INTRODUCTION
Attention-deficit/hyperactivity disorder (ADHD) is a chronic neurodevelopmental condition that begins in early childhood and can persist across the lifespan. According to nationally representative data from the United States, the estimated prevalence of ADHD among children and adolescents aged 4-17 years ranged between 10.08% and 10.47% during the period from 2017 to 2022 [1]. In Türkiye, a multicenter study conducted among primary school-aged children reported a prevalence rate of 12.4% for ADHD [2]. Although the prevalence of ADHD has increased over the years, the condition is not only a risk factor for comorbid psychiatric disorders but can also adversely affect an individual’s academic performance, occupational functioning, social and romantic relationships, and overall quality of life [3]. Although genetic factors are considered the primary cause of ADHD, the contribution of environmental influences (notably exposure to endocrine-disrupting chemicals [EDCs]) should not be overlooked. The rising prevalence and increasing recognition of ADHD have underscored the need to investigate the role of environmental factors in the etiology of the disorder. EDCs are defined as exogenous (non-naturally occurring) substances or mixtures that can interfere with any stage of hormonal processes. Exposure to these substances occurs via multiple environmental sources, such as plastic materials, cosmetics and personal care products, food additives, and air pollution [4-6].
There is a growing body of evidence suggesting that exposure to environmental chemicals, particularly during sensitive developmental periods such as the prenatal period and early childhood, may be associated with alterations in attentional processes [7]. In addition to persistent organic pollutants, evidence regarding the potential health associations of other, less persistent EDCs has also been increasing [8]. Considering routes of exposure and hormonal regulatory processes, children and adolescents are thought to be more vulnerable to potential biological impacts of these compounds [9]. Among these EDCs, parabens have become an increasing focus of interest due to their widespread use and continuous exposure in daily life. Parabens belong to the group of EDCs and are widely used as preservatives in cosmetic, food, and pharmaceutical products [10-12]. These compounds are synthetic preservatives designed to inhibit the growth of bacteria, fungi, and other microorganisms in products [11]. The most commonly encountered paraben types are methyl paraben, ethyl paraben, propyl paraben, and butyl paraben [13]. Humans are exposed to parabens through various routes, including dermal absorption, inhalation of contaminated air, and the ingestion of dust, food, and water [14]. Pharmacokinetic studies have shown that, following dermal exposure, the elimination half-lives of methyl paraben, ethyl paraben, and propyl paraben are 12.2, 12.0, and 9.3 hours, respectively [15]. Oral exposure, on the other hand, has been reported to be associated with a shorter half-life [16]. After exposure, parabens are rapidly metabolized in the liver and are excreted mainly through urine [17,18]. The associations between methyl paraben, ethyl paraben, propyl paraben, and butyl paraben and attention, hyperactivity, and impulsivity have been examined in only a limited number of studies. For example, Baker et al. [19] demonstrated that higher methyl paraben levels measured in meconium (reflecting prenatal exposure) were associated with an increased risk of ADHD at ages 6-7. Foreman et al.8 reported that urinary propyl paraben concentrations measured during adolescence were associated with ADHDlike symptoms. In contrast, Shoaff et al. [20], who also assessed concurrent adolescent exposure using urinary biomarkers, did not find a significant association.
Evidence suggesting an association between phthalate exposure and ADHD is comparatively stronger [21,22]. Phthalate metabolites are widely used in food processing and packaging materials, personal care products (e.g., cosmetics, perfumes), and pharmaceuticals, leading to exposure across all age groups [23,24]. Phthalate metabolites are not covalently bound to plastics and can therefore readily leach into the environment. They can enter the human body through oral, dermal, and inhalation exposure routes [25]. Shoaff et al. [20] demonstrated that concurrent urinary phthalate metabolite concentrations measured during adolescence were associated with increased ADHD-related behavioral problems. Another study also reported a significant association between phthalate exposure and increased maladaptive behaviors during adolescence [26].
Epidemiological studies suggest that prenatal and earlychildhood exposure to parabens and phthalate metabolites may be associated with ADHD-like behaviors [19,27,28]. However, most existing evidence derives from prenatal and early childhood exposure cohorts, whereas studies specifically examining concurrent adolescent exposure remain limited. Yet adolescence represents a critical period for brain development, during which behavioral problems related to hormonal changes, as well as structural and functional brain alterations, are frequently observed [29]. In this context, exposure to EDCs during adolescence is of particular importance because of its potential associations [8].
The primary aim of this study was to compare urinary concentrations of paraben and phthalate groups between adolescents diagnosed with ADHD and age- and sex-matched healthy controls. The secondary aim was to examine behavioral and lifestyle factors associated with levels of chemical exposure in the body, including recent personal care product use and selected consumption behaviors, in order to describe potential exposure patterns during adolescence. Given the short biological half-lives of parabens and phthalates, single spot urine measurements are expected to primarily reflect recent exposure. Therefore, the concurrent assessment of behavioral variables alongside biological measurements may facilitate a contextual interpretation of the observed findings.
METHODS
Participants
This study was approved by the Pamukkale University Non-Interventional Clinical Research Ethics Committee (Decision Date: 07 February 2023, Decision No: 03, Document No: E-60116787-020-328696). Following ethics committee approval and subsequent Scientific Research Project funding from our university, adolescents aged 12-17 years who presented to the Department of Child and Adolescent Psychiatry at Pamukkale University Faculty of Medicine and were diagnosed with ADHD, along with age- and sex-matched healthy controls, were included in the study. Written informed consent was obtained from all participants and their legal guardians.
The diagnosis and severity of ADHD were determined through a comprehensive psychiatric assessment, a clinical interview conducted in accordance with Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) diagnostic criteria, the Conners-Wells Adolescent Self-Report Scale-Short Form completed by the adolescents, and the Conners Parent Rating Scale-Revised Short Form completed by their parents.
