Abstract
PURPOSE: Machine learning has shown promise to detect useful subgroups of patients with sepsis from gene expression and protein data. This approach has rarely been deployed in metabolomic datasets. Metabolomic data are of interest as they capture effects from the genome, proteome, and environmental. We aimed to discover metabolic sub-phenotypes of septic shock, examine their temporal stability and association with clinical outcome. METHODS: Analysis was performed in two double-blind randomized trials in septic shock (LeoPARDS (1402 samples from 470 patients) and VANISH (493 samples from 173 patients)). Patients were included soon after the onset of shock and had serum collected at up to four time points. Metabolic clusters were identified from 474 metabolites using k-means clustering in LeoPARDS and predicted in VANISH with an elastic net classifier. RESULTS: Three sub-phenotypes were found. The main determinants of cluster membership were lipid species, especially lysophospholipids. Low lysophospholipid sub-phenotypes were associated with higher circulating cytokine levels. Persistence of low lysophospholipid sub-phenotypes was associated with higher mortality compared to the high lysophospholipid sub-phenotype (LeoPARDS: cluster 2 odds ratio 3.66 (95% CI 1.88-7.20), p = 0.0001, cluster 3 2.49 (1.29-4.81), p = 0.006; VANISH: cluster 2 4.13 (1.17-15.61), p = 0.03), cluster 3 3.22 (1.09-9.92), p = 0.04, vs cluster 1). We found no heterogeneity of treatment effect for any of the trial interventions by baseline metabolic sub-phenotype. CONCLUSION: Three metabolic subgroups exist in septic shock which evolve over time. Persistence of low lysophospholipid sub-phenotypes is associated with mortality. Monitoring these subgroups could help identify patients at risk of poor outcome and direct novel therapies such as lysophospholipid supplementation. REGISTRATION: Clinicaltirals.gov Identifiers, VANISH: ISRCTN 20769191, LeoPARDS: ISRCTN12776039.