Associations of a mixture of urinary phthalate metabolites with blood lipid traits: A repeated-measures pilot study

Journal Pre-proof Associations of a mixture of urinary phthalate metabolites with blood lipid traits: A repeated-measures pilot study Qingqing Zhu, Jian Hou, Wenjun Yin, Fang Ye, Tian Xu, Juan Cheng, Zhiqiang Yu, Lin Wang, Jing Yuan PII:

S0269-7491(19)33806-0

DOI:

https://doi.org/10.1016/j.envpol.2019.113509

Reference:

ENPO 113509

To appear in:

Environmental Pollution

Received Date: 13 July 2019 Revised Date:

5 October 2019

Accepted Date: 27 October 2019

Please cite this article as: Zhu, Q., Hou, J., Yin, W., Ye, F., Xu, T., Cheng, J., Yu, Z., Wang, L., Yuan, J., Associations of a mixture of urinary phthalate metabolites with blood lipid traits: A repeated-measures pilot study, Environmental Pollution (2019), doi: https://doi.org/10.1016/j.envpol.2019.113509. 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. © 2019 Published by Elsevier Ltd.

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Associations of a mixture of urinary phthalate metabolites with blood lipid traits: a

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repeated-measures pilot study

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Qingqing Zhua,b,1, Jian Houa,b,1,2, WenjunYina,b, Fang Yea,b, Tian Xua,b, Juan Chenga,b,

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Zhiqiang Yuc, Lin Wangb*, Jing Yuana,b*

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a

Department of Occupational and Environmental Health and bKey Laboratory of

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Environment and Health, Ministry of Education & Ministry of Environmental Protection, and

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State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji

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Medical College, Huazhong University of Science and Technology, Hangkong Road 13,

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Wuhan 430030, Hubei, PR. China;

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c

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and Resources, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences,

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Guangzhou, 510640, PR. China;

State Key Laboratory of Organic Geochemistry, Guangdong Key Laboratory of Environment

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*These authors contributed equally to this work.

These authors contributed equally to this work.

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Zhengzhou University, Zhengzhou, Henan, China.

Present address: Department of Epidemiology and Biostatistics, College of Public Health,

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*Corresponding Author: Dr. Jing Yuan, E-mail: [email protected]

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*Co-correspondence author: Dr. Lin Wang, E-mail: [email protected]

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Abbreviations

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BKMR, Bayesian kernel machine regression; BMI, body mass index; CI, confidence interval;

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Cr, creatinine; DEHP, di (2-ethydlhexyl) phthalate; HDL-C, high density lipoprotein

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cholesterol ; LDL-C, low density lipoprotein cholesterol; LME, liner mixed-effect regression;

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LOD, limits of detection; MMP, mono-methyl phthalate; MEP, mono-ethyl phthalate; MiBP,

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mono-iso-butyl phthalate; MnBP, mono-N-butyl phthalate; MEHP, mono-2-ethylhexyl

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phthalate; MEHHP, mono-(2-ethyl-5-hydroxyhexyl) phthalate; MEOHP,

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mono-(2-ethyl-5-oxyhexyl) phthalate; MBzP, mono-benzyl phthalate; MiNP, mono-iso-nonyl

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phthalate; PIP, posterior inclusion probability; PPAR, peroxisome proliferator-activated

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receptor; TC, total cholesterol; TG, triglycerides.

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Abstract

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Evidence is available about the associations of phthalates or their metabolites with blood

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lipids, however, the mixture effects of multiple phthalate metabolites on blood lipid traits

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remain largely unknown. In this pilot study, 106 individuals at three age groups of <18, 18-

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and ≥60 years were recruited from the residents (n=1240) who were randomly selected from

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two communities in Wuhan city, China. The participants completed the questionnaire survey

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and physical examination as well as provided urine samples in the winter of 2014 and the

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summer of 2015. We measured urinary levels of nine phthalate metabolites using a

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high-performance liquid chromatography-tandem mass spectrometry. We estimated the

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associations of individual phthalate metabolite with blood lipid traits by linear mixed effect

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(LME) models, and assessed the overall association of the mixture of nine phthalate

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metabolites with blood lipid traits using Bayesian kernel machine regression (BKMR) models.

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LME models revealed the negative association of urinary mono-2-ethylhexyl phthalate

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(MEHP) with total cholesterol (TC) as well as of urinary mono-benzyl phthalate or urinary

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MEHP with low density lipoprotein cholesterol (LDL-C). BKMR models revealed the

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negative overall association of the mixture of nine phthalate metabolites with TC or LDL-C,

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and DEHP metabolites (especially MEHP) had a greater contribution to TC or LDL-C levels

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than non-DEHP metabolites. The findings indicated the negative overall association of the

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mixture of nine phthalate metabolites with TC or LDL-C. Among nine phthalate metabolites,

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MEHP was the most important component for the changes of TC or LDL-C levels, implying

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that phthalates exposure may disrupt lipid metabolism in the body.

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Keywords: Bayesian kernel machine regression model; lipid traits; phthalates.

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Capsule: Exposure to multiple phthalates was negatively related to blood TC or LDL-C

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values

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1. Introduction

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Phthalates are a group of hormone-mimicking compounds. These kind of compounds are

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widely used as plasticizers in various plastic products (Hauser and Calafat, 2005). They are

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easily released from the plastic products into the environment owing to they are not

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chemically bound to the plastics (Erythropel et al., 2014). Thus, humans are often

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simultaneously exposed to multiple phthalates through the routes of migration from packaged

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foods, drinking water, and inhalation of indoor dust (Guo et al., 2011; Wang et al., 2018).

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After entering the body, phthalates are rapidly metabolized, and eliminated primarily via

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urine (Frederiksen et al., 2007; Wittassek et al., 2011). Moreover, recent studies report the

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ecological behavior and toxicological effects of phthalates, including endocrine disruption,

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increased risk for obesity, type II diabetes, male infertility or breast cancer (Benjamin et al.,

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2017; Kim et al., 2019).

