The purpose of our study was to investigate the relationship between concentrations of urinary substances in single urine samples from participants in the national dietary survey Riksmaten Adolescent 2016-17 (RMA) and demographic, lifestyle and dietary factors among the participants. A urine sample was taken from each participant (spot urine), who also recorded what they ate and drank the day before the sampling. The study included the plastic chemicals phthalates, phthalate alternatives and bisphenols, the pesticides chlorpyrifos and pyrethroids, phosphorus flame retardants/plasticizers (PFRs), polyaromatic hydrocarbons (PAHs), the biocide triclosan (TCS), the UV filter substance BP3 and the preservative BHA. Associations between substance levels in urine and food consumption the day before sampling were investigated using log-linear multivariate regression analysis. Furthermore, the associations with important personal characteristics such as demographic factors (age, sex, country of birth, parental education level), BMI, sampling day of the week and calendar month, lifestyle (smoking, snus, alcohol) and residence (urban/rural, flooring, longitude/latitude) were also evaluated. The results were analyzed using log-linear multivariate regression analysis, where the associations between the concentration of a single urinary substance (dependent variable) and all the above explanatory variables (independent variables) were analyzed simultaneously in a regression model. Using this method of statistical analysis, a statistically significant association between concentrations of a substance in urine and an independent variable in the multivariate regression model should be interpreted as the effect of the independent variable after the result has been controlled (adjusted) for the effects of the other independent variables included in the model.
A variety of associations between substance concentrations and variables related to demographic/residential/lifestyle/dietary factors were observed among RMA participants, and also between concentrations and time of urine collection (month, day of week). An important thing to bear in mind when interpreting the results is that the more statistical tests that are performed, the greater the risk of falsely observing relationships that do not actually exist (chance findings). In cases where chance is not involved, the observed associations may in some cases be due to the independent variables in question being direct sources of exposure of the substances. In other cases, the relationships may be due to the fact that the independent variables studied are indicators of unknown underlying factors that are the actual sources of exposure.
Urine concentrations of MEP, a metabolite of the phthalate DEP, the metabolites of the phthalate alternative DiNCH, the bisphenol 4,4-BPF, the PAH metabolite 2-OH-PH, TCS and BP3 increased with increasing age (on average 5-13% per year of age). Concentrations of metabolites of the phthalate DEHP and a metabolite of the PFR TBOEP (BBOEP) decreased with increasing age (-5.3% to -6% per year). The concentrations of MEP and MBP (metabolite of the phthalate DBP), the bisphenol 4,4-BPF, a PAH metabolite, TCS, BHA and BP3 were on average 14%-70% lower among boys than among girls. The age- and gender-dependent relationships can to some extent be due to gender differences in the use of cosmetics/skin care/hygiene products that contain some of the substances studied. Concentrations of most phthalate, chlorpyrifos and pyrethroid metabolites, were on average 25% -54% lower among participants born in high-income countries than among those born in low-income countries, while the pattern for BP3 was the opposite (57% higher). The parental level of education, participant BMI, and use of tobacco products (snus/smoking) and alcohol had basically no effect on the exposure. Overall, the results show that there are differences in exposure patterns among different demographic groups of young people in Sweden
Residential factors also showed associations with concentrations of the substances, which may at least partially be due to differences in exposure from the indoor environment. Participants living in rural areas at the time of urine sampling had on average 13%-73% higher concentrations of phthalate and DiNCH metabolites than those living in urban environments. Replacement of different types of flooring materials over the last 10 years appeared to affect exposure to some phthalates/phthalate alternatives. Metabolites of the phthalates DBP and BBzP, the chlorpyrifos metabolite TCP, and the bisphenol BPS showed increasing concentrations the further north in Sweden the participants lived (4%-8% per latitudinal unit), while DPP and BP3 decreased northward in the country (-3.6% to -8.1% per latitudinal unit). Metabolites of PFR, as well as TCP and BPA showed increasing concentrations the further east in the country the participants lived (3.0%-7.3% per longitudinal unit).
Different seasonal and weekday patterns of time spent indoors and product use could hypothetically, in some cases, explain why the concentrations of a large number of phthalate and DiNCH metabolites varied with sampling month, as was also the case for the PFR metabolites DPP and BBOEP, the PAH metabolites 2 -OH-PH and 1-HP, and TCP. The metabolites of the phthalates DEP, BBzP and DEHP, as well as BBOEP, BPS and 2-OH-PH varied depending on which day of the week the sampling took place.
Concentrations of many different urinary substances decreased with increased consumption of fish, dairy (≤3% fat), processed meat, cheese and vegetables the day before sampling. DEHP metabolites and metabolites of PFR (mainly DPP) showed this pattern in relation to all the above-mentioned food groups. This suggests that there are underlying exposures that decrease with increased consumption of the foods in question. In contrast to this, concentrations of metabolites of several phthalates, BBOEP, and BHA increased with increased ice cream consumption the day before urine sampling. Phthalate and DiNCH metabolites also increased with increased consumption of baked goods.
Associations with two long-term dietary patterns were also investigated, i.e. the degree of healthy diet (SHEIA15) and the degree of varied diet (RADDS). SHEIA15 (Swedish Healthy Eating Index for Adolescents) is based on the Nordic nutritional recommendations and the higher SHEIA15 score a participant has, the healthier is the participant's eating pattern. RADDS (The Riksmaten Adolescent's Diet Diversity Score) is based on a quantification of each individual participant's variability in dietary habits, built around the Swedish Food Agency's dietary recommendations for a varied diet. The higher a participant's score, the more varied is the participant's diet. An increased degree of healthy diet was related to reduced exposure to the phthalate DPHP, PFR TPP, BPS and BP3, while a more varied diet was associated with reduced exposures to the phthalates DEP, BBzP, DEHP, DiNP, and DPHP and to BPA.
The regression models used in the study only explained 9-24% (BPA lowest, MBzP highest) of the variation in substance concentrations in the urine samples. RMA was primarily designed as a dietary survey, and in addition to the dietary registration that participants completed, they also answered a questionnaire that were primarily focused on questions related to dietary habits. There was limited space in the questionnaire for questions that would be of value for investigating other sources of exposure in addition to the diet. Examples of such data are the use of cosmetics/hygiene/body care products and of various products with plastic/rubber components, as well as the use of cleaning products at home and at school. In these cases, information is needed about use during day/days before sampling. In addition, information would be valuable about times of stay in different types of buildings the day/days before sampling (home, school, training facilities, leisure facilities, etc.), year of construction of the buildings and the type of floor/wall/ceiling material and surface material that the rooms had in which the participant spent time in. Several studies have shown that sampling the first urine in the morning provides a more reliable estimate of exposure the previous days than a urine sample taken at any time during the day. Therefore, it is desirable that the urine samples are taken directly in the morning before the day's activities begin. If this is not possible, information about at what time of day that the sampling occurred would also be valuable. In that case, information on the above-mentioned variables during the sampling day, until the sample is taken, should also be collected. The present study also showed that the concentrations of certain substances in the urine varied in a south-north and west-east direction in Sweden and were dependent on if the participant lived on the country-side or in an urban area. Moreover, variation in exposure was observed depending on the season and day of the week that the urine samples were taken. Information about these factors is thus important in future studies. The study also shows that the participant's country of birth contributes to some extent to the variation in substance exposure, which shows the importance of having information about this in future studies. Information about parental birth countries, as well as how long the participant has lived in Sweden is also of importance. These variables likely capture variation in lifestyle factors that contribute to variation in exposure, and which are difficult to capture with survey questions. Overall, the study also shows that there are a number of demographic/housing/lifestyle/dietary factors that can be confounders in future studies of health effects linked to urine substance exposure of young people in Sweden. Some of these factors are important to control for in statistical analyzes of associations between exposure and health.