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Low-level environmental phthalate exposure associates with urine metabolome alteration in a Chinese male cohort Jie Zhang, Liangpo Liu, Xiaofei Wang, Qingyu Huang, Meiping Tian, and Heqing Shen Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b00034 • Publication Date (Web): 03 May 2016 Downloaded from http://pubs.acs.org on May 8, 2016

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Environmental Science & Technology

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Low-level environmental phthalate exposure associates with urine metabolome

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alteration in a Chinese male cohort

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Jie Zhang*, Liangpo Liu, Xiaofei Wang, Qingyu Huang, Meiping Tian, Heqing Shen*

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Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of

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Sciences, China

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*To whom correspondence may be addressed:

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Jie Zhang, Institute of Urban Environment, Chinese Academy of Sciences, 1799 Jimei Road,

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Xiamen, 361021, China; Telephone/Fax: (86)-592-6190523; E-mail: [email protected]

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Heqing Shen, Institute of Urban Environment, Chinese Academy of Sciences, 1799 Jimei Road,

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Xiamen, 361021, China; Telephone/Fax: (86)-592-6190771; E-mail: [email protected]

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ACS Paragon Plus Environment


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Abstract

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The general population is exposed to phthalates through various sources and routes. Integration of

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omics data and epidemiological data is a key step towards directly linking phthalate

16

bio-monitoring data with biological response. Urine metabolomics is a powerful tool to identify

17

exposure biomarkers and delineate the modes of action of environmental stressors. The objectives

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of this study are to investigate the association between low-level environmental phthalate

19

exposure and urine metabolome alteration in male population, and to unveil the metabolic

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pathways involved in the mechanisms of phthalate toxicity. In this retrospective cross-sectional

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study, we studied the urine metabolomic profiles of 364 male subjects exposed to low-level

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environmental phthalates. Di(2-ethylhexyl) phthalate (DEHP) and dibutyl phthalate (DBP) are the

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most widely used phthalates. ΣDEHP and MBP (the major metabolite of DBP) were associated

24

with significant alteration of global urine metabolome in the male population. We observed

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significant increase in the levels of acetylneuraminic acid, carnitine C8:1, carnitine C18:0, cystine,

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phenylglycine, phenylpyruvic acid and glutamylphenylalanine; and meanwhile, decrease in the

27

levels

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methylglutaconic acid, hydroxyl-PEG2 and keto-PGE2 in high exposure group. The observations

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indicated that low-level environmental phthalate exposure associated with increased oxidative

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stress and fatty acid oxidation and decreased prostaglandin metabolism. Urea cycle, tryptophan

31

and phenylalanine metabolism disruption was also observed. The urine metabolome disruption

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effects associated with ΣDEHP and MEP were similar, but not identical. The multi-biomarker

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models presented AUC values of 0.845 and 0.834 for ΣDEHP and MEP, respectively. The

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predictive accuracy rates of established models were 81% for ΣDEHP and 73% for MEP. Our

of

carnitine

C16:2,

diacetylspermine,

alanine,

taurine,


tryptophan,

ornithine,

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results suggest that low-level environmental phthalate exposure associates with urine metabolome

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disruption in male population, providing new insight into the early molecular events of phthalate

37

exposure.

38



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Introduction

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The ubiquitousness of phthalates results in widespread exposure to these chemicals among the

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general population. Phthalates has been tightly linked to adverse health outcomes, such as diabetes,

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allergy symptoms and reproductive abnormalities.1-4 Phthalates are rapidly converted into

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mono-phthalates in human body,

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bio-monitoring indicators. However, mono-phthalates are not enough to characterize adverse

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effects due to their irrelevance with molecular response to the exposure. Mechanisms of response

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and biomarkers of susceptibility to phthalates are interrelated, thus the development of new

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strategies- especially the identification of early effect biomarkers using omics approaches (e.g.

