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The functional roles of IGF-1 variants in the susceptibility and clinical outcomes of mild traumatic brain injury

Journal of Biomedical Science volume 26, Article number: 94 (2019) Cite this article

Abstract

Background

Insulin-like growth factor 1 (IGF-1) is an important pleiotropic hormone that exerts neuroprotective and neuroreparative effects after a brain injury. However, the roles of IGF-1 variants in mild traumatic brain injury (mTBI) are not yet fully understood. This study attempted to elucidate the effects of IGF-1 variants on the risk and neuropsychiatric outcomes of mTBI.

Methods

Based on 176 recruited mTBI patients and 1517 control subjects from the Taiwan Biobank project, we first compared the genotypic distributions of IGF-1 variants between the two groups. Then, we analyzed associations of IGF-1 variants with neuropsychiatric symptoms after mTBI, including anxiety, depression, dizziness, and sleep disturbances. Functional annotation of IGF-1 variants was also performed through bioinformatics databases.

Results

The minor allele of rs7136446 was over-represented in mTBI patients compared to community-based control subjects. Patients carrying minor alleles of rs7136446 and rs972936 showed more dizziness and multiple neuropsychiatric symptoms after brain injury.

Conclusions

IGF-1 variants were associated with the risk and neuropsychiatric symptoms of mTBI. The findings highlight the important role of IGF-1 in the susceptibility and clinical outcomes of mTBI.

Background

Traumatic brain injury (TBI) is a global health issue that has become significantly more prominent over the past two decades, with the prevalence rate increasing by 8.4% between 1990 and 2016 [1]. TBI is defined as a disruption of normal brain function caused by an external mechanical force impacting the head [2]. The leading causes of TBI include falls, being struck by or striking an object, and traffic accidents [3]. Patients suffering from TBI can be divided into three categories according to the Glasgow Coma Scale (GCS). A GCS score of 3~8 is categorized as severe TBI; 9~12 is moderate and 13~15 is mild TBI (mTBI) [4]. Approximately 80~90% of TBI patients are diagnosed with mTBI, and most recover quickly without any treatment. However, a significant minority of patients experience persistent symptoms, which can affect the quality of life [5]. The most common symptoms accompanying mTBI include dizziness, depression, anxiety, and sleep disturbances [6, 7]. These symptoms negatively impact long-term outcomes of mTBI and subsequently increase social and economic burdens [8,9,10,11]. Therefore, the identification of crucial factors that influence risk and prognosis of mTBI is urgently needed.

Genetic variations are among the most important determinants of the pathophysiology of brain injury. Dretsch et al. reported that soldiers carrying the brain-derived neurotrophic factor (BDNF) Met/Met genotype had a higher incidence of mTBI compared to non-Met/Met carriers [12]. In a prospective cohort study, interleukin-6 receptor (IL-6R) and apolipoprotein E (APOE) variants were significantly associated with the risk of concussion among 1056 college athletes [13]. A relationship between TAU variants and concussion history was also reported in rugby union players [14]. In addition, the effects of genetic variation on the prognosis and clinical outcomes of mTBI were widely described [15, 16].

Insulin-like growth factor 1 (IGF-1) is an important pleiotropic hormone that is involved in various physiological functions. IGF-1 exhibits both neuroprotective and neuroreparative effects following brain injury [17, 18]. Decreased serum IGF-1 levels were reported in TBI patients [19, 20], and in animal studies, circulating IGF-1 was correlated with brain injury-induced cognitive dysfunction and anxiety behaviors [21, 22]. Furthermore, changes in IGF-1 expression in local brain regions were reported during the acute phase following brain injury [23,24,25]. Thus, accumulating evidence suggests that IGF-1 plays important roles in the pathophysiology and recovery of brain injury. However, the roles of IGF-1 variants in mTBI patients have not yet been investigated.

This clinical study attempted to elucidate the effects of IGF-1 variants on the susceptibility and neuropsychiatric symptoms of mTBI. Based on 176 recruited mTBI patients and 1517 controls from the Taiwan Biobank (TWB) project [26], we compared the genotypic distributions of selected IGF-1 single nucleotide polymorphisms (SNPs) in mTBI patients and controls. Then, we analyzed associations of these SNPs with the four most common neuropsychiatric symptoms accompanying mTBI: anxiety, depression, dizziness, and sleep disturbances. Further analyses were conducted to explore the SNPs-sex interaction effects on these neuropsychiatric symptoms. In addition, we evaluated the expression levels of IGF-1 across brain regions, cis-expression quantitative trait loci (cis-eQTL) and potential functions of the identified SNPs using bioinformatics databases.

Methods

Participant recruitment

Patients diagnosed with mTBI in an emergency department (ED) were recruited from three Taipei Medical University (TMU)-affiliated hospitals, including TMU Hospital, Wan Fang Hospital, and Shuang Ho Hospital. The inclusion criteria were that mTBI patients be aged at least 20 years, had an accelerated or decelerated closed injury to the head, and presented to the ED within 6 h of onset of symptoms. The exclusion criteria were as follows: (a) a history of significant ear surgery; (b) a penetrating head injury; (c) pregnancy; (d) a history of dementia or mental disorder; (e) a uremia, liver cirrhosis, heart failure, pulmonary edema, coagulopathy or renal dysfunction; (f) ischemic or hemorrhagic stroke; (g) with an in vivo magnetic implant or pacemaker; and (h) the patient had either died or had already received cardiopulmonary resuscitation before arrival at the ED. Patients with a brain injury caused by abuse or assault were also excluded from the analysis. In addition, the 1517 community-based subjects from the Taiwan Biobank (TWB) were used as our controls. The TWB aims to build a nationwide research database by creating large-scale community-based and hospital-based cohorts in the Taiwanese population [26].

