Personalized medicine using omics approaches in acute respiratory distress syndrome to identify biological phenotypes.

In the last decade, research on acute respiratory distress syndrome (ARDS) has made considerable progress. However, ARDS remains a leading cause of mortality in the intensive care unit. ARDS presents distinct subphenotypes with different clinical and biological features. The pathophysiologic mechanisms of ARDS may contribute to the biological variability and partially explain why some pharmacologic therapies for ARDS have failed to improve patient outcomes. Therefore, identifying ARDS variability and heterogeneity might be a key strategy for finding effective treatments. Research involving studies on biomarkers and genomic, metabolomic, and proteomic technologies is increasing. These new approaches, which are dedicated to the identification and quantitative analysis of components from biological matrixes, may help differentiate between different types of damage and predict clinical outcome and risk. Omics technologies offer a new opportunity for the development of diagnostic tools and personalized therapy in ARDS. This narrative review assesses recent evidence regarding genomics, proteomics, and metabolomics in ARDS research.

Machine learning-driven serum metabolomics: insights into the transition to early-stage heart failure with preserved ejection fraction

Introduction: Metabolomics has emerged as a powerful tool in providing readouts of early disease states before clinical manifestation. Here we used the predictive power of Unsupervised Hierarchical Clustering Analysis (UHCA) and Automated Machine Learning (AutoML) algorithms to identify serum metabolic panels in a population at risk of developing HFpEF. Methods: We studied 215 subjects staged as non-HF, pre-HFpEF and early-stage HFpEF(es-HFpEF). We evaluated clinical, laboratory, echocardiographic, and NMR-based metabolomics of blood serum data. UHCA and AutoML were used to explore metabolic fingerprints potentially related to clinical features or HFpEF. We used Metabolite Set Enrichment Analysis to explore biochemical pathways. Results: The UHCA identified three major patients (P) and two metabolites (M) clusters (Figure). The P clusters were associated with HFpEF stages, cardiac remodeling, diastolic dysfunction, and sex (Pearson Chi-square, p < 0.05) and M clusters with glycine and serine metabolism and urea cycle pathways (FDR-adjusted p-value < 0.002). Considering non-HFpEF and es-HFpEF groups, AUROC mean for feature subset combinations was 0.897 and the highest AUROC (0.995) combined metabolites, clinical, laboratory and echo features. Of the 64 models trained that included metabolites as input, serine (25), uridine (17), 2-oxoglutarate (14), citrate (14), 2-aminobutyrate (13) and taurine (13) were observed more frequently with feature importance value greater than zero. The metabolites with higher sum values of feature importance were serine (0.173), uridine (0.131), 2-aminobutyrate (0.123), choline (0.098) and dimethylamine (0.087). Conclusions: This study revealed characteristic metabolite profiles in the sera of patients at risk of developing HFpEF. These metabolite panels can add information for classificatory algorithms development and contribute to the understanding of HFpEF pathophysiology.