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Animal experiments showed that exposure to di (2-ethydlhexyl) phthalate (DEHP) and

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diethyl phthalate affect serum lipids levels (Pradhan et al., 2018) through activating the

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peroxisome proliferator-activated receptors (PPARs, a nuclear receptor superfamily with a

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key role in hepatica fatty acid synthesis and lipid oxidation) (Engel et al., 2017; Hayashi et al.,

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2011). Limited researches are available on the associations of phthalates or their metabolites

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with lipids levels, and the obtained results regarding the associations between them remain

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inconsistent. For example, mono (2-ethylhexyl) phthalate (MEHP) and mono-methyl

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phthalate (MMP) were found to be positively associated with triglycerides (TG) and low

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density lipoprotein cholesterol (LDL-C), respectively (Han et al., 2019; Olsen et al., 2012);

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the negative associations of MEHP with TG, mono-3-carboxypropyl phthalate, mono-ethyl

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phthalate (MEP) or dibutyl phthalate metabolites with total cholesterol (TC) and LDL-C were

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found (Jia et al., 2015; Perng et al., 2017); but no associations between urinary phthalate

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metabolites and serum lipids were found (Trasande and Attina, 2015; Yaghjyan et al., 2015). 4

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Additionally, most of these studies reported the associations of individual phthalate

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metabolite (such as MEHP, MEP and MMP) with blood lipids (Olsen et al., 2012; Perng et al.,

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2017), however, the overall associations of the mixture of multiple phthalate metabolites with

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blood lipid traits remain unclear. Considering human being are simultaneously exposed to

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multiple phthalates, thus adverse effect of phthalate mixtures on human health need to be

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paid attention (Billionnet et al., 2012; Carlin et al., 2013; Chiu et al., 2018).

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The traditional multiple linear regression model is generally used to analyze the

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correlations between different phthalate metabolites and health outcomes. However, high

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correlations between chemicals may distort the real associations between individual chemical

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and health outcomes, when multiple chemicals were simultaneously considered in one

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multiple linear regression model (Chiu et al., 2018; Czarnota et al., 2015; Marill, 2004);

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additionally, the interactions with each other among chemicals on health outcome were

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neglected, when the impact effect of individual chemical on outcome at a time is analyzed.

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Bayesian kernel machine regression (BKMR) model is a relatively novel statistical method,

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which can flexibly model the joint effects of a mixture of multiple compounds based on a

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kernel function (Bobb et al., 2015). It has recently been applied to assess adverse health

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effects of a mixture of multi-pollutants, allowing for potential interaction and non-linearity

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effects (Chiu et al., 2018; Valeri et al., 2017).

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In the present study, traditional linear mixed-effect regression (LME) model was applied to

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estimate the associations of individual phthalate metabolites with blood lipid indices

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(including TG, TC, LDL-C or high density lipoprotein cholesterol (HDL-C)). BKMR model

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was used to assess potential non-linear effect and the interaction of the mixture of nine

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phthalate metabolites on each of blood lipid indices. Particularly, we investigated the overall

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association of the mixture of nine phthalate metabolites with each of blood lipid indices, the

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association of individual phthalate metabolite in the mixture with each of blood lipid indices 5

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when the other phthalate metabolites were concurrently fixed at the same specific percentiles

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of their levels, and further identified the contribution of individual phthalate metabolite to

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changes in value of each lipid.

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2. Materials and methods

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2.1 Study population

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A total of 1240 residents were recruited from two communities in Wuhan city, China using

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stratified random cluster sampling method. The participants had lived in the communities for

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at least two years and had no plans to move out of the communities in the next year. A pilot

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study with repeated measurements of 106 individuals from the participants was

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synchronously conducted (20 individuals were randomly selected from each of three age

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groups (<18, 18-, and ≥60 years) in each community). Among 120 individuals from the two

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communities, individuals (n=106) who completed the questionnaire, physical examination,

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measurements of indoor and personal PM2.5 levels in the winter and summer seasons were

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finally included in the study as described elsewhere (Yin et al., 2017). Individuals in this

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study were free for chronic respiratory disease, cardiovascular and cerebrovascular diseases,

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and cancer. No differences were found in the general characteristics between the whole

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population and individuals in this study (Table S1). The study was approved by the Medical

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Research Ethics Committee of Tongji Medical College, Huazhong University of Science and

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Technology. Informed consent was obtained from all participants prior to this study.

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2.2 Questionnaire survey

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Individuals (n=106) in the pilot study participated the questionnaire survey and physical

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examinations in the winter of 2014. However, the number of participants dropped to 103 in

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the summer of 2015 (two of them gave up the physical examinations owing to changes of

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their workplaces, one died of choking of foreign bodies stuck throat). Data on demographic 6

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and socioeconomic characteristics, lifestyle (such as smoking and drinking status, dietary

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habits, and physical activity), individual and family histories of diseases were collected by

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face-to-face interview. Smoking and drinking status were defined as described elsewhere

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(Yang et al., 2014).

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2.3 Physical examination

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In each season, measurements of body weight and height were performed by the physicians.

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The fasting venous blood samples were collected for routine blood test (including erythrocyte

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count, leukocyte count, platelet count, and mean platelet volume) and blood biochemical

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index (including fasting blood glucose, blood lipids, alanine transaminase, and bilirubin).

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Blood lipid traits (including TC, TG, LDL-C, and HDL-C) values were measured using an

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automatic blood biochemical analyzer (KHB450, Kehua Bio-engineering Co., Ltd., Shanghai,

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China) (Allain et al., 1974). Additionally, the participants provided their urine samples over

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three consecutive days from the date of the personal physical examination. The collected

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urine samples were split and stored at -20°C for further analysis.

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2.4 Urinary phthalate metabolites concentrations

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Urine samples (2.0 mL each) were used to analyze urinary concentrations of nine phthalate

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metabolites (including MEHP, mono-(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP),

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mono-(2-ethyl-5-oxyhexyl) phthalate (MEOHP), MMP, MEP, mono-iso-butyl phthalate

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(MiBP), mono-N-butyl phthalate (MnBP), mono-benzyl phthalate (MBzP), and

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mono-iso-nonyl phthalate (MiNP)) using a high-performance liquid chromatography-tandem

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mass spectrometry (HPLC-1100, Agilent Technologies Co., Santa Clara, CA; API-4000 mass

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spectrometry system, Applied Biosystems/MDS Sciex, USA) according to the reported

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method (Dewalque et al., 2014) with minor modifications. The limits of detections (LODs)

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for MEHP, MEHHP, MEOHP, MMP, MEP, MiBP, MnBP, MBzP, and MiNP were 5.09, 0.28,

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0.24, 1.33, 0.53, 1.90, 1.03, 0.54, and 5.20 pg, respectively. In the urinary samples (n=627), 7

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the numbers of detected MBzP and MiNP were 615 (98.1%) and 511 (81.5%), respectively.