Study procedures

The mTBI patients were assessed by an emergency medicine specialist. Blood samples and self-reported questionnaires were collected from each patient by a well-trained study nurse in the first week after the mTBI. For the TWB controls, summarized genotype frequency data for each SNP using next-generation sequencing are accessible through Taiwan View (https://taiwanview.twbiobank.org.tw/index). This protocol was approved by the TMU-Joint Institutional Review Board (TMU-JIRB) (nos.: P980803 and 201003008), and written informed consent was received from each mTBI patient.

Self-reported questionnaires

Beck anxiety inventory (BAI)

The BAI is a 21-item self-reported questionnaire that measures the severity of anxiety symptoms. Each item is rated on a 4-point scale from 0 (not at all) to 3 (severely). The total score ranges 0~63, with 0~7 indicating none or minimal, 8~15 mild, 16~25 moderate, and 26~63 severe anxiety. A cutoff score of the BAI of > 7 was used to indicate anxiety symptoms [27].

Beck depression inventory (BDI)

The BDI is a 21-item self-reported questionnaire that evaluates the cognitive, behavioral, and physiological symptoms associated with depression. Each item is rated on a 4-point scale from 0 (not depressed) to 3 (severely depressed). The total score ranges 0~63, with 0~9 indicating normal, 10~18 mild, 19~29 moderate, and 30~63 severe depression. A cutoff score of the BDI of > 9 was used to indicate the presence of depressive symptoms [28].

Dizziness handicap inventory (DHI)

The DHI is a 25-item self-reported questionnaire to assess the impacts of dizziness on a subject’s quality of life. The DHI has three sub-domains representing functional, emotional, and physical aspects. Each item has three response levels that contribute to the total score which ranges 0~100. A total score of 0~30 indicates a mild, 31~60 a moderate, and 61~100 a severe handicap. A cutoff score of the DHI of > 30 was used to define dizziness-related impairment [29].

Pittsburgh sleep quality index (PSQI)

The PSQI is a 19-item self-reported questionnaire that consists of seven domains: sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction. Each domain is scored from 0 (no difficulty) to 3 (severe difficulty). The total score ranges 0~21, with a higher score indicating worse sleep quality. A cutoff score of the PSQI of > 8 was chosen to indicate sleep problems [30, 31].

Assay of IGF-1

Blood samples were collected from mTBI patients. A radioimmunoassay (RIA) with an IGF-1-RIA-CT (KIP1588) Kit (DIAsource, ImmunoAssays SA, Nivelles, Belgium) was used to verify serum IGF-1 levels of patients in the first week following their brain injury [32].

DNA extraction

DNA was extracted from peripheral blood samples of recruited mTBI patients. Blood cells were first treated with 0.5% sodium dodecyl sulfate (SDS) lysis buffer, and then a proteinase K solution (1 mg/mL) was applied for 4 h at 60 °C to digest nuclear proteins. Total DNA was harvested using a Gentra extraction kit (Qiagen, Valencia, CA, USA) followed by 70% alcohol precipitation.

Genotyping of IGF-1

Five tagging SNPs of IGF-1 with a minimum allele frequency (MAF) of > 10% were selected from the HapMap Han Chinese database (http://www.hapmap.org/) (Fig. 1). Characteristics of these SNPs are shown in Additional file 1: Table S1. The IGF-1 SNPs were genotyped using a TaqMan Allelic Discrimination Assay (Applied Biosystems, Foster City, CA, USA). A polymerase chain reaction (PCR) used a 96-well microplate with an ABI 9700 Thermal Cycler (Applied Biosystems). Thermal cycle conditions of the PCR were as follows: denaturing at 95 °C for 10 min, followed by 40 cycles of denaturing at 95 °C for 15 s, and annealing and extension at 60 °C for 1 min. StepOne software (vers. 2.2.2, Applied Biosystems) was used to detect and analyze the fluorescence intensity.

Fig. 1
figure 1

Graphical overview of the genotyped IGF-1 gene

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Functional annotation data query

We queried the tissue-specific expression quantitative trait loci (eQTL) from the GTEx Portal (http://www.gtexportal.org/home/) to evaluate correlations between the SNPs and gene expression profiles [33]. To further assess possible functions of SNPs, bioinformatics databases, including dbSNP, HaploReg V4.1, and RegulomeDB were used to search for the identified SNPs [34,35,36].

Claims data from longitudinal health insurance database 2005 (LHID2005)

The data were retrieved from LHID2005 to evaluate the consequent risk of neuropsychiatric symptoms after mTBI in the Taiwanese population. The LHID2005 is a subset that contains one million beneficiaries of the National Health Insurance (NHI) program randomly sampled from the NHI Research Database (NHIRD) in 2005. The mTBI cohort included adults who had a newly diagnosed mTBI identified by ICD-9-CM codes: “skull fracture” (800.0, 800.5, 801.0, 801.5, 803.0, 803.5, 804.0, 804.5), “concussion” (850.0, 850.1, 850.5, 850.9), “intracranial injury of unspecified nature” (854.0), and “head injury, unspecified” (959.01) during 2010. An age- and sex-matched non-mTBI cohort was randomly selected from the remaining participants of the LHID2005. The date of initial diagnosis with mTBI was assigned as the index date. A new diagnosis of anxiety (ICD-9-CM 300), depression (ICD-9-CM 296.*, 300.4, 311), dizziness (ICD-9-CM 780.4), and sleep disorders (ICD-9-CM 780.51, 780.53, 780.57, 307.4, 327.23) during the year following index date were recorded. This protocol was approved by the TMU-JIRB (no.: 201309033).

RNA sequencing (RNA-seq) data from aging, dementia and TBI study

The available datasets from Aging, Dementia and TBI Study were used to assess the correlation between brain IGF-1 expression levels and Alzheimer’s disease (AD) among individuals with mild-moderate TBI exposure. The Aging, Dementia and TBI study includes 377 autopsy samples collected from the temporal neocortex (TCx), parietal neocortex (PCx), parietal white matter (FWM), and hippocampus (HIP) of 107 aged brain donors from the Adult Changes in Thought (ACT) study [37]. The RNA integrity number (RIN) corrected, TbT-normalized RNA-seq data was downloaded through the Gene Expression Omnibus (GEO) database (GSE104687). The de-identified clinical metadata was obtained from the Aging, Dementia and TBI Study website (http://aging.brain-map.org/).

