Predicting the mortality of pneumonia-induced direct lung injury using serum metabolomics

Pneumonia-induced acute respiratory distress syndrome (ARDS) presents a significant source of morbidity and mortality in the ICU with no current treatment beyond supportive measures. A good predictive method to determine mortality of ARDS currently does not exist. As such, it is important to identify potential biomarkers that can predict mortality of pneumonia induced ARDS. Multivariate statistical analysis of 1H-NMR analyzed data yielded a predictive model that separated patient cohorts based on 28-day mortality. This study was a critical step forward to potentially develop new diagnostic and treatment options for those afflicted with pneumonia-induced ARDS

Metabolomics in severe traumatic brain injury: a scoping review

Abstract Background Diagnosis and prognostication of severe traumatic brain injury (sTBI) continue to be problematic despite years of research efforts. There are currently no clinically reliable biomarkers, though advances in protein biomarkers are being made. Utilizing Omics technology, particularly metabolomics, may provide new diagnostic biomarkers for sTBI. Several published studies have attempted to determine the specific metabolites and metabolic pathways involved; these studies will be reviewed. Aims This scoping review aims to summarize the current literature concerning metabolomics in sTBI, review the comprehensive data, and identify commonalities, if any, to define metabolites with potential clinical use. In addition, we will examine related metabolic pathways through pathway analysis. Methods Scoping review methodology was used to examine the current literature published in Embase, Scopus, PubMed, and Medline. An initial 1090 publications were identified and vetted with specific inclusion criteria. Of these, 20 publications were selected for further examination and summary. Metabolic data was classified using the Human Metabolome Database (HMDB) and arranged to determine the ‘recurrent’ metabolites and classes found in sTBI. To help understand potential mechanisms of injury, pathway analysis was performed using these metabolites and the Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Database. Results Several metabolites related to sTBI and their effects on biological pathways were identified in this review. Across the literature, proline, citrulline, lactate, alanine, valine, leucine, and serine all decreased in adults post sTBI, whereas both octanoic and decanoic acid increased. Hydroxy acids and organooxygen compounds generally increased following sTBI, while most carboxylic acids decreased. Pathway analysis showed significantly affected glycine and serine metabolism, glycolysis, branched-chain amino acid (BCAA) metabolism, and other amino acid metabolisms. Interestingly, no tricarboxylic acid cycle metabolites were affected. Conclusion Aside from a select few metabolites, classification of a metabolic profile proved difficult due to significant ambiguity between study design, sample size, type of sample, metabolomic detection techniques, and other confounding variables found in sTBI literature. Given the trends found in some studies, further metabolomics investigation of sTBI may be useful to identify clinically relevant metabolites

Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying

Abstract Background The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-Cov2 virus has become the greatest health and controversial issue for worldwide nations. It is associated with different clinical manifestations and a high mortality rate. Predicting mortality and identifying outcome predictors are crucial for COVID patients who are critically ill. Multivariate and machine learning methods may be used for developing prediction models and reduce the complexity of clinical phenotypes. Methods Multivariate predictive analysis was applied to 108 out of 250 clinical features, comorbidities, and blood markers captured at the admission time from a hospitalized cohort of patients (N = 250) with COVID-19. Inspired modification of partial least square (SIMPLS)-based model was developed to predict hospital mortality. Prediction accuracy was randomly assigned to training and validation sets. Predictive partition analysis was performed to obtain cutting value for either continuous or categorical variables. Latent class analysis (LCA) was carried to cluster the patients with COVID-19 to identify low- and high-risk patients. Principal component analysis and LCA were used to find a subgroup of survivors that tends to die. Results SIMPLS-based model was able to predict hospital mortality in patients with COVID-19 with moderate predictive power (Q2 = 0.24) and high accuracy (AUC > 0.85) through separating non-survivors from survivors developed using training and validation sets. This model was obtained by the 18 clinical and comorbidities predictors and 3 blood biochemical markers. Coronary artery disease, diabetes, Altered Mental Status, age > 65, and dementia were the topmost differentiating mortality predictors. CRP, prothrombin, and lactate were the most differentiating biochemical markers in the mortality prediction model. Clustering analysis identified high- and low-risk patients among COVID-19 survivors. Conclusions An accurate COVID-19 mortality prediction model among hospitalized patients based on the clinical features and comorbidities may play a beneficial role in the clinical setting to better management of patients with COVID-19. The current study revealed the application of machine-learning-based approaches to predict hospital mortality in patients with COVID-19 and identification of most important predictors from clinical, comorbidities and blood biochemical variables as well as recognizing high- and low-risk COVID-19 survivors