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All the others (including MEHP, MEHHP, MEOHP, MMP, MEP, MiBP and MnBP) were

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detected in all urine samples. Values below the LODs were assigned a value equal to the

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LOD divided by the square root of 2 (Barr et al., 2006). The recovery rate of the method

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ranged from 74.6 to 104.8%. Urinary creatinine concentrations were measured using an

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automated clinical chemistry analyzer (BS200, Mindray Bio-medical Electronics co. LTD,

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Shenzhen, China). Urinary concentrations of nine phthalate metabolites were corrected by

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urine creatinine concentrations. Data are expressed as µg/mmol creatinine (µg/mmol Cr). The

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3-days moving average values of nine phthalate metabolites were used to estimate phthalate

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exposure for each individual.

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2.5 Covariates

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To estimate the associations of phthalate metabolites with blood lipid traits, we adjusted for

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the same covariates in LME and BKMR models, including gender (male/female), age

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(continuous), educational level (≤ 9/> 9 years), smokers (yes/no), passive smokers (yes/no),

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drinkers (yes/no), poultry and meat intake (≤ 1/> 1 time/day), exercise (yes/no), BMI

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(continuous), diabetes (yes/no), hypertension (yes/no), and seasonal factors (winter/summer).

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The covariates were selected based on biological consideration and the clues for potential

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confounders in the previous studies (Franklin et al., 2014; Wannamethee and Shaper, 1992).

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2.6 Statistical analysis

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Descriptive statistics was conducted to summarize the demographic characteristics and

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distributions of urinary phthalates metabolites concentrations. Kolmogorov-Smirnov test was

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used to analyze the normality of data. Measurement data with normal distributions were

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expressed as mean ± standard deviation. Data with non-normal distribution were presented by

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median with interquartile range (IQR). Due to their skewed distributions, urinary phthalate

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metabolites concentrations and blood lipids values as continuous variables were 8

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ln-transformed prior to further analysis. The sum of DEHP metabolites (ΣDEHP) including

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MEHP, MEHHP and MEOHP was calculated as a new exposure indicator. We calculated

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Pearson correlation coefficients to determine the associations of urinary phthalate metabolites

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among each other. The sample size of the pilot study was 209 (including 106 individuals in

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the winter plus 103 ones in the summer). However, after excluding individuals with missing

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data on blood lipid traits (n=11) and poultry and meat intake (n=19), finally data on 179

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observations (including 96 individuals in the winter plus 83 ones in the summer) were used in

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the final analysis. Subsequently, we proposed the LME models to estimate the associations

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between urinary phthalates metabolites and blood lipids indices (including TC, TG, LDL-C,

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and HDL-C) based on the repeated data from the same participants in the two seasons. Nine

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phthalate metabolites and ΣDEHP were analyzed separately using LME models (each

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phthalate metabolite was analyzed in one LME model at a time). The regression coefficient β

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was obtained from the LME model. The estimated percent changes in blood lipids levels

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were calculated according to the equation: 100% × [exp (β) - 1]. The Bonferroni correction

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was used to adjust the p values from LME models to account for multiple comparisons in the

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analysis.

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To assess associations of the mixture of nine phthalate metabolites with blood lipids

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indices, we further fitted BKMR models with each lipid as the dependent variable. The

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models were flexibly proposed with 25,000 iterations by a Markov chain Monte Carlo

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algorithm based on a Gaussian kernel function (Bobb et al., 2015). Multiple components with

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a component-wise selection and hierarchical variable selection are used to select variables

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when constructing a BKMR model. Whereas, the hierarchical variable selection is easy to

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identify the correlations of components among each other rather than the component-wise

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selection, when there are the higher correlations of components each other among the mixture

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of components (Bobb et al., 2015; Coker et al., 2018). Based on the Pearson correlation 9

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coefficients among phthalate metabolites and the similar exposure sources of them (Zhang et

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al., 2019), we grouped urinary DEHP metabolites (including MEHP, MEHHP and MEOHP)

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together as Group1 and non-DEHP metabolites (including MMP, MEP, MiBP, MnBP, MBzP

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and MiNP) together as Group2, respectively. Considering that the repeated-measures design

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of this study, BKMR model with a random intercept was constructed based on the two

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measures of the study items for each individual (Bobb et al., 2015; Coker et al., 2018). The

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BKMR model was given below:

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Yij = h [Group1 = (MEHPij, MEHHPij, MEOHPij), Group2 = (MMPij, MEPij, MiBPij, MnBPij,

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MBzPij, MiNPij)] +βTZij+ eij

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Where i and j correspond to each participant and the clinical visit, respectively. Yij represents

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individual blood lipids value (TC, TG, LDL-C or HDL-C). h () represents a Gaussian kernel

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exposure-response function. The coefficient βT is effect estimates for each blood lipids value

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of the covariates mentioned above; eij represents the residuals.

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BKMR model allows us to infer the mixture effect of chemicals on health outcomes, by

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calculating the posterior mean estimate for changes (or percent changes) in the body indices

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along with changes in the concentration of each chemical in the mixture (Valeri et al., 2017;

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Zhang et al., 2019). To evaluate overall association of the mixture of nine phthalate

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metabolites with each lipids, we calculated the estimated percent change (95% confidence

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intervals, CI) in each lipids value, when comparing the effects of nine phthalate metabolites

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concentrations concurrently fixed at the same percentile (the 10th, 20th, 30th, 40th, 50th, 60th,

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70th, 80th or 90th percentile) and the effects of nine phthalate metabolites concentrations

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fixed at the corresponding medians. To evaluate association of individual phthalate

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metabolite with each of blood lipid indices, we further calculated the percent changes (95%

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CIs) of each lipid value with an IQR change in urinary concentration of individual phthalate

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metabolite, when the other eight phthalate metabolites were concurrently fixed at the 25th, 10

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50th or 75th percentile of their concentrations. We also calculated the group posterior

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inclusion probability (PIP) to represent the probability of a mixture group (i.e., Group1)

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included in the final model after 25,000 iterations. Consequently, we calculated the

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conditional PIP based on the group PIP to reflect the probability of an individual phthalate

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metabolite in Group1 or Group2 included in the final model. Finally, we assessed the relative

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ranking of the Group1 and Group2 based on the group PIP values, and evaluated the specific

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contribution of each phthalate metabolite to each lipid value based on the conditional PIP

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values. The threshold value for PIP to draw the inference on variable “importance” was

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usually set as 0.5 (Coker et al., 2018; Zhang et al., 2019).

