Traumatic injury is a leading cause of death and is associated with significant morbidity.1,2 With the advancement of optimal resuscitation strategies, more severely injured patients survive the initial insult of injury but are often prone to organ failure and hospitalizations requiring prolonged critical care. A recent multiomic analysis demonstrated an early “Systemic Storm” pattern of broad increases in circulating proteins, metabolites, and endothelial damage markers following severe trauma that varied by patient outcome.3 These findings also corroborated previously described early activation of the coagulation and complement cascades, neuroendocrine stress responses, and evidence of endotheliopathy in severely injured humans.4–8 Based on these studies, it is now accepted that the host response to injury can be associated with divergent outcomes, even in similarly injured humans.3,9–11
Severe injury with shock can lead to both direct and indirect organ injury and dysfunction. Recent reports indicate that ongoing cardiac injury is an underappreciated feature in patients who remain critically ill after injury.12–15 However, the longitudinal assessment of tissue-specific damage biomarkers in severely injured humans has been limited to the evaluation of a small number of molecules.16 Multiomics applied to longitudinal blood samples from trauma patients offers the opportunity to characterize tissue-specific injury biomarker patterns more broadly and assess the association of these patterns with patient outcomes.
The present study used high-dimensional proteomic and metabolomic data sets derived from blood samples of severely injured humans to identify organ injury patterns. We provide a hierarchy of organ injury markers that change over time and correlate these changes with outcomes and markers of cellular metabolism.
PATIENTS AND METHODS
The primary analysis was performed on representative plasma samples from patients enrolled in the Prehospital Air Medical Plasma (PAMPer) trial with key findings externally validated in the Study of Tranexamic Acid During Air and Ground Medical Prehospital Transport (STAAMP) trial. Both trials were prospective, multicenter, double-blind randomized control trials examining the effect of prehospital thawed plasma (PAMPer) or tranexamic acid (STAAMP) compared with standard of care in patients at risk for hemorrhage after injury (at least one episode of hypotension [systolic blood pressure (SBP), <90 mm Hg] and tachycardia [heart rate, >108 beats per minute] or any episode of severe hypotension [SBP, <70 mm Hg; PAMPer]; at least one episode of hypotension [SBP, ≤90 mm Hg] or tachycardia [heart rate, ≥110 beats per minute; STAAMP]). Detailed study protocols are publicly available.17,18 The primary trials were approved by the US Food and Drug Administration, Human Research Protection Offices of the US Department of Defense, and the institutional review boards of the participating sites.17,18 The current analysis is a secondary analysis of these published data sets with deidentified patient information.
Citrated and Ethylenediaminetetraacetic acid plasma samples for patients enrolled in both trials were collected at the time of arrival to definitive trauma care (time point 0 hours) and at 24 hours and 72 hours after admission and stored in a −80°C freezer. Representative citrated samples with at least 30 samples per subgroup (PAMPer, n = 112, all time points; STAAMP, n = 188, 0 and 24 hours) were subjected to multiplexed proteomic analysis using the SomaScan assay (SomaLogic Inc., Boulder, CO) to achieve a statistical power of at least 0.8.19) This proteomic platform technology and performance characteristics have previously been described but briefly uses DNA-based binding reagents (aptamers) to quantify relative circulating binding epitopes availability for more than 7,000 proteins. In the case of duplicate proteins inherent in the assay because of multiple SOMAmer reagent targets, both protein targets were examined and labeled “protein name_1 or 2” to differentiate the targets.3,20,21
Details for the measurement of untargeted metabolomics have previously been reported.21 Briefly, a representative subset of Ethylenediaminetetraacetic acid plasma samples from the PAMPer trial (n = 145) were subjected to an untargeted metabolomics assay for 898 metabolites (liquid chromatography mass spectrometry; Metabolon Inc., Durham, NC). Raw data was peak identified and underwent quality control processing using Metabolon's internal software and library of available purified standard compounds to provide information on the relative levels of measured metabolites.
