Metabolomics and Precision Medicine in Trauma: The State of the Field

A metabolomic and proteomic analysis of pathologic hypercoagulability in traumatic brain injury patients after dura violation

BACKGROUND

The coagulopathy of traumatic brain injury (TBI) remains poorly understood. Contradictory descriptions highlight the distinction between systemic and local coagulation, with descriptions of systemic hypercoagulability despite intracranial hypocoagulopathy. This perplexing coagulation profile has been hypothesized to be due to tissue factor release. The objective of this study was to assess the coagulation profile of TBI patients undergoing neurosurgical procedures. We hypothesize that dura violation is associated with higher tissue factor and conversion to a hypercoagulable profile and unique metabolomic and proteomic phenotype.

METHODS

This is a prospective, observational cohort study of all adult TBI patients at an urban, Level I trauma center who underwent a neurosurgical procedure from 2019 to 2021. Whole blood samples were collected before and then 1 hour following dura violation. Citrated rapid and tissue plasminogen activator (tPA) thrombelastography (TEG) were performed, in addition to measurement of tissue factory activity, metabolomics, and proteomics.

RESULTS

Overall, 57 patients were included. The majority (61%) were male, the median age was 52 years, 70% presented after blunt trauma, and the median Glasgow Coma Score was 7. Compared with pre-dura violation, post-dura violation blood demonstrated systemic hypercoagulability, with a significant increase in clot strength (maximum amplitude of 74.4 mm vs. 63.5 mm; p < 0.0001) and a significant decrease in fibrinolysis (LY30 on tPAchallenged TEG of 1.4% vs. 2.6%; p = 0.04). There were no statistically significant differences in tissue factor. Metabolomics revealed notable increases in metabolites involved in late glycolysis, cysteine, and one-carbon metabolites, and metabolites involved in endothelial dysfunction/arginine metabolism/responses to hypoxia. Proteomics revealed notable increase in proteins related to platelet activation and fibrinolysis inhibition.

CONCLUSION

A systemic hypercoagulability is observed in TBI patients, characterized by increased clot strength and decreased fibrinolysis and a unique metabolomic and proteomics phenotype independent of tissue factor levels.

A Multidimensional Bioinformatic Platform for the Study of Human Response to Surgery

OBJECTIVE

To design and establish a prospective biospecimen repository that integrates multi-omics assays with clinical data to study mechanisms of controlled injury and healing.

SUMMARY BACKGROUND DATA

Elective surgery is an opportunity to understand both the systemic and focal responses accompanying controlled and well-characterized injury to the human body. The overarching goal of this ongoing project is to define stereotypical responses to surgical injury, with the translational purpose of identifying targetable pathways involved in healing and resilience, and variations indicative of aberrant peri-operative outcomes.

METHODS

Clinical data from the electronic medical record combined with large-scale biological data sets derived from blood, urine, fecal matter, and tissue samples are collected prospectively through the peri-operative period on patients undergoing fourteen surgeries chosen to represent a range of injury locations and intensities. Specimens are subjected to genomic, transcriptomic, proteomic, and metabolomic assays to describe their genetic, metabolic, immunologic, and microbiome profiles, providing a multidimensional landscape of the human response to injury.

RESULTS

The highly multiplexed data generated includes changes in over 28,000 mRNA transcripts, 100 plasma metabolites, 200 urine metabolites, and 400 proteins over the longitudinal course of surgery and recovery. In our initial pilot dataset, we demonstrate the feasibility of collecting high quality multi-omic data at pre- and post-operative time points and are already seeing evidence of physiologic perturbation between timepoints.

CONCLUSIONS

This repository allows for longitudinal, state-of-the-art genomic, transcriptomic, proteomic, metabolomic, immunologic, and clinical data collection and provides a rich and stable infrastructure on which to fuel further biomedical discovery.

