臺灣博碩士論文加值系統

全氟烷基化合物(perfluoroalkyl substances, PFASs)，鄰苯二甲酸酯(phthalate esters, PAEs)，雙酚A(bisphenol A, BPA)是普遍存在的內分泌干擾化學物質，可能影響野生動物和人類健康。雙酚F(bisphenol F, BPF)和雙酚S(bisphenol S, BPS)是BPA的替代物；然而，研究顯示它們的潛在危害並不亞於BPA。對羥基苯甲酸酯防腐劑(parabens)通常添加到個人保健產品、食品和藥品中，以抑制微生物生長和延長產品保存期限，但是其亦屬內分泌干擾化學物質。食物為一般大眾暴露化學物質的主要來源，雖然台灣目前有國民膳食營養調查，但較少生物檢體的內在劑量資料，為了評估人體暴露於這些化學物質的內在劑量，生物偵測是一種很好的方法。血清中化學物質濃度與身體內總量保持平衡；尿液則可顯示短時間經由人體代謝途徑濃度之情形。本研究使用尿液和血清做為生物基質，可以更全面的代表化學物在人體之內在劑量。
本研究針對尿液及血清中11種全氟烷基化合物(PFASs)、6種鄰苯二甲酸酯代謝物(PAE metabolites)、4種防腐劑(parabens)、雙酚A(BPA)、雙酚F(BPF)及雙酚S(BPS)，完成儀器分析方法開發，使用Waters UPLC I-Class極致液相層析搭配Waters Xevo TQ-XS串聯式質譜儀，游離介面為負電灑游離。11種全氟碳化合物(PFASs)、6種鄰苯二甲酸酯代謝物(PAE metabolites)、4種防腐劑(paraben)、雙酚S(BPS)使用之液相層析管柱為Waters CORTECS (30 × 2.1 mm, 1.6 μm)，有機動相為甲醇，水性動相為0.1% 醋酸水溶液(pH 3.26)，採梯度流析，流速為0.40 mL/min，管柱溫度為40°C，層析時間（連同管柱再平衡）為10分鐘；雙酚A(BPA) 及雙酚F(BPF)則使用Waters BEH C18 層析管柱(50 × 2.1 mm, 1.7 μm)，有機動相為甲醇，水性動相為10 mM N-甲基嗎啡林水溶液(pH 9.65)，採梯度流析，流速為0.40 mL/min，管柱溫度為40°C，層析時間(連同管柱再平衡)為6.3分鐘。質譜儀以MRM多重反應偵測模式進行離子監測。製作標準品校正回歸線之線性範圍為0.5-500 ng/mL，線性相關係數平方r2皆為0.990以上，儀器偵測極限範圍為0.44-899 fg及儀器定量極限範圍為4.38-1024 fg，具有良好的偵測靈敏度。
本研究開發尿液的樣本前處理的方法，使用β-glucuronidase 與 arylsulfatase (ALS) 兩種混合之酵素於37°C培養40分鐘，用於切斷接合物(conjugates)，並以Sirocco 96-well plate進行萃取，偵測尿液中化學物質之總濃度(free plus deconjugates)，並進行方法之確效。血清前處理方法為應用實驗室先前開發之方法，使用Ostro 96-well plate進行萃取，因增加待測物種類，故一樣進行方法確效。PFASs、PAE代謝物、BPA及其替代物和Parabens在尿液方法之基質效應和萃取效率範圍分別為62-107%和92-117%。血清之基質效應和萃取效率範圍分別為88-125%、65-109%。同日和異日差異之定量偏差和相對偏差多數小於20%。尿液中的每個待測物之定性極限範圍為1.87-247 pg/mL，定量極限範圍為5.95-868 ng/mL；血清中的每個化合物之定性極限範圍為4.14-621 pg/mL，定量極限範圍為11.1-1719 pg/mL，顯示開發之方法有良好的靈敏度。隨後應用開發之方法進行真實樣本分析。
樣本來自於台大醫院兒童(National Taiwan University Children Hospital, NTUCH)於2018年2月至11月期間收集之336個配對的尿液和血清樣本，在尿液樣本的方面，因缺少一個creatinie濃度資料故尿液最終樣本數為335個。短碳鏈的PFASs (除PFPeA)、PAE代謝物、BPA及其替代物和Parabens類在尿液中均有很高的陽性率(80-100%)；短碳和長碳之PFASs、PAE代謝物(除MBzP)、BPS和methyl、propyl parabent在血清有很高的偵測頻率(93-100%)。Methyl paraben在兩個基質皆有很高的檢出率(100%和97%)以及相對paraben類中的其他待測物有較高的幾何平均濃度(43.1 μg/g cr and 2.17 ng/mL)。最後將濃度資料＜LOQ，但＞LOD之數值，以1/2 LOQ計算；＜LOD之數值一樣以1/2 LOQ計算。刪去沒有問卷之樣本數，以濃度資料和問卷進行配對後，分別為318個尿液樣本和319個血清樣本，搭配問卷資料與飲食調查之複回歸分析，並訂定p-value<0.05有顯著影響。尿液樣本的統計結果發現，豬肉和軟體類海鮮的消費量與PFAS濃度的增加呈正相關。使用塑料容器、攝取加工過的雞蛋，豬肉和甜飲和尿液中PAE代謝物濃度的增加有正相關的影響。攝食速食和高油甜食與parabens濃度增加呈正相關。使用乳液與尿液中的methyl paraben濃度式正相關。在血清中，較高的PFAS濃度與診斷的過敏症顯著相關。塑料容器的使用對血清PFAS，PAE代謝物和BPS濃度的增加有顯著影響；暴露二手菸、吃速食和零食對血清parabens的濃度上升有顯著影響。二手煙暴露對血清中PFASs、PAE代謝物濃度的增加有顯著影響。攝食高油甜食、速食與PFAS、PAE代謝物和parabens血中濃度的增加呈顯著正相關。本研究的结果顯示了哪些行為及飲食可能影響尿液及血清中待測物濃度上升，以提供未來優先管控的項目，以降低或解決孩童暴露情形。

