Interactions between ambient air pollution and obesity on lung function in children: The Seven Northeastern Chinese Cities (SNEC) Study

Journal Pre-proof Interactions between ambient air pollution and obesity on lung function in children: The Seven Northeastern Chinese Cities (SNEC) Study

Xiumei Xing, Liwen Hu, Yuming Guo, Michael S. Bloom, Shanshan Li, Gongbo Chen, Steve Hung Lam Yim, Namratha Gurram, Mo Yang, Xiang Xiao, Shuli Xu, Qi Wei, Hongyao Yu, Boyi Yang, Xiaowen Zeng, Wen Chen, Qiang Hu, Guanghui Dong PII:

S0048-9697(19)34388-8

DOI:

https://doi.org/10.1016/j.scitotenv.2019.134397

Reference:

STOTEN 134397

To appear in:

Science of the Total Environment

Received date:

15 April 2019

Revised date:

23 August 2019

Accepted date:

9 September 2019

Please cite this article as: X. Xing, L. Hu, Y. Guo, et al., Interactions between ambient air pollution and obesity on lung function in children: The Seven Northeastern Chinese Cities (SNEC) Study, Science of the Total Environment (2019), https://doi.org/10.1016/ j.scitotenv.2019.134397

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© 2019 Published by Elsevier.

Journal Pre-proof Interactions between ambient air pollution and obesity on lung function in children: The Seven Northeastern Chinese Cities (SNEC) Study Xiumei Xing1,†, Liwen Hu1,†, Yuming Guo2,†, Michael S. Bloom1,3, Shanshan Li3, Gongbo Chen4, Steve Hung Lam Yim5, Namratha Gurram1,2, Mo Yang1, Xiang Xiao1, Shuli Xu1, Qi Wei1, Hongyao Yu1, Boyi Yang1, Xiaowen Zeng1, Wen Chen1, Qiang Hu6,*,Guanghui Dong1,* 1

Guangdong Provincial Engineering Technology Research Center of Environmental Pollution

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and Health Risk Assessment, Guangzhou Key Laboratory of Environmental Pollution and

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Health Risk Assessment, Department of Occupational and Environmental Health, School of

Department of Epidemiology and Preventive Medicine, School of Public Health and

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Public Health, Sun Yat-sen University, Guangzhou 510080, China

Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia Departments of Environmental Health Sciences & Epidemiology and Biostatistics,

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University at Albany, State University of New York, Rensselaer, New York 12144, USA Department of Global Health, School of Health Sciences, Wuhan University, Wuhan, China

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Department of Geography and Resource Management, The Chinese University of Hong

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Kong, ShatinN.T., Hong Kong, China 6 †

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Department of Pediatric Surgery, Weifang People's Hospital, Weifang 261041, China These authors contributed equally to this work and should be listed as the first author. Address correspondence to:

Guanghui Dong, MD, PhD, Professor, Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou 510080, China. Email:[email protected];

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Journal Pre-proof [email protected]; Qiang Hu, MD, PhD, Professor, Department of Pediatric Surgery, Weifang People's Hospital,

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Weifang 261041, China. [email protected]

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Journal Pre-proof Abstract Children are vulnerable to air pollution-induced lung function deficits, and the prevalence of obesity has been increasing in children. To evaluate the joint effects of long-term PM1 (particulate matter with an aerodynamic diameter ≤ 1.0 μm) exposure and obesity on children’s lung function, a cross-sectional sample of 6,740 children (aged 7-14 years) was enrolled across seven northeastern Chinese cities from 2012-2013. Weight and lung function, including forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1), peak

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expiratory flow (PEF), and maximal mid-expiratory flow (MMEF), were measured according

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to standardized protocols. Average PM1, PM2.5, PM10 and nitrogen dioxide (NO2) exposure

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levels were estimated using a spatiotemporal model, and sulphur dioxide (SO2) and ozone (O3)

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exposure were estimated using data from municipal air monitoring stations. Two-level logistic regression and general linear models were used to analyze the joint effects of body mass index

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(BMI) and air pollutants. The results showed that long-term air pollution exposure was

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associated with lung function impairment and there were significant interactions with BMI. Associations were stronger among obese and overweight than normal weight participants (the

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adjusted odds ratios (95% confidence intervals) for PM1 and lung function impairments in three increasing BMI categories were 1.50 (1.07-2.11) to 2.55 (1.59-4.07) for FVC < 85% predicted, 1.44 (1.03-2.01) to 2.51 (1.53-4.11) for FEV1 < 85% predicted, 1.34 (0.97-1.84) to 2.04 (1.24-3.35) for PEF < 75% predicted, and 1.34 (1.01-1.78) to 1.93 (1.26-2.95) for MMEF < 75% predicted). Consistent results were detected in linear regression models for PM1, PM2.5 and SO2 on FVC and FEV1 impairments (PInteraction < 0.05). These modification effects were stronger amongst females and older participants. These results can provide policy makers with more comprehensive information for to develop strategies for preventing air pollution induced children’s lung function deficits among children. Keywords: Air pollution, Particulate matter, Obesity, Lung function, Children

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Abbreviations BMI, body mass index; CI, confidence interval; FEV1, forced expiratory volume in 1s; FVC, forced vital capacity; FEF25-75, forced expiratory flow at 25% and 75% of the pulmonary volume; IL-1β, interleukin-1 beta; IL-6, interleukin-6; IQR, interquartile ranges; MMEF, maximal mid-expiratory flow; MODIS, Moderate Resolution Imaging Spectroradiometer;

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NO2, nitrogen dioxide; O3, ozone; OR, odds ratio; OS, oxidative stress; PEF, peak expiratory flow; PM1, particulate matter with an aerodynamic diameter of 1.0 μm or less; PM2.5,

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particulate matter with an aerodynamic diameter of 2.5 μm or less; PM10, particulate matter

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with an aerodynamic diameter of 10 μm or less; RMB, Chinese Yuan; SD, standard deviations;

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SNEC, Seven Northeastern Cities; SO2, sulphur dioxide; TNF-α, tumor necrosis factor-α;

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WHO, World Health Organization.

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Journal Pre-proof 1. Introduction Impacts on lung function during early life stage may have long term implications for respiratory health. Children with poor lung function are more prone to respiratory problems as adults than children with normal lung function (Bui et al., 2018). Therefore, identifying modifiable risk factors for adverse lung function in early life is of great importance (Manuck et al., 2016). Ambient air pollution is one key factor of interest to respiratory health

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investigators worldwide (Gordon et al., 2014; Guan et al., 2016; Milanzi et al., 2018; Tsui et al., 2018), especially in developing countries (WHO, 2018). Thus, elucidating the impacts of

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chronic ambient air pollution on children’s lung function may help to prevent respiratory

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health problems in later life.