Lastly, we examined the endothelial damage markers syndecan-1, thrombomodulin, and sVEGFR1 from a representative subset of the PAMPer trial (n = 112, time point 0 hours; n = 103, time point 24 hours). Commercially available immunoassays were used to quantify endothelial damage marker measurements as previously described.7,22
The patients were categorized into outcome groups using clinical data from the primary trials. Patients who died within 72 hours of injury were excluded from this analysis. The remaining patients were classified as nonresolvers (died >72 hours or required ≥7 days of critical care) or resolvers (survived to 30 days and required <7 days of critical care). For proteomic comparisons, admission (time point 0 hours) plasma samples from a minimally injured cohort (median Injury Severity Score [ISS] average of 1; n = 30) in the standard care arm of the STAAMP trial were uses as a control group. Age- and sex-matched nonfasting healthy volunteers (n = 17) were analyzed with metabolomic samples and served as a control group for metabolomic comparisons.23
Established tissue damage biomarkers for the cardiac, renal, neurologic, hepatic, and pulmonary systems were identified through a literature review and were cross-referenced with tissue-specific proteomes and transcriptomes from the Human Protein Atlas. Proteins with tissue-specific enrichment (≥4× expression compared with any other tissue) or elevation (≥4× expression compared with the average of all other tissues) in respective organs were analyzed.24
Raw proteomic data underwent quality analysis and normalization using multiple hybridization and calibration scale factors. Raw peak intensity data from the untargeted metabolomics layer were normalized based on the run-day blocks for each metabolite by a median normalization to equal 1. The normalized data in each layer were log2 transformed to approximate a normal distribution. For heat map visualization, transformed values were autoscaled as a z score across each sample.
Nonparametric Kruskal-Wallis and Mann-Whitney U tests with false discovery rate correction assessed differences in protein expression across outcome groups (corrected level of significance; p < 0.1). Correlations between biomarkers were evaluated using Spearman's rank-order correlation. Heat maps and line/box plots of proteins and metabolites of interest were used for data visualization. For preprocessing of metabolomic features, volcano plots of log-fold changes between outcome groups and adjusted p values at 24 and 72 hours were used to select the most discriminatory metabolomic features between outcome groups. The log-2–fold change in concentration of metabolites between 24 and 72 hours was also assessed to identify metabolites with diverging trajectories between outcome groups (Supplemental Digital Content, Supplementary Fig. 1, https://links.lww.com/TA/C866). Statistical analysis was performed using R version 4.1.2 (Vienna, Austria).25 A Spearman's correlation network that included 0 hours values of cardiac and endothelial injury markers as well as fatty acid metabolite levels was generated using SciPy and NetworkX libraries of Python programming language v3.10 (Amsterdam, Netherlands).26 This study used and followed the Standards for Reporting of Diagnostic Accuracy Studies guidelines to ensure proper reporting (Supplemental Digital Content, Supplementary Data 1, https://links.lww.com/TA/C867).
RESULTS
The primary analysis was performed on a representative cohort of the PAMPer trial, and key findings were externally validated using a representative cohort from the STAAMP trial. Both cohorts were consisted of mostly blunt injury (87% [PAMPer], 81% [STAAMP], p = 0.22); however, PAMPer patients were more severely injured than STAAMP patients with higher median ISSs (27 vs. 14; p < 0.01), shock index (1.5 vs. 1.1, p < 0.01), rates of traumatic brain injury (TBI; 57% vs. 25%, p < 0.01), intensive care unit (ICU) length of stay (9 vs. 3, p < 0.01), and 30-day mortality (21% vs. 3%, p < 0.01).
Patient and injury characteristics stratified by outcome group are summarized in Table 1. In the PAMPer cohort, nonresolvers were more frequently Black (12% vs. 0%, p = 0.04), non-Hispanic (96% vs. 82%, p = 0.04), injured by blunt mechanism (91% vs. 76%, p = 0.04), and were more severely injured (ISS, 30 vs. 22; p < 0.01) with higher rates of TBI (65% vs. 38%, p < 0.01) compared with resolvers. Nonresolvers also had worse outcomes compared with resolvers in the PAMPer trial (31% vs. 0% 30-day mortality, p < 0.01; ICU length of stay 13 vs. 3 days, p < 0.01). Injury and outcome characteristics between nonresolvers and resolvers in the STAAMP cohort mirrored those of the PAMPer cohort with higher rates of blunt injury mechanism (89% vs. 76%, p = 0.02), TBI (51% vs. 14%, p < 0.01), higher ISS (27 vs. 10, p < 0.01), 30-day mortality (3% vs. 0%, p = 0.06), and ICU length of stay (11 vs. 2 days, p < 0.01) in the nonresolvers.
