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2019 New Investigator Award Competition

An Aging-Related Single-Nucleotide Polymorphism is Associated With Altered Clinical Outcomes and Distinct Inflammatory Profiles in Aged Blunt Trauma Patients

Lamparello, Ashley J.*; Namas, Rami A.*,†; Schimunek, Lukas*; Cohen, Maria; El-Dehaibi, Fayten*; Yin, Jinling*; Barclay, Derek*; Zamora, Ruben*,†; Billiar, Timothy R.*,†; Vodovotz, Yoram*,†

Author Information
doi: 10.1097/SHK.0000000000001411

Abstract

INTRODUCTION

Advanced age is independently associated with worse clinical outcomes following trauma, including multiple organ failure, longer intensive care unit (ICU) and hospital lengths of stay, and mortality (1–4). Alterations in the immune and inflammatory systems following traumatic injury detected in aged trauma patients are likely to contribute to these adverse outcomes (4). We have shown that aged blunt trauma patients exhibit a distinct pattern of circulating chemokines and cytokines when compared to young patients (5). Our results showed that these age-related inflammatory alterations are not simply a matter of suppressed inflammation but rather characterized by increased levels of two C-X-C motif chemokines (CXCL), namely CXCL10/interferon gamma-induced protein 10 (IP-10) and CXCL9/monokine induced by interferon gamma (MIG) (5).

In addition to the post-traumatic inflammatory response, genetic variability in the form of single-nucleotide polymorphisms (SNPs) is associated with clinical complications and adverse outcomes following injury (6, 7). The contribution of individual genetic determinants of aging to the adverse clinical outcomes and altered inflammation mediator networks characteristic of aged trauma patients, however, is unknown. We hypothesized that an aging-related SNP associated with longevity would stratify older trauma patients according to outcome and inflammatory profiles.

In this study, we focused on a candidate SNP, rs2075650 in an intron of the gene encoding for TOMM40 (translocase of outer mitochondrial membrane 40) on chromosome 19, which has been associated with survival in old age in genome-wide association studies (GWAS) of aging (8, 9). The major (A) allele of rs2075650 is associated with longevity while the minor (G) allele is associated with 2–4 fold higher risk of Alzheimer disease (10–12) and lowers the probability of reaching 90-year old by nearly 30% (8). We investigated the role of the minor allele of rs2075650 in relation to clinical outcomes and inflammation biomarker patterns in both young and aged patients following blunt trauma. While trauma studies vary in their definition of “aged” (13), we chose 65-year old as a cutoff based on the US Census Bureau definition of the “older population” (10), as we have done in a previous study (5). The aged cohort was compared to a cohort of young patients (18–30 years old) to determine if physiological alterations as a function of rs2075650 were already present in the young.

Our results revealed significant differences in the requirement for mechanical ventilation as well as in the inflammatory response of aged trauma patients with the AA genotype as compared with those with the AG or GG genotype. In particular, the AA genotype in the aged was associated with selective differences in the levels of a subset of immune mediators and protection from lung injury. Interestingly, these results were not seen in the young patients with AA or AG/GG genotypes, suggesting that the influence of rs2075650 may increase with advanced age.

PATIENTS AND METHODS

Subjects and blood sampling

Blunt trauma patients were enrolled in this study after University of Pittsburgh IRB approval and informed consent. Inclusion criteria were 18 years of age or older, admission to the ICU, and expectation to survive beyond the initial 24 h after injury. Exclusion criteria were isolated head injury, brain death criteria, and pregnancy. Whole blood was sampled three times within the first 24 h, starting with the initial blood draw on arrival, and then daily from day 1 to 7 after injury. Demographic and clinical data were collected from the inpatient electronic medical records and trauma registry database.

