Abstract
Objective
To investigate the associations between socio-demographic status, lifestyle factors, dietary patterns and urinary total phthalate concentration in a cohort of South Australian men.
Method
We randomly selected 1527 males aged 39 to 84 from wave two of the Men Androgen Inflammation Lifestyle Environment and Stress (MAILES) study. Total phthalate concentration was examined in fasting morning urine samples. Socio-demographic and lifestyle factors were assessed by questionnaire. Food intake was assessed by food frequency questionnaire (FFQ). Dietary patterns were constructed using factor analysis.
Results
Total phthalates were detected in 99.6% of the urine samples. The overall geometric mean (95% CI) of total phthalate concentration was 112.4 (107.5–117.5) ng/mL. The least square geometric means (LSGMs) of total phthalate concentration were significantly higher among people who were obese (127.8 ng/mL), consuming less than two serves fruit per day (125.7 ng/mL) and drinking more than one can (375mL) of carbonated soft drink per day (131.9 ng/mL). Two dietary patterns were identified: a prudent dietary pattern and a western dietary pattern. Both the western dietary pattern (p = 0.002) and multiple lifestyle risk factors including smoking, obesity, insufficient physical activity and the highest quartile of the western dietary pattern (p<0.001), were positively associated with total phthalate levels. There was no significant relationship between total phthalate concentration and socio-demographic status.
Conclusion
Phthalate exposure is ubiquitous and positively associated with lifestyle risk factors in urban dwelling Australian men.
Introduction
Public concern has been raised about Phthalates due to ubiquitous exposure and concern regarding an association with detrimental health effects such as obesity, diabetes and cardiovascular disease. Phthalates are diesters of 1,2-benzenedicarboxylic acid (phthalic acid) [1] widely used in a variety of industrial and consumer products to increase the transparency, flexibility and durability of plastic [2]. They are also used as a solvent in personal care products, medications and dietary supplements [3, 4]. When used as a plasticiser, phthalates are not physically bonded to the material backbone resulting in their leaching [5] and, consequently, their detection in environmental samples such as surface water, soil and air [6], from where they may enter the human food chain [6].
The contamination of foods with phthalates may also occur from food packaging, food processing, storage and transport [7]. The concentrations of phthalates vary according to the nature of the food; levels are higher in foods with a greater fat content [5, 8, 9]. A high level of phthalates is found in spicy dressings and butter in Germany [8], fish products in the United Kingdom (UK) [5] and pork products in the United States (US) [9]. Phthalate exposure appears to be driven by dietary habit [10]. In an Italian study, high levels of certain phthalates were found in people consuming canned food, drinking alcohol and using plastic containers to store fatty foods [11].
Phthalates from contaminated food are absorbed via the gut, and diet is considered to be the major pathway of phthalate exposure [8]. Phthalates can also be absorbed by inhalation of contaminated air or dust, and through the skin following the use of personal care products. The contributions of these sources to phthalate exposure are considered to be minor [12–14]. The relationship between patterns of food intake and lifestyle factors and phthalate exposure has not, as far as we can tell, been reported.
Following absorption, most phthalates are rapidly metabolized to monoesters and excreted via the kidneys, appearing in the urine within 24–48 h [2, 15]. Some phthalates are stored in fat and can be found in human fluids beyond 48 h, which reflects the possibility of bioaccumulation [16, 17].
Some population-based studies have been undertaken to determine phthalate exposure levels. The Canadian Health Measures Survey (CHMS), which examined urine samples collected by mobile clinics from 2007 to 2009, detected 11 phthalate metabolites in more than 90% of Canadians [18]. Prior examination conducted by the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2010 demonstrated the presence of phthalate metabolites in over 70% of Americans [19–21]. A study conducted in China in 2010 found that the presence of 14 phthalate metabolites in spot urine samples was ubiquitous [22].
Levels of phthalate metabolites are considered to vary according to socio-demographic status [19–21, 23–26]. However, the associations of phthalates and socio-demographic status are inconsistent in different populations, with no consistent pattern of change by socioeconomic status and ethnicity [21, 26]. Studies in Spain, China and central Italy considered that the regional variation might due to the level of industrial pollution, lifestyle and socioeconomic status developed in the region [11, 26, 27]. In a study comparing seven Asian countries, it was found that Kuwait had the highest level of phthalates compared to India, China, Vietnam, Japan, Korea, and Malaysia [28]. Similar studies have not, as far as we can establish, been undertaken in Australia, but are of potential importance given the health concerns attributed to phthalate exposure.
The aforementioned studies compared phthalate exposure by examining several phthalate metabolites. However, these findings may be biased by possible over-exposure to specific phthalates for a short period. In this study, we measured total phthalate levels in fasting morning urine samples collected from an urban, community-based cohort of South Australian men, to investigate the association of socio-demographic status, lifestyle factors and dietary patterns with total urinary phthalate concentration.
