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| 題名: | NMDA假說、認知功能、與精神分裂症
NMDA Hypothesis, Cognitive Function, and Schizophrenia |
| 作者: | 林潔欣;Chieh-Hsin Lin |
| 貢獻者: | 臨床醫學研究所博士班 |
| 關鍵詞: | 精神分裂症;社會認知功能;神經認知功能;功能性結果;臨床症狀;G72;NMDA;schizophrenia;social cognition;neurocognition;functional outcome;clinical symptoms;G72;NMDA |
| 日期: | 2013-09-25 |
| 上傳時間: | 2014-10-02 09:45:43 (UTC+8) |
| 出版者: | 中國醫藥大學 |
| 摘要: | 背景：精神分裂症一個相當複雜的精神疾病，也是世界上最容易造成嚴重而持續之功能障礙的失能疾病之一。認知功能之缺損，包括神經認知功能與社會認知功能，過去一直被認為是精神分裂症的核心症狀之一，且與預後及功能不佳有關。然而，認知功能 (包括神經與社會認知功能)、臨床症狀及功能性預後之間的複雜關係仍不清楚。
N-methyl-D-aspartate (NMDA) 神經傳導功能低下與精神分裂症有密切關聯；而證據也顯示NMDA訊息傳導對認知功能來說相當重要。G72蛋白可能在NMDA訊息傳導的調控上扮演了重要的角色。因此，是否G72蛋白在精神分裂症病患身上有過度表現(over-expressed)的現象，而能當作輔助診斷的生物標記？值得進一步研究。另一個有趣的議題是：是否結合G72蛋白與認知功能可以提供更好的辨別力來區分精神分裂症病患與健康人？我們於是進行了三個研究來嘗試回答這些問題。
方法：研究一：302位漢族慢性精神分裂症病患接受認知功能 (以[MATRICS] Consensus Cognitive Battery來施測，包括涵蓋神經與社會認知功能等七個認知向度)施測、臨床症狀 (包括正性、負性及憂鬱症狀)及功能性預後 (由整體功能評估量表及生活品質量表來代表)之評估。
研究二：測量兩群不同的精神分裂症病患與健康對照組之周邊血液的G72蛋白濃度。以Receiver operating characteristic (ROC) curve找出區分精神分裂症病患與健康對照組的最佳切點。
研究三：測量精神分裂症病患與健康對照組之周邊血液的G72蛋白濃度，並以MATRICS Consensus Cognitive Battery (MCCB)來評估其認知功能。以ROC找出區分精神分裂症病患與健康對照組的最佳切點。
結果：研究一：以SEM模式分析發現臨床症狀是精神分裂症病患的認知功能與功能性結果之間的中介者。在基本模式中，認知功能與功能性預後之間有顯著相關性。在中介模式裡，認知功能與功能性預後之間則由臨床症狀(主要是負性症狀)所調控。
研究二：在learning set中，長期服藥之精神分裂症病患 (平均 = 4.43 ± 2.84, n = 39)的G72蛋白濃度顯著高於健康對照組 (1.17 ± 0.57, p < 0.001, n = 30)。最佳切點為2.017，可達到敏感度0.97和特異性0.93 (ROC之曲線下面積為0.986)。而testing set中，未服藥之精神分裂症病患 (平均 = 3.64 ± 1.80, n = 27)的G72蛋白濃度顯著高於健康對照組 (1.13 ± 0.58, p < 0.001, n = 30)。最佳切點為2.131，可達到敏感度0.78和特異性0.96 (ROC之曲線下面積為0.896)。
研究三：結合G72蛋白濃度與認知功能 (敏感度0.957，特異性0.953，ROC之曲線下面積為0.988)可達到比G72蛋白濃度 (敏感度0.936，特異性0.930，ROC之曲線下面積為0.983)或認知功能 (敏感度0.907，特異性0.915，ROC之曲線下面積為0.969)其中一種單獨預測更好的辨別力。
結論：這些研究不僅提出慢性精神分裂症病患的認知功能與功能性預後之間的關係受臨床症狀(主要是負性症狀)所調控，也藉由發現周邊血液的G72蛋白濃度有潛力作為精神分裂症之生物標記而支持NMDA理論。更進一步顯示結合生物標記與表現型特徵可提供更好的鑑別力來區分精神分裂症病患和健康人。若後續的研究能以前瞻性的設計及較大的樣本數再度確認這些研究結果，將有助於釐清認知缺損的機轉，並在未來發展出臨床可應用的精神分裂症生物標記。
Background: Schizophrenia is a very complex mental disease and one of most disabling disorders worldwide that bring forth severe and persistent functional impairment. Deficits in cognitive functions, including both neuro- and social-cognition, are core symptoms of schizophrenia and associated with poor functional outcome. However, the complex relationship among cognitive function (both neuro- and social-cognition), clinical symptoms, and outcome remains unclear.