Healthy control participants were evaluated through a DSM-5-based structured clinical interview conducted by a child and adolescent psychiatry specialist, and no active psychiatric disorders were identified. Accordingly, because the Conners- Wells Adolescent Self-Report Scale and the Conners’ Parent Rating Scale assess ADHD symptom severity, these scales were not administered to healthy control participants.
Urine sample collection and processing
Urine samples from adolescents with ADHD and healthy controls were collected in glass containers to avoid contact with plastic. Samples were transported under appropriate conditions to the Medical Physiology Laboratory of Pamukkale University Faculty of Medicine. Urine specific gravity (SG) was measured at the time of sample collection. Samples with SG values outside the range of 1.005-1.030 were excluded as part of quality control procedures to minimize the effects of extreme hypo- or hyperhydration. As a result, all samples included in the final analyses fell within a narrow and clinically acceptable SG range, indicating comparable hydration status across participants. Therefore, no additional SG- or creatininebased normalization was applied in the primary statistical analyses.
All analytical procedures were performed using only glass pipettes, glass containers, and glass tubes to prevent contamination from plastic materials. For each participant, urine samples were divided into two separate glass vials—one for paraben analyses and one for phthalate analyses—allowing each set of analyses to be performed independently. All samples were stored at -80°C in ultra-low temperature freezers until the completion of the study.
Paraben analysis
A total of seven paraben derivatives (methyl paraben, ethyl paraben, isopropyl paraben, propyl paraben, isobutyl paraben, butyl paraben, and benzyl paraben) were analyzed. Paraben measurements were performed using a high-performance liquid chromatography (HPLC) system (Thermo Scientific UltiMate 3000 HPLC System; Thermo Fisher Scientific Inc.). Chromatographic separation was achieved with a C18 column (Inertsil ODS-4, 100 Å, 5 μm, 4.6×250 mm; GL Sciences Inc.). The mobile phase consisted of 60% ultrapure water and 40% acetonitrile. The flow rate was set to 1.2 mL/min, the injection volume to 20 μL, the column temperature to 25°C, the DAD detector wavelength to 214 nm, and the total runtime to 3 minutes.
Calibration standards were prepared using mixed solutions of each paraben analyte at five different concentrations (100, 250, 500, 750, and 1,000 μg/L), and calibration curves were constructed for all analytes. The coefficient of determination (R²) values exceeded 99.95% for all analytes, indicating excellent calibration accuracy. The mean slope value was calculated as 1.2515. Additionally, the offset value was 0.0000 across all calibrations, supporting the suitability of the linear calibration model.
The HPLC analytical conditions and chromatographic parameters were adapted from a validated method described by Özcan et al.12 for the simultaneous determination of multiple parabens in pharmaceutical and cosmetic matrices, with minor laboratory-specific optimizations.
Sample pretreatment
Urine samples (100-700 mg) were extracted using 6.25 mL of a 1:1 ethanol-acetone mixture. The mixture was placed in glass tubes, shaken, and left at room temperature overnight. The following day, samples were centrifuged at 2,500 rpm for 10 minutes, and the resulting supernatant was transferred into a clean glass tube. The remaining pellet was re-extracted under the same conditions with 1.5 mL of the ethanol-acetone mixture, centrifuged, and the newly obtained supernatant was combined with the initial one.
The combined supernatant fractions were evaporated to dryness under a clean air stream at room temperature for 2-3 hours in a fume hood. The dried extracts were reconstituted in 3 mL of 70% methanol, vortex-mixed, and stored at -20°C overnight. On the next day, the mixture was centrifuged at 3,200 rpm for 20 minutes at 4°C using a pre-cooled rotor. The upper phase was transferred to a clean tube. The remaining pellet was washed again with 70% methanol, centrifuged under the same conditions, and the supernatants were pooled. The final extracts were transferred into HPLC vials and prepared for analysis. Limits of detection (LODs) for each paraben analyte are provided in Supplementary Table 1. The limit of quantification (LOQ) was defined as three times the LOD (LOQ=3×LOD).
In accordance with established analytical chemistry standards, LOD values were calculated using the equation LOD=k·sB/m, where sB denotes the standard deviation (SD) of the blank signal—obtained either through repeated blank measurements or, in chromatographic assessments, from the variability of multiple baseline data points—and m represents the slope of the calibration curve. The constant k, which reflects the selected LOD criterion, typically ranges between 2 and 3; in the present study, k was conservatively set at 3 to ensure stringent detection thresholds.
Instrumental precision of the HPLC-UV method was high, with coefficients of variation (CV) remaining below 10% for all paraben analytes. Calibration curves demonstrated excellent linearity (R² >0.99), and spike-recovery experiments yielded recovery rates within the accepted bioanalytical range of 80%-120%, supporting analytical reliability.
Phthalate analysis
A total of ten phthalate metabolites were analyzed: di-nhexyl phthalate, benzyl butyl phthalate, di-n-octyl phthalate, bis(2-ethylhexyl) phthalate, diamyl phthalate, bis(2-methoxyethyl) phthalate, di-n-butyl phthalate, isobutyl phthalate, diethyl phthalate, and dimethyl phthalate. Phthalate analyses were performed using a gas chromatography-mass spectrometry (GC-MS) system.
Stock solutions (50 mg/mL) were prepared for each phthalate and diluted with methanol to obtain five calibration levels ranging from 10 to 50 μg/mL, corresponding to a 2-μL injection volume. All standard solutions were stored at 4°C, and calibration curves were constructed for each analyte.
Ultra-high-purity helium (99.999995%) was used as the carrier gas. To prevent contamination, all glassware was washed twice with acetone and hexane and subsequently baked at 150°C for 1 hour. All plastic laboratoryware was removed from the laboratory environment during the analyses.
