Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China.
Department of health policy research, Henan Academy of Medical Sciences, Zhengzhou, China.
Environ Res. 2021 Mar;194:110632. doi: 10.1016/j.envres.2020.110632. Epub 2020 Dec 18.
Socio-economic status (SES) and air pollutants are thought to play an important role in human obesity. The evidence of interactive effect between SES and long-term exposure to mixture of air pollutants on obesity is limited, thus, this study is aimed to investigate their interactive effects on obesity among a rural Chinese population.
A total of 38,817 individuals were selected from the Henan Rural Cohort Study. Structural equation modeling (SEM) was applied to construct the latent variables of low SES (educational level, marital status, family yearly income, and number of family members), air pollution (particulate matter with aerodynamics diameters ≤ 1.0 μm, ≤ 2.5 μm or ≤ 10 μm, and nitrogen dioxide) and obesity (body mass index, waist circumference, waist-to-hip ratio, waist-to-height ratio, body fat percentage and visceral fat index). Generalized linear regression models were used to assess associations between the constructed latent variables. Interaction plots were applied to describe interactive effect of air pollution and low SES on obesity and biological interaction indicators (the relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP) and synergy index (S)) were also calculated.
Increased latent variables of low SES and mixture of air pollution were associated with a higher odds of latent variable of obesity (odds ratios (OR) (95% confidence interval (CI)) were 1.055 (1.049, 1.060) and 1.050 (1.045, 1.055)). The association of the mixture of air pollutants on obesity was aggravated by increased values of the latent variable of low SES (P < 0.001). Furthermore, the values of RERI, AP and S were 0.073 (0.051, 0.094), 0.057 (0.040, 0.073) and 1.340 (1.214, 1.479), respectively, indicating an additive effect of estimated latent variable of low SES and air pollution on obesity.
These findings suggested that low SES aggravated the negative effect of mixture of air pollutants on obesity, implying that individuals with low SES may be more susceptible to exposure to high levels of mixture of air pollutants related to increased risk of prevalent obesity.
社会经济地位（SES）和空气污染物被认为在人体肥胖中起着重要作用。SES 与长期暴露于空气污染物混合物之间相互作用对肥胖的影响证据有限，因此，本研究旨在调查 SES 和空气污染物混合物对中国农村人群肥胖的相互作用。
本研究共从河南农村队列研究中选取了 38817 名参与者。结构方程模型（SEM）被用来构建 SES （教育水平、婚姻状况、家庭年收入和家庭成员数量）、空气污染（空气动力学直径≤1.0μm、≤2.5μm 和≤10μm 的颗粒物以及二氧化氮）和肥胖（体重指数、腰围、腰臀比、腰高比、体脂百分比和内脏脂肪指数）的潜在变量。广义线性回归模型用于评估构建的潜在变量之间的关联。交互作用图用于描述空气污染和 SES 对肥胖的交互作用，同时还计算了交互作用的相对超额风险（RERI）、归因比例（AP）和协同指数（S）。
SES 降低和空气污染物混合物的增加与肥胖的潜在变量呈正相关（比值比（OR）（95%置信区间（CI）为 1.055（1.049，1.060）和 1.050（1.045，1.055））。空气污染物混合物对肥胖的影响随着 SES 潜在变量的增加而加剧（P<0.001）。此外，RERI、AP 和 S 的值分别为 0.073（0.051，0.094）、0.057（0.040，0.073）和 1.340（1.214，1.479），表明 SES 和空气污染物对肥胖的影响具有相加作用。
这些发现表明，SES 降低会加剧空气污染物混合物对肥胖的负面影响，这表明 SES 较低的个体可能更容易接触到与肥胖患病率增加相关的高水平空气污染物混合物，从而增加肥胖的风险。