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Several previous epidemiological studies investigated associations between ambient air

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pollution exposure and children’s lung function, mostly for exposure to particulate matter (PM) with an aerodynamic diameter of 2.5 μm or less (PM2.5), and 10 μm or less (PM10),

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nitrogen dioxide (NO2), sulphur dioxide (SO2), and ozone (O3). Most (Amster et al., 2014; Barone-Adesi et al., 2015; Gauderman et al., 2015; Götschi et al, 2008; Jung et al., 2017;

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Knibbs et al., 2018; Schultz et al., 2017; Tam et al., 2016; Tsui et al., 2018; Vrijheid et al., 2016; Yu et al., 2018), but not all (Fuertes et al., 2015; Milanzi et al., 2018; Rovira et al., 2014; Spyratos et al., 2015) studies, suggested that short or long-term air pollutant exposures impair children’s lung function. However, few studies have characterized respiratory health impacts for the smaller and potentially more toxic PM size fraction of less than 1 µm aerodynamic diameter (PM1), which is characterized by a high surface area to volume ratio, and has greater potential for deleterious biological interactions with respiratory tissues and risks for adverse health outcomes than larger particles (Borm et al., 2006; Buczyńska et., al 2014; Hu et al., 2018; Mei et al., 2018; Pejhan et al., 2019; Yang et al., 2019). In a systematic Medline search, we identified only three panel studies that evaluated the potential adverse effects of PM1

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Journal Pre-proof exposure on lung function in children (Moshammer et al., 2006; Zwozdziak et al., 2016) and adolescents (Ghozikali et al., 2018). All of these were performed in areas with relatively low ambient PM1 levels, i.e., 22 μg/m3 (8-h average) (Zwozdziak et al., 2016), 21.5 μg/m3 (daily average) (Ghozikali et al., 2018), and 15.03 μg/m3 (annual average) (Moshammer et al., 2006), respectively. Therefore, it is essential to evaluate the respiratory effects of long-term exposure to higher PM1 levels in children.

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In addition, given dramatically increasing obesity rates in children (Pan et al., 2018;

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WHO, 2017), body mass index (BMI) has become of great interest to respiratory health investigators (Bekkers et al., 2015; Dixon & Peters, 2018; Fretzayas et al., 2018; Forno et al.,

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2018 ). Researchers found that obesity was an important modifier of associations between

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ambient air pollutant exposures and health outcomes in children, including blood pressure and

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hypertension (Dong et al., 2015), cognition (Calderón-Garcidueñas et al., 2016), and respiratory symptoms or asthma (Dong et al., 2013; LeMasters, et al., 2015; Soh et al., 2018).

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For instance, in one of our previous studies, we observed consistent and statistically

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significant interactions between ambient air pollution exposure and obesity on respiratory symptoms and asthma in 30,056 children, using a standardized American Thoracic Society Epidemiologic Standardization Project questionnaire (Dong et al., 2013). Yet, direct measures of lung function, such as spirometry, are more objective and provide great detail on airway obstruction than possible using questionnaires, reducing outcome misclassification and increasing the sensitivity and specificity of epidemiologic studies of environmental pollutants and lung function (Abellan et al., 2019; Gaffin et al., 2010; Miller and Marty 2010; Pijnenburg et al., 2015). However, to the best of our knowledge, there is limited evidence on the joint effects of chronic ambient air pollution exposure and obesity on lung function in children.

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Journal Pre-proof Therefore, in this study, we hypothesized that obesity interacts with ambient air pollutants, including PM1, on children’s lung function. Specially, we proposed that obesity enhances adverse respiratory responses to ambient air pollution exposure among children, and that the effect of air pollution is significantly stronger among obese children than among normal-weight children. To test this hypothesis, we analyzed data from the Seven Northeastern Cities (SNEC) study (Dong et al., 2014, 2015; Hu et al., 2017), a large population-based investigation of lung function, BMI, and air pollution exposure in China.

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The results of this study will inform implementation of regulations to protect children’s

2. Methods

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2.1. Study sites and subject recruitment

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WHO or other government agencies in the future.

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respiratory health, and will play a pivotal role in setting standards for PM1 proposed by the

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The SNEC study is a population-based cross-sectional investigation of air pollution exposure and health effects in Chinese children. A detailed description of the study protocol

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was previously published (Dong et al., 2013, 2015; Hu et al., 2017). Briefly, to maximize air pollutant exposure gradients and minimize their inter correlations, we chose 24 urban districts from Anshan, Benxi, Dandong, Dalian, Fushun, Liaoyang, and Shenyang, seven cities in northeast of China, based on 2009 to 2011 air quality monitoring data. In each district, we randomly selected one or two elementary schools and one or two middle schools within 2 km of a municipal air monitoring station. The locations of the study cities and selected schools are shown in Figure S1. Finally, within each selected school, we randomly selected one or two classes from each grade level and enrolled 7,326 eligible participants. After enrollment, classroom teachers described the study protocol to participants’ parent/guardian and

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Journal Pre-proof distributed a study questionnaire, which could be completed while attending a “Parent’s Night” or at home and returned to the classroom teacher in a sealed envelope. Of 7,326 enrolled participants, 7,109 (97.0%) completed both the questionnaire and spirometry. We excluded 279 children (3.9%) for living less than two years in the study district, and 90 children (1.3%) for missing data, such as age and gender. Finally, 6,740 children were analyzed in this study.

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Written informed consent was collected from the parent/guardian of each child. The

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study protocol was approved by the Sun Yat-sen University Institutional Human Ethics Committee (Ethics Approval Number: 026) and complied with the Declaration of

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Helsinki-Ethical Principles for Medical Research Involving Human Subjects.