TABLE 1 -
Patient and Injury Characteristics of Representative Samples from PAMPer and STAAMP Cohorts Subjected to Proteomic Analysis Stratified by Outcome Group
|
PAMPer |
STAAMP |
| Characteristic |
Nonresolving (n = 78) |
Resolving (n = 34) |
p*
|
Low ISS Controls (n = 30) |
Nonresolving (n = 37) |
Resolving (n = 121) |
p*
|
| Age, median (IQR), y |
42 (26–66) |
48 (28–63) |
0.79 |
32 (25–47) |
48 (31–59) |
33 (23–49) |
0.002 |
| Male sex, n (%) |
58 (74) |
25 (74) |
0.93 |
21 (70) |
31 (834) |
92 (76) |
0.32 |
| Race, n (%) |
|
|
0.005 |
|
|
|
0.25 |
| White |
68 (87) |
29 (85) |
|
26 (87) |
33 (89) |
89 (74) |
|
| Black |
9 (12) |
0 (0) |
|
2 (7) |
2 (5) |
14 (12) |
|
| Other |
0 (0) |
3 (9) |
|
0 (0) |
0 (0) |
2 (2) |
|
| Unknown |
1 (1) |
2 (6) |
|
2 (7) |
2 (5) |
16 (13) |
|
| Ethnicity, n (%) |
|
0.04 |
|
|
|
0.14 |
| Hispanic |
1 (1) |
1 (3) |
|
1 (3) |
4 (11) |
6 (5) |
|
| Non-Hispanic |
75 (96) |
28 (82) |
|
26 (87) |
31 (84) |
96 (79) |
|
| Unknown |
2 (3) |
5 (15) |
|
3 (10) |
2 (5) |
19 (16) |
|
| Injury mechanism, n (%) |
|
0.04 |
|
|
|
0.02 |
| Blunt |
71 (91) |
26 (76) |
|
27 (90) |
33 (89) |
92 (76) |
|
| Penetrating |
7 (9) |
8 (24) |
|
3 (10) |
2 (5) |
28 (23) |
|
| Both blunt and penetrating |
0 (0) |
0 (0) |
|
|
2 (5) |
1 (1) |
|
| Initial Glasgow Coma Scale, median (IQR) |
3 (3–12) |
14 (8–15) |
<0.001 |
15 (12–15) |
9 (3–15) |
15 (11–15) |
<0.001 |
| Initial Glasgow Coma Scale <8, n (%) |
<0.001 |
|
|
|
<0.001 |
| Yes |
51 (65) |
9 (26) |
|
7 (23) |
17 (46) |
23 (19) |
|
| No |
27 (35) |
25 (74) |
|
23 (77) |
20 (54) |
98 (81) |
|
| TBI, n (%) |
0.007 |
|
|
|
|
| Yes |
51 (65) |
13 (38) |
|
2 (7) |
19 (51) |
17 (14) |
<0.001 |
| No |
27 (35) |
21 (62) |
|
28 (93) |
16 (43) |
104 (86) |
|
| Missing |
0 (0) |
0 (0) |
|
0 (0) |
2 (5) |
0 (0.0) |
|
| Prehospital SBP, median (IQR), mm Hg |
72 (65–81) |
79 (64–84) |
0.37 |
144.5 (125–156) |
76 (70–85) |
114 (90–130) |
<0.001 |
| Prehospital heart rate, median (IQR) |
121 (109–128) |
117 (109.5–125.5) |
0.42 |
117 (112–121) |
119 (105–145) |
120 (112–130) |
0.88 |
| Prehospital intubation, n (%) |
0.001 |
|
|
|
<0.001 |
| Yes |
51 (65) |
11 (32) |
|
6 (20) |
21 (57) |
20 (17) |
|
| No |
27 (35) |
23 (68) |
|
24 (80) |
16 (43) |
101 (84) |
|
| Prehospital CPR, n (%) |
|
0.35 |
|
|
|
0.07 |
| Yes |
2 (3) |
0 (0) |
|
1 (3) |
1 (3) |
0 (0) |
0.07 |
| No |
76 (97) |
34 (100) |
|
29 (97) |
36 (97) |
121 (100) |
|
| Prehospital packed red blood cell transfusion, n (%) |
0.66 |
|
|
|
0.4 |
| Yes |
22 (28) |
11 (32) |
|
0 (0) |
3 (8) |
16 (13) |
|
| No |
56 (72) |
23 (68) |
|
30 (100) |
34 (92) |
105 (87) |
|
| Prehospital crystalloid volume, median (IQR), mL |
800 (0–1,400) |
775 (0–1,500) |
0.62 |
275 (0–500) |
1,000 (300–1,600) |
400 (150–1,000) |
0.004 |
| Prehospital thawed plasma, n (%) |
0.04 |
|
|
|
NA |
| Yes |
42 (54) |
11 (32) |
|
NA |
NA |
NA |
|
| No |
36 (46) |
23 (68) |
|
NA |
NA |
NA |
|
| Prehospital TXA, n (%) |
NA |
|
|
|
0.5 |
| TXA |
NA |
NA |
|
0 (0) |
17 (46) |
48 (40) |
|
| No TXA |
NA |
NA |
|
30 (100) |
20 (54) |
73 (60) |
|
| TXA dose, n (%) |
NA |
|
|
|
0.01 |
| Placebo |
NA |
NA |
|
30 (100) |
20 (54) |
73 (60) |
|
| 1 g |
NA |
NA |
|
0 (0) |
6 (16) |
12 (10) |
|
| 2 g |
NA |
NA |
|
0 (0) |
11 (30) |
17 (14) |
|
| 3 g |
NA |
NA |
|
0 (0) |
0 (0) |
