Predicting the diagnosis of various mental disorders in a mixed cohort using blood-based multi-protein model: a machine learning approach
Predicting the diagnosis of various mental disorders in a mixed cohort using blood-based multi-protein model: a machine learning approach
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使用基于血液的多蛋白模型预测混合队列中各种精神障碍的诊断：一种机器学习方法
DOI：
10.1007/s00406-022-01540-3
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发表时间：
2022-12-25
影响因子：
4.7
通讯作者：
Yuan, Yonggui
中科院分区：
文献类型：
--
作者：
Chen, Suzhen;Chen, Gang;Yuan, Yonggui
缺乏客观的精神障碍诊断方法，对诊断的可靠性提出了挑战。本研究旨在建立一种简便、实用的血清多蛋白诊断方法，用于诊断抑郁症(MDD)、精神分裂症(SZ)、双相情感障碍(BPD)和惊恐障碍(PD)。检测了 患者(n= 50例)、SZ患者(n= 50例)、BPD患者(n=BPD 55例)和PD患者(50例健康对照组)的血清脑源性神经营养因子、血管内皮生长因子(非首字母缩写)、C反应蛋白和皮质醇水平。采用线性判别分析(LDA)建立多分类模型对这些精神障碍进行分类。采用留一法交叉验证(LOOCV)和五重交叉验证方法验证LDA模型的准确性和稳定性。LDA模型中包含了所有五种血清蛋白，在对MDD、SZ、BPD、PD和HC组进行分类时，发现其总体准确率高达96.9%。LOOCV的LDA模型和5倍交叉验证(研究内重复)的多分类准确率分别达到96.9%和96.5%，证明了基于血液的多蛋白LDA模型在混合队列中对常见精神障碍进行分类的可行性。这些结果表明，使用LDA结合与不同精神障碍发病机制相关的多个蛋白质可能是一种新的、相对客观的精神障碍分类方法。临床医生应考虑结合多种血清蛋白客观诊断精神障碍。
The lack of objective diagnostic methods for mental disorders challenges the reliability of diagnosis. The study aimed to develop an easily accessible and useable objective method for diagnosing major depressive disorder (MDD), schizophrenia (SZ), bipolar disorder (BPD), and panic disorder (PD) using serum multi-protein. Serum levels of brain-derived neurotrophic factor (BDNF), VGF (non-acronymic), bicaudal C homolog 1 (BICC1), C-reactive protein (CRP), and cortisol, which are generally recognized to be involved in different pathogenesis of various mental disorders, were measured in patients with MDD (n= 50), SZ (n= 50), BPD (n= 55), and PD along with 50 healthy controls (HC). Linear discriminant analysis (LDA) was employed to construct a multi-classification model to classify these mental disorders. Both leave-one-out cross-validation (LOOCV) and fivefold cross-validation were applied to validate the accuracy and stability of the LDA model. All five serum proteins were included in the LDA model, and it was found to display a high overall accuracy of 96.9% when classifying MDD, SZ, BPD, PD, and HC groups. Multi-classification accuracy of the LDA model for LOOCV and fivefold cross-validation (within-study replication) reached 96.9 and 96.5%, respectively, demonstrating the feasibility of the blood-based multi-protein LDA model for classifying common mental disorders in a mixed cohort. The results suggest that combining multiple proteins associated with different pathogeneses of mental disorders using LDA may be a novel and relatively objective method for classifying mental disorders. Clinicians should consider combining multiple serum proteins to diagnose mental disorders objectively.