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2.2. Ambient and personal air pollution assessment

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We used a spatiotemporal model to predict daily particulate matter concentrations (PM1,

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PM2.5, and PM10) and NO2 for 2009 to 2012, at a 0.1° × 0.1° spatial resolution. We described the spatiotemporal prediction model in detail in a prior publication (Chen et al., 2018). In brief, Target

(https://darktarget.gsfc.nasa.gov/)

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Dark

(https://deepblue.gsfc.nasa.gov/),

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satellite-based

and

Deep

Blue

Moderate

Resolution

Imaging

Spectroradiometers (MODIS) data processing algorithms, were combined to collect aerosol optical depth data. We developed a spatiotemporal model to link ground-monitored PM1, PM2.5, PM10, and NO2 data with the MODIS aerosol optical depth data and with other spatial and temporal predictors (e.g., urban cover, forest cover, weather data, and calendar month). Each participant’s home address was geocoded as a geographical longitude and latitude and superimposed over predicted daily PM1, PM2.5, PM10, and NO2 concentration grids. Exposure parameters were then calculated by averaging annual PM1, PM2.5, PM10, and NO2 concentrations over the four-year period from 2009-2012. The results of a 10-fold

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Journal Pre-proof cross-validation showed that R2 values for daily and annual predictions were 55% and 75% for PM1, 83% and 86% for PM2.5, 78% and 81% for PM10, and 64% and 72% for NO2, respectively. The Root Mean Squared Error (RMSE) values for daily and annual predictions were 20.5 µg/m3 and 8.8µg/m3 for PM1, 18.1 µg/m3 and 6.9 µg/m3 for PM2.5, 31.5 µg/m3 and 14.4 µg/m3 for PM10, and 12.4µg/m3 and 6.5 µg/m3 for NO2, respectively. We also collected SO2 and O3 concentrations, measured at municipal air monitoring

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stations found in each study district, as described previously (Dong et al., 2014, 2015; Hu et al., 2017). These pollutants were measured hourly on a continuous basis, following standards

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established by State Environmental Protection Administration of China. For this study, we

pollution exposure.

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2.3. Obesity and overweight definition

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used four-year mean SO2 and O3 concentrations as surrogates of participants’ long-term air

Each of the participating children completed a physical examination in school from April

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2012 to May 2013. All investigators were trained and assessed on the study procedures, and

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those who passed qualifying examinations were permitted to conduct physical examinations according to the World Health Organization (WHO) standard protocol (WHO, 2008). Electronic scales were used to measure weight and height to the nearest 0.1 kg and f 0.1 cm, respectively. Body mass index (BMI) was calculated as weight divided by height squared as kg/m2. We defined “overweight” and “obese” as BMI greater than the 85th and 95th percentiles of the age (1 month intervals) and sex-specific BMI distributions for children, respectively, according to U.S. Centers for Disease Control and Prevention BMI growth charts. 2.4. Lung function measurement We previously described the lung function measurements in detail (Hu et al., 2017). In brief, lung function measurements were conducted in schools by two experienced technicians

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Journal Pre-proof according to American Thoracic Society and European Respiratory Society standards, from April 2012 to May 2013. Children were tested in the standing position, wearing a nose clip, and in a quiet and comfortable room. Two portable electronic spirometers (MIR Spirolab, Italy) were used to test forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1), peak expiratory flow (PEF), and maximal mid-expiratory flow (MMEF). Using previously published predicted spirometric values for children in northeast China as the reference (Ma et al., 2013), we defined impaired lung function as observed FVC < 85%

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predicted value, FEV1% < 85% predicted value, PEF < 75% predicted value, or MMEF < 75%

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2.5. Questionnaire survey and covariates

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predicted value.

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Participants’ parent/guardian completed a standardized questionnaire to capture

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socio-demographics (such as age, gender, and residence), socio-economic status (such as education levels and household income), behavioural habits (e.g., passive smoking,

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breastfeeding, home coal using, keeping a pet in the home, and home renovation), and other

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health information (e.g., family history of illnesses including atopy). We classified parental education levels as: ≥ high school or < high school. Annual household income was categorized as < 5,000 RMB (Chinese Yuan), 5,000-9,999 RMB, 10,000-29,999 RMB, 30,000-100,000 RMB, or > 100,000 RMB. Passive tobacco smoke exposure was defined as living with someone who smokes cigarettes daily in the house. A dichotomous (no/yes) history of having been breastfed was assessed by asking if the mother breastfed the child for at least three months. Home coal use information was obtained by inquiring about use of coal for cooking or space heating. We also captured home pet ownership by asking if parents/guardians kept any pets in the home. Home renovation was assessed by asking about any renovations in the home within the past 2 years. A family history of atopy was defined as a clinical diagnosis of hay fever, allergies (including allergic dermatitis, allergic 10

Journal Pre-proof conjunctivitis, and eczema), asthma, or bronchial asthma in a biologic parent or grandparent. Child’s asthma status was defined as “yes” if the parent/guardian reported that a doctor had ever diagnosed the child as having asthma. 2.6. Statistical analysis We assessed the data for normality using the Shapiro-Wilks test and homogeneity of variances across BMI categories using Bartlett’s test for unequal variances. We calculated

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relative frequencies for categorical variables; and for continuous variables, we calculated

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mean ± standard deviation (SD). Differences between groups of overweight, obesity, and normal weight were tested using analysis of variance (ANOVA) or chi-square tests as

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appropriate.

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We used two-level logistic regression models to analyze associations between

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dichotomous impaired lung function outcomes and continuous air pollutants as predictors, as described in detail in the Supplementary information. Briefly, in the first level, we included

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BMI as a predictor of child’s lung impairment for each lung function measure in individual

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models, adjusted for age, gender, passive tobacco smoke exposure, parental education, breastfeeding status, household income, home coal use, keeping a pet in the home, and family history of atopy. In the second-level, we regressed district-specific intercepts and regression coefficients on district-specific air pollutant levels. We then combined the two levels to allow for the magnitudes of the BMI and air pollutant effects to vary according to district. To analyze cross-level interactions between BMI and air pollutants, we introduced cross-product terms between the child-level and district-level variables, reflecting BMI as a modifier of the associations between air pollutants and respiratory function. We exponentiated the resulting regression coefficient to provide odds ratios (OR) and their 95% confidence intervals (CI) controlling for confounding variables. We entered age, gender, passive tobacco

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Journal Pre-proof smoke exposure, parental education, breastfed status, household income, home coal use, keeping a pet in the home, home renovation in the past 2 years, family history of atopy, and ambient temperature into regression models based on literature evidence for associations with air pollution exposure and respiratory function. In the interest of parsimony, we retained potential confounding variables in the final model only if estimated regression effects for air pollutants changed by at least 10% compared to an unadjusted model.

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We also assessed absolute differences in the results of lung function tests associated with chronic exposure to air pollutants in different BMI groups, using general linear models

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adjusted for age, gender, household income, parental education, passive tobacco smoke

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exposure, breastfed status, keeping a pet in the home, home coal use, family history of atopy,

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and study district. Estimates were scaled to the interquartile range (IQR, i.e., 25th percentile

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to 75th percentile of exposure concentrations) for each pollutant, including: 13.1 μg/m3 for PM1, 10.0 μg/m3 for PM2.5, 13.8 μg/m3 for PM10, 7.3 μg/m3 for NO2, 23.4 μg/m3 for SO2, and

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46.3 μg/m3 for O3.