Multi-omics strategies for personalized and predictive medicine: past, current, and future translational opportunities

Precision medicine is driven by the paradigm shift of empowering clinicians to predict the most appropriate course of action for patients with complex diseases and improve routine medical and public health practice. It promotes integrating collective and individualized clinical data with patient specific multi-omics data to develop therapeutic strategies, and knowledgebase for predictive and personalized medicine in diverse populations. This study is based on the hypothesis that understanding patient's metabolomics and genetic make-up in conjunction with clinical data will significantly lead to determining predisposition, diagnostic, prognostic and predictive biomarkers and optimal paths providing personalized care for diverse and targeted chronic, acute, and infectious diseases. This study briefs emerging significant, and recently reported multi-omics and translational approaches aimed to facilitate implementation of precision medicine. Furthermore, it discusses current grand challenges, and the future need of Findable, Accessible, Intelligent, and Reproducible (FAIR) approach to accelerate diagnostic and preventive care delivery strategies beyond traditional symptom-driven, disease-causal medical practice.

Plasma profiling reveals a blood-based metabolic fingerprint of obstructive sleep apnea

INTRODUCTION

Obstructive sleep apnea (OSA) is a chronic, heterogeneous and multicomponent disorder with associated cardiovascular and metabolic alterations. Despite being the most common sleep-disordered breathing, it remains a significantly undiagnosed condition.

OBJECTIVE

We examined the plasma metabolome and lipidome of patients with suspected OSA, aiming to identify potential diagnosis biomarkers and to provide insights into the pathophysiological mechanisms underlying the disease. Additionally, we evaluated the impact of continuous positive airway pressure (CPAP) treatment on the circulating metabolomic and lipidomic profile.

MATERIAL AND METHODS

Observational-prospective-longitudinal study including 206 consecutive subjects referred to the sleep unit. OSA was defined as an apnea-hypopnoea index ≥ 15 events/h after polysomnography (PSG). Patients treated with CPAP were followed-up for 6 months. Untargeted plasma metabolomic and lipidomic profiling was performed using liquid chromatography coulpled to massspectrometry.

RESULTS

A plasma profile composed of 33 metabolites (mainly glycerophospholipids and bile acids) was identified in OSA vs. non-OSA patients. This profile correlated with specific PSG measures of OSA severity related to sleep fragmentation and hypoxemia. Machine learning analyses disclosed a 4-metabolites-signature that provided an accuracy (95% CI) of 0.98 (0.95-0.99) for OSA detection. CPAP treatment was associated with changes in 5 plasma metabolites previously altered by OSA.

CONCLUSIONS

This analysis of the circulating metabolome and lipidome reveals a molecular fingerprint of OSA, which was modulated after effective CPAP treatment. Our results suggest blood-based biomarker candidates with potential application in the personalized management of OSA and suggest the activation of adaptive mechanisms in response to OSA-derived hypoxia.

Precision medicine in trauma: a transformational frontier in patient care, education, and research

Purpose

Trauma is the leading cause of death before the age of 45 in the United States. Precision medicine (PM) is the most advanced scientific form of medical practice and seeks to gather data from the genome, environmental interactions, and lifestyles. Relating to trauma, PM promises to significantly advance our understanding of the factors that contribute to the physiologic response to injury.

Methods

We review the status of PM-driven trauma care. Semantic-based methods were used to gather data on genetic/epigenetic variability previously linked to the principal causes of trauma-related outcomes. Data were curated to include human investigations involving genomics/epigenomics with clinical relevance identifiable early after injury.

Results

Most studies relevant to genomic/epigenomic differences in trauma are specific to traumatic brain injury and injury-related sepsis. Genomic/epigenomic differences rarely encompass other relevant factors, such as coagulability and pharmacogenomics. Few studies describe clinical use of genomics/epigenomics for therapeutic intervention in trauma care, and even fewer attempt to incorporate real-time genomic/epigenomic information to precisely guide clinical decision-making.

Conclusion

Considering that genomics/epigenomics, environmental exposures, and lifestyles are most likely to be of significant medical relevance in advancing the field of trauma, the lack of application of concepts and methodologies from PM to trauma education, research, practice, and community wellness is underwhelming. We suggest that significant effort be given to incorporate the tools of what is becoming the “new medicine”.

Persistent metabolomic alterations characterize chronic critical illness after severe trauma

BACKGROUND

Following trauma, persistent inflammation, immunosuppression, and catabolism may characterize delayed recovery or failure to recover. Understanding the metabolic response associated with these adverse outcomes may facilitate earlier identification and intervention. We characterized the metabolic profiles of trauma victims who died or developed chronic critical illness (CCI) and hypothesized that differences would be evident within 1-week postinjury.

