Combining proteomic markers to construct a logistic regression model for polycystic ovary syndrome - Tong, Cheng; Wu, Yue; Zhuang, Zhenchao; Wang, Zhejiong; Yu, Ying (Frontiers in Endocrinology, 2023)
Proteomics technology has been used in various fields in recent years for the exploration of novel markers and the study of disease pathogenesis, and has become one of the most important tools for researchers to explore unknown areas. However, there are fewer studies related to the construction of clinical models using proteomics markers. In our previous study we used DIA proteomics to screen for proteins that were significant in 31 PCOS patients compared to women of normal reproductive age. In the present study, we constructed a logistic model using these protein markers, where HIST1H4A (OR=1.037) was an independent risk factor for polycystic ovary syndrome and TREML1 (OR=0.976) were protective factors for the disease. The logistic regression model equation is: Logit (PCOS) =0.036*[ HIST1H4A]-0.024*[TREML1]-16.368. The ROC curve analyzing the diagnostic value of the model has an AUC value