Long-term exposure to air pollution might increase prevalence of osteoporosis in Chinese rural population

Journal Pre-proof Long-term exposure to air pollution might increase prevalence of osteoporosis in Chinese rural population Dou Qiao, Jun Pan, Gongbo Chen, Hao Xiang, Runqi Tu, Xia Zhang, Xiaokang Dong, Yan Wang, Zhicheng Luo, Huiling Tian, Zhenxing Mao, Wenqian Huo, Gongyuan Zhang, Shanshan Li, Yuming Guo, Chongjian Wang PII:

S0013-9351(20)30156-0

DOI:

https://doi.org/10.1016/j.envres.2020.109264

Reference:

YENRS 109264

To appear in:

Environmental Research

Received Date: 7 October 2019 Revised Date:

16 January 2020

Accepted Date: 15 February 2020

Please cite this article as: Qiao, D., Pan, J., Chen, G., Xiang, H., Tu, R., Zhang, X., Dong, X., Wang, Y., Luo, Z., Tian, H., Mao, Z., Huo, W., Zhang, G., Li, S., Guo, Y., Wang, C., Long-term exposure to air pollution might increase prevalence of osteoporosis in Chinese rural population, Environmental Research (2020), doi: https://doi.org/10.1016/j.envres.2020.109264. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Inc.

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Title:

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Long-term exposure to air pollution might increase prevalence of osteoporosis in

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Chinese rural population

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Running title:

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Air pollution and osteoporosis

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Authors:

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Dou Qiaoa†, Jun Panb†, Gongbo Chend, Hao Xiangd, Runqi Tua, Xia Zhanga, Xiaokang

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Donga, Yan Wanga, Zhicheng Luoa, Huiling Tiana, Zhenxing Maoa, Wenqian Huoa,

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Gongyuan Zhanga, Shanshan Lic, Yuming Guoa,c, Chongjian Wanga*

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Authors affiliations:

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a

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Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China.

b

Department of Orthopaedic Surgery, The Second Affiliated Hospital and Yuying

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Children’s Hospital of Wenzhou Medical University; The Second Clinical Medical

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school, Wenzhou Medical University, Wenzhou, Zhejiang, PR China.

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c

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Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.

d

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Department of Global Health, School of Health Sciences, Wuhan University, Wuhan, China

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†

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* Correspondence author

Dou Qiao and Jun Pan contributed equally to this work.

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Dr. Chongjian Wang & Yuming Guo

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Department of Epidemiology and Biostatistics

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College of Public Health, Zhengzhou University

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100 Kexue Avenue, Zhengzhou, 450001, Henan, PR China 1

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Phone: +86 371 67781452

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Fax: +86 371 67781919

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E-mail: [email protected] & [email protected]

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The word counts of abstract: 234

The word counts of text: 2700

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The number of table: 4

The number of figure: 2

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The number of references: 40

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Ethics approval

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Ethical approval for this study was obtained from the “Zhengzhou University Life

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Science Ethics Committee”, and written informed consent was obtained from all

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participants. Ethic approval code: [2015] MEC (S128)

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Clinical Trial Registration

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The Henan Rural Cohort has been registered at the Chinese Clinical Trial Registry

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(Registration

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http://www.chictr.org.cn/showproj.aspx?proj=11375

number:

ChiCTR-OOC-15006699).

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2

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What is already known on this subject?

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Little evidence exists about the association between air pollution and osteoporosis,

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especially in developing countries. In addition, previous studies on particulate matter

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pollution are mainly focused on PM2.5 and PM10 (particulate matter with an

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aerodynamic diameter ≤10 µm), but studies on PM1 (particulate matter with an

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aerodynamic diameter ≤1 µm) on osteoporosis are limited.

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What does this study add?

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We found that there existed a significantly positive dose-response relationship

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between air pollution and osteoporosis in Chinese rural population after controlling for

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potential confounders. Physical activity may weaken the adverse effects of air pollution

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on osteoporosis. In addition, assuming causal association, the population attributable

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fractions were 20.29% for PM1, 23.20% for PM2.5, 24.36% for PM10 and 22.29% for

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NO2, respectively. It was estimated that 20.29%-24.36% of the osteoporosis cases could

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be reduced among participants, if air pollution could be reduced to below the

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corresponding the first quartile levels. Our findings have important implications for

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development of environmental health policy.

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3

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Abstract

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Objectives: The associations of long-term exposure to air pollutions with osteoporosis

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are rarely reported, especially in rural China. This study aimed to explore the

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association among rural Chinese population.

