Key result
A logistic regression model combining proteomic markers HIST1H4A and TREML1 yielded an AUC of 0.977 for the diagnosis of polycystic ovary syndrome, with HIST1H4A identified as an independent risk factor (OR 1.037).
Why the study?
Few studies have investigated the construction of clinical diagnostic models using proteomics markers.
Does a logistic regression model combining proteomic markers (HIST1H4A and TREML1) accurately diagnose polycystic ovary syndrome in women of reproductive age?
Case-Control (n=62)
No
Does a logistic regression model combining proteomic markers (HIST1H4A and TREML1) accurately diagnose polycystic ovary syndrome in women of reproductive age?
Odds Ratio: 1.037 (95% CI 1.012–1.063)
p-value: p=0.004
A novel logistic regression model combining serum proteomic markers HIST1H4A and TREML1 demonstrates high diagnostic accuracy for polycystic ovary syndrome, offering a potential new tool for clinical diagnosis.
Should not yet alter PCOS diagnostic practice; hypothesis-generating for proteomic markers pending prospective validation.
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.
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Cheng et al. (2023) conducted a case-control in Polycystic ovary syndrome (n=62). HIST1H4A level vs. Lower HIST1H4A level was evaluated on Polycystic ovary syndrome diagnosis (OR 1.037, 95% CI 1.012-1.063, p=0.004). A logistic regression model combining proteomic markers HIST1H4A and TREML1 yielded an AUC of 0.977 for the diagnosis of polycystic ovary syndrome, with HIST1H4A identified as an independent risk factor (OR 1.037).
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