A preliminary study on the early warning role of DL-malic acid in atrial fibrillation occurrence among patients with hyperuricemia - Li, Dayong; Jiao, Huachen; Liu, Donghai; Li, Zeng; Lv, Shunxin; Jin, Haoluo; Yan, Xipeng (Frontiers in Cardiovascular Medicine, 2025)
BackgroundThere have been sufficient previous studies demonstrating that hyperuricemia (HUA) is closely associated with the occurrence of atrial fibrillation (AF).The incidence of AF in patients with hyperuricemia is higher than that in the general population. Therefore, it is meaningful to explore the serum markers of AF in the HUA population and establish early warning indicators.ObjectiveTo preliminarily explore the correlation between HUA and AF at the metabolomics level, and to identify a group of metabolites with potential predictive power for AF that can be used for further large-scale studies.MethodsThis study used untargeted metabolomics technology to detect serum metabolites of patients with AF, patients with AFHUA, and control group. Receiver operator characteristic (ROC) curve were used to analyze differential metabolites.ResultsUltimately, multiple metabolites such as L-Threonine, DL-Malic acid, L-Valine, L-Cysteine were identified as early warning markers of AF in patients with HUA. Combined ROC curve using these four metabolites between the AFHUA/Control comparison group and the AFHUA/AF comparison group showed good predictive efficacy, with Area Under the ROC Curve (AUC) = 0.923 (P < 0.001) in the AFHUA/Control comparison group and AUC = 0.714 (P < 0.001) in the AFHUA/AF comparison group. This provides an early predictive method for patients who may develop atrial fibrillation among those with hyperuricemia. And offers new approaches for the prevention and treatment of atrial fibrillation.ConclusionThis study indicates that serum metabolomics can be specifically used to predict the probability of AF occurrence in individuals with HUA, and has identified metabolites such as L-Threonine, DL-Malic acid, L-Valine, and L-Cysteine that possess potential predictive efficacy.