Metabolomics in critical care medicine: a new approach to biomarker discovery

Purpose: To present an overview and comparison of the main metabolomics techniques (1H NMR, GC-MS, and LC-MS) and their current and potential use in critical care medicine. Source: This is a focused review, not a systematic review, using the PubMed database as the predominant source of references to compare metabolomics techniques. Principal Findings: 1H NMR, GC-MS, and LC-MS are complementary techniques that can be used on a variety of biofluids for metabolomics analysis of patients in the Intensive Care Unit (ICU). These techniques have been successfully used for diagnosis and prognosis in the ICU and other clinical settings; for example, in patients with septic shock and community-acquired pneumonia. Conclusion: Metabolomics is a powerful tool that has strong potential to impact diagnosis and prognosis and to examine responses to treatment in critical care medicine through diagnostic and prognostic biomarker and biopattern identificatio

Plasma lipid profiling for the prognosis of 90-day mortality, in-hospital mortality, ICU admission, and severity in bacterial community-acquired pneumonia (CAP)

Abstract Introduction Pneumonia is the most common cause of mortality from infectious diseases, the second leading cause of nosocomial infection, and the leading cause of mortality among hospitalized adults. To improve clinical management, metabolomics has been increasingly applied to find specific metabolic biopatterns (profiling) for the diagnosis and prognosis of various infectious diseases, including pneumonia. Methods One hundred fifty bacterial community-acquired pneumonia (CAP) patients whose plasma samples were drawn within the first 24 h of hospital admission were enrolled in this study and separated into two age- and sex-matched cohorts: non-survivors (died ≤ 90 days) and survivors (survived > 90 days). Three analytical tools, 1H-NMR spectroscopy, GC-MS, and targeted DI-MS/MS, were used to prognosticate non-survivors from survivors by means of metabolic profiles. Results We show that quantitative lipid profiling using DI-MS/MS can predict the 90-day mortality and in-hospital mortality among patients with bacterial CAP compared to 1H-NMR- and GC-MS-based metabolomics. This study showed that the decreased lysophosphatidylcholines and increased acylcarnitines are significantly associated with increased mortality in bacterial CAP. Additionally, we found that decreased lysophosphatidylcholines and phosphatidylcholines (> 36 carbons) and increased acylcarnitines may be used to predict the prognosis of in-hospital mortality for bacterial CAP as well as the need for ICU admission and severity of bacterial CAP. Discussion This study demonstrates that lipid-based plasma metabolites can be used for the prognosis of 90-day mortality among patients with bacterial CAP. Moreover, lipid profiling can be utilized to identify patients with bacterial CAP who are at the highest risk of dying in hospital and who need ICU admission as well as the severity assessment of CAP

Plasma metabolomics for the diagnosis and prognosis of H1N1 influenza pneumonia

Abstract Background Metabolomics is a tool that has been used for the diagnosis and prognosis of specific diseases. The purpose of this study was to examine if metabolomics could be used as a potential diagnostic and prognostic tool for H1N1 pneumonia. Our hypothesis was that metabolomics can potentially be used early for the diagnosis and prognosis of H1N1 influenza pneumonia. Methods 1H nuclear magnetic resonance spectroscopy and gas chromatography-mass spectrometry were used to profile the metabolome in 42 patients with H1N1 pneumonia, 31 ventilated control subjects in the intensive care unit (ICU), and 30 culture-positive plasma samples from patients with bacterial community-acquired pneumonia drawn within the first 24 h of hospital admission for diagnosis and prognosis of disease. Results We found that plasma-based metabolomics from samples taken within 24 h of hospital admission can be used to discriminate H1N1 pneumonia from bacterial pneumonia and nonsurvivors from survivors of H1N1 pneumonia. Moreover, metabolomics is a highly sensitive and specific tool for the 90-day prognosis of mortality in H1N1 pneumonia. Conclusions This study demonstrates that H1N1 pneumonia can create a quite different plasma metabolic profile from bacterial culture-positive pneumonia and ventilated control subjects in the ICU on the basis of plasma samples taken within 24 h of hospital/ICU admission, early in the course of disease

Using metabolomics to predict severe traumatic brain injury outcome (GOSE) at 3 and 12 months