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metabolomics) is of particular importance.6 Furthermore, the integration of omics data with

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mechanistic and epidemiological data is a key step towards linking exposure biomarkers and

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susceptibility to disease mechanisms and outcomes.7

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Metabolomics studies from our group

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disrupted many important metabolic pathways in rodents and other experimental models. Animal

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models used in toxicological studies have been criticized due to the lack of similarities to human

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exposure conditions, in particular through the use of high doses and short term exposure. The best

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strategy for identification of metabolic disturbances in environmental health is to directly target on

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human population6. Metabolomics offers three distinguished advantages in studying human–

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environment interactions at molecular level: (1) metabolic fingerprint represents actual functional

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status of the organism, which can be mechanistically related to organism phenotype; (2)

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metabolomics focuses more on questions than hypotheses, thus it can discover unexpected

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associations between metabolite responses and environmental exposure 8; (3) metabolic changes

5

and thus urinary mono-phthalates are commonly used as

9

and others

10-14

have demonstrated phthalate exposure


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can be observed in biological fluids (e.g. urine, blood), making it possible to directly identify

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potential biomarkers that may indicate the early effects of environmental exposure in humans.

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Recently, researchers began to apply environmental metabolomics to epidemiological studies.

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Differential urine metabolomes have been characterized for male welders vs. office workers

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volunteers living near a closed zinc smelter of cadmium pollution16, pregnant women exposed to

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low-dose/complex pesticides17, and diabetic/non-diabetic population exposed to inorganic arsenic

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through drinking water18. These studies investigated the metabolome alteration of the populations

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with occupational exposure

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known about low-level environmental exposure in general population.

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In this study, we aimed at answering the following questions: What is the influence of low-level

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environmental phthalate exposure on the urine metabolome in general male population? Which

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metabolic pathways are involved in the mechanisms of phthalate toxicity? We performed liquid

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chromatography - mass spectrometry based urine metabolomic analyses of 364 Chinese men in

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order to identify discriminant metabolites, and we further investigated disrupted metabolic

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pathways associated with the early effects of phthalates exposure.

15

or the populations living in highly polluted areas,

16-18

15

,

but little is

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Methods

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Subject demographics and sample collection

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The retrospective, cross-sectional study was approved by the institutional ethics committee and

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conducted under the Helsinki Declaration. Three hundred and sixty four Chinese adult men

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(ethnically Han) were enrolled from affiliated hospitals of Nanjing Medical University (NJMU).

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They were the volunteers of NJMU Fertility Study. Informed consent was obtained from all



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participants. The data including age, weight, height, smoking and alcohol drinking status,

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education level, annual income, and occupation were collected by questionnaire. The subjects with

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diabetes mellitus were excluded to avoid any ambiguity. Spot morning urine samples were

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collected for mono-phthalate determination and metabolic profile acquisition. After collection, the

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samples were transported in ice to the lab in 24 hours, and then stored in -80°C prior to analysis.

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Urinary phthalate metabolite analysis

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Seven major phthalate metabolites were determined using liquid chromatography - mass

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spectrometry, including monomethyl phthalate (MMP), monoethyl phthalate (MEP), monobutyl

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phthalate (MBP), monobenzyl phthalate (MBzP), mono-2-ethylhexyl phthalate (MEHP),

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mono-2-ethyl-5-oxohexyl phthalate (MEOHP) and mono-(2-ethyl-5-hydroxylhexyl) phthalate

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(MEHHP). The details of sample preparation, standard solutions and instrumental analysis have

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been described previously

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β-glucuronididase, purified by solid-phase extraction, and analyzed by isotope dilution

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high-performance liquid chromatography - tandem mass spectrometry. The limits of detection

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(LODs) of MMP, MEP, MBP, MBzP, MEHP, MEOHP and MEHHP were 0.08, 0.10, 0.10, 0.14,

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0.07, 0.06 and 0.10 µg/L, respectively. The relative standard deviation (RSD) values of peak area

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for these phthalate metabolites ranged from 2.2 to 13.9%. Urinary concentrations of phthalate

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metabolites were expressed as creatinine-adjusted concentrations. Di(2-ethylhexyl) phthalate

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(DEHP) and dibutyl phthalate (DBP) are the most widely used phthalates. MBP is the major

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metabolite of DBP. ΣDEHP was calculated based on mass sum of major metabolite concentrations

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(MEHP, MEHHP and MEOHP) 20.

104

Urine metabolome analysis

19

. Briefly, urinary phthalate metabolites were deconjugated using


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Details of sample preparation, metabolic profiling acquisition, data processing, biomarker

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screening and identification, quality control procedures were described in Supporting Information.

