0 15 40.5 .241 No 15 75.0 22 59.5 Variables ε4+ allele group (N = 20) ε4– allele group (N = 37) pa M SD M SD Age 20.30 1.42 20.27 1.47 .941 Years of education 13.95 1.47 13.59 1.17 .321 WTAR FSIQ 105.45 5.84 106.32 7.98 .672 Days tested post-injury 15.35 33.47 13.49 17.43 .783 N % N % pb Sex Male 16 80.0 28 75.7 .710 Female 4 20.0 9 24.3 Ethnicity Caucasian 14 70.0 27 73.0 .812 Other 6 30.0 10 27.0 Concussion historyc 0 6 30.0 12 32.4 .982 1 9 45.0 16 43.2 2 or more 5 25.0 9 24.3 History of ADHD/LD Yes 3 15.0 1 2.7 .083 No 17 85.0 36 97.3 Loss of consciousness Yes 3 15.0 3 8.1 .418 No 17 85.0 34 91.9 Retrograde amnesia Yes 3 15.0 5 13.5 .877 No 17 85.0 32 86.5 Anterograde amnesia Yes 5 25.0 15 40.5 .241 No 15 75.0 22 59.5 Notes: WTAR FSIQ = Wechsler Test of Adult Reading Full Scale IQ; ADHD = attention-deficit/hyperactivity disorder; LD = learning disability. aIndependent samples t-tests were used to determine whether there were group differences for age, years of education, WTAR FSIQ, and days tested post-injury. bChi-square analyses were used to determine whether there were group differences for sex, ethnicity, concussion history, history of ADHD/LD, loss of consciousness, retrograde amnesia, and anterograde amnesia. cConcussion history refers to the total number of previous concussions participants sustained prior to being enrolled in the present study. Mean Neurocognitive Standardized Scores Table 2 lists the domain-specific composites and their associated variables. The memory composite showed good internal consistency (7 items; Cronbach’s α = 0.87) and the executive functioning composite showed acceptable internal consistency (11 items; Cronbach’s α = 0.76). Table 2. Variables comprising each domain-specific composite Memory composite Executive functioning, attention, & processing speed composite ImPACT Verbal Memory Composite Vigil Average Delay ImPACT Visual Memory Composite ImPACT Visuomotor Speed Composite BVMT-R Total Immediate Recall ImPACT Reaction Time Composite BVMT-R Delayed Recall SDMT Total Correct HVLT-R Total Immediate Recall CTMT 1 Time HVLT-R Delayed Recall CTMT 2 Time SDMT Incidental Memory Digit Span Forward Digit Span Backward Stroop Word Time Stroop Color-Word Time PSU Cancellation Test Total Correct Memory composite Executive functioning, attention, & processing speed composite ImPACT Verbal Memory Composite Vigil Average Delay ImPACT Visual Memory Composite ImPACT Visuomotor Speed Composite BVMT-R Total Immediate Recall ImPACT Reaction Time Composite BVMT-R Delayed Recall SDMT Total Correct HVLT-R Total Immediate Recall CTMT 1 Time HVLT-R Delayed Recall CTMT 2 Time SDMT Incidental Memory Digit Span Forward Digit Span Backward Stroop Word Time Stroop Color-Word Time PSU Cancellation Test Total Correct Independent samples t-tests showed no significant differences between athletes with and without the ε4 allele across the neurocognitive composite variables (all p > .05; d = 0.16–0.18; see Table 3). Table 3. Mean differences between ε4+ and ε4– participants: post-concussion neurocognitive variables Neurocognitive variable ε4+ allele group (N = 20) ε4– allele group (N = 37) t p Cohen’s effect sizes (d) M SD M SD Neurocognitive composites Memory 97.61 12.61 99.60 12.75 0.57 .574 0.16 EF 99.12 9.39 100.83 9.88 0.63 .529 0.18 Neurocognitive variability indices ASD score 15.95 5.28 13.51 3.71 −2.04 .047 0.53 MD score 62.89 31.46 51.59 17.26 −1.49 .149 0.45 Neurocognitive variable ε4+ allele group (N = 20) ε4– allele group (N = 37) t p Cohen’s effect sizes (d) M SD M SD Neurocognitive composites Memory 97.61 12.61 99.60 12.75 0.57 .574 0.16 EF 99.12 9.39 100.83 9.88 0.63 .529 0.18 Neurocognitive variability indices ASD score 15.95 5.28 13.51 3.71 −2.04 .047 0.53 MD score 62.89 31.46 51.59 17.26 −1.49 .149 0.45 Notes: EF = executive functioning; ASD = average standard deviation; MD = maximum discrepancy. Cohen’s effect sizes (d): 0.20 = small; 0.50 = medium; 0.80 = large. Total Number of Impaired Test Scores The number of impaired test scores for each participant was calculated based on the procedures described earlier. When applying the >1.5 SD threshold, the mean number of impaired scores obtained post-concussion across the entire sample was 1.79 (SD = 2.40; Mdn = 1; range = 0–11). Using a cutoff of 3 to establish “impaired” and “not impaired” groups, 14 athletes (24.6%) fell in the impaired group. Chi-square analysis showed that significantly more ε4+ athletes (40.0%; 8 of 20) fell in the impaired group compared with ε4– athletes (16.2%; 6 of 37), χ2(1, N = 57) = 3.96, p = .046, φ = 0.26 (see Fig. 1). Fig. 1. View largeDownload slide Chi-square analyses were used to determine whether there were significant differences between athletes with and without the ε4 allele who fell in the impaired group at the 1.5 SD threshold. Results indicate that 40.0% (8 of 20) of ε4+ participants fell in the impaired group, compared with only 