BACKGROUND Overweight and obesity impose a considerable individual and social burden, and the urban environments might encompass factors that contribute to obesity. Nevertheless, there is a scarcity of research that takes into account the simultaneous interaction of multiple environmental factors. OBJECTIVES Our objective was to perform an exposome-wide association study of body mass index (BMI) in a multicohort setting of 15 studies. METHODS Studies were affiliated with the Dutch Geoscience and Health Cohort Consortium (GECCO), had different population sizes (688-141,825), and covered the entire Netherlands. Ten studies contained general population samples, others focused on specific populations including people with diabetes or impaired hearing. BMI was calculated from self-reported or measured height and weight. Associations with 69 residential neighborhood environmental factors (air pollution, noise, temperature, neighborhood socioeconomic and demographic factors, food environment, drivability, and walkability) were explored. Random forest (RF) regression addressed potential nonlinear and nonadditive associations. In the absence of formal methods for multimodel inference for RF, a rank aggregation-based meta-analytic strategy was used to summarize the results across the studies. RESULTS Six exposures were associated with BMI: five indicating neighborhood economic or social environments (average home values, percentage of high-income residents, average income, livability score, share of single residents) and one indicating the physical activity environment (walkability in 5-km buffer area). Living in high-income neighborhoods and neighborhoods with higher livability scores was associated with lower BMI. Nonlinear associations were observed with neighborhood home values in all studies. Lower neighborhood home values were associated with higher BMI scores but only for values up to €300,000. The directions of associations were less consistent for walkability and share of single residents. DISCUSSION Rank aggregation made it possible to flexibly combine the results from various studies, although between-study heterogeneity could not be estimated quantitatively based on RF models. Neighborhood social, economic, and physical environments had the strongest associations with BMI. https://doi.org/10.1289/EHP13393.

中文翻译：

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使用随机森林模型的新颖元分析方法进行体重指数的全暴露关联研究。


背景技术超重和肥胖给个人和社会带来了相当大的负担，并且城市环境可能包含导致肥胖的因素。然而，考虑多种环境因素同时相互作用的研究却很少。目标 我们的目标是在 15 项研究的多队列环境中进行体重指数 (BMI) 的全暴露组关联研究。方法 研究隶属于荷兰地球科学与健康队列联盟 (GECCO)，具有不同的人口规模 (688-141,825)，覆盖整个荷兰。十项研究包含一般人群样本，其他研究则侧重于特定人群，包括糖尿病患者或听力受损者。 BMI 是根据自我报告或测量的身高和体重计算得出的。探讨了与 69 个住宅区环境因素（空气污染、噪音、温度、社区社会经济和人口因素、食品环境、驾驶性和步行性）的关联。随机森林 (RF) 回归解决了潜在的非线性和非加性关联。在缺乏 RF 多模型推理的正式方法的情况下，使用基于排名聚合的元分析策略来总结所有研究的结果。结果 六项暴露与 BMI 相关：五项指示社区经济或社会环境（平均房价、高收入居民的百分比、平均收入、宜居性得分、单身居民的比例），一项指示身体活动环境（5 岁以下居民的步行能力）公里缓冲区）。居住在高收入社区和宜居性得分较高的社区与较低的体重指数相关。 在所有研究中都观察到与邻里房屋价值的非线性关联。较低的社区房屋价值与较高的 BMI 分数相关，但仅限于价值不超过 30 万欧元的情况。协会对于步行适宜性和单身居民比例的指示不太一致。讨论 尽管无法根据 RF 模型定量估计研究间的异质性，但排名聚合使得灵活组合各种研究的结果成为可能。社区社会、经济和物理环境与 BMI 的关联最强。 https://doi.org/10.1289/EHP13393。