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Methods: A total of 8,033 participants (18-79 years) derived from the Henan Rural

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Cohort Study (n=39259) were included in this cross-sectional study. Exposure to air

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pollutants was estimated using machine learning algorithms with satellite remote

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sensing, land use information, and meteorological data [including particulate matter

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with aerodynamic diameters ≤1.0 µm (PM1), ≤2.5 µm (PM2.5), and ≤10 µm (PM10), and

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nitrogen dioxide (NO2)]. The bone mineral density of each individual was measured by

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using ultrasonic bone density apparatus and osteoporosis was defined based on the

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T-score ≤ -2.5. Multiple logistic regression models were used to examine the association

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of air pollution and osteoporosis prevalence.

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Results: We observed that per 1 µg/m3 increase in PM1, PM2.5, PM10 and NO2 were

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associated with a 14.9%, 14.6%, 7.3%, and 16.5% elevated risk of osteoporosis.

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Compared with individuals in the first quartile, individuals in the fourth quartile had

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higher odds ratio (OR) of osteoporosis (P-trend < 0.001), the ORs (95% confidence

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interval) were 2.08 (1.72, 2.50) for PM1, 2.28 (1.90, 2.74) for PM2.5, 1.93 (1.60, 2.32)

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for PM10, and 2.02 (1.68, 2.41) for NO2. It was estimated that 20.29%-24.36% of

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osteoporosis cases could be attributable to air pollution in the rural population from

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China.

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Conclusions: Long-term exposure to air pollutants were positively associated with

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high-risk of osteoporosis, indicated that improving air quality may be beneficial to

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improve rural residents health.

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Keywords: Air pollutants; Particulate matter; PM1; Osteoporosis; Rural population

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4

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1. Introduction1

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Osteoporosis has become a serious problem worldwide. In 2013-2014, the prevalence

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rate of osteoporosis and low bone mass in older adults was about 6 to 11% and 28 to

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45% respectively in the United States (Looker et al. 2017). Previous study reported that,

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in 2014, the prevalence rates of osteoporosis for women in the United Kingdom, France

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and Japan was 9%, 15%, and 38%, whereas, in men, the prevalence was 1%, 8%, and

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4%, respectively (Wadeet al. 2014). In China, the total prevalence of osteoporosis at the

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age over 60 years was 36% (23% in males and 49% in females) (He et al. 2016).

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Osteoporosis is an important determinant for the development and progression of

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fracture, which linked to a significantly higher mortality and a heavy burden on families

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and society. It is predicted that by 2050 the annual number and costs of

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osteoporosis-related fractures will increase to 5.99 million, and $25.43 billion in China,

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respectively (Si et al. 2015). China has been experiencing an increased serious financial

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burden due to osteoporosis-related adverse health effect (Lin et al. 2015).

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Air pollutions have decreased or stabilized in many countries but remained high in

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China, which is bearing the highest disease burden due to air pollution all over the

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world (Cohen et al., 2017). Long-term exposure to air pollution has been linked to

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increased mortality and morbidity of cardiovascular disease (Lin et al. 2017; Yang et al.

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2019a; Yang et al. 2019b), respiratory disease (Lin et al. 2018; Liu et al. 2017), diabetes

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(Liang et al. 2019), lung cancer (Zhanget al. 2020), impaired cognition (Griffiths and

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Mudway 2018; Zhanget al. 2018), and many more, and shorten life expectancy (United

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States Environmental Protection Agency, 2009). Several studies (Table S1) so far 1

Abbreviation: BMD, bone mineral density; BMI, body mass index; CI, confidence interval; NO2, nitrogen dioxide; OR, odds ratio; PM10, particulate matter with an aerodynamic diameter ≤10 µm; PM2.5, particulate matter with an aerodynamic diameter ≤2.5 µm; PM1, particulate matter with an aerodynamic diameter ≤1µm; SD, standard deviation. 5

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explored the possible effect of air pollution on bone mineral density (BMD) and

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osteoporosis. Some of these studies found a weak but statistically significant inverse

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association between air pollution and BMD (Alvaer et al. 2007; Alver et al. 2010; Prada

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et al. 2017). Yet, the significant association between air pollutants and BMD were not

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found in other study (Chen et al. 2015). In addition, most of studies focused on

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Caucasians (Alvaer et al. 2007; Alver et al. 2010; Chen et al. 2015; Prada et al. 2017),

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or specific populations including the elderly (Alvaer et al. 2007; Alver et al. 2010) and

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adolescents (Liu et al. 2015). The effects of air pollution exposure on BMD and

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osteoporosis were rarely assessed among rural Chinese population. Although the effects

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of air pollution are borderless (Imura. 2013), considering that the complex composition

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and concentrations of air pollution vary greatly in different countries, it is urgent to

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explore the relationship in Chinese population. Furthermore, previous studies on

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particulate matter pollution mainly focused on PM2.5 and PM10 (particulate matter with

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an aerodynamic diameter ≤2.5 µm and 10 µm), none of the previous studies considered

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PM1 (particulate matter with an aerodynamic diameter ≤1 µm). Because of smaller

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particle size, PM1 may be more harmful than coarser particles (Lin et al. 2016).