Methods
Study design and participants
This study used cross-sectional data from wave two of the Men Androgen Inflammation Lifestyle Environment and Stress (MAILES) study, a prospectively-followed cohort of urban, randomly selected community-dwelling men aged 35 and over at enrolment [29]. Fasting morning spot urine samples were available from men aged 39 to 84 who attended follow-up clinic visits and also completed detailed questionnaires to document socio-demographic status and health related behaviours (including food intakes), as well as health status and medication use (n = 1527 out of 2038 eligible participants of MAILES II). The study was approved by the research ethics committees of the Royal Adelaide Hospital and the North Western Adelaide Health Service, and informed consent was obtained from all participants. A written informed consent form was given to the eligible participants who signed in-clinic.
Measurements of phthalates
First-morning urine samples were collected in the clinic after an overnight fast, in 125 mL Nalgene containers (Thermo-Fisher Scientific Inc., Rochester NY, USA), aliquot into 30 mL Nalgene containers, stored on ice up to 2 h post-collection before being stored at -80°C. Total phthalates were measured using liquid chromatography tandem mass spectrometry (LC-MS/MS). The method extracts phthalic acid (PA) from participant urine samples (total PA, after hydrolysis of the glucuronides with 3 mol/L HCl), calibration standards (containing 13C4-PA, added as an external calibration standard and 2H4-PA, added as internal standard to all samples) and quality controls (containing 13C4-PA and 2H4-PA) using solid phase extraction (Strata-X polymeric reversed-phase, Phenomenex, Lane Cove, NSW, Australia). The analytes were separated by HPLC on a phenyl hexyl column (Luna, 50 x 2.0 mm, 3 μ, Phenomenex, Lane Cove, NSW, Australia) preceded by a 2 mm cartridge of the same material, and the eluate monitored with an API3000 MS/MS (AB Sciex, Mulgrave, Vic, Australia) operating in negative Multiple Reaction Monitoring (MRM) mode. The singly-charged Q1/Q3 transition for PA is 165.0/121.0/77.0 atomic mass units (amu), for 13C4-PA is 169.1/124.0/79.0 and 169.0/124.0/79.0 amu for 2H4-PA. The concentration of PA was calculated from a calibration curve of the ratio of the peak area of 13C4-PA/2H4-PA versus concentrations of 13C4-PA. The data were acquired and processed with Analyst 1.4 linked directly to the mass spectrometer. The assay range was from 10 to 1500 ng/mL and had a run time of approximately 10 min per sample at a flow rate of 0.2 mL/min. To prevent contamination from plastics during processing, the connection with plastic lids or containers was minimized. For concentrations of total phthalates (n = 6) below the limit of quantification (LOQ) of 10 ng/ml, a value of the LOQ divided by two was given [18].
Dietary patterns
Dietary intakes were assessed by a validated food frequency questionnaire (FFQ) [30]. Participants (n = 1503) who have completed FFQ reported the frequency that particular food items were consumed in the previous 12 months (never, monthly, weekly and daily). The frequencies of monthly and weekly food consumption were converted to daily frequencies using daily equivalent coefficients [30]. A total number of 128 food items including alcohol (beer, wine and spirit) and beverages (carbonated soft drink, tea and coffee) were aggregated into 40 groups based on a previous study in Australia (S1 Table) [30]. The 40 food groups were used to construct dietary patterns through factor analysis. There were a number of missing data for the daily consumption of fruit (n = 4), vegetables (n = 4), soft drinks (n = 4) and alcohol (n = 2). These were assigned a value of zero when the dietary patterns were constructed. Factor analysis was performed using the principle component factor method. The numbers of factors were retained in factor analysis by considering an Eigenvalue that was greater than one and a Scree plot. We tried two to five factor solutions. Two factors were finally used in the factor analysis according to the interpretability of derived patterns. Factors obtained were rotated using the (orthogonal) Varimax procedure to facilitate interpretability and ensure orthogonality [30]. Factor loadings were plotted in graphs in order to visualize the composition of food patterns. The major contributors of the dietary patterns was considered as food items with factor loadings greater than 0.3. The scores were predicted for both derived dietary patterns (prudent dietary and western dietary) and divided into quartiles for further analyses.
Socio-demographic and lifestyle factors
The participants’ socio-demographic data (including age, marital status, employment status, the highest educational level and annual household income), smoking status and physical activity by Active Australia survey definition [31] were collected using a standardised self-reported questionnaire [29]. Socio-demographic status including education was categorized according to the 13th biennial report of the Australian Institute of Health and Welfare (AIHW) [32]. The sufficient physical activity was defined as undertaking 150 mins or more of activity per week [31]. Body mass index (BMI) was calculated from weight (kg) divided by the square of height (m) with weight and height measured without shoes and in light clothing at the clinic [29]. A multiple lifestyle risk factors model was constructed based on smoking, obesity, undertaking insufficient physical activity and the highest quartile of western dietary pattern (each scored 1).