Chromatographic separation was achieved using an Rxi-5-ms GC capillary column (30 m×0.25 mm ID×1.0 μm; Restek Corporation). The inlet temperature was set to 280°C in splitless mode. The following oven temperature program was applied: an initial temperature of 70°C held for 1 minute; ramped to 230°C at 20°C/min and held for 2 minutes; then increased to 290°C at 5°C/min and held for 2 minutes; and finally raised to 300°C at 10°C/min and held for 2 minutes. The split flow rate was set to 45 mL/min, and the carrier gas flow rate to 1.5 mL/min.
The GC-MS analytical workflow and quality control procedures were adapted from the HERMOSA study protocols, with minor laboratory-specific optimizations to accommodate the available instrumentation [24].
Sample pretreatment
The cartridges (Phenomenex Solid Phase Extraction [SPE] cartridges; Phenomenex) were preconditioned sequentially with 3 mL methanol followed by 3 mL Milli-Q water (0.15 M ammonium acetate buffer, pH 2-3). Subsequently, 1.0 mL urine samples were loaded onto the cartridges. After sample loading, the cartridges were washed sequentially with 4 mL Milli-Q water, 4 mL sodium bicarbonate buffer (pH 8.5; prepared by dissolving 1.05 g sodium bicarbonate in 250 mL Milli-Q water), and 4 mL methanol.
The analytes were eluted with 5 mL methanol containing 2% formic acid (v/v). The eluate was then evaporated to near dryness at 45°C in a water bath under a gentle stream of nitrogen. Finally, the residue was reconstituted with 100 μL of a 60:40 methanol-water mixture (v/v) prior to analysis. LOD for phthalate analytes are presented in Supplementary Table 2. The LOQ was defined as three times the LOD (LOQ=3×LOD).
A linear regression analysis was performed between the peak areas obtained at different concentration levels and the corresponding analyte concentrations as part of the analytical validation process. The validity and reliability of the method were evaluated based on this linear relationship. The calibration curve demonstrated excellent linearity across the tested range, confirming the suitability of the method for quantitative analysis.
Instrumental precision was high, with CV remaining below 2.3% for all detected phthalate metabolites. Calibration linearity exceeded R²=99.95% across all analytes, further supporting analytical reliability.
Data collection tools
Sociodemographic data form
The sociodemographic data form used in this study was developed by the research team to assess the basic sociodemographic characteristics of the adolescents and their parents, as well as potential exposure-related variables.
The section directed to adolescents includes variables such as age, educational status, foods consumed within 24 hours prior to urine collection, use of personal care products, consumption of caffeinated beverages, and the amount of fast food and canned food consumed. These data were collected to evaluate potential exposure risk factors during the 24-hour period preceding urine sampling. Height and weight measurements were obtained during the urine sampling process.
The section directed to parents includes basic sociodemographic variables such as age, educational level, and income level.
The Revised Conners’ Parent Rating Scale-Short Form
The Revised Conners’ Parent Rating Scale-Short Form (CPRS-R/S) is a 27-item parent-report instrument developed by Conners to screen for ADHD and disruptive behavior disorders. The Turkish validity and reliability study of the scale was conducted by Kaner et al. [30], demonstrating that the measure possesses adequate psychometric properties [31]. The items are rated on a 4-point Likert scale with the response options “never,” “rarely,” “often,” and “very often,” scored as 0, 1, 2, and 3, respectively. Higher scores on the scale indicate increased levels of symptoms characteristic of disruptive behavior disorders. The CPRS-R/S consists of four subscales: Oppositional Behaviors, Cognitive Problems/Inattention, Hyperactivity, and the ADHD index.
Conners-Wells’ Adolescents Self-Report Scale-Short Form
The Conners-Wells’ Adolescents Self-Report Scale-Short Form (CASS-S) was developed by Conners et al. [32] to assess conduct problems, impulse-control difficulties, attention deficits, hyperactivity, and symptoms of ADHD in adolescents. The scale assesses adolescents’ self-reported behaviors over the past month and is designed for individuals aged 12 to 17 years. The Turkish adaptation, including its validity and reliability study, was conducted by Kaner et al. [33] The items are rated on a 4-point Likert scale (“never,” “sometimes,” “often,” and “very often”), scored as 0, 1, 2, and 3, respectively. Higher scores indicate that the adolescent experiences the problems described in the scale at a greater severity. The CASS-S comprises four subscales: Conduct Problems, Cognitive Problems/Inattention, Hyperactivity, and the ADHD index.
Statistical analyses
In the present study, all measurements of parabens and phthalate metabolites that were below the analytical detection limit (<LOD) or reported as “not detected” were imputed using the commonly applied LOD/2 substitution method. To improve distributional normality and reduce the influence of extreme values, all chemical concentration variables were subsequently log10-transformed. After log10 transformation, distributional assumptions for parametric analyses were re-evaluated and were found to be sufficiently met. Therefore, all subsequent group comparisons and correlation analyses were conducted using parametric tests on transformed variables only; no parametric analyses were performed on raw, untransformed concentration data.
In addition to numerical comparisons, box plots were used to visualize the distribution of urinary chemical concentrations in each group, displaying the median and interquartile ranges (Figure 1).
Statistical analyses were conducted using jamovi version 2.6. Categorical variables, including demographic characteristics and cosmetic use habits, were summarized as frequencies and percentages. Continuous demographic and anthropometric variables (e.g., age, weight, and body mass index [BMI]) were described using means, SDs, medians, minimums, and maximums.
