Phthalate exposure increased the risk of early renal impairment in Taiwanese without type 2 diabetes mellitus

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Phthalate exposure increased the risk of early renal impairment in Taiwanese without type 2 diabetes mellitus Jung-Wei Changa, Kai-Wei Liaob,c, Chien-Yuan Huangd, Han-Bin Huange, Wan-Ting Changb, Jouni J.K. Jaakkolaf,g, Chih-Cheng Hsuh, Pau-Chun Cheni,j,k,l, Po-Chin Huangb,m,n,o,∗ a

Institute of Environmental and Occupational Health Sciences, School of Medicine, National Yang-Ming University, Taipei, Taiwan National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan c School of Food Safety, College of Nutrition, Taipei Medical University, Taipei, Taiwan d Department of Occupational Medicine, Chi-Mei Medical Center, Tainan, Taiwan e School of Public Health, National Defense Medical Center, Taipei, Taiwan f Center for Environmental and Respiratory Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland g Medical Research Center, University of Oulu and Oulu University Hospital, Oulu, Finland h Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan i Institute of Occupational Medicine and Industrial Hygiene, National Taiwan University College of Public Health, Taipei, Taiwan j Department of Public Health, National Taiwan University College of Public Health, Taipei, Taiwan k Department of Environmental and Occupational Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan l Office of Occupational Safety and Health, National Taiwan University College of Medicine and Hospital, Taipei, Taiwan m Research Center for Environmental Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan n Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan o Department of Safety, Health and Environmental Engineering, National United University, Miaoli, Taiwan b

ARTICLE INFO

ABSTRACT

Keywords: Phthalate metabolites Cumulative risk assessment Microalbumin Early renal impairment

Studies have suggested that phthalates may be a risk factor for microalbuminuria, whereas little is known regarding their nephrotoxic effects on adults. We enrolled 311 participants (≥18 y, N = 241; < 18 y, N = 70) who provided questionnaire information as well as blood and urine samples from a nationally cross-sectional study. Urinary phthalate metabolites were analyzed through liquid chromatography–tandem mass spectrometry. From the renal function index, we measured the serum level of blood urea nitrogen (BUN), and the urinary levels of microalbumin, albumin, protein and creatinine. We used multiple logistic regressions and a cumulative risk assessment of renal effect to evaluate the relationship between phthalate exposure and renal function in our participants. We aimed to assess the relationship between phthalate exposure and renal function including serum level of BUN, and urinary levels of microalbumin, albumin, protein, and creatinine in 311 participants (≥18 y, N = 241; < 18 y, N = 70) from a population-based study. The multiple logistic regression showed that the adjusted odds ratio of the highest tertile of estimated di-2-ethylhexyl phthalate (DEHP) daily intake in participants ≥18 y for early renal impairment (microalbumin > 1.9 mg/dL) was 9.40 times higher (95% confidence interval = 1.67–52.84) than the lowest tertile. The cumulative hazard index of phthalate-induced nephrotoxicity (HInephro) was significantly positively associated with microalbumin (β: 0.98, P < 0.001), BUN (β: 0.19, P = 0.002), and urine protein (β: 0.75, P = 0.001) in participants ≥18 y without type 2 diabetes mellitus after adjusting for confounding factors, but not in those < 18 y. Our findings suggest that daily exposure to DEHP and its metabolites were significantly positively associated with an increased risk of higher microalbumin in Taiwanese ≥18 y. Comprehensive or mechanistic studies are required to elucidate these associations.

1. Introduction Over the past decade, Taiwan had the highest end-stage renal

disease (ESRD) prevalence in the world according to the US Renal Data System annual report (US Renal Data System, 2017). According to the Ministry of Health and Welfare, Executive Yuan, kidney diseases, such

∗ Corresponding author. National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan. 35, Keyan Road, Zhunan Town, Miaoli County 350, Taiwan, ROC. E-mail address: [email protected] (P.-C. Huang).

https://doi.org/10.1016/j.ijheh.2019.10.009 Received 30 April 2019; Received in revised form 21 October 2019; Accepted 21 October 2019 1438-4639/ © 2019 Elsevier GmbH. All rights reserved.

Please cite this article as: Jung-Wei Chang, et al., International Journal of Hygiene and Environmental Health, https://doi.org/10.1016/j.ijheh.2019.10.009

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Abbreviations

(DEP) (DnBP) (DiBP) (BBzP) (DiNP) (LOD) (BUN) (pH) (ACR) (eGFR) (CCr) (DI) (CE) (HQ) (ND) (KDIGO)

(ESRD) End-stage renal disease (Type 2 DM) Type 2 diabetes mellitus (DEHP) Di-2-ethylhexyl phthalate (MEHP) Mono-ethylhexyl phthalate (MMP) Mono-methyl phthalate (MEP) Mono-ethyl phthalate (MnBP) Mono-n-butyl phthalate (MiBP) Mono-iso-butyl phthalate (MBzP) Mono-benzyl phthalate (MiNP) Mono-iso-nonyl phthalate (MEOHP) Mono-(2-ethyl-5-oxo-hexyl) phthalate (MEHHP) Mono-(2-ethyl-5-hydroxyhexyl) phthalate (MECPP) Mono-(2-ethyl-5-carboxypentyl) phthalate (MCMHP)Mono-(2-carboxymethylhexyl) phthalate (DMP) Dimethyl phthalate as nephrosis (kidney inflammation) and renal syndrome, were the ninth leading cause of death among Taiwanese individuals in 2016. Possible proposed explanations included aging, type 2 diabetes mellitus (type 2 DM), and other chronic diseases (such as hypertension). The contribution of environmental nephrotoxicants still remains largely unknown. Reports of impaired kidney function resulting from occupational exposure to some substances, especially heavy metals, suggested that moderate environmental exposures might lead to chronic kidney disease (CKD) (Udenze et al., 2012). However, this does not explain why Taiwan has the highest prevalence of ESRD among other Asian countries with similar prevalence of type 2 DM or other chronic diseases. Phthalates are endocrine disruptors, and studies on human biomonitoring have reported ubiquitous phthalate exposure in the general population in the United States., Canada, Europe, South Korea, and Taiwan (Frederiksen et al., 2010, 2013; Goen et al., 2011; Huang et al., 2015; Kim et al., 2016; Koch et al., 2017; Saravanabhavan et al., 2013; Schwedler et al., 2017; Zota et al., 2014). Several animal studies have revealed that exposure to high doses of di-2-ethylhexyl phthalate (DEHP) may cause renal tubular damage, chronic progressive nephropathy, renal tubular degeneration, and decreased kidney weight (David et al., 2000; Ward et al., 1986; Wood et al., 2014; Wu et al., 2018). Moreover, mono-ethylhexyl phthalate (MEHP) was discovered to have an obvious nephrotoxic effect on cultured kidney epithelial cells in vitro, as experimental studies (Rothenbacher et al., 1998; Wu et al., 2018) reported chronic progressive nephropathy and renal tubule pigmentation in rats with chronic DEHP exposure (David et al., 2000). Several epidemiological studies have suggested that DEHP exposure in children may increase the risk of renal dysfunction (Trasande et al., 2014; Tsai et al., 2016), especially in those exposed to phthalate-tainted products. Only one recent study attempted to explore the relationship of phthalate exposure and potential renal function in a Chinese adult population (Chen et al., 2019). However, little is known regarding the relationship between phthalate exposure and subclinical nephrotoxicity in other populations. We aimed to evaluate the association between phthalate exposure and renal function factors by using a dose-based and cumulative risk assessment approach for the nephrotoxic effects in the general Taiwanese population.