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We next conducted subgroup and sensitivity analyses to assess the robustness of the BMI-air pollution interaction estimates, including stratification by age and gender and using passive tobacco smoke exposure as a substitute for air pollution exposure. The GLIMMIX procedure in SAS 9.4 (SAS Institute, Cary, NC USA) was used for statistical analyses of data. P < 0.05 was defined as statistical significances for main effects and P < 0.10 for interactions. 3. Results 3.1. Characteristics of study population The characteristics of 6,740 children analyzed in this study are shown in Table 1. The mean (±SD) age of the children was 11.6 ± 2.1 years (range from 7 to 14 years), 3,382 (50.2%)

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Journal Pre-proof were male, 4,751 (70.5%) were breastfed, and 1,390 (20.6%) had a family history of atopy. The overall prevalences of lung function impairment for FVC, FEV1, PEF and MMEF were 11.3%, 8.6%, 6.8%, and 9.4%, respectively. The overall four year average (±SD) PM1, PM2.5, PM10, NO2, SO2, and O3 concentrations were 47.5 ± 6.6 μg/m3, 54.5 ± 6.1 μg/m3, 96.5 ± 9.8 μg/m3, 34.0 ± 4.6 μg/m3, 50.7 ± 13.8 μg/m3 and 96.8 ± 156.3 μg/m3, respectively. 3.2. Interactions between air pollution and BMI on lung function

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The odds of lung function impairment related to air pollution exposure are presented in

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Table 2. Overall, air pollution, especially PM1, was positively associated with lung function impairment for all children, irrespective of BMI. There were fairly consistent interactions for

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PM1, PM2.5, and PM10 exposure with BMI on FVC, FEV1, PEF, and MMEF impairments,

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with higher ORs among obese and overweight children than in normal weight children.

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Specifically, the OR per IQR PM1 and four lung function impairments were 1.93-2.55 in obese, 1.69-2.40 in overweight, and 1.34-1.50 in normal weight participants. For FVC and

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FEV1 impairment, there were also interactions between NO2 exposure (PInteraction < 0.001) and

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SO2 exposure (PInteraction = 0.013) and obesity. The interaction between obesity and NO2 exposure on MMEF was likewise statistically significant (PInteraction = 0.031), although the association was different from the null only among obese children. These findings suggest that there are interactions between obesity and chronic exposure to ambient air pollutants, including PM1, on children’s lung function impairment. Table 3 presents the absolute differences in lung function tests associated with air pollution exposure, grouped by BMI. The associations between PM1, PM2.5, and SO2 with FVC and FEV1 were statistically significant across all BMI categories, and strongest in the obese category. Consistently lower FVC and FEV1 values ranged from -24.74 mL (95% CI: -50.16, -0.69) to -240.52 mL (95% CI: -308.17, -172.87), respectively, for one IQR higher PM1, PM2.5, and SO2. The associations between FVC and greater PM2.5 and PM10 were also

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Journal Pre-proof stronger in overweight and obese children than in normal weight children. For PEF and MMEF, only SO2 showed significant association across all BMI categories, with the strongest association in obese children (-343.49 mL/s, 95%CI: -467.46, -219.52 and -280.97 mL/s, 95% CI: -371.23, -190.71, respectively) and diminished in lower BMI categories. These results suggest that associations exist between chronic air pollution exposure and impaired lung function in children, especially among those with higher BMI.

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3.3. Subgroup analyses and sensitivity analyses

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As shown in Figure 1, when stratified by age, we found statistically significant interactions between BMI categories and PM1 or PM2.5 concentrations in which associations

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with lung function impairment were stronger for overweight and obese children than for

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normal weight children. The modification effects also appeared to be more pronounced in

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older children than in younger children (i.e., aged 10-14 years, all PInteraction < 0.001). Similar patterns were detected for elementary school children and middle school children using linear

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regression models, as shown in Tables S1 and S2, respectively.

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When stratified by gender, overweight and obese children still appeared to be more vulnerable to the adverse effects of air pollution than normal weight children, especially in girls (Table S3 for boys and Table S4 for girls). For example, the differences in FVC with one IQR higher PM1 were -15.80 mL (95% CI: -69.53, 37.93), -17.57 mL (95% CI: -111.71, 76.58), and -115.44 mL (95% CI: -204.05, -26.84), respectively, in three BMI categories among boys (PInteraction = 0.034). Comparatively, the differences in girls were more dramatic, including -55.00 mL (95% CI: -90.82, -19.19), -181.35 mL (95% CI: -266.02, -96.69), and -280.45 mL (95% CI: -386.86, -174.04) in three BMI categories, respectively. The results suggested that overweight and obese girls may be more vulnerable than boys to chronic air pollution exposure associated lung function impairment.

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Journal Pre-proof Our sensitivity analyses showed similar interactions with BMI categories when using passive tobacco smoke exposure as a substitute for air pollutants (Table 4). The ORs for passive tobacco smoke (from anyone) exposure and the four measures of lung function impairment were 1.43-2.03 in obese, 1.16-2.01 in overweight, and 0.99-1.29 in normal weight children. The findings suggest that the overweight and obese children were more vulnerable to associations between passive tobacco smoke exposure and lung function impairment than

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normal weight children.

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4. DISCUSSION 4.1. Key findings

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The present study explored the joint effects of chronic ambient air pollution exposure

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and obesity on children’s lung function in areas with relatively high air pollution levels. In

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addition to widely studied air pollutants, including PM2.5, PM10, NO2, SO2, and O3, we also focused on rarely studied PM1. We consistently found positive associations between chronic

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air pollution exposure and lung function impairment in children, with stronger associations

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among children with higher BMI than among those with lower BMI. In particular, we found statistically significant interactions between annual average concentrations of PM1, PM2.5, and SO2 and BMI categories on measurements of FVC and FEV1 in Chinese school children. 4.2. Comparison with other published studies and interpretations As previously mentioned, prior studies did not investigate the joint effect of ambient air pollutants and obesity on children’s lung function. However, several previous epidemiological studies considered BMI as a modifier of associations between air pollution and clinical respiratory health outcomes in children. Lu et al. (2013), investigated 148 African American children with persistent asthma over one year of follow-up, and found that overweight or obesity increased susceptibility to adverse respiratory effects induced by indoor PM2.5 and NO2 exposures. In our own previous work, we also found consistent and statistically

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Journal Pre-proof significant joint effects between air pollution exposure and obesity on adverse respiratory symptoms and asthma in children, with odds ratios from 1.17 (95% CI: 1.01-1.36) for wheeze per IQR higher PM10 to 1.50 (95% CI: 1.21, 1.87) for phlegm and cough (95% CI: 1.24, 1.81) per IQR higher NO2 (Dong et al., 2013). The current study expands upon that previous work by incorporating objective lung function measures in children using spirometry. In addition to studies conducted in children, several investigators described joint effects for ambient air pollution and obesity on lung function in adults (Adam et al., 2015; Bennett et