16.2% (6 of 37) of ε4– athletes, χ2(1, N = 57) = 3.96, p = .046, φ = 0.26. Fig. 1. View largeDownload slide Chi-square analyses were used to determine whether there were significant differences between athletes with and without the ε4 allele who fell in the impaired group at the 1.5 SD threshold. Results indicate that 40.0% (8 of 20) of ε4+ participants fell in the impaired group, compared with only 16.2% (6 of 37) of ε4– athletes, χ2(1, N = 57) = 3.96, p = .046, φ = 0.26. Variability in Neurocognitive Standardized Scores The ASD and MD scores were calculated according to the procedures described previously. Independent samples t-tests revealed that ε4+ athletes showed a significantly greater amount of performance variability than ε4– athletes (t(55) = −2.04, p < .05, d = 0.53; see Table 3) as assessed by the ASD score, but no significant differences were observed between allele groups when assessing the MD score (t(25) = −1.49, p = .149, d = 0.45; see Table 3). The mean of the ASD score was 14.37 (SD = 4.44; Mdn = 14.32; range = 5.97–32.15). “High” and “low” variability groups were created using the mean; 27 athletes (47.4%) were classified as having high variability. Chi-square analysis showed that significantly more ε4+ athletes (65.0%; 13 of 20) fell in the high ASD variability group compared with ε4– athletes (37.8%; 14 of 37), χ2(1, N = 57) = 3.84, p = .050, φ = 0.26 (see Fig. 2a). Fig. 2. View largeDownload slide Chi-square analyses were used to determine whether there were significant differences between athletes with and without the ε4 allele who fell in the high variability groups. Results indicate that 65.0% (13 of 20) of ε4+ participants fell in the high ASD group, compared with only 37.8% (14 of 37) of ε4– athletes, χ2(1, N = 57) = 3.84, p = .050, φ = 0.26 (a). As for the MD results, 55.0% (11 of 20) of ε4+ participants fell in the high MD group, compared with only 27.0% (10 of 37) of ε4– athletes, χ2(1, N = 57) = 4.37, p = .037, φ = 0.28 (b). Fig. 2. View largeDownload slide Chi-square analyses were used to determine whether there were significant differences between athletes with and without the ε4 allele who fell in the high variability groups. Results indicate that 65.0% (13 of 20) of ε4+ participants fell in the high ASD group, compared with only 37.8% (14 of 37) of ε4– athletes, χ2(1, N = 57) = 3.84, p = .050, φ = 0.26 (a). As for the MD results, 55.0% (11 of 20) of ε4+ participants fell in the high MD group, compared with only 27.0% (10 of 37) of ε4– athletes, χ2(1, N = 57) = 4.37, p = .037, φ = 0.28 (b). The mean of the MD score was 55.55 (SD = 23.60; Mdn = 48.32; range = 23.77–165.27). Again, “high” and “low” variability groups were created using the mean; 21 athletes (36.8%) were classified as having high variability. Chi-square analysis showed that significantly more ε4+ athletes (55.0%; 11 of 20) fell in the high MD variability group compared with ε4– athletes (27.0%; 10 of 37), χ2(1, N = 57) = 4.37, p = .037, φ = 0.28 (see Fig. 2b). Discussion The primary purpose of the present study was to examine the relationship between the ε4 allele of the APOE gene and neurocognitive performance following concussion. To our knowledge, this represents the first attempt in the sports concussion literature to examine such relationships, and our findings suggest that genetic factors contribute to post-concussion neuropsychological outcomes. Unlike existing studies, in addition to evaluating mean neurocognitive performance-level differences between ε4 carriers and non-carriers, we also examined post-concussion neurocognitive impairments and variability in neurocognitive standardized scores. We hypothesized that, when compared to concussed ε4– athletes, concussed ε4+ athletes would demonstrate worse neurocognitive performance. Our findings partially supported this hypothesis. Although we did not find differences between ε4 allele groups when examining mean neurocognitive standardized scores, a greater proportion of participants with the ε4 allele (compared to athletes without an ε4 allele) exhibited at least 3 or more impaired scores across the test battery based on scores falling more than 1.5 SD below the mean. Additionally, when evaluating the ASD score—a measure of within-person, across-neuropsychological test variability—we found that ε4+ athletes exhibited a greater amount of neurocognitive performance variability than ε4– athletes. Likewise, a larger proportion of ε4+ athletes compared to ε4– athletes were classified as having “high” intraindividual variability as assessed by the ASD score. When evaluating the MD score—the difference between an athletes’ highest and lowest standard score—we found that athletes with an ε4 allele had an average MD score of greater than 4 SDs, whereas athletes without an ε4 allele had an average MD score of under 3.5 SDs. Moreover, a significantly greater proportion of ε4+ athletes compared with