19 (16) |
|
| ISS, median (IQR) |
30 (22–41) |
22 (14–24) |
<0.001 |
1 (0–1) |
27 (18–38) |
10 (1–17) |
<0.001 |
| Head AIS score, median (IQR) |
3 (2–4) |
0 (0–3) |
<0.001 |
0 (0–0) |
3 (0–5) |
0 (0–0) |
<0.001 |
| Abdomen AIS score, median (IQR) |
2 (0–3) |
2 (0–2) |
0.29 |
0 (0–0) |
0 (0–3) |
0 (0–0) |
<0.001 |
| Chest AIS score, median (IQR) |
3 (2–3) |
3 (0–3) |
0.19 |
0 (0–0) |
3 (2–3) |
0 (0–3) |
<0.001 |
| External AIS score, median (IQR) |
1 (0–1) |
1 (0–1) |
0.77 |
0 (0–1) |
1 (0–1) |
1 (0–1) |
0.65 |
| Extremity AIS score, median (IQR) |
2 (0–3) |
2 (0–3) |
0.17 |
0 (0–0) |
2 (0–3) |
1 (0–3) |
0.005 |
| Face AIS score, median (IQR) |
0 (0–2) |
0 (0–1) |
0.1 |
0 (0–0) |
0 (0–1) |
0 (0–1) |
0.09 |
| 24-h Mortality, n (%) |
|
1 |
|
|
|
1 |
| No |
78 (100) |
34 (100) |
|
30 (100) |
37 (100) |
121 (100) |
|
| 30-d Mortality, n (%) |
|
<0.001 |
|
|
|
0.06 |
| Yes |
24 (31) |
0 (0) |
|
1 (3) |
1 (3) |
0 (0) |
|
| No |
54 (69) |
34 (100) |
|
29 (97) |
34 (92) |
121 (100) |
|
| Missing |
0 (0) |
0 (0) |
|
0 (0) |
2 (5) |
0 (0) |
|
| ICU length of stay, median (IQR), d |
13 (9–18) |
3 (2–5) |
<0.001 |
0 (0–0) |
11 (9–16) |
2 (0–3) |
<0.001 |
*p Values comparing nonresolvers to resolvers.
AIS, Abbreviated Injury Scale; NA, not applicable; TXA, tranexamic acid.
Literature review and cross-reference with the Human Protein Atlas24 identified 41 tissue-specific damage biomarkers (8 cardiac, 6 renal, 16 neurologic, 7 hepatic, 4 pulmonary) represented in the SomaScan 7K proteomic platform. The analyzed biomarkers are listed according to tissue-specificity, organ system, and protein function in Supplemental Digital Content (Supplementary Table 1, https://links.lww.com/TA/C868).
We first visualized the relative plasma levels of the 41 biomarkers across outcome groups and time in a heat map of normalized z scores (Fig. 1). We also examined these biomarkers across injury severity groups (mild [ISS, ≤15], moderate [ISS, 16–24], severe [ISS, >25]). This identified an early (time point 0 hours) outcome and injury severity–based near-indiscriminate release of the analyzed biomarkers. Interestingly, the release of these circulating biomarkers was largely reversed in all outcome and injury severity–based groups by 24 hours. However, at the 72 hours time point, nonresolvers demonstrated a sustained relatively higher level of these biomarkers across all organs except the renal system. Importantly, the sustained relative elevations of these biomarkers across outcome groups at 72 hours were not significantly associated with injury severity (Fig. 1; Supplemental Digital Content, Supplementary Fig. 2, https://links.lww.com/TA/C869). Furthermore, these biomarker patterns were independent of the treatment arm in the PAMPer trial, as there were no significant differences in biomarker concentrations between standard care and plasma arms except heart fatty acid binding protein_2 at 24 hours. Examination of these biomarkers in the STAAMP cohort confirmed the broad elevation at time point 0 hours among resolvers and nonresolvers. Similar to the PAMPer cohort, resolvers demonstrated near complete resolution of the elevation of these biomarkers, whereas nonresolvers had ongoing increases in all organ group biomarkers except the renal system at time point 24 hours.
