Alzheimer’s Disease Genetic Risk Influences Recovery Among Individuals Receiving Traumatic Brain Injury Rehabilitation

Abstract

Evidence supports the role of traumatic brain injury (TBI) as a risk factor for Alzheimer’s disease (AD), and it has been shown that some individuals with TBI experience post-injury cognitive decline; however, it is currently not clear whether genetic risk for AD may influence TBI recovery. This study investigated the association of AD genetic risk with TBI recovery, assessed with the Functional Independence Measure (FIM) at 1-year post-injury, in the TBI Model Systems cohort (N = 176). AD genetic risk was calculated using an AD polygenic risk score (PRS) measuring 37 SNPs previously shown to be associated with AD risk independently of Apolipoprotein E (APOE) genotype, the major known risk factor for late-onset AD. A linear regression model predicting FIM score at 1-year follow-up, including AD PRS, FIM score at hospital discharge, three genetic ancestry components, sex, age and education at follow-up, and APOE e4 allele carrier status, was significant (F(2,154) = 12.729, p < 0.001); the model showed that higher AD PRS predicted worse FIM outcome scores (beta = −0.167, t = 2.226, p = 0.027). Post-hoc testing showed similar though less significant effects for the FIM motor and cognitive subscales. These preliminary findings highlight the need for larger-scale investigations of genetic factors and fluid biomarkers as additional predictors of recovery following TBI, as well as risk for cognitive decline.

Keywords

genetic factors neurodegenerative disorders outcome measures rehabilitation traumatic brain injury

Introduction

There is evidence that some patients experience cognitive decline following traumatic brain injury (TBI), particularly those with moderate to severe injuries.^1–5 Within the TBI Model Systems (TBIMS) National Database, 22% of people with moderate/severe TBI died within 5 years of injury, and another 30% showed cognitive and behavioral decline over this period.⁶ Additionally, brain injury has been shown to increase the risk of dementia and Alzheimer’s disease (AD) in particular.^7,8 As cognitive decline is common following TBI, and TBI survivors are at greater risk for dementia and AD, we hypothesized that genetic risk for AD may also influence patient recovery following TBI.

Polygenic risk scores (PRS) utilize patient genotype data and prior results from large studies of genetic risk for diseases like AD to calculate individuals’ cumulative genetic risk for disease. This method is advantageous as it allows for the evaluation of genetic risk association with outcomes in data sets that would normally be underpowered for genetic studies. This pilot study in the TBIMS evaluates the association of participants’ AD genetic risk, calculated with a PRS, with TBI outcomes 1-year post injury on the Functional Independence Measure (FIM) scale.⁹ The FIM includes both a motor and cognitive component, permitting post-hoc analysis of these subscales as well. We hypothesized that individuals with higher AD genetic risk would have lower (worse) scores on the FIM, and that these would be driven by cognition dysfunction.

Methods

TBIMS pilot biosample cohort

The TBIMS National Database (NDB) has been extensively described in previous publications.^10,11 The TBIMS NDB participation criteria are: ≥ 16 years old at injury; status post-complicated mild to severe TBI as defined by TBI with post-traumatic amnesia (PTA) > 24 h, or loss of consciousness > 30 min, Glasgow Coma Scale (GCS) score in the Emergency Department < 13, or neuroimaging abnormalities consistent with TBI; admitted to the Emergency Department within 72 hours of the TBI; and received acute care & inpatient rehabilitation for the TBI. This TBIMS pilot biosample cohort was comprised from the Indiana University (IU), Ohio State University, and North Texas TBIMS participants. Participants were enrolled either during acute inpatient rehabilitation following TBI (blood sample collected) or via telephone follow-up (saliva sample collected). Demographic, clinical, and TBI-related data were collected at baseline post-injury, as well as at 1-year follow-up per TBIMS protocol.¹²

Genotyping and data quality control

Blood or saliva was sent to the Indiana University Genetics Biobank, where DNA was extracted; DNA passing quality control was obtained for N = 190 participants, and APOE SNPs rs429358 and rs7412 were genotyped with a custom 96-SNP chip on the Fluidigm microfluidic platform to generate APOE ε2/ε3/ε4 alleles. DNA from these participants was also sent to the Children’s Hospital of Philadelphia Center for Applied Genomics (CAG), where genotyping with the Illumina Global Screening Array v3.0 was performed to generate genome-wide association study (GWAS) microarray data. Data for 189 participants was returned; one sample failed quality control at the CAG.

