Steroid Hormone Profiling in Hyperandrogenism and Non-hyperandrogenism Women with Polycystic Ovary Syndrome

Biochemical classification diagnosis of polycystic ovary syndrome based on serum steroid hormones

Polycystic ovary syndrome (PCOS) is a metabolic disorder with clinical heterogeneity. PCOS women with non-hyperandrogenemia (NA) might be misdiagnosed due to a lack of diagnostic markers. This study aims to systematically analyze the differences in steroid hormones between PCOS women with hyperandrogenemia (HA) and NA, and to screen classification diagnosis models for PCOS. The serum samples from 54 HA-PCOS, 79 NA-PCOS and 60 control women (Non-PCOS) aged between 18 and 35 were measured by an integrated steroid hormone-targeted quantification assay using LC-MS/MS. The levels of serum androgens, corticosteroids, progestins and estrogens in the steroid hormone biosynthesis pathway were analyzed in PCOS and Non-PCOS women. Eight machine learning methods including Linear Discriminant Analysis (LDA), K-nearest Neighbors (KNN), Boosted Logistic Regression (LogitBoost), Naive Bayes (NB), C5.0 algorithm (C5), Random Forest (RF), Support Vector Machines (SVM), and Neural Network (NNET) were performed, evaluated and selected for classification diagnosis of PCOS. A 10-fold cross-validation on the training set was performed. The whole metabolic flux from cholesterol to downstream steroid hormones increased significantly in PCOS, especially in HA-POCS women. The RF model was chosen for the classification diagnosis of HA-PCOS, NA-PCOS, and Non-PCOS women due to the maximum average accuracy (0.938, p<0.001), AUC (0.989, p<0.001), and kappa (0.906, p<0.001), and the minimum logLoss (0.200, p<0.001). Five steroid hormones including testosterone, androstenedione, total 2-methoxyestradiol, total 4-methoxyestradiol, and free estrone were selected as the decision trees for the simplified RF model. A total of 37 women were included in the validation set. The diagnostic sensitivity for HA-PCOS, NA-PCOS, and Non-PCOS was 100 %, 93.3 % and 91.7 %, respectively. HA-PCOS, NA-PCOS, and Non-PCOS women showed obvious different steroid hormone profiles. The simplified RF model based on two androgens and three estrogens could be effectively applied to the classification diagnosis of PCOS, further reducing the missed diagnosis rate of NA-PCOS.

Advances in Mass Spectrometry-Based Blood Metabolomics Profiling for Non-Cancer Diseases: A Comprehensive Review

Blood metabolomics profiling using mass spectrometry has emerged as a powerful approach for investigating non-cancer diseases and understanding their underlying metabolic alterations. Blood, as a readily accessible physiological fluid, contains a diverse repertoire of metabolites derived from various physiological systems. Mass spectrometry offers a universal and precise analytical platform for the comprehensive analysis of blood metabolites, encompassing proteins, lipids, peptides, glycans, and immunoglobulins. In this comprehensive review, we present an overview of the research landscape in mass spectrometry-based blood metabolomics profiling. While the field of metabolomics research is primarily focused on cancer, this review specifically highlights studies related to non-cancer diseases, aiming to bring attention to valuable research that often remains overshadowed. Employing natural language processing methods, we processed 507 articles to provide insights into the application of metabolomic studies for specific diseases and physiological systems. The review encompasses a wide range of non-cancer diseases, with emphasis on cardiovascular disease, reproductive disease, diabetes, inflammation, and immunodeficiency states. By analyzing blood samples, researchers gain valuable insights into the metabolic perturbations associated with these diseases, potentially leading to the identification of novel biomarkers and the development of personalized therapeutic approaches. Furthermore, we provide a comprehensive overview of various mass spectrometry approaches utilized in blood metabolomics research, including GC-MS, LC-MS, and others discussing their advantages and limitations. To enhance the scope, we propose including recent review articles supporting the applicability of GC×GC-MS for metabolomics-based studies. This addition will contribute to a more exhaustive understanding of the available analytical techniques. The Integration of mass spectrometry-based blood profiling into clinical practice holds promise for improving disease diagnosis, treatment monitoring, and patient outcomes. By unraveling the complex metabolic alterations associated with non-cancer diseases, researchers and healthcare professionals can pave the way for precision medicine and personalized therapeutic interventions. Continuous advancements in mass spectrometry technology and data analysis methods will further enhance the potential of blood metabolomics profiling in non-cancer diseases, facilitating its translation from the laboratory to routine clinical application.

Androgen excess: a hallmark of polycystic ovary syndrome

Polycystic ovarian syndrome (PCOS) is a metabolic, reproductive, and psychological disorder affecting 6-20% of reproductive women worldwide. However, there is still no cure for PCOS, and current treatments primarily alleviate its symptoms due to a poor understanding of its etiology. Compelling evidence suggests that hyperandrogenism is not just a primary feature of PCOS. Instead, it may be a causative factor for this condition. Thus, figuring out the mechanisms of androgen synthesis, conversion, and metabolism is relatively important. Traditionally, studies of androgen excess have largely focused on classical androgen, but in recent years, adrenal-derived 11-oxygenated androgen has also garnered interest. Herein, this Review aims to investigate the origins of androgen excess, androgen synthesis, how androgen receptor (AR) signaling mediates adverse PCOS traits, and the role of 11-oxygenated androgen in the pathophysiology of PCOS. In addition, it provides therapeutic strategies targeting hyperandrogenism in PCOS.