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In the past few decades, people in rural areas directly relied on burning wood, coal,

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and straw for cooking, and these behaviors are still very popular in rural areas and have

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not been strictly controlled at present (Balmes 2019). Burned fuels often emit a complex

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pollutant mixture of particulate matter and other toxic compounds at concentrations

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much higher than most urban ambient pollution levels (Baumgartneret al. 2011). In

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addition, as more and more factories move from urban to suburban areas, rural

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pollutants related adverse health effect could not be ignored. This study aimed to

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investigate the relationship of air pollutants [PM1, PM2.5, PM10 and nitrogen dioxide

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(NO2)] on osteoporosis among rural Chinese population. 6

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2. Methods

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2.1 Study subjects

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The study subjects were derived from Henan Rural Cohort Study (Registration

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number: ChiCTR-OOC-15006699). More details on Henan Rural Cohort Study have

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been provided elsewhere (Liu et al. 2019). In brief, from July 2015 to September 2017,

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a total of 39,259 participants aged 18-79 years old (including young, middle age, and

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older individuals) completed the standardized questionnaires by trained interviewers. Of

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these, only the 8,033 subjects completed BMD measurement and air pollutions

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evaluation were included in the present analysis. This study was approved by the

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Zhengzhou University Life Science Ethics Committee (Code: [2015] MEC (S128)), and

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all participants signed a written informed consent. The present study was conducted

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according to the Declaration of Helsinki on research.

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2.2 Assessment of bone mineral density

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The BMD (g/cm2) of the calcaneus for non-dominant foot was measured three times

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using a quantitative ultrasound bone density apparatus (Hologic Sahara, America), when

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subject is sitting with hips bent about 90 degrees, knees about 40 degrees, and ankles

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about 10 degrees. If the subject’s non-dominant foot had an ankle fracture previously,

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measure the dominant foot instead. The BMD T-score was calculated from the

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manufacturer-provided reference data, which was derived from a database of Chinese

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individuals. The average of the three readings was taken for analysis (Wu et al. 2019).

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Participants were defined as osteoporosis if the T-score ≤ -2.5 (World Health

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Organization, 1994).

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2.3 Exposure assessment

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A detailed description of the exposure assessment was described elsewhere (Chen et

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al. 2018b; Chen et al. 2018c; Chen et al. 2018d). Briefly, daily concentrations of 4 air 7

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pollutants (PM1, PM2.5, PM10, and NO2) were estimated using machine learning

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algorithms (random forests model) with satellite remote sensing, land use information,

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and meteorological data. The spatial resolution for the spatial temporal model was

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0.1°×0.1°. Compared with ground-level measurements of air pollutants, the spatial

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temporal model showed a good predictive ability. Table S2 presented the performance

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of model and accuracy of estimation for daily and annual average of air pollutants. The

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10-foldcross-validation (CV) R2 and root mean squared prediction error (RMSE) for

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annual average of PM1, PM2.5, PM10, and NO2 were 75% and 8.8µg/m3, 86% and

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6.9µg/m3, 81% and 14.4µg/m3, and 72% and 6.5µg/m3, respectively (Chen et al. 2018a;

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Chen et al. 2018c; Zhang et al. 2019). Three-year average concentrations to each air

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pollutant of participants before the baseline survey were calculated and developed as

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substitutes for long-term air pollution exposure.

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2.4 Assessment of potential covariates

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A standardized questionnaire was used to collect information on demographic,

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socioeconomic and lifestyle factors (e.g., smoking, alcohol drinking, dietary habits,

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physical activity levels, and medical history) (Liu et al. 2018a). Average monthly

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individual income was divided into <500 RMB, 500-999 RMB, and ≥1000 RMB, based

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on the previous study and the actual income status in China (Songet al. 2017; Zhaoet al.