Study design

This was a retrospective study of a prospectively maintained clinical, biobank, and genomic database of 453 blunt trauma patients at a Level 1 trauma center. Aged patients (65–90 years old) were stratified into groups based on their genotype of rs2075650: homozygous for the major (A) allele (AA genotype, n = 77) or minor (G) allele carriers (AG/GG genotypes, n = 17). An additional comparison was made with a cohort of young patients (18–30 years old). The young cohort was propensity-matched stringently to the aged patients based on sex ratio and injury severity score (ISS) using IBM SPSS statistics software to avoid the potential impact of these variables on any results. The young patients were also stratified into groups based on their genotype of rs2075650, AA (n = 56) or AG/GG (n = 19). In order to ensure that findings associated with rs2075650 were specific to that SNP and not random, control groups were generated based on the genotype of a SNP which has no current reported clinical significance and a minor allele frequency of at least 25%. The control SNP selected was rs5966792 on chromosome X in the intron of DIAPH2 (diaphanous related formin 2). Aged patients were stratified into groups based on their genotype of rs5966792: homozygous for the major (T) allele (TT genotype, n = 30) or minor (C) allele carriers (TC/CC genotypes, n = 64).

DNA sampling and single-nucleotide polymorphism genotyping

DNA was extracted from whole blood samples using the QIAamp DNA Blood Midi Kit (QIAGEN, Valencia, Calif) as per the manufacturer's specifications. SNP genotyping was performed with 200 ng of genomic DNA input using the Human Core Exome-24 v1.1 BeadChip (Illumina, San Diego, Calif) following the manufacturer's Infinium HTS Assay protocol. Briefly, DNA was denatured in 0.1N NaOH and neutralized prior to isothermal amplification. Amplified DNA was fragmented and then hybridized to locus-specific 50mers that make up the array for 16–24 h with rocking at 48°C. After removal of unbound or non-specifically annealed DNA, single-base extension of the 50mer oligonucleotides was performed with labeled nucleotides, which were scanned using an Illumina iScan with autoloader 2.x. Data analysis was performed using Illumina Genome Studio 2.0.

Validation of genotyping with real-time PCR

In order to validate the genotyping of rs2075650, all patient samples included in this study were regenotyped. Real-time PCR was carried out in a MicroAmp Enduraplate Optical 96-well plate using TaqMan SNP genotyping assay kits (Assay ID: C___3084828_20, Catalog number: 4351379) for human rs2075650 study (Applied Biosystems, Waltham, Mass). The reactions were carried out using 20 ng of human genomic DNA in a total volume of 20 μl. PCR involved standard cycles including a pre-read at 60°C for 30 s, initial denature at 95°C for 5 min, 40 cycles of denaturation at 95°C for 15 s, annealing/extension at 60°C for 1 min, and post-read at 60°C for 30 s using a QuantStudio 3 system (Applied Biosystems, Waltham, Mass). Endpoint reads were done on the plates. Results were analyzed using QuantStudio Design and Analysis Software v1.4.3.

Multiplex assay of inflammation biomarkers

Blood samples were centrifuged and plasma was stored at −80°C for subsequent analysis of inflammatory mediators. A Luminex 100 IS analyzer (Luminex) and Human Cytokine/Chemokine MILLIPLEX Panel kit (Millipore Corporation) were used to measure plasma levels of CXCL10/IP-10, CXCL9/MIG, C-C motif chemokine ligand (CCL) 11/eotaxin, interleukin (IL)-1β, IL-1 receptor antagonist (IL-1RA), IL-2, soluble IL-2 receptor-α (sIL-2Ra), IL-4, IL-5, IL-6, IL-7, CXCL8/IL-8, IL-10, IL-13, IL-15, IL-17A, interferon (IFN)-α, IFN-γ, CCL3/macrophage inflammatory protein (MIP)-1α, CCL4/MIP-1β, CCL2/monocyte chemoattractant protein (MCP)-1, granulocyte-macrophage colony-stimulating factor (GM-CSF), and tumor necrosis factor (TNF)-α. Human T helper 17 MILLIPLEX Panel kit was used to measure IL-9, IL-21, IL-22, IL-23, IL-17E/25, and IL-33. NO2/NO3 levels were measured by a Greiss Reagent colorimetric assay (Cayman Chemical, Ann Arbor, Mich). Soluble ST2 was measured by a sandwich ELISA assay (R&D Systems, Minneapolis, MN).

Statistical analyses

All data were analyzed using SigmaPlot 11 software (Systat Software) and GraphPad Prism (GraphPad). Statistical differences between groups were determined by either Mann–Whitney U test or chi-square as appropriate. Group by time interaction of plasma inflammatory mediators’ levels was determined by two-way ANOVA. A P value < 0.05 was considered statistically significant for analyses of clinical data. A P value < 0.001 was considered statistically significant for analyses of biomarker data.

