Integrated metabolomics and transcriptomics to reveal biomarkers and mitochondrial metabolic dysregulation of premature ovarian insufficiency.

Background: The metabolic characteristics of premature ovarian insufficiency (POI), a reproductive endocrine disease characterized by abnormal sex hormone metabolism and follicle depletion, remain unclear. Metabolomics is a powerful tool for exploring disease phenotypes and biomarkers. This study aims to identify metabolic markers and construct diagnostic models, and elucidate the underlying pathological mechanisms for POI. Methods: Non-targeted metabolomics was utilized to characterize the plasma metabolic profile of 40 patients. The metabolic markers were identified through bioinformatics and machine learning, and constructed an optimal diagnostic model by classified multi-model analysis. Enzyme-linked immunosorbent assay (ELISA) was used to verify antioxidant indexes, mitochondrial enzyme complexes, and ATP levels. Finally, integrated transcriptomics and metabolomics were used to reveal the dysregulated pathways and molecular regulatory mechanisms of POI. Results: The study identified eight metabolic markers significantly correlated with ovarian reserve function. The XGBoost diagnostic model was developed based on six machine learning models, demonstrating its robust diagnostic performance and clinical applicability through the evaluation of receiver operating characteristic (ROC) curve, decision curve analysis (DCA), calibration curve, and precise recall (PR) curve. Multi-omics analysis showed that mitochondrial respiratory chain electron carrier (CoQ10) and enzyme complex subunits were down-regulated in POI. ELISA validation revealed an elevation in oxidative stress markers and a reduction in the activities of antioxidant enzymes, CoQ10, and mitochondrial enzyme complexes in POI. Conclusion: Our findings highlight that mitochondrial dysfunction and energy metabolism disorders are closely related to the pathogenesis of POI. The identification of metabolic markers and predictive models holds significant implications for the diagnosis, treatment, and monitoring of POI.

Establishment of predictive model for analyzing clinical pregnancy outcome based on IVF-ET and ICSI assisted reproductive technology.

In order to explore the predictive model for analyzing clinical pregnancy outcomes based on IVF-ET (in vitro fertilization and embryo transfer) and ICSI (Intracytoplasmic sperm injection) assisted reproductive technology (ART). METHODS: this study selected the embryo transfer (fresh) patients who received IVF-ET or ICSI treatment in the First Affiliated Hospital of Guangxi Medical University as the subjects. Moreover, the controlled ovarian stimulation (COS) and follow-up were conducted to collect relevant data for analysis, and finally a prediction model was established. RESULTS: The results showed that the patients were divided into different ovarian response groups at first. The age, bFSH and bFSH/bLH were the highest in the poor ovarian response group (POR), followed by the normal ovarian response group (NOR) and the lowest in the high ovarian response group (HOR). The area under the ROC curve was 0.669 according to the predictive model of pregnancy-related factors. The confidence interval of 94% was 0.629-0.697, with statistical significance (P = 0.000, P < 0.01). CONCLUSION: it can be concluded that in clinical pregnancy, for many related factors, regression equation can be used to establish a prediction model to diagnose the success rate of pregnancy. In conclusion, a prediction model can be built based on the relevant experimental results, to provide experimental reference ideas for increasing the success rate of ART in late clinical pregnancy, which is of great research significance.