Perfluoroalkyl substances (PFASs), phthalates (PAEs), and bisphenol A (BPA) were ubiquitous endocrine disrupting chemicals that might affect wildlife and human health. Bisphenol F (BPF) and bisphenol S (BPS) were alternatives to BPA; however, studies had shown that their potential hazards were not less than BPA. Parabens were commonly added to personal health products, foods and pharmaceuticals to inhibit microbial growth and extended product shelf life, but they were also endocrine disrupting chemicals. Food was the main source of exposure for the general population. Although Taiwan has national dietary nutrition surveys, there is less internal exposure dose data for exposure assessment. In order to evaluate the internal dose of human exposure to these chemicals, biomonitoring is a good method. The concentration of chemical substances in the serum was balanced with the total amount in the body; urine showed the concentration of the metabolic pathway through the human body for a short time. This study used urine and serum as biological matrix to more fully represent the dose of chemicals in human body.
This study developed a method for quantifying 11 perfluoroalkyl substances (PFASs), six phthalate esters (PAEs) metabolites, four parabens, bisphenol A (BPA) and its substitutes including bisphenol S (BPS) and bisphenol F (BPF) in urine and serum using ultra-performance liquid chromatograph coupled with tandem mass spectrometer (UPLC-MS/MS) at negative electrospray ionization (ESI-) mode. To separate the analytes, we use two different chromatographic conditions, which were Waters CORTECS C18 column (30 x 2.1 mm, 1.6 µm) and Waters BEH C18 column (50 x 2.1 mm, 1.7 µm) combining with two different aqueous buffers, 0.1 % acetic acid(aq) and 10-mM N-methylmorpholine(aq), respectively. The linear ranges of calibration curve from 0.5 to 500 ng/mL with the square of correlation coefficient (r2) greater than 0.990. The instrument detection limits (IDLs) range were 0.44-899 fg and the instrument quantitative limits (IQLs) range were 4.38-1024 fg, indicated good instrumental detection sensitivity.