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2019). According to the smoking index of the WHO (Ediriweera et al. 2011), smoking

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status was grouped into never smoking, light smoking, moderate smoking, and heavy

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smoking. In accordance with the daily alcohol intake of WHO and the dietary guidelines

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for Chinese residents (Ediriweera et al. 2011; Chinese Nutrition Society, 2011), drinking

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was divided into four categories: never drinking, light drinking, moderate drinking, and

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heavy drinking. The Body mass index (BMI) was calculated as the weight (kg) divided

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by the square of the height (m). 8

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2.5 Statistical analysis

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Continuous and categorical characteristic variables between subjects with and

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without osteoporosis were presented as mean ± standard deviation (SD) and numbers

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(percentages), and were compared using Student’s t-test and chi-squared test. Pearson

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correlation test was used to assess the correlation between air pollutants. The

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concentrations of air pollutants were categorized into 4 groups based on their

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distribution quartiles, ranging from Q1 (the lowest concentration) to Q4 (the highest

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concentration).

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Logistic regression models were performed to evaluate the odds ratios (ORs) and

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95% confidence intervals (CIs) of per 1 µg/m3 increase, per 1 SD increase and 4 groups

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of air pollutants with risk of osteoporosis after adjusting potential confounders. Given

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the high correlations among air pollutants, only single-pollutant models were applied.

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To explore the dose-response relationship between air pollution concentrations and

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osteoporosis, restricted cubic splines recommended by Loic Desquilbet and François

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Mariotti (Desquilbet and Mariotti 2010) were performed with three knots at the 25th,

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50th and 75th percentiles of concentrations.

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Subgroup analyses were performed to examine whether the associations between air

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pollution and osteoporosis were modified by sex, age, smoking status, drinking status,

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vegetables and fruits intake and physical activity. The statistical difference between the

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subgroups was examined by calculating the 95% CI of the difference using the formula

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recommended by Schenker and Gentleman (Schenker and Gentleman 2001). The

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detailed calculation formula was provided in the supplementary material page 2. To test

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the robustness of the results, sensitivity analyses were conducted by including 1, 2, 4,

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and 5-year average concentrations of air pollutants before the baseline survey, instead of

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the 3-year average, and excluding participants with cancer, liver disease, and kidney 9

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disease.

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2.6 Estimating attributable osteoporosis risk

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The osteoporosis burden attributable to air pollutants was estimated using 2 indicators,

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attributable cases and population attributable fraction (Lin et al. 2017). Given that the

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concentration of PM2.5 and PM10 were far beyond the WHO’s Air Quality Guidelines

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and the guideline for PM1 has not been proposed by WHO or any other organizations,

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the first quartile (P25) value of the corresponding air pollutants was used as the reference,

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to explore the population attributable fraction of osteoporosis. That means the

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population attributable fractions were evaluated in participants who exposed to a

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relatively high air pollution compared to those with relatively low air pollution in rural

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China.

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Data analysis were performed using SAS 9.1 (SAS Inst., Cary, NC) and R 3.4, with a

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P value <0.05 considered as statistically significant for a two-tailed test.

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3. Results

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3.1 Characteristics of study participants

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Among the 8,033 participants, 1,636 participants with osteoporosis were identified.

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The crude and age-standardized prevalence of osteoporosis were 20.37% and 14.54%,

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respectively (Table 1). In general, compared with non-osteoporosis participants, those

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with osteoporosis comprised a greater proportion of females, older individuals. Besides,

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participants with osteoporosis were of lower education level, lower average monthly

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individual income, lower physical activity, lower BMI, less likely to smoke and drink,

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while, more likely to be unmarried/divorced/widowed than those without osteoporosis.

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In addition, there were no significant differences in age between these 8033 participants

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and non-included participants (Table S3).

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The descriptive statistics of air pollution concentrations, as well as their pairwise 10

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correlations were summarized in Table 2. The concentration ranges of PM1, PM2.5, PM10,

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and NO2 were 53.3-70.9 µg/m3, 69.7-85.0 µg/m3, 123.6-148.8 µg/m3, and 34.0-48.8

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µg/m3, respectively. Overall, the air pollutants were strongly correlated with each other.

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3.2 Associations between air pollutants and osteoporosis

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As shown in Table 3 and Fig. 1, the present study found that increased concentrations

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of air pollutants were associated with elevated risk of osteoporosis, after adjusting for

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potential confounders. That is to say, compared with the first quartile, subjects in the

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fourth quartile had higher odds of osteoporosis (P-trend < 0.001). The ORs (95% CIs)

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were 2.08 (1.72, 2.50) for PM1, 2.280 (1.90, 2.74) for PM2.5, 1.93 (1.60, 2.32) for PM10,

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and 2.02 (1.68, 2.42) for NO2 (Fig. 1). When air pollutants were analyzed as continuous

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variables, per 1 µg/m3 increase in PM1, PM2.5, PM10 and NO2 were associated with a

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14.9%, 14.6%, 7.3%, and 16.5% elevated risk of osteoporosis, respectively (Table 3).

