Multidisciplinary Diagnosis and Treatment of Polycystic Ovary Syndrome

The investigators collected clinical data and serum samples of patients with polycystic ovary syndrome (PCOS) in this study, used statistical software such as SPSS for date analysis, and used experimental techniques such as ELISA and flow cytometry to detect serum samples, aiming to explore the relationship between the body anthropometry, skin conditions, psychosomatic status, diet, sleep, exercise, glucose and lipid metabolism, gonadal hormones, and body fat distribution in patients with polycystic ovary syndrome, and to discovery new biomarkers. Multidisciplinary exploration of the mechanisms of disease occurrence and development, the establishment of a PCOS multicenter, multidisciplinary and multidimensional clinical research database, combined with the established statistical analysis strategy for big data and analysis, to promote the realization of more accurate personalized medicine.