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Briefly, the urine metabolic profiles were acquired using a liquid chromatography/time-of-flight

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mass spectrometer, and then processed with Profile Analysis 2.0 to obtain a feature table. The

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table was Pareto-scaled and introduced to SIMCA-P v11.5 software (Umetrics, Sweden) for

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multivariate statistical analysis. Principal component analysis (PCA) was performed to cluster the

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samples, and identified the outliers. The outliners were the samples far away from the cluster

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center in PCA score plot. The samples were possibly contaminated during the storage and

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preparation processes. After careful inspection of raw chromatograms, the outliers (n=2) were

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removed from the dataset. ΣDEHP, MBP and MEP concentration was classified into tertiles.

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Accordingly, the dataset were separately categorized into three groups (1st tertile, 2nd tertile and 3rd

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tertile), and defined as low, medium and high exposure groups, respectively. MBzP was detected

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in 52.7% of the subjects, thus the dataset was divided into two groups (detected vs. undetected).

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Phthalate exposure-oriented PLS-DA (partial least squares discriminant analysis) models were

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established, in which internal levels of phthalates were used as classifiers. The biomarkers were

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screened from the PLS-DA models according to the following criteria: (1) VIP scores > 2; (2)

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jack-knifing confidence interval > 0; (3) p-value between low and high exposure groups < 0.05;

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and (4) the features significantly (p < 0.05) correlated with phthalate concentrations after adjusted

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by age, BMI, smoke and alcohol drinking status; (5) the features significantly (p < 0.05) correlated

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with semen volume, concentration, motility, progression, and motion parameters were excluded.

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Statistical analysis

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All the statistical analysis was performed using SPSS 18 (SPSS Inc.). The nonparametric



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Mann-Whitney test was used to evaluate the significant difference of each potential biomarker

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between the groups. Spearman correlation was performed to investigate the association between

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demographic factors, phthalates and biomarkers. Partial correlation analysis was performed to

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investigate the association between the biomarkers and phthalate exposure after adjustment of age,

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BMI, smoking and alcohol drinking status. P-values of < 0.05 were considered statistically

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significant.

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Receiver operating characteristic (ROC) curves

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Classical univariate ROC analyses of potential biomarkers were performed using SPSS 18 (SPSS

135

Inc.). Multivariate analyses of combinational biomarker patterns were performed using online

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ROCCET (ROC Curve Explorer & Tester) software (http://www.roccet.ca/ROCCET/).

137 138

Results

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Population characteristics

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The demographic characteristics of study population are listed in Table 1. The participants are

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male residents of Nanjing city aging from 19 to 44 years. BMI exhibited a wide range of 16.5–

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32.9 kg/m2 (median 23.7 kg/m2). Half of the participants never consume tobacco and alcohol.

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Urinary phthalate levels

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Urinary phthalate levels are listed in Table 2. MEP, MBP, MEHP, MEHHP, and MEOHP were

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detected in all the samples. MBzP were detected in 52.7% of the subjects. MMP were only

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detected in 3.6% of the subjects, thus it was not included in metabolomics analysis. We detected

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much higher concentrations of MBP, MEP and MMP than other phthalate metabolites. Statistical

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analysis showed ΣDEHP were highly correlated with its major metabolites (r= 0.90, 0.99 and 0.98


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for MEHP, MEHHP and MEOHP, respectively) (see SI Table S1). In addition, we also observed a

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moderate correlation between ΣDEHP and MBP (r=0.55) (see SI Table S1). Correlation analysis

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showed a slight positive correlation (r ≤ 0.22) between phthalate metabolites and BMI (except

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MBzP and MEHP), but no significant correlation was observed between phthalates and age. The

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tertiles of ΣDEHP and MBP (see SI Table S2) also showed no significant difference in education

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level, annual income, occupation, smoking and alcohol drinking status. Moreover, no significant

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difference of age, BMI and phthalate levels was observed between smokers and non-smokers, and

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neither was observed between alcohol drinkers and non-alcohol drinkers (see SI Table S3 and S4).