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Simultaneously, per 1 SD increase in PM1, PM2.5, PM10 and NO2 were associated with a

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28.5%, 29.6%, 32.4% and 29.7% elevated risk of osteoporosis. The restricted cubic

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spline regressions illustrated that the associations between osteoporosis and air

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pollutants tended to be linear (Fig. 2).

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The results of subgroup analyses showed that age, drinking status, vegetables and

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fruits intake and physical activity could modify the air pollution-osteoporosis

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association (Table S4). Lower ORs were observed in the subgroup with high physical

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activity, suggesting that higher physical activity may weaken the adverse effects of air

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pollution on osteoporosis. The associations between air pollutants and osteoporosis

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were generally stronger among never drinkers. In addition, the ORs of osteoporosis

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associated NO2 were significantly higher among participants aged ≥ 65 years and with

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more vegetables and fruits intake than those <65 years, with less vegetables and fruits

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intake, respectively. We did not observe significant differences by gender. Sensitivity 11

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analyses yielded similar results when using multi-annual average concentrations of air

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pollutants as the exposure variable (Table S5) or excluding participants with cancer,

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liver disease, and kidney disease (Table S6).

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3.3 Estimating attributable osteoporosis risk

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Estimates of the osteoporosis burden attributable to air pollutions were illustrated in

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Table 4. Because of higher air pollutions than the reference, the attributable osteoporosis

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cases were 332 (95% CI, 240-432), 380 (95% CI, 274-496), 398 (95% CI, 290-507),

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and 365 (95% CI, 253-487) for PM1, PM2.5, PM10 and NO2, respectively. The

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corresponding population attributable fractions were 20.29% for PM1, 23.20% for PM2.5,

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24.36% PM10 and 22.29% for NO2. It was estimated that 20.29%-24.36% of the

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osteoporosis cases could be prevented among participants, if air pollution could be

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reduced to below the corresponding P25 levels.

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4. Discussion

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The findings from this population-based epidemiological study indicated that a

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significantly positive dose-response relationship between air pollution and osteoporosis

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exists, after adjusting for potential confounders. We also found lower OR values in the

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subgroup with high physical activity, suggesting that higher physical activity may

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weaken the adverse effects of air pollution on osteoporosis. Moreover, it was estimated

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that 20.29%-24.36% of the osteoporosis cases could be prevented among participants, if

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air pollution could be reduced to below the corresponding P25 levels.

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Several studies investigated associations between air pollution exposure and BMD or

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osteoporosis, but the findings are inconsistent (Alvaer et al. 2007; Alver et al. 2010;

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Chang et al. 2015; Chen et al. 2015; Liu et al. 2015; Prada et al. 2017). Consistent with

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our current findings, a study based on 9.2 million Medicare enrollees of the United

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States reported that long-term exposure to black carbon and higher PM2.5 concentrations 12

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were related to an excess of longitudinal bone loss and a greater risk of osteoporotic

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fracture (Prada et al. 2017). Meanwhile, evidence from population-based Oslo Health

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Study has revealed a weak but statistically significant inverse association between PM2.5,

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PM10 and total body BMD in elderly men (Alvaer et al. 2007; Alver et al. 2010). Similar

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results have also been declared in a retrospective cohort study in a Taiwanese population,

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in which the highest quartile group of carbon monoxide and NO2 were significantly

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associated with an 84% and 60% increased risk of osteoporosis, respectively (Chang et

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al. 2015). Besides, it was also found that proximity to freeways was associated with

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reduced BMD, but ambient air pollutants (NO2, O3, and PM2.5) were not significantly

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associated with BMD (Chen et al. 2015). The reasons for the complicated results may

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be related to the differences in characteristics of study population, sources or

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compositions of air pollutants, measurements of outcomes, and different statistical

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methods, as well as some studies that are not totally consistent with the evidence.

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The biological mechanisms underlying links between air pollutants and bone mineral

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metabolism are not fully elucidated. However, increasing evidence shows that enhanced

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inflammatory responses caused by air pollution exposures can be identified as a

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contributory factor (Chen et al. 2015). Stimulatory effects of pro-inflammatory

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cytokines on osteoclastogenesis have been reported before (Lorenzo et al. 2008;

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Manolagas 2000). Characterized by elevated levels of pro-inflammatory markers

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including interleukin, tumor necrosis factor alpha, and C-reactive protein, chronic

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inflammation has been considered as the mediators between air pollution and

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osteoporosis. Air pollution can not only create an aggravated inflammatory response but

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also induce immune activation (Deiuliis et al. 2012). Accumulated incidence and

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prevalence of osteoporosis were discovered in subjects with inflammatory and

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immunologic disorders, although there is no direct evidence linking immune activation 13

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and BMD (Mikuls et al. 2005). Furthermore, atmospheric pollution-induced cellular

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oxidative stress is probably one of the pathogenic mechanisms. It was reported that

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oxidative stress causes bone loss by activating T cells in estrogen-deficient mice

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through enhanced bone marrow dendritic cell activation (Grassiet al. 2007).

