Untargeted Metabolomics Analysis Using UHPLC-Q-TOF/MS Reveals Metabolic Changes Associated with Hypertension in Children

Metabolomic signatures for blood pressure from early to late adolescence: findings from a U.S. cohort

INTRODUCTION

Metabolite signatures for blood pressure (BP) may reveal biomarkers, elucidate pathogenesis, and provide prevention targets for high BP. Knowledge regarding metabolites associated with BP in adolescence remains limited.

OBJECTIVES

Investigate the associations between metabolites and adolescent BP, both cross-sectionally (in early and late adolescence) and prospectively (from early to late adolescence).

METHODS

Participants are from the Project Viva prospective cohort. During the early (median: 12.8 years; N = 556) and late (median: 17.4 years; N = 501) adolescence visits, we conducted untargeted plasma metabolomic profiling and measured systolic (SBP) and diastolic BP (DBP). We used linear regression to identify metabolites cross-sectionally associated with BP at each time point, and to assess prospective associations of changes in metabolite levels from early to late adolescence with late adolescence BP. We used Weighted Gene Correlation Network Analysis and Spearman's partial correlation to identify metabolite clusters associated with BP at each time point.

RESULTS

In the linear models, higher androgenic steroid levels were consistently associated with higher SBP and DBP in early and late adolescence. A cluster of 59 metabolites, mainly composed of androgenic steroids, correlated with higher SBP and DBP in early adolescence. A cluster primarily composed of fatty acid lipids was marginally associated with higher SBP in females in late adolescence. Multiple metabolites, including those in the creatine and purine metabolism sub-pathways, were associated with higher SBP and DBP both cross-sectionally and prospectively.

CONCLUSION

Our results shed light on the potential metabolic processes and pathophysiology underlying high BP in adolescents.

Hub gene ELK3-mediated reprogramming lipid metabolism regulates phenotypic switching of pulmonary artery smooth muscle cells to develop pulmonary arterial hypertension induced by PM2.5

Fine particulate matter (PM2.5) as an environmental pollutant is related with respiratory and cardiovascular diseases. Pulmonary arterial hypertension (PAH) was characterized by incremental pulmonary artery pressure and pulmonary arterial remodeling, leading to right ventricular hypertrophy, and finally cardiac failure and death. The adverse effects on pulmonary artery and the molecular biological mechanism underlying PM2.5-caused PAH has not been elaborated clearly. In the current study, the ambient PM2.5 exposure mice model along with HPASMCs models were established. Based on bioinformatic methods and machine learning algorithms, the hub genes in PAH were screened and then adverse effects on pulmonary artery and potential mechanism was studied. Our results showed that chronic PM2.5 exposure contributed to increased pulmonary artery pressure, pulmonary arterial remodeling and right ventricular hypertrophy in mice. In vitro, PM2.5 induced phenotypic switching in HPASMCs, which served as the early stage of PAH. In mechanism, we investigated that PM2.5-mediated mitochondrial dysfunction could induce phenotypic switching in HPASMCs, which was possibly through reprogramming lipid metabolism. Next, we used machine learning algorithm to identify ELK3 as potential hub gene for mitochondrial fission. Besides, the effect of DNA methylation on ELK3 was further detected in HPASMCs after PM2.5 exposure. The results provided novel directions for protection of pulmonary vasculature injury, against adverse environmental stimuli. This work also provided a new idea for the prevention of PAH, as well as provided experimental evidence for the targeted therapy of PAH.

Exploring the causal role of multiple metabolites on ovarian cancer: a two sample Mendelian randomization study

BACKGROUND

The mechanisms and risk factors underlying ovarian cancer (OC) remain under investigation, making the identification of new prognostic biomarkers and improved predictive factors critically important. Recently, circulating metabolites have shown potential in predicting survival outcomes and may be associated with the pathogenesis of OC. However, research into their genetic determinants is limited, and there are some inadequacies in understanding the distinct subtypes of OC. In this context, we conducted a Mendelian randomization study aiming to provide evidence for the relationship between genetically determined metabolites (GDMs) and the risk of OC and its subtypes.

METHODS

In this study, we consolidated genetic statistical data of GDMs with OC and its subtypes through a genome-wide association study (GWAS) and conducted a two-sample Mendelian randomization (MR) analysis. The inverse variance weighted (IVW) method served as the primary approach, with MR-Egger and weighted median methods employed for cross-validation to determine whether a causal relationship exists between the metabolites and OC risk. Moreover, a range of sensitivity analyses were conducted to validate the robustness of the results. MR-Egger intercept, and Cochran's Q statistical analysis were used to evaluate possible heterogeneity and pleiotropy. False discovery rate (FDR) correction was applied to validate the findings. We also conducted a reverse MR analysis to validate whether the observed blood metabolite levels were influenced by OC risk. Additionally, metabolic pathway analysis was carried out using the MetaboAnalyst 5.0 software.

RESULTS

In MR analysis, we discovered 18 suggestive causal associations involving 14 known metabolites, 8 metabolites as potential risk factors, and 6 as potential cancer risk reducers. In addition, three significant pathways, "caffeine metabolism," "arginine biosynthesis," and "citrate cycle (TCA cycle)" were associated with the development of mucinous ovarian cancer (MOC). The pathways "caffeine metabolism" and "alpha-linolenic acid metabolism" were associated with the onset of endometrioid ovarian cancer (OCED).

CONCLUSIONS

Our MR analysis revealed both protective and risk-associated metabolites, providing insights into the potential causal relationships between GDMs and the metabolic pathways related to OC and its subtypes. The metabolites that drive OC could be potential candidates for biomarkers.

Melatonin as a therapeutic agent for alleviating endothelial dysfunction in cardiovascular diseases: Emphasis on oxidative stress

The vascular endothelium is vital in maintaining cardiovascular health by regulating vascular permeability and tone, preventing thrombosis, and controlling vascular inflammation. However, when oxidative stress triggers endothelial dysfunction, it can lead to chronic cardiovascular diseases (CVDs). This happens due to oxidative stress-induced mitochondrial dysfunction, inflammatory responses, and reduced levels of nitric oxide. These factors cause damage to endothelial cells, leading to the acceleration of CVD progression. Melatonin, a natural antioxidant, has been shown to inhibit oxidative stress and stabilize endothelial function, providing cardiovascular protection. The clinical application of melatonin in the prevention and treatment of CVDs has received widespread attention. In this review, based on bibliometric studies, we first discussed the relationship between oxidative stress-induced endothelial dysfunction and CVDs, then summarized the role of melatonin in the treatment of atherosclerosis, hypertension, myocardial ischemia-reperfusion injury, and other CVDs. Finally, the potential clinical use of melatonin in the treatment of these diseases is discussed.