A prospective study. In clinical practice, it is challenging to tell major depressive disorder (MD-D) apart from bipolar disorder depressive episodes (BD-D). This study focuses on using inflammatory proteins as biomarkers to differentiate MD-D from BD-D. Admission plasma was collected from 30 healthy participants, 30 BD-D patients, and 30 MD-D patients. Ten samples from each group were randomly chosen for analysis using Olink Proteomics, and all samples were confirmed with ELISA. Then, we established a nomogram prediction model to assess the diagnostic potential biomarkers and their cut-off points. The study revealed that patients with MD-D exhibited notably elevated levels of IL6, MCP-3, TGF-α, TNFRSF9, and IL17-A compared to healthy control, with their cut-off values being 2.705 pg/ml, 374.5 pg/ml, 51.5 pg/ml, 328.5 pg/ml, and 2.905 pg/ml, respectively. Significantly higher levels of IL17-A and IFN-γ were found in MD-D patients compared to BD-D individuals and their cut-off values were 3.645 pg/ml, and 40.4 pg/ml, respectively. Subsequently, we created a nomogram prediction model that exhibited excellent consistency in the correction curve and proved to be clinically practical based on decision curve analysis. Our research indicated plasma inflammatory proteins associated with MD-D and could serve as potential biomarkers for it. Moreover, we discovered their cut-off values. A prediction model was created by us to accurately predict MD-D from BD-D patients, giving clinicians a novel approach to quickly distinguish MD-D and implement early targeted treatments. Larger samples are also necessary to further verify our results.
Xie et al. (2026) studied this question.