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al., 2007, 2016; Cole-Hunter et al., 2018; Karottki et al.,2014; Kim et al., 2017; Matt et al.,

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2016; Schikowski et al., 2013). Kim et al. (2017), evaluated associations of PM10 and NO2

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concentrations with FEV1 and FVC in 1,876 Korean men (aged 51.6 ± 5.7 years), and found

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stronger inverse associations among men with abdominal adiposity. A Swiss cohort study

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(n=4,664 aged 18-60 years) reported statistically significant interactions between improved air quality over 10 years (i.e., PM10 decline) and average BMI on lung function improvement

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measured as FVC, FEV1/FVC, FEF25–75, and FEF25–75/FVC (Schikowski et al., 2013). Despite the different study populations, the results of these previous investigations provide important

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support for our hypothesis, given that children’s lung function is likely to be more vulnerable to the toxic effects of ambient air pollution than adults’. Whereas a wide range of studies reported associations between higher BMI and poorer lung function in adults and children, we identified more complex patterns of vulnerability when we stratified the analysis according to age. For older middle school children, the association of air pollutants and lung function deficits among the obese were the strongest, followed by overweight and normal weight participants, and with statistically significant interactions. We found similar trends among younger elementary school children, but the odds ratios were stronger than those of the older children. Previous studies reported different associations between obesity and lung function deficit in different age groups (Chen et al,

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Journal Pre-proof 2017; Ekström et al., 2018; Forno et al., 2018; Robinson, 2014). In younger children, FVC and FEV1 values increased with greater BMI. But after puberty, the increasing trend plateaued and then decreased with greater BMI expressed (as an inverted “U-shaped” pattern as seen in adults). Pérez-Padilla et al (2006) reported and inverted “U-shaped” dose-response curve between BMI and lung function in children more than 12 years old (i.e., middle school age in China). Similar patterns reported for older (middle school) children and adults further confirmed our findings. However, given the limited data available, more age-related studies,

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especially in young children, are needed to characterize the joint effect of ambient air

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pollutants and overweight/obesity on children’s lung function.

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In a sensitivity analysis, we also observed stronger associations for passive tobacco

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smoke exposure and lung function impairment (particularly FVC, FEV1, and MMEF) among

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obese/overweight children than among normal weight children. Our results agreed with those from previous studies of a general population by Pistelli et al. (2008), which reported that the

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lung function impairment effects of obesity was significantly greater in smokers than in never smokers. Another study, investigating 9,719 adults, also reported a positive association of

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BMI with FEV1/FVC ratio and the association was most pronounced among smokers (Çolak et al., 2015). Our findings were also in accordance with studies on additive (and perhaps also synergistic) interactions of obesity and tobacco smoke on other respiratory health outcomes, such as bronchial hyperresponsiveness (Sposato and Scalese, 2014). Since passive smoke is a major source of indoor air pollution and an important risk factor for children’s lung health, our findings have significant public health implications for policy-makers and individuals, and suggest that urgent strategies to reduce both indoor and outdoor air pollutants should be taken, especially for children with higher BMIs. 4.3. Potential underlying mechanisms

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Journal Pre-proof Both air pollution exposure (Ghio et al., 2012; Jalava et al., 2007; Nick et al., 2016; Park et al., 2011) and obesity (Consilvio et al., 2010; Fernández-Sánchez et al., 2011; Marseglia et al., 2015; McMurray et al., 2016) can promote airway inflammation and oxidative stress (OS). Adipose tissue can produce adipokines (leptin, adiponectin, plasminogen activator inhibitor, and macrophage migration inhibitory factor) to mediate the inflammatory response, and adipocytes can release proinflammatory cytokines (TNF-α, IL-1β and IL-6) (Cinkajzlová et al., 2017; Engin et al., 2017; Kim et al., 2015; Rajala et al., 2003; Tourniaire et al., 2013), which

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are related to persistent inflammation and oxidative stress. Air pollutants, especially PM, can

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induce oxidative damage and pulmonary inflammation (Clifford et al., 2018; Dauchet et al.,

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2018; Ghio et al., 2012; Nick et al., 2016), diminish insulin sensitivity and increase β-cell

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dysfunction (Alderete et al., 2017; Chen et al., 2016; Haberzettl et al., 2016), and promote adipokine production. Systemic inflammation and OS are associated with altered cell

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proliferation, airway and tissue remodeling (Anderson et al., 2013; Churg et al., 2003), and

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poor respiratory function (Barrett et al., 2013; Inselman et al., 2004; Ochs-Balcom et al., 2005; Salome et al., 2010). The stronger associations between air pollutants and poor lung function

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among obese and overweight children in this study, might be explained in part by the cumulative effects of enhanced systemic inflammation and OS. An epigenetic mechanism may also be important, as suggested by diverse changes in Nrf2 and catalase expression presumably instigated by altered DNA methyltransferase activities in the lung tissue of mice jointly exposed to an obesogenic diet and aqueous PM extracts (Pardo et al., 2018). Finally, as overweight and obese children tend to have larger FVC than normal weight children, higher air pollutant exposures were likely, which may have also contributed to greater impairment. 4.4. Strengths and limitations The present study has several strengths. Firstly, to the best of our knowledge, no other studies have demonstrated BMI-modification of air pollution-respiratory health associations in

18

Journal Pre-proof children using spirometry, an objective measure of lung function. Our findings will help to develop more effective prevention and intervention policies, especially for children with higher BMIs. Secondly, we evaluated the respiratory effects of PM1 in addition to more commonly studied air pollutants. PM1 is more prone to deposit deeper in the lung than PM2.5 and PM10, potentially posing a greater health risk. Furthermore, PM1 was reported to be the major component of PM2.5 in China (Chen et al., 2017; Yang et al., 2018). Our findings show that the adverse lung function effects of chronic PM1 exposure in children deserves serious

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attention, especially for overweight and obese children. Finally, in sensitivity analyses, we

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demonstrated the robustness of the interaction of obesity and air pollution on lung function

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during an early life stage.

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While our study offers important strengths, there are also several limitations. Primarily, we are

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unable to assess temporality of the associations between the exposure and the health outcome, given our cross-sectional study design. Thus, we cannot preclude the possibility for “reverse

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causality”, and so these cross-sectional results require confirmation using a prospective design.