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Urine metabolome

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PCA and PLS-DA models were established to investigate the metabolic disruptions (see SI Table

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S5). In PCA scoring plots, low exposure group was mostly separated from high exposure group,

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but medium exposure group could not be separated from low and high exposure groups (see

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Figure 1 and SI Figure S1). We also investigated the influence of smoking and alcohol drinking on

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urine metabolome. The developed PCA models could not provide differentiation for non-smokers

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vs. smokers and non-alcohol drinkers vs. alcohol drinkers (see SI Figure S1). In order to further

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differentiate the groups and screen potential biomarkers, PLS-DA analysis was separately

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performed for phthalates (low exposure group vs. high exposure group) and smoking/alcohol

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drinking status. The strict criteria of permutation tests (see SI Figure S2) were used to validate the

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models: (1) the R2 and Q2 values in the permutation test were lower than the original ones; and (2)

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the Y-axis intercept for Q2 is below zero. Finally, robust PLS-DA models were only established

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for MBP and ΣDEHP. Good separations of metabolic profiles were observed between low and

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high exposure groups in MBP and ΣDEHP models (Figure 1). No satisfactory PLS-DA models



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were generated for MEP, MBzP and smoking/alcohol drinking status (see SI Table S5), thus these

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factors were not examined further in analysis.

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Biomarker screening was conducted according to the protocol described in experimental section.

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The study cohort consisted of the volunteers enrolled in NJMU Fertility Study, which was

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designed to investigate the association between environmental exposure and semen quality. In this

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study, we aimed to investigate the association between metabolome alteration and phthalate

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exposure, and identify the disrupted metabolic pathways associated with early effects of phthalates

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exposure. Since male infertility status may alter the metabolome

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correlated with semen parameters were removed from the biomarker candidate list (n=2 for MBP,

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n=1 for ΣDEHP). These removed metabolites have little influence on the overall findings of this

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study. Finally, twelve biomarkers were selected from MBP model, and five biomarkers were

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selected from ΣDEHP model (Table 3). These tentatively identified biomarkers were key nodes of

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the early effects associated with phthalate exposure in male population. The biomarkers were

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closely involved in amino acid metabolism, mitochondrial beta-oxidation of fatty acids, and

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prostaglandin metabolism. Except for acetylneuraminic acid and phenylglycine, the biomarkers

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showed similar trend change in both MBP and ΣDEHP models. As shown in Table 3, in high

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exposure group, five amino acids and their related metabolites (i.e. alanine, taurine, tryptophan,

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ornithine, and methylglutaconic acid) significantly decreased, while other amino acid derivatives

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(i.e. cystine, phenylglycine, phenylpyruvic acid and glutamylphenylalanine) significantly

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increased. Carnitines play a key role in mitochondrial beta-oxidation of fatty acids. Carnitine C8:1

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and carnitine C18:0 increased after phthalate exposure. However, another long chain carnitine -

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carnitine C16:2 exhibited significant decrease in high MEP and ΣDEHP group. Two

21

, the metabolites significantly


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prostaglandins – hydroxyl-PGE2 and keto-PGE2 dramatically decreased in high MBP or ΣDEHP

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group. The change of acetylneuraminic acid was observed only in high MBP group, while the

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decrease of diacetylspermine was noted in both groups. Most biomarkers showed significant

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dose-dependent changes in three groups (see SI Figure S3).

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ROC analysis

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The closer the AUC value approaches to 1, the better the model provides diagnostic performance.

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For MBP model, 12 biomarkers gave AUC values between 0.7 and 0.9 (Table 3), indicating their

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moderate discrimination ability. For ΣDEHP model, 10 biomarkers gave AUC values between 0.7

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and 0.8 (Table 3). Multi-biomarker models were further established for MBP and ΣDEHP.

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Combinations of top ten biomarkers turned out to be the best indicators for both MBP and ΣDEHP

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models (Figure 2). Confusion matrix showed the predictive accuracy of the model as the

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percentage of correctly classified samples in a given class. The predicative accuracy was about 72%

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for MBP model and 80% for ΣDEHP model (Figure 2).

206 207

Discussion

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Humans are daily exposed to phthalates through various sources and routes (diet, dust, air, oral

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and respiratory). Together with the use of population characteristics data, understanding human

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metabolome responses to phthalate exposure will provide new fingerprinting tools in assessing

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health risk and understanding their toxicity.

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The noticeable phthalates are DEHP with more than 75% of its total usage in plastic products,

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followed by customer and personal care related chemicals i.e., DBP and DEP 22, 23. In this study,

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we detected higher concentrations of DBP and DEHP metabolites than others. Furthermore,



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correlation analysis revealed strong associations between these two phthalates (R2=0.546),

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indicating they may have common exposure source (mainly through diet) for the investigated

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population. The levels of urinary phthalate metabolite in the study were significantly lower than

218

occupational exposure

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Chinese report22, but were lower than those observed in the National Health and Nutrition

220

Examination Survey 4.