;)
Figure 1: Heat map of tissue-specific biomarkers stratified by time point and outcome in PAMPer cohort. Number of subjects in each group: low ISS controls, 30; resolving 0 hours, 34; nonresolving 0 hours, 78; resolving 24 hours, 32; nonresolving 24 hours, 72; resolving 72 hours, 32; and nonresolving 72 hours, 71. *Represents significant elevation with adjusted p value of <0.1 across outcome groups.
Given the significant differences in demographics and injury mechanisms between outcome groups, we explored differences in biomarker concentrations across sex, race, prehospital shock, TBI, and injury mechanism. Interestingly, there were no significant differences in biomarker concentrations across sex (n = 83 [male], n = 29 [female]) and shock (any SBP <70 mm Hg before arrival to trauma center; n = 73 [shock], n = 39 [no shock]) at all time points. When we compared patients with TBI (n = 64) with patients without TBI (n = 48), there were significant differences in 18 neurologic, 9 cardiac, 7 hepatic, 5 pulmonary, and 5 renal biomarker concentrations at time point 0 hours. However, by 72 hours, there were only significant differences in concentrations of five neurologic, one cardiac, and one pulmonary protein. Similarly, patients who suffered blunt (n = 97) compared with penetrating (n = 15) injury mechanism demonstrated significant differences in 22 neurologic, 8 cardiac, 7 hepatic, 4 pulmonary, and 6 renal protein concentrations at time point 0. Again, by 72 hours, there were only significant differences in four neurologic, two cardiac, one renal, and one pulmonary protein concentrations across injury mechanism.
While biomarkers for all tissue groups (except renal) exhibited sustained elevation among nonresolvers compared with resolvers at 72 hours, this effect was most pronounced for the cardiac biomarkers. We therefore investigated the time-dependent trends in cardiac biomarkers across outcome groups. The relative concentrations of four representative cardiac damage biomarkers across time are depicted in Figure 2. While the trajectory of these cardiac biomarkers over time was similar between outcome groups, the relative concentrations of these proteins were highest in nonresolvers compared with resolvers at all time points (Fig. 2A). Many of the cardiac damage markers returned to within the baseline range observed in low ISS controls in resolvers by 72 hours. In contrast, nonresolvers demonstrated sustained elevation (above the baseline range of the low ISS control group) in all cardiac damage biomarkers except myosin binding protein C (cardiac type) and fatty-acid binding protein_2 at 72 hours (Fig. 2B and C). Lastly, among the analyzed cardiac damage biomarkers, the time-dependent trend in troponin T uniquely revealed a linear increase in relative concentration over time with significant differences between outcome groups occurring only at the 72 hours time point (Fig. 2D).
Figure 2: Time-dependent relative concentrations of analyzed cardiac damage
biomarkers stratified by patient outcome. Dot and error bars represent mean and SD of respective outcome groups. Black line and gray bars represent mean and SD of relative protein concentration in low injury severity control group at time point 0 hours. *Signifies significant difference in relative concentration between resolving and nonresolving after false discovery rate correction. Number of subjects the same as
Figure 1.
To assess the effect of direct chest injury on cardiac injury biomarkers, we compared cardiac damage biomarkers in patients with at least moderate chest injury (chest Abbreviated Injury Scale score, >2) with those with mild to no chest injury (chest Abbreviated Injury Scale score, ≤2). This demonstrated relative elevation in plasma concentrations of myoglobin (cardiac), heart fatty acid–binding protein (cardiac), pulmonary surfactant–associated protein D (pulmonary), and advanced glycosylation end product–specific soluble receptor (pulmonary) at 0 hours among patients with a direct chest injury. By 24 hours, only pulmonary surfactant–associated protein D (pulmonary) was significantly elevated among those with chest injury, and there were no significant differences in relative concentrations of these biomarkers between chest injury groups at 72 hours.
Lastly, to clinically correlate our outcome group findings, we examined trends in tissue-specific damage biomarkers across vasopressor use as a proxy of cardiac dysfunction. We extrapolated vasopressor use within the first 3 days of admission from Denver multiple organ scores collected in the primary PAMPer trial.27 This demonstrated an almost exclusive elevation in cardiac damage biomarkers at 24 hours and elevation of all examined cardiac damage biomarkers at 72 hours among those on vasopressors compared with those not requiring vasopressors.
