Association between environmental quality and diabetes in the USA
Association between environmental quality and diabetes in the USA
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DOI：
10.1111/jdi.13152
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发表时间：
2019-10-21
影响因子：
3.2
通讯作者：
Sargis, Robert M.
中科院分区：
文献类型：
--
作者：
Jagai, Jyotsna S.;Krajewski, Alison K.;Sargis, Robert M.
目的/引言热量过剩和缺乏体力活动不能完全解释糖尿病患病率的上升。个别环境污染物可破坏葡萄糖稳态并促进代谢功能障碍。然而，累积暴露对糖尿病风险的影响尚不清楚。材料与方法建立了县级环境质量指数，该指数由5个领域组成，以获取多因素的环境暴露。环境质量指数与县级年度年龄调整人口糖尿病患病率估计值相关联。使用随机截距混合效应线性回归模型估计患病率差异（PD，每10万人的年差异）和95%置信区间（CI）。协会的整体环境质量和特定领域的指数进行了评估，所有的分析分层的四个农村-城市阶层。结果环境质量最高/最差的县与环境质量最低/最好的县相比，环境质量差的县糖尿病总患病率较低。城乡阶层之间的关联各不相同;总体而言，在城市化程度较低和人口稀少的阶层中，环境质量较好与糖尿病总患病率较低相关。当考虑所有县时，良好的社会人口环境与较低的总糖尿病患病率相关（患病率差异2.77，95%置信区间2.71-2.83），表明社会人口环境差的县的年患病率为2.77/10万人，高于社会人口环境好的县。结论越来越多的注意力集中在环境暴露作为糖尿病发病机制的贡献者，目前的研究结果表明，糖尿病预防的综合方法必须包括干预措施，以改善环境质量。
Aims/Introduction Caloric excess and physical inactivity fail to fully account for the rise of diabetes prevalence. Individual environmental pollutants can disrupt glucose homeostasis and promote metabolic dysfunction. However, the impact of cumulative exposures on diabetes risk is unknown. Materials and Methods The Environmental Quality Index, a county-level index composed of five domains, was developed to capture the multifactorial ambient environmental exposures. The Environmental Quality Index was linked to county-level annual age-adjusted population-based estimates of diabetes prevalence rates. Prevalence differences (PD, annual difference per 100,000 persons) and 95% confidence intervals (CI) were estimated using random intercept mixed effects linear regression models. Associations were assessed for overall environmental quality and domain-specific indices, and all analyses were stratified by four rural-urban strata. Results Comparing counties in the highest quintile/poorest environmental quality to those in the lowest quintile/best environmental quality, counties with poor environmental quality demonstrated lower total diabetes prevalence rates. Associations varied by rural-urban strata; overall better environmental quality was associated with lower total diabetes prevalence rates in the less urbanized and thinly populated strata. When considering all counties, good sociodemographic environments were associated with lower total diabetes prevalence rates (prevalence difference 2.77, 95% confidence interval 2.71-2.83), suggesting that counties with poor sociodemographic environments have an annual prevalence rate 2.77 per 100,000 persons higher than counties with good sociodemographic environments. Conclusions Increasing attention has focused on environmental exposures as contributors to diabetes pathogenesis, and the present findings suggest that comprehensive approaches to diabetes prevention must include interventions to improve environmental quality.