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研究生： 王莉芸
Wang, Li-Yun
論文名稱： 第二型糖尿病和躁鬱症藥物情緒穩定劑使用風險相關性
Use of Mood Stabilizers and Risk of Type 2 Diabetes Mellitus in Patients with Bipolar Disorder
指導教授： 陳立宗
Chen, Li-Tzong
學位類別： 博士
Doctor
系所名稱： 醫學院 - 臨床藥學與藥物科技研究所
Institute of Clinical Pharmacy and Pharmaceutical sciences
論文出版年： 2023
畢業學年度： 111
語文別： 英文
論文頁數： 132
中文關鍵詞： 肝糖合成酶激酶-3 、鋰鹽 、丙戊酸 、卡馬西平 、第2型糖尿病 、躁鬱症
外文關鍵詞： glycogen synthase kinase-3 (GSK-3), lithium, valproate, carbamazepine, diabetes mellitus type 2 (type 2 DM), bipolar disorder
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    • 目的：
    肝糖合成酶激酶-3於人體糖代謝中扮演重要角色，而GSK-3使用於體外與動物實驗已被發現與第2型糖尿病相關。鋰鹽為目前已使用於臨床躁鬱症病患治療之GSK-3抑制劑，而許多抗精神病藥物已被證明具有較高產生代謝性疾病之風險。然而，躁鬱症患者服用鋰鹽導致第2型糖尿病之風險國內外各研究結果仍不一致。因此本研究之目的為利用健保資料庫探討肝糖合成酶激酶-3抑制劑鋰鹽對於躁鬱症病患之第2型糖尿病產生之關聯。
    方法：
    本研究使用台灣衛生福利資料科學中心健保資料庫200萬隨機抽樣歸人檔進行迴溯性世代研究並利用國際疾病分類標準碼篩選2000至2015年間20歲以上新診斷躁鬱症(ICD-9-CM: 296.0-296.1, 296.4–296.9; ICD-10-CM: F30, F31)單獨或併用情感穩定劑之病患，主要分析藥物包含鋰鹽(lithium)、拉莫三嗪(lamotrigine)、卡馬西平(Carbamazepine)和丙戊酸（valproate）。於本分析追蹤條件為病患第一次使用之主要分析藥物之時間區間，追蹤期間為自開始用藥持續至第2型糖尿病 (ICD-9-CM codes 250; ICD-10-CM: E11)發生或2016年止，排除條件為主要分析藥物使用少於12周者及資料不全者。Kaplan-Meier估計值用於評估鋰鹽、拉莫三嗪、卡馬西平和丙戊酸此4組與第2型糖尿病產生之關聯。本研究亦計算4組藥物耗用標準化之累積定義每日劑量與其處方時間與第2型糖尿病產生之關聯。Cox回歸分析評估病患產生第2型糖尿病之風險比且調整可能影響第2型糖尿病相關共病症與用藥之變項。
    結果：
    本研究納入5386名診斷使用主要分析藥物之躁鬱症病患(鋰鹽965人、拉莫三嗪253人、丙戊酸2558人、卡馬西平1610人)。患者平均年齡依序分別為40.04、36.06、37.67與50.02歲，男性比例分別為44.77%、30.83%、44.8%與43.11%。於共病症部分，在Charlson共病症指數(CCI)評分(0, 1, >=2)之條件下，分數1或>=2分別為7.51%/3.95%於拉莫三嗪組別、6.94%/3.83%於鋰鹽組別、10.75%/5%於丙戊酸組別、17.83%/13.42%於卡馬西平組別。追蹤期間發生第2型糖尿病之中位數於此四組別為2.93、3.18、5.6與4年。Kaplan-Meier存活分析中估算於第10年之第2型糖尿病發生機率為17.58%於卡馬西平組別、11.34%於拉莫三嗪組別、11.21%於鋰鹽組別、9.81%於丙戊酸組別。Cox回歸分析下高累積劑量之鋰鹽雖然未達統計顯著性但與第2型糖尿病風險降低有關。
    結論：
    本研究第一組分析結果顯示使用丙戊酸產生第2型糖尿病之風險最低，其次是拉莫三嗪與鋰鹽，而使用卡馬西平產生第2型糖尿病之風險最高。考慮躁鬱症持續時間與各組於年齡、性別、共併症和共併藥物之差別，於第二組分析結果探得年齡為影響最大之因素。Cox回歸校正年齡分析顯示鋰鹽組別和拉莫三嗪組別之第2型糖尿病發生率最低，丙戊酸組別次之，卡馬西平組別第2型糖尿病發生率仍最高。本研究結果顯示鋰鹽使用具減少躁鬱症病患之第2型糖尿病發展之趨勢。雖仍需更多後續分析，本研究亦顯示GSK-3抑製劑於第2型糖尿病減緩應用之可能性。

    Objective: Glycogen synthase kinase-3 (GSK-3) plays an important role in glucose metabolism. GSK-3inhibitor usage is associated with diabetes mellitus type 2 (T2DM)-like syndromes in vitro and in vivo. Lithium is a GSK-3 inhibitor used in clinical treatment. Many antipsychotic drugs were shown to associate with metabolic disorders. However, the association between T2DM and lithium usage in bipolar disorder (BD) patients remains controversial. In this study we examined the association between lithium and T2DM in BD patients in Taiwan with a real-world study setting.
