Establishment of the circadian metabolic phenotype strategy in spontaneously hypertensive rats: a dynamic metabolomics study

Background Circadian rhythms play a fundamental role in the progression of cardiovascular events. Almost all cardiovascular diseases have a circadian misalignment usually characterized by changes in metabolites. This study aimed to dynamically monitor rhythmic biomarkers, to elucidate the metabolic pathways that are potentially under circadian control in spontaneously hypertensive rats (SHRs), and to eventually establish a circadian metabolic phenotype strategy based on metabolomics. Methods In this study, an untargeted metabolomics technology was used to dynamically monitor changes in serum metabolites between SHR model group and WKY control group. Liquid chromatography-mass spectrometry (LC–MS) combined with multivariate statistical analysis was applied to identify markers of hypertension rhythm imbalance. The concentrations of amino acids and their metabolites identified as markers were quantified by a subsequent targeted metabolomics analysis. Overall, these approaches comprehensively explored the rhythm mechanism and established a circadian metabolic phenotype strategy. Results The metabolic profile revealed a disorder in the diurnal metabolism pattern in SHRs. Moreover, multivariate statistical analysis revealed metabolic markers of rhythm homeostasis, such as arginine, proline, phenylalanine, citric acid, l -malic acid, succinic acid, etc., accompanied by an imbalance in hypertension. The key metabolic pathways related to rhythm imbalance in hypertension were found by enrichment analysis, including amino acid metabolism, and the tricarboxylic acid cycle (TCA). In addition, the quantitative analysis of amino acids and their metabolites showed that the changes in leucine, isoleucine, valine, taurine, serine, and glycine were the most obvious. Conclusions In summary, this study illustrated the relationship between metabolites and the pathways across time on hypertension. These results may provide a theoretical basis for personalized treatment programmes and timing for hypertension.