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Secondly, the exposure assessments based on a spatiotemporal model and municipal air monitoring stations might misclassify some participants. However, the errors were likely to be non-differential according to lung function with any resulting bias towards the null hypothesis. Thirdly, the air pollution assessment also did not discriminate between time spent outdoors and time spent indoors, which may have further misclassified air pollutant exposure for some participants. Indoor air pollutants are likely to impact children’s lung function, especially passive smoke exposure (Sood et al., 2018). However, we adjusted for passive smoke exposure, a major source of indoor air pollution, as well as keeping a pet in the home, and home coal use in our regression models and so the impact of unmeasured air pollutants was likely modest. We also found a similar pattern of associations between air pollution exposure, obesity, and lung function impairment when substituting passive tobacco smoke exposure for

19

Journal Pre-proof ambient air pollutant concentrations in a sensitivity analysis, suggesting that indoor air pollutants have similar associations as outdoor pollutants. However, a future study incorporating a more comprehensive exposure assessment including indoor as well as outdoor air pollutants is necessary for a more definitive result. Fourthly, we obtained participant characteristics according to self-report, so misclassification of baseline covariates may exist, leading to underestimated effects and residual confounding. Finally, due to resource limitations, our obesity measures were based on BMI alone. BMI has been shown to be a valid

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and reliable indicator of absolute adiposity in children (Glässer et al., 2011; Laurson et al.,

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2011). However, other related adiposity indexes should also be considered in future study,

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such as the percentage of fat mass, skinfold thickness, and visceral fat content, which might

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provide more information about the actual impacts of fat on air pollution induced lung function

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deficits in children.

5. Conclusions

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The results of this large epidemiologic study suggest that overweight and obesity may

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enhance the harmful effects of air pollution on children’s lung function in China, particularly among girls and older children. However, future longitudinal studies incorporating individual level monitoring for outdoor and indoor air pollutants as well as comprehensive measures of adiposity are needed to confirm the results and to clarify the biological mechanisms for the associations. Declaration of interests There are no conflicts to declare.

Acknowledgments We gratefully acknowledge all participants in SNEC study for their cooperation and assistance.

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Journal Pre-proof Funding This work was supported by Major Program of National Natural Science Foundation of China (91543208),

Science

and

Technology

Program

of

Guangzhou

(201807010032,

201803010054), the National Natural Science Foundation of China (81872582, 81673128, 81703179, 81001255), Guangdong Provincial Natural Science Foundation Team Project (2018B030312005), National Key Research and Development Program of China (2016YFC0207000), Fundamental Research Funds for the Central Universities (17ykpy14,

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17ykpy16), and Guangdong Province Natural Science Foundation (2018B05052007,

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2016A030313342, 2017A050501062).

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Journal Pre-proof Figure Legend

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Fig. 1. Adjusted OR (95% CI) for impaired lung function and average ambient air pollutant concentrations, according to BMI categories and age group. A, for age < 10 years (7-10 years); B, for age ≥10 years (10-14 years). The associations are adjusted for age, gender, parental education, breastfeeding status, income, home coal use, house pet, family history of atopy, temperature during investigation, and study district. Effects are expressed for an interquartile range change for each pollutant (13.1 μg/m3 for PM1, 10.0 μg/m3 for PM2.5). P-values for interaction between average PM and BMI categories on lung function measures.

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Journal Pre-proof Table 1. Demographic, lung function, and annual air pollutant exposure characteristics for children participating in the seven northeastern cities study according according to BMI categories. Obese (n=1,154) 11.2 (2.1) 819 (71.0) 798 (69.2) 781 (67.7)

Total (n=6,740) 11.6 (2.1) 3382 (50.2) 4751 (70.5) 4211 (62.5)

522 (11.5) 604 (13.4) 1608 (35.6) 1605 (35.5) 179 (4.0) 2132 (47.2) 500 (11.1) 955 (21.1) 1611 (35.7) 7.6 (7.9) 4203 (93.0) 911 (20.2)

107 (10.0) 143 (13.4) 370 (34.6) 400 (37.5) 48 (4.5) 516 (48.3) 97 (9.1) 222 (20.8) 393 (36.8) 7.5 (7.1) 1006 (94.2) 248 (23.2)

129 (11.2) 129 (11.2) 416 (36.0) 432 (37.4) 48 (4.2) 633 (54.9) 79 (6.9) 258 (22.4) 412 (35.7) 7.7 (7.6) 1082 (93.8) 231 (20.0)

758 (11.2) 876 (13.0) 2394 (35.5) 2437 (36.2) 275 (4.1) 3281 (48.7) 676 (10.0) 1435 (21.3) 2416 (35.9) 7.6 (7.7) 3291 (93.3) 1390 (20.6)

2.6 (0.7) 2.4 (0.7) 4.7 (1.4) 3.3 (1.0)

2.7 (0.7) 2.5 (0.7) 4.9 (1.4) 3.4 (1.0)

2.9 (0.8) 2.7 (0.8) 5.1 (1.5) 3.5 (1.1)

2.6 (0.8) 2.5 (0.7) 4.8 (1.4) 3.4 (1.1)

470 (10.4) 356 (7.9) 298 (6.6) 393 (8.7)

135 (12.6) 102 (9.6) 70 (6.6) 110 (10.3)

154 (13.3) 120 (10.4) 90 (7.8) 131 (11.4)

759 (11.3) 578 (8.6) 458 (6.8) 634 (9.4)

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Overweight (n=1,068) 11.4 (2.0) 555 (52.0) 736 (68.9) 716 (67.0)

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Age (years), mean (SD)a Malea Breastfeeding Parent education ≥ higher schoola Family income per year <5,000 RMB 5,000-9,999 RMB 10,000-29,999 RMB 30,000-100,000 RMB >100,000 RMB Passive smoking exposure sourcea Home coal usea Home pet keeping Home renovation in recent two years Exercise per week (hour), mean (SD) Parents as responders Family history of atopy Spirometric parameters, mean (SD) FVC (L)a FEV1 (L)a PEF (L/s)a MMEF (L/s)a Lung function status FVC < 85% predicteda FEV1 < 85% predicteda PEF < 75% predicted MMEF < 75% predicteda Air pollutants concentrations (μg/m3)b PM1a PM2.5a PM10 a NO2a SO2a O3a

Normal weight (n=4,518) 11.7 (2.1) 2008 (44.4) 3217 (71.2) 2714 (60.1)

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47.2 (6.4) 48.0 (6.6) 47.8 (6.6) 47.5 (6.6) 54.3 (6.1) 55.1 (6.3) 54.9 (6.2) 54.5 (6.1) 96.1 (9.7) 97.2 (10.2) 97.0 (9.8) 96.5 (9.8) 33.9 (4.6) 34.3 (4.9) 34.3 (4.6) 34.0 (4.6) 50.1 (14.1) 51.8 (13.4) 52.4 (12.3) 50.7 (13.8) 101.8 (162.5) 91.4 (149.1) 82.2 (136.0) 96.8 (156.3)

Abbreviation: FEV1,forced expiratory volume in 1s; FVC, forced vital capacity; MMEF, maximal mid-expiratory flow; NO2, nitrogen dioxide; O3, ozone; PEF, peak expiratory flow; PM1, particles with an aerodynamic diameter ≤ 1.0 μm; PM2.5, particles with an aerodynamic diameter ≤ 2.5μm; PM10, particles with an aerodynamic diameter ≤ 10 μm; SO2, sulfur dioxide; RMB, Chinese Yuan; SD, Standard Deviation. a P < 0.05 for difference among three BMI categories using Chi square (categorical variables) or ANOVA (continuous variables) tests. b Four years (2009-2012) average concentrations of ambient air pollutants in 24 districts.