GWAS data were converted to PLINK format with GenomeStudio (Illumina, Inc., San Diego, CA). PLINK v1.9 was used to perform quality control, including variant and sample filtering for <95% call rate, removal of monomorphic and duplicate variants, as well as those that were not in Hardy-Weinberg Equilibrium (p < 1 × 10⁻⁶), and sample sex check for concordance with reported sex.^13,14 One sample was dropped for low call rate, while another participant could not be linked to an ID in the TBIMS database; the remaining 187 participants were used for analysis. GWAS data for these participants were merged with genotype data from the 1,000 Genomes Project, and multidimensional scaling was performed in PLINK to compute genetic ancestry.¹⁵ Principal components analysis was used to generate PCs 1, 2, and 3, which were included as covariates to account for differences in genetic ancestry (see Supplementary Fig. S1 for more information). Given the small sample size, individuals of diverse genetic ancestry were not removed from the analysis; however, main findings were replicated in individuals with White non-Hispanic ancestry only (data not shown) to verify the effect direction was consistent with reported results in the total cohort.

GWAS data were imputed with the Michigan Imputation Server, using the 1,000 genomes reference population with the mixed population method selected.^15,16 PLINK v2 was used to extract data for SNPs with R² ≥ 0.3 into pgen format.¹³

Polygenic scores for AD were reviewed within the Polygenic Score Catalog (http://www.pgscatalog.org/), to select the most relevant score with sufficient validation.¹⁷ This analysis used PGS000898, a validated AD PRS generated from a study by de Rojas and colleagues, which includes 39 SNPs significantly associated with AD case/control status.¹⁸ Score data were downloaded and used to calculate a summed AD PRS from TBIMS GWAS data using the PLINK v1.9 score function. Two SNPs did not have data passing quality control, yielding scores summed from 37 SNPs (see Supplementary Data). Scores did not include APOE SNPs to allow for independent assessment of the effect of APOE ε4 allele carrier status in the analyses. For visualization, AD PRS was standardized to z-scores.

Demographic, clinical, and outcome measures

Data assessed for this pilot study included demographic factors such as sex, age, and years of education. Age at follow-up was calculated as an imputed score, including age at follow-up when available, as well as calculated age, which was obtained by adding the follow-up interval to age at baseline and substituted for missing follow-up age. A binned composite TBI severity measure (cTBI) was also calculated; injuries were categorized as moderate/severe TBI if participants had a GCS < 13, or if GCS was missing, by PTA > 1 day; injuries in patients who did not meet either of these selection criteria were categorized as mild TBI. Only 176 participants had sufficient data to calculate cTBI; analyses excluded individuals missing cTBI.

FIM total scores as well as motor and cognitive scores at 1-year follow-up were used as outcomes measures in the analyses. FIM scores at discharge were included as covariates in analysis.

Data sharing

Data for TBIMS is available through the TBIMS National Data and Statistical Center (https://www.tbindsc.org/). GWAS and AD PRS data are available to qualified researchers on reasonable request to the corresponding author.

Statistical analysis

Statistical analyses were performed in SPSS Statistics v27 (IBM, Corp., Armonk, NY). Participants were assessed for differences in demographic, clinical, and genetic data by cTBI group, using ANOVA or chi-square analyses as appropriate. Assessment of FIM scores using Kolmogorov-Smirnov and Shapiro-Wilk tests showed that FIM scores were not normally distributed (p < 0.001), so all FIM scores were log-10 transformed prior to analyses. Chi-square analysis of AD PRS divided into quintile groups with cTBI was performed to assess distribution of risk between severity groups.

Linear regression using a backwards calculation (starting with all predictors, with stepwise removal of non-significant [p < 0.05] predictors) was performed, using FIM total score at 1-year follow-up as the dependent variable. Predictors in the model included: FIM total score at discharge, age and years of education at 1-year follow-up, sex, cTBI, genetic ancestry principal components 1, 2, and 3, APOE ε4 allele carrier status, and AD PRS. Post-hoc analyses included linear regression testing with FIM motor or cognitive scores substituted for total scores. AD PRS quintile groups were also assessed for differences in mean FIM total score at 1-year follow-up by ANOVA.

Results

Of the 190 participants with DNA available for genotyping, 188 had GWAS data that passed quality control (see Genotyping and Data Quality Control for details). Of these, 176 had phenotype data, including cTBI scores, available for analysis. Comparison of participant demographic and clinical information for these individuals showed that those with complicated moderate/severe TBI were younger and had completed less education than those with mild TBI (Table 1). Sex, reported race and ethnicity, APOE ε4 carrier status, FIM total score at 1-year follow-up, and AD PRS quintile groups were not significantly different between injury severity groups.

Table 1.