    Methods: This population-based retrospective cohort study is conducted by using the Longitudinal Health Insurance Database 2005 (LHID 2005) from the Health and Welfare Data Science Center. Diagnosed BD patients were extracted (ICD-9-CM: 296.0-296.1, 296.4–296.9; ICD-10-CM: F30, F31) as aged older than 20 and with usage of lithium, lamotrigine, carbamazepine or valproate from 2000-2015. The date of first prescription is the index date, and patients diagnosed with T2DM (ICD-9-CM codes 250; ICD-10-CM: E11) before the index date was excluded. Patients were included in the analysis based on their index exposure as the period of first index drug usage, and the outcome of interest is newly diagnosed T2DM before the end of this study. Patients who used second-generation antipsychotics (SGA) and index drugs less than 12 weeks and those without complete medical information were also excluded. Finally, patients with mono-index drug were divided into groups of (1) lithium (2) valproate (3) carbamazepine (4) lamotrigine. Kaplan-Meier estimate was used to evaluate T2DM risk among 4 groups. The cumulative define daily dose (cDDD) and prescription days were calculated for index drugs. The Cox regression was used to evaluate hazard ratios (HRs) and 95% confidence intervals (CI) for covariates including comorbidity and co-medication.
    Results: A total of 5386 BD patients with index drug usage were included in the study cohort and were divided into lithium group (Li, n=965), lamotrigine group (LTG, n=253), valproate group (VA, n=2558) and carbamazepine group (CBZ, n=1610). The average age (SD) of the patients were 40.04 (13.04), 36.06 (12.08), 37.67(13.68) and 50.02 (16.73) years, and the proportions of male were 44.77%, 30.83%, 44.8% and 43.11%. The Charlson comorbidity index (CCI) score (0, 1, >=2) was also computed for each group. CCI scores of “1/>=2” were 7.51%/3.95% (lamotrigine), 6.94%/3.83% (lithium), 10.75% 5% (valproate) and 17.83%/13.42% (carbamazepine), respectively. The median follow-up durations of time to T2DM event were 2.93, 3.18, 5.65 and 4 years. The Kaplan-Meier estimates of 10-year time-to-event (T2DM) rates are 11.34% (lamotrigine), 11.21% (lithium), 9.81% (valproate) and 17.58% (carbamazepine). The Cox regression analysis showed that high cumulative doses of lithium were associated with a decreased risk for T2DM (p >0.05).
    Conclusion: Our analysis indicated that the group of valproate was associated with lower T2DM risk, followed by groups of lamotrigine and lithium, and the group of carbamazepine associated with the highest T2DM risk in BD patients. Analyzing additional factors such as BD duration and demographic characteristics (age, gender, comorbidity and co-medications) among the index drug groups, in hazard ratios analysis we found age is an important factor affecting survey. After age adjustment by Cox regression we found groups lithium and lamotrigine were associated with lower T2DM risk, followed by group valproate, and group carbamazepine were associated with the highest T2DM risk in BD patients. Our result suggested the association between lithium usage and minor T2DM reduction. This study shows the potential of the clinical application of GSK-3 inhibitors in T2DM.

    中文摘要 I ABSTRACT III ACKNOWLEDGEMENT V CONTENT VI TABLE OF CONTENTS VII FIGURE OF CONTENTS VIII APPENDICES X 1. INTRODUCTION 1 2.LITERATURE REVIEW 3 2.1 EPIDEMIOLOGY OF BIPOLAR DISORDER 3 2.2 RISK FACTORS OF BIPOLAR DISORDER 4 2.3 COMORBIDITIES OF BIPOLAR DISORDER 4 2.4 PHARMACOTHERAPY FOR BIPOLAR DISORDER 5 2.5 GLYCOGEN SYNTHASE KINASE-3, GSK3 6 2.6 GSK3 AND TYPE 2 DIABETES 7 2.7 TYPE 2 DIABETES, T2DM 8 2.7.1 T2DM 8 2.7.2 MEDICATION OF T2DM 9 2.7.3 NEW TARGETS FOR T2DM DRUGS 15 2.7.4 PREVENTION OF T2DM 17 2.8 GSK-3 AND BIPOLAR DISORDER AND LITHIUM 20 2.9 LITHIUM AND TYPE 2 DIABETES RISK 21 3. STUDY OBJECTIVES 23 4. MATERIALS AND METHODS 24 4.1 DATABASES LHID2005 24 4.2 INCLUSION AND EXCLUSION CRITERIA 25 4.3 DRUG CATEGORIES AND DOSAGE 25 4.4 STUDY GROUPS IN T2DM RISK INVESTIGATION 27 4.5 OUTCOME MEASUREMENTS 27 4.6 ANALYSIS VARIABLES 28 4.7 SENSITIVITY TESTING AND SUBGROUP ANALYSES 29 4.8 STATISTICAL ANALYSIS 29 5. RESULT 30 5.1 PREVALENCE OF PHARMACOTHERAPY IN BIPOLAR DISORDER 30 5.2 DEMOGRAPHIC CHARACTERISTICS OF INDEX DRUG GROUP 31 5.3 ASSOCIATION BETWEEN INDEX DRUG USAGE AND T2DM 33 5.4 ASSOCIATION BETWEEN INDEX DRUG DOSAGE AND T2DM 34 5.5 ASSOCIATION BETWEEN DEMOGRAPHIC CHARACTERISTIC/COMORBIDITY/COMEDICATION AND T2DM IN INDEX DRUG GROUPS 35 5.6 SENSITIVE TESTS AND SUBGROUP ANALYSIS 36 5.6.1 ASSOCIATION BETWEEN ORDER OF MEDICATION AND T2DM IN INDEX DRUG GROUPS 36 5.6.2 ASSOCIATION BETWEEN SGA AND T2DM IN INDEX DRUGS GROUPS 37 5.6.3 ASSOCIATION BETWEEN AGE and T2DM IN INDEX DRUGS GROUPS 38 6. DISCUSSION 40 7. CONCLUSION 47 8. REFERENCES 87 Table of contents TABLE 1: ADHERENCE OF BIPOLAR MEDICATION AFTER DIAGNOSIS 48 TABLE 2: BASELINE CHARACTERISTICS OF STUDY GROUPS 49 TABLE 3: INCIDENCE DENSITY RATE (IR) OF INDEX DRUGS GROUPS 53 TABLE 4: MOOD STABILIZERS EXPOSURE AND HAZARD RATIOS FOR T2DM RISK 54 TABLE 5: HAZARD RATIOS FOR T2DM IN COMORBIDITY 56 TABLE 6: HAZARD RATIOS FOR T2DM IN CO-MEDICATION 58 TABLE 7: HAZARD RATIOS FOR COMORBIDITY OF T2DM IN AGE FILTRATION 60 TABLE 8: A HAZARD RATIOS FOR CO-MEDICATION OF T2DM IN AGE FILTRATION 64 Figure of contents FIGURE 1: DIAGRAM OF FOLLOW-UP TIME IN STUDY COHORT 66 FIGURE 2: STUDY FLOW CHART AND GROUPING 68 FIGURE 3: THE PROPORTIONS OF DRUG CLASSES IN BIPOLAR PATIENTS 69 FIGURE 4: THE PATIENT NUMBERS WITH EACH DRUG CLASS BEFORE/AFTER BIPOLAR DIAGNOSIS 71 FIGURE 5: THE PROPORTIONS OF DRUG CLASSES IN BIPOLAR PATIENTS FOR THE FIRST TIME 73 FIGURE 6: THE QUANTITIES OF SEXUAL AND AGE IN BIPOLAR PATIENTS 75 FIGURE 7: KAPLAN-MEIER ESTIMATE OF TIME TO T2DM BY FOUR GROUPS 76 FIGURE 8: KAPLAN-MEIER ESTIMATE OF TIME TO ANTI-T2DM MEDICATIONS BY FOUR GROUPS 77 FIGURE 9: DIAGRAM OF DRUGS ORDER ANALYSIS IN MODEL2 78 FIGURE 10: KAPLAN-MEIER ESTIMATE OF TIME TO T2DM BY MONO-DRUG THERAPY GROUPS 79 FIGURE 11: KAPLAN-MEIER ESTIMATE OF TIME TO ANTI-T2DM DRUGS BY MONO-THERAPY GROUPS 80 FIGURE 12: KAPLAN-MEIER ESTIMATE OF TIME TO T2DM BY MULTI-DRUGS OF FIRST MEDICATION 81 FIGURE 13: GROUPING OF INDEX DRUGS COMBINED SGA IN SUBGROUP ANALYSIS 82 FIGURE 14: KAPLAN-MEIER ESTIMATE OF TIME TO T2DM BY STUDY GROUPS COMBINED SGA USAGE 83 FIGURE 15: KAPLAN-MEIER ESTIMATE OF TIME TO T2DM WITH AGE GROUPS 85 FIGURE 16: KAPLAN-MEIER ESTIMATE OF TIME TO T2DM BY STUDY GROUPS ADJUSTING AGE 86 Appendices APPENDIX 1: STUDIES OF PREVENTION OF TYPE 2 DIABETES 117 APPENDIX 2: ICD-9/ 10- CM CODE FOR STUDY COMORBIDITY 118 APPENDIX 3: ATC CODE FOR STUDY DRUGS 119 APPENDIX 4: SOURCE OF EACH VARIABLE 126 APPENDIX 5: THE DEYO–CHARLSON COMORBIDITY INDEX (CCI) 128 APPENDIX 6: TAIWAN CONSENSUS: MANIC PHASE TREATMENT 130 APPENDIX 7: TAIWAN CONSENSUS: DEPRESSIVE PHASE TREATMENT 131 APPENDIX 8: TAIWAN CONSENSUS: MAINTENANCE PHASE TREATMENT 132

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