研究背景：糖尿病為當今重要的公共衛生議題，且造成社會醫療成本的增加。血糖異常包含糖尿病前期及糖尿病的狀態，而糖尿病前期為發展為糖尿病的病程。雖然有研究探討空氣污染物對血糖異常的影響，但皆未探討空氣污染物之間的交互作用。另一方面，鮮少研究探討空氣污染物與血糖異常惡化之間的關聯。

研究目的：本研究旨在探討空氣污染物，包括懸浮微粒(PM10)、細懸浮微粒(PM2.5)、一氧化碳、二氧化氮、二氧化硫、臭氧，對於血糖異常發生及其惡化的相關性。

材料與方法：本研究為回溯性世代研究。研究對象選取自2002至2017年參與美兆健康檢查的族群，排除條件為地址資料缺失者、僅參與一次健檢者、及初次健檢時自訴患有糖尿病、癌症、心血管疾病者。本研究擬以混合土地利用迴歸模型估算參與者第一次接受健檢當月及前23個月之空氣污染物之平均濃度，再以Cox迴歸分析評估各空氣污染物和血糖異常(包含糖尿病前期及糖尿病)的之發生及糖尿病前期之惡化的關聯，並評估各污染物間的相關性；若兩兩空氣污染物高度相關，則進行雙空氣污染物模型分析。

結果:在CO暴露組中，Q3與Q4的血糖異常發生風險顯著增加 6.8%與13.9%。在PM10暴露組中，Q3與Q4的糖尿病前期惡化風險顯著上升8.9%與17.1%。PM2.5有相似的現象，其風險顯著上升15.5%與18%。此外，在雙污染物模型中發現CO與NO2之間存在交互作用。然而，本研究亦發現NO2與血糖異常的發生與惡化皆具負相關性。

結論: 本研究顯示，氣態污染物與懸浮微粒對血糖異常具有不同的相關性。PM2.5與PM10主要與血糖異常的惡化有關，而CO則與血糖異常的發生相關，且其相關性在NO2暴露較低時更為明顯，顯示存在交互作用。然而，NO2可能對血糖異常具潛在的保護作用。

Background: Diabetes is an important public health issue today, causing increased social and medical costs. Dysglycemia includes prediabetes and diabetes stages. Prediabetes is a pathway for the development of diabetes. Although there are studies examining the effects of air pollutants on dysglycemia, interactions of air pollutants have not been evaluated. Furthermore, few studies investigated the associations between air pollutants and the progression of dysglycemia.
Objective: The objective of this study is to evaluate the effects of air pollutants on the incidence and progression of dysglycemia, including particulate matter (PM10), fine particulate matter (PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide, and ozone (O3).
Material and Methods: The current study recruited participants from MJ medical examination program from 2002 to 2017. We excluded those who were lack of information on home addresses, received health examination for only once, or had been diagnosed with diabetes, cancer, or cardiovascular disease at the first health examination. The hybrid land-use regression model was used to estimate the mean exposure level of air pollutants of each individual during the month of the health examination and the 23 months prior to the examination. Cox regressions were used to evaluate the associations of air pollutants with the incidence and progression of dysglycemia. We also assessed the correlations among the pollutants and applied two-pollutant models when two air pollutants were found to be highly correlated.
Results: The incidence of dysglycemia in the Q3 and Q4 CO exposure groups increased by 6.8% and 13.9%, respectively. Meanwhile, the risk of dysglycemia progression in the Q3 and Q4 PM10 exposure groups increased by 8.9% and 17.1%, respectively. Similarly, for PM2.5 exposure, the increased risks were 15.5% and 18%, respectively. A two-pollutant model revealed an interaction between CO and NO2. However, NO2 had a negative association with the incidence and progression of dysglycemia.
Conclusion: The current study found that gaseous pollutants and particulate matter had different relationships with dysglycemia. PM2.5 and PM10 were associated with the risk of dysglycemia progression. CO was associated with the incidence of dysglycemia, and the increases in the risk were even larger after stratification by NO2. However, NO2 had a negative association with dysglycemia.

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