Di-ethyl phthalate Di-n-butyl phthalate Di-iso-butyl phthalate Benzyl butyl phthalate Di-iso-nonyl phthalate Limit of detection Blood urea nitrogen Potential of hydorgen Albumin-to-creatinine ratio Estimated glomerular filtration rate Creatinine clearance rate Daily intake Creatinine excretion Hazard quotient Non-detectable Kidney Disease: Improving Global Outcomes

of each city in Taiwan, we selected 17 townships of 11 cities or counties and a remote island region (Penghu County) during May and December 2013. All participants were Taiwanese and aged 7 y or older. We excluded pregnant and breast-feeding women, individuals with serious diseases (e.g., cancer or renal dialysis), older individuals with dementia who had lost the ability to communicate, and those who resided in nursing homes, military units, or jails. Five hundred individuals were interviewed, out of which 394 signed a written inform consent and participated in this study (response rate: 78.8%). We excluded 74 participants who provided no blood or urine samples or had a urine creatinine concentration < 30 mg/dL or ≥300 mg/dL (WHO, 1996). We double-checked the questionnaire answers pertaining to medical history of excretory system failure to exclude participants who had responded “yes” (N = 9). Finally, the study population included 241 adults (≥18 y) and 70 children/adolescents (< 18 y) (Fig. S1). Information regarding personal characteristics (age, sex, income, and education), and lifestyle (smoking, drinking, and betel nut chewing) was obtained through the questionnaire. Although betel nut chewing is not common habit in Taiwan (~12%) or western countries, some studies have found that the prevalence or risk of chronic kidney disease with betel nut chewing was significantly higher than those without this habit (Chou et al., 2009; Wang et al., 2018). The Research Ethics Committee of the National Health Research Institutes (No. EC1020206) in Taiwan approved this study, and we obtained informed consent from each participant prior to their participation as well as additional signatures from the parents of children/ adolescents. 2.2. Phthalate metabolite analysis Details of the applied analytical method were described in our previous studies (Huang et al., 2015, 2017; Liao et al., 2018). In brief, a first morning urine sample (20 mL) was collected in a polypropylene container in the early morning from each participant. After collection, samples were transferred to a prewashed glass bottle with acetonitrile and stored at −80 °C until analysis. The urine sample (100 μL) with ammonium acetate (20 μL, > 98%; Sigma-Aldrich Lab., Inc., USA), βglucuronidase (10 μL, E. coli K12; Roche Biomedical, Germany), and a mixture of ten isotopic (13C4) phthalate metabolite standards (100 μL, Cambridge Isotope Lab., Inc., USA) was incubated (37 °C, 90 min) for analysis. Through online liquid chromatography-tandem mass spectrometry (API 3000; Applied Biosystems, Foster City, CA, USA), we analyzed 11 urinary phthalate metabolites: mono-methyl phthalate (MMP), monoethyl phthalate (MEP), mono-n-butyl phthalate (MnBP), mono-iso-butyl phthalate (MiBP), mono-benzyl phthalate (MBzP), mono-iso-nonyl

2. Methods 2.1. Study population Participants in this cross-sectional study were enrolled from the Taiwan Environmental Survey for Toxicants (TEST) 2013 (Huang et al., 2017). The recruitment process was described in the previous study (Huang et al., 2015). Briefly, considering the density and urbanization 2

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phthalate (MiNP), MEHP, mono-(2-ethyl-5-oxo-hexyl) phthalate (MEOHP), mono-(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), mono(2-ethyl-5-carboxypentyl) phthalate (MECPP), and mono-(2-carboxymethylhexyl) phthalate (MCMHP) which are biomarkers for exposure to the seven commonly used phthalates [dimethyl phthalate (DMP), diethyl phthalate (DEP), di-n-butyl phthalate (DnBP), di-iso-butyl phthalate (DiBP), benzyl butyl phthalate (BBzP), di-iso-nonyl phthalate (DiNP), and DEHP]. Urinary creatinine levels were measured by spectrophotometric methods, and phthalate metabolite levels were divided by urinary creatinine levels and expressed as “μg/g creatinine” to account for urinary volume correction. One blank, repeated quality control (QC) sample was included in each batch of analyzed samples. Concentrations of blank samples was to be less than 2 fold the method detection limit. The QC sample was spiked in pooled urine samples with a mixture of phthalate metabolite standards (20–50 ng/mL) in each sample. The relative percent difference for the repeated sample, as well as recovery of the QC sample, was to be less than ± 30%. The limits of detection (LOD) for MEHP, MEOHP, MEHHP, MECPP, MCMHP, MnBP, MiBP, MEP, MiNP, MBzP, and MMP were 0.7, 0.3, 0.3, 0.3, 0.1, 1.0, 1.0, 0.3, 0.1, 0.3, and 0.3 ng/mL, respectively.