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Metabolome analysis

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After phthalate determination, we followed a non-targeted metabolic profiling approach to

223

characterize urine metabolome, to further offer insights into metabolic disruption in male

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population. The metabolome represents the fingerprint of all small, low molecular weight

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metabolites in a biological cell, tissue, organ or organism, which are the end products of cellular

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processes. In this study, an environmental exposure-oriented metabolome strategy was used,

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which was reported in our previous paper regarding arsenic exposure

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method, the low exposure group was well discriminated from the high exposure group, suggesting

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that the exposure to DBP and DEHP associated with substantial global metabolic variations in

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male population. The individual’s metabolism differs from person to person, resulting in

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metabolome variation. The phthalate exposure-oriented metabolomic results were derived from

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statistical analysis of hundreds of participants, thus the uncertainty induced by individual’s

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metabolism variation were reduced to a certain extent. Furthermore, phthalate exposure-oriented

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PLS-DA analysis and sequent statistical analysis could exclude the metabolic features associated

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with the variation of individual’s metabolism.

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Recently, a few study reported cigarette smoking 26, 27 and alcohol consumption 28 induced serum

24

. The data were comparable to those of our previous report19 and other


25

. By using PLS-DA

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or blood metabolomic differences in humans. In this study, we did not observe significant urine

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metabolome alteration. The main reason is the composition of urine is significantly different from

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serum and blood. Study population difference our study and previous reports 26-28 may be another

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reason. No significant difference of MBP/ΣDEHP biomarkers was observed between alcohol

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drinkers and non-alcohol drinkers; neither was observed between non-smokers and smokers. This

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indicated the biomarkers were not associated with smoking/alcohol drinking status.

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Totally 16 differential metabolites were putatively identified in MBP/ΣDEHP statistical models.

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Several important metabolic pathways were associated with phthalate exposure, for example, an

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increase in fatty acid beta-oxidation (carnitine C8:1 and carnitine C18:0), a decrease in

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prostaglandin metabolism (hydroxyl-PGE2 and keto-PGE2) and disturbed amino acid metabolism

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(alanine, taurine, tryptophan, ornithine, cystine and derivatives). The identified metabolites also

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showed similar trend in both MBP and ΣDEHP models, indicating these two phthalates may exert

249

similar disruption effect on human metabolism network. This finding is consistent with previous

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rodent-based metabolomics study, where the authors observed similar change of 39 lipid and

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amino acid metabolites for the DEHP and DBP exposure in male rats. 12

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Phthalate exposure associates with increased fatty acid oxidation and oxidative stress

253

Carnitines have been used as potential metabolic modulators to modulate mitochondrial

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metabolism and alleviate oxidative stress

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C8:1) were significantly increased in the subjects exposed to high level of phthalates, pointing to

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possible changes in fatty acid β-oxidation associated with phthalate exposure. Altered

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acylcarnitine levels have also been observed in adipocytes, brain and reproductive organs of

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phthalate treated mice or rats. 13, 30 Recently, DEHP treatment was reported to elevate carnitine

29

. Main acylcarnitines (carnitine C18:0 and carnitine



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acetyltransferase/palmitoyltransferase activities and up-regulate the expression of peroxisomal and

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mitochondrial β-oxidation enzymes in rats

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(PPARs) are closely involved in the regulation of carnitine homeostasis and mitochondrial

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function. Therefore, we propose that PPARs dis-regulation is a potential mechanism by which

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phthalates can exert their effects on lipid metabolism in male population.

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Dis-regulated carnitine metabolism observed in our study also connects phthalate exposure with

265

increased oxidative stress. Elevated acylcarnitine expression is a positive response to oxidative

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stress induced by phthalate exposure. Glutathione is a key antioxidant, preventing reactive oxygen

267

species to damage cellular components. We observed increased levels of cysteine and

268

glutamylphenylalanine which are important precursors and intermediates in glutathione synthesis.

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These findings support the hypothesis that environmental phthalate exposure causes oxidative

270

stress-related effects in male population.

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Phthalate exposure associates with decreased prostaglandin metabolism

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Our data revealed the association between phthalate exposure and prostaglandin pathway, which is

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tightly involved in sexual development, inflammatory responses and hormone regulation.