Data-driven modeling

Dynamic Bayesian Network (DyBN) inference was carried out to define the most likely single-network structure that best characterizes the dynamic interactions among systemic inflammatory mediators across time, in the process suggesting likely feedback structures that define central nodes. The networks might also suggest possible mechanisms by which progression of the inflammatory response differs within a given experimental group. In this analysis, time courses of unprocessed inflammatory mediator measurements from each patient were used as input for a DyBN inference algorithm, implemented in Matlab (MathWorks) essentially as described previously by our group for the study of systemic acute inflammation (14, 15). Dynamic Network Analysis (DyNA) was carried out to define the central inflammatory network nodes as a function of time, age, and genotype. Using inflammatory mediator measurements, networks were created during three consecutive time periods (0–8 h, 8–16 h, and 16–24 h) using Matlab (16, 17). Connections (network edges) were created if the Pearson correlation coefficient between any two inflammatory mediators (network nodes) at the same time interval was greater than or equal to a threshold of 0.7. Connections represent trajectories of inflammatory mediators that move in parallel, either in the same or opposite direction. The network complexity for each time interval was calculated using the following formula: sum (N1 + N2 … + Nn)/(n – 1), where N represents the number of connections for each mediator and n is the total number of mediators analyzed.

RESULTS

rs2075650 genotype is associated with altered requirements for mechanical ventilation in aged patients following traumatic injury

We first compared aged patients with the AA genotype of rs2075650 (aged AA, n = 77) to the G allele carriers (aged AG/GG, n = 17) (Table 1). There was no significant difference in age, sex, mechanism of injury, comorbidities, or ISS between the aged groups. The mean ISS of both groups was considered to be in the moderate range (ISS: 15–24). In terms of clinical outcomes, there also was no difference in ICU length of stay, hospital length of stay, mean Marshall multiple organ dysfunction score from days 1 to 7, rate of nosocomial infection, or post-discharge disposition. Aged patients with the AA genotype had a significantly lower requirement for ventilation (P = 0.042) and more ventilation-free days (P = 0.044). Importantly, there was no significant difference in pulmonary comorbidities such as asthma or chronic obstructive pulmonary disease (COPD), degree of traumatic injury to the chest according to the Abbreviated Injury Scale (AIS) (Suppl. Fig. 1A, Supplemental Digital Content 1, http://links.lww.com/SHK/A912), or thoracic interventions between the aged SNP-stratified sub-groups.

Table 1
Table 1:
Demographics and clinical outcomes of the aged (65–90 years old) rs2075650 AA (n = 77) and AG/GG (n = 17) cohorts

We next sought to determine if rs2075650 genotype was associated with altered clinical outcomes in young patients as well. Accordingly, we performed the same analysis of clinical data in a cohort of young patients with the same ISS as the aged cohort and compared young patients with the AA genotype (young AA, n = 56) to the G allele carriers (young AG/GG, n = 19) (Table 2). There was no significant difference in key demographics of the young AA and AG/GG groups, including age, sex, mechanism of injury, and comorbidities. There was no difference in ISS or degree of injury to the various body regions by AIS analysis (Suppl. Fig. 1B, Supplemental Digital Content 1, http://links.lww.com/SHK/A912). There was also no difference in any of the measured clinical outcomes, including requirement for ventilation or ventilation-free days.

Table 2
Table 2:
Demographics and clinical outcomes of the young (18–30 years old) rs2075650 AA (n = 56) and AG/GG (n = 19) cohorts

We next restratified the overall cohort of aged patients according to their genotype of the previously identified control SNP, rs5966792 on chromosome X. This analysis was performed to validate that any findings of our candidate longevity-associated SNP were specific to that SNP and not random. Aged patients with the TT genotype of the control SNP (n = 30) were compared to those with the TC or CC genotype (n = 64). There were no significant differences in any assessed demographic or clinical outcome between the two control groups (Suppl. Table 1, Supplemental Digital Content 2, http://links.lww.com/SHK/A913), supporting the notion that our findings with rs2075650 were specific to this SNP.

rs2075650 genotype is associated with differentially altered dynamic patterns of systemic inflammatory mediators between aged and young trauma patients

