Department of Epidemiology, School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, Zhejiang, PR China.
Department of Infection Prevention and Control, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, 310016, Zhejiang, PR China.
Semin Arthritis Rheum. 2024 Jun;66:152445. doi: 10.1016/j.semarthrit.2024.152445. Epub 2024 Mar 30.
Limited research has been conducted on the association between long-term exposure to air pollutants and the incidence of gout.
This study aims to assess the individual and combined effects of prolonged exposure to five air pollutants (NO, NO, PM, PM and PM) on the incidence of gout among 458,884 initially gout-free participants enrolled in the UK Biobank.
Employing a land use regression model, we utilized an estimation method to ascertain the annual concentrations of the five air pollutants. Subsequently, we devised a weighted air pollution score to facilitate a comprehensive evaluation of exposure. The Cox proportional hazards model was utilized to investigate the association between ambient air pollution and gout risk. Interaction and stratification analyses were conducted to evaluate age, sex, BMI, and genetic predisposition as potential effect modifiers in the air pollution-gout relationship. Furthermore, mediation analyses were conducted to explore the potential involvement of biomarkers in mediating the association between air pollution and gout.
Over a median follow-up time of 12.0 years, 7,927 cases of gout were diagnosed. Significant associations were observed between the risk of gout and a per IQR increase in NO (HR: 1.05, 95 % CI: 1.02-1.08, p = 0.003), NO (HR: 1.04, 95 % CI: 1.01-1.06, p = 0.003), and PM (HR: 1.03, 95 % CI: 1.00-1.06, p = 0.030). Per IQR increase in the air pollution score was associated with an elevated risk of gout (p = 0.005). Stratified analysis revealed a significant correlation between the air pollution score and gout risk in participants ≥60 years (HR: 1.05, 95 % CI: 1.02-1.09, p = 0.005), but not in those <60 years (p = 0.793), indicating a significant interaction effect with age (p-interaction=0.009). Mediation analyses identified five serum biomarkers (SUA:15.87 %, VITD: 5.04 %, LDLD: 3.34 %, GGT: 1.90 %, AST: 1.56 %) with potential mediation effects on this association.
Long-term exposure to air pollutants, particularly among the elderly population, is associated with an increased risk of gout. The underlying mechanisms of these associations may involve the participation of five serum biomarkers.
目前，有关长期暴露于空气污染物与痛风发病率之间关联的研究较为有限。
本研究旨在评估在英国生物库中招募的 458884 名最初无痛风的参与者中，长期暴露于 5 种空气污染物（NO、NO、PM、PM 和 PM）对痛风发病率的个体和联合影响。
我们采用土地使用回归模型，利用估计方法确定了这 5 种空气污染物的年浓度。随后，我们构建了加权空气污染评分，以全面评估暴露情况。采用 Cox 比例风险模型探讨了环境空气污染与痛风风险之间的关联。进行交互作用和分层分析，以评估年龄、性别、BMI 和遗传易感性作为空气污染与痛风关系中的潜在效应修饰因素。此外，还进行了中介分析，以探讨生物标志物在介导空气污染与痛风之间关联中的潜在作用。
在中位随访时间 12.0 年期间，诊断出 7927 例痛风病例。NO（HR：1.05，95%CI：1.02-1.08，p=0.003）、NO（HR：1.04，95%CI：1.01-1.06，p=0.003）和 PM（HR：1.03，95%CI：1.00-1.06，p=0.030）每增加一个 IQR，痛风风险均显著增加。空气污染评分每增加一个 IQR，与痛风风险升高相关（p=0.005）。分层分析显示，在≥60 岁的参与者中，空气污染评分与痛风风险之间存在显著相关性（HR：1.05，95%CI：1.02-1.09，p=0.005），但在<60 岁的参与者中无相关性（p=0.793），表明与年龄存在显著交互作用（p 交互=0.009）。中介分析确定了五个血清生物标志物（SUA：15.87%，VITD：5.04%，LDLD：3.34%，GGT：1.90%，AST：1.56%），它们可能在这种关联中具有潜在的中介作用。
长期暴露于空气污染物，特别是在老年人群中，与痛风发病风险增加有关。这些关联的潜在机制可能涉及五种血清生物标志物的参与。