Statistical analysis

R 3.2.0 (http://www.r-project.org/; http://cran.r-project.org/) was used for all statistical analyses. Continuous normally and non-normally distributed variables were respectively presented as the mean ± standard deviation (SD) and median [interquartile range]. Categorical variables were presented as the number (%). The genotypic distributions were assessed for Hardy-Weinberg equilibrium (HWE) using a χ2 goodness-of-fit test. Cochran-Armitage test was used to evaluate differences in genotypic distributions between mTBI patients and controls. A logistic regression under the additive model was performed to estimate associations of SNPs with each phenotype. Age and sex were included as covariates in the regression model. Multiple testing correction was carried out using the Bonferroni correction, and p-values of < 0.05 were considered statistically significant. The SNP-sex interaction analysis was performed by adding SNP-by-sex interaction term in the regression model. A chi-square test and two-sample t-test were used respectively to assess the data from LHID2005 and Aging, Dementia and TBI Study.

Results

Participant characteristics

In a nationwide population-based analysis, mTBI cohort showed a higher proportion of developing neuropsychiatric symptoms than non-mTBI cohort during the one-year follow-up period (Additional file 1: Figure S1). To elucidate whether the IGF-1 variation is a potential contributing factor, 176 mTBI patients were recruited. Demographic characteristics were summarized in Table 1. The mean age was 39 (range 20~83) years. Females accounted for 68.8% of the total recruited patients. The main causes of injury were transportation accidents (54.5%) and falls (30.1%). Meanwhile, 1517 subjects from a community-based cohort of the TWB were used as controls in our study. The mean age of controls was 49.5 (range 30~70) years. Females accounted for 49.7%. Most control subjects were recruited from northern and southern Taiwan (Northern: 38.2%, Central: 18.0%, Southern: 42.5%, and Eastern: 1.3%). In the control group, the genotype frequency of each SNP was obtained for analysis.

Table 1 Basal characteristics of patients with mild traumatic brain injury (mTBI)
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Associations between IGF-1 variants and mTBI susceptibility

We first compared the genotypic distribution of each SNP between mTBI patients and TWB controls. As shown in Table 2, a significant difference in the distribution of rs7136446 was found between the two groups even after multiple testing correction (p = 0.008, Bonferroni = 0.040). The C allele of rs7136446 was over-represented in the case group compared to the community-based control group. In addition, rs972936 showed a borderline level of statistical significance (p = 0.053, Bonferroni = 0.265). A higher proportion of the T allele of rs972936 was found in mTBI cases compared to the controls.

Table 2 Association of insulin-like growth factor 1 (IGF-1) variants with susceptibility to mild traumatic brain injury (mTBI)
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Associations between IGF-1 variants and emotional symptoms following mTBI

BAI and BDI scores were respectively used to evaluate anxiety and depressive symptoms. As shown in Table 3, rs972936 showed an association with the BAI score. Patients carrying the T allele had a higher BAI score than that with the C allele. However, significance was not retained after multiple testing correction (p = 0.049, Bonferroni = 0.980). In our analysis, there was no significant association between IGF-1 variants and BDI scores (Table 4).

Table 3 Beck Anxiety Inventory (BAI) scores among mild traumatic brain injury (mTBI) patients stratified by different insulin-like growth factor 1 (IGF-1) genotypes
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Table 4 Beck Depression Inventory (BDI) scores among mild traumatic brain injury (mTBI) patients stratified by different insulin-like growth factor 1 (IGF-1) genotypes
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Associations between IGF-1 variants and dizziness following mTBI

The DHI score was used to interpret the severity of dizziness symptoms, as shown in Table 5. Patients carrying the C allele of rs7136446 had a higher risk of dizziness symptoms than patients carrying the T allele. The statistical significance remained consistent after multiple testing correction (p = 0.0004, Bonferroni = 0.008). In addition, patients carrying the T allele of rs972936 also revealed a higher risk of dizziness than those carrying the C allele (p = 0.008, Bonferroni = 0.160).

Table 5 Dizziness Handicap Inventory (DHI) scores among mild traumatic brain injury (mTBI) patients stratified by different insulin-like growth factor 1 (IGF-1) genotypes
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Associations between IGF-1 variants and sleep disturbances following mTBI

PHQI total score was used to evaluate the sleep problems of patients who had experienced an mTBI. In the present analysis, we found no statistically significant association between IGF-1 variants and the PHQI total score (Table 6).

Table 6 Pittsburgh Sleep Quality Index (PSQI) scores among mild traumatic brain injury (mTBI) patients stratified by different insulin-like growth factor 1 (IGF-1) genotypes
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The sex-specific effects of IGF-1 variants following mTBI

One hundred seventy-six mTBI patients were further stratified to evaluate the sex-specific effects of IGF-1 variants on neuropsychiatric symptoms (Additional file 1: Table S2). Patients carrying the minor alleles of rs972936 and rs7136446 showed a higher DHI score in males and females, respectively (rs972936 in males: p = 0.037, Bonferroni = 0.740; rs7136446 in females: p = 0.001, Bonferroni = 0.020) (Additional file 1: Table S5). Meanwhile, patients carrying the minor allele of rs7136446 showed a higher PSQI score in females, but not in males (rs7136446 in females: p = 0.035, Bonferroni = 0.700) (Additional file 1: Table S6). To confirm these findings, the interaction analyses between SNPs and sex were performed to elucidate whether the impacts of rs972936 and rs7136446 were significantly different between males and females. However, we found no significant interaction effects of these SNPs with sex on the neuropsychiatric symptoms following mTBI (Additional file 1: Table S7).