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Prostaglandin E2 (PGE2) is the most biologically active prostaglandin and widely distributed in

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various tissues. In our study, the PGE2 metabolites hydroxy- and keto- PGE2 were observed to

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decrease significantly, indicating that phthalate exposure associates with decreased prostaglandin

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metabolism. This finding is consistent with previous in-vivo and in-vitro studies

278

that phthalates are endocrine-disrupting compounds. Phthalates inhibit the prostaglandin pathway

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through releasing cytokine, activating PGD and PGE receptors, activating PPAR pathways, and

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inhibiting the COX-mediated conversion of arachidonic acid to prostaglandins 34, 35.

31,32

. Peroxisome proliferator-activated receptors


33, 34

supporting

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Phthalate exposure associates with amino acid metabolism disruption

282

Serum uric acid has been recently reported to have a significant positive correlation with serum

283

DEHP level in humans.

284

ornithine cycle). Combining our observations, we concluded that phthalate exposure associated

285

with disrupted urea cycle. Alanine and ornithine metabolism-related enzymes (e.g. alanine

286

aminotransferase ALT, ornithine decarboxylase ODC) may be important toxic target. Phthalate

287

exposure significantly elevated the ALT level

288

disrupt alanine synthesis and polyamine pathway. The disregulation of polyamine pathway was

289

further supported by decreased diacetylspermine level observed in our study.

290

Tryptophan is another important essential amino acid. Previous reports revealed DBP and DEHP

291

may increase the conversion ratio of tryptophan to niacin through the inhibition of

292

α-amino-β-carboxymuconate-esemialdehyde decarboxylase38. Although niacin level was not

293

measured, the decreased level of tryptophan observed in our study partially supported previous

294

findings. Phenylalanine metabolism was also probably disturbed by phthalates, indicated by

295

increased

296

glutamylphenylalanine. The increased phenylalanine was observed in male rats treated with 3000

297

ppm DEHP for 4 weeks 12, which was consistent with our hypothesis.

298

ROC analysis

299

The identified biomarkers were the indicators of phthalate exposure because they represented the

300

disrupted pathways. The biomarkers distributed in several important pathways, thus the

301

combinational biomarker pattern represented the overall disrupted metabolic network. As expected,

302

combined biomarker pattern provided higher discrimination power than single biomarkers. The

levels

of

36

its

Alanine and ornithine are involved in urea cycle (also known as

two

important

36

and ODC activity

intermediates

-


37

, which were capable to

phenylpyruvic

acid

and


303

multi-biomarker models (top ten biomarker combination) presented AUC values of 0.834 and

304

0.845 for MBP and ΣDEHP, respectively. Usually, a biomarker with AUC >0.85 is acceptable for

305

most clinical applications. Therefore, the combinational biomarker pattern has great potential in

306

assessing health risk associated with phthalate exposure in the actual environment.

307 308

Our study suggests a tight relationship between phthalate exposure and urine metabolome

309

disruption in male population, and highlights potential mechanisms that deserve further study.

310

However, it has several limitations: (1) Control of cohort quality remains a big challenge for

311

epidemiological metabolome studies. Due to the difficulty in performing full clinic diagnosis for

312

all participants, a small number of the subjects with diseases were possibly included in the study.

313

Although the developed PLS-DA model in combination with correlation analysis could greatly

314

minimize the influence of these factors on metabolome analysis, excluding these participants will

315

enhance the credibility of this study. (2) Individual metabolic differences may impact both urine

316

metabolome and phthalate metabolite concentrations. Although the biomarkers were screened with

317

strict procedure, we could not completely eliminate the uncertainty induced by individual

318

metabolic differences. (3) Phthalates have different modes of toxic action, thus they may have

319

different influence on urine metabolome. So far, more than 20 phthalates have been used in a large

320

variety of products. Major phthalate metabolites have been investigated in this study, but further

321

study is needed to elucidate the impact of unmeasured phthalates. (4) Humans are daily

322

simultaneously exposed to a variety of harmful chemicals, and some of them possibly have similar

323

influences on metabolic pathway. In this study, the identified phthalate biomarkers were

324

completely different from those obtained from arsenic study 25, and the combinational biomarker


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pattern provided accurate assessment for phthalate exposure. However, uncertainty might be

326

introduced into the results due to the incapability of characterizing disruption effect of single

327

exposure for all harmful chemicals. Nevertheless, our data still provided direct evidence that

328

phthalate exposure was associated with urinary metabolome disruption in male adult population,

329

which could be useful in population based risk assessments of phthalate exposure.

