Low-level environmental phthalate exposure associates with urine metabolome. alteration in a Chinese male cohort

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1 Supporting Information 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, Heqing Shen* Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, China *To whom correspondence may be addressed: Jie Zhang, Institute of Urban Environment, Chinese Academy of Sciences, 1799 Jimei Road, Xiamen, , China; Telephone/Fax: (86) ; jzhang@iue.ac.cn Heqing Shen, Institute of Urban Environment, Chinese Academy of Sciences, 1799 Jimei Road, Xiamen, , China; Telephone/Fax: (86) ; hqshen@iue.ac.cn 11 pages, 5 tables, 4 figures Details of sample preparation and metabolome analysis Spot morning urine samples were collected, transported in ice to the lab in 24 hours, and then stored in -80 C prior to analysis. Same samples were used for mono-phthalate determination and metabolic profile acquisition, thus the sampling introduced uncertainty was waived. Urine samples were thawed and diluted with 1:1 deionized water. After centrifugation, the samples were passed through 0.22 µm filtered syringes. The filtered urine was used for metabolic profile acquisition. Quality control (QC) sample was prepared by mixing aliquots of each sample. Urine metabolic profile acquisition was performed by using an Ultimate-3000 HPLC (Dionex, USA) coupled to a MicrOTOF-Q II mass spectrometer (Bruker Daltonics, USA). Chromatographic separation was performed on a Kinetex core-shell C18 column ( mm, 2.6 µm) (Phenomenex, USA) at a flow rate of 200 µl/min. The mobile phase was a mixture of (A) H 2 O with 0.1% formic acid and (B) acetonitrile with 0.1% formic acid. The gradient program: 0 min, 5% B; 1.5 min, 20% B; 10 min, 60% B; 10.1 min, 95% B; 12 min 95% B; 12.1 min, 5% B; 15 min, 5% B. The column was maintained at 30 o C. Sample injection volume was 5 µl. The mass spectrometer was operated under positive electrospray ionization. The scan range was 50 to 1000 m/z. Data was collected in centroid mode. Capillary voltage and end-plate offset potential were set at 4500 V and -500 V, respectively. Nebulizer gas pressure was set at 0.6 bar, and dry gas flow rate at 6 L/min at 200 o C. All samples were analyzed in a random fashion to avoid artifactual uncertainties related to injection order and to eliminate the effect of gradual changes of instrument sensitivity in whole batch runs. The MS/MS mode was also used to identify potential biomarkers. Argon was used as a collision gas, and the collision energy was adjusted from 10 ev to 40 ev for each analyte. One QC and one blank sample were periodically injected to monitor the stability and reproducibility of analytical instruments and sample carryover during batch analysis. All raw chromatograms were processed with Profile Analysis 2.0 (Bruker, USA). The main parameters were: retention time range = min, mass window = 0.5 Dalton, and retention time window = 1 min. To reduce variations from sample injection and enrichment factors, the intensity of extracted metabolites was normalized to the total areas for all urine samples. After peak deconvolution, alignment, integration and normalization, a table containing retention time, exact mass pairs and normalized peak areas were extracted S1