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Additionally, we used BKMR models to estimate potential non-linear dose-response

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functions of each phthalate metabolite with each lipid indicator, when the other eight

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phthalate metabolites were fixed at the corresponding median levels of them. Moreover, we

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used BKMR models to estimate interactions of individual phthalate metabolite with each

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lipid indicator. The interactions were also assessed by estimating the dose-response curve of

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one phthalate metabolite, when levels of another phthalate metabolite was fixed at the

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corresponding 25th, 50th, or 75th percentile with the other seven phthalate metabolites were

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simultaneously fixed at the corresponding median values of them, respectively. In the

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estimation, an alteration in the dose-response curve of each phthalate metabolite occurred

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along with one of other phthalate metabolites levels fixed at different percentiles, indicating

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the interaction between them. The parallel shifts of dose-response curve of one phthalate

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metabolite were all found along with one of other phthalate metabolites levels fixed at the

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corresponding different percentiles, indicating no interaction between them (Kupsco et al.,

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2019; Valeri et al., 2017).

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Additionally, we constructed LME models and BKMR models when the measure values of phthalate metabolites as independent variables and urinary creatinine values as a confounder 11

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(Barr et al., 2005) for sensitivity analysis. Data analyses were conducted using R statistical

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software (version3.4.2; R Foundation for Statistical Computing) and SPSS 12.0 statistical

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software (SPSS Inc. Chicago, IL). P-values less than 0.05 were considered significance.

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3. Results

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3.1 Characteristics of participants

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Table 1 shows the characteristics of the individuals. Average age of 106 participants at

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baseline was 46.2 ± 23.8 years. Among them 54.7% were females. Most of them were non-

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smokers (89.6%) and non-drinkers (84.9%). The 3-days moving average values for nine

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urinary phthalate metabolites were ranked from high to low below: MnBP (30.75 µg/mmol

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Cr) > MiBP (30.19 µg/mmol Cr) > MEP (13.59 µg/mmol Cr) > MEHHP (8.76 µg/mmol Cr) >

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MEHP (7.41 µg/mmol Cr) > MEOHP (5.42 µg/mmol Cr) > MMP (5.19 µg/mmol Cr) > MiNP

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level (0.48 µg/mmol Cr) > MBzP (0.12 µg/mmol Cr) in the winter, and MnBP (91.86

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µg/mmol Cr) > MiBP (47.68 µg/mmol Cr) > MEP (21.52 µg/mmol Cr) > MEHHP (6.49

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µg/mmol Cr) > MMP (5.47 µg/mmol Cr) > MEHP (5.01 µg/mmol Cr) > MEOHP (4.56

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µg/mmol Cr) > MiNP (0.23 µg/mmol Cr) > MBzP (0.14 µg/mmol Cr ) in the summer.

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3.2 Correlations among urinary phthalate metabolites

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Figure 1 displays that urinary metabolites of DEHP (MEHP, MEOHP and MEHHP) were

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moderately to highly correlated among each other (r values ranging from 0.58 to 0.98),

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whereas urinary metabolites of non-DEHP were weakly to moderately correlated among each

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other (r values ranging from -0.03 to 0.66).

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3.3 Associations of urinary phthalate metabolites with blood lipids

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Figure 2 shows the results of LME models for the associations between urinary phthalate

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metabolites and blood lipid traits. LME models indicated that TC values were decreased by

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-4.2% (95%CI: -8.0, -0.2%) and -5.9% (95%CI: -9.8, -1.8%), respectively, with each one-unit 12

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increase in ln-transformed urinary concentrations of MMP and MEHP. Besides, LDL-C

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values were correspondingly reduced by -3.4% (95%CI: -6.3, -0.4%), -10.2% (95%CI: -15.8,

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-4.2%) and -4.4% (95%CI: -8.3, -0.4%), respectively, with each one-unit increase in

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ln-transformed urinary concentrations of MEP, MEHP and MBzP. However, no significant

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association was found between each phthalate metabolite and TG or HDL-C. Additionally, no

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significant association was found between ΣDEHP and each of lipid indices. After Bonferroni

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correction, only the relations for MEHP to TC (p=0.03) and LDL-C (p=0.02) was significant.

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Figure 3 displays the results from BKMR models, revealing the negative overall

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association of the mixture of nine phthalate metabolites with TC or LDL-C values, but no

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significant overall association with TG or HDL-C values was found, by comparing the

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percent changes in each lipids value when urinary concentrations of nine phthalate

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metabolites each were concurrently fixed at the corresponding median and concurrently fixed

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at the same percentile ranging from the 10th to 90th percentiles of their concentrations.

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Figure 4 shows the results of BKMR models for the associations of individual phthalate

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metabolite with blood lipid traits. We found that with an IQR increase in ln-transformed

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urinary concentrations of MBzP or MEHP was associated with a decrease of -3.0% (95%CI:

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-5.1, -0.7%), -3.3% (95%CI: -5.5, -1.1%) or -3.6% (-5.8, -1.3%) as well as of -7.6% (95%CI:

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-10.6, -4.4%), -7.8% (95%CI: -10.6, -4.9%) or -8.2% (95%CI: -11.1, -5.2%) in TC values,

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when the other eight phthalate metabolites concentrations were fixed concurrently at the 25th,

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50th or 75th percentiles of their concentrations. Additionally, an IQR increase in

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ln-transformed urinary MiBP concentrations was related to a 2.7% (95%CI: 0.6, 4.9%), 2.8%

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(95%CI: 0.7, 4.9%) or 2.8% (95%CI: 0.7, 5.0%) increase in TC values, when the other eight

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phthalate metabolites were fixed concurrently at the 25th, 50th or 75th percentiles of their