Occupational exposure
Participants were asked if they ever worked in a range of industries which were potentially exposed to hazardous substance. The listed industries include plastic fumes/plastic industry, cleaning agents industry, solvents/paints/remover industry, benzene industry, anaesthetics industry, printing ink industry, glue industry, wood dust/mining industry, asbestos industry, diesel fumes industry, lead industry, soldering fumes industry, pesticide/crop spraying industry, other chemical industry (not specified), other fumes industry (not specified). One more question was also asked to clarify if people are currently working in those industries. The occupational exposure status was created by categorizing the participants into three groups including none, ex-workers and current workers.
Statistical analysis
As the total urinary phthalate concentration were log-normal distributed, the geometric mean (GM) was calculated to represent overall phthalate exposure. Two multiple linear regression models were constructed. To compare the difference of the GM of total phthalate concentration across socio-demographic status and lifestyle factors, a multivariable linear regression model was produced to obtain the mutually adjusted least square geometric means (LSGMs) and 95% confidence intervals (CIs) of total phthalates concentration. The model includes socio-demographic status such as age, education, employment status, marital status, household annual income, and lifestyle factors such as smoking, BMI, physical activity, and daily consumption of alcohol, fruit, vegetables and carbonated soft drink [5, 18, 33]. The LSGMs were obtained through margins command with exponential option [34]. The pairwise comparisons across levels of factor variables were conducted after the regression. Hierarchical linear regression models were built to estimate the associations of total phthalate concentration with quartiles of dietary patterns and multiple lifestyle risk factors by adjusting for a broad range of known and potential confounders [20, 35]. We built three models: model 1 adjusted for age; level 2 with additional adjustment for the remained socio-demographic status including education, employment status, marital status and annual household income; level 3 further adjusted for the lifestyle factors including current smoking, BMI and physical activity for dietary patterns. Wald test was performed to compare the regression coefficients among groups. A sensitivity test was conducted to assess the effect of occupational exposure. A p-value ≤ 0.05 was considered to be significant. All analyses were performed and graphs drawn using STATA version 12 (StataCorp, College Station, TX, USA).
Results
The sample characteristics are shown in Table 1. The mean age of participants was 59.6 years. Among all men, 46.7% had a diploma or higher education attainment; 48.1% worked full-time and more than one third (35.5%) were retired. Over 60% of the participants reported that their gross annual household income was greater than AU$40,000. The majority of the participants in the study were overweight or obese (81.3%), alcohol drinkers (88.9%) and consumed fewer than five serves of vegetables per day (95.9%). There were 14.6% and 15.3% of total participants who were current smokers and drinking more than one can of carbonated soft drink per day respectively.
Table 1. Basic sample characteristic.
| n (%) | |
|---|---|
| Overall | 1527 (100.0) |
| Education a | |
| Up to high school | 426 (28.2) |
| Trade/apprenticeship | 380 (25.1) |
| Certificate/diploma | 487 (32.2) |
| Degree or higher | 220 (14.5) |
| Employment a | |
| Full time | 717 (48.1) |
| Part time/unemployed/student/other | 243 (16.3) |
| Retired | 529 (35.5) |
| Marital status a | |
| Married/living with a partner | 1169 (78.7) |
| Separated/divorced/widowed | 225 (15.2) |
| Never married | 91 (6.1) |
| Household annual income a | |
| Up to $40,000 | 531 (37.0) |
| $40,001 to $80,000 | 504 (35.2) |
| $80,001 or more | 399 (27.8) |
| Smoking a | |
| Non/ex-smokers | 1285 (85.4) |
| Current smokers | 219 (14.6) |
| Body Mass Index (BMI) a | |
| Underweight/normal (< = 24.9 kg/m2) | 285 (18.7) |
| Overweight (25.0–29.9 kg/m2) | 723 (47.4) |
| Obesity (> = 30.0 kg/m2) | 518 (33.9) |
| Physical activity a | |
| Sufficient physical activity | 597 (60.6) |
| Insufficient physical activity | 388 (39.4) |
| Alcohol per day a b | |
| Non alcohol drinkers | 166 (11.1) |
| Alcohol drinkers | 1335 (88.9) |
| Fruit per day a b | |
| 2 serves or more | 609 (40.6) |
| Less than 2 serves | 890 (59.4) |
| Vegetables per day a b | |
| 5 serves or more | 62 (4.1) |
| Less than 5 serves | 1437 (95.9) |
| Carbonated soft drinks per day a b | |
| 1 can or less | 1270 (84.7) |
| More than 1 can | 229 (15.3) |
a Missing categories were not reported.
b Questions from food frequency questionnaire (FFQ).
Two dietary patterns were identified and the labelled rotated factor loadings are plotted in Fig. 1. The two dietary patterns accounted for 16.9% of the total variance of food intake. Factor 1 was named as the prudent dietary pattern as it had high factor loadings on fruit, vegetables, fish, legumes, nuts and juices. Factor 2 was named as the western dietary pattern as it had high factor loadings on high fat dairy, take-away food, processed meat, carbonated soft drink, white bread, saturated spread and potato cooked with fat.
Fig 1. The factor loadings of factor analysis for dietary patterns.
Two dietary patterns which are prudent and western dietary pattern were generated using the principle component factor method of factor analysis.
Total phthalates were detected in 99.6% of the urine samples. The overall GM (95% CI) of total phthalate concentration was 112.4 (107.5–117.5) ng/mL (S2 Table). The adjusted LGSMs of total phthalate concentration among socio-demographic status and lifestyle factors are plotted in Fig. 2. There were no significant differences in total phthalate concentration among socio-demographic status. However, the LGSM of total phthalate concentration was significantly higher among obese men (127.8 ng/mL vs 108.8 ng/mL, p = 0.019), as well as those consuming less than two serves of fruit (125.7 ng/mL vs 103.6 ng/mL, p<0.001) and drinking more than one can (375 mL) of carbonated soft drink, per day (131.9 ng/mL vs 110.7 ng/mL, p = 0.001).
Fig 2. The LSGM of phthalate concentration among socio-demographic status and lifestyle factors.
Margins with exponential transferring and marginsplot syntax were used to plot after linear regression mutually adjusting for age, education, employment status, marital status, household annual income, smoking, body mass index (BMI), physical activity, alcohol consumption per day, fruit consumption per day, vegetables consumption per day and carbonated soft drinks consumption per day.
The associations of total phthalate concentration with dietary patterns and multiple lifestyle risk factors are shown in Tables 2 and 3. The western dietary pattern was positively associated with the LSGM of the urine phthalate concentration (p = 0.002), of which increased by 18.7% in-group with the highest quartile of western dietary pattern compared to the lowest quartile (125.7 ng/mL vs 105.9 ng/mL). A marginally inversed association was found in those with the prudent dietary pattern (p = 0.043). Nearly 80% of people had at least one lifestyle risk factor. The phthalate concentration increased with the number of lifestyle risk factors (p<0.001); people with three or more lifestyle risk factors had 22.8% higher concentrations than those who had no lifestyle risk factors.
Table 2. The association between dietary patterns and urinary total phthalate concentration.
| n | LSGM a d (95% CI) | β1 (SE) b | β2 (SE) c | β3 (SE) d | |
|---|---|---|---|---|---|
| Prudent dietary pattern e | |||||
| Quartile 1 (lowest) | 376 | 120.7 (109.4–132.1) | 0 (ref) | 0 (ref) | 0 (ref) |
| Quartile 2 | 376 | 124.5 (113.1–135.9) | 0.02 (0.06) | 0.03 (0.07) | 0.03 (0.07) |
| Quartile 3 | 376 | 112.8 (102.3–123.3) | -0.10 (0.06) | -0.08 (0.07) | -0.07 (0.07) |
| Quartile 4 (highest) | 375 | 107.4 (97.3–117.4) | -0.12 (0.06) | -0.12 (0.07) | -0.11 (0.07) |
| P value for trend | 0.024 | 0.028 | 0.047 | ||
| Western dietary pattern e | |||||
| Quartile 1 (lowest) | 376 | 105.9 (95.8–115.9) | 0 (ref) | 0 (ref) | 0 (ref) |
| Quartile 2 | 376 | 108.3 (98.2–118.3) | 0.07 (0.06) | 0.02 (0.07) | 0.02 (0.07) |
| Quartile 3 | 376 | 125.3 (113.9–136.8) | 0.21 (0.06) | 0.17 (0.07) | 0.17 (0.07) |
| Quartile 4 (highest) | 375 | 125.7 (113.9–137.6) | 0.23 (0.06) | 0.20 (0.07) | 0.17 (0.07) |
| P value for trend | <0.001 | <0.001 | 0.002 |
a LSGM: Least Square Geometric Mean.
b Model adjusted to age.
c Model adjusted to age, education, employment status, marital status and annual household income.
d Model adjusted to age, education, employment status, marital status, annual household income, current smoking, body mass index (BMI) and physical activity.
e Derived from food frequency questionnaire (FFQ).
Table 3. The association of a multiple lifestyle risk factors with urinary total phthalate concentration.
| n | LSGM a c (95% CI) | β1 (SE) b | β2 (SE) c | |
|---|---|---|---|---|
| Lifestyle risk factors d | ||||
| None | 311 | 103.9 (93.3–114.5) | 0 (ref) | 0 (ref) |
| 1 risk factor | 599 | 108.0 (100.0–115.9) | 0.03 (0.06) | 0.04 (0.06) |
| 2 risk factors | 431 | 130.2 (118.9–141.4) | 0.23 (0.07) | 0.23 (0.07) |
| 3 factors or more | 186 | 127.6 (111.0–144.3) | 0.29 (0.08) | 0.21 (0.09) |
| P value for trend | <0.001 | <0.001 |
a LSGM: Least Square Geometric Mean.
b Model adjusted to age.
c Model adjusted to age, education, employment status, marital status and annual household income.
d Multiple lifestyle risk factors include smoking, obesity, insufficient physical activity and highest quartile of western dietary pattern.
We conducted a sensitivity analysis to test the effect of occupational exposure (S3 Table). After adjusted for age, remained socio-demographic status and lifestyle factors respectively, there was no significant association between occupational exposure and phthalate levels.
Discussion
In this cross-sectional study we detected the presence of total phthalates in the urine of almost all participants. This indicates that at least for urban dwelling South Australian men there is ubiquitous exposure to phthalates. The positive association of a western dietary pattern, higher carbonated soft drink consumption, lower fruit consumption and obesity with total phthalate concentration suggests that lifestyle factors and chronic disease risk are co-associated with phthalate exposure.
Although previous studies [18, 19, 24, 26] have shown almost universal exposure to phthalates, as far as we are aware, this is the first population study to explore the relationship between urinary phthalate concentration, dietary patterns and lifestyle risk factors. It is also the first study of phthalate exposure in an Australian population. In Australia, people with unhealthy lifestyles tend to consume more fast or convenience foods [36]. As dietary intake is the primary route of phthalate exposure [37], it seems most likely that phthalate exposure result from an unhealthy dietary pattern of processed and packaged foods [5, 7–9] often associated with other unhealthy behaviours and obesity [38, 39]. The association of carbonated soft drink consumption with phthalate exposure has been reported previously [40]. Most carbonated soft drinks are consumed from containers either made of, or lined with, material that can leach phthalates into the fluid [41]. It is also possible that the contamination occurs during processing [42].
In contrast The inverse association between the prudent dietary pattern and urinary phthalate reflects the consumption of more fresh vegetables, fruit, nuts and fish and less high fat, packaged and processed foods [5], and consequently less exposure to phthalates.
The higher urinary phthalate in obese men may reflect patterns of food intake, but since the observation is independent of diet it is more likely to be the result of phthalate accumulation in adipose tissue. A recent study in rats has shown that dibutyl phthalate (DBP) accumulates in adipose tissues [17].
The absence of an association between age and total phthalate concentration is in accordance with other studies [18, 24, 25]. There was also no association found in this study between urinary total phthalate concentration and socioeconomic status, however, this differs from the findings of previous studies [19–21, 23, 26, 43–46]. Firstly, this may be because we examined total phthalates rather than specific phthalate metabolites which were measured in all of previous studies. Secondly, some of these studies only recruited maternal women or women at reproductive age, which are different from our target population [20, 21, 26, 43–46]. Thirdly, results from the studies using NHANES data showed an inconsistent pattern over time [19–21, 23]. Finally, the association between the level of some phthalate metabolites and education, household income and social class remain in conflict among a number of studies on women [20, 21, 26, 43–46]. Therefore, our findings among socioeconomic status are incomparable with previous studies due to different study designs, different target population and different regions. It is surprising that occupational exposure did not affect the total phthalate concentration in this study. They may due to a good occupational protection or that participants were absent from work 24–48 h before the urine collection. However, further research may need to investigate this association.
The strength of this study is the measurement of total urinary phthalates from a large sample of community dwelling men from whom detailed information relating to socio-demographic status, lifestyle and diet was obtained. Total phthalate concentration is less subject to variation as compared to specific phthalate metabolites measured in previous studies [20], and is a good indicator of the overall impact exposure to multiple sources of environmental phthalates [47–49].
The limitations of this study include its cross-sectional nature and so causal relationships between lifestyle factors and total phthalate exposure cannot be established. Also while the men in the study are broadly representative of Australian urban dwelling men aged 39–84 years, the findings cannot be generalised beyond this group. In addition, we did not have detailed information on smoking. We were unable to adjust for the day-to-day variation in phthalate exposure, although this may not affect the overall direction of the associations. Finally, we did not collect contemporaneous dietary records and therefore we are relying on the “usual dietary pattern” rather than exposure over the previous 24 hours.
Conclusion
As the first study to explore the total phthalate exposure among Australians, we found that phthalates are ubiquitous. Lifestyle factors and dietary habits contribute more to phthalate exposure than socio-demographic status. This extends our understanding on the determinants of total phthalate exposure in Australia. Our findings also support that dietary intake is a major route of phthalate exposure, with carbonated soft drink being an important contributor. People consuming more fatty foods are more likely to have a high concentration of phthalates in the body. Therefore, adopting a healthy lifestyle may be an option to reduce phthalate exposure. Given the ubiquitous but markedly varying exposure to phthalates future research is needed to explore the relationships between total phthalate exposure and health risks such as obesity, diabetes and cardiovascular disease [1].
Supporting Information
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Data Availability
The data from Men Androgen Inflammation Lifestyle Environment and Stress (MAILES) study are third party data which can be obtained through contacting Sean Martin (sean.martin@adelaide.edu.au) or Janet Grant (jane.grant@adelaide.edu.au).
Funding Statement
Funding was provided by National Health and Medical Research Council (NHMRC), grant number #627227, to the University of Adelaide. The funders had no role in study design, data collection and analysis,decision to publish, or preparation of the manuscript.
References
- 1. Goodman M, Lakind JS, Mattison DR. Do phthalates act as obesogens in humans? A systematic review of the epidemiological literature. Crit Rev Toxicol. 2014;44(2):151–75. 10.3109/10408444.2013.860076 [DOI] [PubMed] [Google Scholar]
- 2. National Industrial Chemicals Notification and Assessment Scheme (NICNAS). Existing Chemical Hazard Assessment Report—Phthalate Hazard Compendium. Canberra: Department of Health and Aging NICNAS; 2008. 10.1002/etc.2672 [DOI] [Google Scholar]
- 3. Hernandez-Diaz S, Mitchell AA, Kelley KE, Calafat AM, Hauser R. Medications as a potential source of exposure to phthalates in the U.S. population. Environ Health Perspect. 2009;117(2):185–9. 10.1289/ehp.11766 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Kelley KE, Hernandez-Diaz S, Chaplin EL, Hauser R, Mitchell AA. Identification of phthalates in medications and dietary supplement formulations in the United States and Canada. Environ Health Perspect. 2012;120(3):379–84. 10.1289/ehp.1103998 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Bradley EL, Burden RA, Bentayeb K, Driffield M, Harmer N, Mortimer DN, et al. Exposure to phthalic acid, phthalate diesters and phthalate monoesters from foodstuffs: UK total diet study results. Food Addit Contam Part A Chem Anal Control Expo Risk Assess. 2013;30(4):735–42. 10.1080/19440049.2013.781684 [DOI] [PubMed] [Google Scholar]
- 6. Bradley EL, Burden RA, Leon I, Mortimer DN, Speck DR, Castle L. Determination of phthalate diesters in foods. Food Addit Contam Part A Chem Anal Control Expo Risk Assess. 2013;30(4):722–34. 10.1080/19440049.2013.781683 [DOI] [PubMed] [Google Scholar]
- 7.Guo Y, Zhang Z, Liu L, Li Y, Ren N, Kannan K. Occurrence and Profiles of Phthalates in Foodstuffs from China and Their Implications for Human Exposure. J Agric Food Chem. 2012. [DOI] [PubMed]
- 8.Heinemeyer G, Sommerfeld C, Springer A, Heiland A, Lindtner O, Greiner M, et al. Estimation of dietary intake of bis(2-ethylhexyl)phthalate (DEHP) by consumption of food in the German population. International journal of hygiene and environmental health. 2013. [DOI] [PubMed]
- 9. Schecter A, Lorber M, Guo Y, Wu Q, Yun SH, Kannan K, et al. Phthalate concentrations and dietary exposure from food purchased in New York State. Environ Health Perspect. 2013;121(4):473–94, 94e1-4. 10.1289/ehp.1206367 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Koch HM, Lorber M, Christensen KL, Palmke C, Koslitz S, Bruning T. Identifying sources of phthalate exposure with human biomonitoring: Results of a 48h fasting study with urine collection and personal activity patterns. International journal of hygiene and environmental health. 2013. [DOI] [PubMed]
- 11.Tranfo G, Papaleo B, Caporossi L, Capanna S, De Rosa M, Pigini D, et al. Urinary metabolite concentrations of phthalate metabolites in Central Italy healthy volunteers determined by a validated HPLC/MS/MS analytical method. International journal of hygiene and environmental health. 2012. [DOI] [PubMed]
- 12. Rudel RA, Perovich LJ. Endocrine disrupting chemicals in indoor and outdoor air. Atmos Environ (1994). 2009;43(1):170–81. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. U.S. CDC. Chemical fact sheet—phthalate U.S.: Department of Health and Human Services; 2013. [Google Scholar]
- 14. Wittassek M, Koch HM, Angerer J, Bruning T. Assessing exposure to phthalates—the human biomonitoring approach. Mol Nutr Food Res. 2011;55(1):7–31. 10.1002/mnfr.201000121 [DOI] [PubMed] [Google Scholar]
- 15. Toxicity and Exposure Assessment for Children's Health (TEACH). TEACH chemical summary—phthalates U.S.: Environmental Protection Agency (EPA); 2006. [Google Scholar]
- 16. Genuis SJ, Beesoon S, Lobo RA, Birkholz D. Human elimination of phthalate compounds: blood, urine, and sweat (BUS) study. ScientificWorldJournal. 2012;2012:615068 10.1100/2012/615068 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Zeng Q, Wei C, Wu Y, Li K, Ding S, Yuan J, et al. Approach to distribution and accumulation of dibutyl phthalate in rats by immunoassay. Food Chem Toxicol. 2013;56:18–27. 10.1016/j.fct.2013.01.045 [DOI] [PubMed] [Google Scholar]
- 18.Saravanabhavan G, Guay M, Langlois E, Giroux S, Murray J, Haines D. Biomonitoring of phthalate metabolites in the Canadian population through the Canadian Health Measures Survey (2007–2009). International journal of hygiene and environmental health. 2013. [DOI] [PubMed]
- 19. Silva MJ, Barr DB, Reidy JA, Malek NA, Hodge CC, Caudill SP, et al. Urinary levels of seven phthalate metabolites in the U.S. population from the National Health and Nutrition Examination Survey (NHANES) 1999–2000. Environ Health Perspect. 2004;112(3):331–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Kobrosly RW, Parlett LE, Stahlhut RW, Barrett ES, Swan SH. Socioeconomic factors and phthalate metabolite concentrations among United States women of reproductive age. Environ Res. 2012;115:11–7. 10.1016/j.envres.2012.03.008 [DOI] [PubMed] [Google Scholar]
- 21.Zota AR, Calafat AM, Woodruff TJ. Temporal Trends in Phthalate Exposures: Findings from the National Health and Nutrition Examination Survey, 2001–2010. Environ Health Perspect. 2014. [DOI] [PMC free article] [PubMed]
- 22. Guo Y, Wu Q, Kannan K. Phthalate metabolites in urine from China, and implications for human exposures. Environment international. 2011;37(5):893–8. 10.1016/j.envint.2011.03.005 [DOI] [PubMed] [Google Scholar]
- 23. Koo JW, Parham F, Kohn MC, Masten SA, Brock JW, Needham LL, et al. The association between biomarker-based exposure estimates for phthalates and demographic factors in a human reference population. Environ Health Perspect. 2002;110(4):405–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Olsen L, Lampa E, Birkholz DA, Lind L, Lind PM. Circulating levels of bisphenol A (BPA) and phthalates in an elderly population in Sweden, based on the Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS). Ecotoxicol Environ Saf. 2012;75(1):242–8. 10.1016/j.ecoenv.2011.09.004 [DOI] [PubMed] [Google Scholar]
- 25. Fromme H, Bolte G, Koch HM, Angerer J, Boehmer S, Drexler H, et al. Occurrence and daily variation of phthalate metabolites in the urine of an adult population. International journal of hygiene and environmental health. 2007;210(1):21–33. [DOI] [PubMed] [Google Scholar]
- 26. Casas L, Fernandez MF, Llop S, Guxens M, Ballester F, Olea N, et al. Urinary concentrations of phthalates and phenols in a population of Spanish pregnant women and children. Environment international. 2011;37(5):858–66. 10.1016/j.envint.2011.02.012 [DOI] [PubMed] [Google Scholar]
- 27. Chen L, Zhao Y, Li L, Chen B, Zhang Y. Exposure assessment of phthalates in non-occupational populations in China. Sci Total Environ. 2012;427–428:60–9. [DOI] [PubMed] [Google Scholar]
- 28. Guo Y, Alomirah H, Cho HS, Minh TB, Mohd MA, Nakata H, et al. Occurrence of phthalate metabolites in human urine from several Asian countries. Environ Sci Technol. 2011;45(7):3138–44. 10.1021/es103879m [DOI] [PubMed] [Google Scholar]
- 29.Grant JF, Martin SA, Taylor AW, Wilson DH, Araujo A, Adams RJ, et al. Cohort Profile: The Men Androgen Inflammation Lifestyle Environment and Stress (MAILES) Study. Int J Epidemiol. 2013. [DOI] [PMC free article] [PubMed]
- 30. Schoenaker DA, Dobson AJ, Soedamah-Muthu SS, Mishra GD. Factor analysis is more appropriate to identify overall dietary patterns associated with diabetes when compared with Treelet transform analysis. J Nutr. 2013;143(3):392–8. 10.3945/jn.112.169011 [DOI] [PubMed] [Google Scholar]
- 31. AIHW. The Active Australia Survey: a guide and mannual for implimentation, analysis and reporting Canberra: Australian Institue Health and Welfare (AIHW); 2003. [Google Scholar]
- 32. AIHW. Australia's health 2012 Canberra: Australian institute of Health and Welfare (AIHW); 2012. AUS 156–13. [Google Scholar]
- 33. Lang IA, Galloway TS, Scarlett A, Henley WE, Depledge M, Wallace RB, et al. Association of urinary bisphenol A concentration with medical disorders and laboratory abnormalities in adults. JAMA. 2008;300(11):1303–10. 10.1001/jama.300.11.1303 [DOI] [PubMed] [Google Scholar]
- 34. StataCorp;. Stata Reference Manual. College Station, TX: Stata Press; 2013. [Google Scholar]
- 35. Feng X, Astell-Burt T. Neighborhood socioeconomic circumstances and the co-occurrence of unhealthy lifestyles: evidence from 206,457 Australians in the 45 and up study. PLoS One. 2013;8(8):e72643 10.1371/journal.pone.0072643 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Mohr P, Wilson C, Dunn K, Brindal E, Wittert G. Personal and lifestyle characteristics predictive of the consumption of fast foods in Australia. Public Health Nutr. 2007;10(12):1456–63. [DOI] [PubMed] [Google Scholar]
- 37. Rudel RA, Gray JM, Engel CL, Rawsthorne TW, Dodson RE, Ackerman JM, et al. Food packaging and bisphenol A and bis(2-ethyhexyl) phthalate exposure: findings from a dietary intervention. Environ Health Perspect. 2011;119(7):914–20. 10.1289/ehp.1003170 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Olinto MT, Willett WC, Gigante DP, Victora CG. Sociodemographic and lifestyle characteristics in relation to dietary patterns among young Brazilian adults. Public Health Nutr. 2011;14(1):150–9. 10.1017/S136898001000162X [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Petersen SB, Rasmussen MA, Strom M, Halldorsson TI, Olsen SF. Sociodemographic characteristics and food habits of organic consumers—a study from the Danish National Birth Cohort. Public Health Nutr. 2012:1–10. [DOI] [PMC free article] [PubMed]
- 40. Khedr A. Optimized extraction method for LC-MS determination of bisphenol A, melamine and di(2-ethylhexyl) phthalate in selected soft drinks, syringes, and milk powder. J Chromatogr B Analyt Technol Biomed Life Sci. 2013;930C:98–103. [DOI] [PubMed] [Google Scholar]
- 41. Guo Z, Wei D, Wang M, Wang S. Determination of six phthalic acid esters in orange juice packaged by PVC bottle using SPE and HPLC-UV: application to the migration study. J Chromatogr Sci. 2010;48(9):760–5. [DOI] [PubMed] [Google Scholar]
- 42. Kappenstein O, Vieth B, Luch A, Pfaff K. Toxicologically relevant phthalates in food. EXS. 2012;101:87–106. 10.1007/978-3-7643-8340-4_4 [DOI] [PubMed] [Google Scholar]
- 43. Adibi JJ, Hauser R, Williams PL, Whyatt RM, Calafat AM, Nelson H, et al. Maternal urinary metabolites of Di-(2-Ethylhexyl) phthalate in relation to the timing of labor in a US multicenter pregnancy cohort study. Am J Epidemiol. 2009;169(8):1015–24. 10.1093/aje/kwp001 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44. Wolff MS, Engel SM, Berkowitz GS, Ye X, Silva MJ, Zhu C, et al. Prenatal phenol and phthalate exposures and birth outcomes. Environ Health Perspect. 2008;116(8):1092–7. 10.1289/ehp.11007 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45. Ye X, Pierik FH, Hauser R, Duty S, Angerer J, Park MM, et al. Urinary metabolite concentrations of organophosphorous pesticides, bisphenol A, and phthalates among pregnant women in Rotterdam, the Netherlands: the Generation R study. Environ Res. 2008;108(2):260–7. 10.1016/j.envres.2008.07.014 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46. Peck JD, Sweeney AM, Symanski E, Gardiner J, Silva MJ, Calafat AM, et al. Intra- and inter-individual variability of urinary phthalate metabolite concentrations in Hmong women of reproductive age. Journal of exposure science & environmental epidemiology. 2010;20(1):90–100. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47. Meeker JD, Sathyanarayana S, Swan SH. Phthalates and other additives in plastics: human exposure and associated health outcomes. Philos Trans R Soc Lond B Biol Sci. 2009;364(1526):2097–113. 10.1098/rstb.2008.0268 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48. Heudorf U, Mersch-Sundermann V, Angerer J. Phthalates: toxicology and exposure. International journal of hygiene and environmental health. 2007;210(5):623–34. [DOI] [PubMed] [Google Scholar]
- 49. Wormuth M, Scheringer M, Vollenweider M, Hungerbuhler K. What are the sources of exposure to eight frequently used phthalic acid esters in Europeans? Risk Anal. 2006;26(3):803–24. [DOI] [PubMed] [Google Scholar]
Associated Data
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Data Availability Statement
The data from Men Androgen Inflammation Lifestyle Environment and Stress (MAILES) study are third party data which can be obtained through contacting Sean Martin (sean.martin@adelaide.edu.au) or Janet Grant (jane.grant@adelaide.edu.au).