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Additionally, vitamin D deficiency is also a factor linking air pollution to

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osteoporosis. Insufficient vitamin D can cause low serum calcium level, thereby

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inducing osteoclast activity and mobilization of calcium from the skeleton into the

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extracellular space (Moller and Loft 2010). Air pollution may be related to fewer

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outdoor activities, which may result in less solar ultraviolet B (UVB) exposure.

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Insufficient UVB absorption by the skin is one of the major causes of vitamin D

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deficiency. More studies are warranted to clarify the biological mechanisms between air

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pollutant exposure and lower BMD.

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Our findings showed a significantly lower effect of air pollution exposure on

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osteoporosis among participants who had high-level physical activity, suggesting that

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higher physical activity may weaken the adverse effects of air pollution on osteoporosis.

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Physical activity, which increases bone strength and reduces calcium loss, is known to

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be a protective factor for osteoporosis (Troyet al. 2018). We also observed that the

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impacts of NO2 on osteoporosis were significantly higher among participants aged ≥ 65

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years. Since bone density gradually decreases with age after reaching the peak, this may

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explain the age difference in this association.

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Different from previous research results, those who had more vegetables and fruits

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intake were found to have higher OR of osteoporosis due to increase in NO2. Such

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inconsistence might be due to the freshness of vegetables and fruits, cooking method

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and personal hygiene. One possible explanation is that people in countryside typically

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do not wash fruits before eating, so more air pollutants deposited on the surface of the 14

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peel was ingested. In addition, even the fruits and vegetables have rotted, people usually

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remove the decayed parts and continue to eat them. We observed larger ORs of

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osteoporosis for participants who were never-drinkers. Existing evidence has indicated

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that lower alcohol consumption could increase calcitonin production that inhibits bone

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resorption (Vantyghem et al. 2007). Moderate alcohol intake may be associated with

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increased estrogen levels in postmenopausal women (Onland-Moret et al. 2005), and

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these may exert a beneficial effect on BMD. Light to moderate alcohol intake can also

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increase the level of serum estradiol, an inhibitor of bone remodeling, and reduces the

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rate of bone loss (Berg et al. 2008).

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To the best of our knowledge, this is the first study to explore associations between

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long-term air pollution and osteoporosis among rural China adults. Individual’s air

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pollution exposures were estimated by satellite remote sensing data and a random

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forests approach, which showed high predictive ability (Liu et al. 2018b). Nevertheless,

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several limitations in our study should also be noted. First, because of cross-sectional

345

design of study, therefore, causal and temporal associations could not be inferred.

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However, a wide range of covariates were considered in our analysis to insure the

347

reliability of the analysis. Second, the BMD of calcaneus was measured by ultrasonic

348

bone density apparatus, because the application of dual-energy X-ray absorptiometry is

349

very limited in large-scale population studies. Although our study only had calcaneus

350

measurements as a surrogate measure of bone health, several studies have confirmed the

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high predictive ability of QUS of the calcaneus in predicting osteoporotic fractures

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(Hanset al. 1996; Khawet al. 2004; Hartl al. 2002). The same method has been applied

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in an elderly Chinese population (Tian et al. 2015) and some large cohort studies (Li et

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al. 2019; Stiles et al. 2017). Besides, there was no significant difference between the

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QUS and X-ray when using ROC analysis to separate normal from osteoporotic subjects 15

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(Guglielmi and de Terlizzi 2009). Third, although there were no significant differences

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in the mean age between these 8033 participants and those who were not included, the

358

distribution of included participants in the 18-39 age group appeared to be smaller.

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That's also a limitation.

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5. Conclusion

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In summary, our findings demonstrated that there existed a significantly positive

362

dose-response relationship between air pollution and osteoporosis among Chinese rural

363

population after controlling for potential confounders. Physical activity may weaken the

364

adverse effects of air pollution on osteoporosis. Moreover, it was estimated that

365

20.29%-24.36% of the osteoporosis cases could be prevented in rural China, if air

366

pollution could be reduced to below the corresponding P25 levels. Our findings have

367

important implications for developing environmental health policy.

368

Acknowledgements

369

The authors thank all of the participants, coordinators, and administrators for their

370

support during the research. In addition, the authors would like to thank Tanko Abdulai

371

for his critical reading of the manuscript.

