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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 郭柏秀(Po-Hsiu Kuo) | |
| dc.contributor.author | Shiau-Shian Huang | en |
| dc.contributor.author | 黃孝先 | zh_TW |
| dc.date.accessioned | 2022-11-25T06:34:47Z | - |
| dc.date.copyright | 2022-02-21 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-02-09 | |
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Jansen R, Penninx B, Madar V, Xia K, Milaneschi Y, Hottenga J, Hammerschlag A, Beekman A, Van Der Wee N, Smit JJMp: Gene expression in major depressive disorder. 2016, 21(3):339-347. 149. Jones KA, Thomsen CJM, Neuroscience C: The role of the innate immune system in psychiatric disorders. 2013, 53:52-62. 150. Coleman JR, Lester KJ, Keers R, Roberts S, Curtis C, Arendt K, Bögels S, Cooper P, Creswell C, Dalgleish T: Genome-wide association study of response to cognitive-behavioural therapy in children with anxiety disorders. The British Journal of Psychiatry 2016, 209(3):236-243. 151. Brown T, DiBenedetti D, Danchenko N, Weiller E, Fava M: Symptoms of anxiety and irritability in patients with major depressive disorder. J Depress Anxiety 2016, 5(3):237. 152. Edoardo G, Magri C, Minelli A, Valsecchi P, Monchieri S, Borsani G, Sacchetti E, Gennarelli M: Study of the genetic architecture behind mood disorders by whole exome sequencing on a large Italian pedigree. 1……… | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/82273 | - |
| dc.description.abstract | 憂鬱症是一常見且嚴重的精神疾病，其特性反覆發作容易形成慢性化，影響病人甚鉅，也是所有疾病造成失能負擔排行第二名的疾病，是相當重要的公衛議題。藥物一直在發展進步，然而研究顯示有接受治療的病人當中，超過一半無法達到症狀緩解，持續受到疾病的影響，有許多人進展成難治型憂鬱症。在臨床上患者的治療反應存在相當大的變異性，因此值得探討其背後原因。可能原因之一是憂鬱症群體的異質性，醫師透過診斷症狀學，評估眾多憂鬱症狀來診斷患者，囊括了許多不同特性的患者，患者基因特性也可能有很大的差別。另一方面憂鬱症患者常併有其他精神科診斷或是內外科的共病，也使得病情複雜化影響治療效果。探討這些遺傳特性、臨床共病對於療效的變異有助於了解疾病潛在機轉。探討療效預測因子，找出重要的生物標記，將可協助診斷與制定相關的治療計畫。遺傳訊息屬於靜態資訊，進一步尋找療效的動態預測因子對於臨床治療的指引是很重要的。近年來針對憂鬱症研究，許多學者將心力投注於非侵入性、經濟效應性高的腦部檢查，如腦電波。過往一些小型研究利用腦波功率譜發現具有潛力可以用來預測療效的指標，然而結果重現性並不高。近期許多研究將焦點轉至腦部功能性連結與憂鬱症的關聯性。綜合上述，本研究有幾個研究目標:(1)研究漢氏憂鬱量表分數，執行因素分析探討憂鬱個案分類，降低異質性；(2a)研究憂鬱症治療效果相關基因位點；(2b) 研究憂鬱症藥物治療副作用相關基因位點；(3)利用多基因風險評分探討帶有憂鬱症疾病位點與療效之關聯性；(4)研究難治型憂鬱症臨床早期特性及終身特性；(5)研究共病於難治型憂鬱症風險之影響性；(6)探討腦波功能性連結在健康對照組與憂鬱患者的差異性；(7)探討功能性連結訊號與療效關聯性。本研究期望能了解憂鬱症疾患的異質性，探討治療變異原因，以推論生物機制，結合動態、靜態預測因子，甚至將具有潛力的指標結合，以協助未來在重鬱症診斷、與預估療效的參考。本論文包含三種研究設計:(1)基因研究; (2)世代追蹤研究; (3)臨床腦波觀察性研究。研究最後發現憂鬱症狀分數可以降維區分為五個因子，在五個因子中也發現許多與療效相關的基因位點。多基因風險評分發現五個因子中somatic anxiety有著最顯著的關係。本研究也報告許多抗憂鬱劑副作用相關位點。憂鬱症療效基因進一步的路徑分析發現，與免疫、神經功能相關。世代研究發現難治型憂鬱症，在憂鬱患者占35%。我們發現了許多早期及終生的風險因子，例如初期使用較高的鎮靜安眠藥劑量或是女性有較高的風險變成難治型憂鬱症。患者有較多的精神科共病也有加成風險，接近七成未來變成難治型憂鬱症，值得早期注意。腦波研究則發現患者初期的功能性連結較健康對照組差，在治療一週之後有所提升並且接近健康對照組的表現。而對於治療有反應的患者比起無反應組，在高頻的腦波頻帶顯示有較好的功能性連結訊號，且此現象沒有隨著治療而改變。隨著治療而改變的訊號主要出現在低頻的訊號。患者功能性連結的改善(betweenness centrality)與患者症狀學改善在部分結果呈現正相關，其結果對於未來預測患者療效提供更進一步的研究指引。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-25T06:34:47Z (GMT). No. of bitstreams: 1 U0001-0802202211020800.pdf: 6148956 bytes, checksum: 270ce6a18da9ed5b6046481d395e63b8 (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | "口試委員會審定書 I 致謝 Ⅱ 中文摘要 Ⅲ ABSTRACT V LIST OF TABLES XI LIST OF FIGURES XII SUPPLEMENTARY MATERIALS A ND APPE XIII LIST OF ABBREVIATION XIV CHAPTER 1 INTRODUCTION 1 1.1 The main issue for major depressive disorder (MDD) 1 1.1.1 Epidemiology of MDD 1 1.1.2 Risk factors and pathogenesis of MDD 1 1.1.3 Challenges of treatment of MDD: treatment variation 3 1.2 Genetics and MDD: clinical implications for disease risk, and treatment response 4 1.2.1 Genetics and MDD: linkage and candidate genes study 4 1.2.2 Genome-wide association studies for MDD 5 1.2.3 Genetic Findings for Treatment Response in MDD 6 1.2.4 Polygenic risk score of MDD 8 1.2.5 Pharmacogenetic studies for side effects of antidepressant 9 1.3 Treatment-resistant depression (TRD): a crucial public health issue 10 1.3.1 Epidemiology of TRD 10 1.3.2 Staging models and definition of TRD 11 1.3.3 Relevant clinical features and risk factors of TRD 12 1.4 Electroencephalography (EEG) biomarkers for depression 13 1.4.1 EEG biomarkers for MDD 14 1.4.2 EEG: potential biomarkers for perdition of treatment response in MDD 15 1.4.3 Functional connectivity in EEG 16 CHAPTER 2 SPECIFIC AIMS AND RESEARCH BLUEPRINT 18 2.1 Specific Aims 18 2.2 Research Blueprint 18 CHAPTER 3 PROJECT1: THE GENETIC STUDY 20 3.1 Method 20 3.1.1 Data source and study participants 20 3.1.2 Assessments and measurements 20 3.1.3 Genotyping, imputation, and quality control 21 3.1.4 GWAS for treatment responses, side effects, and mixed model 22 3.1.5 Statistical analysis 23 3.2 Results 24 3.2.1 Perform factors analysis of HAM-D 24 3.2.2 Potential genetic variants on for treatment response in MDD 25 3.2.3 Mixed model for repeated measurements of HAM-D at all visits 26 3.2.4 PRS under different P-Threshold values and pathways analysis 26 3.2.5 Potential genetic variants on for side effects in MDD 27 3.3 Discussion 28 CHAPTER 4 PROJECT2: THE PROSPECTIVE COHORT STUDY FOR TRD 33 4.1 Method 33 4.1.1 Data source and study participants 33 4.1.2 Operational definition of TRD and relevant clinical variables 33 4.1.3 Statistical analysis 35 4.2 Results 36 4.2.1 Identify the lifetime characteristics of TRD in Taiwan 36 4.2.2 Identify the early characteristics of TRD in Taiwan 37 4.2.3 Multiple Cox regression model of TRD 38 4.2.4 Survival analyses of TRD 39 4.3. Discussion 39 CHAPTER 5 PROJECT3: THE EEG CLINICAL STUDY 47 5.1 Methods 47 5.1.1 Study design and study participants 47 5.1.2 HCs, and study exclusion criteria 47 5.1.3 Study assessments and outcome 48 5.1.4 EEG Recording 49 5.1.5 EEG data preprocessing and analysis 49 5.1.6 FNC and phase locking value 50 5.1.7 Graph theory 50 5.1.8 Statistical analysis 51 5.2 Results 52 5.2.1 Basic data between control, responder, and non-responder in EEG study 52 5.2.2 Detecting ability of treatment response in EEG between band power and FNC 52 5.2.3 Correlation between FNC and severity of depression or change of FNC and HAM-D scores 53 5.2.4 The difference of FNC in EEG between responder and non-responder 53 5.2.5 The changes in syndromal severity in participants in the EEG and genetic studies 54 5.3 Discussion 55 CHAPTER 6 CONCLUSIONS AND OVERALL DISCUSSION 60 6.1 Conclusions 60 6.2 Overall discussion 62 REFERENCES 65 LIST OF TABLES Table 1. Demographic and clinical characteristics of participants in the genetic study 85 Table 2. Latent syndromal factors of Hamilton depression rating scale 86 Table 3. Associated index SNP for '% change' of GWAS 88 Table 4. Associated index SNP for binary outcome of response of GWAS 89 Table 5. Associated index SNP for '% change' of GWAS by mixed model for repeated measurement 92 Table 6. Polygenic risk score under different p-value threshold for continuous treatment response.1 93 Table 7. Pathway network analysis for treatment response in Major depressive disorder (MDD) 95 Table 8. Associated index SNP for 'treatment emergent suicidal ideation' and ' sexual side effect ' of GWAS 96 Table 9. Lifetime demographic and clinical characteristics between non-TRD and TRD patients 98 Table 10. Early comorbidities, prescription pattern, and behavior of seeking medical care between non-TRD and TRD patients 99 Table 11. Multivariable Cox regression1 analysis for risk of treatment-resistant depression 101 Table 12. Demographic and clinical characteristics of patients and control in the EEG study 102 Table 13. Demographic and clinical characteristics of non-responsive and responsive group (EEG study) 103 Table 14. Comparison of relative power in delta, theta, alpha, and beta frequency band. 104 LIST OF FIGURES Figure 1. Research Blueprint 105 Figure 2. Study flowchart and study aims of the genetic study 106 Figure 3. Study flowchart and study aims of the TRD cohort study 107 Figure 4. Study flowchart and study aims of the EEG clinical study 108 Figure 5. Screen plot for an exploratory factor analysis 109 Figure 6. Change in syndromal severity during follow-up 110 Figure 7. Manhattan plot for a continuous response “%change of HAM-D” 111 Figure 8. Manhattan plot for a binary outcome response “≥50% improvement” 112 Figure 9. Specific protein-protein interaction subnetwork for treatment response in major depressive disorder (MDD) 113 Figure 10. The distribution of side effects (treatment emergent suicidal ideation and sexual side effect) 114 Figure 11. Survival curves of TRD in different models 115 Figure 12. Step of EEG examination 116 Figure 13. Graph theory-based analyses between patients at baseline and healthy controls 117 Figure 14. Graph theory-based analyses between patients at week-1 and healthy controls 118 Figure 15. Graph theory-based analyses between non-responders at baseline 119 Figure 16. Graph theory-based analyses between non-responders at week-1 120 Figure 17. Functional connectivity among responders at baseline and week-1 121 Figure 18. Comparison of the changes in syndromal severity between participants in the EEG and genetic studies during follow-up 122 SUPPLEMENTARY MATERIALS Supplement Table 1. Pharmacogenetics studies for treatment responses 123 Supplement Table 2. ISPC participating sites and study description 126 Supplement Table 3. The correlation of change in average betweenness centrality and change in HAM-D after adjusted 126" | |
| dc.language.iso | en | |
| dc.subject | 治療模式 | zh_TW |
| dc.subject | 憂鬱症 | zh_TW |
| dc.subject | 治療效果變異 | zh_TW |
| dc.subject | 難治型憂鬱症 | zh_TW |
| dc.subject | 基因路徑分析 | zh_TW |
| dc.subject | 全基因組 | zh_TW |
| dc.subject | 共病症 | zh_TW |
| dc.subject | 腦波 | zh_TW |
| dc.subject | 腦部功能性網路連結 | zh_TW |
| dc.subject | 多基因風險評分 | zh_TW |
| dc.subject | Electroencephalography | en |
| dc.subject | Functional network connectivity | en |
| dc.subject | Prescription pattern | en |
| dc.subject | Comorbidities | en |
| dc.subject | Side effects | en |
| dc.subject | Pathway analysis | en |
| dc.subject | Genome-wide | en |
| dc.subject | Syndromal factors | en |
| dc.subject | Treatment response variation | en |
| dc.subject | Major depression | en |
| dc.subject | Treatment-resistant depression | en |
| dc.title | 探討憂鬱症治療療效變異:憂鬱症治療療效預測之臨床特性、腦波及遺傳特徵研究 | zh_TW |
| dc.title | "Investigation of the treatment variation of major depression and prediction of treatment response of antidepressants effects using clinical characteristics, genetic data, and electroencephalogram tool" | en |
| dc.date.schoolyear | 110-1 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 陳為堅(Chia-Lin Chung),謝明憲(Rong-Kuen Chen),尤香玉,蕭朱杏 | |
| dc.subject.keyword | 憂鬱症,治療效果變異,難治型憂鬱症,基因路徑分析,全基因組,共病症,腦波,腦部功能性網路連結,多基因風險評分,治療模式, | zh_TW |
| dc.subject.keyword | Major depression,Treatment-resistant depression,Electroencephalography,Treatment response variation,Syndromal factors,Genome-wide,Pathway analysis,Side effects,Comorbidities,Prescription pattern,Functional network connectivity, | en |
| dc.relation.page | 126 | |
| dc.identifier.doi | 10.6342/NTU202200365 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2022-02-09 | |
| dc.contributor.author-college | 公共衛生學院 | zh_TW |
| dc.contributor.author-dept | 流行病學與預防醫學研究所 | zh_TW |
| dc.date.embargo-lift | 2024-01-01 | - |
| 顯示於系所單位： | 流行病學與預防醫學研究所 | |
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