糖尿病是全球最常見的疾病之一，若未妥善控制，將引發多種併發症，其中腎臟病更是台灣十大死因之一。然而，目前的研究尚未能明確證實糖尿病與腎臟病之間的因果關係。因此，本研究採用孟德爾隨機化（Mendelian Randomization, MR）方法，以探討兩者之間的潛在因果關聯。研究中以糖化血色素作為糖尿病相關指標，並選用血中尿素氮、血中肌酸酐、eGFR、尿液肌酸酐、尿中微蛋白及ACR作為腎功能指標。
由於這些生物指標會隨時間變化，若未將時間因素納入考量，可能導致推論偏差。傳統MR分析並未處理時間變異問題，因此本研究進一步應用多變量孟德爾隨機化（Multivariable Mendelian Randomization, MVMR）及時間變化孟德爾隨機化（Time-Varying Mendelian Randomization, TVMR），以觀察不同時間或年齡層下的因果關係差異；同時使用CAUSE模型考慮具水平多效性的遺傳工具，以避免多效性干擾因果推論，最後再比較各方法所得結果。
研究結果顯示，在傳統MR方法中，雙樣本分析大多未發現糖化血色素與腎功能指標具有顯著關聯；然而，單樣本分析則呈現大部分指標與糖化血色素之間存在顯著關聯。CAUSE模型在單樣本與雙樣本間的結果差異不大，顯示將水平多效性納入模型後可有效降低偏誤。多變量孟德爾隨機化於單樣本與雙樣本中亦呈現相近結果，但部分指標不具顯著性。至於時間變化孟德爾隨機化，其結果相對不佳，僅顯示糖化血色素與尿中微蛋白間存在顯著相關。
綜合上述，本研究結果支持糖化血色素與部分腎功能指標之間可能存在一定的因果關係。未來研究可利用具有更多測量時間點的大型資料庫，以增加樣本數與分析可信度。同時，時間因素仍是影響疾病的重要關鍵，需在研究設計中更加審慎納入考量。以上結果可作為醫學領域後續研究與臨床應用的重要參考。

Diabetes mellitus is one of the most prevalent diseases worldwide, and poor glycemic control can lead to multiple complications, among which kidney disease is a leading cause of mortality in Taiwan. However, existing studies have not clearly established a causal relationship between diabetes and kidney disease. Therefore, this study applied Mendelian Randomization (MR) to investigate the potential causal association between diabetes-related traits and kidney function. Glycated hemoglobin (HbA1c) was used as the indicator for diabetes, while blood urea nitrogen, serum creatinine, estimated glomerular filtration rate (eGFR), urinary creatinine, urinary microalbumin, and albumin-to-creatinine ratio (ACR) were employed as indicators of kidney function.
Since these biomarkers vary over time, failing to account for temporal dynamics may bias the results. Traditional MR does not address time-dependent changes; hence, this study further employed Multivariable Mendelian Randomization (MVMR) and Time-Varying Mendelian Randomization (TVMR) to explore potential differences in causal associations across different time points or age groups. In addition, the CAUSE model was used to account for horizontal pleiotropy by identifying pleiotropic genetic variants, thereby reducing potential bias, and results across different methods were compared.
The findings indicated that, under traditional MR, most two-sample analyses did not reveal significant associations between HbA1c and kidney function indicators, whereas one-sample analyses demonstrated significant associations for the majority of indicators. Results from the CAUSE model showed little difference between one-sample and two-sample analyses, suggesting that accounting for pleiotropy effectively reduced bias. Similarly, MVMR produced consistent results across both one-sample and two-sample settings, though some associations were not statistically significant. In contrast, TVMR yielded less robust findings, with HbA1c showing a significant association only with urinary microalbumin.
In conclusion, this study provides evidence supporting a potential causal relationship between HbA1c and certain kidney function indicators. Future research may benefit from the use of large-scale databases with repeated measurements to increase statistical power and robustness. Moreover, as time remains a critical factor influencing disease progression, it should be carefully incorporated into study designs. These findings offer valuable insights for future research and potential clinical applications.