372

Funding

373

This research was supported by the Foundation of National Key Program of Research

374

and Development of China (Grant NO: 2016YFC0900803), National Natural Science

375

Foundation of China (Grant NO: 81573243, 21806146), Henan Provincial Science Fund

376

for Distinguished Young Scholars (Grant NO: 164100510021), Science and Technology

377

Innovation Talents Support Plan of Henan Province Colleges and Universities (Grant

378

NO: 14HASTIT035), High-level Personnel Special Support Project of Zhengzhou

379

University (Grant NO: ZDGD13001). YG was supported by the Career Development

380

Fellowship of the Australian National Health and Medical Research Council 16

381

(APP1107107 & APP1163693). SL was supported by the Early Career Fellowship of

382

Australian National Health and Medical Research Council (#APP1109193).The funders

383

had no role in the study design, data collection and analysis, decision to publish, or

384

preparation of the manuscript.

385

Authors’ contribution

386

CW and YG conceived and designed the experiments. DQ, JP GC, HX, RT, XZ, XD,

387

YW, ZL and HT gathered data. DQ, XZ, ZM, and SL analyzed the data and take

388

responsibility for the integrity and accuracy of the information. WH and GZ contributed

389

to the reagents/ materials/ analysis tools. DQ drafted the manuscript. All authors

390

critically revised the manuscript. All authors have approved the final manuscript.

391

Declaration of interest

392

All authors have read and approved this version of the article, and declared that they

393

had no competing or financial interests to disclosure.

394

Availability of data and material

395

The data are available from the corresponding author on reasonable request.

396

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Table 1. Demographic characteristics of study participants

Characteristics

Total (n=8033) 55.8±10.8 3001 (37.4)

Age (mean ±SD) Men, n (%) Education level, n (%) Elementary school or below 3705 (46.1) Junior high school 3320 (41.3) High school or above 1005 (12.5) Average monthly individual income, n (%) <500 RMB 2723 (33.9) 500-1000 RMB 2451 (30.5) ≥1000 RMB 2859 (35.6) Marital status, n (%) Married/cohabitating 7296 (90.8) Unmarried/divorced/widowed 737 (9.2) Smoking, n (%) Never 6007 (74.8) Light 375 (4.7) Moderate/Heavy 1647 (20.5) Drinking, n (%) Never 6277 (78.1) Light 1119 (13.9) Moderate/Heavy 637 (7.9) Physical activity, n (%) Low 2273 (28.3) Moderate 3039 (37.8) High 2721 (33.9) Dietary habits (g/day), (mean ±SD) Meat and poultry 46.0±64.9 Fishery products 4.1±7.4 Vegetables and fruits 524.4±262.5 Soy products 30.7±44.6 2 BMI (kg/m ), (mean ±SD) 24.6±3.5 621 Abbreviations: SD, standard deviation; BMI, body mass index.

Non-osteoporosis (n=6397) 54.6±10.6 2564 (40.1)

Osteoporosis (n=1636) 60.5±10.3 437 (26.7)

2728 (42.7) 2821 (44.1) 847 (13.2)

977 (59.8) 499 (30.5) 158 (9.7)

P-value <0.001 <0.001 <0.001

<0.001 2096 (32.8) 1951 (30.5) 2350 (36.7)

627 (38.3) 500 (30.6) 509 (31.1)

5901 (92.3) 496 (7.7)

1395 (85.3) 241 (14.7)

<0.001

<0.001 4685 (73.3) 332 (5.2) 1376 (21.5)

1322 (80.8) 43 (2.6) 271 (16.6) <0.001

4900 (76.6) 959 (15.0) 538 (8.4)

1377 (84.2) 160 (9.8) 99 (6.1) 0.240

1784 (27.9) 2434 (38.1) 2179 (34.1)

489 (29.9) 605 (37.0) 542 (33.1)