Hypofunction of N-methyl-D-aspartate (NMDA) neurotransmission is involved in schizophrenia; and evidence suggests that NMDA signaling is important for cognitive function. G72 protein may play an important role in the modulation of NMDA signaling. Whether G72 protein is over-expressed in patients with schizophrenia and can serve as a diagnostic biomarker deserves further investigation. It is also interesting whether the combination of G72 protein level and cognitive functions provides a better discriminative power in differentiating patients with schizophrenia and healthy individuals. We conducted 3 studies attempting to answer the above questions.
Methods: Study 1: Three hundred and two Han-Chinese patients with chronically stable schizophrenia received evaluation of cognitive function (using the Measurement and Treatment Research to Improve Cognition in Schizophrenia [MATRICS] Consensus Cognitive Battery, including 7 domains covering neurocognition and social cognition), clinical symptoms (including positive, negative and depressive symptoms), and functional outcome as assessed by Global Assessment of Functioning Scale and Quality of Life Scale.
Study 2: G72 protein level was measured in peripheral plasma in patients with schizophrenia and healthy controls in two independent cohorts. Receiver operating characteristic (ROC) curve was conducted to determine the optimal cutoff values of G72 protein level for schizophrenia patients vs. healthy controls.
Study 3: G72 protein level and cognitive functions assessed by the MATRICS Consensus Cognitive Battery (MCCB) were measured in patients with schizophrenia and healthy controls. ROC curve was conducted to determine the optimal cutoff value of the combination of G72 protein level and cognitive function for schizophrenia patients vs. healthy controls.
Results: Study 1: SEM identified clinical symptoms as a mediator between cognitive function (including all 7 domains of MATRICS) and functional outcome in schizophrenia. The relationship between cognitive function and functional outcome was significant in the basic model. In the mediation model, the link between cognitive function and functional outcome was mediated by clinical symptoms, mainly negative symptoms.
Study 2: In the learning set, the G72 protein level was higher in medicated schizophrenia patients (mean = 4.43 ± 2.84, n = 39) when compared with healthy individuals (1.17 ± 0.57, p < 0.001, n = 30). The optimal cutoff value, 2.017, between age- and gender-matched medicated schizophrenia patients and healthy subjects generated a sensitivity of 0.97 and specificity of 0.93 (area under curve [AUC] of ROC = 0.986). For the testing set, the G72 protein level was higher in drug-free schizophrenia patients (mean = 3.64 ± 1.80, n = 27) than the second group of healthy individuals (1.13 ± 0.58, p < 0.001, n = 30). A cutoff of 2.131 differentiated matched drug-free schizophrenia from healthy subjects with a sensitivity of 0.78 and specificity of 0.96 (AUC = 0.896).
Study 3: The combination of G72 protein level and cognitive function generated a better discriminative power between schizophrenia patients and healthy subjects (sensitivity = 0.957, specificity = 0.953, AUC of ROC = 0.988) than either G72 protein level (sensitivity = 0.936, specificity = 0.930, AUC of ROC = 0.983) or cognitive function solely (sensitivity = 0.907, specificity = 0.915, AUC of ROC = 0.969).
Conclusions: The studies not only suggest that clinical symptoms, mainly negative symptoms, mediate the influence of neurocognition and social cognition on functional outcome of schizophrenia but also support the NMDA hypothesis by discovering peripheral G72 protein level as a potential biomarker for schizophrenia. Moreover, the combination of biomarker and phenotype yields better differentiating power between patients with schizophrenia and normal controls. If reconfirmed by further studies with a prospective design and larger sample sizes, the results of our studies could help to elucidate the mechanism of cognitive deficits and develop a clinically useful biomarker for schizophrenia in the future. |
| 顯示於類別: | [臨床醫學研究所] 博碩士論文
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