Associations between IGF-1 variants and multiple neuropsychiatric symptoms

Having multiple neuropsychiatric symptoms would make recovery more difficult for mTBI patients. Therefore, we compared the genotypic distributions between patients with all four neuropsychiatric symptoms (n = 18) and patients without any symptoms (n = 61). The characteristics are shown in Table 7. The genotypic distributions of both rs7136446 and rs972936 showed a difference between the two groups. The C allele of rs7136446 and the T allele of rs972936 were over-represented in the group with multiple neuropsychiatric symptoms (rs7136446: p = 0.005, Bonferroni = 0.025; rs972936: p = 0.041, Bonferroni = 0.205) (Table 8).

Table 7 Basal characteristics of mild traumatic brain injury (mTBI) patients stratified by the presence or absence of multiple neuropsychiatric symptoms
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Table 8 Multiple neuropsychiatric symptoms of mild traumatic brain injury (mTBI) patients stratified by different insulin-like growth factor-1 (IGF-1) genotypes
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eQTLs and functional annotation

The eQTLs for the two most significant SNPs were accessed from the GTEx Portal. After the multiple testing correction, rs731446 showed an ability to alter the expression of WASH complex subunit 3 (WASHC3) in several tissues. rs972936 can alter the expression of poly (ADP-ribose) polymerase 1 (PARP-1) - binding protein (PARPBP) and WASHC3 across different types of tissues (Additional file 1: Table S8). Results of functional annotations using HaploReg V4.1 and Regulome DB are shown in Additional file 1: Table S9.

Association between IGF-1 expression and Alzheimer’s disease (AD) after mTBI

We further search the corrections of rs972936 and rs7136446 with IGF-1 expressions across 13 brain tissue types through GTEx with a p-value less than 0.05. As shown in Additional file 1: Table S10, the risk alleles of both SNPs in the present study showed lower levels of IGF-1 expression in the hippocampus, an important brain region for learning and memory. We therefore evaluate the effects of hippocampal IGF-1 expression on AD among individuals with TBI exposure. The RNA-seq data of 107 older brain donors from Aging, Dementia, and TBI Study were retrieved. In total, 53 donors had a history of mild-moderate TBI with loss of consciousness. Of the 53 donors, 14 died with a clinical diagnosis of AD and 26 died with no dementia (Table 9). The levels of IGF-1 expression were significantly different between the two groups in the hippocampus (p = 0.008) and parietal cortex (p = 0.019) (Table 10). In the donors without TBI exposure, we find no statistically significant differences of IGF-1 expression levels between two groups (Additional file 1: Table S11).

Table 9 Characteristics of brain donors with a history of traumatic brain injury (TBI) exposure stratified by the presence or absence of Alzheimer’s disease (AD) from GEO database (GSE104687)
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Table 10 Association between insulin-like growth factor 1 (IGF-1) expression levels and Alzheimer’s disease (AD) in brain donors with a history of traumatic brain injury (TBI)
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Discussion

The IGF-1 signaling pathway is involved in numerous brain diseases [38]. IGF-1 binding to its receptor (IGF-1R) initiates intracellular signaling that subsequently activates two major pathways: phosphatidyl-inositol 3-kinase/ protein kinase B (PI3K/AKT) and mitogen-activated protein kinase/ extracellular signal-regulated kinase (MAPK/ERK). Activation of these pathways modulates various brain functions, such as glucose utilization, neurogenesis, synaptic plasticity, and angiogenesis [18]. These functions may then influence primary responses of an individual’s brain after being struck by an external force as well as the secondary damage following a brain injury.

In this study, we found that IGF-1 variants (rs7136446 and rs972936) were associated with the susceptibility and neuropsychiatric symptoms of mTBI. The minor allele of rs7136446 was over-represented in mTBI patients compared to a community-based control population. In addition, patients carrying the minor alleles of these two SNPs showed more dizziness and multiple neuropsychiatric symptoms in the first week after brain injury. These early neuropsychiatric symptoms largely reflect a disturbance of brain functions and could also predict long-term consequences following mTBI [10, 39,40,41]. Our findings support the idea that IGF-1 variants are important factors for the pathophysiology of mTBI.

In addition, the minor alleles of rs7136446 and rs972936 showed lower IGF-1 expressions in the hippocampus. Meanwhile, brain donors diagnosed with AD also showed lower levels of hippocampal IGF-1 expression than those with no dementia among individuals with TBI exposure. These findings provide additional clues that IGF-1 variation may not only affects early neuropsychiatric symptoms but also neurodegeneration after brain injury. The regulatory effects of IGF-1 signaling on amyloid-β (Aβ) deposition and tau phosphorylation were reported in previous animal studies [42, 43]. In addition, hypoxia repressed the activities of IGF-1 signaling in zebrafish embryos [44, 45]. IGF-1 signaling in human astrocytes displayed the capacity to protect neurons from oxidative stress [46]. These pathophysiological processes had also been reported as the possible link between TBI and AD [47].

In the subgroup analyses by gender, the results indicated that rs972936 and rs7136446 may exert more influence on males and females, respectively. However, in the SNPs-sex interaction analyses, we find no significant interaction between each SNP and gender. It seems that gender may not the strong co-regulator participated in the correlations between IGF-1 variants and early neuropsychiatric symptoms after mTBI. However, previous studies revealed that the effects of IGF-1 variants on the risk of some cancers were modified by sex or menopausal status [48, 49]. In addition, the cross-interactions between estrogen and IGF-1 signaling were widely reported in the brain [50, 51]. The estrogen and IGF-1 co-regulated the PI3K/AKT and MAPK/ERK pathways which stimulate the adult neurogenesis and promote the neuroprotection in the brain regions [52]. Therefore, even though we didn’t find the strong gender-specific effects of SNPs chosen for the present study, the associations between sex hormones, IGF-1, and mTBI are worth further investigation.

