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Environmental Health

, 17:32 | Cite as

Childhood and adolescent phenol and phthalate exposure and the age of menarche in Latina girls

  • Alexandra M. Binder
  • Camila Corvalan
  • Antonia M. Calafat
  • Xiaoyun Ye
  • Verónica Mericq
  • Ana Pereira
  • Karin B. Michels
Open Access
Research
Part of the following topical collections:
  1. Children’s Environmental Health

Abstract

Background

The age of menarche has been associated with metabolic and cardiovascular disease, as well as cancer risk. The decline in menarcheal age over the past century may be partially attributable to increased exposure to endocrine disrupting chemicals (EDCs).

Methods

We assessed the influence of 26 phenol and phthalate biomarkers on the timing of menarche in a longitudinal cohort of Chilean girls. These EDCs were quantified in urine collected prior to the onset of breast development (Tanner 1; B1), and during adolescence (Tanner 4; B4). Multivariable accelerated failure time (AFT) models were used to analyze associations between biomarker concentrations and the age of menarche adjusting for body mass index (BMI) Z-score and maternal education, accounting for within-subject correlation.

Results

Several biomarkers were significantly associated with the age at menarche; however, these associations were dependent on the timing of biomarker assessment. A log(ng/ml) increase in B1 concentrations of di(2-ethylhexyl) phthalate biomarkers was associated with later menarche (hazard ratio (HR): 0.77; 95% CI: 0.60, 0.98), whereas higher B1 concentrations of 2,5-dichlorophenol and benzophenone-3 were associated with earlier menarche (HR: 1.13; 95% CI: 1.01, 1.27; HR: 1.17; 95% CI: 1.06, 1.29, respectively). Elevated B4 concentrations of monomethyl phthalate were similarly associated with earlier menarche (HR: 1.30; 95% CI: 1.10, 1.53). The impact of monoethyl phthalate and triclosan concentrations on pubertal timing were significantly modified by BMI Z-score. Higher monoethyl phthalate and triclosan concentrations were associated with earlier menarche among overweight or obese girls, but not among those that were normal weight.

Conclusions

This study identifies modulation of sexual maturation by specific EDC biomarkers in Latina girls.

Keywords

Menarche Puberty Phenols Phthalates Endocrine disrupting chemicals 

Abbreviations

24-DCP

2,4-dichlorophenol

25-DCP

2,5-dichlorophenol

AFT

Accelerated failure time

B1

Tanner 1

B4

Tanner 4

BP-3

Benzophenone-3

BPA

Bisphenol A

B-PB

Butyl paraben

BPF

Bisphenol F

BPS

Bisphenol S

DEHP

of di (2-ethylhexyl) Phthalate

EDC

Endocrine disrupting chemical

E-PB

Ethyl paraben

MBP

Mono-n-butyl phthalate

MBzP

Monobenzyl phthalate

MCNP

Mono carboxyisononyl phthalate

MCOP

Mono carboxyisooctyl phthalate

MCPP

Mono-3-carboxypropyl phthalate

MECPP

Mono(2-ethyl-5-carboxypentyl) phthalate

MEHHP

Mono(2-ethyl-5-hydroxyhexyl) phthalate

MEHP

Mono(2-ethylhexyl) phthalate

MEOHP

Mono(2-ethyl-5-oxohexyl) phthalate

MEP

Monoethyl phthalate

MHBP

Mono-hydroxybutyl phthalate

MHiBP

Mono-hydroxyisobutyl phthalate

MiBP

Mono-isobutyl phthalate

MMP

Monomethyl phthalate

MNP

Mono-isononyl phthalate

M-PB

Methyl paraben

P-PB

Propyl paraben

TCS

Triclosan

Background

Determinants of pubertal timing are of public health concern because of the association between the age of onset and adult disease susceptibility. Among females, early menarche has been associated with an increased risk of type 2 diabetes, cardiovascular disease, and reproductive cancers, including breast [1, 2, 3]. The average age of menarche has decreased from 16-17 years at the end of the nineteenth century, to less than 13 years across Europe and the United States, likely due to improvements in nutrition and general health [4, 5]. While the age of menarche was thought to have stabilized in the past 50 years, more recent studies have suggested that the age of menarche has continued to decline in South America, the United States, and many countries in Europe [6, 7, 8, 9]. The secular trend towards earlier menarche has been observed across race/ethnicity groups, despite differences in timing [7, 10, 11, 12]. Within the United States, the average age of menarche is earlier among Hispanic and black females relative to white females, adjusting for BMI, as well as social and economic indicators [7, 13]. Across Europe and South America, the fastest decline has been among impoverished girls, in spite of earlier menarche being previously more common among affluent families [6, 8, 9, 14, 15].

The two primary hypotheses for this shift towards earlier menarche are the growing childhood obesity epidemic and increasing exposure to endocrine disruptor chemicals (EDCs) [16, 17, 18]. EDCs can influence the endocrine system through a multitude of mechanisms, including competitive binding to hormone receptors to modulation of hormone synthesis and metabolism [19]. There is building evidence that EDCs can affect proper function across a number of different tissues, including breast tissue and female reproductive organs [19], as well as influencing the neuroendocrine control of reproduction. Results of both in vivo studies and a few longitudinal human cohorts suggest some of these compounds may be obesogenic, with early life exposure associated with childhood body size [20, 21, 22, 23]. Phenols and phthalates are two classes of EDCs of particular concern due to their use in an extensive array of products, including plastics, building materials, personal care products, insecticides, and herbicides [24]. In this study, we analyzed the association between pre-pubescent and pubescent biomarkers of exposure to select phenols and phthalates and the timing of menarche within a longitudinal cohort of Chilean girls born in 2002–2003. Two cross-sectional studies and four longitudinal studies have analyzed the influence of childhood EDC exposure on the age of menarche [25, 26, 27, 28, 29, 30]. The results of these studies have been largely discordant, potentially reflecting regional and socioeconomic influences on exposure profile, differences in the age of biomarker measurement, and possible effect modification by race/ethnicity. For this study, we quantified a broad panel of 26 phenol and phthalate urinary biomarker concentrations prior to the onset of breast development (Tanner 1; B1) and at Tanner 4 (B4). This study provides unique insight into the influence of these compounds on pubertal timing in Latina girls.

Methods

Study population

Our study population was a random subset of 200 girls part of the longitudinal Growth and Obesity Cohort Study (GOCS) with urine samples collected at B1 (ages 6.7 to 9.6 years; median age: 7.9 years) and B4 (ages 9.4 to 13.1 years; median age: 11.2 years). The GOCS children were born in 2002–2003, and are representative of the low to middle-income families in Santiago, Chile. A description of the cohort has been provided elsewhere [31]. Starting in 2009, breast development was assessed by two trained dietitians (kappa with pediatric endocrinologist = 0.85) by visual inspection using Tanner’s rating scale approximately every 6 months [32]. Palpation was additionally used to distinguish breast Tanner 1 from Tanner 2. Sex and age-adjusted BMI Z-score at each visit were calculated based on the Centers for Disease Control and Prevention (CDC) growth charts. Girls were categorized as having excess weight (overweight or obese) if their BMI Z-score was equal or above the 85th percentile. Informed consent was obtained from all parents or guardians of children before the start of data collection.

Biomarker measurements

Fasting spot urine samples were collected between 10 AM and 12 PM in polypropylene sterile cups, and were immediately vortexed and aliquoted. Concentrations of 26 phenol and phthalate biomarkers were quantified in urines collected at breast Tanner 1 (B1) and Tanner 4 (B4) from 200 girls (400 samples). The analytical measurements were performed at the CDC National Center for Environmental Health Laboratory using previously described analytical methods [33, 34]. Concentrations below the limit of detection (LOD) were given an imputed value equal to LOD/sqrt(2) [35]. Biomarker concentrations (ng/ml) were corrected for specific gravity. Dilution adjustment was performed using the formula Pc = P[(1.015–1)/(SG-1)], where Pc is the specific gravity-corrected biomarker concentration, P is the observed biomarker concentration, SG is the specific gravity of the urine sample, and 1.015 is the median SG of the study population [36, 37]. To calculate the summation of di(2-ethylhexyl) phthalate (DEHP) metabolites (⅀DEHP), concentrations of MEHP, MEOHP, MEHHP, and MECPP were converted to nmol/L before being added together. The analysis of blinded specimens by the CDC laboratory was determined not to constitute engagement in human subjects’ research.

Age of menarche

Prior to the onset of B4, girls were asked to report the date of their first menstrual bleeding at each 6-month visit. After achieving B4, girls were contacted by study dieticians every 3 months to survey whether the girl had reached menarche. During this phone interview, a questionnaire was used to differentiate menarche from other potential causes of vaginal bleeding, such as vaginal infection, urinary infection or trauma. Longitudinal follow-up of participants enabled the confirmation of menarche onset.

Statistical methods

Multivariable accelerated failure time (AFT) models were used to assess the influence of individual phenol and phthalate biomarker concentrations on time to menarche, assuming a Weibull distribution. To account for possible confounding by body size, models were adjusted for BMI Z-score at EDC measurement [16, 17, 18, 38, 39]. Models were additionally adjusted for maternal education as an indicator of socioeconomic status, which is related to both exposure profile and age of menarche [6, 8, 9, 14, 15, 40]. Sensitivity analyses were conducted further adjusting for the mother’s age of menarche (recall; ≤11, 12, 13,≥14 years), which may capture confounding by transgenerational exposures correlated with socioeconomic status. These analyses were restricted to the subset of the families that reported mother’s age of menarche (N = 181), assuming recall data was missing completely at random. A cluster statement was used to account for within-subject correlation between B1 and B4 biomarker measurements. Accordingly, inference was based on robust standard errors estimated using the Huber sandwich estimator. For incident cases, survival time was the age at menarche, estimated based on time between the self-reported date of first menses and date of birth. Survival time for right censored individuals was the age at last clinic visit, based on the time between date of last visit and date of birth. Time-varying associations between biomarker concentration (log(ng/ml)) and the age of menarche were investigated by adding Tanner stage as well as an interaction between biomarker concentrations and Tanner stage to our models. Association estimates from the AFT models were reported as the relative change in hazard of menarche (hazard ratio; HR) associated with a log(ng/ml) increase in biomarker concentration, which generally corresponded to a change from the lowest concentration quartile to the highest quartile. If the interaction between Tanner stage and concentration on timing of menarche was significant (Wald test; p < 0.05), the reported associations were stratified by Tanner stage. Similarly, a Wald test was used to assess effect modification by BMI Z-score. If the interaction term significantly improved model fit (p < 0.05), the influence of the biomarker on the timing of menarche was reported separately for overweight/obese girls and normal weight girls. To identify potential non-monotonic dose-response relationships, we additionally modeled tertiles of biomarker concentration, with tertile cut-points stratified by Tanner stage. Significant trends across categories were evaluated by modeling the log(median) concentration within tertiles as a continuous variable. To visualize the change in the timing of menarche, we computed median age at menarche for tertiles of urinary concentrations using the baseline survivor function of multivariable adjusted AFT models. All statistical analysis was performed in R Version 3.3.1 and figures were generated using ggplot2 [41].

Results

The influence of EDC exposure across childhood and adolescence on the age of menarche was assessed among 200 Chilean girls born in 2002–2003. The median age at urine collection prior to the onset of breast development (B1) was 7.9 years and 11.2 years at Tanner 4 (Table 1). Proportion of samples with measurements above the limit of detection, as well as the geometric mean (95% CI) biomarker concentrations at B1 and B4 are reported in Additional file 1: Tables S1a and S1b. Analysis was restricted to the subset of 21 biomarkers with detectable concentrations at Tanner 1 and Tanner 4 in at least 75% of the girls. These 21 biomarkers included: 2,4-dichlorophenol, 2,5-dichlorophenol, benzophenone-3, BPA, MBP, MBzP, MCNP, MCOP, MCPP, MECPP, MEHHP, MEHP, MEOHP, MEP, methyl paraben, MHBP, MHiBP, MiBP, MMP, propyl paraben, triclosan; excluded were: MNP, B-PB, BPF, BPS, and E-PB (see Abbreviation List; Additional file 1: Table S1a and S1b). For reference, geometric mean (95% CI) phenol and phthalate creatine-adjusted urinary biomarker concentrations among U.S. females of a similar age range from the 2011–2012 National Health and Nutrition Examination Survey are reported in Additional file 1: Table S2. In both the U.S. and Chilean populations, most biomarker concentrations decreased with age (Additional file 1: Table S1a and S1b; Additional file 1: Table S2). The intra-individual correlation between phenol and phthalate biomarkers at B1 and B4 was moderate to low (Additional file 1: Table S3). The mean age of menarche was 12.0 years; the median age was similarly 12.0 years (95% CI: 11.9–12.2 years). At the time of analysis, 18 girls had not reached menarche or had dropped out of the cohort. Of those that achieved menarche during the course of the study, 10.5% (n = 21) reached menarche prior to the first detection of B4. The occurrence of menarche prior to B4 did not significantly modify the linear association between any biomarkers and the age of menarche in adjusted models. Therefore, B4 biomarker measurements for these 21 girls were included in all subsequent models. Biomarker concentrations at B1 were not significantly correlated with BMI Z-score. Similarly, most B4 biomarker concentrations were not correlated with B4 BMI Z-score, with the exception of monoethyl phthalate (MEP) and 2,5-dichlorophenol, which had moderate positive associations with BMI Z-score (ρ = 0.21 and ρ = 0.22, respectively). Additional study population characteristics are outlined in Table 1.
Table 1

Study population characteristics

Characteristic

Distribution

Age [years; median (range)]

 At B1 EDC Measurement

7.9 (6.7–9.6)

 At B4 EDC Measurement

11.2 (9.4–13.1)

Attained Menarche during Study Category [count (%)]

 Yes

182 (91.0)

 No

18 (9.0)

Age at Menarche [years; median (range)]

12.0 (9.4–13.8)

BMI Z-score [median (range)]

 At B1 EDC Measurement

0.5 (−2.2–2.4)

 At B4 EDC Measurement

0.5 (−2.7–2.2)

BMI Category [count (%)]

 At B1 EDC Measurement

  Under-weight or Normal weight (<85th Percentile)

147 (73.5)

  Overweight or Obese (≥85th percentile)

53 (26.5)

 At B1 EDC Measurement

 

  Under-weight or Normal weight (<85th Percentile)

137 (68.5)

  Overweight or Obese (≥85th percentile)

63 (31.5)

Height Z-score [median (range)]

 At B1 EDC Measurement

−0.1 (−2.6–2.1)

 At B4 EDC Measurement

0.1 (−2.1–2.3)

Maternal Education [count (%)]

 Post-Secondary Education

46 (23.0)

 No Post-Secondary Education

154 (77.0)

We did not detect a significant linear impact of average concentration across Tanner stages on the age of menarche for any biomarker (Table 2). However, the association between urinary biomarker concentrations and the timing of menarche was significantly modified by the Tanner stage at biomarker measurement for 2,5-dichlorophenol, benzophenone-3, monomethyl phthalate (MMP), and all of the metabolites of DEHP after adjusting for BMI Z-score and maternal education (Table 2). Further adjustment for mother’s age of menarche (≤11, 12, 13, ≥14 years) did not change the shape or strength of the associations between EDC biomarker levels and daughter’s age of menarche (Additional file 1: Tables S4 and S5). For ease of interpretation, and to identify potential non-linear influences, we evaluated the association between tertiles of each biomarker and the timing of menarche in adjusted models (Table 3; Fig. 1; T1=lowest tertile, T2=middle tertile, T3=highest tertile). Among the subset of biomarkers for which we did not detect significant effect modification by Tanner stage at measurement, we found a non-monotonic change in the timing of menarche between tertiles of monobenzyl phthalate (MBzP) across puberty (Table 3; Fig. 1). While there was a later age of menarche among the second tertile of MBzP relative to the first (HR: 0.70; 95% CI: 0.54, 0.93), the highest tertile was not significantly different from the lowest (Table 3; Fig. 1).
Table 2

Menarche hazard ratio (95% CI) associated with log(ng/ml) increase in each EDC biomarker across puberty

Biomarker

Unadjusted Model

Adjusted Modela

Interaction with Tanner stageb

Interaction with BMIc

2,4-Dichlorophenol

0.98 (0.87, 1.10)

1.00 (0.89, 1.12)

0.246

0.062

2,5-Dichlorophenol

1.05 (0.97, 1.14)

1.04 (0.95, 1.14)

0.017*

0.975

Benzophenone-3

1.03 (0.97, 1.09)

1.05 (0.99, 1.11)

0.009**

0.235

BPA

0.90 (0.78, 1.05)

0.92 (0.79, 1.06)

0.362

0.790

MBP

0.95 (0.84, 1.07)

0.96 (0.85, 1.08)

0.565

0.396

MBzP

0.98 (0.87, 1.09)

0.94 (0.84, 1.05)

0.729

0.891

MCNP

0.95 (0.82, 1.10)

0.91 (0.79, 1.05)

0.392

0.797

MCOP

0.95 (0.81, 1.10)

0.92 (0.78, 1.09)

0.638

0.790

MCPP

0.91 (0.80, 1.04)

0.91 (0.79, 1.05)

0.157

0.252

ΣDEHPd

1.04 (0.88, 1.22)

1.02 (0.87, 1.19)

0.002**

0.783

MECPP

1.05 (0.89, 1.24)

1.02 (0.87, 1.20)

0.004**

0.855

MEHHP

1.03 (0.89, 1.18)

1.01 (0.88, 1.16)

0.002**

0.643

MEHP

1.01 (0.85, 1.20)

1.01 (0.85, 1.20)

0.000***

0.909

MEOHP

1.02 (0.88, 1.19)

1.01 (0.87, 1.16)

0.004**

0.852

MEP

1.10 (0.99, 1.23)

1.09 (0.98, 1.21)

0.177

0.033*

Methyl Paraben

1.00 (0.94, 1.07)

1.00 (0.94, 1.07)

0.861

0.576

MHBP

1.02 (0.89, 1.16)

1.02 (0.89, 1.17)

0.486

0.488

MHiBP

1.04 (0.88, 1.23)

1.04 (0.88, 1.24)

0.426

0.920

MiBP

1.02 (0.87, 1.19)

1.01 (0.86, 1.19)

0.955

0.671

MMP

1.05 (0.93, 1.19)

1.08 (0.97, 1.22)

0.017*

0.262

Propyl Paraben

0.99 (0.92, 1.05)

0.99 (0.93, 1.05)

0.495

0.160

Triclosan

0.98 (0.90, 1.07)

1.00 (0.92, 1.08)

0.956

0.033*

aAccelerated failure time model adjusting for BMI Z-score, and maternal education

bp-value for interaction between continuous biomarker concentration and Tanner stage at biomarker measurement in adjusted models

cp-value for interaction between continuous biomarker concentration and BMI Z-score in adjusted models

dunits in log(nmol/l) reflecting the log transformed summation of DEHP metabolite concentrations

*p < 0.05, **p < 0.01, ***p < 0.001

Table 3

Menarche hazard ratio (95% CI) between tertiles of each EDC biomarker across pubertya

Biomarker

Middle vs Lowest Tertile

Highest vs Middle Tertile

Highest vs Lowest Tertile

Trendb (p-value)

2,4-Dichlorophenol

1.03 (0.79, 1.35)

1.01 (0.79, 1.30)

1.04 (0.80, 1.37)

0.701

BPA

1.06 (0.81, 1.38)

0.86 (0.67, 1.11)

0.91 (0.70, 1.20)

0.695

MBP

0.95 (0.74, 1.23)

1.00 (0.76, 1.31)

0.95 (0.69, 1.31)

0.749

MBzP

0.70* (0.54, 0.93)

1.16 (0.88, 1.55)

0.82 (0.62, 1.08)

0.155

MCNP

0.99 (0.77, 1.27)

0.91 (0.67, 1.23)

0.89 (0.67, 1.19)

0.433

MCOP

0.94 (0.72, 1.22)

0.99 (0.75, 1.30)

0.93 (0.68, 1.27)

0.661

MCPP

0.92 (0.67, 1.26)

0.84 (0.63, 1.13)

0.77 (0.57, 1.04)

0.116

MEP

1.00 (0.74, 1.34)

1.20 (0.88, 1.62)

1.19 (0.90, 1.57)

0.212

Methyl Paraben

0.96 (0.71, 1.29)

1.14 (0.84, 1.54)

1.09 (0.81, 1.47)

0.575

MHBP

0.94 (0.72, 1.23)

1.10 (0.88, 1.39)

1.04 (0.76, 1.41)

0.808

MHiBP

0.98 (0.76, 1.26)

1.09 (0.86, 1.39)

1.07 (0.82, 1.39)

0.654

MiBP

0.85 (0.63, 1.15)

1.13 (0.85, 1.50)

0.96 (0.74, 1.26)

0.763

Propyl Paraben

0.83 (0.60, 1.13)

1.20 (0.85, 1.68)

0.99 (0.73, 1.33)

0.918

Triclosan

0.85 (0.66, 1.10)

1.05 (0.84, 1.30)

0.90 (0.67, 1.20)

0.536

aAccelerated failure time model adjusting for BMI Z-score and maternal education; restricting to the subset of associations for which there was no significant interaction between Tanner stage at biomarker measurement and biomarker concentration on timing of menarche (p > 0.05)

bTrend evaluated by modeling the log(median) concentration within tertiles as a continuous variable

*p < 0.05, **p < 0.01, ***p < 0.001

Fig. 1

Median menarcheal age (95% CI) between tertiles of EDC biomarker concentrations across Tanner stages. Significant difference (p < 0.05) between: tertiles 1 and 2 indicated by A, tertiles 2 and 3 indicated by B, and tertiles 1 and 3 indicated by C

Increased B1 concentrations of 2,5-dichlorophenol and benzophenone-3 were associated with decreased time to menarche (Table 4). In contrast, higher B1 concentrations of the DEHP biomarkers mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), mono(2-ethyl-5-oxohexyl) phthalate (MEOHP), and mono(2-ethylhexyl) phthalate (MEHP) were associated with later menarche (Table 4). Girls in the highest tertile of B1 2,5-dichlorophenol concentration had an earlier age of menarche compared to those in the lowest tertile (Table 5; HR: 1.58; 95% CI: 1.09, 2.29), corresponding to 4.09 months earlier median age of menarche (Fig. 2). The influence of benzophenone-3 and the DEHP metabolites on the relative time to menarche was less linear (Table 5; Fig. 2). The median age of menarche was 4.10 months earlier among girls in the second tertile of B1 benzophenone-3 concentration compared to the lowest tertile (HR: 1.58; 95% CI: 1.12, 2.22), but the change in timing was not significant between the lowest and highest tertiles (Table 5). For all of the secondary oxidative DEHP biomarkers quantified (MEHHP, MECPP, and MEOHP), the median age of menarche was 6.57 to 7.37 months later among girls in the middle tertile relative to those in the lowest tertile of B1 concentration (Table 5). For MEHHP, girls in the highest tertile at B1 also had a significantly later age of menarche, but the median delay was not as great as that among girls in the second tertile (Table 5; Fig. 2).
Table 4

Menarche hazard ratio (95% CI) associated with log(ng/ml) increase in biomarker stratified by Tanner stagea

 

Tanner Stage

Biomarker

B1

B4

2,5-Dichlorophenol

1.13* (1.01, 1.27)

0.96 (0.85, 1.08)

Benzophenone-3

1.17** (1.06, 1.29)

0.99 (0.91, 1.06)

ΣDEHPb

0.77* (0.60, 0.98)

1.24 (0.97, 1.57)

MECPP

0.79 (0.61, 1.01)

1.24 (0.97, 1.58)

MEHHP

0.77* (0.62, 0.96)

1.22 (0.98, 1.51)

MEHP

0.80* (0.65, 0.98)

1.20 (0.98, 1.47)

MEOHP

0.78* (0.63, 0.97)

1.20 (0.96, 1.50)

MMP

0.96 (0.81, 1.14)

1.30** (1.10, 1.53)

aAccelerated failure time model adjusting for BMI Z-score and maternal education; including an interaction between Tanner stage at biomarker measurement and concentration; restricted to subset of associations for which the interaction with the timing of biomarker measurement was significant (p < 0.05)

bunits in log(nmol/l) reflecting the log transformed summation of DEHP metabolite concentrations

*p < 0.05, **p < 0.01, ***p < 0.001

Table 5

Menarche hazard ratio (95% CI) between tertiles of biomarker stratified by Tanner stage at measurementa

Biomarker

Tanner Stage

Middle vs Lowest Tertile

Highest vs Middle Tertile

Highest vs Lowest Tertile

Trendb (p-value)

2,5-Dichlorophenol

 

1

1.35 (0.93, 1.98)

1.17 (0.81, 1.68)

1.58* (1.09, 2.29)

0.019*

4

1.16 (0.79, 1.69)

0.79 (0.55, 1.14)

0.91 (0.62, 1.35)

0.521

Benzophenone-3

 

1

1.58** (1.12, 2.22)

0.89 (0.60, 1.34)

1.41 (0.96, 2.06)

0.142

4

0.87 (0.61, 1.24)

1.07 (0.74, 1.54)

0.93 (0.63, 1.37)

0.743

ΣDEHP

 

1

0.43*** (0.31, 0.61)

1.56* (1.09, 2.25)

0.67* (0.46, 0.98)

0.140

4

0.93 (0.64, 1.36)

1.52* (1.01, 2.28)

1.42 (0.93, 2.17)

0.083

MECPP

 

1

0.43*** (0.30, 0.62)

1.63** (1.15, 2.33)

0.71 (0.47, 1.05)

0.232

4

0.92 (0.64, 1.34)

1.53* (1.02, 2.31)

1.41 (0.93, 2.15)

0.082

MEHHP

 

1

0.47*** (0.33, 0.68)

1.44 (1.00, 2.09)

0.68* (0.47, 0.99)

0.064

4

1.05 (0.73, 1.52)

1.29 (0.87, 1.90)

1.36 (0.89, 2.06)

0.148

MEHP

 

1

0.78 (0.54, 1.14)

0.94 (0.64, 1.38)

0.73 (0.50, 1.09)

0.122

4

1.05 (0.74, 1.49)

1.40 (0.93, 2.10)

1.47 (0.98, 2.20)

0.061

MEOHP

 

1

0.43*** (0.30, 0.62)

1.92*** (1.34, 2.75)

0.83 (0.59, 1.17)

0.313

4

1.07 (0.73, 1.55)

1.30 (0.87, 1.95)

1.39 (0.91, 2.12)

0.120

MMP

 

1

1.31 (0.89, 1.93)

0.82 (0.55, 1.22)

1.07 (0.74, 1.56)

0.672

4

0.98 (0.68, 1.42)

1.45* (1.01, 2.10)

1.42* (1.02, 1.99)

0.032*

aEstimating Tanner stage-specific association between biomarker and age of menarche adjusting BMI Z-score and maternal education, including an interaction between biomarker tertile and Tanner stage; restricted to subset of associations for which the interaction with Tanner stage was significant (p < 0.05)

bTrend evaluated by modeling the log(median) concentration within tertiles as a continuous variable

*p < 0.05, **p < 0.01, ***p < 0.001

Fig. 2

Median menarcheal age (95% CI) between tertiles of EDC biomarker concentrations stratified by Tanner stage

A log(ng/ml) increase in B4 concentrations of MMP was associated with a faster time to menarche (Table 4; HR: 1.30; 95% CI: 1.10, 1.53). The influence of B4 concentrations of MMP on the age of menarche was relatively linear. Among girls in the highest B4 concentration tertile, the median age of menarche was 3.15 months earlier than girls in the lowest MMP tertile (Table 5; HR: 1.42; 95%: 1.02, 1.99).

For MEP and triclosan, the influence on the age of menarche was significantly modified by age and sex-adjusted BMI Z-score (Table 2). The average effect of triclosan across various body sizes was negligible, whereas there was a shift towards earlier menarche among girls with higher concentrations of MEP (Table 2). Stratifying by BMI category, a log(ng/ml) increase in triclosan concentration was associated with earlier age of menarche (Table 6; HR: 1.16; 95% CI: 1.01, 1.34) among girls that were either overweight or obese (≥85th percentile BMI Z-score). Similarly, a log(ng/ml) increase in MEP among overweight or obese girls was associated with earlier menarche (HR: 1.24; 95%CI: 1.05, 1.47), with the median age of menarche 5.03 months earlier in the highest compared to the lowest tertile (Fig. 3).There was no significant association with either triclosan or MEP among girls that were normal weight.
Table 6

Menarche hazard ratio (95% CI) associated with log(ng/ml) increase of biomarker stratified by BMI percentilea

 

BMI Percentile

Biomarker

<85th

≥85th

MEP

1.02 (0.90, 1.17)

1.24* (1.05, 1.47)

Triclosan

0.93 (0.84, 1.02)

1.16* (1.01, 1.34)

aAccelerated failure time model adjusting for BMI Z-score and maternal education; restricted to subset of associations for which the interaction with BMI Z-score was significant (p < 0.05)

*p < 0.05, **p < 0.01, ***p < 0.001

Fig. 3

Median menarcheal age (95% CI) between tertiles of EDC biomarker concentrations stratified by BMI percentile

Discussion

In this study, we report changes in the timing of menarche associated with earlier childhood and adolescent biomarker urinary concentrations of several phthalates and phenols. For most of these biomarkers, the strength of the association was dependent on the timing of biomarker measurement, suggesting specific periods of susceptible hormone-driven development. High childhood (Tanner 1) urinary concentrations of DEHP metabolites were associated with later menarche. In contrast, increased childhood levels of 2,5-dichlorophenol and benzophenone-3 were associated with early menarche. Increased adolescent (Tanner 4) concentrations of MMP were similarly associated with earlier menarche. This sensitivity to exposure window may partially explain some discordance between prior studies of childhood EDC exposure and the age of menarche, which evaluated biomarker concentrations at various ages [25, 26, 27, 28].

Among this cohort of Latina girls, increased urinary concentrations of DEHP biomarkers during childhood were associated with later menarcheal age. High pre-pubertal concentrations of these biomarkers have been associated with decreased concentrations of adolescent androgens, suggesting DEHP may inhibit adrenal androgen synthesis in humans [29]. This anti-androgenic influence of DEHP is further supported by the later age of pubarche among girls with relatively high concentrations of DEHP metabolites [42, 43]. Another mechanism by which childhood DEHP exposure may influence pubertal timing is through its influence on concentrations of IGF-1 and thyroid hormones [44, 45, 46, 47]. DEHP may also act by modifying the production of luteinizing hormone and follicle-stimulating hormone, reducing estrogen formation, or through the activation of peroxisome proliferation-activated receptors [48]. Similar to our findings, one longitudinal study of German adolescents recently reported that higher urinary concentrations of DEHP metabolites from ages 8–10 years were associated with decreased odds of having reached menarche at subsequent visits [30]. Other studies have not reported the same association between urinary DEHP biomarker concentrations and age of menarche. A cross-sectional study of U.S. girls 12–16 years reported the summation of phthalate urinary biomarker concentrations was not associated with the onset of menarche [27]. A smaller cross-sectional study of Hispanic girls in Mexico City also did not report a significant influence of these exposures [28]. In contrast, greater concentrations of DEHP biomarkers between 7 and 15 years were associated with increased odds of earlier menarche within a cohort of Chinese girls [26]. Our study results suggest that the influence of the biomarkers was modified by pubertal stage at the time of exposure assessment, and suggested the correlation in DEHP biomarker concentrations across puberty was weak. Therefore, these disparate findings may partially reflect differences in exposure window.

In contrast to the influence of the DEHP biomarkers, increased concentrations of 2,5-dichlorophenol, benzophenone-3, and MMP were associated with earlier menarche. 2,5-dichlorophenol is the major biomarker of para-dichlorobenzene, a chemical used in moth repellants, room deodorants, and fumigants. Two prior studies of U.S. girls reported a similar association between 2,5-dichlorophenol and age of menarche, which is consistent with the estrogenic activity 2,5-dichlorophenol has demonstrated in vitro and in vivo [25, 27, 49]. The sunscreen agent benzophenone-3 has also been shown to have estrogenic characteristics, including exerting uterotrophic effects in rats, simulating proliferation of breast cancer cell lines, as well as binding and activating estrogen receptors [50, 51]. While these compounds may have a similar mechanism of action, prior studies did not report a significant association between benzophenone-3 and menarcheal age [25, 27]. In our study, only pre-pubertal concentrations of 2,5-dichlorophenol and benzophenone-3 were associated with earlier menarche. Conversely, MMP concentrations close to the onset of menarche were associated with earlier timing. Two studies of Chinese girls reported conflicting associations between MMP concentrations and pubertal timing [52, 53]. The parent compound of MMP, dimethyl phthalate (DMP), can be found in insect repellants and plastics. While no estrogenic effect of DMP has been demonstrated in vivo, concentrations of MMP among children 5–7 years have been inversely associated with concentrations of IGF-1, with no significant impact on thyroid function [54]. The potential influence on the regulation of growth and development suggests the need for further investigation into the impact of MMP, or its precursor DMP, on adolescent development.

BMI significantly modified the influence of both MEP and triclosan on age of menarche. An increase in either biomarker was only associated with earlier menarche among girls that were either overweight or obese. Among U.S. children, neither association was significantly modified by BMI; however, the association with MEP was in a similar direction among all girls [25, 27]. It is possible that these differences in effect modification by BMI are due to race/ethnicity disparities in adolescent body composition [55]. Compared to white U.S. girls, Asian and Hispanic adolescents have greater trunk fat placement independent of total body fat [55]. Furthermore, Hispanic girls tend to have higher percent body fat than white girls, adjusting for body size [56]. It is possible the action of MEP and triclosan may be modified by elements of metabolic profile shaped by fat distribution, such as modulations in insulin resistance or leptin production [18, 57]. Alternatively, the observed statistical interaction with BMI may reflect an influence of MEP and triclosan on both adiposity and menarcheal age. Prenatal exposure to MEP has been associated with decreased childhood BMI Z-score [23], but this association has been inconsistent across populations [22]. In one cross-sectional study, triclosan exposure was similarly associated with a decrease in BMI and waist circumference among children [58], but this correlation was not observed in a second cross-sectional study [38]. Given the impact of childhood adiposity on pubertal timing [59, 60, 61, 62, 63], the potential influence of EDCs on adiposity raises important consideration of the direction of causation. Specifically, EDC exposure could have a direct effect on menarcheal age, or influence adiposity to impact pubertal timing, or influence pubertal timing to impact adiposity. These theories are not mutually exclusive, and should be addressed by follow-up studies.

One limitation to this study is that we cannot account for the influence of earlier EDC exposure on pubertal timing that may have partially confounded the observed associations due to consistent home environment. For example, there is indication that exposure to certain EDCs in utero, such as DEHP, may influence both childhood adiposity and pubertal timing [21, 22, 23, 28]. A second limitation is that while this study did measure urinary biomarkers at two developmental time points, concentrations in a single spot urine may not be representative of average exposure. Nonetheless, prior longitudinal assessments of EDC concentrations suggest that spot measurements can estimate relative EDC exposures over the course of several months to a year due to recurrent exposures [64, 65, 66, 67]. A third important consideration is that these girls were primarily from low socioeconomic status families, which may have an exposure profile that is not representative of all children and adolescents. However, given the faster decline in menarcheal age among impoverished girls [6, 8, 9, 14, 15], the determinants of pubertal timing within this subset of the population is of particular public health concern. Finally, this study did not account for potential synergistic or antagonistic impacts of EDC co-exposures on pubertal timing. In addition to the studied biomarkers, exposure to other types of unmeasured EDCs, such as persistent organic pollutants, may have modified our observed associations with pubertal development. These interactions may contribute to the lack of reproducibility in the associations between EDC biomarkers and the onset of menarche across populations. Given the concurrence of EDCs in consumer products and the home environment [68], it is difficult to disentangle the independent influence of any single chemical on menarcheal age. However, we hope these findings spur additional research into modifiable sources of EDC exposure during childhood and adolescence.

Conclusions

Exposure to certain phenols and phthalates during specific stages of pubertal development was associated with menarcheal age in Chilean girls. This study highlights the necessity of capturing critical windows of exposure during periods of rapid physiological change. Additionally, the unique associations observed in this cohort emphasize the need to integrate investigations from across the world to identify population-specific sensitivities, which can guide global health recommendations. As data collection in this longitudinal cohort continues, we plan to explore the impact of EDC exposure on additional facets of pubertal development, such as the onset of regular cycling. These shifts in pubertal timing may have both immediate psychological implications for these girls, and may indicate changes in the predisposition to adverse health in adulthood. Girls with early puberty have been shown to suffer from higher rates of adolescent depression, anxiety, and delinquent behavior [69, 70, 71, 72]. Early onset of menarche has also been associated with increased risk of type-2 diabetes, cardiovascular disease, and cancer risk. Future studies will be necessary to directly link the variation in pubertal timing associated with early life EDC exposure to adult health, as well as the potential direct effect of childhood/adolescent EDC exposure on adult health independent of age of menarche.

Notes

Acknowledgements

We acknowledge Manori Silva, Ella Samandar, Jim Preau, Prabha Dwivedi, Xiaoliu Zhou, and Tao Jia who performed the measurements of phthalates and phenols biomarkers.

Funding

This work was supported by Public Health Service grant R01CA158313 from the National Cancer Institute, National Institutes of Health, U.S. Department of Health and Human Services (to KBM).

Availability of data and materials

Please contact author for data requests.

Disclaimer

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. Use of trade names is for identification only and does not imply endorsement by the CDC, the Public Health Service, or the US Department of Health and Human Services.

Authors’ contributions

AMB, CC, AP, and KBM designed the research plan; CC, VM, and AP acquired the data; AMC and XY measured the urinary biomarkers; AMB, CC, AP, and KBM interpreted the data; AMB performed the statistical analysis and wrote the paper; AMB, AMC, CC, VM, AP, and KBM provided critical revision of the manuscript for important intellectual content; AMB and KBM had primary responsibility for final content. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

Ethics approval and consent to participate

The study protocol was approved by the Ethics Committee of the Institute of Nutrition and Food Technology, University of Chile. The study was also reviewed and approved by the IRB of the University of California, Los Angeles. Informed consent was obtained from all parents or guardians of children before the start of data collection.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary material

12940_2018_376_MOESM1_ESM.docx (27 kb)
Additional file 1: Table S1a. Phthalate metabolite urinary concentrations corrected for specific gravity in a Chilean girls cohort (n = 200). Table S1b. Phenol biomarker urinary concentrations corrected for specific gravity in a Chilean girls cohort (n = 200). Table S2. Geometric mean (95% CI) creatine-adjusted urinary EDC biomarker concentrations and age at measurement in the 2011–2012 U.S. National Health and Nutrition Examination Survey among females 5 to 14 years. Table S3. Spearman correlation between EDC SG-adjusted biomarker urinary concentrations (ng/ml) at B1 and B4 in Chilean cohort (n = 200). Table S4. Sensitivity analysis: menarche hazard ratio (95% CI) associated with log(ng/ml) increase in each EDC biomarker across puberty adjusting for mother's age of menarche. Table S5. Sensitivity analysis: menarche hazard ratio (95% CI) associated with log(ng/ml) increase in biomarker stratified by Tanner stage adjusting for mother's age of menarche. (DOCX 27 kb)

References

  1. 1.
    He C, Zhang C, Hunter DJ, Hankinson SE, Louis GMB, Hediger ML, et al. Age at menarche and risk of type 2 diabetes: results from 2 large prospective cohort studies. Am J Epidemiol. 2009;  https://doi.org/10.1093/aje/kwp372.
  2. 2.
    Lakshman R, Forouhi NG, Sharp SJ, Luben R, Bingham SA, Khaw K-T, et al. Early age at menarche associated with cardiovascular disease and mortality. J Clin Endocrinol Metab. 2009;94:4953–60.CrossRefGoogle Scholar
  3. 3.
    Hsieh CC, Trichopoulos D, Katsouyanni K, Yuasa S. Age at menarche, age at menopause, height and obesity as risk factors for breast cancer: associations and interactions in an international case-control study. Int J Cancer J Int Cancer. 1990;46:796–800.CrossRefGoogle Scholar
  4. 4.
    Wyshak G, Frisch RE. Evidence for a secular trend in age of menarche. N Engl J Med. 1982;306:1033–5.CrossRefGoogle Scholar
  5. 5.
    Euling SY, Selevan SG, Pescovitz OH, Skakkebaek NE. Role of environmental factors in the timing of puberty. Pediatrics. 2008;121(Supp l 3):S167–71.CrossRefGoogle Scholar
  6. 6.
    Ong KK, Ahmed ML, Dunger DB. Lessons from large population studies on timing and tempo of puberty (secular trends and relation to body size): the European trend. Mol Cell Endocrinol. 2006;254–255:8–12.CrossRefGoogle Scholar
  7. 7.
    McDowell MA, Brody DJ, Hughes JP. Has age at menarche changed? Results from the National Health and nutrition examination survey (NHANES) 1999-2004. J Adolesc Health Off Publ Soc Adolesc Med. 2007;40:227–31.CrossRefGoogle Scholar
  8. 8.
    Morris DH, Jones ME, Schoemaker MJ, Ashworth A, Swerdlow AJ. Secular trends in age at menarche in women in the UK born 1908-93: results from the breakthrough generations study. Paediatr Perinat Epidemiol. 2011;25:394–400.CrossRefGoogle Scholar
  9. 9.
    Junqueira Do Lago M, Faerstein E, De Souza Lopes C, Werneck GL. Pró-Saúde study (Rio de Janeiro, Brazil). Family socio-economic background modified secular trends in age at menarche: evidence from the Pró-Saúde study (Rio de Janeiro, Brazil). Ann Hum Biol. 2003;30:347–52.CrossRefGoogle Scholar
  10. 10.
    Freedman DS, Khan LK, Serdula MK, Dietz WH, Srinivasan SR, Berenson GS. Relation of age at menarche to race, time period, and anthropometric dimensions: the Bogalusa heart study. Pediatrics. 2002;110:e43.CrossRefGoogle Scholar
  11. 11.
    Chumlea WC, Schubert CM, Roche AF, Kulin HE, Lee PA, Himes JH, et al. Age at menarche and racial comparisons in US girls. Pediatrics. 2003;111:110–3.CrossRefGoogle Scholar
  12. 12.
    Anderson SE, Must A. Interpreting the continued decline in the average age at menarche: results from two nationally representative surveys of U.S. girls studied 10 years apart. J Pediatr. 2005;147:753–60.CrossRefGoogle Scholar
  13. 13.
    Wu T, Mendola P, Buck GM. Ethnic differences in the presence of secondary sex characteristics and menarche among US girls: the third National Health and nutrition examination survey, 1988-1994. Pediatrics. 2002;110:752–7.CrossRefGoogle Scholar
  14. 14.
    Dossus L, Kvaskoff M, Bijon A, Fervers B, Boutron-Ruault M-C, Mesrine S, et al. Determinants of age at menarche and time to menstrual cycle regularity in the French E3N cohort. Ann Epidemiol. 2012;22:723–30.CrossRefGoogle Scholar
  15. 15.
    Amigo H, Vásquez S, Bustos P, Ortiz G, Lara M. Socioeconomic status and age at menarche in indigenous and non-indigenous Chilean adolescents. Cad Saude Publica. 2012;28:977–83.CrossRefGoogle Scholar
  16. 16.
    Buck Louis GM, Gray LE, Marcus M, Ojeda SR, Pescovitz OH, Witchel SF, et al. Environmental factors and puberty timing: expert panel research needs. Pediatrics. 2008;121(Suppl 3):S192–207.CrossRefGoogle Scholar
  17. 17.
    Biro FM, Greenspan LC, Galvez MP. Puberty in girls of the 21st century. J Pediatr Adolesc Gynecol. 2012;25:289–94.CrossRefGoogle Scholar
  18. 18.
    Kaplowitz PB. Link between body fat and the timing of puberty. Pediatrics. 2008;121(Suppl 3):S208–17.CrossRefGoogle Scholar
  19. 19.
    Gore AC, Chappell VA, Fenton SE, Flaws JA, Nadal A, Prins GS, et al. EDC-2: the Endocrine Society’s second scientific statement on endocrine-disrupting chemicals. Endocr Rev. 2015;36:E1–150.CrossRefGoogle Scholar
  20. 20.
    Kim SH, Park MJ. Phthalate exposure and childhood obesity. Ann Pediatr Endocrinol Metab. 2014;19:69–75.CrossRefGoogle Scholar
  21. 21.
    Buckley JP, Engel SM, Mendez MA, Richardson DB, Daniels JL, Calafat AM, et al. Prenatal phthalate exposures and childhood fat mass in a new York City cohort. Environ Health Perspect. 2016;124:507–13.Google Scholar
  22. 22.
    Valvi D, Casas M, Romaguera D, Monfort N, Ventura R, Martinez D, et al. Prenatal phthalate exposure and childhood growth and blood pressure: evidence from the Spanish INMA-Sabadell birth cohort study. Environ Health Perspect. 2015;123:1022–9.CrossRefGoogle Scholar
  23. 23.
    Buckley JP, Engel SM, Braun JM, Whyatt RM, Daniels JL, Mendez MA, et al. Prenatal phthalate exposures and body mass index among 4- to 7-year-old children: a pooled analysis. Epidemiol Camb Mass. 2016;27:449–58.CrossRefGoogle Scholar
  24. 24.
    CDC. Fourth National Report on human exposure to environmental chemicals. Atlanta: Centers for Disease Control and Prevention National Center for Environmental Health Divsion of Laboratory Sciences; 2012.Google Scholar
  25. 25.
    Wolff MS, Pajak A, Pinney SM, Windham GC, Galvez M, Rybak M, et al. Associations of urinary phthalate and phenol biomarkers with menarche in a multiethnic cohort of young girls. Reprod Toxicol Elmsford N. 2017;67:56–64.CrossRefGoogle Scholar
  26. 26.
    Zhang Y, Cao Y, Shi H, Jiang X, Zhao Y, Fang X, et al. Could exposure to phthalates speed up or delay pubertal onset and development? A 1.5-year follow-up of a school-based population. Environ Int. 2015;83:41–9.CrossRefGoogle Scholar
  27. 27.
    Buttke DE, Sircar K, Martin C. Exposures to endocrine-disrupting chemicals and age of menarche in adolescent girls in NHANES (2003-2008). Environ Health Perspect. 2012;120:1613–8.CrossRefGoogle Scholar
  28. 28.
    Watkins DJ, Téllez-Rojo MM, Ferguson KK, Lee JM, Solano-Gonzalez M, Blank-Goldenberg C, et al. In utero and peripubertal exposure to phthalates and BPA in relation to female sexual maturation. Environ Res. 2014;134:233–41.CrossRefGoogle Scholar
  29. 29.
    Mouritsen A, Frederiksen H, Sørensen K, Aksglaede L, Hagen C, Skakkebaek NE, et al. Urinary phthalates from 168 girls and boys measured twice a year during a 5-year period: associations with adrenal androgen levels and puberty. J Clin Endocrinol Metab. 2013;98:3755–64.CrossRefGoogle Scholar
  30. 30.
    Kasper-Sonnenberg M, Wittsiepe J, Wald K, Koch HM, Wilhelm M. Pre-pubertal exposure with phthalates and bisphenol a and pubertal development. PLoS One. 2017;12:e0187922.CrossRefGoogle Scholar
  31. 31.
    Corvalán C, Uauy R, Stein AD, Kain J, Martorell R. Effect of growth on cardiometabolic status at 4 y of age. Am J Clin Nutr. 2009;90:547–55.CrossRefGoogle Scholar
  32. 32.
    Tanner J. Growth at adolescense. Oxford: Blackwell Scientific Publications; 1962.Google Scholar
  33. 33.
    Ye X, Kuklenyik Z, Needham LL, Calafat AM. Automated on-line column-switching HPLC-MS/MS method with peak focusing for the determination of nine environmental phenols in urine. Anal Chem. 2005;77:5407–13.CrossRefGoogle Scholar
  34. 34.
    Silva MJ, Samandar E, Preau JL, Reidy JA, Needham LL, Calafat AM. Quantification of 22 phthalate metabolites in human urine. J Chromatogr B Analyt Technol Biomed Life Sci. 2007;860:106–12.CrossRefGoogle Scholar
  35. 35.
    Hornung RW, Reed LD. Estimation of average concentration in the presence of nondetectable values. Appl Occup Environ Hyg. 1990;5:46–51.CrossRefGoogle Scholar
  36. 36.
    Boeniger MF, Lowry LK, Rosenberg J. Interpretation of urine results used to assess chemical exposure with emphasis on creatinine adjustments: a review. Am Ind Hyg Assoc J. 1993;54:615–27.CrossRefGoogle Scholar
  37. 37.
    Teass AW, Biagini RE, DeBord G, Hull RD. Application of biological monitoring methods. NIOSH man anal method. Cincinnati: National Institute for Occupational Safety and Health Division of Physical Sciences and Engineering; 1998.Google Scholar
  38. 38.
    Buser MC, Murray HE, Scinicariello F. Association of urinary phenols with increased body weight measures and obesity in children and adolescents. J Pediatr. 2014;165:744–9.CrossRefGoogle Scholar
  39. 39.
    Hatch EE, Nelson JW, Qureshi MM, Weinberg J, Moore LL, Singer M, et al. Association of urinary phthalate metabolite concentrations with body mass index and waist circumference: a cross-sectional study of NHANES data, 1999-2002. Environ Health Glob Access Sci Source. 2008;7:27.Google Scholar
  40. 40.
    Tyrrell J, Melzer D, Henley W, Galloway TS, Osborne NJ. Associations between socioeconomic status and environmental toxicant concentrations in adults in the USA: NHANES 2001-2010. Environ Int. 2013;59:328–35.CrossRefGoogle Scholar
  41. 41.
    Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer-Verlag; 2009.CrossRefGoogle Scholar
  42. 42.
    Frederiksen H, Sørensen K, Mouritsen A, Aksglaede L, Hagen CP, Petersen JH, et al. High urinary phthalate concentration associated with delayed pubarche in girls. Int J Androl. 2012;35:216–26.CrossRefGoogle Scholar
  43. 43.
    Wolff MS, Teitelbaum SL, McGovern K, Windham GC, Pinney SM, Galvez M, et al. Phthalate exposure and pubertal development in a longitudinal study of US girls. Hum Reprod Oxf Engl. 2014;29:1558–66.CrossRefGoogle Scholar
  44. 44.
    Boas M, Frederiksen H, Feldt-Rasmussen U, Skakkebæk NE, Hegedüs L, Hilsted L, et al. Childhood exposure to phthalates: associations with thyroid function, insulin-like growth factor I, and growth. Environ Health Perspect. 2010;118:1458–64.CrossRefGoogle Scholar
  45. 45.
    Tsai Y-A, Lin C-L, Hou J-W, Huang P-C, Lee M-C, Chen B-H, et al. Effects of high di(2-ethylhexyl) phthalate (DEHP) exposure due to tainted food intake on pre-pubertal growth characteristics in a Taiwanese population. Environ Res. 2016;149:197–205.CrossRefGoogle Scholar
  46. 46.
    Huang H-B, Pan W-H, Chang J-W, Chiang H-C, Guo YL, Jaakkola JJK, et al. Does exposure to phthalates influence thyroid function and growth hormone homeostasis? The Taiwan environmental survey for toxicants (TEST) 2013. Environ Res. 2017;153:63–72.CrossRefGoogle Scholar
  47. 47.
    Thankamony A, Ong KK, Ahmed ML, Ness AR, Holly JMP, Dunger DB. Higher levels of IGF-I and adrenal androgens at age 8 years are associated with earlier age at menarche in girls. J Clin Endocrinol Metab. 2012;97:E786–90.CrossRefGoogle Scholar
  48. 48.
    Lovekamp-Swan T, Davis BJ. Mechanisms of phthalate ester toxicity in the female reproductive system. Environ Health Perspect. 2003;111:139–45.CrossRefGoogle Scholar
  49. 49.
    Versonnen BJ, Arijs K, Verslycke T, Lema W, Janssen CR. In vitro and in vivo estrogenicity and toxicity of o-, m-, and p-dichlorobenzene. Environ Toxicol Chem. 2003;22:329–35.CrossRefGoogle Scholar
  50. 50.
    Schlumpf M, Cotton B, Conscience M, Haller V, Steinmann B, Lichtensteiger W. In vitro and in vivo estrogenicity of UV screens. Environ Health Perspect. 2001;109:239–44.CrossRefGoogle Scholar
  51. 51.
    Takatori S, Kitagawa Y, Oda H, Miwa G, Nishikawa J, Nishihara T, et al. Estrogenicity of metabolites of benzophenone derivatives examined by a yeast two-hybrid assay. J Health Sci. 2003;49:91–8.CrossRefGoogle Scholar
  52. 52.
    Chou Y-Y, Huang P-C, Lee C-C, Wu M-H, Lin S-J. Phthalate exposure in girls during early puberty. J Pediatr Endocrinol Metab JPEM. 2009;22:69–77.CrossRefGoogle Scholar
  53. 53.
    Shi H, Cao Y, Shen Q, Zhao Y, Zhang Z, Zhang Y. Association between urinary phthalates and pubertal timing in Chinese adolescents. J Epidemiol. 2015;25:574–82.CrossRefGoogle Scholar
  54. 54.
    Wu W, Zhou F, Wang Y, Ning Y, Yang J-Y, Zhou Y-K. Exposure to phthalates in children aged 5-7years: associations with thyroid function and insulin-like growth factors. Sci Total Environ. 2017;579:950–6.CrossRefGoogle Scholar
  55. 55.
    Novotny R, Going S, Teegarden D, Van Loan M, McCabe G, McCabe L, et al. Hispanic and Asian pubertal girls have higher android/gynoid fat ratio than whites. Obes Silver Spring Md. 2007;15:1565–70.CrossRefGoogle Scholar
  56. 56.
    Ellis KJ, Abrams SA, Wong WW. Body composition of a young, multiethnic female population. Am J Clin Nutr. 1997;65:724–31.CrossRefGoogle Scholar
  57. 57.
    Shalitin S, Phillip M. Role of obesity and leptin in the pubertal process and pubertal growth--a review. Int J Obes Relat Metab Disord J Int Assoc Study Obes. 2003;27:869–74.CrossRefGoogle Scholar
  58. 58.
    Li S, Zhao J, Wang G, Zhu Y, Rabito F, Krousel-Wood M, et al. Urinary triclosan concentrations are inversely associated with body mass index and waist circumference in the US general population: experience in NHANES 2003-2010. Int J Hyg Environ Health. 2015;218:401–6.CrossRefGoogle Scholar
  59. 59.
    Lee JM, Appugliese D, Kaciroti N, Corwyn RF, Bradley RH, Lumeng JC. Weight status in young girls and the onset of puberty. Pediatrics. 2007;119:e624–30.CrossRefGoogle Scholar
  60. 60.
    Davison KK, Susman EJ, Birch LL. Percent body fat at age 5 predicts earlier pubertal development among girls at age 9. Pediatrics. 2003;111:815–21.CrossRefGoogle Scholar
  61. 61.
    Aksglaede L, Juul A, Olsen LW, Sørensen TIA. Age at puberty and the emerging obesity epidemic. PLoS One. 2009;4:e8450.CrossRefGoogle Scholar
  62. 62.
    Rubin C, Maisonet M, Kieszak S, Monteilh C, Holmes A, Flanders D, et al. Timing of maturation and predictors of menarche in girls enrolled in a contemporary British cohort. Paediatr Perinat Epidemiol. 2009;23:492–504.CrossRefGoogle Scholar
  63. 63.
    Biro FM, Greenspan LC, Galvez MP, Pinney SM, Teitelbaum S, Windham GC, et al. Onset of breast development in a longitudinal cohort. Pediatrics. 2013;132:1019–27.CrossRefGoogle Scholar
  64. 64.
    Calafat AM, Longnecker MP, Koch HM, Swan SH, Hauser R, Goldman LR, et al. Optimal exposure biomarkers for nonpersistent Chemicals in Environmental Epidemiology. Environ Health Perspect. 2015;123:A166–8.CrossRefGoogle Scholar
  65. 65.
    Johns LE, Cooper GS, Galizia A, Meeker JD. Exposure assessment issues in epidemiology studies of phthalates. Environ Int. 2015;85:27–39.CrossRefGoogle Scholar
  66. 66.
    Teitelbaum SL, Britton JA, Calafat AM, Ye X, Silva MJ, Reidy JA, et al. Temporal variability in urinary concentrations of phthalate metabolites, phytoestrogens and phenols among minority children in the United States. Environ Res. 2008;106:257–69.CrossRefGoogle Scholar
  67. 67.
    Engel LS, Buckley JP, Yang G, Liao LM, Satagopan J, Calafat AM, et al. Predictors and variability of repeat measurements of urinary phenols and parabens in a cohort of shanghai women and men. Environ Health Perspect. 2014;122:733–40.Google Scholar
  68. 68.
    Ferguson KK, Colacino JA, Lewis RC, Meeker JD. Personal care product use among adults in NHANES: associations between urinary phthalate metabolites and phenols and use of mouthwash and sunscreen. J Expo Sci Environ Epidemiol. 2017;27:326–32.CrossRefGoogle Scholar
  69. 69.
    Kaltiala-Heino R, Kosunen E, Rimpelä M. Pubertal timing, sexual behaviour and self-reported depression in middle adolescence. J Adolesc. 2003;26:531–45.CrossRefGoogle Scholar
  70. 70.
    Stice E, Presnell K, Bearman SK. Relation of early menarche to depression, eating disorders, substance abuse, and comorbid psychopathology among adolescent girls. Dev Psychol. 2001;37:608–19.CrossRefGoogle Scholar
  71. 71.
    Reardon LE, Leen-Feldner EW, Hayward C. A critical review of the empirical literature on the relation between anxiety and puberty. Clin Psychol Rev. 2009;29:1–23.CrossRefGoogle Scholar
  72. 72.
    Blumenthal H, Leen-Feldner EW, Trainor CD, Babson KA, Bunaciu L. Interactive roles of pubertal timing and peer relations in predicting social anxiety symptoms among youth. J Adolesc Health Off Publ Soc Adolesc Med. 2009;44:401–3.CrossRefGoogle Scholar

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© The Author(s). 2018

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  • Alexandra M. Binder
    • 1
  • Camila Corvalan
    • 2
  • Antonia M. Calafat
    • 3
  • Xiaoyun Ye
    • 3
  • Verónica Mericq
    • 4
  • Ana Pereira
    • 2
  • Karin B. Michels
    • 1
  1. 1.Department of Epidemiology, Fielding School of Public HealthUniversity of CaliforniaLos AngelesUSA
  2. 2.Institute of Nutrition and Food TechnologyUniversity of ChileSantiagoChile
  3. 3.Division of Laboratory Sciences, National Center for Environmental HealthCenters for Disease Control and PreventionAtlantaUSA
  4. 4.Institute of Maternal and Child ResearchUniversity of ChileSantiagoChile

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