目錄
圖目錄	IV
表目錄	V
第一章	緒論	1
1.1	研究背景與動機	1
1.2	研究目的	2
第二章	文獻探討	3
2.1	孟德爾隨機化（Mendelian Randomization）	3
2.2	多變量孟德爾隨機化（Multivariable MR）	6
2.3	時間變化孟德爾隨機化（Time-Varying MR, TVMR）	7
第三章	資料介紹	10
3.1	台灣人體生物資料庫	10
3.2	糖化血色素（HbA1c）	11
3.3	微血管併發症	12
3.3.1	血中尿素氮（BUN）	14
3.3.2	血中肌酸酐（CREATININE）	15
3.3.3	估計腎絲球過濾率（eGFR）	16
3.3.4	尿液肌酸酐（CREATININE_URINE）	17
3.3.5	尿中微蛋白（MICROALB）	18
3.3.6	尿液白蛋白與肌酸酐比值（ACR）	19
第四章	研究方法	21
4.1	研究流程	21
4.2	品質管制（Quality Control）	23
4.3	全基因組關聯研究（GWAS）	24
4.4	表型全基因組關聯研究（PheWAS）	25
4.5	孟德爾隨機化方法（MR）	25
4.5.1	單樣本孟德爾隨機化方法	28
4.5.2	雙樣本孟德爾隨機化方法	28
4.5.3	CAUSE 模型	29
4.6	多變量孟德爾隨機化方法（MVMR）	31
4.7	時間變化孟德爾隨機化方法（TVMR）	33
4.7.1	真實資料	36
4.7.2	模擬資料	37
第五章	研究結果	38
5.1	品質管制（Quality Control）	38
5.2	全基因組關聯分析（GWAS）	40
5.3	表型全基因組關聯研究（PheWAS）	42
5.4	孟德爾隨機化方法(MR)	45
5.4.1	單樣本孟德爾隨機化方法	45
5.4.2	雙樣本孟德爾隨機化方法	53
5.4.3	CAUSE 模型	58
5.5	多變量孟德爾隨機化方法	69
5.5.1	單樣本MVMR	69
5.5.2	雙樣本MVMR	74
5.6	時間變化孟德爾隨機化方法	76
5.7	方法比較	83
5.7.1	MR模型比較	83
5.7.2	解決MVMR異質性問題	88
第六章	結論與討論	90
6.1	結論	90
6.2	討論	91
第七章	參考文獻	94
第八章	附錄	102

圖目錄
圖 3 - 1糖化血色素與血中尿素氮之散佈圖	15
圖 3 - 2糖化血色素與血中肌酸酐之散佈圖	16
圖 3 - 3糖化血色素與eGFR之散佈圖	17
圖 3 - 4糖化血色素與尿中肌酸酐之散佈圖	18
圖 3 - 5糖化血色素與尿中微蛋白之散佈圖	19
圖 3 - 6 糖化血色素與ACR之散佈圖	20
圖 4 - 1研究流程圖	22
圖 4 - 2 孟德爾隨機化示意圖	26
圖 4 - 3 Causal 示意圖	29
圖 4 - 4 遺傳變異不受干擾因子影響示意圖	30
圖 4 - 5遺傳變異經干擾因子影響暴露示意圖	31
圖 4 - 6 多變量孟德爾隨機化示意圖	32
圖 4 - 7 時間變化孟德爾隨機化示意圖	34
圖 5 -  1尿中微蛋白以特徵函數為基底之TVMR結果	82
圖 5 - 2尿中微蛋白以多項式為基底之TVMR結果	82

表目錄
表 2 - 1 MR相關的研究	5
表 3 - 1糖化血色素統計量	12
表 3 - 2結果變數統計量	14
表 3 - 3 eGFR分級	16
表 4 - 1 QC步驟說明	23
表 4 - 2糖化血色素調整情況	37
表 5 - 1 QC結果	39
表 5 - 2單樣本MR結果	52
表 5 - 3雙樣本MR結果	57
表 5 - 4單樣本CAUSE模型結果	63
表 5 - 5雙樣本CAUSE模型結果	68
表 5 - 6未調整單樣本MVMR結果	73
表 5 - 7調整單樣本MVMR結果	73
表 5 - 8未調整雙樣本MVMR結果	76
表 5 - 9單樣本調整TVMR工具變量強度	80
表 5 - 10單樣本未調整TVMR工具變量強度	80
表 5 - 11雙樣本未調整TVMR工具變量強度	80
表 5 - 12單樣本調整之真實資料水平多效性	81
表 5 - 13雙樣本未調整之水平多效性	81
表 5 - 14單樣本調整之模擬資料水平多效性	81
表 5 - 15單樣本未調整之真實資料水平多效性	81
表 5 - 16方法比較	86
表 5 - 17有因果關係的方法中具水平多效性之SNP	87
表 5 - 18具異質性之血中肌酸酐之cause結果	89

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