SNPs rs7136446 and rs972936 have been studied in several human traits and diseases. Huuskonen et al. found that rs7136446 was associated with maximal force production and body composition [53], while Aberg et al. demonstrated that patients carrying the major allele of rs7136446 showed favorable functional outcomes after ischemic stroke [54]. In addition, the rs972936 SNP was significantly associated with susceptibility to Alzheimer’s disease and Parkinson’s disease in a Han Chinese population [55, 56]. Although both rs7136446 and rs972936 are located in intronic regions of IGF-1, these reports provide evidence that the alleles indeed influence human traits and diseases. Functional annotations using bioinformatics databases revealed that rs7136446 and rs972936 can alter the expression level of WASHC3, also known as Coiled-coil domain-containing protein 53 (CCDC53). Interestingly, a previous animal study showed that Ccdc53 expression exhibited a maximum negative fold-change (− 2.42) in mTBI induced hippocampal gene expression profiles compared to a sham-operated group [57, 58]. Moreover, these SNPs are located in a region with known histone modifications, DNAse, and regulatory motifs. Furthermore, evidence from recent studies supports the involvement of epigenetic modulations following brain injury which can further affect the recovery after mTBI [59, 60]. Taken together, these studies provide tantalizing clues as to the possible mechanisms that could underlie the connection between the identified SNPs and mTBI pathophysiology.

There are several limitations to our study. First, we used 1517 community-based control subjects from the TWB project as our comparison group. Summary statistics of the SNPs were accessed through the Taiwan View website. However, previous injury histories of these subjects were lacking, so we cannot exclude the possibility that some subjects may have experienced an mTBI in the past. Nevertheless, the present findings still offer important information about the effects of IGF-1 on the risk of mTBI. Well-designed prospective studies that include large sample sizes of both mTBI cases and controls are needed to fully elucidate corrections between IGF-1 variants and mTBI. Second, we used a candidate gene approach to evaluate the roles of IGF-1 variants in mTBI outcomes. However, the influence of variants in other unexamined genes cannot be ruled out. Since the pathophysiology of mTBI depends on complex regulation by multiple signaling pathways, a genome-wide approach may yield further insights into genetic variations and prognoses of mTBI.

Conclusions

In this study, we found associations of IGF-1 variants with susceptibility and neuropsychiatric outcomes of mTBI, highlighting the important roles of IGF-1 in the pathophysiology of mTBI. Further studies focusing on the IGF-1 signaling pathway are needed to elucidate the mechanism underlying this association. The role of epigenetic modulations in the risk of mTBI is especially important for further investigation.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

AD:

Alzheimer’s disease

BAI:

Beck Anxiety Inventory

BDI:

Beck Depression Inventory

CCDC53:

Coiled-coil domain-containing 53

DHI:

Dizziness Handicap Inventory

ED:

Emergency department

eQTL:

Expression quantitative trait loci

GCS:

Glasgow Coma Scale

GTEx:

Genotype-tissue expression

IGF-1:

Insulin-like growth factor 1

LHID2005:

Longitudinal Health Insurance Database 2005

MAPK/ERK:

Mitogen-activated protein kinase/ extracellular signal-regulated kinase

mTBI:

Mild traumatic brain injury

PI3K/AKT:

Phosphatidyl-inositol 3-kinase/ protein kinase B

PSQI:

Pittsburgh Sleep Quality Index

SNP:

Single-nucleotide polymorphism

TBI:

Traumatic brain injury

TWB:

Taiwan Biobank

WASHC3:

WASH complex subunit 3

References

  1. James SL, Theadom A, Ellenbogen RG, Bannick MS, Montjoy-Venning W, Lucchesi LR, et al. Global, regional, and national burden of traumatic brain injury and spinal cord injury, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2019;18(1):56–87.

  2. McKee AC, Daneshvar DH. The neuropathology of traumatic brain injury. Handb Clin Neurol. 2015;127:45–66.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Asemota AO, George BP, Bowman SM, Haider AH, Schneider EB. Causes and trends in traumatic brain injury for United States adolescents. J Neurotrauma. 2013;30(2):67–75.

    Article  PubMed  Google Scholar 

  4. Teasdale G, Jennett B. Assessment of coma and impaired consciousness. A practical scale. Lancet (London, England). 1974;2(7872):81–4.

    Article  CAS  Google Scholar 

  5. Blennow K, Brody DL, Kochanek PM, Levin H, McKee A, Ribbers GM, et al. Traumatic brain injuries. Nat Rev Dis Prim. 2016;2:16084.

    Article  PubMed  Google Scholar 

  6. Prince C, Bruhns ME. Evaluation and treatment of mild traumatic brain injury: the role of neuropsychology. Brain Sci. 2017;7(8):105.

    Article  PubMed Central  Google Scholar 

  7. Eme R. Neurobehavioral outcomes of mild traumatic brain injury: a mini review. Brain Sci. 2017;7(5):46.

    Article  PubMed Central  Google Scholar 

  8. Iverson GL, Gardner AJ, Terry DP, Ponsford JL, Sills AK, Broshek DK, et al. Predictors of clinical recovery from concussion: a systematic review. Br J Sports Med. 2017;51(12):941–8.

    Article  PubMed  Google Scholar 

  9. Chan LG, Feinstein A. Persistent sleep disturbances independently predict poorer functional and social outcomes 1 year after mild traumatic brain injury. J Head Trauma Rehabil. 2015;30(6):E67–75.

    Article  PubMed  Google Scholar 

  10. Chamelian L, Feinstein A. Outcome after mild to moderate traumatic brain injury: the role of dizziness. Arch Phys Med Rehabil. 2004;85(10):1662–6.

    Article  PubMed  Google Scholar 

  11. van der Naalt J, Timmerman ME, de Koning ME, van der Horn HJ, Scheenen ME, Jacobs B, et al. Early predictors of outcome after mild traumatic brain injury (UPFRONT): an observational cohort study. Lancet Neurol. 2017;16(7):532–40.

    Article  PubMed  Google Scholar 

  12. Dretsch MN, Williams K, Emmerich T, Crynen G, Ait-Ghezala G, Chaytow H, et al. Brain-derived neurotropic factor polymorphisms, traumatic stress, mild traumatic brain injury, and combat exposure contribute to postdeployment traumatic stress. Brain Behav. 2016;6(1):e00392.