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Journal Pre-proof Table 2. Adjusted OR (95% CI) of impaired lung function associated with annual air pollutant concentrations in children, according to BMI categoriesa. Obese (n=1,154) OR (95% CI)

Interaction P Value

2.40 (1.47-3.92) 1.95 (1.33-2.87) 1.64 (1.19-2.28) 1.65 (1.14-2.40) 1.53 (1.03-2.27) 1.04 (0.98-1.11)

2.55 (1.59-4.07) 2.03 (1.40-2.95) 1.70 (1.23-2.34) 1.63 (1.13-2.36) 1.69 (1.12-2.56) 1.07 (1.00-1.14)

<0.001b <0.001b <0.001b <0.001b 0.036b 0.220

2.15 (1.28-3.62) 2.05 (1.36-3.07) 1.85 (1.31-2.60) 1.74 (1.18-2.57) 1.48 (0.93-2.37) 1.01 (0.92-1.10)

2.51 (1.53-4.11) 2.23 (1.52-3.27) 1.86 (1.34-2.57) 1.75 (1.21-2.55) 2.39 (1.46-3.91) 1.06 (0.98-1.15)

<0.001b <0.001b <0.001b <0.001b 0.013b 0.657

1.81 (1.04-3.14) 1.44 (0.94-2.22) 1.24 (0.86-1.79) 1.15 (0.76-1.73) 1.81 (1.03-3.16) 1.01 (0.91-1.11)

2.04 (1.24-3.35) 1.81 (1.22-2.69) 1.63 (1.16-2.28) 1.44 (0.98-2.13) 1.37 (0.79-2.37) 1.06 (0.97-1.15)

0.068b 0.028b 0.023b 0.120 0.530 0.750

1.69 (1.07-2.65) 1.37 (0.96-1.95) 1.21 (0.90-1.64) 1.13 (0.81-1.59) 1.43 (0.91-2.24) 1.01 (0.93-1.10)

1.93 (1.26-2.95) 1.74 (1.24-2.43) 1.57 (1.18-2.10) 1.59 (1.14-2.24) 1.86 (1.17-2.95) 1.04 (0.96-1.12)

0.024b 0.016b 0.012b 0.031b 0.491 0.419

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Overweight (n=1,068) OR (95% CI)

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Normal weight (n=4,518) Pollutants (μg/m3) OR (95% CI) FVC < 85% predicted PM1 1.50 (1.07-2.11) PM2.5 1.29 (0.98-1.70) PM10 1.18 (0.93-1.51) NO2 1.13 (0.86-1.49) SO2 1.27 (0.98-1.65) O3 1.05 (1.00-1.10) FEV1 < 85% predicted PM1 1.44 (1.03-2.01) PM2.5 1.34 (1.03-1.74) PM10 1.27 (1.01-1.60) NO2 1.20 (0.92-1.55) SO2 1.41 (1.01-1.96) O3 1.05 (0.98-1.12) PEF < 75% predicted PM1 1.34 (0.97-1.84) PM2.5 1.17 (0.91-1.52) PM10 1.07 (0.85-1.33) NO2 1.01 (0.78-1.30) SO2 1.40 (0.98-2.02) O3 1.06 (1.00-1.14) MMEF < 75% predicted PM1 1.34 (1.01-1.78) PM2.5 1.24 (0.99-1.56) PM10 1.14 (0.94-1.39) NO2 1.13 (0.90-1.41) SO2 1.53 (1.13-2.08) O3 1.05 (0.99-1.12)

Abbreviation: BMI, body mass index; CI, confidence intervals; FEV1, forced expiratory volume in 1s; FVC, forced vital capacity; MMEF, maximal mid-expiratory flow; NO2, nitrogen dioxide; OR, odds ratios; O3, ozone; PEF, peak expiratory flow; PM1, particles with an aerodynamic diameter ≤ 1.0 μm; PM2.5, particles with an aerodynamic diameter ≤ 2.5 μm; PM10, particles with an aerodynamic diameter ≤ 10 μm; SO2, sulfur dioxide. a Adjusted for age, gender, smoking exposure, parental education, breastfeeding status, income, home coal use, house pet, family history of atopy, temperature during investigation, and study district. Estimate was scaled to the interquartile range (IQR: Range from 25th to 75th percentile of district specific concentrations) for each pollutant (air pollutants were estimated using a spatial statistical model: 13.1 μg/m3 for PM1, 10.0 μg/m3 for PM2.5, 13.8 μg/m3 for PM10, 7.3 μg/m3 for NO2, air pollutants were measured by local air monitoring station: 23.4 μg/m3 for SO2 and 46.3 μg/m3 for O3). b Statistically significant difference at P < 0.10.

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Journal Pre-proof Table 3. Estimated absolute change (95% CI) in pulmonary function test (PFT) associated with annual air pollutant concentrations in children, according to BMI categoriesa. Normal weight (n=4,518) Estimate (95% CI)

Obese (n=1,154) Interaction Estimate (95% CI) P Value -167.15(-237.79, -96.50) -120.29(-177.67, -62.90) -73.23(-123.62, -22.84) -39.39(-96.77, 17.99) -240.52(-308.17, -172.87) -21.80(-34.06, -9.53)

<0.001b <0.001b 0.031b 0.212 <0.001b 0.161

-68.55(-126.35, -10.75) -54.48(-100.85, -8.12) -34.70(-74.14, 4.74) -12.51(-56.08, 31.07) -143.12(-193.65, -14.04(-23.09,-92.59) -5.00)

-121.06(-180.40, -61.71) -87.51(-135.69, -39.33) -52.81(-95.08, -10.54) -24.87(-72.97, 23.24) -230.68(-287.01, -174.35) -17.83(-28.11, -7.54)