The heart typically uses fatty acids as an energy substrate, and fatty acids are transported into the mitochondria of myocytes as acyl carnitines.28,29 Cardiac metabolism can shift to burn ketones and glucose in stress states.30,31 We evaluated the longitudinal changes in circulating metabolites known to be involved in cardiac metabolism (fatty acid [n = 159], ketone [n = 2], and glucose [n = 6] pathway metabolites).30 Preprocessing of these metabolomic features through volcano plots identified acyl cholines and acyl carnitines conjugated to monosaturated, polyunsaturated, and long chain saturated fatty acids as the most discriminatory features between outcome groups at 24 and 72 hours (Supplemental Digital Content, Supplementary Fig. 1, https://links.lww.com/TA/C866). Visualization of these metabolites in a heat map depicts an overall early (time 0 hours) loss of fatty acid metabolites among resolvers and nonresolvers compared with healthy controls (Fig. 3). This was followed by selective increases in multiple long-chain acyl cholines and a subset of saturated long chain-, polyunsaturated-, and monounsaturated-conjugated acyl carnitines among resolvers compared with nonresolvers by 72 hours (Fig. 3).
Figure 3: Heat map of plasma acyl carnitine and acyl choline fatty acid metabolites stratified by time point and patient outcome. Number of subjects in each group: healthy controls, 17; resolving 0 hours, 43; nonresolving 0 hours, 102; resolving 24 hours, 42; nonresolving 24 hours, 100; resolving 72 hours, 43; and nonresolving 72 hours, 99.
The time-dependent trajectories of a representative acyl carnitine, acyl choline, and medium-chain fatty acid as well as pyruvate are shown in Figure 4, respectively. Dihomo-linoleoylcarnitine (C20:2) demonstrated a relative decrease from 0 to 24 hours among both resolvers and nonresolvers, with a subsequent relative increase compared with baseline concentrations at 72 hours only among resolvers (Fig. 4A). In contrast, palmitoylcholine was lower compared with healthy controls at time 0 hours, which was more pronounced in nonresolvers compared with resolvers (Fig. 4B). The relative concentration increased in both nonresolvers and resolvers over time, approaching levels in healthy controls in both outcome groups by 72 hours. Interestingly, caprylate (8:0), a medium-chain fatty acid associated with myocardial oxidative stress,32 was much lower compared with healthy controls in both outcome groups at 0 and 24 hours and increased in concentration in only nonresolvers at 72 hours (Fig. 4C). Pyruvate, a systemic marker of anaerobic glucose metabolism, was elevated compared with healthy controls in both resolvers and nonresolvers at 0 hours and remained elevated at 24 hours in nonresolvers (Fig. 4D). Taken together, these observations show that circulating levels of cardiac energy substrates inversely correlate with ongoing cardiac myocyte injury, while markers of metabolic stress associate with cardiac injury biomarkers.
Figure 4: Representative time-dependent trajectories of acyl carnitines, acyl cholines, medium chain fatty acids, and lactate across outcome groups. Depicted trajectories are representative of metabolites in acyl carnitine (
A), acyl choline (
B), medium chain fatty acids (
C), and pyruvate (
D). Number of subjects the same as
Figure 3.
We next examined the correlation between cardiac damage biomarkers and the proinflammatory cytokines interleukin-6 and tumor necrosis factor α (TNF-α), which have been shown to modulate cardiac stress.15,33–35 This revealed positive correlations between both proinflammatory cytokines and all measured cardiac damage biomarkers across the three time points. Overall, cardiac damage biomarkers had stronger correlations with TNF-α compared with interleukin-6. These correlations were the most prominent at the 0 hours time point with brain natriuretic peptide 32 (BNP; ρ = 0.95–0.98) and atrial natriuretic factor (ANF; ρ = 0.94–0.95) demonstrating the highest correlations with TNF-α. Similar significant positive correlations were observed at 24 hours between brain natriuretic peptide 32 (ρ = 0.79–0.85) and atrial natriuretic factor (ρ = 0.71–0.72) with TNF-α. At 72 hours, brain natriuretic peptide 32 (ρ = 0.85–0.86) remained most strongly correlated to TNF-α.