METHODS

Venous blood samples from trauma victims with shock who survived at least 7 days were analyzed using mass spectrometry. Subjects who died or developed CCI (intensive care unit length of stay of ≥14 days with persistent organ dysfunction) were compared with subjects who recovered rapidly (intensive care unit length of stay, ≤7 days) and uninjured controls. We used partial least squares discriminant analysis, t tests, linear mixed effects regression, and pathway enrichment analyses to make broad comparisons and identify differences in metabolite concentrations and pathways.

RESULTS

We identified 27 patients who died or developed CCI and 33 who recovered rapidly. Subjects were predominantly male (65%) with a median age of 53 years and Injury Severity Score of 36. Healthy controls (n = 48) had similar age and sex distributions. Overall, from the 163 metabolites detected in the samples, 56 metabolites and 21 pathways differed between injury outcome groups, and partial least squares discriminant analysis models distinguished injury outcome groups as early as 1-day postinjury. Differences were observed in tryptophan, phenylalanine, and tyrosine metabolism; metabolites associated with oxidative stress via methionine metabolism; inflammatory mediators including kynurenine, arachidonate, and glucuronic acid; and products of the gut microbiome including indole-3-propionate.

CONCLUSIONS

The metabolic profiles in subjects who ultimately die or develop CCI differ from those who have recovered. In particular, we have identified differences in markers of inflammation, oxidative stress, amino acid metabolism, and alterations in the gut microbiome. Targeted metabolomics has the potential to identify important metabolic changes postinjury to improve early diagnosis and targeted intervention.

LEVEL OF EVIDENCE

Prognostic/epidemiologic, level III.

Biomarkers in traumatic brain injury: new concepts

Traumatic brain injury is a multifaceted condition that encompasses a spectrum of injuries: contusions, axonal injuries in specific brain regions, edema, and hemorrhage. Brain injury determines a broad clinical and disability spectrum due to the implication of various cellular pathways, genetic phenotypes, and environmental factors. It is challenging to predict patient outcomes, to appropriately evaluate the patients, to determine a suitable treatment strategy and rehabilitation program, and to communicate with patient relatives. Biomarkers detected from body fluids are potential evaluation tools for traumatic brain injury patients. These may serve as internal indicators of cerebral damage, delivering valuable information about the dynamic cellular, biochemical, and molecular environments. The diagnostic and prognostic value of biomarkers tested both in animal models of traumatic brain injury is still under question, despite a considerable scientific literature. Recent publications emphasize that a more realistic approach involves combining multiple types of biomarkers with other investigative tools (imaging, outcome scales, and genetic polymorphisms). Additionally, there is increasing interest in the use of biomarkers as tools for treatment monitoring and as surrogate outcome variables to facilitate the design of distinct randomized controlled trials. This review highlights the latest available evidence regarding biomarkers in adults after traumatic brain injury and discusses new approaches in the evaluation of this patient group.

Detecting direct oral anticoagulants in trauma patients using liquid chromatography-mass spectrometry: A novel approach to medication reconciliation

BACKGROUND

Accurate medication reconciliation in trauma patients is essential but difficult. Currently, there is no established clinical method of detecting direct oral anticoagulants (DOACs) in trauma patients. We hypothesized that a liquid chromatography-mass spectrometry (LCMS)-based assay can be used to accurately detect DOACs in trauma patients upon hospital arrival.

METHODS

Plasma samples were collected from 356 patients who provided informed consent including 10 healthy controls, 19 known positive or negative controls, and 327 trauma patients older than 65 years who were evaluated at our large, urban level 1 trauma center. The assay methodology was developed in healthy and known controls to detect apixaban, rivaroxaban, and dabigatran using LCMS and then applied to 327 samples from trauma patients. Standard medication reconciliation processes in the electronic medical record documenting DOAC usage were compared with LCMS results to determine overall accuracy, sensitivity, specificity, and positive and negative predictive values (PPV, NPV) of the assay.

