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研究生:簡君如
研究生(外文):Chun-Ju Chien
論文名稱:一、產前暴露酚類化合物與兒童兩歲與七歲時神經發展影響之相關性二、頭髮中皮質醇作為慢性壓力生物指標
論文名稱(外文):Part 1: Prenatal phenolic compounds exposure and neurobehavioral development at 2 and 7 years of agePart 2: Measurement of cortisol in human hair as a biological marker of chronic stress
指導教授:陳保中陳保中引用關係
指導教授(外文):Pau-Chung Chen
口試委員:謝武勳陳家揚李永凌
口試委員(外文):Wu-Shiun HsiehChia-Yang ChenYung-Ling Lee
口試日期:2015-06-24
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:職業醫學與工業衛生研究所
學門:醫藥衛生學門
學類:公共衛生學類
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:86
中文關鍵詞:兒童神經發展雙酚A壬基酚辛基酚嬰幼兒綜合發展測驗魏氏兒童智力量表產前暴露頭髮皮質醇慢性壓力液相層析串聯質譜儀
外文關鍵詞:Children neurodevelopmentbisphenol AnonylphenoloctylphenolCDIITWISCprenatal exposurehaircortisolchronic stressliquid chromatography-tandem mass spectrometry
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第一部分
研究背景及目的:雙酚A (Bisphenol A, BPA)是一種類雌激素且常被使用於日常生活用品中,如熱感應紙、環氧樹脂等等。另外壬基酚 (Nonyl phenol, NP)和辛基酚 (Octyl phenol, OP)會被使用在例如含有界面活性劑的清潔劑當中等等。這些酚類化合物會模擬雌激素在體內的運作,已被認為是內分泌干擾物,俗稱的環境荷爾蒙。文獻指出胎盤屏障對於BPA, NP, OP的阻擋能力較低,因此可能對於胎兒的影響甚鉅。在動物實驗當中,產前暴露雙酚A可能影響大鼠大腦皮質生成、認知缺陷、空間學習與工作記憶障礙。對於動物實驗中,產前暴露壬基酚造成後代神經行為發展影響的結果並不一致。在動物實驗中看到產前暴露酚類化合物可能對於兒童神經行為發展有影響,然而在人類在這方面的研究不多,因此本篇研究進行臍帶血中的雙酚A、壬基酚和辛基酚濃度與分別於兒童兩歲及七歲時候的神經行為發展之間的相關性。另外以性別作分層,以了解酚類化合物對於男生與女生影響是否有不同。
材料與方法:本篇研究對象來自台灣出生世代研究(Taiwan Birth Panel Study),本次分析的研究對象分別於兩歲(208位)與七歲(148位)時候的兒童。每次收案皆有請主要照顧者填寫問卷以了解兒童各時期的基本資料及狀況。臍帶血中酚類化合物的濃度使用極致效能液相層析串聯質譜儀 (ultra-performance liquid chromatography-tandem mass spectrometry) 進行分析。兩歲時候的神經發展使用CDIIT(Comprehensive Developmental Inventory for Infants and Toddlers)做評估,CDIIT有五大項評估項目,分別是認知、語言、動作、社會能力及身邊事務處理能力。七歲時的神經發展使用魏氏兒童智力量表第四版(Wechsler Intelligence Scale for Children (WISC-IV))作為評估的工具。統計分析的部分使用卡方檢定、t test進行基本資料的分析。利用臍帶血中酚類化合物的濃度及各項評估神經發展的分數及可能的干擾因子進行複線性迴歸(multiple linear regression)分析。
結果:臍帶血中的BPA、NP和OP的檢出率分別為55.9%、77.6% 和68.3%。在兩歲的分析個案中BPA、NP和OP濃度的中位數分別為3.2 (ng/mL)、72.6 (ng/mL)和3.3 (ng/mL)。在七歲的分析個案中BPA、NP和OP濃度的中位數分別為3.2 (ng/mL)、49.3 (ng/mL)和6.6 (ng/mL)。兒童七歲時候,在不分性別情況下,臍帶血中BPA濃度和總IQ分數、語言表達和知覺推理三個項目有顯著的負相關。而在做性別分層後,觀察到,BPA可能對於男性的總IQ分數和語言表達有負向影響;女性的總IQ分數、知覺推理和工作記憶會降低的部分可能是受到產前暴露BPA的影響。
結論:因此本篇研究推論產前暴露到BPA可能會對於兒童七歲時候的神經認知方面的發展有所影響,且在男性與女性影響的面向有不同。另外本篇沒有觀察到產前暴露NP和OP對於兒童神經發展有統計上的相關性,未來需要更多的研究來探討驗證相關的結果。

