Circulating metabolic biomarkers predict incident sepsis: a large-scale population study in the UK Biobank

Currently, there is an absence of large-scale research focusing on the metabolome profiles of individuals prior to the development of sepsis. This study aimed to evaluate the associations of circulating Nuclear Magnetic Resonance (NMR) metabolic biomarkers with the risk of incident sepsis and the predictive ability of these metabolites for sepsis.

The analysis utilized plasma metabolomic data measuring through NMR from the UK Biobank, which involved baseline plasma samples of 106,533 participants. The multivariable-adjusted Cox proportional hazard models were used to assess the associations of each circulating NMR metabolite biomarker with risk of incident sepsis. The full cohort was randomly assigned to a training set (n = 53,267) and a test set (n = 53,266) to develop and validate the sepsis risk prediction model. In training set, the least absolute shrinkage and selection operator (LASSO) and stepwise Cox regression analyses were used to develop the prediction model. In test set, the predictive ability of conventional risk factors-based and combined metabolic biomarkers prediction model was assessed by Harrell's C-index. The incremental predictive power of the metabolic biomarkers was evaluated with continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI).

A total of 90 circulating metabolic biomarkers were significantly associated with risk of incident sepsis (all FDR adjusted P value < 0.05). Of these, triglycerides related lipid sub-classes, glycolysis, ketone bodies, and inflammation related metabolite biomarkers, creatinine, and phenylalanine were positively associated with risk of incident sepsis, while most of other lipid sub-classes, albumin, histidine, fatty acid and cholines related metabolic biomarkers were negatively associated with risk of sepsis. The Harrell's C-index of the conventional prediction model was 0.733 (95% CI: 0.722, 0.745) for incident sepsis; after adding the circulating NMR metabolic biomarkers to the conventional prediction model, the Harrell's C-index increased to 0.741 (95% CI: 0.730, 0.753) for incident sepsis. In addition, the continuous NRI and IDI were 0.022 (95% CI: 0.015, 0.043, P < 0.05) and 0.009 (95% CI: 0.006, 0.014, P < 0.05).

This study identified multiple plasma metabolic biomarkers were associated with risk of incident sepsis. The addition of these metabolic biomarkers to the conventional risk factors-based model significantly improved the prediction precision.