Metabolomics and machine learning approaches for diagnostic and prognostic biomarkers screening in sepsis.

BACKGROUND: Sepsis is a life-threatening disease with a poor prognosis, and metabolic disorders play a crucial role in its development. This study aims to identify key metabolites that may be associated with the accurate diagnosis and prognosis of sepsis. METHODS: Septic patients and healthy individuals were enrolled to investigate metabolic changes using non-targeted liquid chromatography-high-resolution mass spectrometry metabolomics. Machine learning algorithms were subsequently employed to identify key differentially expressed metabolites (DEMs). Prognostic-related DEMs were then identified using univariate and multivariate Cox regression analyses. The septic rat model was established to verify the effect of phenylalanine metabolism-related gene MAOA on survival and mean arterial pressure after sepsis. RESULTS: A total of 532 DEMs were identified between healthy control and septic patients using metabolomics. The main pathways affected by these DEMs were amino acid biosynthesis, phenylalanine metabolism, tyrosine metabolism, glycine, serine and threonine metabolism, and arginine and proline metabolism. To identify sepsis diagnosis-related biomarkers, support vector machine (SVM) and random forest (RF) algorithms were employed, leading to the identification of four biomarkers. Additionally, analysis of transcriptome data from sepsis patients in the GEO database revealed a significant up-regulation of the phenylalanine metabolism-related gene MAOA in sepsis. Further investigation showed that inhibition of MAOA using the inhibitor RS-8359 reduced phenylalanine levels and improved mean arterial pressure and survival rate in septic rats. Finally, using univariate and multivariate cox regression analysis, six DEMs were identified as prognostic markers for sepsis. CONCLUSIONS: This study employed metabolomics and machine learning algorithms to identify differential metabolites that are associated with the diagnosis and prognosis of sepsis patients. Unraveling the relationship between metabolic characteristics and sepsis provides new insights into the underlying biological mechanisms, which could potentially assist in the diagnosis and treatment of sepsis. TRIAL REGISTRATION: This human study was approved by the Ethics Committee of the Research Institute of Surgery (2021-179) and was registered by the Chinese Clinical Trial Registry (Date: 09/12/2021, ChiCTR2200055772).

The burden of premature ventricular contractions predicts adverse fetal and neonatal outcomes among pregnant women without structural heart disease: A prospective cohort study.

BACKGROUND: Premature ventricular contractions (PVCs) may increase during pregnancy, however, few studies have evaluated the relationship between PVCs and the pregnant outcomes. HYPOTHESIS: PVCs may increase the adverse fetal/neonatal outcomes in pregnant women. METHODS: Six thousand one hundred and forty-eight pregnant women were prospectively enrolled in our center between 2017 and 2019 in the study. The average PVC burden was determined by calculating the number of PVCs in total beats. Those who had a PVC burden >0.5% were divided into two groups based on the presence or absence of adverse fetal or neonatal events. The adverse outcomes were compared between the groups to assess the impact of PVCs on pregnancy. RESULTS: A total of 103 (1.68%) women with a PVC burden >0.5% were recorded. Among them, 17 adverse events (12 cases) were documented, which was significantly higher than that among women without PVCs (11.65% vs. 2.93%, p < .01). The median PVC burden among pregnant women with PVCs was 2.84% (1.02%-6.1%). Furthermore, compared with that of the women without adverse events, the median PVC burden of women with adverse fetal or neonatal outcomes was significantly higher (9.02% vs. 2.30%, p < .01). Multivariate logistic regression analysis demonstrated that not the LVEF, heart rate and bigeminy, but only the PVC burden was associated with adverse fetal or neonatal outcomes among pregnant women with PVCs (OR: 1.34, 95% CI [1.11-1.61], p < .01). CONCLUSIONS: Frequent PVCs have adverse effects on pregnancy, and the PVC burden might be an important factor associated with adverse fetal and neonatal outcomes among pregnant women with PVCs.