Abstract
ObjectiveTo identify metabolomic biomarkers predictive of Intensive Care Unit (ICU) mortality in adults.RationaleComprehensive metabolomic profiling of plasma at ICU admission to identify biomarkers associated with mortality has recently become feasible.MethodsWe performed metabolomic profiling of plasma from 90 ICU subjects enrolled in the BWH Registry of Critical Illness (RoCI). We tested individual metabolites and a Bayesian Network of metabolites for association with 28-day mortality, using logistic regression in R, and the CGBayesNets Package in MATLAB. Both individual metabolites and the network were tested for replication in an independent cohort of 149 adults enrolled in the Community Acquired Pneumonia and Sepsis Outcome Diagnostics (CAPSOD) study.ResultsWe tested variable metabolites for association with 28-day mortality. In RoCI, nearly one third of metabolites differed among ICU survivors versus those who died by day 28 (N = 57 metabolites, p<.05). Associations with 28-day mortality replicated for 31 of these metabolites (with p<.05) in the CAPSOD population. Replicating metabolites included lipids (N = 14), amino acids or amino acid breakdown products (N = 12), carbohydrates (N = 1), nucleotides (N = 3), and 1 peptide. Among 31 replicated metabolites, 25 were higher in subjects who progressed to die; all 6 metabolites that are lower in those who die are lipids. We used Bayesian modeling to form a metabolomic network of 7 metabolites associated with death (gamma-glutamylphenylalanine, gamma-glutamyltyrosine, 1-arachidonoylGPC(20:4), taurochenodeoxycholate, 3-(4-hydroxyphenyl) lactate, sucrose, kynurenine). This network achieved a 91% AUC predicting 28-day mortality in RoCI, and 74% of the AUC in CAPSOD (p<.001 in both populations).ConclusionBoth individual metabolites and a metabolomic network were associated with 28-day mortality in two independent cohorts. Metabolomic profiling represents a valuable new approach for identifying novel biomarkers in critically ill patients.
Highlights
More than 4 million patients are admitted to intensive care units (ICUs) each year in the United States. [1] Despite marked improvements in care for these critically ill patients, [2,3] approximately 500,000 die in the Intensive Care Unit (ICU) each year. [1] Identifying biomarkers that distinguish the patients at highest risk for poor outcomes who could be targeted for novel therapies and clinical trials is critical. [4].Investigating metabolomics to identify novel biomarkers in the ICU is promising for several reasons
Both individual metabolites and a metabolomic network were associated with 28-day mortality in two independent cohorts
[5] While metabolomics was previously highly time-intensive and technologically limited, and could not plausibly be done on a large scale, technological advances in gas and lipid chromatography and mass spectroscopy make such study possible. [6,7] metabolomic profiling has been performed in multiple complex trait diseases, in each case identifying important novel biologic pathways that contribute to pathogenesis and prognosis
Summary
More than 4 million patients are admitted to intensive care units (ICUs) each year in the United States. [1] Despite marked improvements in care for these critically ill patients, [2,3] approximately 500,000 die in the ICU each year. [1] Identifying biomarkers that distinguish the patients at highest risk for poor outcomes who could be targeted for novel therapies and clinical trials is critical. [4].Investigating metabolomics to identify novel biomarkers in the ICU is promising for several reasons. [6,7] metabolomic profiling has been performed in multiple complex trait diseases (notably recent work in cancer [8], diabetes [9], tuberculosis [10] and, most recently, septic shock in children [11]), in each case identifying important novel biologic pathways that contribute to pathogenesis and prognosis. For all of these reasons, we performed metabolic profiling to identify prognosticators of 28-day mortality in a cohort of 90 critically ill adult ICU patients. We compare our results to recent work that examined metabolomics in septic shock using these same metabolomics profiles but different analytical methods. [12]
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