We also investigated the association of the endothelial damage marker syndecan-1 and cardiac damage biomarkers, which demonstrated significant positive correlations for all analyzed cardiac damage biomarkers. Atrial natriuretic factor (ρ = 0.58) and brain natriuretic peptide 32 (ρ = 0.57) had the strongest overall correlations with syndecan 1 at all time points. Lastly, we generated a Spearman's correlation network to visualize the relationships among cardiac damage markers, fatty acid metabolites, and markers of endotheliopathy at 0 hours (Fig. 5). Besides high correlation levels between the cardiac markers, the network highlights positive associations between several cardiac damage markers and endothelial injury (syndecan-1, thrombomodulin, and sVEGFR1). In addition to positive correlations between 3-glycerophosphatate and ANF, BNP, and cardiac fatty acid binding protein, the latter shows moderate positive correlations with several acyl carnitines.
Figure 5: Network of highly correlated (Spearman's coefficient, >0.5) cardiac metabolites (SomaLogic), fatty acid metabolites (Metabolon) and markers of endotheliopathy enzyme-linked immunosorbent assay (ELISA) in PAMPer patients at 0 hours.
DISCUSSION
Through a comprehensive, high-throughput plasma proteomic analysis, we identified evidence of sustained elevation of tissue-specific damage biomarkers among patients with complicated clinical courses following injury. Most prominent were elevations in cardiac-specific biomarkers. These findings were externally validated in plasma samples from an additional cohort of traumatically injured patients. Furthermore, while the release of circulating tissue-specific damage biomarkers was associated with injury severity, mechanism, and TBI at admission, by 72 hours after admission, elevation of cardiac-specific biomarkers was not associated with injury severity or pattern. These findings add to a body of evidence that indicates that the host response to injury significantly contributes to prognosis and outcomes following trauma.3,9–11
Evidence of cardiac stress independent of direct chest injury after traumatic injury has been previously characterized as trauma-induced secondary cardiac injury.12–15 This phenomenon was described in a cohort of trauma patients without direct thoracic injury who experienced adverse cardiac events during initial hospitalization. Patients with trauma-induced secondary cardiac injury demonstrated increased risk of poor outcomes including mortality. Trauma-induced secondary cardiac injury is known to be associated with increases in circulating levels of brain natriuretic peptide, heart-type fatty acid binding protein, and troponin I at 24 and 72 hours after admission.12 We expand this list to include 10 cardiac biomarkers and confirm the unique pattern of troponin I as a biomarker that shows a progressive increase over time as opposed to sustained elevations for most of the other biomarkers.12 By including injury biomarkers representative of several major organ systems, we can conclude that ongoing myocardial injury stands out in comparison with secondary injury of other organs. Our analysis indicates that cardiac stress at 72 hours after injury may prognosticate a complicated clinical course lasting beyond 7 days and identify patients who could benefit from cardioprotective therapy.
Given the evidence of prolonged cardiac stress in nonresolvers, we explored potential mechanistic underpinnings including an investigation of circulating metabolites involved in cardiac metabolism. We identified an early loss of acyl carnitines conjugated to monosaturated, polysaturated, and long-chain saturated fatty acids in both outcome groups with resolution to baseline healthy control concentrations among resolvers only. Acyl carnitines assist in the transport of fatty acids into the mitochondria of myocytes for subsequent β-oxidation and derivation of cardiac energy substrates.29,36 The sustained loss of acyl carnitines observed in nonresolvers in our study suggests a potential metabolic-driven mechanism to the cardiac stress identified in this outcome group. Current knowledge about cardiac metabolism after trauma is limited, but extrapolations from other cardiac stressed states such as heart failure demonstrate decreased fatty oxidation rates and reduced expression of fatty acid transporters and oxidation enzymes.30,31,37,38 Shifts in cardiac metabolism after multiple injuries has been investigated in a porcine model, which showed an early shift in cardiomyocyte substrate utilization from fatty acid oxidation to glucose metabolism with subsequent increase in fatty acid transporters such as heart fatty acid binding protein at later time points (72 hours).39 Our findings on the loss of circulating acyl cholines and acyl carnitines raise the possibility that loss of fatty acid substrates could contribute to cardiac stress after injury and create an energy crisis in the heart.
Furthermore, the loss of several acyl cholines after injury followed by a more rapid return in resolvers is intriguing. The biological role of these molecules is uncertain; however, previous investigation supports an effect on blood pressure based on the chain length of the acyl choline molecules.40,41 In addition, recent in vitro studies suggest that a subset with fatty acids ranging from 18 to 22 carbons in length may regulate acetylcholine signaling and hence could regulate cardiovascular function.42
While we did not identify significant differences in systemic markers of glucose metabolism between outcome groups, our findings support the previous large animal work by demonstrating an increase in circulating pyruvate, a carbohydrate metabolite at time point 0 hours.39 Lastly, our exploration of medium-chain fatty acids showed overall depletion in both outcome groups with a selective increase at later time points only among nonresolvers. Previous investigation suggests that increased concentrations of medium-chain fatty acids correlate to myocardial oxidative stress and atrophy.32 Taken together, the differential time-dependent trends in cardiac metabolism substrates between resolvers and nonresolvers suggest, at least in part, a metabolic driven mechanism for the prolonged cardiac stress observed in patients with complicated clinical courses following injury.