    Article  PubMed  Google Scholar 

  13. Terrell TR, Abramson R, Barth JT, Bennett E, Cantu RC, Sloane R, et al. Genetic polymorphisms associated with the risk of concussion in 1056 college athletes: a multicentre prospective cohort study. Br J Sports Med. 2018;52(3):192–8.

    Article  PubMed  Google Scholar 

  14. Abrahams S, Mc Fie S, Patricios J, Suter J, September AV, Posthumus M. Toxic TAU: the TAU gene polymorphisms associate with concussion history in rugby union players. J Sci Med Sport. 2019;22(1):22–8.

    Article  PubMed  Google Scholar 

  15. Bennett ER, Reuter-Rice K, Laskowitz DT. Frontiers in neuroscience genetic influences in traumatic brain injury. In: Laskowitz D, Grant G, editors. Translational research in traumatic brain injury. Boca Raton: CRC Press/Taylor and Francis Group (c) 2016 by Taylor & Francis Group, LLC; 2016.

    Google Scholar 

  16. Davidson J, Cusimano MD, Bendena WG. Post-traumatic brain injury: genetic susceptibility to outcome. Neuroscientist. 2015;21(4):424–41.

    Article  PubMed  CAS  Google Scholar 

  17. Madathil SK, Saatman KE. Frontiers in neuroengineering IGF-1/IGF-R signaling in traumatic brain injury: impact on cell survival, neurogenesis, and behavioral outcome. In: Kobeissy FH, editor. Brain neurotrauma: molecular, neuropsychological, and rehabilitation aspects. Boca Raton: CRC Press/Taylor & Francis (c) 2015 by Taylor & Francis Group, LLC.; 2015.

    Google Scholar 

  18. Mangiola A, Vigo V, Anile C, De Bonis P, Marziali G, Lofrese G. Role and importance of IGF-1 in traumatic brain injuries. Biomed Res Int. 2015;2015:736104.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Sanus GZ, Tanriverdi T, Coskun A, Hanimoglu H, Is M, Uzan M. Cerebrospinal fluid and serum levels of insulin-like growth factor-1 and insulin-like growth factor binding protein-3 in patients with severe head injury. Ulus Travma Acil Cerrahi Dergisi = Turkish J Trauma Emerg Surg. 2007;13(4):281–7.

    Google Scholar 

  20. Wagner J, Dusick JR, McArthur DL, Cohan P, Wang C, Swerdloff R, et al. Acute gonadotroph and somatotroph hormonal suppression after traumatic brain injury. J Neurotrauma. 2010;27(6):1007–19.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Ozdemir D, Baykara B, Aksu I, Kiray M, Sisman AR, Cetin F, et al. Relationship between circulating IGF-1 levels and traumatic brain injury-induced hippocampal damage and cognitive dysfunction in immature rats. Neurosci Lett. 2012;507(1):84–9.

    Article  PubMed  CAS  Google Scholar 

  22. Baykara B, Aksu I, Buyuk E, Kiray M, Sisman AR, Baykara B, et al. Progesterone treatment decreases traumatic brain injury induced anxiety and is correlated with increased serum IGF-1 levels; prefrontal cortex, amygdala, hippocampus neuron density; and reduced serum corticosterone levels in immature rats. Biotech Histochem. 2013;88(5):250–7.

    Article  PubMed  CAS  Google Scholar 

  23. Madathil SK, Evans HN, Saatman KE. Temporal and regional changes in IGF-1/IGF-1R signaling in the mouse brain after traumatic brain injury. J Neurotrauma. 2010;27(1):95–107.

    Article  PubMed  Google Scholar 

  24. Schober ME, Block B, Beachy JC, Statler KD, Giza CC, Lane RH. Early and sustained increase in the expression of hippocampal IGF-1, but not EPO, in a developmental rodent model of traumatic brain injury. J Neurotrauma. 2010;27(11):2011–20.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Walter HJ, Berry M, Hill DJ, Logan A. Spatial and temporal changes in the insulin-like growth factor (IGF) axis indicate autocrine/paracrine actions of IGF-I within wounds of the rat brain. Endocrinology. 1997;138(7):3024–34.

    Article  PubMed  CAS  Google Scholar 

  26. Chen CH, Yang JH, Chiang CWK, Hsiung CN, Wu PE, Chang LC, et al. Population structure of Han Chinese in the modern Taiwanese population based on 10,000 participants in the Taiwan biobank project. Hum Mol Genet. 2016;25(24):5321–31.

    PubMed  PubMed Central  CAS  Google Scholar 

  27. Beck AT, Epstein N, Brown G, Steer RA. An inventory for measuring clinical anxiety: psychometric properties. J Consult Clin Psychol. 1988;56(6):893–7.

    Article  PubMed  CAS  Google Scholar 

  28. Beck AT, Ward CH, Mendelson M, Mock J, Erbaugh J. An inventory for measuring depression. Arch Gen Psychiatry. 1961;4:561–71.

    Article  PubMed  CAS  Google Scholar 

  29. Jacobson GP, Newman CW. The development of the dizziness handicap inventory. Arch Otolaryngol Head Neck Surg. 1990;116(4):424–7.

    Article  PubMed  CAS  Google Scholar 

  30. Buysse DJ, Reynolds CF 3rd, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh sleep quality index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193–213.

    Article  PubMed  CAS  Google Scholar 

  31. Fictenberg NL, Putnam SH, Mann NR, Zafonte RD, Millard AE. Insomnia screening in postacute traumatic brain injury: utility and validity of the Pittsburgh sleep quality index. Am J Phys Med Rehabil. 2001;80(5):339–45.

    Article  PubMed  CAS  Google Scholar 

  32. Sung CW, Chen KY, Chiang YH, Chiu WT, Ou JC, Lee HC, et al. Heart rate variability and serum level of insulin-like growth factor-1 are correlated with symptoms of emotional disorders in patients suffering a mild traumatic brain injury. Clin Neurophysiol. 2016;127(2):1629–38.