We next sought to determine how circulating levels of inflammatory mediators changed across time in our study groups, hypothesizing that rs2075650 genotype would be associated with differential inflammatory responses to injury (see Excel file, Supplemental Digital Content 3, for primary inflammation biomarker data, http://links.lww.com/SHK/A914). In the aged cohort, patients with the AA genotype had significantly lower levels of two out of 31 mediators from admission to day 7, IL-25 and IL-33 (Fig. 1A). Over this same time period, the aged AA patients had significantly higher levels of TNF-α compared with the aged AG/GG patients (Fig. 1A). Interestingly, these findings were different from those found in the analysis of circulating biomarker levels of the young cohort (Fig. 1B). Young patients with the AA genotype had significantly lower levels of only one mediator, IP-10, from admission to day 7, but significantly higher levels of eight out of 31 total mediators (Fig. 1B). The higher mediators included IL-1RA, sIL-2Rα, IL-4, IL-7, IL-13, IL-17A, IFN-γ, and MIP-1α. Young AA patients generally showed more robust levels of inflammatory mediators as compared with the young AG/GG patients. These findings suggest that the rs2075650 genotype is associated with differences in the levels of select inflammatory mediators after polytrauma, and that the nature of the differences is influenced by age.

Fig. 1
Fig. 1:
Time course analysis from time of injury up to day 7 of inflammatory mediators that were significantly different between the (A) aged AA versus AG/GG cohorts and (B) young AA versus AG/GG cohorts.Data presented as mean ± SEM. Statistical significance set at P < 0.001 by two-way ANOVA

Circulating biomarker levels were also analyzed as a function of the control SNP genotype. When comparing aged patients with the TT genotype of rs5966792 to those with the TC/CC genotypes, only one mediator, IP-10, was significantly higher from admission to day 7 in the TT group (Suppl. Fig. 2, Supplemental Digital Content 4, http://links.lww.com/SHK/A915).

We next performed dynamic systemic network analyses to determine if inflammation biomarker patterns could distinguish between the AA and AG/GG rs2075650 genotypes in the aged and young. DyBN is a tool used to suggest feedback structures in inferred dynamic networks (18). This analysis performed in the aged patients showed key differences in inflammatory programs between the genotypes (Fig. 2, A and B). DyBN of the aged AA patients demonstrated two central mediators, IL-23 and MIG, both of which displayed self-feedback so are inferred as central nodes. MIG also had downstream effects on IP-10 and MCP-1. DyBN of the aged AG/GG patients displayed some similarity to that of the AA patients. The cytokine IL-23 was again a central node in this network, affecting IL-25 and MIG, the latter of which also influenced IP-10. To determine if key inflammatory mediator networks were dependent on age, we also performed DyBN in the young cohort (Fig. 2, C and D). IL-23 was again a central node in both the young AA and AG/GG patients. In the young AA patients, IL-23 had downstream effects only on IL-25 while in the young AG/GG patients, IL-23 affected IL-22, MCP-1, and MIG in addition to IL-25. Notably, MIG was absent in the network of the young AA patients, and IP-10 was absent in the networks of both young patient sub-groups.

Fig. 2
Fig. 2:
Dynamic Bayesian Network (DyBN) suggests differential expression of dynamic networks among the aged and young.A, DyBN inference of the aged AA patients suggests both IL-23 and MIG are central nodes, and MIG has downstream effects on IP-10 and MCP-1. B, DyBN inference of the aged AG/GG patients suggests IL-23 is a central node and directly affects IL-25 and MIG, the latter of which also has downstream effects on IP-10. C, DyBN inference of the young AA patients suggests IL-23 is a central node and only affects IL-25 whereas (D) DyBN inference of the young AG/GG patients suggests IL-23 is a central node and directly affects four mediators (IL-22, IL-25, MIG, and MCP-1).

