Determinants of phthalate exposure among a U.S.-based group of Latino workers

Background: Phthalates are endocrine disrupting compounds linked to various adverse health effects. U.S. national biomonitoring data indicate that select minority subgroups may suffer disparate exposures to phthalates. Still, exposures and their respective determinants among these subgroups are not well characterized. Objective: We sought to examine determinants of phthalate exposure in a subsample of US-based Latino adults. Methods: We conducted a cross-sectional study on 94 Latino immigrant adults in Maryland. Participants were >18 years of age and working in a service-based industry. We administered an interviewer-administered questionnaire to capture information on potential exposure determinants (e.g., demographic characteristics, consumer product use, and workplace exposures and behaviors) and using HPLC/MS-MS we quantified concentrations of 9 urinary phthalate metabolites: monoethyl phthalate (MEP, diethyl phthalate metabolite); mono-n-butyl phthalate (MBP, di-n-butyl phthalate metabolite); mono-isobutyl phthalate (MiBP, di-isobutyl phthalate metabolite; monobenzyl phthalate (MBzP, benzylbutyl phthalate metabolite); molar sum of di-2-ethylhexyl phthalate or DEHP metabolites [mono-2-ethylhexyl phthalate (MEHP), mono-(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), mono-(2-ethyl-5-oxohexyl) phthalate (MEOHP), and mono-(2-ethyl-5-carboxypentyl) phthalate (MECCP)]; and mono(3-carboxypropyl) phthalate (MCPP, a non-specific metabolite of several phthalates including di-n-butyl phthalate and di-n-octyl phthalate). DEHP was analyzed as the molar sum of four metabolites (ΣDEHP = MEHP + MEHHP + MECPP + MEOHP). Spearman correlations, Wilcoxon-Mann-Whitney, and Kruskal-Wallis tests were conducted to assess bivariate associations between metabolite concentrations and potential exposure determinants. Covariates associated with metabolites at p < 0.10 in bivariate analyses were included in multivariable linear regression models to assess the independent effects of predictors on metabolite concentrations. Results: Uncorrected median phthalate metabolite concentrations were lower in our study population (<LOD-12.8 μg/L) compared to those reported in the US general population (1.0–28.8 μg/L) and adult populations in other countries. Geometric mean specific gravity-corrected concentrations for metabolites detected in >50% of samples ranged between 1.4 and 23.6 μg/L. While we observed some significant associations with select predictors in our bivariate analysis, select associations were attenuated in multivariable regression models. In our final multivariable linear regression models, we found that use of bleach (β = 1.15, 95%CI:0.30, 2.00) and consumption pasta/rice/noodles (β = 0.87, 95%CI: 0.27, 1.46) was positively associated with MBzP concentrations. MEP concentrations were inversely associated with use of furniture polish (β = −1.17, 95%CI: 2.21, −0.12) and use of scented dryer sheets (β = −1.08, 95%CI: 2.01, −0.14). Lastly, ΣDEHP concentrations were inversely associated with use of degreaser (ß_DEHP = −0.65, 95%CI: 1.25, −0.05). Conclusions: In this predominantly U.S.-based Central American subsample of adults, we observed lower metabolite concentrations than those previously reported in other U.S. studies and other countries. Our findings could be due, in part, to temporal trends in phthalate exposures and cultural differences related to exposure-related behaviors. While some exposure determinants were identified in our bivariate analyses, results from multivariable regression models did not provide clear results as many associations were attenuated. Environmental exposures may vary within minority subgroups and should be explored further in future studies to further inform exposure mitigation strategies.