    Article  PubMed  Google Scholar 

  33. Lonsdale J, Thomas J, Salvatore M, Phillips R, Lo E, Shad S, et al. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013;45(6):580–5.

  34. Ward LD, Kellis M. HaploReg v4: systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease. Nucleic Acids Res. 2016;44(D1):D877–81.

    Article  PubMed  CAS  Google Scholar 

  35. Boyle AP, Hong EL, Hariharan M, Cheng Y, Schaub MA, Kasowski M, et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 2012;22(9):1790–7.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001;29(1):308–11.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Miller JA, Guillozet-Bongaarts A, Gibbons LE, Postupna N, Renz A, Beller AE, et al. Neuropathological and transcriptomic characteristics of the aged brain. eLife. 2017;6:e31126.

  38. Trejo JL, Carro E, Garcia-Galloway E, Torres-Aleman I. Role of insulin-like growth factor I signaling in neurodegenerative diseases. J Mol Med (Berlin, Germany). 2004;82(3):156–62.

    Article  CAS  Google Scholar 

  39. Lau BC, Kontos AP, Collins MW, Mucha A, Lovell MR. Which on-field signs/symptoms predict protracted recovery from sport-related concussion among high school football players? Am J Sports Med. 2011;39(11):2311–8.

    Article  PubMed  Google Scholar 

  40. Dischinger PC, Ryb GE, Kufera JA, Auman KM. Early predictors of postconcussive syndrome in a population of trauma patients with mild traumatic brain injury. J Trauma. 2009;66(2):289–96 discussion 96-7.

    Article  PubMed  Google Scholar 

  41. Rao V, McCann U, Han D, Bergey A, Smith MT. Does acute TBI-related sleep disturbance predict subsequent neuropsychiatric disturbances? Brain Inj. 2014;28(1):20–6.

    Article  PubMed  Google Scholar 

  42. Cheng CM, Tseng V, Wang J, Wang D, Matyakhina L, Bondy CA. Tau is hyperphosphorylated in the insulin-like growth factor-I null brain. Endocrinology. 2005;146(12):5086–91.

    Article  PubMed  CAS  Google Scholar 

  43. Giuffrida ML, Tomasello F, Caraci F, Chiechio S, Nicoletti F, Copani A. Beta-amyloid monomer and insulin/IGF-1 signaling in Alzheimer's disease. Mol Neurobiol. 2012;46(3):605–13.

    Article  PubMed  CAS  Google Scholar 

  44. Kamei H, Ding Y, Kajimura S, Wells M, Chiang P, Duan C. Role of IGF signaling in catch-up growth and accelerated temporal development in zebrafish embryos in response to oxygen availability. Dev (Cambridge, England). 2011;138(4):777–86.

    Article  CAS  Google Scholar 

  45. Kajimura S, Aida K, Duan C. Insulin-like growth factor-binding protein-1 (IGFBP-1) mediates hypoxia-induced embryonic growth and developmental retardation. Proc Natl Acad Sci U S A. 2005;102(4):1240–5.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Ratcliffe LE, Vazquez Villasenor I, Jennings L, Heath PR, Mortiboys H, Schwartzentruber A, et al. Loss of IGF1R in human astrocytes alters complex I activity and support for neurons. Neuroscience. 2018;390:46–59.

    Article  PubMed  CAS  Google Scholar 

  47. Ramos-Cejudo J, Wisniewski T, Marmar C, Zetterberg H, Blennow K, de Leon MJ, et al. Traumatic brain injury and Alzheimer's disease: the cerebrovascular link. EBioMedicine. 2018;28:21–30.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Shi J, Aronson KJ, Grundy A, Kobayashi LC, Burstyn I, Schuetz JM, et al. Polymorphisms of insulin-like growth factor 1 pathway genes and breast Cancer risk. Front Oncol. 2016;6:136.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Ong J, Salomon J, te Morsche RH, Roelofs HM, Witteman BJ, Dura P, et al. Polymorphisms in the insulin-like growth factor axis are associated with gastrointestinal cancer. PLoS One. 2014;9(3):e90916.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Huffman J, Hoffmann C, Taylor GT. Integrating insulin-like growth factor 1 and sex hormones into neuroprotection: implications for diabetes. World J Diabetes. 2017;8(2):45–55.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Garcia-Segura LM, Arevalo MA, Azcoitia I. Interactions of estradiol and insulin-like growth factor-I signalling in the nervous system: new advances. Prog Brain Res. 2010;181:251–72.

    Article  PubMed  CAS  Google Scholar 

  52. Sohrabji F. Estrogen-IGF-1 interactions in neuroprotection: ischemic stroke as a case study. Front Neuroendocrinol. 2015;36:1–14.

    Article  PubMed  CAS  Google Scholar 

  53. Huuskonen A, Lappalainen J, Oksala N, Santtila M, Hakkinen K, Kyrolainen H, et al. Common genetic variation in the IGF1 associates with maximal force output. Med Sci Sports Exerc. 2011;43(12):2368–74.

    Article  PubMed  CAS  Google Scholar 

  54. Aberg ND, Olsson S, Aberg D, Jood K, Stanne TM, Nilsson M, et al. Genetic variation at the IGF1 locus shows association with post-stroke outcome and to circulating IGF1. Eur J Endocrinol. 2013;169(6):759–65.

    Article  PubMed  CAS  Google Scholar 

  55. Wang W, Yu JT, Tan L, Liu QY, Wang HF, Ma XY. Insulin-like growth factor 1 (IGF1) polymorphism is associated with Alzheimer's disease in Han Chinese. Neurosci Lett. 2012;531(1):20–3.

    Article  PubMed  CAS  Google Scholar 

  56. Xiao Y, Cen L, Mo M, Chen X, Huang S, Wei L, et al. Association of IGF1 gene polymorphism with Parkinson’s disease in a Han Chinese population. J Gene Med. 2017;19(4):e2949.