TBI Model Systems Genetics Cohort Demographics

Variable	Mild TBI (N = 68)	Moderate/severe TBI (N = 108)	X² or F (p value)
Sex count (%) male	46 (67.6%)	75 (69.4%)	0.063 (0.802)
Race count (%) White	53 (81.5%)	91 (88.3%)	1.510 (0.219)
Ethnicity count (%) Hispanic	2 (3.1%)	7 (6.9%)	1.116 (0.291)
Age at 1-year mean (SD) [range]^a	55.11 (19.64) [20–91]	36.77 (15.29) [17–85]	48.019 (<0.001)
Education at 1-year mean (SD) [range]	14.58 (2.58) [10–20]	12.96 (2.13) [8–18]	18.847 (<0.001)
APOE ε4 carrier count (%)	23 (33.8%)	23 (21.3%)	3.392 (0.066)
Raw FIM Total Score at 1-year mean (SD) [range]	119.86 (10.42) [53–126]	119.27 (7.70) [88–126]	0.169 (0.681)

^a13 individuals missing 1Y age—listed mean for each category for all individuals with data was used to replace missing age values for analyses (imputed age).

TBI, traumatic brain injury; SD, standard deviation.

Linear regression analysis of FIM total score at 1-year follow-up showed that the model explained 13.2% of the variance and was a significant predictor of FIM score (F(2,154) = 12.729, p < 0.001), though only AD PRS (beta = −0.167, t = −2.226, p = 0.027) and FIM total score at discharge (beta = 0.348, t = 4.632, p < 0.001) were significant predictors included in the final model. Results indicate that individuals at lower genetic risk for AD have higher FIM scores, indicating increased independent function at 1-year follow-up (Fig. 1).

FIG. 1.

FIM Total Score and AD PRS z-score. Scatterplot of FIM total score at 1 year (1Y) follow-up by AD PRS z-scores. Fit line slope indicates that individuals with lower AD PRS have higher FIM scores (better functional independence) at 1Y follow-up.

Post-hoc assessment of FIM cognitive and motor scores shows similar directions of effect, though AD PRS is not a statistically significant predictor for either subscale score (results not shown). A basic ANOVA of AD PRS quintile group differences in mean FIM total score at 1-year follow-up showed a small, non-significant [F(4,157) = 0.932, p = 0.447] range from the lowest risk group [FIM mean = 121.48, standard deviation (SD) = 4.07] to the highest risk group (FIM mean = 118.21, SD = 13.45).

Discussion

These results not only indicate that genetic factors that increase risk for AD could influence recovery trajectories following TBI but also highlight the need for larger, more detailed studies of the role of genetics in TBI recovery in diverse populations. Findings show that individuals with higher genetic risk for AD show lower levels of independence after brain injury and that modeling polygenic score from GWAS as well as APOE accounts for more of this risk than only evaluating APOE genotype. It is important to note that an ANOVA test for differences in AD PRS quintile groups showed FIM total score means at 1-year follow-up indicated high functional independence for all risk groups, with all mean scores > 117 out of 126. However, it should be noted that only about 10% of participants had FIM < 110 at 1-year follow-up; a larger sample size with more impaired participants is required to fully assess the clinical and functional implications of genetic risk for disease.

This study has some additional limitations that are important to note. Due to the small sample size, this study is not well-powered to investigate differences in genetic risk stratified by sex, interaction effects of age and environment with genetic risk on longer-term functional outcomes, or differences related to different genetic risk in individuals of diverse genetic ancestry. Given that studies of individuals with non-European genetic ancestry have shown different genetic risk for AD, it is possible that these findings may not translate well to diverse populations. Larger, more diverse studies are also needed to further investigate the translatability of these findings outside the TBIMS cohort, such as to TBI cohorts with milder injuries or pediatric populations. This investigation was also limited by measures that are not specifically targeting AD-related cognitive metrics. However, despite these limitations, the results show that higher AD genetic risk may influence recovery following TBI, particularly in White non-Hispanic individuals. It is critical for future studies to expand these analyses to larger, more diverse populations to investigate the contribution of specific risk factors to TBI recovery, and to examine the impact of genetic risk on longer-term recovery following injury. Studies leveraging larger sample sizes could compare different genetic contributions to TBI recovery metrics, such as comparing the effects of AD-specific genetic risk and a genetic score for general cognition.¹⁹ There are likely additional elements of genetic risk captured with other polygenic scores and/or approaches, which will enable a more complete assessment of risk for poor recovery following TBI.²⁰ Identification of additional risk factors for poor outcomes following TBI could enable prioritization of participants for preventative or therapeutic clinical or intervention trials and facilitate improved clinician counseling of patients following injury.

Conclusions

The TBIMS represents a valuable untapped resource for longitudinal studies of genetics and fluid biomarkers of TBI recovery and risk for AD. It will be important for larger studies to replicate this finding, establish this score’s relative utility as a biomarker compared to other metrics of concussion recovery, and investigate whether individuals at high risk for AD might benefit from cognitive behavioral therapeutic or pharmaceutical interventions to facilitate recovery following TBI.

Transparency, Rigor, and Reproducibility Summary

This study was not formally preregistered. The lead author with primary responsibility for the analysis certifies that the analysis plan (linear regression of total FIM scores at follow-up to assess AD PRS as a predictor, post-hoc analysis of FIM sub-scores) was specified prior to data analysis. For this pilot study, a sample size of 190 participants was planned based on the availability of DNA collections. The actual sample size following all exclusions for missing samples and/or data was 176 participants, and the observed effect size for linear regression of total FIM scores at 1-year follow-up (adjusted R² = 0.132) was ƒ2 = 0.152. Blood was collected for 190 participants, and GWAS returned for 189. Of these, 187 participants had GWAS data passing quality control. The final sample size of 176 included all participants with GWAS, relevant demographic and clinical data, and information required to calculate the cTBI score. Handling of biofluid samples was performed by team members blinded to relevant participant characteristics. Generation of GWAS data was performed by the Children’s Hospital of Philadelphia, also blinded to relevant participant characteristics. All equipment and analytical reagents used to perform assays or measurements on biospecimens are widely available from commercial sources. The key inclusion criteria for TBIMS are established standards. Replication is planned once additional data becomes available. De-identified data from this study are publicly available through the TBIMS National Data and Statistical Center (https://www.tbindsc.org/). GWAS and AD PRS data are available from the researchers on request. DNA used to conduct the study was obtained by the Indiana University Department of Physical Medicine & Rehabilitation, and is currently banked at the Indiana University Genetics Biobank; these samples are currently stored for internal use only. This article will be published under a Creative Commons Open Access license, and the preprint will be freely available on MedRxiv (https://www.medrxiv.org/).

Authors’ Contributions

K.N.H.N.: Conceptualization (equal); data curation (equal); investigation and analysis (equal); funding acquisition (lead); methodology (lead); project administration (lead); software (supporting); supervision (lead); visualization (supporting); writing—original draft (lead); writing—review and editing (lead). E.W.: Data curation (equal); investigation and analysis (equal); visualization (supporting); writing—original draft (supporting); writing—review and editing (supporting). M.A.: Data curation (equal); software (lead); writing—review and editing (supporting). J.D.C.: Writing—review and editing (supporting). J.A.B.: Writing—review and editing (supporting). B.C.McD: Conceptualization (supporting); funding acquisition (supporting); methodology (supporting); writing—review and editing (equal). A.J.S.: Conceptualization (supporting); funding acquisition (supporting); methodology (supporting); writing—review and editing (equal). T.M.F.: writing—review and editing (supporting). F.M.H.: Conceptualization (equal); funding acquisition (supporting); methodology (supporting); writing—review and editing (equal).

Footnotes

Acknowledgments

The authors thank the larger CBRAIN group at Indiana University for providing their expertise and insight for this project. They are grateful for the efforts of the Traumatic Brain Injury-Model Systems study in collaboration with the Indiana University Genetics Biobank for collecting the biospecimens that enabled this pilot project. Finally, they would like to thank all of the Traumatic Brain Injury Model Systems participants, without whom this study would not have been possible.

Author Disclosure Statement

A.J.S. has received in-kind support from Avid Radiopharmaceuticals, a subsidiary of Eli Lilly (PET tracer precursor) and Gates Ventures, LLC and Sanofi (Proteomics panel assays on IADRC and KBASE participants as part of the Global Neurodegeneration Proteomics Consortium), gift funds (GV) supporting technical contributions to the GRIP platform, funding to IU by the Alzheimer’s Drug Discovery Foundation’s Diagnostics Accelerator (ADDF), and he has participated in Scientific Advisory Boards (Bayer Oncology, Eisai, Novo Nordisk, and Siemens Medical Solutions USA, Inc) and an Observational Study Monitoring Board (MESA, NIH NHLBI), as well as External Advisory Committees for multiple NIA grants. He also serves as Editor-in-Chief of Brain Imaging and Behavior, a Springer-Nature Journal.

The other authors state they have no conflicts of interest (including competing interest, personal financial interests, funding conflicts, employment conflicts, or other competing interests that would inappropriately affect the integrity of the research reported or objectivity of the review of the article by the reviewer or Editor).

Funding Information

This project was funded by the Indiana State Department of Health, the Indiana University Department of Physical Medicine & Rehabilitation, and grants from the National Institute on Disability, Independent Living, and Rehabilitation Research (Indiana TBI Model Systems grants 90DP0036, 90DPTB0002, 90DPTB0022). NIDILRR is a Center within the Administration for Community Living (ACL), Department of Health and Human Services (HHS). The contents of this publication do not necessarily represent the policy of NIDILRR, ACL, HHS, and you should not assume endorsement by the Federal Government.

This research was also supported in part by the Indiana Genomics Initiative, and by Lilly Endowment, Inc., through its support for the Indiana University Pervasive Technology Institute.

Supplemental Material

Abbreviations

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