Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing 100069, PR China.
Department of Otolaryngology, Head and Neck Surgery, Beijing TongRen Hospital, Capital Medical University, Beijing, PR China; Beijing Key Laboratory of Nasal Diseases, Beijing Institute of Otolaryngology, Beijing, PR China.
Environ Int. 2023 Jul;177:108031. doi: 10.1016/j.envint.2023.108031. Epub 2023 Jun 12.
We evaluated the association between ambient particulate matter (PM) exposure and eosinophilic chronic rhinosinusitis with nasal polyps (CRSwNP), and predicted the CRSwNP recurrence risk using machine learning algorithms.
In total, 1,086 patients with CRSwNP were recruited from nine hospitals in China during 2014-2019. The average annual concentrations of ambient PMs before surgery were assessed using satellite-based daily concentrations of PM and PM for a 1 × 1-km area. Linear regression and logistic regression models were used to evaluate the associations of PM exposure with eosinophilia and risks of eosinophilic CRSwNPs. In addition, mediation effect analysis was used to validate the interrelationships of the aforementioned factors. Finally, machine learning algorithms were used to predict the recurrence risks of CRSwNPs.
There was a significantly increased risk of eosinophilic CRSwNPs with each 10 μg/m increase in PM, with odds ratios (ORs) of 1.039 (95% confidence interval [CI] = 1.007-1.073) for PM and 1.058 (95% CI = 1.007- 1.112) for PM. Eosinophils had a significant mediation effect, which accounted for 52% and 35% of the relationships of CRSwNP recurrence with PM and PM, respectively. Finally, we developed a naïve Bayesian model to predict the risk of CRSwNP recurrence based on PM exposure, inflammatory data, and patients' demographic factors.
Increased PM exposure is associated with an increased risk of eosinophilic CRSwNP in China. Therefore, patients with eosinophilic CRSwNP should reduce PM exposure to mitigate its harmful impacts.
我们评估了环境颗粒物（PM）暴露与伴有鼻息肉的嗜酸性慢性鼻-鼻窦炎（CRSwNP）之间的关系，并使用机器学习算法预测了 CRSwNP 的复发风险。
本研究共纳入了 2014 年至 2019 年期间中国 9 家医院的 1086 例 CRSwNP 患者。使用基于卫星的每日 PM 和 PM 浓度来评估手术前的环境 PM 年平均浓度，覆盖面积为 1×1 公里。线性回归和逻辑回归模型用于评估 PM 暴露与嗜酸性粒细胞增多症以及嗜酸性粒细胞性 CRSwNP 风险之间的关联。此外，还进行了中介效应分析来验证上述因素之间的相互关系。最后，使用机器学习算法预测 CRSwNP 的复发风险。
随着 PM 浓度每增加 10μg/m，发生嗜酸性粒细胞性 CRSwNP 的风险显著增加，PM 的比值比（OR）为 1.039（95%置信区间[CI]：1.007-1.073），PM 的 OR 为 1.058（95% CI：1.007-1.112）。嗜酸性粒细胞具有显著的中介效应，分别占 CRSwNP 复发与 PM 和 PM 之间关系的 52%和 35%。最后，我们基于 PM 暴露、炎症数据和患者的人口统计学因素，开发了一个朴素贝叶斯模型来预测 CRSwNP 复发的风险。
在中国，PM 暴露增加与嗜酸性粒细胞性 CRSwNP 风险增加相关。因此，患有嗜酸性粒细胞性 CRSwNP 的患者应减少 PM 暴露，以减轻其有害影响。