    Article  CAS  Google Scholar 

  57. Tweedie D, Rachmany L, Rubovitch V, Zhang Y, Becker KG, Perez E, et al. Changes in mouse cognition and hippocampal gene expression observed in a mild physical- and blast-traumatic brain injury. Neurobiol Dis. 2013;54:1–11.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Tweedie D, Rachmany L, Kim DS, Rubovitch V, Lehrmann E, Zhang Y, et al. Mild traumatic brain injury-induced hippocampal gene expressions: the identification of target cellular processes for drug development. J Neurosci Methods. 2016;272:4–18.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Wong VS, Langley B. Epigenetic changes following traumatic brain injury and their implications for outcome, recovery and therapy. Neurosci Lett. 2016;625:26–33.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Schober ME, Ke X, Xing B, Block BP, Requena DF, McKnight R, et al. Traumatic brain injury increased IGF-1B mRNA and altered IGF-1 exon 5 and promoter region epigenetic characteristics in the rat pup hippocampus. J Neurotrauma. 2012;29(11):2075–85.

    Article  PubMed  Google Scholar 

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Acknowledgements

We are grateful to acknowledge the advice of Dr. Yun-Jin Jiang. In addition, we would like to express our gratitude to Dr. Wei-Pin Chang and Dr. Hou-Wei Chu for enhancing the quality of revised manuscript.

Funding

This work was supported by grants from Ministry of Science and Technology (MOST104–2923-B-038-004-MY2, MOST105–2628-B-038-001-MY4), Ministry of Health and Welfare (MOHW105-TDU-B-212-133018), National Health Research Institutes (08A1-MGGP08–037), and Taipei Medical University (DP2–108–21121-01-N-05-01, 106–5807–001-400).

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Authors and Affiliations

  1. Ph.D. Program for Neural Regenerative Medicine, College of Medical Science and Technology, Taipei Medical University and National Health Research Institutes, Taipei, Taiwan

    Yu-Jia Wang & Kai-Yun Chen

  2. Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, Taiwan

    Henry Sung-Ching Wong & Wei-Chiao Chang

  3. Department of Surgery, College of Medicine, Taipei Medical University, Taipei, Taiwan

    Chung-Che Wu & Yung-Hsiao Chiang

  4. Department of Neurosurgery, Taipei Medical University Hospital, Taipei, Taiwan

    Chung-Che Wu & Yung-Hsiao Chiang

  5. Institute of Injury Prevention and Control, College of Public Health and Nutrition, Taipei Medical University, Taipei, Taiwan

    Wen-Ta Chiu

  6. Department of Pharmacy, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan

    Wei-Chiao Chang

  7. Master Program for Clinical Pharmacogenomics and Pharmacoproteomics, School of Pharmacy, Taipei Medical University, Taipei, Taiwan

    Wei-Chiao Chang

  8. Department of Medical Research, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan

    Wei-Chiao Chang

  9. Pain Research Center, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan

    Wei-Chiao Chang

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  1. Yu-Jia Wang
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  2. Henry Sung-Ching Wong
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  4. Yung-Hsiao Chiang
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  5. Wen-Ta Chiu
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  7. Wei-Chiao Chang
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Contributions

YJW: study concept and design; implementation of experiments; analysis and interpretation of data; drafting of the manuscript. HSCW: analysis and interpretation of data; drafting of the manuscript. CCW, YHC, and WTC: data acquisition; drafting of the manuscript. KYC, WCC: study concept and design; drafting of the manuscript; supervising the work. All authors read and approved the final version of the manuscript.

Corresponding authors

Correspondence to Kai-Yun Chen or Wei-Chiao Chang.

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Ethics approval and consent to participate

This study was approved by the Taipei Medical University - Joint Institutional Review Board (TMU-JIRB nos.: P980803, 201003008, 201309033). Samples were collected from patients at three TMU-affiliated hospitals after they had provided written informed consent.

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The authors declare that they have no competing interests.

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Supplementary information

Additional file 1: Figure S1.

Proportion of subjects developing neuropsychiatric symptoms among mTBI and non-mTBI cohorts during the 1-year follow-up period from the index date. Table S1. The allele frequency of single nucleotide polymorphisms (SNPs) in different ethnic groups. Table S2. Basal characteristics of patients with mild traumatic brain injury (mTBI) stratified by sex. Table S3. Sex-stratified analyses for Beck Anxiety Inventory (BAI) score. Table S4. Sex-stratified analyses for Beck Depression Inventory (BDI) score. Table S5. Sex-stratified analyses for Dizziness Handicap Inventory (DHI) score. Table S6. Sex-stratified analyses for Pittsburgh Sleep Quality Index (PSQI) score. Table S7. Single nucleotide polymorphisms (SNPs)-sex interaction analyses for neuropsychiatric symptoms following mild traumatic brain injury (mTBI). Table S8. Expression quantitative trail loci (eQTL) results from Genotype-tissue expression (GTEx). Table S9. Functional annotation by HaploReg V4.1 and RegulomeDB. Table S10. Expression quantitative trait loci (eQTL) results of rs7136446 and rs972936 with insulin-like growth factor 1 (IGF-1) expressions in brain tissues from Genotype-tissue expression (GTEx). Table S11. Association between insulin-like growth factor 1 (IGF-1) expression levels and Alzheimer’s disease (AD) in brain donors with no history of traumatic brain injury (TBI).

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Wang, YJ., Wong, H.SC., Wu, CC. et al. The functional roles of IGF-1 variants in the susceptibility and clinical outcomes of mild traumatic brain injury. J Biomed Sci 26, 94 (2019). https://doi.org/10.1186/s12929-019-0587-9

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Keywords

  • Mild traumatic brain injury
  • Genetic variants
  • Insulin-like growth factor 1
  • Anxiety
  • Depression
  • Dizziness
  • Sleep disorders

Journal of Biomedical Science

ISSN: 1423-0127