背景：慢性肝病及肝硬化在2020為第十大死因、肝細胞癌是第二大癌症死因。除了病毒性肝炎及酒精性肝炎外，非酒精性脂肪肝疾病（Non-alcoholic fatty liver disease，NAFLD）做為肝臟疾病的致病原因越來越受到重視。除了過去大家熟知的肝病三部曲「肝炎、肝硬化、肝細胞癌」之外，肝細胞癌可能在沒有肝纖維化或肝硬化的情況下就發展出，主要與胰島素阻抗與肥胖引起慢性全身性發炎反應及促進致癌機轉有關。糖尿病患者中NAFLD盛行率估計超過50%的，是一般族群的兩倍。然而篩檢因為缺乏非侵入性且有指標性的篩檢工具，NAFLD的常規篩檢至今仍不可行。目前，鮮少有研究以糖尿病族群為主要研究對象，探討非病毒性、非酒精性肝硬化及肝細胞癌（non-alcoholic-, non-hepatitis B- and C-related liver cirrhosis, hepatocellular carcinoma, NANBNC-LC, HCC）之流行病學研究。
目的：本研究旨在探討第二型糖尿病中NANBNC-LC/HCC的發生密度及危害對比值；並利用中介因子分析，探討NANBNC-LC在糖尿病與NANBNC-HCC之間的中介情形。
方法：本研究採用以族群為基礎回溯性世代追蹤研究設計，使用2006-2017年「衛生福利部統計處衛生福利資料科學中心」發行之去識別化全人口健保資料及死因統計資料，納入2007-2010一年內門急診或住院中主次診斷碼中有兩次第二型糖尿病診斷之患者，往前追蹤到2006排除研究終點及其他肝病共病及肝臟疾病危險因子，同時也排除2006至追蹤結束前有病毒性肝炎之個案後，以1：1個別配對診斷日期、年齡與性別選取對照組世代，並往後追蹤至肝硬化及肝癌發生、死亡、或研究結束（2017/12/31）。使用Cox比例風險模式估計出糖尿病世代發生NANBNC-LC/HCC之危害對比值。本研究也使用Baron & Kenny中介模式驗證方法，以及中介分析定量方法評估NANBNC-LC在糖尿病與NANBNC-HCC之間的中介角色。
結果：糖尿病與對照組世代分別有944,120與944,119人，分別在平均追蹤8.98與9.77年後，NANBNC-LC在糖尿病與對照組的發生密度分別為10.30/10000人年與3.38/ 10000人年，粗危害對比值為3.04（95%C.I.=2.92-3.17），校正潛在干擾因子後之危害對比值為2.73（95%C.I.=2.62-2.85）。NANBNC-HCC的發生率在糖尿病與對照世代分別追蹤8.98與9.76年後，發生密度分別為10.30/10000人年與4.75/10000人年。糖尿病與NANBNC-HCC之粗危害對比值及校正潛在干擾因子後之危害對比值分別為2.18（95%C.I.=2.10-2.26）與2.02（95%C.I.= 1.95-2.10）。採用次分佈瞬間危險估算以死亡為競爭風險後之結果後，NANBNC-LC之粗風險對比值與校正風險對比值分別為2.83（95%C.I.=2.42-3.32）與2.55（95%C.I.=2.17-3.00）。NANBNC-HCC的相對應數值則為1.95（95%C.I.=1.69-2.23）與1.76（95%C.I.=1.52-2.03）。若不考慮NANBNC-LC首次出現是否在NANBNC-HCC之前，本研究利用Baron & Kenny方法發現NANBNC-LC在糖尿病與NANBNC-HCC的時序相關中扮演部份中介的角色；而定量分析發現NANBNC-LC之中介效應百分比為11.97%；若考慮NANBNC-LC及NANBNC-HCC的時序性，則Baron & Kenny中介模式分析之結果仍顯示肝硬化是糖尿病與肝標癌的中介因子，但其定量之中介效應百分比僅剩1.08%。
結論：本研究發現糖尿病相對於對照組，有較高的NANBNC-LC/HCC發生風險；此外，NANBNC-LC在糖尿病與NANBNC-HCC之間僅扮演部份中介的角色，若進一步考慮就醫資料中NANBNC-LC與NANBNC-HCC之時序關係，則NANBNC-LC的中介效應即變的不明顯。

There have been limited of epidemiological studies on the relationship between non-alcoholic-, non-B-, non-C-hepatitis (NANBNC) LC and HCC. This population-based retrospective cohort study, using National Health Insurance data between 2006-2017 in Taiwan, investigate this relationship. Patients with >=2 clinical visits for type 2 diabetes mellitus (T2DM) in 2007-2010 were identified. Those who had prior histories of comorbidity and risk factors for liver disease as early as 2006 were excluded. We also excluded parients with viral hepatitis during follow up period. Cox proportional hazard regression model with competing risk event was used. We used Baron & Kenny method and quantative mediation analysis for the potential mediation of NANBNC-LC between T2DM and NANBNC-HCC. We enrolled 944,120 T2DM patients and 944,119 matched controls. Incidence density of NANBNC-LC was estimated at 10.30/10000 and 3.38/10000 person-years for T2DM and controls, respecitvely, representing a covariate adjusted HR of 2.73 (95%C.I.=2.62-2.85). The corresponding figures for NANBNC-HCC were 10.30/10000 person-years, 4.75/10000 person-years, and 2.02 (95%C.I.=1.95-2.10). In competing risk analyses, adjusted HR of NANBNC-LC and NANBNC-HCC was slightly attenatued at 2.55 (95%C.I.=2.17-3.00) and 1.76 (95%C.I.=1.52-2.03), respectively. Mediation analyses that did not consider the temporal sequence between NANBNC-LC and NANBNCHCC showed that NANBNC-LC may partially mediate between T2DM and NANBNC-HCC, with a proportion mediated of 11.97%. When temporal sequence was considered, the proportion mediated reduced to 1.08%. In summary, T2DM was associated with higher risks of both NANBNC-LC and NANBNC-HCC. Additionally, NANBNC-LC only slightly mediated between T2DM and NANBNC-HCC.

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