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concentrations. We found that an IQR increase in ln-transformed urinary concentrations of

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MBzP or MEHP was associated with a corresponding decrease of -6.4% (95%CI: -9.7, 13

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-2.9%), -7.3% (95%CI: -10.5, -4.0%) or -7.9% (-11.3, -4.4%) in LDL-C values or a decrease

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of -13.6% (95%CI: -18.0, -8.8%), -14.7% (95%CI: -18.7, -10.4%) or -16.1% (-20.2, -11.7%)

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in LDL-C values, when the other eight phthalate metabolites were fixed concurrently at their

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corresponding the 25th, 50th or 75th percentiles. An IQR increase in ln-transformed urinary

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concentrations of MiNP or MnBP was associated with a corresponding decrease of -7.1%

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(95%CI: -12.5, -1.42%), -7.7% (95%CI:-12.8, -2.2%) or -8.2% (-13.5, -2.6%) or a

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corresponding decrease of -7.6% (95%CI: -13.5, -1.2%), -8.5% (95%CI:-14.3, -2.4%) or -9.5%

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(-15.3, -3.2%) in TG values; an IQR increase in ln-transformed concentrations of urinary

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MMP was associated with a corresponding decrease of -1.6% (95%CI: -3.2, -0.1%), -1.6%

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(95%CI: -3.1, 0.0%) or -1.4% (95%CI: -3.0, 0.1%) in HDL-C values, when the other eight

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phthalate metabolites were fixed concurrently at their corresponding the 25th, 50th or 75th

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percentiles.

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Table 2 presents the results from BKMR models regarding the values of group PIPs and

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conditional PIPs for blood lipid traits. The group PIPs value of Group1 was higher than that of

324

Group2 for TC or LDL-C. Moreover, the group PIPs value of Group2 was higher than that of

325

Group1 for TG and was the same as that of Group1 for HDL-C. Of the conditional PIPs in

326

Group1, the conditional PIPs value of MEHP for TC or LDL-C was more than 0.9. However,

327

the conditional PIPs values of Group2 ranged from 0.08 to 0.39.

328

Results of BKMR models only indicated the non-linear dose-response association between

329

MEHP and TC, when the other eight phthalate metabolites were fixed at the corresponding

330

median levels of them (Figure S1). BKMR models revealed the interaction of MEHP with

331

MEP on TC, MEHP with MEP on LDL-C, MEHP with MnBP or MiBP on HDL-C, when

332

levels of another phthalate metabolite was fixed at the corresponding percentile of 25th, 50th,

333

or 75th with the other seven phthalate metabolites were fixed at the corresponding median

334

values of them, respectively (Figure S2). Results of the sensitivity analysis by using values 14

335

of phthalate metabolites as independent variables and creatinine as confounder were

336

consistence with the previous analysis, in which the creatinine-adjusted levels of phthalate

337

metabolites were as independent variables (Figure S3-S5). Moreover, the sensitivity analysis

338

indicated TC values was correspondingly reduced by -3.0% (95%CI: -5.6, -0.4%), with each

339

one-unit increase in ln-transformed urinary concentrations of MBzP (Figure S3); a negative

340

association of the mixture of nine phthalate metabolites with TG, and a positive overall

341

association of the mixture of nine phthalate metabolites with HDL-C (Figure S4); when the

342

other eight phthalate metabolites were fixed concurrently at the 25th, 50th or 75th percentiles

343

of their levels, an IQR increase in ln-transformed urinary levels of MEHP was associated

344

with a corresponding decrease of -8.8% (95%CI: -15.5, -1.5%), -9.1% (95%CI:-15.4, -2.3%)

345

or -9.2% (-15.5, -2.5%) in TG values and an corresponding increase of 2.5% (95%CI: -0.1,

346

5.1%), 2.5% (95%CI:0.1, 5.0%) or 2.6% (0.2, 5.1%) in HDL-C values (Figure S5).

347 348 349

4. Discussion In the present study, LME models revealed the negative associations of urinary levels of

350

MMP or MEHP with TC values and urinary levels of MBzP, MEP or MEHP with LDL-C

351

values. BKMR models further confirmed the associations, except for the association of

352

urinary levels of MMP or MEP with values of TC or LDL-C. The difference in the results

353

between the two models may be due to the residual confounding effect caused by other

354

phthalate metabolites, and the contributions of the joint effects of the other chemicals to

355

false-positive or false-negative results (Czarnota et al., 2015). Furthermore, BKMR models

356

revealed the negative overall association of urinary levels of the mixture of nine phthalate

357

metabolites with TC or LDL-C, the negative association of higher MBzP with decreased TC,

358

higher MiNP or MnBP with decreased TG, higher MMP with decreased HDL-C as well as

15

359

the positive association of higher MiBP with increased TC, when the other eight phthalate

360

metabolites were concurrently fixed at the 25th, 50th or 75th percentiles of their levels.

361

Few researches reported the negative association of blood lipids with phthalate metabolites.

362

For example, Jia et al. found that the maternal blood MEHP was negatively associated with

363

values of TG or several fatty acids (such as palmitic and oleic) in pregnant Japanese women

364

(n=318) (Jia et al., 2015). Perng et al. reported the negative association between urinary

365

levels of mono-3-carboxypropyl phthalate, MEP or dibutyl phthalate metabolites and values

366

of TC or LDL-C in boys as well as the negative association of ΣDEHP levels with LDL-C

367

values in girls among 248 Mexican youth aged 8-14 years (Perng et al., 2017). Whereas,

368

results of this study is inconsistent with the conclusions of the above-mentioned studies. The

369

reason for this may be partially due to the lacking of considering the effects of other phthalate

370

metabolites simultaneously in addition to the differences in the basic characteristics (such as

371

sex and age), sample size, and phthalate exposure concentrations.

