1H-nuclear magnetic resonance analysis reveals dynamic changes in the metabolic profile of patients with severe burns

Abstract

Abstract Background Severe burn injury causes a hypermetabolic response, resulting in muscle protein catabolism and multiple organ damage syndrome. However, this response has not yet been continuously characterized by metabolomics in patients. This study aims to quantify temporal changes in the metabolic processes of patients with severe burns. Methods We employed 1H-nuclear magnetic resonance (NMR) spectroscopy to scrutinize metabolic alterations during the initial 35 days following burn injury in a cohort of 17 adult patients with severe burns, with 10 healthy individuals included as controls. Plasma specimens were collected from patients on postburn days 1, 3, 7, 14, 21, 28 and 35. After performing multivariate statistical analysis, repeated-measures analysis of variance and time-series analysis, we quantified changes in metabolite concentrations. Results Among the 36 metabolites quantified across 119 samples from burn patients, branched-chain amino acids, glutamate, glycine, glucose, pyruvate, lactate, trimethylamine N-oxide and others exhibited obvious temporal variations in concentration. Notably, these metabolites could be categorized into three clusters based on their temporal characteristics. The initial response to injury was characterized by changes in lactate and amino acids, while later changes were driven by an increase in fatty acid catabolism and microbial metabolism, leading to the accumulation of ketone bodies and microbial metabolites. Conclusions Metabolomics techniques utilizing NMR have the potential to monitor the intricate processes of metabolism in patients with severe burns. This study confirmed that the third day after burn injury serves as the boundary between the ebb phase and the flow phase. Furthermore, identification of three distinct temporal patterns of metabolites revealed the intrinsic temporal relationships between these metabolites, providing clinical data for optimizing therapeutic strategies.