2 from raw chromatograms. The intensity of each metabolite is normalized to the MSTUS prior to multivariate analysis, thus the effects of urine dilution on metabolome analysis is eliminated (J. Chromatogr. B 2009, 877:547).The missing values were removed according to 80% rule. The remaining missing values were assumed to be below the limit of detection for a particular metabolite, and were imputed with the half of observed minimum for that particular metabolite. All data in this table were Pareto-scaled and then introduced to SIMCA-P v11.5 software (Umetrics, Sweden) for multivariate statistical analysis. A tight QC clustering was observed in the scores plot following principal component analysis (Figure S1). These evidences indicated good quality of the dataset. Principal component analysis (PCA) was performed to cluster the samples, and identified the outliers. The outliners were the samples far away from the cluster center in PCA score plot. The samples were possibly contaminated during the storage and preparation processes. After careful inspection of raw chromatograms, the outliers (n=2) were removed from the dataset. ΣDEHP, MBP and MEP concentration was classified into tertiles. Accordingly, the dataset were separately categorized into three groups (1st tertile, 2nd tertile and 3rd tertile), and defined as low, medium and high exposure groups, respectively. MBzP was detected in 52.7% of the subjects, thus the dataset was divided into two groups (detected vs. undetected). It should be noted the definition of low, medium and high exposure were only applicable for investigated population, whose phthalate levels were much lower than occupationally exposed population. Phthalate exposure-oriented PLS-DA (partial least squares discriminant analysis) models were established, in which internal levels of phthalates were used as classifiers. A 999-time permutation test was carried out to evaluate the developed PLS-DA models. Biomarkers were screened from the developed phthalate-based PLS-DA models. Specifically, the features were selected as the potential biomarkers of each phthalate according to the following criteria: (1) VIP scores of the features > 2; (2) jack-knifing confidence interval of the features > 0; (3) p-value of the features between low and high exposure groups < 0.05; and (4) the features significantly (p < 0.05) correlated with phthalate concentrations after adjusted by age, BMI, smoke and drinking status; (5) the features significantly (p < 0.05) correlated with semen volume, concentration, motility, progression, and motion parameters were excluded. Biomarker identification was conducted as described previously. The detailed method for biomarker identification was described previously. Briefly, a pooled urine sample was subjected to MS and MS/MS analysis to acquire accurate mass, isotopic pattern and fragment ions for potential biomarkers. Possible formulas of potential biomarkers were calculated using Data Analysis 4.0 software (Bruker Daltonics, USA). Element number restriction, LEWIS check, and the isotopic pattern, hydrogen/carbon element ratio check were used to reduce the number of candidate formulas. Structural information of potential biomarkers was searched using the HMDB database ( the METLIN database ( and Massbank database ( (20 mda was set as the accepted mass difference). The candidates at the top of the list were further validated by searching against the databases or by comparing characteristics of their product ions with Mass Frontier software (Thermo, US). Biomarker identities were finally confirmed by comparison with commercial standards. When standards were unavailable, identities were tentatively derived from online databases and literature. S2

3 Figure S1. A PCA scores plot of human urine samples ( ) and QCs ( ) obtained by HPLC/QTOF-MS. S3

4 Figure S2. Score plots of MEP (A), smoking status (B), alcohol drinking status (C) based PCA and PLS-DA models. never and past alcohol drinker group; current alcohol drinker group. low exposure group; medium exposure group; high exposure group; never smoker/alcohol drinker and past smoker/alcohol drinker group; current smoker/drinker group. No valid PLS-DA model was generated for alcohol drinking status. S4

5 Figure S3.The 999-time permutation tests conducted for each PLS-DA model. A = MBP, B = ΣDEHP, C = MEP, D = smoking status. The vertical axis gives the R2Y and Q2Y-values of each model. The horizontal axis represents the correlation coefficient between the real Y and the permuted Y. The PLS model is strongly significant. The R2Y and Q2Y of the real model are always larger than the corresponding values of the models fitted to the permuted responses. S5

6 Figure S4. Representative dot plots for fold changes of identified differential metabolites from ΣDEHP and MBP models. The colored dots denote low exposure subjects (green), medium exposure subjects (blue) and high exposure subjects (red). The Y- and X-axes denote the fold change relative to low exposure group and the group name, respectively. The stars indicates * P<0.05, ** P<0.01, and *** P< S6

7 * p<0.05; ** p<0.01. Table S1. Spearman correlations among creatinine-adjusted phthalate concentrations and demographic factors MEP MEHHP MEOHP MBP MBzP MEHP DEHP Age BMI MEP ** 0.24 ** 0.37 ** ** 0.27 ** * MEHHP 0.26 ** ** 0.54 ** 0.23 ** 0.83 ** 0.99 ** ** MEOHP 0.24 ** 0.98 ** ** 0.26 ** 0.83 ** 0.98 ** * MBP 0.37 ** 0.54 ** 0.55 ** ** 0.48 ** 0.55 ** ** MBzP ** 0.26 ** 0.19 ** ** 0.25 ** MEHP 0.25 ** 0.83 ** 0.83 ** 0.48 ** 0.25 ** ** ΣDEHP 0.27 ** 0.99 ** 0.98 ** 0.55 ** 0.25 ** 0.90 ** * Age BMI 0.11 * 0.14 ** 0.11 * 0.21 ** * S7

8 Table S2. Distribution of smoking/alcohol drinking status, education level, annual income, profession for tertiles of phthalates. MBP ΣDEHP 1st tertile 2nd tertile 3rd tertile 1st tertile 2nd tertile 3rd tertile Smoking status Never Past Current Alcohol drinking status Never Past Current Education level < College > College Missing data Annual income < 20,000 RMB > 20,000 RMB Missing data Profession White collar Blue collar Missing data S8

9 Table S3. Comparison of subject characteristics between smokers and non-smokers, and between alcohol drinkers and non- alcohol drinkers. a Mann-Whitney U test. Characteristic Smoker (n=147) Non-smoker (n=217) Mean ± SD Median (5th, 95th) Mean ± SD Median (5th, 95th) p a Age (years) 29.2 ± (23.0, 37.7) 28.9 ± (22.3, 37.6) BMI (kg/m2) 23.5 ± (19.0, 29.3) 23.8 ± (18.7, 29.4) Alcohol drinker (n=169) Non- alcohol drinker (n=195) Mean ± SD Median (5th, 95th) Mean ± SD Median (5th, 95th) p a Age (years) 29.3 ± (23.0, 38.2) 28.8 ± (22.1, 37.4) BMI (kg/m2) 23.8 ± (19.0, 29.4) 23.6 ± (18.9, 29.0) S9

10 Table S4. Comparison of urinary phthalate levels between smokers and non-smokers, and between alcohol drinkers and non- alcohol drinkers. Metabolite Smoker (n=147) Non-smoker (n=217) Percent > LOD Mean ± SD Median (5th, 95th) Percent > LOD Mean ± SD Median (5th, 95th) p a MEP ± (3.4, ) ± (3.14, 241.9) MBP ± (7.6, 446.6) ± (5.7, 493.6) MBzP ± (0.1, 34.2) ± (0.2, 4.6) MEHP ± (1.1, 55.9) ± (1.1, 31.4) MEHHP ± (4.0, 136.9) ± (4.2, 86.1) MEOHP ± (2.0, 60.9) ± (1.8, 43.3) MMP ± (0.3, 75.6) ± (6.2, 112.0) ΣDEHP 72.0 ± (8.3, 245.6) 68.3 ± (8.5, 155.3) Metabolite Alcohol drinker (n=169) Non- alcohol drinker (n=195) Percent > LOD Mean ± SD Median (5th, 95th) Percent > LOD Mean ± SD Median (5th, 95th) p a MEP ± (4.9, 484.0) ± (3.0, 294.8) MBP ± (6.6, 452.5) ± (6.8, 379.3) MBzP ± (0.1, 12.3) ± (0.2, 5.5) MEHP ± (1.0, 40.0) ± (1.6, 37.9) MEHHP ± (3.5, 117.9) ± (4.6, 88.8) MEOHP ± (1.7, 55.6) ± (2.1, 45.2) MMP ± (0.3, 112.0) c ± (13.0, 33.1) ΣDEHP 62.9 ± (6.6, 198.6) 75.9± (8.9, 162.3) a Mann-Whitney U test. S10

11 Table S5. Established PCA and PLS-DA models by using phthalates or smoking and alcohol drinking history as the classifiers Classifier Numbers of principal components R2X R2Y Q2(cum) PCA models a MEP MBP ΣDEHP Smoking history b Alcohol drinking history c PLS-DA models d MEP MBP ΣDEHP Smoking history b Alcohol drinking history c NA e a the models were established using low exposure group vs. high exposure group. b smoking history was defined as either 1 = never smoker and past smoker or 2 = current smoker. c alcohol drinking history was defined as either 1 = never drunk and past alcohol drinker or 2 = current alcohol drinker. d the models were established using all the samples. e not available. S11

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