ARDS metabolic fingerprints: characterization, benchmarking, and potential mechanistic interpretation.

In this study, we aimed to identify acute respiratory distress syndrome (ARDS) metabolic fingerprints in selected patient cohorts and compare the metabolic profiles of direct versus indirect ARDS and hypoinflammatory versus hyperinflammatory ARDS. We hypothesized that the biological and inflammatory processes in ARDS would manifest as unique metabolomic fingerprints that set ARDS apart from other intensive care unit (ICU) conditions and could help examine ARDS subphenotypes and clinical subgroups. Patients with ARDS (n = 108) and ICU ventilated controls (n = 27) were included. Samples were randomly divided into 2/3 training and 1/3 test sets. Samples were analyzed using 1H nuclear magnetic resonance spectroscopy and gas chromatography-mass spectrometry. Twelve proteins/cytokines were also measured. Orthogonal partial least squares discriminant analysis (OPLS-DA) was used to select the most differentiating ARDS metabolites and protein/cytokines. Predictive performance of OPLS-DA models was measured in the test set. Temporal changes of metabolites were examined as patients progressed through ARDS until clinical recovery. Metabolic profiles of direct versus indirect ARDS subgroups and hypoinflammatory versus hyperinflammatory ARDS subgroups were compared. Serum metabolomics and proteins/cytokines had similar area under receiver operator curves when distinguishing ARDS from ICU controls. Pathway analysis of ARDS differentiating metabolites identified a dominant involvement of serine-glycine metabolism. In longitudinal tracking, the identified pathway metabolites generally exhibited correction by 7-14 days, coinciding with clinical improvement. ARDS subphenotypes and clinical subgroups were metabolically distinct. However, our identified metabolic fingerprints are not ARDS diagnostic biomarkers, and further research is required to ascertain generalizability. In conclusion, patients with ARDS are metabolically different from ICU controls. ARDS subphenotypes and clinical subgroups are metabolically distinct.

Evolution of ARDS biomarkers: Will metabolomics be the answer?

To date, there is no clinically agreed-upon diagnostic test for acute respiratory distress syndrome (ARDS): the condition is still diagnosed on the basis of a constellation of clinical findings, laboratory tests, and radiological images. Development of ARDS biomarkers has been in a state of continuous flux during the past four decades. To address ARDS heterogeneity, several studies have recently focused on subphenotyping the disease on the basis of observable clinical characteristics and associated blood biomarkers. However, the strong correlation between identified biomarkers and ARDS subphenotypes has yet to establish etiology; hence, there is a need for the adoption of other methodologies for studying ARDS. In this review, we will shed light on ARDS metabolomics research in the literature and discuss advances and major obstacles encountered in ARDS metabolomics research. Generally, the ARDS metabolomics studies focused on identification of differentiating metabolites for diagnosing ARDS, but they were performed to different standards in terms of sample size, selection of control cohort, type of specimens collected, and measuring technique utilized. Virtually none of these studies have been properly validated to identify true metabolomics biomarkers of ARDS. Though in their infancy, metabolomics studies exhibit promise to unfold the biological processes underlying ARDS and, in our opinion, have great potential for pushing forward our present understanding of ARDS.

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 identification.