Urinary concentrations of parabens and phthalates were treated as continuous variables. For descriptive reporting, geometric means were calculated. Behavioral outcomes measured by the Conners’ rating scales were also analyzed as continuous variables. Group comparisons for continuous variables were performed using independent samples t-tests when variance homogeneity assumption was met and Welch’s t-tests otherwise. Effect sizes were reported using Cohen’s d.
To examine the influence of sociodemographic characteristics, one-way analysis of variance was conducted. Furthermore, Pearson correlation analyses were performed to investigate the associations between log-transformed paraben and phthalate concentrations and Conners parent and self-report scale scores. In addition, to evaluate whether group differences in chemical exposure levels could be explained by sociodemographic factors and product-use behaviors, multivariable and mediation analyses were conducted. First, a two-step binary logistic regression analysis was performed with group membership (0=healthy control, 1=ADHD) as the dependent variable. In Model 1, household income, maternal education, paternal education, sex, and BMI were entered simultaneously as covariates. In Model 2, log10-transformed chemical exposure variables that showed significant between-group differences in univariate analyses (propyl paraben, dimethyl phthalate, diethyl phthalate, and di-n-hexyl phthalate) were added to the model. Multicollinearity was assessed using variance inflation factors, and all values were within acceptable limits (<2.0). Model fit was evaluated using likelihood ratio tests, classification accuracy, and area under the receiver operating characteristic curve.
Additionally, to examine whether chemical concentrations differed according to product-use behaviors and whether these associations varied by diagnostic group, factorial analysis of covariance (ANCOVA) models were conducted separately for each chemical analyte. In these models, diagnostic group (ADHD vs. healthy control) and product-use variables (cosmetic use in the last 24 hours, face-hand-body cream use, and face/body wash or gel use) were included as fixed factors, and household income, maternal education, paternal education, and BMI were included as covariates. Main group and product-use terms, as well as group×product-use interaction terms, were tested. Partial eta-squared (η²) values were reported as standardized magnitude indices in ANCOVA models.
To further examine whether personal care product use was associated with the relationship between ADHD status and chemical exposure levels, multiple mediation analyses were conducted using cosmetic use in the last 24 hours, face/body cream use, and face/body wash/gel use as potential intermediary variables, while maternal education, paternal education, household income, and BMI were included as covariates. Indirect associations were evaluated using bias-corrected bootstrap confidence intervals with 5,000 resamples. A significance level of p<0.05 was adopted for all statistical tests.
RESULTS
Demographic characteristics
The patient and healthy control groups were comparable in terms of basic demographic and anthropometric characteristics. A total of 80 individuals (39 patients [48.8%] and 41 healthy controls [51.2%]) were included in the study. Of the participants, 45.0% were female (n=36) and 55.0% were male (n=44). The mean age was 14.5 years (SD=1.6; range=12-17 years). There were no significant differences between the patient and healthy control groups in terms of sex distribution (p=0.486), age (in years or months), height, or weight (all p> 0.60). Similarly, no significant sex differences were found in age or school grade level (p>0.05) (Table 1).
In contrast, several sociodemographic characteristics differed significantly between groups. Marked differences were observed between the groups in terms of sociodemographic variables. Maternal education was concentrated at higher levels in the patient group, whereas primary and high school education were more frequent among mothers in the healthy group (χ²(4, n=80)=23.05, p<0.001). Similarly, there was a significant difference in paternal education (χ²(5, n=80)=16.14, p=0.006); university and postgraduate education were more common in the patient group, whereas primary and middle school education were more common in the healthy group. No significant differences were found between groups regarding family structure (p=0.370). In contrast, household income differed significantly between groups (χ²(3, n=80)=16.18, p=0.001) (Table 2). No significant sex differences were found in parental education level, family structure, or household income (p>0.05).
Use of cosmetic products
Adolescents in the patient group reported more frequent use of several cosmetic and personal care products compared with healthy controls. Specifically, cosmetic product use, face/body cream use, and face/body wash/gel use were more common in the patient group, whereas deodorant/perfume use did not differ between groups (Table 3). Product use patterns differed markedly by sex, with females reporting substantially higher use across most product categories. Females showed higher use of cosmetic products, deodorant/perfume, and face/body creams than males, while no sex differences were observed for toothpaste/soap or face/body wash/gel use (Table 3). Among female participants, group differences were limited to selected product categories. Face/body cream use and face/body wash/gel use were higher in female patients compared with healthy females, whereas no group differences were observed for toothpaste/soap, cosmetic product use, or deodorant/perfume use (Table 4). Among male participants, cosmetic product use and face/body wash/gel use were more frequent in patients than in healthy controls. No group differences were observed for deodorant/perfume or face/body cream use among males (Table 4). Makeup use did not differ between patient and healthy groups in the overall sample or among female participants. Because none of the male participants reported using makeup products, subgroup analyses were restricted to females, and no significant group differences were observed (Table 4).
Fast food, coffee, cigarette, and alcohol consumption
No significant differences were observed between patient and healthy groups in fast food, coffee, cigarette, or alcohol consumption within the last 24 hours.
Consumption patterns were comparable across all variables, and no alcohol use was reported in either group (Table 5).
Attention to the content of products
Adolescents in the patient group reported greater attention to specific product ingredients compared with healthy controls. While general attention to ingredients did not differ between groups, attention to paraben content and sodium lauryl sulfate (SLS) content was more frequent in the patient group. Attention to phthalate content was reported only in the patient group and showed a trend-level group difference (Table 6).
No significant sex differences were observed in attention to product ingredients.
Female and male participants showed comparable levels of general ingredient attention and attention to paraben, phthalate, and SLS content (Table 6).
Comparison of paraben levels between patient and healthy groups
Propyl paraben levels were higher in adolescents with ADHD compared with healthy controls, whereas no significant group differences were observed for the remaining paraben derivatives. In addition, 1-phenoxy-2-propanol showed a trend-level difference, with higher levels in the patient group, although this did not reach conventional statistical significance (Table 7).