Daily intake(µ g/kg/day) =

UEsum × CEsmoothed MWd × FUE × 1000 MWm

1

(1) UEsum is molar urinary excretion sum of the measured urinary phthalate metabolites. (2) CEsmoothed is smoothed creatinine excretion (CE) rates based on body weight (BW), height (ht), age and sex (Mage et al., 2004, 2008). The formulae of CEsmoothed estimates for adults and children/adolescents in this study are listed below.

Adults ( 18 y) CE = 1.93 × (140 CE = 1.64 × (140

age) × BW1.5× ht 0.5× 10 6…(male) age) × BW1.5× ht0.5× 10 6…(female)

Children/adolescents (7 18 y) CE=ht×{6.265 + 0.0564×(ht 168)}×10 3…ht < 168cm…(male) CE=ht×{6.265 + 0.2550×(ht 168)}×10 3…ht 168cm…(male) CE=2.045× ht×exp{0.01552×(ht 90)}×10 3…(female)

(2)

(3)

(3) Age (y) and ht (cm) were obtained from the questionnaire. (4) FUE, the molar fraction, represents the molar ratio between the excreted amount of specific metabolites of each phthalate corresponding to the dietary intake of the parent phthalate (0.69 for MMP, MEP; 0.84 for MBP; 0.73 for MBzP; 0.059 for MEHP; 0.150 for MEOHP; 0.233 for MEHHP; 0.185 for MECPP; and 0.042 for MCMHP) (Koch et al., 2007; Wittassek et al., 2011).

2.3. Measurement of renal function and other parameters in serum and urine The concentrations of blood urea nitrogen (BUN), blood creatinine, and blood uric acid were measured with serum. The concentrations of urinary creatinine, microalbumin, potential of hydorgen (pH), protein, albumin, and uric acid were measured with urine. Morning blood samples from each participant were centrifuged for 20 min at 4 °C immediately after the collection and then stored at −80 °C until analysis. All analyses were blindly performed in random order by the technician from the laboratory certified by the Taiwan Accreditation Foundation (No. 1673) and recognized by the International Laboratory Accreditation Cooperation Mutual Recognition Arrangement. Serum levels of BUN, blood uric acid, and blood creatinine, and urine levels of microalbumin, urinary creatinine, and uric acid were quantified using Beckman Coulter SYNCHRON DxC 800 System (Beckman Coulter Inc., Brea, CA, USA). Assay sensitivities for uric acid, microalbumin, and urinary creatinine in urine were 0.5 mg/dL, 0.2 mg/dL, and 10 mg/dL, respectively. Assay sensitivities for BUN, blood uric acid and blood creatinine in serum were 5 mg/dL, 5.0 mg/dL and 0.1 mg/dL, respectively. Urine pH and protein levels were determined using ARKRAY Aution Sticks 10 EA (ARKRAY Factory, Inc, Shiga, Japan). Albumin-tocreatinine ratio (ACR) was calculated by dividing microalbumin value by urinary creatinine concentration. Estimated glomerular filtration rate (eGFR) was calculated using Modification of Diet in Renal Disease (MDRD) equation: 186 × (serum creatinine)−1.154 × (age)−0.203 × (0.742, if female) × (1.212, if black). Estimated creatinine clearance rate (CCr) was calculated using Cockcroft–Gault formula: [(140 − age) × weight]/(serum creatinine × 72) × (0.85 if female) (Levey et al., 2007). Participants were considered to have diabetes if (1) their fasting plasma glucose was ≥126 mg/dL, or (2) they were currently using insulin and oral hypoglycemic agents. We defined subjects as early CKD if 60 ≤ eGFR < 90 mL/min/1.73 m2 and urine protein > 14 mg/L.

2.5. Cumulative risk assessment: hazard quotient and hazard index To assess the risk of each participant from each phthalate, we used the following formula for hazard quotient (HQ):

HQ =

DI Reference limit value

4

Hazard index (HI) < 1 indicated low probability of adverse effects from exposure to several chemicals (Benson, 2009). Estimated HI values of the cumulative hazard index of nephrotoxicity were calculated from RfD (HQ of DiNP and DEHP). The most reliable NOAELs, established in oral studies in animals, have been used and uncertainty factor (UF) of 100 was derived. The NOAEL of kidney toxicity for DEHP and DiNP were 28.9 and 88 mg/kg/day, respectively (European Chemicals Bureau, 2003, 2008).

Reference dose (RfD) =

NOAEL UF

,where UF = 100

where UF = 100 HInephro = HQDiNP

5 RfD

+ HQDEHP

RfD

6

2.6. Statistical methods We separated our participants into two groups according to age (≥18 and < 18 y). A subgroup analysis was conducted to exclude 45 adults with a self-reported history of other endocrine diseases. Since no differences were observed between adults with and without a self-reported history of other endocrine diseases (data not shown), we eventually included these participants in the analysis Based on the CKD Guideline for clinical cutoff points of microalbumin (1.9 mg/dL), ACR (30 mg/g creatinine), and eGFR (60 mL/min/1.73 m2), we categorized the participants into normal and abnormal groups with early renal impairment (Levin and Stevens, 2014). Descriptive statistics on participant demographics were presented. The Mann–Whitney U test and Kruskal–Wallis test were used to compare levels of phthalate metabolites and renal function factors between

2.4. Daily intake estimation To evaluate adverse renal effects caused by chronic phthalate exposure, we calculated corresponding daily intake (DI) of each phthalate using method described by Koch et al. (2007). Calculations of phthalates DIs were based on urinary phthalate metabolites as described in our previous study (Chang et al., 2017). The DI estimates were based on the following equations and parameters: 3

