Hu Li-Kun, Liu Yu-Hong, Yang Kun, Chen Ning, Ma Lin-Lin, Yan Yu-Xiang
Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing, 100069, China.
Municipal Key Laboratory of Clinical Epidemiology, Beijing, 100069, China.
Environ Sci Pollut Res Int. 2023 Mar;30(14):40507-40518. doi: 10.1007/s11356-023-25132-3. Epub 2023 Jan 7.
Evidence of associations between ambient fine particulate matter (PM) and risks of decline of kidney function and hyperuricemia is limited. We aimed to investigate the associations between long-term exposure to PM with decline of kidney function and hyperuricemia in China. We conducted a two-stage study based on China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2015. Cox proportional hazard regression models and restricted cubic splines were used to evaluate the associations of PM with risks of decline of kidney function and hyperuricemia. Latent class trajectory models (LCTM) were used to identify trajectories of PM from 2011 to 2015 in the sensitivity analysis. A total of 9760 participants were included in baseline analysis, and 5902 participants were in follow-up analysis. PM was associated with the risks of decline of kidney function [hazard ratio (HR): 2.14; 95% confidence interval (CI): (1.03, 4.44)] and hyperuricemia [HR 1.40 (95% CI: 1.10, 1.79)] in the second quartile group versus the lowest quartile group of PM. We also observed nonlinear relationships between PM and the risks of the decline of kidney function and hyperuricemia (P < 0.001). In sensitivity analysis, four trajectory groups were identified. "Maintaining a high PM" [odds ratio (OR): 2.20; 95%CI: (1.78, 2.73)] and "moderately high starting PM then steadily decreased" [OR (95%CI): 5.15 (1.55, 16.13)] were associated with hyperuricemia risk, using "low starting PM then steadily decreased" trajectory as reference. In conclusion, improved air quality is essential for prevention of decline of kidney function and hyperuricemia.
环境细颗粒物（PM）与肾功能下降及高尿酸血症风险之间关联的证据有限。我们旨在研究中国长期暴露于PM与肾功能下降及高尿酸血症之间的关联。我们基于2011年至2015年的中国健康与养老追踪调查（CHARLS）进行了一项两阶段研究。采用Cox比例风险回归模型和受限立方样条来评估PM与肾功能下降及高尿酸血症风险的关联。在敏感性分析中，使用潜在类别轨迹模型（LCTM）来识别2011年至2015年的PM轨迹。共有9760名参与者纳入基线分析，5902名参与者纳入随访分析。与PM最低四分位数组相比，PM第二四分位数组与肾功能下降风险[风险比（HR）：2.14；95%置信区间（CI）：（1.03，4.44）]及高尿酸血症[HR 1.40（95%CI：1.10，1.79）]相关。我们还观察到PM与肾功能下降及高尿酸血症风险之间存在非线性关系（P＜0.001）。在敏感性分析中，识别出四个轨迹组。以“低起始PM然后稳步下降”轨迹为参照，“维持高PM”[优势比（OR）：2.20；95%CI：（1.78，2.73）]和“起始PM中度高然后稳步下降”[OR（95%CI）：5.15（1.55，16.13）]与高尿酸血症风险相关。总之，改善空气质量对于预防肾功能下降和高尿酸血症至关重要。