Product use and paraben levels
Urinary paraben levels did not differ according to the use of toothpaste, cosmetic products, perfume/deodorant, face/body cream, or shower gel within the last 24 hours.
In contrast, fast food consumption was associated with higher propyl paraben levels. Propyl paraben levels were higher in participants who consumed fast food (mean=0.77, SD=0.70) than in those who did not (mean=0.53, SD=0.22, t(78)=-2.29, p=0.025, Cohen’s d=-0.54), indicating a medium effect size. For all other paraben derivatives, no significant differences were observed according to fast food consumption.
Coffee, cigarette, and alcohol consumption, as well as general makeup use, were not associated with urinary paraben levels. No significant differences were observed for any paraben type across these exposure-related variables (all p>0.05).
For descriptive purposes, Table 8 summarizes geometric mean urinary concentrations of paraben and phthalate metabolites (back-transformed from log10-transformed values) stratified by personal care product use within the preceding 24 hours, providing an overview of exposure patterns according to recent product-use behaviors.
Comparison of phthalate metabolite levels between patient and healthy groups
Several phthalate metabolites showed differences in concentrations between groups; however, given the relatively low detection frequencies for some analytes, these findings should be interpreted with caution and considered exploratory. No differences were observed for the remaining analytes (Table 9). Ten phthalate metabolites were screened; however, bis(2-ethylhexyl) phthalate and di-n-octyl phthalate were not detected in either group and are therefore not presented in the tables.
Additional multivariable analyses indicated that the association between dimethyl phthalate and ADHD status remained statistically significant after adjustment for sociodemographic factors; however, this finding should be interpreted with caution given the low detection frequency of this analyte. In multivariable logistic regression models, maternal education remained independently associated with ADHD group membership, whereas household income, paternal education, sex, and BMI were not independently associated with ADHD status. Model performance improved after inclusion of chemical exposure variables, and dimethyl phthalate remained significantly associated with ADHD status (Supplementary Tables 3 and 4).
Mediation analyses suggested that product-use behaviors did not substantially mediate the observed exposure differences. Cosmetic use behaviors did not show significant indirect associations between ADHD status and propyl paraben, dimethyl phthalate, diethyl phthalate, or di-n-hexyl phthalate levels. A modest indirect association was observed only for benzyl butyl phthalate through face/body cream use, whereas the overall association with diagnostic group was not significant (Supplementary Table 5).
Product use and phthalate levels
Recent cosmetic product use showed selective associations with urinary phthalate metabolites.
Diethyl phthalate levels were higher among cosmetic product users (t_w(33.9)=-2.05, p=0.048, d=-0.52). In addition, dimethyl phthalate (p=0.053), bis(2-methoxyethyl) phthalate (p=0.050), and di-n-hexyl phthalate (p=0.051) demonstrated trend-level associations, whereas no significant differences were observed for the remaining analytes (all p>0.11).
Specific personal care products were associated with distinct phthalate metabolites. Deodorant/perfume use was associated with higher diamyl phthalate levels (t_w(59.9)=-2.10, p=0.040, d=-0.48). Face/hand/body cream use was associated with higher benzyl butyl phthalate levels (t_w(67.3)=2.46, p=0.016, d=0.53), and face/body wash/gel use was associated with higher di-n-hexyl phthalate levels (t_w(70.9)=-2.02, p=0.047, d=-0.45).
Dietary factors showed limited associations with phthalate exposure. No significant associations were observed between fast food consumption and phthalate levels (all p>0.05). Coffee consumption was associated with higher diethyl phthalate levels (t_w(68.4)=-2.59, p=0.012, d=-0.44), whereas benzyl butyl phthalate levels were higher among participants who did not consume coffee (t_w(68.0)=3.39, p=0.001, d=0.58).
Exposure-related factors and diagnostic group differences in chemical levels
Diagnostic group (ADHD vs. healthy control) and product-use variables (cosmetic use in the last 24 hours, face- hand-body cream use, and face/body wash or gel use) were included as fixed factors in factorial ANCOVA models conducted separately for each analyte. Household income, maternal education, paternal education, and BMI were included as covariates. Main group and product-use terms, as well as group×product-use interaction terms, were examined. For paraben compounds (methyl, ethyl, propyl, isopropyl, isobutyl, butyl, and benzyl paraben), neither diagnostic group nor product-use variables showed significant associations (all pvalues> 0.05), and no significant group×product-use interaction terms were observed.
For dimethyl phthalate, a significant difference was observed between diagnostic groups (F(1, 71)=7.97, p=0.006, η²=0.099), with higher levels in the ADHD group after adjusting for product-use variables and their interaction terms.
For diamyl phthalate, the overall model was significant (F(8, 71)=2.45, p=0.021); however, no significant association was observed for diagnostic group (p=0.446). In contrast, a significant interaction term was observed between face-hand- body cream use and face/body wash or gel use (F(1, 71)=4.31, p=0.042, η²=0.049), suggesting that analyte levels differed across specific combinations of product-use categories rather than by diagnostic status.
For di-n-hexyl phthalate, the overall model was also significant (F(8, 71)=5.90, p<0.001), with no significant association observed for diagnostic group (p=0.379). However, significant interaction terms were observed between cosmetic use in the last 24 hours and face-hand-body cream use (F(1, 71)=10.72, p=0.002, η²=0.108); between cosmetic use and face/body wash or gel use (F(1, 71)=11.26, p=0.001, η²=0.114); and between face-hand-body cream use and face/body wash or gel use (F(1, 71)=4.00, p=0.049, η²=0.040).