330 331

In summary, this study demonstrated the association between low-level environmental phthalate

332

exposure and global urine metabolome disruption in male population. Our results also suggested

333

that phthalate exposure associated with increased oxidative stress and fatty acid oxidation,

334

decreased prostaglandin metabolism, and disrupted amino acid metabolism. The multi-biomarker

335

ROC models showed great potential in assessing health risk associated with phthalate exposure in

336

the actual environment.

337 338

Supporting Information Available

339

Spearman correlations among creatinine-adjusted phthalate concentrations and demographic

340

factors (Table S1); distribution of smoking/alcohol drinking status, education level, annual

341

income, profession for tertiles of phthalates (Table S2); comparison of subject characteristics

342

between smokers and non-smokers, and between alcohol drinkers and non- alcohol drinkers

343

(Table S3); comparison of urinary phthalate levels between smokers and non-smokers, and

344

between alcohol drinkers and non- alcohol drinkers (Table S4); established PCA and PLS-DA

345

models by using phthalates or smoking and alcohol drinking history as the classifiers (Table S5); a

346

PCA scores plot of human urine samples and QCs obtained by HPLC/QTOF-MS (Figure S1);

347

score plots of MEP, smoking status, alcohol drinking status based PCA and PLS-DA models



348

(Figure S2); the 999-time permutation tests conducted for each PLS-DA model (Figure S3);

349

representative dot plots for fold changes of identified differential metabolites from ΣDEHP and

350

MBP models (Figure S4). This material is available free of charge via the Internet at

351

http://pubs.acs.org.

352 353

Acknowledgements

354

This work was financially supported by the National Natural Science Foundation of China

355

(21407143, 21407144 and 21307126), Youth Innovation Promotion Association of CAS

356

(2015246), Natural Science Foundation of Fujian Province (2013J01063) and the Ningbo Science

357

and Technology Fund (2014A610284).

358 359

Running title: Urine metabolome of phthalate exposure

360 361

Competing financial interests: The authors declare no competing interests.

362 363

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Figure Legends

492

Figure 1. Scoring plots of the developed PCA and PLS-DA models of MBP (A) and ΣDEHP (B).

493

▲ low exposure group; ◆ medium exposure group; □ high exposure group.

494

Figure 2. ROC curves, probability views and confusion matrix of the combined biomarker patterns

495

for MBP (A) and ΣDEHP (B). ROC curves are generated by Monte-Carlo cross validation using

496

balanced subsampling. The predicted class probabilities were calculated for each sample using the

497

developed ROC models.

498 499



Page 26 of 31

Table 1. Characteristics of the study population (n = 364) Characteristic

Mean ± SD

Median (5th, 95th)

Age (years) Height (cm) Weight (kg) BMI (kg/m2) Smoking status Never Past Current Alcohol drinking status Never Past Current

29.0 ± 4.7 173.0 ± 5.5 71.1 ± 10.9 23.7 ± 3.2

28.5 (22.6, 37.5) 173.0 (165.0, 181.8) 70.0 (55.0, 90.0) 23.7 (19.0, 29.3)

n (%)

194 (53.3%) 23 (6.3%) 147 (40.4%) 172 (47.3%) 23 (6.3%) 169 (46.4%)

Education level < College > College Missing data Annual income < 20,000 RMB > 20,000 RMB Missing data

86(23.7%) 117(32.1%) 161(44.2%)

Occupation White collar Blue collar Missing data

119(32.7%) 146(40.1%) 99(27.2%)

171(47.0%) 136(37.4%) 57(15.6%)


Page 27 of 31


Table 2. Urinary concentrations (µg/g creatinine) of phthalates (n = 364). Metabolite

Percent>LOD

Median (5th, 95th)

Low tertile

Medium tertile

High tertile

MEP MBP MBzP MEHP MEHHP MEOHP MMP

100 100 52.7 100 100 100 3.6

28.2 (3.2, 339.5) 47.1 (6.9, 448.0) 0.8 (0.1, 7.6) 8.0 (1.1, 38.5) 22.5 (4.1, 90.7) 10.7 (2.0, 46.8) 24.4 (0.3, 112.0) a