This study developed a method for pretreatment to detect the total concentration of chemicals in urine samples. Enzymes of β-glucuronidase and arylsulfatase (ALS) were added to urine samples and performed at 37°C for 40 min for deconjugation. Then, urine samples were pre-treated with Sirocco 96-well plates. The serum pretreatment method used Ostro 96-well plate which was a method previously developed by our laboratory and the method was confirmed by increasing the type of the analytes to be tested. The matrix effects and extraction efficiencies of PFASs, PAE metabolites, BPA and its substitutes and parabens in the urine method ranged from 62-107% and 92-117%, respectively. The matrix effect and extraction efficiency of serum ranged from 88-125% and 65-109%, respectively. The %bias and %RSD of the inter- and intra-day less than 20%. LODs of urine was 1.87-247 pg/mL, and LOQs was 5.95-868 ng/mL; LODs of serum was 4.14-621 pg/mL, and LOQs was 11.1-1719 pg/mL.
This study analyzed 336 paired urine and serum samples which were acquired at the National Taiwan University Children Hospital (NTUCH) during February to November 2018. One urine sample lacked its creatinie concentration data, so the final number of urine samples for statistical analysis was 335. Urine contained short-chain PFASs (except for PFPeA), PAE metabolites, BPA and its substitutes and parabens showing high positive rate (80-100%); serum had high detection frequencies (93-100%) on PFASs, metabolites (except for MBzP), BPS and methyl, and propyl paraben. Methyl paraben had a high detection rate in both matrixes (100% and 97%) and a relatively high GM concentration (43.1 μg/g creatinine and 2.17 ng/mL) to other substances in parabens. For statistics, the concentration data <LOQ, but >LOD value was replace to 1/2 LOQ; ＜LOD value was replace to 1/2 LOQ. Those samples without questionnaire were deleted. Finally, 318 urine and 319 serum samples were processed using multiple regression analysis, and the significant level was set at p<0.05. The statistical results of urine samples were found that consumption of pork and mollusks had a positive correlation with the increase in the concentration of PFASs. There were positive influences between using plastics container, ingesting processed egg, pork, sweet drink and the increasing in the concentrations of PAE metabolites in the urine. The increasing levels of parabens were positively correlated with fast food and high-oil sweets. The concentration of methyl paraben increased positively by applying lotion. In serum, higher PFAS concentrations were significantly associated with diagnosed allergies. The use of plastic containers had a significant effect on the increase in serum PFAS, PAE metabolites and BPS concentrations; the exposure to second-hand smoke, fast food consumption and snacks had significantly positive effects with increasing parabens concentrations in serum. Secondhand smoke exposure had a significant impact on the increase in PFASs, PAE metabolites concentrations. The consumption of high-oil sweets and fast food had positive significant correlations with the increase in the concentrations of PFASs and PAE metabolites in serum. The results of the study show which behaviors and diets may affect the increase in urine and serum concentrations of analytes to provide future priority control programs to reduce child exposure.

致謝 i
中文摘要 ii
Abstract v
Contents ix
List of figures xii
List of tables xiii
Chapter 1. Introduction 1
1.1 Perfluroroalkyl substances (PFASs) 1
1.2 Phthalate esters (PAEs) and metabolites 4
1.3 Bisphenol A and its analogues 5
1.4 Parabens.. 7
1.5 Deconjugation 8
1.6 Objectives 10
Chapter 2. Methods 13
2.1 Reagents and materials 13
2.2 Sample collection and storage 15
2.3 Sample preparation 16
2.3.1 Urine sample preparation 16
2.3.3 Serum sample preparation 17
2.4 Instrumental analysis 18
2.4.1 Mass spectrometric conditions 18
2.4.2 UPLC conditions 19
2.5 Method validation 20
2.5.1 Matrix effect and extraction efficiency 20
2.5.2 Accuracy and precision 21
2.6 Identification, quantification, and data analysis 22
2.7 Quality assurance and quality control 23
2.8 Statistical analysis 24
Chapter 3. Results and Discussion 25
3.1 Chromatography 25
3.1.1 Selection of columns and mobile phases 25
3.1.2 Adjustment of UPLC gradient 27
3.2 Optimization of the enzyme incubation time 28
3.3 Method validation 30
3.3.1 Matrix effect and extraction efficiency 30
3.3.2 Accuracy and precision 32
3.4 Identification, quantification, and data analysis 33
3.4.1 IDLs, IQLs, and calibration ranges 33
3.4.2 LODs and LOQs 33
3.5 Real samples 35
Chapter 4. Conclusions 39
Reference 42
Figures 52
Table 67

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