Background: Prognostication is very important to clinicians and families during the early management of severe traumatic brain injury (sTBI), however, there are no gold standard biomarkers to determine prognosis in sTBI. As has been demonstrated in several diseases, early measurement of serum metabolomic profiles can be used as sensitive and specific biomarkers to predict outcomes. Methods We prospectively enrolled 59 adults with sTBI (Glasgow coma scale, GCS ≤ 8) in a multicenter Canadian TBI (CanTBI) study. Serum samples were drawn for metabolomic profiling on the 1st and 4th days following injury. The Glasgow outcome scale extended (GOSE) was collected at 3- and 12-months post-injury. Targeted direct infusion liquid chromatography-tandem mass spectrometry (DI/LC–MS/MS) and untargeted proton nuclear magnetic resonance spectroscopy (1H-NMR) were used to profile serum metabolites. Multivariate analysis was used to determine the association between serum metabolomics and GOSE, dichotomized into favorable (GOSE 5–8) and unfavorable (GOSE 1–4), outcomes. Results Serum metabolic profiles on days 1 and 4 post-injury were highly predictive (Q2 > 0.4–0.5) and highly accurate (AUC > 0.99) to predict GOSE outcome at 3- and 12-months post-injury and mortality at 3 months. The metabolic profiles on day 4 were more predictive (Q2 > 0.55) than those measured on day 1 post-injury. Unfavorable outcomes were associated with considerable metabolite changes from day 1 to day 4 compared to favorable outcomes. Increased lysophosphatidylcholines, acylcarnitines, energy-related metabolites (glucose, lactate), aromatic amino acids, and glutamate were associated with poor outcomes and mortality. Discussion Metabolomic profiles were strongly associated with the prognosis of GOSE outcome at 3 and 12 months and mortality following sTBI in adults. The metabolic phenotypes on day 4 post-injury were more predictive and significant for predicting the sTBI outcome compared to the day 1 sample. This may reflect the larger contribution of secondary brain injury (day 4) to sTBI outcome. Patients with unfavorable outcomes demonstrated more metabolite changes from day 1 to day 4 post-injury. These findings highlighted increased concentration of neurobiomarkers such as N-acetylaspartate (NAA) and tyrosine, decreased concentrations of ketone bodies, and decreased urea cycle metabolites on day 4 presenting potential metabolites to predict the outcome. The current findings strongly support the use of serum metabolomics, that are shown to be better than clinical data, in determining prognosis in adults with sTBI in the early days post-injury. Our findings, however, require validation in a larger cohort of adults with sTBI to be used for clinical practice.Medicine, Faculty ofNon UBCPsychiatry, Department ofReviewedFacultyResearcherPostdoctoralGraduateOthe

Plasma metabolomics for the diagnosis and prognosis of H1N1 influenza pneumonia

Abstract Background Metabolomics is a tool that has been used for the diagnosis and prognosis of specific diseases. The purpose of this study was to examine if metabolomics could be used as a potential diagnostic and prognostic tool for H1N1 pneumonia. Our hypothesis was that metabolomics can potentially be used early for the diagnosis and prognosis of H1N1 influenza pneumonia. Methods 1H nuclear magnetic resonance spectroscopy and gas chromatography-mass spectrometry were used to profile the metabolome in 42 patients with H1N1 pneumonia, 31 ventilated control subjects in the intensive care unit (ICU), and 30 culture-positive plasma samples from patients with bacterial community-acquired pneumonia drawn within the first 24 h of hospital admission for diagnosis and prognosis of disease. Results We found that plasma-based metabolomics from samples taken within 24 h of hospital admission can be used to discriminate H1N1 pneumonia from bacterial pneumonia and nonsurvivors from survivors of H1N1 pneumonia. Moreover, metabolomics is a highly sensitive and specific tool for the 90-day prognosis of mortality in H1N1 pneumonia. Conclusions This study demonstrates that H1N1 pneumonia can create a quite different plasma metabolic profile from bacterial culture-positive pneumonia and ventilated control subjects in the ICU on the basis of plasma samples taken within 24 h of hospital/ICU admission, early in the course of disease

Therapeutic hypothermia attenuates physiologic, histologic, and metabolomic markers of injury in a porcine model of acute respiratory distress syndrome

Abstract Acute respiratory distress syndrome (ARDS) is a lung injury characterized by noncardiogenic pulmonary edema and hypoxic respiratory failure. The purpose of this study was to investigate the effects of therapeutic hypothermia on short‐term experimental ARDS. Twenty adult female Yorkshire pigs were divided into four groups (n = 5 each): normothermic control (C), normothermic injured (I), hypothermic control (HC), and hypothermic injured (HI). Acute respiratory distress syndrome was induced experimentally via intrapulmonary injection of oleic acid. Target core temperature was achieved in the HI group within 1 h of injury induction. Cardiorespiratory, histologic, cytokine, and metabolomic data were collected on all animals prior to and following injury/sham. All data were collected for approximately 12 h from the beginning of the study until euthanasia. Therapeutic hypothermia reduced injury in the HI compared to the I group (histological injury score = 0.51 ± 0.18 vs. 0.76 ± 0.06; p = 0.02) with no change in gas exchange. All groups expressed distinct phenotypes, with a reduction in pro‐inflammatory metabolites, an increase in anti‐inflammatory metabolites, and a reduction in inflammatory cytokines observed in the HI group compared to the I group. Changes to respiratory system mechanics in the injured groups were due to increases in lung elastance (E) and resistance (R) (ΔE from pre‐injury = 46 ± 14 cmH2O L−1, p < 0.0001; ΔR from pre‐injury: 3 ± 2 cmH2O L−1 s−, p = 0.30) rather than changes to the chest wall (ΔE from pre‐injury: 0.7 ± 1.6 cmH2O L−1, p = 0.99; ΔR from pre‐injury: 0.6 ± 0.1 cmH2O L−1 s−, p = 0.01). Both control groups had no change in respiratory mechanics. In conclusion, therapeutic hypothermia can reduce markers of injury and inflammation associated with experimentally induced short‐term ARDS