DyNA was performed next to determine, in a more granular fashion, the dynamic inflammation networks over the first 24 h of admissions in the aged (Fig. 3) and young (Fig. 4). In the aged cohort, the AG/GG patients initially had more connections and increased network complexity than the AA patients (Fig. 3, A and B). However, after 0 to 8 h, the AA patients had more total connections and increased network complexity than the AG/GG patients (Fig. 3, A and B). Notably, there were no negative network connections in the AA patients, but the AG/GG patients had two negative connections from 0–8 h and three from 16–24 h (Fig. 3C). The two negative connections from 0–8 h were inferred between IL-23 and eotaxin as well as between MIP-1α and MIG. The negative connections from 16–24 h were each between IP-10 and another mediator, sIL-2Rα, IL-4, and IL-17A. These negative network connections suggest processes that are actively suppressing specific pathways associated with systemic inflammation. In contrast, the young AA patients initially had slightly more connections and a higher network complexity from 0–8 h as compared with the young AG/GG patients (Fig. 4, A and B). After 0–8 h, the difference in networks between the young groups became greater. From 8 to 24 h, the AG/GG young patients had more connections and higher network complexity than the AA patients (Fig. 4, A and B). This contrasts with the networks of the aged patients, wherein the aged AA patients had increased complexity from 8 to 24 h (Fig. 3, A and B). There was only one negative connection total in the networks of the young (Fig. 4C). This negative network connection in the young AG/GG patients at 16–24 h was inferred between IP-10 and NO2/NO3. This suggests a very muted negative feedback process as compared to our observations in the aged AG/GG patients.

Fig. 3
Fig. 3:
Dynamic network analysis (DyNA) differs among the aged cohorts.A, DyNA of inflammatory mediators in the aged AA and AG/GG groups suggests a differential inflammation profile from 0 to 24 h. B, DyNA network complexity differs between the aged AA and AG/GG genotypes. Network complexity is calculated as sum (N1 + N2 … + Nn)/(n – 1), where N is the number of connections for each mediator and n is the total number of mediators analyzed. C, The number of positive and negative connections differs between the aged AA and AG/GG genotypes.
Fig. 4
Fig. 4:
Dynamic network analysis (DyNA) differs among the young cohorts.A, DyNA of inflammatory mediators in the young AA and AG/GG groups suggests a differential inflammation profile from 0 to 24 h. B, DyNA network complexity differs between the young AA and AG/GG genotypes. Network complexity is calculated as sum (N1 + N2 … + Nn)/(n – 1), where N is the number of connections for each mediator and n is the total number of mediators analyzed. C, The number of positive and negative connections differs between the young AA and AG/GG genotypes.

DISCUSSION

The genetic contributions to aging and longevity are complex and often difficult to untangle from underlying disease burden or other demographic and environmental factors (19). As the proportion of individuals in the United States reaching older age rises (20, 21), the search for potential associations between defined genotypes and specific diseases becomes an increasingly relevant area of research. In parallel, traumatic injury and its sequalae (systemic inflammation, immune dysregulation, and attendant multiple organ dysfunction) are also complex, interrelated processes (22). At the intersection of these two areas of study, trauma in the aging patient population remains a significant cause of morbidity and mortality (2, 4). Early stratification and goal-directed, personalized delivery of care may lead to improved outcomes in the elderly trauma population. As such, the goal of this study was to examine the potential role of an aging-related genetic variant as a biomarker in aged blunt trauma patients. We demonstrate that the presence of a SNP in TOMM40, a gene known to associate with longevity, associates with a lower need for mechanical ventilation and a distinct pattern of inflammatory mediators in aged trauma patients.

The mechanisms underlying the association of the candidate SNP of this study, rs2075650 in TOMM40, with longevity and Alzheimer disease are likely multiple (23, 24). TOMM40 encodes for a protein in the outer mitochondria which is essential for transport of proteins into mitochondria (25). Alterations in the import of mitochondrial proteins can induce oxidative damage, vascular endothelial dysfunction, hypoxia, and mitochondrial dysfunction, all of which can contribute to the pathology of Alzheimer disease (23, 26). This SNP likely also plays a role in lipid metabolism as the minor allele has been associated with dyslipidemia, including increased serum total and LDL cholesterol levels (8, 27–29). As such, any potential future studies of rs2075650 or TOMM40 in the trauma population ought to include analysis focused on mitochondrial dysfunction and lipid metabolism in order to clarify this relationship.

