4.6 Article

Interrelationships Between Walkability, Air Pollution, Greenness, and Body Mass Index

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EPIDEMIOLOGY
卷 28, 期 6, 页码 780-788

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LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/EDE.0000000000000724

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资金

  1. NIH [T32 ES007069, K99 CA201542, P01 CA87969, R01 ES017017, P30 ES000002, UM1 CA186107]
  2. [HL 098048]

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Background: Recent studies have linked urban environmental factors and body mass index (BMI); however, such factors are often examined in isolation, ignoring correlations across exposures. Methods: Using data on Nurses' Health Study participants living in the Northeastern United States in 2006, we estimated associations between neighborhood walkability (a composite of population density, street connectivity, and business access), greenness (from satellite imagery), and ambient air pollution (from satellite-based spatiotemporally resolved PM2.5 predictions and weighted monthly average concentrations of NO2 from up to five nearest monitors) and self-reported BMI using generalized additive models, allowing for deviations from linearity using penalized splines. Results: Among 23,435 women aged 60-87 years, we observed nonlinear associations between walkability and BMI and between PM2.5 and BMI in single-exposure models adjusted for age, race, and individual-and area-level socioeconomic status. When modeling all exposures simultaneously, only the association between walkability and BMI remained nonlinear and nonmonotonic. Increasing walkability was associated with increasing BMI at lower levels of walkability (walkability index <1.8), while increasing walkability was linked to lower BMI in areas of higher walkability (walkability index >1.8). A 10 percentile increase in walkability, right above 1.8 was associated with a 0.84% decrease in log BMI. The relationship between walkability and BMI existed only among younger participants (<71 years old). Conclusions: Neighborhood walkability was nonlinearly linked to lower BMI independent of air pollution and greenness. Our findings highlight the importance of accounting for nonlinear confounding by interrelated urban environmental factors when investigating associations between the environment and BMI.

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