372

Previous study indicated that MEOHP and MEHHP (formed by oxidative metabolism of

373

MEHP) are more appropriate biomarkers for assessing exposure to DEHP than MEHP (Barr

374

et al., 2003). However, the conditional PIP values of MEHP indicated that MEHP may have

375

more contribution to the negative overall association of urinary levels of phthalate

376

metabolites with values of TC or LDL-C rather than MEOHP or MEHHP. The reason for this

377

may be due to MEHP (a lipophilic chemical) is of serum protein-binding characteristics via

378

its free carboxyl group (Griffiths et al., 1988), and the characteristics of MEHP may be

379

changed owing to its oxidative metabolism. Besides, different metabolites of DEHP (such as

380

MEHP and MEHHP) may have diverse effects on health outcomes (Chiu et al., 2018; Liu et

381

al., 2017) as a result of its differential structure and physico-chemical properties (Kratochvil

382

et al., 2019; Viswanathan et al., 2017). In vitro experiment showed that there was

383

accumulated higher intracellular MEHP content in fat cells, which markedly accelerated the 16

384

process of fat decomposition (Chiang et al., 2016). Animal experiments indicated that

385

multiple animal species (including rats, rabbits, and pigs) treated with DEHP dietary

386

exposure appeared lipids metabolic disorders and inhibitions of the cholesterol synthesis in

387

the liver, heart, testes, adrenal gland, brain and reduced plasma cholesterol levels (Bell, 1982).

388

The evidence indicated that the activation of peroxisome proliferator-activated receptor-α

389

(PPAR-α) may be involved in DEHP-induced cholesterol reduction (Hayashi et al., 2011;

390

Nakashima et al., 2013), in which PPAR-α as a ligand-activated transcription factor plays the

391

crucial roles in hepatica fatty acid synthesis and lipid oxidation (Grygiel-Gorniak, 2014;

392

Kimura et al., 2011). Furthermore, few studies showed that MEHP or MBzP induced PPAR-α

393

activation, and MEHP for inducting activation of PPAR-α was stronger than that of MBzP

394

(Engel et al., 2017; Hurst and Waxman, 2003). We found a stronger effect of MEHP on

395

reduced percent changes in values of TC or LDL-C rather than MBzP, which may be due to

396

MEHP or MBzP-induced activation of PPAR-α pathway.

397

Several recent studies found the negative association of urinary MEHP levels with

398

testosterone levels (Chang et al., 2015; Chen et al., 2017) and the positive association of total

399

testosterone derived from cholesterol with values of TC or LDL-C (Kische et al., 2016). We

400

found the negative association of MEHP with values of TC or LDL-C, thus we speculated

401

that MEHP may affect steroid hormone levels by affecting the synthesis of cholesterol.

402

Besides, the findings may provide clues for the association of MEHP exposure with lower sex

403

steroid hormones levels (such as progesterone and free testosterone) (Wen et al., 2017) owing

404

to cholesterols play critical roles in the process of steroidogenesis (Aghazadeh et al., 2015).

405

Similarly, we observed the negative associated between urinary levels of MiNP or MnBP and

406

TG values, which may be related to non-DEHP metabolites-induced activation of PPAR

407

pathway involved in lipid metabolic process (Adibi et al., 2017; Laurenzana et al., 2016).

408

Moreover, the result concerning the negative association of urinary MnBP levels with TG 17

409

values was consistent with the previous study, indicating that individuals with

410

hypertriglyceridemia had lower urinary levels of MnBP than those with normal TG values

411

(Saengkaew et al., 2017). One reason may be due to MnBP-induced activation of PPAR-γ and

412

subsequently accelerated clearance of blood TG, and finally decreased levels of serum lipids

413

(Adibi et al., 2017). Another reason may be that MnBP tend to promote to an increase in the

414

uptake and storage of nonesterified fatty acid in adipose tissue (Laplante et al., 2007).

415

Additionally, MiNP may be considered as an activator of PPARα or PPARγ (Laurenzana et al.,

416

2016), although there is no direct evidence linking the association between MiNP and

417

circulating lipids.

418

BKMR models revealed that non-DEHP metabolites (the group PIPs value of Group2: 0.49)

419

had more contribution to TG rather than DEHP metabolites (that of Group1: 0.34), the

420

finding are similar to the results from an urban cohort study (Adibi et al., 2017) on prenatal

421

phthalates exposure and childhood body size, showing that prenatal non-DEHP phthalate

422

exposures were associated with lower BMI z-score, waist circumference, and fat mass in

423

boys during the early childhood years. However, lipids play an important role in the

424

development of nervous system. An animal study showed that in utero exposure to DEHP

425

altered the brain lipid metabolome of the offspring, which may lead to aberrant

426

neurodevelopment of rats (Xu et al., 2007). A systematic review concluded that prenatal

427

exposure to phthalates exhibited adverse effects on cognitive and behavioral outcomes in

428

children (Ejaredar et al., 2015). Further studies are warranted to investigate adverse effects of

429

phthalates exposure with changes in lipids on neurodevelopmental outcomes.

430

In the present study BKMR model indicated that positive association of MiBP with TC as

431

well as the negative association of MMP with HDL-C. The Prospective Investigation of the

432

Vasculature in Uppsala Seniors Study suggested that the circulating concentrations of MiBP

433

or MMP were positively associated with several obesity indices (including trunk fat mass and 18

434

the trunk/leg-ratio measured by dual-energy X-ray absorptiometry) in elderly women (Lind et

435

al., 2012a) and an increased prevalence of diabetes in elderly (Lind et al., 2012b). These

436

results may provide clues for both MiBP and MMP as risk factors for cardiometabolic

437

diseases such as obesity and diabetes. There was no direct evidence for MiBP-induced

438

activation of PPAR, however, its parent compound was found to be promising in the

439

activation of PPAR receptor (such as impacts of diisobutyl phthalate and other PPAR agonists

440

on steroidogenesis, plasma insulin, and leptin levels in fetal rats). Moreover, MMP can

441

activate either PPARα or PPARγ (Hurst and Waxman, 2003), although the mechanisms

442

underlying the association between MiBP or MMP and circulating lipids need to be further

443

investigated.

444

This study has several strengths. First, owing to the limitation of a single spot-urine sample

445

reflecting individual-levels of pollutants, we repeatedly measured phthalate metabolites levels

446

in the urine samples over 3 consecutive days in each season, which may be helpful to reduce

447

the within-person variability of urinary phthalate metabolites levels. Second, we utilized the

448

advantages of both LME and BKMR models to assess the associations of multiple phthalate

449

metabolites with blood lipid traits. LME models provided the simple relationships between

450

the individual phthalate metabolites and each of lipid indices, and the results were straight

451

forward and easily interpreted. Furthermore, BKMR models were applied to explore the

452

exposure-response functions of each of phthalate metabolites when other phthalate

453

metabolites were simultaneously fixed at certain levels as well as the interactions of multiple

454

phthalate metabolites with each other. This study also has several limitations. First, we did

455

not measure levels of blood lipids over 3 consecutive days, which may cause measurement

456

bias of the outcomes due to the individual variability in the levels of blood lipids daily.