Additionally, TOMM40 is located next to APOE (apolipoprotein E) and is in moderate linkage disequilibrium with SNP rs429358, which defines the ε4 allele of the APOE gene (8). Despite only moderate linkage disequilibrium, GWAS studies report, the association of rs2075650 with longevity is not independent of the effect of APOE-ε4 on longevity (8). The ε4 allele has been identified in several candidate gene studies to be associated with longevity (30, 31), and these results have been confirmed in additional GWAS (32). This SNP is also a major risk factor for the development of late onset familial and sporadic Alzheimer disease (33, 34). We were unable to directly study the role of rs429358 on outcomes in our trauma patient population, as it was not one of the 551,839 SNPs available in our microarray kit. Future studies of genetic variants in aging trauma patients should therefore include APOE-ε4.

Despite this limitation, our findings indicate that the candidate SNP rs2075650 in TOMM40 is associated with differential outcomes and biomarker patterns in aged patients, when comparing those with the major allele (A) to those with the minor allele (G). Aged patients with the AA genotype showed lung-protective effects, with a decreased requirement for ventilation and fewer days on ventilation, when compared with the aged with the minor allele. Unfortunately, we do not have data regarding the smoking habits of our study population, and this may be a potential confounder of these findings. Aged patients with the AA genotype also displayed lower plasma concentrations of two key repair cytokines, notably IL-25 and IL-33 (35). Both of these cytokines are associated with repair of the lung epithelium (36–38). These two cytokines are also involved in the activation of type 2 innate lymphoid cells (ILC2), which are known to play a role in airway inflammation (39–41). Thus, the lower circulating levels of IL-25 and IL-33 in association with a lower need for mechanical ventilation may point to less ILC2-mediated lung injury in the AA group (Fig. 5). However, we have also shown that early elevations in IL-33 can be associated with neutrophil recruitment to the lung in the first 24 h after trauma (35). For this function, the lower levels of IL-33 may contribute to a lower degree of lung injury (Fig. 5).

Fig. 5
Fig. 5:
A working model of the early dysregulated inflammatory dynamics with a trend toward insufficient inflammation in the aged AG/GG cohort (dashed line) as compared with the aged AA cohort (solid line).

Dynamic cytokine and chemokine patterns of both the aged and young by DyBN and DyNA analyses revealed interesting differences as well. While IL-23 was a central node in all DyBN networks, MIG was also a central node in the network of the aged AA patients. IP-10 was also seen in both DyBN networks of the aged and neither network of the young. MIG had direct downstream effects on IP-10 in the aged AA and AG/GG patients. This supports our previously published findings of the major role of MIG and IP-10 in trauma patients 65 to 90 years old, showing increased levels of these two CXC chemokines compared with patients 18 to 30 years old (5). Furthermore, DyNA showed near constant network complexity in both the aged and young AA groups across time from 0 to 24 h, while the aged AG/GG group had its highest network complexity at 0 to 8 h and the young AG/GG group had a peak network complexity at 8 to 16 h. Perhaps the most important difference between patient sub-groups that could be gleaned from DyNA is the inferred, substantial negative feedback on inflammatory networks in the aged AG/GG patients, which suggests a process of active inhibition of inflammation (see below).

Another feature of this study is our inclusion of an additional analysis with a cohort of young patients with the same injury severity as the aged cohort. We chose 18 to 30 years old as the young cohort in order to allow for the greatest discrimination between study groups. We can make several conclusions from our analysis of young patients with the major or minor allele of rs2075650. First, there were no significant differences in clinical outcomes based on genotype of the young patients. It is possible that the lung-protective effects we found in the aged patients with the AA genotype are not realized until older age. At the moderate ISS of this study population, young patients tend not to go on to have complicated clinical courses. Second, young patients with the AA genotype had more significantly increased circulating mediators than the aged. This suggests that young patients in general have a robust inflammatory response following injury regardless of the potential contribution of an aging-related genetic variant.

The control SNP analyses further validate our results of the candidate SNP as non-random and reproducible findings. The control SNP genotypes in the aged patients were not associated with any differences in clinical outcomes and only one significantly different mediator (the chemokine IP-10). We believe that this increases the degree of the association of the candidate SNP with our reported findings, including lung protective effects and altered inflammation in the aged patients with the AA genotype of rs2075650, though our findings regarding IP-10 need to be interpreted cautiously.