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adults and children/adolescents groups. Normality of the distribution for phthalate metabolites concentrations and renal function factors was assessed with Kolmogorov-Smirnov test. Because of the skewed distributions, log transformations (base 10) were performed on those variables to meet the assumption of multiple linear regression. If phthalate metabolite concentrations were lower than LOD, we imputed values between zero and the LOD value for each compound (Royston, 2005; Gascon et al., 2015). The detectable rate was calculated as the number of urine samples with the level of each phthalate metabolite above the detection limit divided by all of the analyzed urine samples. We used ΣDEHPm as cumulative index of DEHP metabolites, which is the sum of molar levels of MEHP, MEHHP, MEOHP, MECPP, and MCMHP; and ΣDBPm as another index, which is the sum of molar levels of MiBP and MnBP. Both indexes are expressed in nmole/mL. We then constructed full log-log regression models with renal function factors as the dependent variables and individual log-transformed urinary phthalate metabolite concentrations as a predictor, with

age (continuous variable), sex (dichotomous), BMI (continuous), cigarette smoking (dichotomous), drinking (dichotomous), and urinary creatinine levels (continuous) as covariates. We also utilized tertiles of urinary phthalate metabolite levels to assess the nonlinear relationship between phthalate exposure and renal function factors by performing logistic regression analyses. Additionally, a log-transformed parameters were incorporated into the generalized additive model (GAM)-penalized regression splines to determine the nonlinear association with risk of higher microalbumin (Royston and Ambler, 1998). The number of knots was then chosen to optimize a fit criterion in the generalized cross-validation (GCV) statistics in the model (Cai and Betensky, 2003; Cao et al., 2010). The smoothing parameter selection was performed by iterative solution to fit the minimization (Wood, 2017). Finally, we also used log-log regression models with renal function factors as the dependent variables and individual log-transformed HInephro as a predictor. P-values of < 0.05 were considered statistically significant. All statistical analyses were performed in SPSS version 22.0.

Table 1 Characteristics of the study population (N = 311). Variables

Adults (≧18 years, N = 241) N (%)

Gender Male Female Age (years, Mean ± SD) 18–40/7–12 40–65/12–18 65 and older BMI (kg/m2), Mean ± SD) Region Northern Taiwan Central Taiwan Southern Taiwan Eastern Taiwan Remote islands Marital status Single Married Divorce/widowed Education ≦Elementary school Junior high school Senior high school ≧College/Graduates Annual family income a < 15,625 15,625–31,250 > 31,250 Cigarette smoking b Yes No Alcohol consumption c Yes No Tea drinking d Yes No Coffee drinking d Yes No Betel nut chewing e Yes No Pesticide use at home Yes No a b c d e

110 (45.6) 131 (54.4) 241 58 (24.1) 117 (48.5) 66 (27.4) 241 74 35 68 39 25

Children/Adolescents (< 18 years, N = 70) Mean ± SD

52.9 ± 16.8

24.8 ± 4.4

(30.7) (14.5) (28.2) (16.2) (10.4)

N (%) 41 (58.6) 29 (41.4) 70 34 (48.6) 36 (51.4) 0 (0.0) 70 24 (34.3) 12 (17.1) 20 (28.6) 6 (8.6) 8 (11.4)

41 (17.0) 180 (74.7) 20 (8.3)

70 (100) 0 (0) 0 (0)

64 34 58 85

35 (50.0) 20 (28.6) 15 (21.4) 0 (0.0)

(26.6) (14.1) (24.1) (35.3)

134 (58.3) 65 (28.3) 31 (13.5)

26 (40.6) 25 (39.1) 13 (20.3)

59 (24.6) 181 (75.4)

0 (0.0) 70 (100)

32 (13.4) 206 (86.6)

0 (0.0) 69 (100)

138 (57.5) 102 (42.5)

33 (47.1) 37 (52.9)

102 (42.3) 139 (57.7)

6 (8.6) 64 (91.4)

17 (7.1) 224 (92.9)

0 (0.0) 70 (100)

57 (23.7) 184 (76.3)

17 (24.3) 53 (75.7)

The currency exchange rate of converting USD to new Taiwan dollar is 1:32. Subjects who self-reported consuming at least one cigarette per day. Subject who self-reported consuming at least one bottle of alcohol drink per week. Subjects who self-reported consuming at least one cup of tea or coffee per week. Subject who self-reported chewing at least one betel nut per week. 4

Mean ± SD

12.6 ± 3.2

20.7 ± 5.5

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3. Results

urinary MMP (β: 0.13; 95% confidence interval [CI] = 0.02 to 0.25; β: 0.03; 95% CI = < 0.01 to 0.05; β: 0.05; 95% CI = < 0.01 to 0.10), which means microalbumin increased by 0.14 × 10−2 mg/dL, BUN increased by 0.03 × 10−2 mg/dL, and ACR increased by 0.05 × 10−2 mg/mmol with each increase of 1% in urinary MMP level, respectively (Table 3). Moreover, the levels of urinary MEHP, MEOHP, MCMHP, and ΣDEHPm were all significant positively associated with BUN (MEHP: β: 0.02, P = 0.043; MEOHP: β: 0.05, P = 0.004; MCMHP: β: 0.02, P = 0.040; ΣDEHPm: β: 0.08, P = 0.005). On the basis of the above mentioned results, MMP, MEHP, MEOHP, MCMHP and ΣDEHPm could possibly have a moderate adverse effect on renal function (Table 3), while no significant associations of phthalate exposure with eGFR, and urine protein were observed (data not presented). The adjusted odds ratio of the highest tertile (T3) of estimated DEHP DI in adults for abnormal microalbumin was 9.40 times higher (95% confidence interval [CI] = 1.67–52.84) than that of the lowest tertile group, followed by the second tertile (T2) (6.68 [1.26–35.52]), indicating a linear relationship between increasing DEHP DI and higher risk for microalbuminuria. We also used GAMs to examine the shape of the relationships between phthalate metabolite concentrations and microalbumin and found no significant departures from linearity (see Fig. S2). No associations were found with the DIs of other phthalates (Table 4, Fig. 1A). Also, we did not observe a similar significant increasing trend for children/adolescents. Furthermore, no increasing trend was noted in between DEHP DI and other renal function factors, such as abnormality of ACR and BUN. Using a multiple regression model, we discovered that cumulative HInephro was significantly positively associated with microalbumin, BUN and protein in adults, including and excluding type 2 DM (models 1 + 2), but not in children/ adolescents (model 3) (Table 5, Fig. 1B). However, a similar trend

3.1. Participant characteristics The present study is based on the data from 311 participants [≥18 y, N = 241 (Adults); < 18 y, N = 70 (Children/adolescents)]. Table 1 lists the demographic characteristics of the participants in this study. The sex distribution was nearly uniform across the study population. In total, 110 men and 131 women comprised adult group, and 41 boys and 29 girls comprised children/adolescents group. The average age and BMI in adults were 52.9 y and 24.8 kg/m2, respectively, and the average age and BMI in children/adolescents were 12.6 y and 20.7 kg/m2, respectively. Abnormality frequencies of ACR (albumin/ creatinine ratio > 30 mg/g creatinine), type 2 DM and urine protein (> 14 mg/dL) were significantly higher in men than in women (P < 0.05) (Table S1). Median levels of urinary ΣDEHPm and ΣDBPm in adults were 0.19 [interquartile range (IQR) = 0.12–0.31] and 0.16 (IQR = 0.09–0.30) nmol/mL, respectively, which were significantly lower than those in children/adolescents [ΣDEHPm: 0.30 (IQR = 0.16–0.46) nmol/mL; ΣDBPm: 0.16 (IQR = 0.12–0.31) nmol/mL] (P < 0.001 and P = 0.001, respectively) (Table 2). Due to the lower detection rate observed in MBzP and MiNP, we did not continue further analysis for these two metabolites. 3.2. Association between urinary phthalate metabolites and renal function factors Multivariate regression models adjusted for the same confounding factors confirmed the association of microalbumin, BUN and ACR with

Table 2 Median and geometric mean levels (ng/mL) of urinary phthalate metabolites and index of renal function among Taiwanese adults and children/adolescents. Variables

Phthalate metabolites (ng/mL) MMP MEP MnBP MiBP MBzP MiNP MEHP MEOHP MEHHP MECPP MCMHP ΣDEHPm (nmole/mL)b ΣDBPm (nmole/mL)b Renal function factor Blood BUN (mg/dL) Creatinine (mg/dL) Uric acid (mg/dL) Urine Creatinine (mg/dL) Microalbumin (mg/dL) pH Protein (mg/dL) Uric acid (mg/day) ACR (mg/mmol) eGFR (60 mL/min/1.73 m2) CCr (mL/min)

Pa

Adults (≥18 years) (N = 241)

Children/Adolescents (< 18 years) (N = 70)

< LOD%

GM

Median (Interquartile range)

< LOD%

GM

Median (Interquartile range)

2.5 7.9 12.0 25.3 77.2 90.0 19.9 5.4 1.2 4.6 36.1

26.5 11.2 10.5 4.21 0.32 0.21 4.04 9.16 17.0 18.0 1.57 0.20 0.11

24.6 12.2 15.8 8.39 0.15 0.15 7.01 10.8 17.2 20.9 3.24 0.19 0.16

1.4 4.3 10.0 18.6 71.4 87.1 21.4 1.4 4.3 4.3 21.4

38.5 14.0 16.1 7.13 0.41 0.25 3.90 17.8 22.0 28.7 3.41 0.29 0.16

43.4 14.8 21.1 13.9 0.15 0.15 7.37 19.5 25.5 34.7 6.09 0.30 0.16

13.1 0.78 5.78

13.1 (10.1, 16.3) 0.79 (0.66, 0.94) 5.9 (4.8, 7.0)

10.4 0.61 5.59

10.3 (6.5, 12.5) 0.60 (0.42, 0.72) 5.7 (3.3, 6.7)

< 0.01 < 0.01 0.330

83.7 0.26 6.02 4.36 33.6 0.345 94.8 95.8

83.0 (52.3, 125.5) 0.1 (0.1, 0.58) 6.5 (5.0, 6.5) 4.0 (2.7, 6.3) 34.0 (23.1, 51.0) 0.256 (0.140, 0.549) 95.1 (82.9, 114.0) 95.6 (76.8, 120.2)

104.1 0.21 6.14 5.68 40.7 0.233 175.7 128.9

100.0 (51.7, 146.6) 0.1 (0.1, 0.41) 6.5 (5.0, 6.5) 5.45 (2.11, 8.80) 43.0 (15.3, 69.5) 0.184 (0.112, 0.481) 171.2 (108.6, 213.7) 120.5 (76.1, 168.2)

0.003 0.506 0.255 0.001 0.026 0.010 < 0.001 < 0.001

(12.4, (5.24, (6.25, (0.15, (0.15, (0.15, (3.11, (6.34, (10.1, (12.0, (0.15, (0.12, (0.09,

55.8) 27.5) 30.5) 17.9) 0.15) 0.15) 12.4) 17.6) 30.4) 32.7) 6.68) 0.31) 0.30)

(18.4, (7.43, (14.1, (4.96, (0.15, (0.15, (2.30, (9.61, (13.9, (19.3, (2.48, (0.16, (0.12,

82.0) 29.7) 37.9) 28.8) 2.28) 0.15) 12.7) 32.3) 39.5) 64.5) 11.5) 0.46) 0.31)

0.007 0.235 0.007 0.016 0.263 0.393 0.908 < 0.001 0.001 < 0.001 < 0.001 < 0.001 0.001

Abbreviations: GM: geometric mean; LOD: limit of detection; MMP: mono-methyl phthalate; MEP: mono-ethyl phthalate; MiBP: mono-iso-butyl phthalate, MnBP: mono-n-butyl phthalate; MBzP: mono-benzyl phthalate; MEHP: mono-ethylhexyl phthalate; MEHHP: mono-(2-ethyl-5-hydroxyhexyl) phthalate; MEOHP: mono-(2ethyl-5-oxo-hexyl) phthalate; MECPP: mono-(2-ethyl-5-carboxypentyl) phthalate; MCMHP: mono-(2-carboxymethylhexyl) phthalate; MiNP: mono-iso-nonyl phthalate; P: p-value; ACR: albumin-to-creatinine ratio; BUN: blood urea nitrogen; eGFR: Estimated glomerular filtration rate. a Mann-Whitney U test calculated for the difference in means between adults and children/adolescents. b ΣDEHPm = sum molar concentrations of MEHP, MEHHP, MEOHP, MECPP and MCMHP; ΣDBPm = sum molar concentrations of MiBP and MnBP. 5

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Table 3 Adjusted regression coefficient and 95% CI for change in renal function index in relation to unit-increased in Log-phthalate metabolites in Taiwanese adults (N = 241). Analyte (ng/mL)

MMP MEP MnBP MiBP MBzP MiNP MEHP MEOHP MEHHP MECPP MCMHP ΣDEHPma ΣDBPmb

Log-microalbuminc Beta

(95%CI)

0.13 0.02 −0.02 < 0.01 < 0.01 0.02 0.05 0.08 0.13 0.12 0.07 0.25 < 0.01

0.02 −0.08 −0.11 −0.08 −0.10 −0.10 −0.04 −0.07 −0.05 −0.01 −0.02 < -0.01 −0.13

Log-BUNc

0.25 0.11 0.07 0.08 0.10 0.15 0.13 0.23 0.32 0.24 0.15 0.50 0.14

Log-ACRc

P

Beta

(95%CI)

0.024 0.727 0.687 0.983 0.930 0.732 0.301 0.323 0.151 0.071 0.123 0.052 0.958

0.03 0.01 −0.01 0.01 < 0.01 0.02 0.02 0.05 0.04 0.02 0.02 0.08 0.02

< 0.01 −0.01 −0.03 −0.01 −0.02 −0.01 < 0.01 0.02 < -0.01 −0.01 < 0.01 0.02 −0.02

0.05 0.03 0.01 0.03 0.03 0.05 0.04 0.08 0.08 0.05 0.04 0.13 0.05

P

Beta

(95%CI)

0.023 0.191 0.449 0.247 0.766 0.176 0.043 0.004 0.055 0.140 0.040 0.005 0.328

0.05 < 0.01 −0.01 < -0.01 < 0.01 0.01 0.02 0.02 0.05 0.04 0.02 0.09 −0.01

< 0.01 −0.04 −0.05 −0.04 −0.04 −0.05 −0.02 −0.04 −0.03 −0.01 −0.01 −0.02 −0.06

P 0.10 0.05 0.03 0.03 0.05 0.06 0.05 0.08 0.13 0.10 0.06 0.19 0.05

0.040 0.826 0.608 0.873 0.887 0.793 0.380 0.548 0.236 0.114 0.184 0.120 0.847

Abbreviations: ACR: albumin-to-creatinine ratio; BUN: blood urea nitrogen. a ΣDEHPm = sum of molar level of MEHP, MEHHP, MEOHP, MECPP, and MCMHP (nmole/mL). b ΣDBPm = sum of molar level of MiBP and MnBP (nmole/mL). c Multiple regression analysis adjusted for age, sex, BMI, urinary creatinine, type II DM, cigarette smoking and alcohol drinking.

between cumulative HInephro and renal function factors was not observed in children/adolescents (model 3).

nephropathy in the present study, whereas this was not observed in those younger than 18 y. Previously, a correlation was observed between the subacute or chronic kidney toxicity or lesions caused by DEHP exposure and peroxisome proliferator–activated receptor (PPAR)-α mediation as a result of peroxisome proliferation (Ward et al., 1998). Animal studies have reported that chronic DEHP exposure could cause chronic progressive nephropathy and the growth of lesions on the kidney, such as the mineralization of renal tubules and papilla (David et al., 2000). As demonstrated by in vitro and in vivo studies, DEHP treatment could cause epithelial-to-mesenchymal transition and the progression of renal fibrosis in renal tubular cells through PPAR downregulation. DEHP exposure potentially worsens the status of renal fibrosis and nephropathy in kidney disease (Wu et al., 2018). DEHP may also cause nephropathy in the renal proximal tubules of rodents (Reubsaet et al., 1991). Rats, exposed to high DEHP doses, exhibited a significantly higher incidence of focal cysts and a significant decrease in creatinine clearance (Crocker et al., 1988). These changes in kidney function were also observed in patients with hemodialysis as a result of the leachability of DEHP from PVC plasticizers containing Visking tubing (Faouzi et al., 1999). Activation of PPAR-gamma was demonstrated with DEHP exposure, which caused an increase in the production of oxidative stress hormones and downregulated expression of insulin receptors and GLUT4 proteins in

4. Discussion The present study showed a significant monotonic dose-response trend between increasing DEHP DI and the risk of higher microalbumin in Taiwanese adults over 18 y (Ptrend < 0.01). We examined the linearity of the associations between phthalate metabolite concentrations and the outcome variables with two different methods of standard regression models and GAMs. While GAM allowed to detect possible nonmonotonic dose-response effect, it did not directly study the risk of higher microalbumin trajectories over daily DEHP intake. We discovered that cumulative HInephro was significant positively associated with microalbumin and BUN after adjustment for significant covariate, and HInephro was associated with protein in all models. Moreover, we used the clinical cutoffs for microalbumin and ACR to assess renal function, which is preferable to a relative percentage of certain population with lower renal function. In addition, we also found that the exclusion of patients with type 2 DM or adjustment for this disease did not affect the strength of association between the risk of renal function factors. Importantly, exclusion of participants with type 2 DM could directly diminish the possibility of adverse renal function by diabetic

Table 4 Associationa between urinary measurement of daily phthalate intakeb and the risk of higher microalbuminc in participants older than 18 y. Phthalates DEHP < 1.280 1.280–2.698 ≥2.698 DnBP < 0.326 0.326–0.730 ≥0.730 DMP < 0.528 0.528–1.024 ≥1.024

Case/N (%)

OR 95%CI

2/79 (2.5) 9/83 (10.8) 11/79 (13.9)

1 6.68 9.40

– 1.26–35.52 1.67–52.84

8/79 (3.3) 5/83 (2.1) 9/79 (3.7)

1 0.91 1.30

– 0.25–3.31 0.43–3.92

4/79 (1.7) 4/83 (1.7) 11/79 (5.8)

1 0.62 3.03

– 0.13–2.91 0.87–10.56

Phthalates DiNP < 0.120 0.120–0.260 ≥0.260 DiBP < 0.140 0.140–0.430 ≥0.430

Case/N (%)

OR 95%CI

Phthalates

Case/N (%)

OR 95%CI

11/79 (4.6) 6/83 (2.5) 5/79 (2.1)

1 0.28 0.28

– 0.08–0.96 0.08–0.95

BBzP < 0.001 0.001–0.010 ≥0.010

10/80 (4.1) 4/81 (1.7) 8/80 (3.3)

1 0.25 0.63

– 0.07–0.98 0.22–1.84

7/79 (2.9) 9/83 (3.7) 6/79 (2.5)

1 1.55 1.03

– 0.49–4.84 0.30–3.53

< 0.220 0.220–0.630 ≥0.630

9/79 (3.7) 7/83 (2.9) 6/79 (2.5)

1 1.15 1.01

– 0.36–3.72 0.30–3.37

Abbreviations: BBzP: benzyl butyl phthalate; CI: confidence interval; DiBP: di-iso-butyl phthalate; DnBP: di-n-butyl phthalate; DEP: di-ethyl phthalate; DEHP: di-2ethylhexyl phthalate; DMP: dimethyl phthalate; DiNP: di-iso-nonyl phthalate; OR: odd ration. a Logistic regression: adjustment of age, sex, type 2 DM, obesity, cigarette smoking and alcohol drinking. b Tertiles of daily phthalate intake (μg/kg/day). c Microalbumin > 1.9 mg/dL. 6

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the livers of Sprague Dawley rats and a normal human hepatocyte line (L02 cells) (Zhang et al., 2017). A moderate amount of urinary albumin could reveal the early stages of kidney injury, being a marker of endothelial dysfunction or an indicator of risk for cardiovascular disease (Weir, 2007). Moreover, much evidence has also highlighted the relationship between nondiabetic microalbuminuria and cardiovascular disease, the underlying mechanism being the contribution of endothelial dysfunction to both atherosclerotic macrovascular disease and renal microvascular disease, for which albuminuria is a marker (Dinneen and Gerstein, 1997; Pedrinelli et al., 2001; Rossing et al., 1996; Stehouwer et al., 1997). In the US National Health and Nutrition Examination Survey, conducted between 2009 and 2010, a total of 677 children were recruited to evaluate the association between phthalate exposure and low-grade albuminuria. DEHP metabolites detected in urine were increased by approximately three-times the normal level, and a 0.55 mg/g increase was observed in ACR (Trasande et al., 2014), which is consistent with our findings. The aforementioned study also revealed a relationship between urinary BPA and albuminuria in the same population (Trasande et al., 2014). Furthermore, scientific evidence indicated that BPA can coexist with other pollutants, such as phthalates, which also exhibited a correlation with consumption levels and potential adverse health effects, specifically causing albuminuria (Kataria et al., 2015; Li et al., 2012; Tsai et al., 2016). Similar to the findings of a Taiwanese study, which assessed children in the plasticizer-contaminated-food episode (Tsai et al., 2016), we confirmed a positive and significant association between the intake of DEHP and the risk of higher microalbumin in adults. However, we further observed this association in adult with background exposure than children in the plasticizer-contaminated-food episode. In one study conducted in Shanghai adults, the co-exposure to certain metabolites, including DEHP and BBzP, were found to be associated with impaired renal function (Chen et al., 2019). Some possible mechanisms for the increased risk of higher microalbumin from phthalate exposure include oxidative stress and lipid peroxidation. Evidence has suggested a possible correlation between oxidative stress and subsequent endothelial function. Oxidative stress is a major pathophysiologic mechanism that underlies cardiometabolic risk and renal injury, and can be induced by exposure to environmental chemicals (Alberti et al., 2009). Moreover, lipid peroxidation causes cellular damage and inflammation, and oxidative stress disturbs the endothelium-derived relaxation factor, promoting vasoconstriction, platelet adhesion, and the release of inflammatory cytokines (Harrison et al., 2003; Singh and Jialal, 2006). Small et al. (2014) reported an association between oxidative stress–triggered mitochondrial

Fig. 1. Association between (1A) urinary measurement of daily phthalate intake and the risk of higher microalbumin; (1B) the HInephro and renal function factors.

Table 5 Log-linear regression analyses of associations between the HInephro and renal function factors. Modelsa

Log-microalbumin

Log-BUN

Beta

(95%CI)

Model 1 Model 2 Model 3

0.964 0.960 0.315

0.440 0.411 −0.572

Models

Log-ACR Beta

(95%CI)

Model 1 Model 2 Model 3

0.131 0.112 −0.213

−0.088 −0.121 −0.590

1.487 1.509 1.203

0.350 0.346 0.163

P

Beta

(95%CI)

< 0.001 0.001 0.480

0.190 0.186 0.073

0.076 0.068 −0.131

P

Log-Protein Beta

(95%CI)

0.241 0.343 0.262

0.772 0.684 0.792

0.353 0.283 0.361

P 0.304 0.304 0.277

0.001 0.002 0.477 P

1.192 1.085 1.223

< 0.001 0.001 < 0.001

Abbreviations: ACR: albumin-to-creatinine ratio; BUN: blood urea nitrogen. a Model 1: Age greater than 18 y, excluding participants with type 2 DM, and adjusted for BMI, sex, age, and smoking and drinking habits; Model 2: Age greater than 18 y and adjusted for BMI, sex, age, smoking and drinking habits, and participants with type 2 DM; Model 3: Age less than 18 y and adjusted for BMI, sex, age, and drinking habits; In model 1 participants with type2 DM were excluded whereas in model 2 participants with type2 DM were included.

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destabilization and both an increase in PPAR-γ phosphorylation and a decrease in PPAR-γ expression in renal proximal tubular cells. Preventative effects against diabetic nephropathy, such as nondiabetic glomerulosclerosis and renal tubulointerstitial fibrosis, can be provided by PPAR-γ agonists (Han et al., 2010; Ma et al., 2001; Yang et al., 2009). Using selenium as an effective redox regulator, Erkekoglu et al. (2012) discovered potential renal toxicity from DEHP and the protective effect of other antioxidants in phthalate-induced renal oxidative stress. Another study reported that maternal exposure to DBP could produce oxidative stress in both renal fibroblasts and tubular epithelial cells, subsequently causing kidney dysplasia and renal fibrosis (Zhu et al., 2017). The protective role of PPAR-α against DEHP-induced glomerulonephritis in mice has also been published (Kamijo et al., 2007; Wu et al., 2018). The half-life of phthalates is short, and a single urinary marker, as studied here, may not reflect the magnitude of microalbuminuria, a condition that accumulates over time. The use of the method of timed urine collections rather than spot urine samples is another adjustment option for urine concentration in future studies. Timed urine collections were recommended in settings, where acute kidney injury was studied and glomerular filtration rapidly changed, resulting in changes in CE (Waikar et al., 2010). Longer periods between urine collections, such as 24 h, commonly regarded as the gold standard, could average out variation and thus decrease the non-differential misclassification from urine osmolality and creatinine measurements (Aylward et al., 2017). However, such collection methods are difficult to employ in large-scale field studies, resulting in missed samples and an increased probability of external contamination (Thomas et al., 1993). In contrast to other studies (Trasande et al., 2014; Tsai et al., 2016), our research did not reveal an association between decreased kidney function and phthalate exposure in participants younger than 18 y. One possible reason for this could be the relatively small size of participants. Additionally, children, adolescents, and young adults constitute less than 5% of the population with ESRD, whose 10-y survival ranges from 70% to 85% (Ferris et al., 2006; Saran et al., 2015). Most studies, which reported an association between chemical (e.g., lead) exposure and renal function, involved adults (mean age of 50 y and older) who might have a high prevalence of comorbid conditions and kidney disease risk factors, such as diabetes mellitus, hypertension, and cigarette smoking (Diamond and Phillips, 2005; Staessen et al., 2001). Our work, however, involved children and adolescents, who are generally free from such comorbidities. Few studies have provided a dose-based or risk-based predictor for extrapolating the adverse renal effects of phthalate exposure (Tsai et al., 2016; Wu et al., 2018). We first utilized the adoption of a HI based on nephrotoxic effect to evaluate the association between cumulative exposure to different phthalates and decreased renal function in the general population. Because type 2 DM is the leading cause of ESRD (Udenze et al., 2012), we determined the existence of consistent positive associations between HInephro and microalbumin among participants, even after excluding those with type 2 DM. Furthermore, we first observed the association between phthalate exposure and renal function in the general adult population. However, only a few studies so far have provided available data which could assess the nephrotoxic effects of DEHP and DiNP (Tsai et al., 2016; Wu et al., 2018). Due to very low detectable rate (around 10%) of MiNP in current study, we cannot provide sufficient evidence about the impact of DiNP on renal function. More studies on nephrotoxic effects of different phthalates exposure are warrant. This study had some limitations that must be addressed in further research. We could not establish causality because of the cross-sectional design of the study. Alternative explanations of our results cannot be fully excluded due to the unmeasured confounding factors in the regression models, such as melamine or other metals (Huang et al., 2017; Wu et al., 2018). An interaction effect has been found between urine melamine levels and past DEHP exposure on urine ACR (Wu et al., 2018). However, there is a paucity of literature on the simultaneous

exposure on microalbumin. Moreover, reverse causation could have occurred, as participants with higher microalbumin could have simply excreted more phthalate metabolites. We used creatinine to adjust phthalate metabolite level and some renal function factors, such as ACR and eGFR, but also recognized that CE varied according to age, sex, muscle mass, and race/ethnicity. However, preferable correcting factors, such as specific gravity and osmolality, were not measured in the urine samples collected in this study (Rose and Post, 2013; Wald, 2013). Additionally, urine osmolality could be more accurate than urine specific gravity for quantifying urine concentration, and a significant correlation was found between them (Cook et al., 2000). Urine osmolality is also less confounded by glucosuria and albuminuria (Turner et al., 2015). One study reported that urine osmolality was significantly influenced by total daily protein intake. Moreover, urine osmolality and creatinine levels exhibited opposite associations, depending on whether participants had chronic kidney disease (Yeh et al., 2015). 5. Conclusions Our findings suggested that higher daily exposure to DEHP and cumulative phthalates was significantly positively associated with an increased risk of higher microalbumin. A moderate amount of albumin could indicate the early stages of kidney disease and may mean that participants were at a higher risk for heart disease. Comprehensive or mechanistic studies are required to elucidate this association. Author contributions PCH conceived and designed the experiments, WTC performed the experiments, and JWC and KWL analyzed the data. PCH and PCC contributed tools for reagents, materials, and analysis, and JWC and PCH wrote the paper. Specimen collection as well as sample arrangement and preparations were managed by WTC; CYH, HBH, JJKJ and CCH contributed to critical revision of the manuscript. Acknowledgments We would like to thank our research assistants, Ms. Wan-Ting Chang and Ms. Wei-Yen Liang for their assistance in data and specimen collection and sample pretreatment as well as Mr. Chien-Jen Wang for his assistance in conducting LC–MS/MS analysis. We are also deeply grateful to the research collaboration of the Nutrition and Health Survey in Taiwan team, Prof. Pan Wen-Harn, Mr. Zheng Chen, and others, and for the support in sampling provided by the Health Promotion Administration, Ministry of Health and Welfare, Taiwan. We would also like to extend thanks to the National Health Research Institutes, Taiwan for their financial support (Grant No.: EH-102-PP-05, EH-103-PP-05, EM-106-PP-12, EM-107-PP-12, EM-108-PP-12), and Ministry of Science of Technology, Taiwan (Grant No.: MOST106-3114B-400-001 and MOST 107-2321-B-002-052). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ijheh.2019.10.009. References Aylward, L.L., Hays, S.M., Zidek, A., 2017. Variation in urinary spot sample, 24 h samples, and longer-term average urinary concentrations of short-lived environmental chemicals: implications for exposure assessment and reverse dosimetry. J. Expo. Sci. Environ. Epidemiol. 27 (6), 582–590. Alberti, K.G., Eckel, R.H., Grundy, S.M., Zimmet, P.Z., Cleeman, J.I., Donato, K.A., Fruchart, J.C., James, W.P., Loria, C.M., Smith Jr., S.C., 2009. Harmonizing the metabolic syndrome: a joint interim statement of the international diabetes federation task force on epidemiology and prevention; national heart, lung, and blood institute; American heart association; world heart federation; international atherosclerosis society; and international association for the study of obesity. Circulation

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