457

Second, the statistical power of this study was very limited to find out the relations of the

458

mixture of nine urinary phthalate metabolites with dyslipidemia (dichotomous outcomes) due 19

459

to the relatively small sample size although the pilot study with repeated measurement was

460

conducted. Therefore, the findings should be interpreted with cautions and need to be

461

confirmed in large prospective studies.

462 463 464

5. Conclusion Both LME and BKMR models revealed the negative associations of MEHP with TC as

465

well as MBzP or MEHP with LDL-C. Besides, the mixture of nine multiple phthalate

466

metabolites exhibited a negative overall effect on TC or LDL-C. Among nine phthalate

467

metabolites, MEHP was the most important component accounting for the changes of TC or

468

LDL-C, indicating that phthalates exposure may disrupt lipid metabolism in the body. Further

469

large prospective researches are needed to confirm the findings and reveal the complex

470

relationship of multiple phthalate metabolites with blood lipid traits.

471 472

Acknowledgments

473

This study was supported by research funds from the Public Sector Program of National

474

Environmental Protection (No. 201409081) and the National Natural Science Foundation of

475

China (No. 81472947).

476 477 478

Declaration of interests We declare that we have no conflicts of interests.

20

479

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662 663

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664

Figure legends

665 666

Figure 1

Correlations of ln-transformed urinary concentrations of nine phthalate

667

metabolites.

668

Dot size is proportional to the magnitude of Pearson correlation coefficients.

669

Abbreviations: Cr, creatinine; MMP, mono-methyl phthalate; MEP, mono-ethyl phthalate;

670

MiBP, mono-iso-butyl phthalate; MnBP, mono-N-butyl phthalate; MEHP, mono-2-ethylhexyl

671

phthalate; MEHHP, mono-(2-ethyl-5-hydroxyhexyl) phthalate; MEOHP,

672

mono-(2-ethyl-5-oxyhexyl) phthalate; MBzP, mono-benzyl phthalate; MiNP, mono-iso-nonyl

673

hthalate.

674 675

Figure 2

Estimated percent changes with 95% confidence intervals in blood lipids each,

676

each one-unit increase in ln-transformed concentrations of urinary phthalate metabolites.

677

Results from linear mixed-effects regression models after adjusted for gender (male/female),

678

age (continuous), educational level (≤ 9/> 9 years), smokers (yes/no), passive smokers

679

(yes/no), drinkers (yes/no), poultry and meat intake (≤ 1/> 1 time/day), exercise (yes/no),

680

body mass index (continuous), diabetes (yes/no), hypertension (yes/no), and seasonal factors

681

(winter/summer).

682

Abbreviations: TC, total cholesterol; TG, triglycerides; LDL-C, low density lipoprotein

683

cholesterol; HDL-C, high density lipoprotein cholesterol; Cr, creatinine; MMP, mono-methyl

684

phthalate; MEP, mono-ethyl phthalate; MiBP, mono-iso-butyl phthalate; MnBP,

685

mono-N-butyl phthalate; MEHP, mono-2-ethylhexyl phthalate; MEHHP,

686

mono-(2-ethyl-5-hydroxyhexyl) phthalate; MEOHP, mono-(2-ethyl-5-oxyhexyl) phthalate;

687

MBzP, mono-benzyl phthalate; MiNP, mono-iso-nonyl phthalate.

688 29

689

Figure 3

Overall associations with 95% confidence intervals of a mixture of nine phthalate

690

metabolites with blood lipid traits.

691

Results from Bayesian kernel machine regression models after adjusted for gender

692

(male/female), age (continuous), educational level (≤ 9/> 9 years), smokers (yes/no), passive

693

smokers (yes/no), drinkers (yes/no), poultry and meat intake (≤ 1/> 1 time/day), exercise

694

(yes/no), body mass index (continuous), diabetes (yes/no), hypertension (yes/no), and

695

seasonal factors (winter/summer).

696

Abbreviations: TC, total cholesterol; TG, triglycerides; LDL-C, low density lipoprotein

697

cholesterol; HDL-C, high density lipoprotein cholesterol; MMP, mono-methyl phthalate;

698

MEP, mono-ethyl phthalate; MiBP, mono-iso-butyl phthalate; MnBP, mono-N-butyl phthalate;

699

MEHP, mono-2-ethylhexyl phthalate; MEHHP, mono-(2-ethyl-5-hydroxyhexyl) phthalate;

700

MEOHP, mono-(2-ethyl-5-oxyhexyl) phthalate; MBzP, mono-benzyl phthalate; MiNP,

701

mono-iso-nonyl phthalate.

702 703

Figure 4

Estimated percent changes with 95% confidence intervals in blood lipid traits, an

704

IQR change in ln-transformed urinary concentrations of individual phthalate metabolite when

705

the other eight phthalate metabolites were fixed concurrently at their corresponding the 25th,

706

50th or 75th percentiles.

707

Results from Bayesian kernel machine regression models after adjusted for gender

708

(male/female), age (continuous), educational level (≤ 9/> 9 years), smokers (yes/no), passive

709

smokers (yes/no), drinkers (yes/no), poultry and meat intake (≤ 1/> 1 time/day), exercise

710

(yes/no), body mass index (continuous), diabetes (yes/no), hypertension (yes/no), and

711

seasonal factors (winter/summer).

712

Abbreviations: TC, total cholesterol; TG, triglycerides; LDL-C, low density lipoprotein

713

cholesterol; HDL-C, high density lipoprotein cholesterol; MMP, mono-methyl phthalate; 30

714

MEP, mono-ethyl phthalate; MiBP, mono-iso-butyl phthalate; MnBP, mono-N-butyl phthalate;

715

MEHP, mono-2-ethylhexyl phthalate; MEHHP, mono-(2-ethyl-5-hydroxyhexyl) phthalate;

716

MEOHP, mono-(2-ethyl-5-oxyhexyl) phthalate; MBzP, mono-benzyl phthalate; MiNP,

717

mono-iso-nonyl phthalate.