第二部分
研究背景及目的:壓力,一種抽象的心理意識,不適當的壓力可能對生理或心理上都會造成不良的影響。當人類面臨壓力時,腎上腺會經由下視丘的刺激分泌皮質醇來應對未知挑戰,而在先前的研究有使用血液、尿液、唾液等生物檢體測量腎上腺皮質醇,頭髮,是一項新興的生物檢體,除了非侵入性、保存方便,還可以回溯個案長期的暴露。本研究欲開發分析方法,以頭髮此種非侵入性的檢體去量化與壓力相關的生物指標,並以測量的慢性壓力結構式問卷與生物指標的濃度作對應。
研究材料與方法:本研究利用超高效能液相層析串聯質譜儀 (UHPLC-MS/MS)進行儀器參數與前處理方法的開發,在頭髮、血清、尿液、唾液中測量皮質醇、可體松、可丁尼、尼古丁、咖啡因與褪黑激素。本研究使用Phenyl 分析管柱,流動相使用含5mM的醋酸銨與0.02%甲酸的甲醇與水,起始相為35%的有機相,分析一支檢體需時8分鐘,流速為每分鐘0.5毫升。本研究利用靠近頭皮三公分的頭髮段,進行清洗、剪碎,並利用甲醇當萃取液,進行攝氏40度為期24小時水浴浸泡,使用0.22 μm PVDF syringe filter 進行過濾、離心、抽乾,上機前以體積比為一比一的甲醇與水溶液回溶。另外收取12位男性與19位女性,共31位成人受試者,進行三個月頭髮、早晨血清、晨尿及唾液的收集,並搭配填寫評估慢性壓力問卷(包含心理壓力強度量表、醫院焦慮憂慮量表、A型人格量表與匹茲堡睡眠量表),以皮爾森相關係數的統計方法進行後續實驗方法驗證。
結果:本研究對於頭髮中皮質醇的定量極限為7.79 (pg/mg),檢出率為百分之八十三點三,中位數為2.2 (pg/mg)。本研究進行各基質間,皮質醇、可體松、可丁尼、尼古丁、咖啡因與褪黑激素濃度的相關性。本研究發現,可體松在血清、尿液、唾液倆倆之間相關性達到統計上的顯著;但皮質醇和可體松在頭髮中和血清、尿液、唾液的相關性並不好。另外,本研究沒有發現頭髮、血清、尿液、唾液中的皮質醇和可體松濃度與A型人格、自覺壓力、睡眠品質、焦慮與憂鬱之間存在顯著的相關性。
結論:本研究發展以液相層析串聯質譜儀來測定頭髮、血清、尿液、唾液中的壓力相關生物指標的分析方法。本研究未觀察到生物指標濃度與A型人格、自覺壓力、睡眠品質、焦慮與憂鬱之間存在顯著的相關性。在未來可以應用實驗分析方法於臨床作為輔助判斷勞工過勞的依據之一。

Part I
Background: Phenolic compounds such as bisphenol A (BPA), nonylphenol (NP), and octylphenol (OP) are known as endocrine-disrupting compounds and commonly used in our life. Their impacts on neurodevelopment of children are inconclusive. The current study aims to investigate the association between umbilical cord blood levels of BPA, NP, OP and neurodevelopmental outcome at 2 and 7 years of age.
Methods: The study was based on Taiwan Birth Panel Study, a prospective birth cohort. We collected cord blood plasma to measure phenolic compounds levels by the ultra-performance liquid chromatography-tandem mass spectrometry. In the follow-up, 208 mother-infant pairs in their 2 years old and 148 mother-infant pairs in 7 years old were recruited in this study. We used Comprehensive Developmental Inventory for Infants and Toddlers (CDIIT) and Wechsler Intelligence Scale for Children (WISC-IV) for neurodevelopmental assessment in 2 years and 7 years of age, respectively. Multiple linear regressions were used for statistical analysis.
Results: The detection rates of BPA, NP, and OP were 55.9%, 77.6%, 68.3%, respectively. In this study, the median of BPA, NP, and OP levels in 2 years old were 3.3, 72.6, and 3.3 (ng/mL). On the other hand, the median levels of BPA, NP, and OP were 3.2, 49.3, and 6.6 (ng/mL). Levels of phenolic compounds were log10-transformed for statistical analysis. Gender stratification was further performed. In the WISC-IV analysis of neurocognitive assessment, we found that both significant negative association and trend between cord blood plasma BPA levels and full scale IQ (p for trend<0.01), verbal comprehension index (p for trend<0.01), and perceptual reasoning index (p for trend<0.01) in total study population. After stratified by sex, significant association were found in full scale IQ (p for trend=0.03) and verbal comprehension (p for trend<0.01) index in boys. In girls, the results demonstrated that prenatal BPA exposure had adverse effects on full scale IQ (p for trend=0.02), perceptual reasoning index (p for trend<0.01), and working memory index (p for trend=0.02). None of the DQs of the CDIIT analysis was significantly associated with phenolic compounds levels in cord blood based on continuous or categorical measures.
Conclusion: Prenatal exposure to BPA may affect neurocognitive development among 7 years of age, and the effects were different between boys and girls. More studies are needed to explore the relationship between NP and OP exposure in utero and childhood neurodevelopment.

Part II
Background: Cortisol was considered as stress biomarker because when we encountered a stressful event, cortisol would be secreted to meet the challenge. Hair was considered as long-term species which could evaluate the exposure in the past. In addition, moderate storing condition and explore specific timing of exposure make hair species getting notice in recent years. Therefore, cortisol levels in hair became a novel biomarker to evaluate chronic stress. The study aimed to develop an analytical method by LC-MS/MS to quantify the concentration of cortisol, cortisone, cotinine, nicotine, caffeine, and melatonin in hair, and also in serum, urine and saliva. In addition, we used structured questionnaire to evaluate stress level to investigate the correlation between concentrations of biomarkers and stress.
Methods: Study subjects recruited in this study were adult volunteers. There were 12 male and 19 female. Morning serum, urine and saliva were collected once a month within three months. Hair sample which near scalp was collected at the third month during study. We developed analytical method by liquid chromatography-tandem mass spectrometry to quantify the concentration of analytes in hair, serum, urine and saliva. Type a personality, consciously stress level, Pittsburgh Sleep Quality Index (PSQI) and Hospital Anxiety and Depression Scale (HADS) were used to evaluate the stress level of the study subjects. Pearson’s correlation coefficients were calculated between analytes concentration in different matrices, and stress-related outcomes assessment.
Results: The limit of detection and limit of quantitation were 2.57 and 7.79 (pg/mg), respectively. The detection rate which above limit of quantitation of cortisol levels in hair was 83.3%. Median of hair cortisol levels in our study population was 2.24 (pg/mg). We found that levels of cortisone but not cortisol in serum, urine, and saliva had significant correlation. We did not find significant correlation between levels of cortisol and cortisone in hair and stress-related outcomes.
Conclusions: The present results suggest that biomarker concentration and stress-related outcome may exist inconsistent. The method could be used in identify the karoshi in the future.

Part I
中文摘要: ii
Abstract: iv
Contents of tables: viii
Introduction: 1
Materials and methods: 3
Study design and population: 3
Phenolic compounds measurement: 4
Comprehensive Developmental Inventory for Infants and Toddlers (CDIIT): 5
Wechsler Intelligence Scale for Children (WISC-IV) 6
Potential confounders: 6
Statistical analysis: 8
Results: 10
Discussion: 12
Reference: 17
Appendix: 33
Appendix 1: 33
Appendix 2: 34

Part II
中文摘要: ii
Abstract: iv
Contents of figures: viii
Contents of tables: ix
Introduction: 38
Materials and methods: 40
Study population: 40
Sample collection: 40
Laboratory analysis: 41
Chemicals and reagents: 41
Preparation of stock and working standards solutions: 41
Sample preparation: 42
LC-MS/MS analysis: 44
Creatinine determination in urine: 45
Method validation: 46
Chronic stress evaluation: 47
Statistical analysis: 47
Results: 49
Method performance and extraction efficiency: 49
Method validation: 49
Cortisol and cortisone levels in each matrix in the study population: 50
Characteristics of stress-related outcomes assessment: 50
Correlation of levels between different matrices: 51
Correlation of levels of analytes and stress-related outcomes: 51
Discussion: 53
Reference: 58
Appendix: 76
Appendix 1: 76
Appendix 2: 78

Part I
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Part II
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