Overall, among phthalate metabolites, only dimethyl phthalate remained statistically different between groups after adjustment for product-use behaviors and interaction terms; however, this finding should be interpreted with caution because of the analyte’s low detection frequency.
Conners parent and adolescent forms
In the ADHD group, both parent- and self-report Conners forms indicated elevated scores across cognitive/inattention and hyperactivity-related domains. In the parent forms, Cognitive Problems/Inattention and the total ADHD index showed relatively higher scores, whereas in the adolescent self-report forms, Conduct Problems, Cognitive Problems/Inattention, Hyperactivity, and the total ADHD index were prominent (Table 10).
A significant sex difference was observed only for the Conners Adolescent ADHD index, with higher scores in girls than in boys. No sex-related differences were observed for the remaining subscale scores (Table 10).
No significant associations were observed between Conners scale scores and sociodemographic variables. Accordingly, these results are not reported in further detail.
Associations between paraben and phthalate exposure and ADHD severity
Several significant associations were observed between urinary chemical concentrations and Conners Parent and Adolescent scale scores. Positive associations were observed between isopropyl paraben and parent-rated Hyperactivity, as well as between isobutyl and butyl parabens and adolescent Hyperactivity. Inverse associations were observed between benzyl paraben and adolescent Conduct Problems, between diethyl phthalate and both adolescent Attention Problems and the Adolescent ADHD index, and between benzyl butyl phthalate and parent-rated Oppositional scores. In addition, dibutyl phthalate showed a positive association with adolescent Attention Problems (Table 11).
DISCUSSION
In this study, urinary concentrations of parabens and phthalate metabolites were compared between adolescents diagnosed with ADHD and their healthy peers. In addition, the associations among biological exposure markers, personal care/cosmetic product use, and ADHD symptom severity were examined in a multidimensional framework. Overall, adolescents in the ADHD group reported significantly more frequent use of cosmetics and selected personal care products within the preceding 24 hours. Several phthalate metabolites showed higher levels in the ADHD group; however, given the relatively low detection frequencies for some analytes, these findings should be interpreted with caution and considered exploratory rather than confirmatory. Levels of propyl paraben and certain phthalate metabolites were associated with fast-food consumption and the use of specific product categories, while some parabens and phthalate metabolites showed significant correlations with relevant ADHD subscales on both parentand self-reported Conners forms. Taken together, these findings provide a preliminary and hypothesis-generating characterization of potential links between environmental EDCs and ADHD symptomatology within a clinically diagnosed adolescent sample. In addition, detection frequencies varied across analytes, with some compounds showing relatively low detection rates. As LOD/2 substitution may introduce bias when detection frequency is low, these findings should be interpreted with caution.
Recent experimental and epidemiological evidence—particularly from prenatal and early-life exposure studies, as well as a smaller number of investigations focusing on concurrent adolescent exposure—converges to suggest that parabens and phthalate metabolites may be implicated in attentional difficulties, hyperactivity, and impairments in executive functioning. Animal studies indicate that several phthalate metabolites can interfere with dopaminergic signaling, prefrontal cortical development, and synaptic plasticity—mechanistic pathways that are critically involved in ADHD. Parallel epidemiological studies in human populations have reported that higher urinary concentrations of certain phthalate metabolites, measured during childhood or adolescence, are associated with inattention, impulsivity, and altered neurobehavioral performance in children and adolescents, although effect sizes and specific metabolites vary across cohorts. Furthermore, adolescence represents a developmental window characterized by heightened vulnerability to environmental exposures due to ongoing maturation of frontostriatal circuits and hormonal transitions, which may modulate susceptibility to EDCrelated neurobehavioral associations.
Within this context, our findings showing elevated use of personal care products and corresponding phthalate metabolite levels in adolescents with ADHD align with emerging literature suggesting behavioral-environmental feedback loops: adolescents with ADHD may engage more frequently in routines or lifestyle behaviors that inadvertently increase exposure to EDCs (e.g., greater fast-food consumption, use of fragranced or convenience products). Conversely, elevated chemical exposure may exacerbate attentional and behavioral dysregulation, although the directionality remains uncertain due to the cross-sectional design of the study. The observed correlations between specific parabens and phthalate metabolites and ADHD subscales—especially those related to inattention and hyperactivity-impulsivity—support the plausibility of exposure-linked behavioral manifestations, though these remain associative rather than causal [8,19-22,26].
In this study, the age- and sex-balanced composition of the sample comprising adolescents with ADHD and their healthy peers represents a notable methodological strength that enhances the internal validity of the findings. The absence of significant group differences in age (in both years and months), height, and weight allowed differences observed in paraben and phthalate concentrations—as well as in behavioral measures— to be interpreted with minimal confounding from developmental stage or physical maturity. Similarly, the comparability of girls and boys with respect to age and grade level increases the likelihood that sex-specific patterns identified in the analyses reflect true sex-related differences rather than artifacts of unequal age distribution.
Conversely, an unexpected and noteworthy pattern emerged in the sociodemographic characteristics of the sample. Adolescents in the ADHD group had higher maternal and paternal education levels and higher family income compared with the healthy control group—a finding open to several interpretations. First, families with higher educational attainment and income may be more likely to recognize early signs of attentional or behavioral difficulties in their children and to seek evaluation at tertiary care centers; this could have resulted in an ADHD sample characterized by relatively high socioeconomic status and easier access to specialized healthcare. Second, higher parental education may heighten expectations regarding children’s academic performance and behavioral functioning, leading parents to perceive symptoms of similar severity as more problematic and to seek professional help more readily.
Thus, the sociodemographic profile observed in our sample may not indicate a causal relationship between ADHD and socioeconomic status; rather, it may reflect differential patterns of healthcare access and diagnostic likelihood across families of varying socioeconomic backgrounds.
The use of personal care products such as makeup items, soaps, and sunscreens may expose individuals to potential EDCs, including phthalate metabolites and parabens [34]. The significantly higher use of cosmetics, deodorants/perfumes, and moisturizers among adolescent girls compared with boys reflects both cultural expectations and developmental patterns characteristic of adolescence. In contrast, the absence of sex differences in the use of essential daily personal care items—such as basic hygiene products and face/body wash— may be explained by the perception of these products as fundamental necessities that are less influenced by gendered behavioral norms. The patterns observed in cosmetic and personal care product use indicate meaningful distinctions across both diagnostic group and sex. Adolescents with ADHD, in particular, reported markedly higher use of cosmetics, face/body creams, and face/body wash products compared with healthy controls. From a sex-based perspective, and consistent with existing literature, female adolescents demonstrated substantially higher usage rates across nearly all product categories relative to males. These differences may be attributed to heightened emphasis placed on body image, self-care routines, and social appearance during adolescence, alongside the influence of social media and peer dynamics—which may particularly contribute to increased cosmetic product use among girls [35,36].
In boys, the pattern of differentiation was even more pronounced and striking. None of the healthy male adolescents reported using cosmetic products, whereas approximately two-thirds of boys with ADHD reported using cosmetics and more than half reported using face/body wash products. Although this finding may initially appear to reflect enhanced self-care behaviors among boys with ADHD, it is more likely indicative of a complex socio-psychological profile.
While the use of cosmetic and personal care products among adolescent boys has increased in recent years, such behaviors remain relatively uncommon in our cultural context. Therefore, the elevated usage rates observed in boys with ADHD may be linked to factors such as body-image concerns, efforts to conform to peer norms, the influence of social media, heightened sensory-seeking tendencies, or impulsivity-driven experimentation [37-39]. When interpreting this pattern, it is essential to consider that socioeconomic status was substantially higher in the ADHD group. The markedly higher parental education levels and family income among adolescents with ADHD may facilitate access to relatively more expensive personal care products—such as cosmetics, shower gels, and face/body creams. This suggests that the elevated product use observed in the ADHD group may be partially mediated by socioeconomic determinants. Consequently, attributing differences in chemical exposure solely to behavioral characteristics may be misleading. Therefore, when interpreting paraben and phthalate concentrations, diagnostic group, sex, and socioeconomic status should be considered concurrently.
The higher frequency of product use observed in the ADHD group in our study may be linked to core characteristics of the disorder, such as impulsivity, heightened novelty seeking, and reward sensitivity; adolescents with these traits may be more inclined to try “fashionable” or trending personal care products more frequently [40]. However, the direction of this association may also operate in the opposite way; that is, frequent use of products with high chemical content may influence neurodevelopmental processes and potentially exacerbate ADHD symptoms. The cross-sectional design of our study does not allow these two possibilities to be disentangled. Nonetheless, the co-occurrence of high product use and elevated chemical exposure with ADHD underscores the need for more detailed investigations into the causal pathways underlying these relationships.
In a study examining the relationship between personal care product use and urinary concentrations of specific phthalates, parabens, and phenols, girls who reported applying makeup daily were found to have higher urinary levels of monoethyl phthalate, methyl paraben, and propyl paraben. Similarly, girls who reported recent use of certain cosmetic products—such as foundation, blush, or mascara—had higher concentrations of monoethyl phthalate, mono-n-butyl phthalate, methyl paraben, and propyl paraben in their urine [34]. In our study as well, the associations between product use and phthalate levels point to potential sources of chemical exposure. In particular, the observation of significant or near-significant increases in diethyl phthalate and several other phthalate metabolites among adolescents who used cosmetic products; the associations of deodorant/perfume use with diisopentyl phthalate, face/body cream use with benzyl butyl phthalate, and face/body wash use with di-n-hexyl phthalate support the possibility that these compounds may enter the body through product formulations or packaging materials. These associations provide important clues regarding potential sources of exposure. Low-molecular-weight phthalate metabolites such as dimethyl phthalate and diethyl phthalate, in particular, are widely used in personal care products [41]. These findings are consistent with the HERMOSA study, which similarly reported positive associations between personal care product use and urinary phthalate and paraben levels [34]. The positive associations observed between cosmetic products and particularly diethyl phthalate and benzyl butyl phthalate suggest that both product formulations and packaging materials may serve as potential sources of exposure [42,43]. The inverse associations observed between coffee consumption and levels of diethyl phthalate and benzyl butyl phthalate should be interpreted with caution, likely reflecting the small number of coffee consumers and the variability in brands and packaging materials. Overall, the findings indicate that cosmetic and personal care products may substantially contribute to phthalate exposure in adolescents. This underscores the importance of integrating chemical-content awareness into clinical followup and preventive mental health practices, particularly for groups with neurodevelopmental disorders such as ADHD.
Although descriptive summaries suggested that several paraben and phthalate metabolites varied according to recent personal care product use, factorial ANCOVA models indicated that, for most analytes, product-use variables were not significantly associated with analyte levels and showed no significant interaction terms with diagnostic group. Notably, for dimethyl phthalate, a significant difference between diagnostic groups remained after adjustment for product-use indicators and their interaction terms, suggesting that higher levels observed in the ADHD group were not fully accounted for by recent cosmetic or hygiene-related behaviors. In contrast, for certain metabolites such as diamyl and di-n-hexyl phthalate, significant interaction terms among product-use variables were observed, indicating that analyte levels differed across specific combinations of product-use categories rather than by diagnostic group alone.
