Quantifying and Examining Reserve in Symptomatic Former National Football League Players

INTRODUCTION

Exposure to repetitive head impacts (RHI), particularly from contact sports, has been associated with later-life cognitive and neuropsychiatric disturbances, as well as the development of neurodegenerative diseases (e.g., chronic traumatic encephalopathy [CTE]) and other pathologies (e.g., white matter degeneration) [1–15]. However, not all individuals exposed to RHI develop cognitive or neuropsychiatric disorders [2, 16–19]. Among those who do, there is heterogeneity in the presence and severity of the presentation and underlying neuropathology [2, 20]. Identification of risk and resilience factors of the late effects of RHI is critical to facilitate disease detection, diagnosis, and treatment and prevention strategies.

The heterogeneous expression of cognitive and neuropsychiatric deficits following RHI exposure may be explained by the reserve hypothesis. Reserve is a heuristic to describe individual variations in cognition and/or function and can be conceptualized as two main components: brain reserve (BR) and cognitive reserve (CR) [21–23]. BR refers to an individual’s neurobiological capital, i.e., resistance to pathology [24–26]. It is often measured by dimensions like gray matter (GM) volume, white matter (WM) volume, or estimated intracranial volume (eTIV) [24–27]. Recent literature has identified eTIV as an appropriate operationalization of BR as it encompasses GM, WM, and cerebrospinal fluid (CSF) measures [27, 28]. CR represents the varying susceptibility of an individual’s cognitive abilities to neuropathology, i.e., resilience to the clinical manifestation of pathology. CR is typically estimated by measures referred to as proxies (i.e., representative measures of reserve). Common CR proxies include estimated premorbid intelligence/reading ability [29, 30], years or level of education [22, 31], and occupational attainment [32, 33]. Higher scores on proxy measures are typically associated with better cognitive performance [34]. The definitions and quantifying methods of reserve are continuously evolving and while recent attempts to clarify these concepts have been made [22, 24], there is currently no consensus. Moreover, evidence suggests that BR and CR independently and interactively contribute to the heterogeneity in one’s resistance and resilience to brain insult [35–37]. As a result, the current investigation refers to CR and BR collectively as reserve, i.e., an entity that accounts for the discrepancy between the degree of brain pathology and the clinical manifestation of pathology [35–37].

Reserve has been extensively studied in Alzheimer’s disease (AD) and AD-related dementias. Higher educational and occupational attainment have been shown to mitigate the clinical and neuropathological expression of AD [38–40], even in APOE ɛ4 carriers [33]. Moreover, higher estimated premorbid intelligence/reading ability has also been associated with improved cognitive recovery following traumatic brain injury (TBI) [29, 30]. Extant studies have also alluded to the possible role of reserve in the heterogeneous presentation of RHI-related deficits [4, 41]; however, formal investigations in this area are limited. Alosco et al. [42] examined the association between occupational attainment and years of education and age of symptom onset among 25 deceased American football players with severe CTE. Here, higher occupational attainment was found to be associated with a later age of symptom onset, but no effect was found for years of education. In addition to highlighting the need for further RHI-focused reserve research, this investigation raised the question of the suitability of traditional proxies for reserve quantification, particularly in elite former contact sport athletes.

Despite the convenience of single proxies for reserve estimation, reserve is a dynamic concept not sufficiently summarized by one component [22, 43]. Representing reserve in this way fails to distinguish between reserve itself and its contributing factors, likely resulting in significant measurement error [43, 44]. Individual proxy use for reserve quantification may also be problematic when considering RHI exposure. Firstly, years of education may not reflect quality of education in former elite athletes [42]. Most former elite athletes obtain 16 years of education with restricted variability due to the close relationship between sporting expertise and the American educational system. As a result, for athletic samples, education may not appropriately reflect complete reserve capacity. It is further considered that occupational attainment may be a poor measure of reserve in samples with RHI exposure. While Alosco et al. [42] found an effect for occupational attainment in their sample, they argue that low occupational attainment may be a consequence of RHI-related pathology (i.e., the pathology leads to functional impairment) rather than reflecting an individual’s complete reserve capacity. Therefore, a more refined measure of reserve is required to further our knowledge of its potential role in RHI-exposed samples and to gain an understanding of how it may contribute to clinical heterogeneity.

One alternative method of reserve quantification is the residual memory variance calculation [45]. Here, variance refers to the variability of a value(s) from the average. To calculate reserve using this method, the residual variance of cognitive performance (e.g., on an episodic memory task) is derived, after accounting for demographic factors (e.g., age, sex) and structural brain characteristics (e.g., total brain volume). This residual component encompasses all individual differences in cognition that cannot be explained by brain structure and demographic variables, thus representing a measure of reserve. This residual method of reserve quantification defines reserve as the discrepancy between one’s predicted and one’s observed level of cognitive performance [45, 46]. Higher positive residuals represent better than expected performance. The residual measure was shown to be correlated with traditional proxy measures of CR, highlighting the plausibility of this method. These findings have since been replicated and extended to different cognitive domains (e.g., executive functioning, language) across multiple samples [46–50]. Moreover, a recent review and meta-analysis of residual methods of reserve quantification strongly supports this technique as a measure of resilience and resistance in aging samples [51]. An important limitation of this approach, however, is that while a proportion of the residual likely represents reserve, there is also likely to be a significant but unknown proportion that is random error [49]. Nonetheless, applying such methodology to an RHI-exposed cohort would be a novel approach towards exploring the evident heterogeneity in symptom expression, or lack of thereof, following RHI exposure.

The objective of this study was to examine the association between reserve and cognitive and neuropsychiatric function in symptomatic former National Football League (NFL) players. The primary objective was to identify optimal measurement of reserve in this population. To do so, we used traditional individual proxy measures of reserve, in addition to the residual variance method of reserve quantification. Episodic memory and executive dysfunction are prominent symptoms following RHI exposure [2, 52–54]. Therefore, we targeted both the episodic memory and executive functioning domains to create residual episodic memory and executive functioning variance variables. The quantification of reserve by means of single proxies is problematic and the residual variance method may prove a suitable alternative to overcome these aforementioned issues in athletic samples.

MATERIALS AND METHODS

RESULTS

Traditional reserve proxies

Linear regression analyses

Regression analyses revealed a statistically significant association between age and racial identity, and several of the neuropsychological and neuropsychiatric outcomes (Supplementary Table 1). Older age correlated with worse performance on TMT Part B (p < 0.01) and NAB List Learning Long Delay Recall (p = 0.01). Black or African American race was associated with worse performance on TMT Parts A (p = 0.01) and B (p = 0.01), NAB List Learning Long Delay Recall (p = 0.03), Animal Fluency (p = 0.03), and the COWAT (p = 0.05). No significant effects were found for the CHII or APOE ɛ4 status on any of the neuropsychological or neuropsychiatric test scores (Supplementary Table 1).

The WRAT-4 had a significant effect on TMT Part A (p = 0.04), NAB List Learning Long Delay Recall (p = 0.04), Animal Fluency (p = 0.04), and the COWAT (p < 0.01). Higher WRAT-4 scores were associated with better test performance. Higher WRAT-4 scores were also associated with lower BRI (p = 0.04) scores, indicating less impairment. The largest effect was evident for the COWAT (β=0.38), followed by NAB List Learning Long Delay Recall (β=0.27), BRI (β=–0.26), TMT Part A (β=0.25), and Animal Fluency (β=0.24) (Fig. 1A; Supplementary Table 2). No significant effects were found for years of education, occupational attainment, or eTIV on any of the neuropsychological or neuropsychiatric test scores (Supplementary Table 2).