ε4– athletes were classified as having “high” intraindividual variability as assessed by the MD score. Taken together, these results suggest that cross-test intraindividual variability may be an important marker of less efficient cognitive processing for ε4+ athletes following concussion. The present findings extend understanding of how the APOE gene may be related to neurocognitive performance following brain injury. Past TBI studies have primarily focused on mean neurocognitive performance when examining differences between ε4 allele groups, and as highlighted previously, mixed findings have resulted. Although some studies reported that possession of at least one ε4 allele confers risk for worse neurocognitive performance post-concussion (Ariza et al., 2006; Crawford et al., 2002), other studies have shown that the ε4 allele does not appear to affect neurocognitive outcomes (Chamelian, Reis & Feinstein, 2004; Ponsford, Rudzki, Bailey, & Ng, 2007). Consistent with these latter studies, the present study found no significant difference in mean cognitive performance between allele groups. Although evaluating and comparing the mean of neurocognitive standardized scores is common, it is recognized that this approach does have limitations. Specifically, utilizing measures of central tendency may mask differential neurocognitive profiles across individuals or may minimize more subtle impairment across neurocognitive tests. In the present study, consideration of additional methods for assessing neurocognitive functioning resulted in the identification of an important relationship that appears to exist between the ε4 allele and neurocognitive performance. Belanger and Vanderploeg (2005) previously demonstrated that there is a relatively subtle effect of sports-related concussion on cognitive outcomes assessed via standard neuropsychological tests. Therefore, it would be expected that the effect of genotype on outcome following concussion may also be subtle, and hence, identifiable only with more sensitive or nuanced measures of cognitive functioning. Intraindividual variability is a construct that has received increased attention in the neuropsychological literature, with several studies demonstrating a relationship between increased variability and poorer neurocognitive performance in clinical samples (Burton, Strauss, Hultsch, Moll, & Hunter, 2006; Cole, Weinberger, & Dickinson, 2011; Holtzer, Verghese, Wang, Hall, & Lipton, 2008; Hultsch, MacDonald, Hunter, Levy-Bencheton, & Strauss, 2000; Rabinowitz & Arnett, 2013). Investigators have proposed that cognitive variability is a consequence of CNS inefficiency arising from disrupted neural connectivity (Kelly, Uddin, Biswal, Castellanos, & Milham, 2008), reduced efficacy of neurotransmitter systems (Backman, Nyberg, Lindenberger, Li, & Farde, 2006), or loss of white matter integrity (Anstey et al., 2007; Fjell, Westlye, Amlien, & Walhovd, 2011; Walhovd & Fjell, 2007). There is even some evidence suggesting that variability may be more sensitive than mean level of performance in detecting cognitive decline (Lovden, Li, Shing, & Lindenberger, 2007). Previous work has found that athletes whose cognitive performance was characterized by a high level of intraindividual variability were more likely to exhibit cognitive decline relative to their baseline performance (Rabinowitz & Arnett, 2013). The findings of the present study support and extend these results by suggesting that increased cognitive variability is associated with APOE genotype. Given the proposed role of the ε4 allele in neuronal maintenance and repair, along with its detrimental relationship to neuropathological processes (Mahley et al., 2006), it is possible that carriers of the ε4 allele are predisposed to experiencing greater fluctuations in their cognitive processing abilities following a brain injury. Given the present findings suggesting a relationship between possession of an ε4 allele and post-concussion performance variability and likelihood of neurocognitive impairment, it is possible that the influence of the ε4 allele on cognitive function is widespread and non-specific. Research suggests that the ε4 allele, compared to the ε2 and ε3 alleles, is associated with oxidative stress, ischemia, inflammation, increased amyloid-beta (Aβ) production, and impaired CNS glucose utilization—all of which can negatively affect cognitive functioning (Mahley et al., 2006). Furthermore, concussion is a diffuse and heterogeneous injury with a non-specific neuropsychological and symptom profile. With this backdrop, our results suggest that, rather than imparting an effect on a specific cognitive process such as memory, the interactive effect of concussion and genetic risk in the form of the ε4 allele results in diffuse CNS involvement which can manifest as impairment across various cognitive domains. This may help to explain the heterogeneous findings