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Updated: Jul 12, 2025
Evaluation of Hepatic Glucose Production in a Polycystic Ovary Syndrome Mouse Model
Published on: March 5, 2022
Combining proteomic markers to construct a logistic regression model for polycystic ovary syndrome
Cheng Tong1,2, Yue Wu1,2, Zhenchao Zhuang1
1Department of Laboratory Medicine, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China.
Introduction:
Proteomics technology has been used in various fields in recent years for the Q6 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.
Methods:
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 this study, we used logistic regression among these protein markers to screen out variables with diagnostic value and constructed logistic regression models.
Results:
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 of 0.977 and a Youden index of0.903, which gives a cutoff value of 0.518 at this point. The model has a sensitivity of 93.5% and a specificity of 96.8%. Calibration curves show fair consistency of the model.
Discussion:
Our study is the first to use proteomic results with clinical biochemical data to construct a logistic regression model, and the model is consistent. However, our study still needs a more complete sample to confirm our findings.