31

Table 1 Descriptive characteristics of the subjects in two seasons Variable

Winter (n=106)

Summer (n=103)a

Gender (Male/Female, n, %)

48/58 (45.3/54.7)

46/57 (44.7/55.3)

Age (Years, mean ± SD)

46.2 ± 23.8

46.4 ± 23.7

Education (≤ 9/> 9 years, n, %)

44/62 (41.5/58.5)

43/60 (41.7/58.3)

Active smoking (Yes/No, n, %)

11/95 (10.4/89.6)

11/92 (89.3/10.7)

Passive smoking (Yes/No, n, %)

42/64 (39.6/60.4)

41/62 (39.8/60.2)

Alcohol use (Yes/No, n, %)

16/90 (15.1/84.9)

16/87 (15.5/84.5)

Exercise (Yes/No, n, %)

64/42 (60.4/39.6)

66/37 (64.1/35.9)

Vegetable and fruit intake (≤ 1/> 1/ missing data time/day, n, %)

63/37/6 (59.4/34.9/5.7)

69/31/3 (67.0/30.1/2.9)

Poultry and meat intake (≤ 1/> 1/ missing data time/day, n, %)

66/31/9 (62.3/29.2/8.5)

73/18/12 (70.9/17.5/11.6)

BMI (kg/m , mean ± SD)

23.7 ± 4.2

23.3 ± 4.2

Hypertension (Yes/No, n, %)

26/80 (24.5/75.5)

25/78 (24.3/75.7)

10/96 (9.4/90.6)

10/93 (9.7/90.3)

TC

4.67 (3.90, 5.61)

4.37 (3.62, 5.16)

TG

1.01 (0.75, 1.44)

1.06 (0.82, 1.78)

LDL-C

2.78 (2.11, 3.60)

2.45 (1.80, 3.20)

1.22 (1.10, 1.40)

1.20 (1.00, 1.30)

5.19 (3.94, 7.63)

5.47 (3.58, 8.41)

Food frequency

2

Diabetes (Yes/No, n, %) b

Blood lipid traits (mmol/L, median, IQR)

HDL-C Urinary phthalate metabolites (µg/mmol Cr, median, IQR)c MMP 32

MEP

13.59 (6.21, 28.43)

21.52 (11.02, 53.19)

MiBP

30.19 (19.48, 46.12)

47.68 (34.29, 80.53)

MnBP

30.75 (20.66, 53.79)

91.86 (62.05, 185.72)

MEHP

7.41 (4.86, 11.1)

5.01 (2.76, 7.84)

MEHHP

8.76 (5.14, 14.83)

6.49 (4.15, 9.32)

MEOHP

5.42 (3.19, 8.62)

4.56 (2.76, 6.25)

MBzP

0.12 (0.08, 0.22)

0.14 (0.08, 0.42)

MiNP 0.48 (0.28, 0.90) 0.23 (0.10, 0.44) Abbreviations: SD: standard deviation; BMI: body mass index; IQR: interquartile range; TC, total cholesterol; TG, triglycerides; LDL-C, low density lipoprotein cholesterol; HDL-C, high density lipoprotein cholesterol ; Cr, creatinine; MMP: mono-methyl phthalate; MEP, mono-ethyl phthalate; MiBP, mono-iso-butyl phthalate; MnBP, mono-N-butyl phthalate; MEHP, mono-2-ethylhexyl phthalate; MEHHP, mono-(2-ethyl-5-hydroxyhexyl) phthalate; MEOHP, mono-(2-ethyl-5-oxyhexyl) phthalate; MBzP, mono-benzyl phthalate; MiNP, mono-iso-nonyl phthalate. a Two subjects gave up the physical examinations owing to changes of their workplaces, another one died of choking of foreign bodies stuck throat. b 11 subjects with missing data on blood lipid traits (two in the winter season and nine in the summer season). c We measured 3-consecutive-day urinary levels of phthalates metabolites each and then corrected by the corresponding urinary creatinine concentrations. The 3-days moving average of urinary phthalate metabolites levels were used to estimate individual phthalate exposure. Data are expressed as µg/mmol Cr. 718

33

Table 2 Posterior inclusion and conditional probabilities of Bayesian kernel machine regression models Lipid traits

Group PIP

Conditional PIP

Group1

Group2

MEHP

MEHHP

MEOHP

MMP

MEP

MiBP

MnBP

MBzP

MiNP

TC

0.89

0.30

0.97

0.02

0.01

0.16

0.39

0.11

0.09

0.17

0.09

TG

0.34

0.49

0.48

0.24

0.28

0.11

0.08

0.14

0.20

0.14

0.33

LDL-C

0.92

0.48

0.93

0.03

0.04

0.10

0.34

0.10

0.14

0.24

0.08

HDL-C 0.51 0.51 0.40 0.32 0.28 0.11 0.11 0.18 0.22 0.19 0.19 Abbreviations: PIP, posterior inclusion probability; TC, total cholesterol; TG, triglycerides; LDL-C, low density lipoprotein cholesterol; HDL-C, high density lipoprotein cholesterol; MMP, mono-methyl phthalate; MEP, mono-ethyl phthalate; MiBP, mono-iso-butyl phthalate; MnBP, mono-N-butyl phthalate; MEHP, mono-2-ethylhexyl phthalate; MEHHP, mono-(2-ethyl-5-hydroxyhexyl) phthalate; MEOHP, mono-(2-ethyl-5-oxyhexyl) phthalate; MBzP, mono-benzyl phthalate; MiNP, mono-iso-nonyl phthalate. Models adjusted for gender, age, education, smoking status, passive smoking, alcohol use, exercise, poultry and meat intake and body mass index (BMI), hypertension, diabetes and seasons; Group1 included MEHP, MEHHP and MEOHP; Group2 included MMP, MEP, MiBP, MnBP, MBzP and MiNP. 719

34

Highlights 1. A mixture of nine phthalate metabolites was negatively associated with TC or LDL-C. 2. Several phthalate metabolites were negatively related to TC or LDL-C. 3. DEHP metabolites had prominent effects on TC or LDL-C than non-DEHP metabolites.

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: