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1 Background
Sports-related concussions have emerged in the past decade as a leading healthcare issue for children and adolescents in the US and abroad. The Centers for Disease Control and Prevention (CDC) define a concussion as a mild traumatic brain injury (mTBI) resulting from a direct impact or forceful motion to the head (CDC, 2018). Although concussions can occur in any sport, they are most common in contact sports such as football, hockey, and soccer (Harmon et al., 2013)
. In the US, it is estimated that children and adolescents account for 78.6% of the sports-related head trauma cases treated in emergency departments
(Gaw and Zonfrillo, 2016) although it is believed that nearly 50% of sports-related concussions go unreported (Harmon et al., 2013). In light of this underreporting and the susceptibility of this population to the effects of sports-related concussions (Halstead et al., 2010; Patel and Reddy, 2010; Yeates, 2010), there is a growing need to understand whether early-life participation in contact sports has implications for later-life cognitive function. Our goal in the proposed analysis is to expand on prior studies by investigating the link between early-life participation in contact sports and late-life cognitive impairment in a unique sample of twins reared together and twins reared apart.2 Study Design and Materials
2.1 The Swedish Adoption/Twin Study of Aging
Detailed descriptions of the Swedish Adoption/ Twin Study of Aging (SATSA) sample have been previously published (Finkel and Pedersen, 2004; Bergeman et al., 1993). Briefly, the longitudinal SATSA sample was drawn from the population-based Swedish Twin Registry (STR) (Magnusson et al., 2013) and contains data on same-sex monozygotic and dizygotic twins that were reared apart and a control sample of twins reared together matched on birth year, country of birth, and gender with survey years in 1984, 1987, 1990, 1993, 2004, 2007, and 2010. SATSA respondents have undergone detailed in-person interviews and health examinations approximately every three years since the baseline survey in 1984. In the 1993 survey wave, responders were asked to report whether they were involved in any sport (e.g., football, ice hockey, or boxing) that may involve a hit on a head (hereafter contact sports). Examining twin pairs concordant and discordant for participation in contact sports allows us to control for biological and familial influences while comparing cognitive outcomes.The SATSA has been approved by an ethics committee at the Karolinska Institutet and the Regional Ethics Review Board in Stockholm.
2.2 Sample Selection Criteria
The SATSA surveyed 2,018 individuals (758 twin pairs; 502 unpaired singletons) in its baseline year in 1984 when participants were, on average, 60.14 years old (SD = 14.02). 1,450 responders (479 twin pairs; 492 unpaired singletons) were retained through the fourth survey wave in 1993 when responders were asked to report whether they participated in contact sports in early-life. Among these 1,450 responders, 1,240 (85.5%) reported not being involved in contact sports, 163 (11.3%) did, and data were missing for 47 (3.22%). Among the 1,403 responders with information about their participation in contact sports, 684 (48.75%) completed in-person interviews and had at least one MMSE score over the survey period. Of the 684, an additional 10 (1.5%) were dropped for reporting uncertain zygosity or being discordant on their rearing status. Of the remaining 674 responders, 79 (11.7%) reported participation in contact sports. Among the 729 responders who provided information on contact sports but had no available MMSE scores, 84 reported playing contact sports (11.6%). Fisher’s test revealed no differences in the missingness of MMSE scores by participation in contact sports (P value 0.05).
2.3 Study Outcomes
Our primary outcome is cognitive impairment suggestive of dementia or mild cognitive impairment (MCI) as assessed through the MMSE. The MMSE is commonly used to assess cognitive functioning, track changes in cognitive function over time, and screen individuals for cognitive impairment (Folstein et al., 1975)
. The SATSA implemented the MMSE in its baseline assessment in 1984 and in each wave that followed through 2010. Table 1 shows descriptive statistics for MMSE scores over the survey period. We will dichotomize MMSE scores such that scores between 0 and 27 are coded as impaired (1) and scores between 28 and 30 are coded as not impaired (0). The traditional MMSE cutoff score is 24, but higher cutoff scores have been proposed to increase diagnostic accuracy in individuals with higher levels of education, and at earlier stages of dementia severity
(Folstein et al., 1991; Rajji et al., 2009). In the absence of a conclusively defined cutoff, we will replicate our semi-parametric illness-death model for MMSE using two additional cutoffs—24 and 30—as recommended in the literature (Folstein et al., 1991; Rajji et al., 2009).In conjunction with the MMSE, the SATSA administered an extensive cognitive battery to assess verbal and spatial ability, memory, and processing speed. Table 2 shows the frequencies of available outcomes data in our sample of eligible responders.
2.4 Primary Analysis
We will model the relationship between participation in contact sports and the onset of cognitive impairment using a semi-parametric illness-death model which accounts for (i) the interval censoring of cognitive impairment between survey waves and (ii) the competing risks with death and study drop-out (Joly et al., 2002; Touraine et al., 2017). The illness-death model provides better estimates of hazard ratios than standard Cox hazard models which ignore interval-censoring and competing risks (Leffondre et al., 2013). We will use a bootstrap clustering (Field and Welsh, 2007) approach with twin pairs as sampling units to account for the twin structure. Coefficient estimates will be obtained using the R package Smooth Hazard (Touraine et al., 2017). We will repeat this analysis in a restricted sample of 29 twin pairs (n = 58) discordant for participation in contact sports.
2.5 Secondary Analysis
We will estimate a series of age and sex adjusted growth curve models for each of seven cognitive test outcomes listed in Table 2 as a function of participation in contact sports. Growth curve models allow us to examine the effects of participation in contact sports on the level of and change in cognitive function over time by including both random (i.e., individuals nested within twin pairs) and fixed (i.e., participation in contact sports) effects. A structured modeling approach will be used to determine the functional form of the growth curve models for each cognitive outcome, independently, while accounting for participation in contact sports. The Bayesian information criterion statistic will be used to compare models. For each cognitive test, the model with the lowest BIC will be selected and re-estimated with the age and sex included. The R code used to estimate each model is provided in Table 3 using the MMSE as an example. All models will be estimated using full-information maximum likelihood. We will cluster the standard errors to account for the twin structure. In addition, we correct for multiplicity by using the Benjamini–Hochberg procedure to estimate p-values adjusted for the false discovery rate
(Cribbie, 2007).We will include the full analytic sample (n=684). A growth curve modeling approach has been used in a previous study which used the SATSA to examine the effects of childhood social class on trajectories of cognitive aging (Ericsson et al., 2017). Because changes in cognition are age-related, we will measure time using chronological age rather than survey year.
References
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3 Appendix
| MMSE | n | Mean | Std. Dev. | Min | Max. |
|---|---|---|---|---|---|
| Wave 1 | 451 | 28.1 | 1.6 | 22 | 30 |
| Wave 2 | 505 | 28.4 | 1.3 | 23 | 30 |
| Wave 3 | 520 | 27.9 | 1.9 | 13 | 30 |
| Wave 4a | 38 | 26.4 | 2.2 | 20 | 29 |
| Wave 5 | 504 | 27.2 | 2.8 | 8 | 30 |
| Wave 6 | 416 | 27.2 | 3.1 | 4 | 30 |
| Wave 7 | 359 | 27.1 | 3.7 | 7 | 30 |
Note. a. Due to reasons related to funding, a telephone interview was used in place of in-person testing for Wave 4 and thus the cognitive assessment was not conducted for all individuals (Pedersen et al., 1991)
| Test | Wave 1 | Wave 2 | Wave 3 | Wave 4a | Wave 5 | Wave 6 | Wave 7 |
|---|---|---|---|---|---|---|---|
| MMSE | 451 | 505 | 520 | 38 | 504 | 416 | 359 |
| 65.9% | 73.8% | 76% | 5.6% | 73.7% | 60.8% | 52.5% | |
| Verbal Ability | 467 | 440 | 457 | 34 | 450 | 368 | 287 |
| 68.3% | 64.3% | 66.8% | 5% | 65.8% | 53.8% | 42% | |
| Spatial Ability | 462 | 438 | 446 | 32 | 434 | 345 | 257 |
| 67.5% | 64% | 65.2% | 4.7% | 63.5% | 50.4% | 37.6% | |
| Memory | 466 | 464 | 494 | 33 | 456 | 379 | 310 |
| 68.1% | 67.8% | 72.2% | 4.8% | 66.7% | 55.4% | 45.3% | |
| Processing Speed | 465 | 462 | 463 | 35 | 470 | 394 | 327 |
| 68% | 67.5% | 67.7% | 5.1% | 68.7% | 57.6% | 47.8% | |
| Digit Span | 492 | 501 | 515 | 35 | 496 | 409 | 343 |
| 71.9% | 73.2% | 75.3% | 5.1% | 72.5% | 59.8% | 50.1% | |
| Delayed Recall | NA | NA | NA | NA | 473 | 394 | 348 |
| 69.2% | 57.6% | 50.9% |
Note. a. Due to reasons related to funding, a telephone interview was used in place of in-person testing for Wave 4 and thus the cognitive assessment was not conducted for all individuals (Pedersen et al., 1991)
| Model | Model Specification |
|---|---|
| Model 1 | lmer(mmse ~ sportsYN + agec + |
| (agec | pair) + (agec | id), data = SATSA, REML = FALSE) | |
| Model 2 | lmer(mmse ~ sportsYN + agec + agec2 + |
| (agec + agec2 | pair) + (agec + agec2 | id), data = SATSA, REML = FALSE) | |
| Model 3 | lmer(mmse ~ sportsYN + agec + agec2 + sportsYN:agec + sportsYN:agec2 + |
| (agec + agec2 | pair) + (agec + agec2 | id), data = SATSA, REML = FALSE) |
Notes.
mmse = Mini-Mental State Exam; sportsYN = binary indicator for participation in contact sports; agec = mean-centered age; agec2 = mean-centered age-squared ; pair = twin-pair identifier; id = individual identifier; REML = restricted maximum likelihood.