reported when evaluating the relationship between the APOE gene and mean-level neurocognitive performance, and would suggest that future studies should continue to examine impaired scores and neurocognitive performance variability following concussion. Limitations The sample size for the present study is 57 cases, which is smaller than the recommended sample size for genetic studies examining a single SNP (248 cases; Hong & Park, 2012), but larger than some previous studies that have examined the effects of APOE genotype on outcome following brain injury (N ranging from 30 to 53; Han et al., 2009; Jordan et al., 1997; Kutner et al., 2000; Sundström et al. 2004). Hence, it is likely that the present study is underpowered to detect the subtler effects of APOE genotype on outcome. However, despite power-limitations, we believe that the present investigation represents a meaningful contribution to the sports concussion literature, wherein there is currently a dearth of research on genetic influences on cognitive outcomes. In fact, as of this writing, the present study represents the largest study to examine genetic influences on short-term cognitive outcomes of sports-related concussion. Another limitation of our study is that the athletes participating in this study were selected from those enrolled in a university-wide concussion management program; hence, the characteristics of the sample and timing of post-concussion referrals were confined by the policies and clinical needs of the program. Thus, we had little control over who was referred for post-concussion testing and when. As a result, the time from injury to assessment varied. It is important to highlight, though, that about three-quarters of the concussed sample were tested within 2 weeks post-injury and the median time from injury to assessment was 5 days. The relatively homogeneous nature of our sample provided an advantageous setting for examining the relationship between APOE genotype and post-concussion neurocognitive functioning in the context of collegiate sports-related concussion. However, findings from this relatively homogenous population with regard to injury mechanism, age, sex, and other clinical and demographic characteristics should be generalized to other populations with great caution. In particular, the relatively small number of female athletes in the sample is a limitation. Given that some research suggests that female athletes suffer poorer outcomes following sports-related concussion (Covassin, Elbin, Harris, Parker, & Kontos, 2012), this is a group that requires further study with regard to genetic influences on post-concussion outcomes. A final limitation of this study is the absence of robust baseline data for comparison. However, a WTAR FSIQ score was obtained for all participants and group comparisons showed that there were no significant differences between athletes with and without an ε4 allele with respect to pre-morbid functioning. Thus, this provides further evidence to suggest that the observed differences in post-concussion neuropsychological performance are not due to baseline differences between the groups, but instead are related to the influence of the APOE ε4 allele on cognitive performance within the context of brain injury or concussion. This limitation will need to be addressed by future research. Conclusions In our study we took a nuanced approach to evaluating neurocognitive performance in a sample of concussed college athletes, and in doing so, identified a link between APOE ε4 carriers and performance on a comprehensive neuropsychological test battery. Our findings suggest a possible relationship between the ε4 allele and post-concussion impairment and neurocognitive performance variability. Future studies should continue to examine the APOE gene and neurocognitive performance in additional samples, including children, adolescents, and older adults with concussion, as well as patients with more severe brain injuries. In order to better understand how genetic factors may mediate outcomes following concussion, it will also be necessary for future studies to determine the exact mechanisms by which the APOE gene exerts its influence on clinical outcomes. Testing the hypotheses proposed above may provide a starting point from which ongoing research can be generated. Finally, research efforts should continue to be carried out on specific samples of patients with TBI so that we may develop a greater understanding of the effects of the APOE gene across the lifespan and in other populations susceptible to brain injury. Funding This work was supported by a grant from the American Psychological Foundation (no grant number exists). Conflict of Interest None declared. 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Archives of Clinical Neuropsychology – Oxford University Press
Published: Feb 1, 2018
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