OPEN IN VIEWER

Fig. 1

Scatterplots of significant relationships between reserve proxies and neuropsychological test scores. A) WRAT-4 and animal fluency (p = 0.04), the COWAT (p < 0.01), NAB (p = 0.04), and TMT A (p = 0.04). B) Residual episodic memory variance and TMT A (p < 0.01), TMT B (p < 0.01), and animal fluency (p < 0.01). C) Residual executive functioning variance and TMT A (p < 0.01) and NAB (p < 0.01). Statistical significance was defined by a false discovery rate adjusted p-value<0.05. Age, racial identity, CHII, and APOE genotype were included as covariates. Non-significant associations presented in Table 4 and Supplementary Table 1 not shown. CHII, cumulative head impact index; WRAT-4, Wide Range Achievement Test-4; COWAT, Controlled Oral Word Association Test; NAB, Neuropsychological Assessment Battery List Learning Long Delay Recall; TMT A, Trail Making Test Part A; TMT B, Trail Making Test Part B; REMV, residual episodic memory variance; REFV, residual executive functioning variance.

DISCUSSION

This study investigated the role of reserve in 89 male, symptomatic, former NFL players and examined multiple methods for the measurement of reserve. Similar to other populations, higher reserve may contribute to the symptom heterogeneity observed within and across samples of former elite American football players. Residual episodic memory variance scores were shown to be associated with better performance on tests of attention and information processing, executive functioning, and semantic fluency, while residual executive functioning variance scores were associated with better performance on tests of attention and information processing and episodic memory. We additionally found that a measure traditionally believed to estimate reading ability was associated with better neuropsychological test performance and decreased neurobehavioral dysregulation, after accounting for covariates. Years of education, occupational attainment, and eTIV were not associated with cognitive test performance or neuropsychiatric symptoms.

Higher reading ability has been shown to mitigate cognitive dysfunction in neurological disorders such as AD and TBI [30, 81–83]. The current investigation indeed observed significant effects for the WRAT-4. Although word-reading tests such as the WRAT-4 have been a commonly used proxy of reserve, they may underestimate premorbid intelligence in individuals who have been exposed to head impacts at an early age [30, 77]. Moreover, the WRAT-4 provides an estimation of one’s reading ability rather than being a direct measure of intelligence itself. While this estimation may capture a proportion of reserve, it likely also encompasses a significant proportion of measurement error. A more comprehensive estimate of premorbid intelligence is needed for future studies in this population. We additionally hypothesized that reserve metrics (e.g., WRAT-4) may moderate the relationship between exposure to RHI and cognitive test performance. No such effects were found. RHI exposure in our sample of former professional athletes was retrospectively estimated using the CHII by applying accelerometer data of American football players at collegiate level. Helmet accelerometer studies are not available for the NFL and thus, as this study focused on professional athletes, the CHII may have underestimated RHI exposure in our sample. Nevertheless, the CHII remains one of the most refined metrics for quantifying RHI exposure. Accelerometer data of professional American football players is needed to overcome this limitation in future studies of this population.

No associations were identified between years of education and cognitive test performance or neuropsychiatric symptom expression. This was also the case for occupational attainment. In former professional American football players, years of education may not capture complete reserve capacity [42]. Most NFL players attain 16 years of education, which may explain the restricted variability in education duration in the current sample. In this all-male sample, variability is also lacking in eTIV [84, 85]. Lastly, in contrast to Alosco et al.’s [42] findings, the current investigation found no significant effect for occupational attainment. Again, >65% of participants in the current study reported high occupational attainment. There are also substantial differences between this sample and Alosco et al., the latter of which included a small sample of brain donors with autopsy-confirmed stage III/IV CTE. Alosco et al. also examined informant-reported age of symptom onset, whereas the present study used objective measures of cognitive and neuropsychiatric function.

In response to the aforementioned limitations of the individual reserve proxies, this study examined more refined approaches of reserve quantification. Residual episodic memory and executive functioning variance variables were created and tested for their association with cognitive and neuropsychiatric function. Residual episodic memory variance showed a significant association with better performance on tests of attention and information processing speed, executive functioning, and semantic fluency. Residual executive functioning variance was similarly associated with better performance on tests of episodic memory and attention and information processing speed performance. Larger effect sizes were evident for the residual variance approach as compared to a test of reading ability in regard to associations with neuropsychological test performance. Unlike reading ability score, the residual cognitive variance scores were not associated with any of the neuropsychiatric measures. This may be because risk factors for neuropsychiatric symptoms in this population are distinct from those used to create the residual cognitive variance scores. Behavioral disturbances in former elite contact sport athletes may also have distinct causes and/or etiologies compared with cognitive impairments. Correlation analyses revealed that a significant correlation existed between a measure of reading ability and residual episodic memory variance. This remains consistent with our findings that, out of the four traditional proxies measured in this study, reading ability was superior. On the other hand, CR is a multidimensional construct and previous research has shown that traditional reserve proxies represent distinct components of reserve, thus each providing a unique contribution to individual performance on cognitive tests [86–88]. Our findings could alternatively suggest that the newly created residual variance variables capture different aspects of reserve than the traditional reserve proxies and reading ability most closely resembles that which is measured by these variables.

The cognitive and neuropsychiatric symptomatology associated with RHI is heterogenous. Even among those with comparable neuropathological load, there can be differences in symptom presence and severity [9, 65]. Not all those subject to RHI experience subsequent cognitive dysfunction or neurobehavioral dysregulation [18, 89]. High reserve in individuals exposed to RHI may be protective against cognitive decline and may explain some of the inconsistent findings in the literature [18, 91]. Individual differences in the susceptibility to age-related brain changes have been examined extensively with regard to reserve [23, 40]. Previous research has shown that high reserve, as measured by traditional reserve proxies, is protective of cognitive decline [92, 93] and the presence of neuropsychiatric symptoms [94, 95]. Furthermore, despite higher severity of AD-related pathology, those with high reserve can still appear clinically similar to those with low reserve and less AD pathology [39]. The exact neural mechanism underlying reserve is currently unknown [39]. Colangeli et al. [96] showed that reserve proxies (i.e., education, occupation, and leisure activities) were associated with activation in the anterior cingulate cortex. Also, Wilson et al. [97] demonstrated that reduced neuronal density (i.e., reserve at the neuronal level) in the locus coeruleus and brain stem neurofibrillary tangles and Lewy bodies were predictive of the rate of cognitive decline in AD. Research has also indicated that the relationship between amyloid plaques and cognitive functioning is moderated by formal years of education [98]. Reserve factors may similarly influence the expression of p-tau and other neuropathologies, as well as resulting clinical syndromes associated with RHI.

There are limitations to our findings. While a number of significant effects were found, the meaningfulness of these effects remains unclear (as shown in Fig. 1). Moreover, the sample included male former NFL players, and it is unclear if our findings generalize to the broader American football population, other contact sports, or female athletes. Future research should focus on exploring alternative methods of reserve/resilience quantification in these other samples. The current research was also cross-sectional. Prospective studies are needed to determine how reserve factors might mitigate cognitive and neuropsychiatric decline. Defining and quantifying reserve, particularly in groups with RHI exposure, is problematic and no consensus has yet been reached. The comparison of results between reserve-based research investigations remains difficult and there is a great need for a more exhaustive and standardized method of reserve estimation. The residual variance method shows promise towards finding a solution to these issues by encompassing multiple aspects of reserve into a single component. Although, the proportion of the residual scores that represent reserve versus random error is unknown, significant measurement error also exists when estimating reserve using traditional proxies. Future research should focus on investigating the applicability of the residual variance approach to reserve quantification in a sample with neuropathologically confirmed CTE to provide further insight into the role of reserve in the clinical expression of this pathology. Furthermore, more refined measures of reserve are required to further understand its predictors. This investigation made use of total brain, hippocampal, and WM-hypo volumes to derive residual variance scores in order to capture the effects of atrophy and cerebrovascular disease. Other brain regions and types of pathology might be more suitable in people exposed to RHI, such as microstructural injury from diffusion tensor imaging. Therefore, future research should focus on exploring alternative methods of capturing the effects of atrophy and cerebrovascular disease.