48.5±69.5 4.3±7.0 538.1±265.2 31.7±45.7 24.8±3.5

36.0±41.2 3.5±8.9 470.7±244.3 26.8±39.5 23.8±3.6

<0.001 <0.001 <0.001 <0.001 <0.001

27

622

Table 2. 3-years average concentrations of ambient air pollutants. Variables

Mean

623 624 625 626 627

Pearson correlation coefficients ##

3-years average concentrations SD

Minimum

Maximum

P25

P50

P75

IQR

> WHO (%)*

PM1

56.6

1.8

53.3

70.9

55.2

56.5

57.7

2.4

none

PM2.5

72.1

1.9

69.7

85.0

70.5

71.7

73.2

2.7

100

PM10

129.1

4.0

123.6

148.8

125.8

128.3

133.0

7.2

100

NO2

37.3

1.7

34.0

48.8

35.9

37.2

37.6

1.8

9.8

PM1

PM2.5

1.00

#

0.93 1.00

PM10

NO2

#

0.86 #

0.92 #

0.71 #

1.00

0.90 #

0.94

1.00

Abbreviations: SD, standard deviation; IQR, interquartile range; PM1, particle with aerodynamic diameter ≤ 1.0 µm; PM2.5, particle with aerodynamic diameter ≤ 2.5 µm; PM10, particle with aerodynamic diameter ≤ 10 µm; NO2, nitrogen dioxide; WHO, World Health Organization air quality guidelines (2005). * Compared to the WHO air quality guidelines (The WHO guidelines for PM2.5, PM10 and NO2 were 10 µg/m3, 20 µg/m3 and 40 µg/m3, respectively). The guideline for PM1 has not been proposed by WHO or any other organizations. # Correlation is significant at the 0.01 level (2-tailed). ## Strong, moderate, and weak correlations were defined as coefficients greater than 0.60, 0.30-0.60, and less than 0.30, respectively.

28

628

Table 3. Associations between ambient air pollutants and osteoporosis* Crude

Fully-adjusted#

PM1

1.09 (1.05, 1.12)

1.15 (1.11, 1.19)

PM2.5

1.12 (1.09, 1.16)

1.15 (1.11, 1.19)

PM10

1.04 (1.03, 1.05)

1.07 (1.06, 1.09)

NO2

1.00 (0.97, 1.03)

1.17 (1.12, 1.22)

PM1

1.16 (1.10, 1.22)

1.29 (1.21, 1.37)

PM2.5

1.25 (1.19, 1.32)

1.30 (1.22, 1.38)

PM10

1.17 (1.11, 1.23)

1.32 (1.24, 1.41)

NO2

1.00 (0.95, 1.05)

1.30 (1.21, 1.40)

Pollutant Per 1 µg/m3 increase

Per 1 SD increase

629 630 631 632 633 634

* Data are OR (95% Confidence Interval). # Data were adjusted for age, gender, education level, marital status, smoking, drinking, physical activity, dietary habits (meat and poultry, fishery products, vegetables and fruits, and soy products), and region. SD, standard deviation; PM1, particle with aerodynamic diameter of 1 µm or less; PM2.5, particle with aerodynamic diameter of 2.5 µm or less; PM10, particle with aerodynamic diameter of 10 µm or less; NO2, nitrogen dioxide.

29

635

Table 4. Estimated osteoporosis burden attributable to air pollutants Pollutant PM1 PM2.5 PM10 NO2

636 637

Attributable Cases (95% CI) 332 (240, 432) 380 (274, 496) 398 (290, 507) 365 (253, 487)

Population Attributable Fraction (%, 95% CI) 20.29 (14.70, 26.38) 23.20 (16.76, 30.30) 24.36 (17.74, 31.01) 22.29 (15.44, 29.74)

PM1, particle with aerodynamic diameter of 1 µm or less; PM2.5, particle with aerodynamic diameter of 2.5 µm or less; PM10, particle with aerodynamic diameter of 10 µm or less; NO2, nitrogen dioxide.

638

30

639

Figure legends

640

Figure 1 Odds ratio (and 95% CIs) of osteoporosis associated with per 1 µg/m3

641

increase in mean exposures to air pollutants. Models were adjusted for age, gender,

642

education level, marital status, incomes, smoking, drinking, physical activity, and

643

dietary habits (meat and poultry, fishery products, vegetables and fruits, and soy

644

products).

645

Abbreviations: PM1, particle with aerodynamic diameter of 1 µm or less; PM2.5,

646

particle with aerodynamic diameter of 2.5 µm or less; PM10, particle with

647

aerodynamic diameter of 10 µm or less; NO2, nitrogen dioxide.

648

Figure 2 OR (solid lines) and 95% CI (dashed lines) for the risk of osteoporosis along

649

with the changes of air pollutants from the restricted cubic splines regression

650

model. Models were adjusted for age, gender, education level, marital status,

651

incomes, smoking, drinking, physical activity, and dietary habits (meat and poultry,

652

fishery products, vegetables and fruits, and soy products).

653

Abbreviations: PM1, particle with aerodynamic diameter of 1 µm or less; PM2.5,

654

particle with aerodynamic diameter of 2.5 µm or less; PM10, particle with

655

aerodynamic diameter of 10 µm or less; NO2, nitrogen dioxide.

656 657 658

31

Highlights:


We explored effect of air pollutions on osteoporosis in mainland China firstly




Air pollutions have to do with elevated prevalence of osteoporosis in rural China




20.29%-24.36% of the osteoporosis cases could be attributed to air pollution




Our findings have great significance on development of environmental health policy

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: