Development and validation of a sensitive and reliable targeted metabolomics method for the quantification of cardiovascular disease-related biomarkers in plasma using ultrahigh-performance liquid chromatography-tandem mass spectrometry.

Abstract

Cardiovascular disease, as a multifactorial disease caused by genetics and environment, has emerged as a leading cause of mortality. The discovery of metabolic biomarkers for the clinical diagnosis, early warning and elucidation of the molecular pathogenesis of cardiovascular disease, using metabolomics, has attracted broad interest. Therefore, this work aimed to develop a sensitive and reliable targeted metabolomics method for the quantification of cardiovascular disease-related biomarkers in plasma. The method was developed and validated using ultrahigh-performance liquid chromatography augmented with tandem mass spectrometry (UHPLC/MS/MS). The LC conditions and MS parameters were optimized using selected reaction monitoring scanning mode to high-throughput and sensitive separation, and could detect 20 metabolic biomarkers in a single experiment. And the linearity, selectivity, accuracy, precision, stability and recovery of the developed method were assessed according to the Bioanalytical Method Validation guidelines of the United States Food and Drug Administration. These quantified metabolic biomarkers are involved in pathways such as aromatic amino acid catabolism (e.g. phenylalanine, tryptophan, tyrosine), trimethylamine N-oxide (TMAO) biosynthesis (e.g. TMAO, choline, carnitine, betaine) and histidine metabolism (e.g. histidine), among others. All analytes exhibited excellent linearities with coefficients of determination greater than 0.99. Accuracies deviated by less than 15% for medium- and high-concentration samples and less than 20% for low-concentration samples, with intra- and inter-day precisions of 1.12-14.12% and 0.30-13.74%, respectively. Recoveries and stabilities also met the analysis requirements of biological samples. The targeted metabolomics method was shown to have a powerful ability to accurately analyze metabolic biomarkers, thereby providing valuable information for large-scale biomarker validation and clarifying the potential material basis of cardiovascular disease for clinical diagnosis or early warning.