We have designed a working model of our overall findings of this study, which illustrates the dysregulated inflammatory dynamics of the aged patients who are minor allele carriers (Fig. 5). After trauma, we believe there is a stepwise progression of the inflammatory response induced by the injury itself which leads to release of damage-associated molecular patterns, followed by chemokine/cytokine release, and culminating in the activation of the Th17 immune response (42). Each step requires a certain threshold for activation. The biomarker network patterns in the aged patients with the AG/GG genotypes indicate disruption of this threshold for upregulation of the Th17 response early after injury, and we speculate that this involves differential activation of ILC2 associated with differential levels IL-25 and IL-33. Specifically, aged patients with the AG/GG genotypes show early negative or depressive connections at 0 to 8 h and 8 to 24 h in their DyNA networks. We hypothesize that these negative connections are driving the inflammatory responses below a certain threshold, so the net result is a reduction in the degree to which Th17 is upregulated. At the same time, the increased circulating levels of the reparative cytokine IL-25 in these patients may result in inhibition of IL-23 and thus the Th17 response. The overall negative impact on the inflammatory response following injury predisposes the aged AG/GG patients to poor outcomes, which manifests here as increased lung injury.

When comparing these results to those seen in the young patients, we find that the young patients with the AG/GG genotypes have a tendency toward a less robust inflammatory response as compared with the young with the AA genotype. Circulating biomarker levels in these patients demonstrate that the AA patients’ responses are broadly pro-inflammatory, while the AG/GG patients are predisposed to anti-inflammatory responses. We believe that these mediator differences in young patients do not result in any differences in clinical outcomes because young patients in general compensate for such differential inflammatory responses or respond more favorably to ICU care despite their different inflammatory predispositions and presumed downstream effect on tissue recovery. However, we hypothesize that this inflammatory predisposition does manifest as important clinical differences in the context of aging, since age represents in essence a second hit along with the traumatic injury itself.

Given the design of this study as a candidate gene-association study, there are several limitations that impact our interpretation of the data. Human longevity is assumed to be determined by many genes, not just one genetic variant as studied here, and there is significant genetic heterogeneity among aged individuals (21). For example, both animal models and candidate gene association studies of longevity have identified important genetic variants involved in metabolism, such as FOXO3A (forkhead box 03A) (43–45) and genes of the insulin signaling pathway, including IGF1R (insulin-like growth factor 1 receptor) (46–48) and AKT1 (AKT serine/threonine kinase 1) (49). These results have not been replicated in GWAS reports, however, possibly because some genetic variants may not exist in some populations. Additional studies of the association of aging-related SNPs with trauma outcomes may benefit from including these genes or from analyzing whole genome sequences. Furthermore, the sample size of our study population is relatively small. Our results may not be broadly applicable to all elderly trauma populations since we sampled patients from a single urban academic medical center. As mentioned above, there is the potential for SNPs in linkage disequilibrium with rs2075650 to affect our analyses. Further studies on genetic variants of aging trauma patients ought to include a larger study population and greater number of patients of various ethnicities and at multiple institutions. In addition, frailty may be an important descriptor of how the immune and inflammatory responses change with aging. A frailty index or score is not routinely recorded in our trauma patients; thus, we are unable to comment directly on this issue in the context of our study. Similarly, the incidence of Alzheimer disease in our study population is unknown. However, we believe that the measurement of inflammation biomarkers takes into account all the factors that contribute to a host response to injury, including frailty and comorbidities. While it may be coincidental, it is interesting to note that the IL-33 locus is associated with Alzheimer disease (50).

Overall, the significant differences between aged blunt trauma patients with the minor allele of rs2075650 compared with those with the homozygous major allele suggest that genetic variants are major contributors to clinical outcomes and biomarker network patterns seen after traumatic injury. Our findings demonstrate a potential novel use for this candidate SNP as a biomarker in the setting of polytrauma in aged patients.

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Keywords:

Aging; biomarkers; blunt trauma; chemokines; cytokines; ICU; inflammatory mediators; single-nucleotide polymorphism; AIS; abbreviated injury scale; APOE; apolipoprotein E; DyBN; Dynamic Bayesian Network; DyNA; Dynamic Network Analysis; GWAS; genome-wide association study; IL; interleukin; ISS; injury severity score; SNP; single-nucleotide polymorphism; TOMM40; translocase of outer mitochondrial membrane 40

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