BMC psychiatry · 2026
Potential biomarkers differentiating major depression from bipolar disorder depressive episodes by Olink Proteomics.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Xie Tingting, Su Yu, Feng Xia, Xu Fangming
Аннотация
STUDY DESIGN: A prospective study.
BACKGROUND: 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.
MATERIALS AND METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
BACKGROUND: 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.
MATERIALS AND METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.