RESULTS

Of 356 patients, 39 (10.96%) were on DOACs: 21 were on apixaban, 14 on rivaroxaban, and 4 on dabigatran. The overall accuracy of the assay for detecting any DOAC was 98.60%, with a sensitivity of 94.87% and specificity of 99.05% (PPV, 92.50%; NPV, 99.37%). The assay detected apixaban with a sensitivity of 90.48% and specificity of 99.10% (PPV, 86.36%; NPV 99.40%). There were three false-positive results and two false-negative LCMS results for apixaban. Dabigatran and rivaroxaban were detected with 100% sensitivity and specificity.

CONCLUSION

This LCMS-based assay was highly accurate in detecting DOACs in trauma patients. Further studies need to confirm the clinical efficacy of this LCMS assay and its value for medication reconciliation in trauma patients.

LEVEL OF EVIDENCE

Diagnostic Test, level III.

A Preliminary Investigation towards the Risk Stratification of Allogeneic Stem Cell Recipients with Respect to the Potential for Development of GVHD via Their Pre-Transplant Plasma Lipid and Metabolic Signature

The clinical outcome of allogeneic hematopoietic stem cell transplantation (SCT) may be influenced by the metabolic status of the recipient following conditioning, which in turn may enable risk stratification with respect to the development of transplant-associated complications such as graft vs. host disease (GVHD). To better understand the impact of the metabolic profile of transplant recipients on post-transplant alloreactivity, we investigated the metabolic signature of 14 patients undergoing myeloablative conditioning followed by either human leukocyte antigen (HLA)-matched related or unrelated donor SCT, or autologous SCT. Blood samples were taken following conditioning and prior to transplant on day 0 and the plasma was comprehensively characterized with respect to its lipidome and metabolome via liquid chromatography/mass spectrometry (LCMS) and gas chromatography/mass spectrometry (GCMS). A pro-inflammatory metabolic profile was observed in patients who eventually developed GVHD. Five potential pre-transplant biomarkers, 2-aminobutyric acid, 1-monopalmitin, diacylglycerols (DG 38:5, DG 38:6), and fatty acid FA 20:1 demonstrated high sensitivity and specificity towards predicting post-transplant GVHD. The resulting predictive model demonstrated an estimated predictive accuracy of risk stratification of 100%, with area under the curve of the ROC of 0.995. The likelihood ratio of 1-monopalmitin (infinity), DG 38:5 (6.0), and DG 38:6 (6.0) also demonstrated that a patient with a positive test result for these biomarkers following conditioning and prior to transplant will be at risk of developing GVHD. Collectively, the data suggest the possibility that pre-transplant metabolic signature may be used for risk stratification of SCT recipients with respect to development of alloreactivity.

A prospective pilot study using metabolomics discloses specific fatty acid, catecholamine and tryptophan metabolic pathways as possible predictors for a negative outcome after severe trauma

Background

We wanted to define metabolomic patterns in plasma to predict a negative outcome in severe trauma patients.

Methods

A prospective pilot study was designed to evaluate plasma metabolomic patterns, established by liquid chromatography coupled to mass spectrometry, in patients allocated to an intensive care unit (in the University Hospital Arnau de Vilanova, Lleida, Spain) in the first hours after a severe trauma (n = 48). Univariate and multivariate statistics were employed to establish potential predictors of mortality.

Results

Plasma of patients non surviving to trauma (n = 5) exhibited a discriminating metabolomic pattern, involving basically metabolites belonging to fatty acid and catecholamine synthesis as well as tryptophan degradation pathways. Thus, concentration of several metabolites exhibited an area under the receiver operating curve (ROC) higher than 0.84, including 3-indolelactic acid, hydroxyisovaleric acid, phenylethanolamine, cortisol, epinephrine and myristic acid. Multivariate binary regression logistic revealed that patients with higher myristic acid concentrations had a non-survival odds ratio of 2.1 (CI 95% 1.1–3.9).

Conclusions

Specific fatty acids, catecholamine synthesis and tryptophan degradation pathways could be implicated in a negative outcome after trauma. The metabolomic study of severe trauma patients could be helpful for biomarker proposal.

Electronic supplementary material

The online version of this article (10.1186/s13049-019-0631-5) contains supplementary material, which is available to authorized users.