0.006b 0.013b 0.149 0.613 <0.001b 0.092b

-135.19(-267.48, -2.90) -97.44(-203.59, 8.71) -55.65(-145.93, 34.63) 6.16(-93.51, 105.84) -344.23(-459.63, -228.83) -27.26(-47.97, -6.55)

-139.98(-269.34, -10.61) -83.62(-188.58, 21.35) -22.80(-114.71, 69.11) 57.20(-47.15, 161.54) -343.49(-467.46, -52.14(-74.35,-219.52) -29.92)

0.899 0.407 0.112 0.016b 0.002b 0.141

-30.60(-128.41, 67.21) -32.05(-110.50, 46.39) -19.17(-85.84, 47.50) 17.35(-56.22, 90.91) -243.31(-328.60, -158.02) -18.17(-33.47, -2.88)

-87.12(-181.67, 7.42) -71.32(-147.97, 5.32) -42.12(-109.22, 24.98) -1.64(-77.90, 74.62) -280.97(-371.23, -28.13(-44.43,-190.71) -11.84)

0.524 0.545 0.537 0.225 0.030b 0.789

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-97.90(-163.24, -32.57) -72.18(-124.61, -19.75) -46.31(-90.93, -1.69) -20.65(-69.96, 28.67) -112.79(-170.41, -20.30(-30.51, -55.17) -10.09)

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FVC (mL) PM1 -41.74(-73.44, -10.04) PM2.5 -24.74(-50.16, -0.69) PM10 -16.72(-38.57, 5.13) NO2 1.12(-23.31, 25.55) SO2 -43.52(-69.34, -17.70) O3 -12.84(-17.19, -8.50) FEV1 (mL) PM1 -40.50(-68.09, -12.91) PM2.5 -28.07(-50.19, -5.94) PM10 -20.38(-39.40, -1.37) NO2 -8.20(-29.46, 13.06) SO2 -61.50(-83.94, -39.07) O3 -8.33(-12.12, -4.54) PEF (mL/s) -154.81(-218.65, PM1 -90.96) -130.66(-181.84, PM2.5 -79.48) PM10 -96.38(-140.38, -52.37) NO2 -65.56(-114.80, -16.32) -167.42(-219.36, SO2 O3 -27.47(-36.24,-115.49) -18.71) MMEF (mL/s) -112.76(-160.79, PM1 -64.74) PM2.5 -90.25(-128.76, -51.74) PM10 -58.61(-91.73, -25.49) NO2 -40.22(-77.27, -3.18) SO2 -179.51(-218.39, O3 -21.24(-27.83,-140.63) -14.65)

Overweight (n=1,068) Estimate (95% CI)

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Abbreviation: BMI, body mass index; CI, confidence intervals; FEV1,forced expiratory volume in 1s; FVC, forced vital capacity; MMEF, maximal mid-expiratory flow; NO2, nitrogen dioxide; OR, odds ratios; O3, ozone; PEF, peak expiratory flow; PM1, particles with an aerodynamic diameter ≤ 1.0 μm; PM2.5, particles with an aerodynamic diameter ≤ 2.5 μm; PM10, particles with an aerodynamic diameter ≤10 μm;.SO2, sulfur dioxide. a

Adjusted for age, gender, smoking exposure, parental education, breastfeeding status, income, home coal use, house pet, family history of atopy, temperature during investigation, and study districts. Estimate was scaled to the interquartile range (IQR: Range from 25th to 75th percentile of district specific concentrations) for each pollutant (air pollutants were estimated using a spatial statistical model: 13.1 μg/m3 for PM1, 10.0 μg/m3 for PM2.5, 13.8 μg/m3 for PM10, 7.3 μg/m3 for NO2, air pollutants were measured by local air monitoring station: 23.4 μg/m3 for SO2 and 46.3 μg/m3 for O3). b

Statistically significant difference at P < 0.10.

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Journal Pre-proof Table 4. Adjusted OR (95% CI) of impaired lung function associated with passive smoke exposure in children, according to BMI categoriesa. Person smoking in the house

Normal weight (n=4518)

Overweight (n=1068)

Obese (n=1154)

Interactio n

OR (95% CI)

OR (95% CI)

OR (95% CI)

P Value

1.93 (1.42-2.63) 3.00 (1.89-4.76) 2.03 (1.57-2.63)

0.027b

FVC< 85% of predicted value Father

1.18 (0.96-1.46)

1.91 (1.38-2.65)

Mother

2.06 (1.50-2.84)

3.72 (2.26-6.12)

Anyone

1.27 (1.06-1.52)

2.01 (1.53-2.64)

0.046b 0.012b

FEV1< 85% of predicted value 1.19 (0.93-1.51)

1.68 (1.17-2.43)

Mother

2.08 (1.47-2.95)

2.48 (1.37-4.47)

Anyone

1.29 (1.05-1.58)

1.81 (1.34-2.44)

Mother

1.07 (0.69-1.67)

Anyone

0.99 (0.79-1.24)

MMEF < 75% of predicted value

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1.16 (0.74-1.82) 1.47 (0.67-3.24)

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1.16 (0.80-1.69)

1.17 (0.94-1.47)

1.49 (1.03-2.14)

Mother

1.35 (0.94-1.93)

1.39 (0.72-2.69)

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Anyone

1.20 (0.99-1.46)

1.45 (1.01-2.09) 3.58 (2.22-5.79) 1.88 (1.42-2.48)

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1.51 (1.13-2.03)

1.25 (0.82-1.89) 1.48 (0.75-2.93) 1.43 (1.02-2.00) 1.45 (1.03-2.05) 1.95 (1.16-3.29) 1.65 (1.26-2.16)

0.220 0.021b 0.021b

0.629 0.601 0.345

0.649 0.485 0.332

Abbreviation: BMI, body mass index; CI, confidence intervals; FEV 1, forced expiratory volume in 1s; FVC, forced vital capacity; MMEF, maximal mid-expiratory flow; OR, odds ratios; PEF, peak expiratory flow. a

Adjusted for age, gender, parental education, breastfeeding status, income, home coal use, house pet, family history of atopy, temperature during investigation, and study district. b

Statistically significant difference at P < 0.10.

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Journal Pre-proof Graphical abstract Highlights 1. Evidence on the long term effects of PM1 on children’s lung function is scarce. 2. Studies of joint effects of PM1 and obesity on children’s lung function are rare. 3. We explored this topic in 6,740 Chinese children from 49 schools in 7 cites. 4. Long-term exposure of PM1 is associated with lung function impairment in children.

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5. Obesity aggravates PM1 effects on lung function, especially in female and older children.

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Figure 1