Assessment of Proteomic Measures Across Serious Psychiatric Illness.

S Charles Schulz, Shauna Overgaard, David J Bond, Rajesh Kaldate
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引用次数: 3

Abstract

The diagnoses of serious psychiatric illnesses, such as schizophrenia, schizoaffective disorder, and bipolar disorder, rely on the subjective recall and interpretation of often overlapping symptoms, and are not based on the objective pathophysiology of the illnesses. The subjectivity of symptom reporting and interpretation contributes to the delay of accurate diagnoses and limits effective treatment of these illnesses. Proteomics, the study of the types and quantities of proteins an organism produces, may offer an objective biological approach to psychiatric diagnosis. For this pilot study, we used the Myriad RBM Discovery Map 250+ platform to quantify 205 serum proteins in subjects with schizophrenia (n=26), schizoaffective disorder (n=20), bipolar disorder (n=16), and healthy controls with no psychiatric illness (n=23). Fifty-seven analytes that differed significantly between groups were used for multivariate modeling with linear discriminant analysis (LDA). Diagnoses generated from these models were compared to SCID-generated clinical diagnoses to determine whether the proteomic markers: 1) distinguished the three disorders from controls, and 2) distinguished between the three disorders. We found that a series of binary classification models including 8-12 analytes produced separation between all subjects and controls, and between each diagnostic group and controls. There was a high degree of accuracy in the separations, with training areas-under-the-curve (AUC) of 0.94-1.0, and cross-validation AUC of 0.94-0.95. Models with 7-14 analytes produced separation between the diagnostic groups, though less robustly, with training AUC of 0.72-1.0 and validation AUC of 0.69-0.89. While based on a small sample size, not adjusted for medication state, these preliminary results support the potential of proteomics as a diagnostic aid in psychiatry. The separation of schizophrenia, schizoaffective disorder, and bipolar disorder suggests that further work in this area is warranted.

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严重精神疾病的蛋白质组测量评估。
严重精神疾病的诊断,如精神分裂症、分裂情感障碍和双相情感障碍,依赖于经常重叠的症状的主观回忆和解释,而不是基于疾病的客观病理生理学。症状报告和解释的主观性导致了准确诊断的延迟,限制了这些疾病的有效治疗。蛋白质组学,研究生物体产生的蛋白质的种类和数量,可能为精神病诊断提供客观的生物学方法。在这项初步研究中,我们使用了Myriad RBM Discovery Map 250+平台,对精神分裂症(n=26)、分裂情感性障碍(n=20)、双相情感障碍(n=16)和无精神疾病的健康对照(n=23)的205种血清蛋白进行了量化。采用线性判别分析(LDA)对57个组间差异显著的分析物进行多变量建模。将这些模型生成的诊断与scid生成的临床诊断进行比较,以确定蛋白质组学标记是否:1)将三种疾病与对照组区分开来,2)区分三种疾病。我们发现,包括8-12个分析物在内的一系列二元分类模型在所有受试者和对照组之间以及每个诊断组和对照组之间产生了分离。分离的准确度较高,训练曲线下面积(AUC)为0.94 ~ 1.0,交叉验证AUC为0.94 ~ 0.95。具有7-14个分析物的模型在诊断组之间产生分离,尽管不太稳健,训练AUC为0.72-1.0,验证AUC为0.69-0.89。虽然基于小样本量,没有调整药物状态,但这些初步结果支持蛋白质组学作为精神病学诊断辅助的潜力。精神分裂症、分裂情感障碍和双相情感障碍的分离表明,在这一领域的进一步工作是有必要的。
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Clinical Schizophrenia and Related Psychoses
Clinical Schizophrenia and Related Psychoses Medicine-Psychiatry and Mental Health
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期刊介绍: The vision of the exciting new peer-reviewed quarterly publication Clinical Schizophrenia & Related Psychoses (CS) is to provide psychiatrists and other healthcare professionals with the latest research and advances in the diagnosis and treatment of schizophrenia and related psychoses. CS is a practice-oriented publication focused exclusively on the newest research findings, guidelines, treatment protocols, and clinical trials relevant to patient care.
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