0.8-15.3 1.5-32.1 0-0.4 0.2-5.3 0.9-14.9 0.5-7.7 0

15.3-54.6 32.1-80.5 0.4-1.2 5.3-12.8 14.9-31.8 7.7-17.2 0-8.5

54.6-10322.3 80.5-1350.4 1.2-623.9 12.8-545.8 31.8-1195.4 17.2-570.9 8.5-112

42.0 (8.5, 166.8)

1.6-28.2

28.2-65.7

65.7-2312.1

ΣDEHP a a

ΣDEHP was the mass sum of individual metabolite concentrations (MEHP, MEHHP and MEOHP).



Page 28 of 31

Table 3. Differentially expressed urinary biomarkers associated with phthalate exposure for male population. ΣDEHP

MBP No.

Biomarkers FC (M/L)

a

b

FC (H/L)

AUC(95% CI)

c

FC (M/L)

a

FC (H/L)b

AUC(95% CI)c

1

Acetylneuraminic acid d

1.14

1.34**

0.66(0.59-0.73)**

1.05**

1.08

0.53(0.46,0.60)

2

Carnitine C8:1 d

1.09**

1.16**

0.69(0.62-0.75) **

1.07*

1.10**

0.63(0.56-0.70) **

3

Carnitine C18:0 e

1.59**

1.74**

0.76(0.70-0.82) **

1.51**

2.06**

0.79(0.73-0.84) **

4

Carnitine C16:2

d

**

**

**

**

**

**

5

Diacetylspermine d

0.66

0.51

0.81(0.76-0.87)

0.80**

0.75**

0.70(0.64-0.77) **

0.72

**

0.64

**

0.73(0.66-0.79)

**

0.74

**

0.59

**

0.72(0.66-0.79)

**

0.69

0.90

0.74(0.67-0.80)

Amino sugar metabolism

Carnitine metabolism, fatty acid beta oxidation, oxidative stress

0.84**

0.62(0.55-0.69) **

0.70

**

0.70(0.63-0.76)

**

Alanine metabolism, glycine and serine metabolism, urea cycle

0.71

**

0.65(0.58-0.71)

**

Bile acid biosynthesis, taurine and hypotaurine metabolism

Polyamine metabolism

Alanine

f

7

Taurine

d

8

Tryptophan d

0.67**

0.6**

0.77(0.71-0.83) **

0.75**

0.63**

0.73(0.66-0.79) **

9

Ornithine e

0.68**

0.54**

0.83(0.77-0.88) **

0.71**

0.61**

0.77(0.71-0.83) **

10

Cystine e

1.21**

1.3**

0.69(0.62-0.76) **

1.27**

1.37**

0.75(0.69-0.81) **

Glutathione metabolism, oxidative stress

11

Phenylglycine d

1.11

1.53*

0.58(0.50-0.65)*

1.06

1.19

0.52(0.44-0.59)

Amino acid metabolism

6

d

**

**

Phenylpyruvic acid

1.34

1.51

0.76**

0.70**

0.77(0.71-0.83) **

0.89**

0.78**

0.70(0.64-0.77) **

14

e

1.58

**

1.72

**

0.74(0.67-0.80)

**

**

1.96

**

0.79(0.73-0.85)

**

0.72

**

0.63

**

0.72(0.66-0.78)

**

0.66

0.54

**

0.73(0.67-0.80)

**

0.81**

0.71**

15

Hydroxyl-PGE2 Keto-PGE2 d

0.75**

0.62**

0.77(0.71-0.83) **

1.36

**

0.70(0.63-0.76)

**

Methylglutaconic acid d

16

0.75(0.69-0.81)

**

0.79

*

13

d

1.7

**

0.87

12

Glutamylphenylalanine

1.53

**

**

0.62

Pathway

0.69(0.62-0.76) **

a

the ratio of the means of medium exposure group and low exposure group. the ratio of the means of high exposure group and low exposure group. c area under curve (95% confidence interval, lower−upper bound) . d screened from MBP-based PLS-DA model. e screened from ΣDEHP-based PLS-DA model. f screened from both MBP and ΣDEHP-based PLS-DA models. * indicates p < 0.05, ** indicates p

Low-Level Environmental Phthalate Exposure ... - ACS Publications

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