School of Public Health, Division of Environmental and Occupational Health Sciences, University of Illinois at Chicago, Chicago, Illinois, USA.
Oak Ridge Institute for Science and Education, National Health and Environmental Effects Research Laboratory, Environmental Public Health Division, U.S. Environmental Protection Agency, Chapel Hill, North Carolina, USA.
J Diabetes Investig. 2020 Mar;11(2):315-324. doi: 10.1111/jdi.13152. Epub 2019 Oct 21.
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.
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.
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.
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.
目的/引言:热量过剩和缺乏体力活动都不能完全解释糖尿病患病率的上升。个体环境污染物会破坏葡萄糖内环境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。
环境质量指数是一个由五个领域组成的县级指数,用于捕捉多因素的环境暴露。将环境质量指数与县级年度年龄调整的人群为基础的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,每年每 10 万人的差异)和 95%置信区间(CI)。评估了整体环境质量和特定领域指数的相关性,所有分析均按四个农村-城市分层进行。
与环境质量最差的最高五分位数/县相比,环境质量较差的县的总糖尿病患病率较低。关联因农村-城市分层而异;在城市化程度较低和人口较少的分层中,整体环境质量较好与总糖尿病患病率较低相关。考虑到所有县,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),这表明环境质量较差的县每年的患病率比社会人口环境较好的县高 2.77 每 10 万人。
人们越来越关注环境暴露作为糖尿病发病机制的致病因素,本研究结果表明,预防糖尿病的综合方法必须包括改善环境质量的干预措施。