In addition to cardiac metabolite substrates, we investigated correlations between cardiac damage biomarkers, proinflammatory mediators, and markers of endotheliopathy. The association of proinflammatory cytokines, particularly TNF-α, with cardiac injury markers is consistent with the prior literature demonstrating an association of admission TNF-α concentrations with adverse cardiac events after trauma.13 In addition, TNF-α has been shown to initiate a signaling pathway contributing to posttraumatic myocardial apoptosis.35 Syndecan-1 also significantly positively correlated with all cardiac damage biomarkers in our cohort, suggesting the association of endotheliopathy with cardiac stress. Endotheliopathy can be initiated early after trauma and contribute to inflammation-associated organ injury; therefore, the association of syndecan-1 with cardiac injury implicates cardiac myocyte endothelium damage.7,43 These findings point to a correlative relationship between endotheliopathy, inflammation, and cardiac stress, which, in concert with our metabolomic findings, suggests multifactorial mechanisms for cardiac stress following traumatic injury.
While our study used high integrity multiomic platforms from clinical trials with excellent randomization, several limitations exist. First, we identified tissue-specific damage biomarkers using a combination of literature review and cross-reference with the Human Protein Atlas. Thus, other tissue-specific damage biomarkers that were not identified in our literature review may warrant investigation, and our definition of tissue specificity is subject to the limitations of the Human Protein Atlas. Second, external validation in the STAAMP cohort was only performed at 0 and 24 hours for the proteomic platform, and therefore, proteomic findings at 72 hours in addition to metabolomic, inflammatory, and endothelial damage findings were not externally validated. Third, limitations in the clinical data collected in the primary randomized trials did not include adverse cardiac events or clinical tests such as troponin I. Fourth, there were significant differences in demographics and injury patterns between outcome groups. While we examined biomarker concentrations across these differences, further investigation with adjustment for these variables is needed to verify that findings are independent of these baseline characteristics. Finally, our exploratory analysis only examined circulating biomarkers in plasma. Further mechanistic studies including organ tissue samples are needed to prove causality and further delineate our findings.
In conclusion, using comprehensive plasma proteomics, we identified an immediate release of biomarkers specific to multiple organs that are associated with patient outcomes following traumatic injury. Biomarkers for liver, pulmonary, neurologic, and particularly cardiac injury remained elevated in patients with complex clinical courses after severe injury. We propose that changes in circulating lipids that are associated with severe injury could contribute to ongoing cardiac stress. While further mechanistic and causal studies are needed, our findings also suggest potential prognostication with early assessment of tissue-specific damage biomarkers and support the exploration of cardioprotective therapeutic targets to improve outcomes in traumatically injured patients.
AUTHORSHIP
All authors have seen and approved the submitted version of the study. S.L., H.M., and T.R.B. had complete access to all data in the study and take full responsibility for data integrity and data analysis accuracy. S.L., B.H., M.K., N.K., J.W., T.R.B., J. Sperry, and M.N. contributed in the concept and design. S.L., H.M., B.H., M.K., N.K., J.W., S.A., J. Scioscia, E.S., A.R., J.B., P.J., J. Stensballe, J.D., U.K., and T.R.B. contributed in the acquisition, analysis, or interpretation of data. All authors contributed in the critical revision of the manuscript for important intellectual content.
ACKNOWLEDGMENTS
The University of Pittsburgh holds a Physician-Scientist Institutional Award from the Burroughs Wellcome Fund (S.L., J.B.). We would like to acknowledge the contribution of collaborators involved in the PAMPer and STAAMP studies for clinical data collection.
This work was supported by US Army Medical Research and Material Command (W81XWG-12-2-0023 to J.L.S.), T32HL098036 from the National Heart, Lung, and Blood Institute (S.L.), and the National Institutes of General Medical Sciences grant R35-GM-127027 to T.R.B.
Funding sources had no role in the conduct, design, data collection/management and analysis, or interpretation of findings in this manuscript. Furthermore, they had no influence in the preparation, review, approval, and submission of this manuscript for publication.
DISCLOSURE
The authors declare no conflicts of interest.
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