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Background: Premature ovarian failure (POF) is a clinical condition characterized by the cessation of ovarian function, leading to infertility. The underlying molecular mechanisms remain unclear, and no predictable biomarkers have been identified. This study aimed to investigate the protein and metabolite contents of serum extracellular vesicles to investigate underlying molecular mechanisms and explore potential biomarkers. Methods: This study was conducted on a cohort consisting of 14 POF patients and 16 healthy controls. The extracellular vesicles extracted from the serum of each group were subjected to label-free proteomic and unbiased metabolomic analysis. Differentially expressed proteins and metabolites were annotated. Pathway network clustering was conducted with further correlation analysis. The biomarkers were confirmed by ROC analysis and random forest machine learning. Results: The proteomic and metabolomic profiles of POF patients and healthy controls were compared. Two subgroups of POF patients, Pre-POF and Pro-POF, were identified based on the proteomic profile, while all patients displayed a distinguishable metabolomic profile. Proteomic analysis suggested that inflammation serves as an early factor contributing to the infertility of POF patients. For the metabolomic analysis, despite the dysfunction of metabolism, oxidative stress and hormone imbalance were other key factors appearing in POF patients. Signaling pathway clustering of proteomic and metabolomic profiles revealed the progression of dysfunctional energy metabolism during the development of POF. Moreover, correlation analysis identified that differentially expressed proteins and metabolites were highly associated, with six of them being selected as potential biomarkers. ROC curve analysis, together with random forest machine learning, suggested that AFM combined with 2-oxoarginine was the best diagnostic biomarker for POF. Conclusions: Omics analysis revealed that inflammation, oxidative stress, and hormone imbalance are factors that damage ovarian tissue, but the progressive dysfunction of energy metabolism might be the critical pathogenic pathway contributing to the development of POF. AFM combined with 2-oxoarginine serves as a precise biomarker for clinical POF diagnosis. [ABSTRACT FROM AUTHOR]

Background: Family socioeconomic status (FSES) serves as a significant determinant for subjective well-being. However, extant research has provided conflicting evidence on the correlation between FSES and adolescent students' subjective well-being (SSWB)., Methods: Data were collected from 12,058 adolescent students (16 years of age) by the Programme for International Student Assessment (PISA) 2018. Multivariate canonical correlation and Mantel test were utilized to investigate the specific connection between FSES and SSWB. Furthermore, a Gaussian EBICglasso graph-theoretical model was used to capture the topological properties of the FSES-SSWB network and reveal the interplay among multifarious components of FSES and SSWB., Results: FSES was positively correlated with SSWB. In the FSES-SSWB network, parental educational attainment and occupation status demonstrated the highest centrality values, thereby contributing significantly to the relationship between FSES and SSWB. However, family wealth, along with educational and cultural resources, displayed lower centrality values, signifying their weaker roles in this relationship., Conclusion: Our findings suggest that symbolic capital, rather than family affluence, exerts a dominant influence on adolescent SSWB. In other words, SSWB may not be detrimentally influenced by a deficiency in monetary resources. However, it is more susceptible to being unfavorably impacted by inferior parental educational attainment and occupational standing., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Wang, Li, Liu, Zhang, Lu and Chen.)