双相情感障碍
判别式
精神分裂症(面向对象编程)
心理学
重性抑郁障碍
精神科
内科学
临床心理学
人工智能
医学
锂(药物)
计算机科学
认知
作者
Dongyoon Shin,Jihyeon Lee,Yeongshin Kim,Junho Park,Daun Shin,Yoojin Song,Eun Jeong Joo,Sungwon Roh,Kyu Young Lee,Sae-Sook Oh,Yong Min Ahn,Sang Jin Rhee,Youngsoo Kim
标识
DOI:10.1021/acs.jproteome.3c00580
摘要
Psychiatric evaluation relies on subjective symptoms and behavioral observation, which sometimes leads to misdiagnosis. Despite previous efforts to utilize plasma proteins as objective markers, the depletion method is time-consuming. Therefore, this study aimed to enhance previous quantification methods and construct objective discriminative models for major psychiatric disorders using nondepleted plasma. Multiple reaction monitoring-mass spectrometry (MRM-MS) assays for quantifying 453 peptides in nondepleted plasma from 132 individuals [35 major depressive disorder (MDD), 47 bipolar disorder (BD), 23 schizophrenia (SCZ) patients, and 27 healthy controls (HC)] were developed. Pairwise discriminative models for MDD, BD, and SCZ, and a discriminative model between patients and HC were constructed by machine learning approaches. In addition, the proteins from nondepleted plasma-based discriminative models were compared with previously developed depleted plasma-based discriminative models. Discriminative models for MDD versus BD, BD versus SCZ, MDD versus SCZ, and patients versus HC were constructed with 11 to 13 proteins and showed reasonable performances (AUROC = 0.890-0.955). Most of the shared proteins between nondepleted and depleted plasma models had consistent directions of expression levels and were associated with neural signaling, inflammatory, and lipid metabolism pathways. These results suggest that multiprotein markers from nondepleted plasma have a potential role in psychiatric evaluation.
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