Taken together, these findings suggest that the observed exposure differences are unlikely to reflect a single determinant. Instead, they are better interpreted within a multivariable framework that considers behavioral, biological, and contextual factors simultaneously. The association between urinary dimethyl phthalate concentrations and ADHD status remained significant after adjustment for sociodemographic covariates, indicating that residual confounding by socioeconomic factors is unlikely. Among the non-chemical variables, only maternal education independently predicted diagnostic group membership, whereas household income, paternal education, sex, and BMI showed no independent associations. The inclusion of chemical exposure variables improved overall model performance, and mediation analyses did not support recent cosmetic use as a primary pathway explaining most betweengroup exposure differences. Consistent with the ANCOVA findings demonstrating a persistent adjusted group effect for dimethyl phthalate after controlling for product use and interaction terms, these results point toward behavior-linked exposure patterns interacting with neurodevelopmental vulnerability during adolescence rather than simple short-term cosmetic exposure. Nevertheless, causal inference is limited by the crosssectional design and the use of single spot urine samples. Longitudinal studies with repeated exposure assessment will be needed to better clarify temporal relationships.
Low-molecular-weight phthalate metabolites such as dimethyl and diethyl phthalate are known to be absorbed primarily through dermal contact from fragranced personal care products and to have short biological half-lives. Therefore, levels obtained from spot urine samples mainly reflect recent exposure and should not be interpreted as direct indicators of chronic toxic burden. However, daily and repetitive use of personal care products may lead to sustained internal exposure over time despite the short half-lives of these compounds, and single-time-point measurements may serve as indirect indicators of habitual exposure patterns at the population level. In this context, the associations observed in our study suggest an interaction between behavior-related exposure and neurodevelopmental vulnerability during adolescence rather than chronic toxic accumulation. At the same time, due to the cross-sectional design of the study, it is not possible to draw conclusions regarding causal direction, and the relationship should therefore be considered bidirectional at the hypothesis level.
The markedly higher cosmetic use observed among male adolescents in the ADHD group raises the possibility that measured low-molecular-weight phthalate levels may partly reflect behavior-related exposure patterns. However, the persistence of a significant difference between diagnostic groups for dimethyl phthalate after adjustment for product-use variables and socioeconomic covariates suggests that the observed group difference was not fully accounted for by recent personal care behaviors alone. These findings are consistent with the possibility that the association between exposure markers and behavioral characteristics may involve more complex pathways.
It has been reported that phthalate exposure during the prenatal and early childhood periods may disrupt dopaminergic pathways and thyroid hormone homeostasis, mechanisms that have been linked to ADHD-like behavioral phenotypes [41,44]. In our study, the finding that these phthalate metabolites were elevated in the ADHD group and were associated with several ADHD subscale scores is consistent with previously proposed mechanistic hypotheses. Evidence from cohort studies has shown that phthalate exposure during early childhood may influence ADHD-related behaviors in middle childhood and adolescence, particularly among girls [44]. Another cohort study also reported that phthalate exposure during adolescence may be associated with ADHD-related behavioral characteristics [20]. Similarly, a recent systematic review and meta-analysis on environmental endocrine disruptors and ADHD risk reported heterogeneous evidence overall; however, consistent risk signals were identified for phthalates, with phthalate exposure associated with a 13% increase in the risk of ADHD [45]. In our study, higher levels of several phthalate metabolites were observed in the ADHD group; however, findings for analytes with lower detection frequencies should be interpreted with caution. These observations are generally in line with previous literature, although they should be interpreted cautiously given the methodological limitations related to detection frequency. Furthermore, systematic reviews and meta-analyses have shown that phthalate exposure during the prenatal and early childhood periods may be associated with ADHD-like symptoms [6,44,46]. Elevated phthalate levels detected during adolescence may partially reflect repeated or habitual exposure patterns across earlier developmental periods, although this cannot be directly inferred from cross-sectional measurements.
Nevertheless, it is not possible to assert definitively that these higher levels lead to ADHD; behavioral patterns commonly associated with ADHD—such as greater consumption of fast food, plastic-packaged products, ready-to-drink beverages, and more frequent use of personal care products—may secondarily increase exposure to these chemicals. Therefore, our findings suggest a bidirectional and complex relationship between ADHD and phthalate exposure, while highlighting the need for longitudinal studies to clarify causality.
Although between-group differences for parabens were more limited, the finding that propyl paraben levels were higher in the ADHD group and were associated with fast-food consumption points to an intersection between food-based and product-based sources of exposure. Consistent with our results, a study conducted among Dutch adolescents aged 13- 15 years reported a trend toward significance, indicating that higher urinary propyl paraben concentrations were associated with poorer attention performance [8]. A study conducted in Taiwan demonstrated that children with ADHD had significantly higher urinary concentrations of methyl paraben and ethyl paraben compared with controls. Elevated ethyl paraben concentrations were associated with an increased risk of ADHD, with this risk being particularly pronounced in boys, indicating an association based on concurrent childhood urinary exposure [47]. Another Taiwanese study, based on a population-based birth cohort, assessed urinary paraben concentrations measured at ages 6-8 years and reported sex-specific associations between parabens and attention performance in children [48]. In contrast, a cohort study conducted in the United States that evaluated urinary paraben exposure during adolescence found no significant association between paraben levels and ADHD-related behaviors [20]. In our study, only propyl paraben was associated with ADHD symptoms. Propyl paraben is a commonly used preservative in cosmetic products; however, given the short biological half-lives of parabens, a single spot urine sample may provide only a limited representation of actual exposure [48]. Moreover, due to the cross-sectional design of the study, causal inferences cannot be drawn. In addition, because propyl paraben showed a low detection frequency in the present study, this finding should also be interpreted with caution.
Strengths and limitations
This study possesses several noteworthy methodological strengths. First, the age- and sex-balanced sample allowed chemical exposure and behavioral findings to be interpreted with minimal influence from developmental confounders. Additionally, ADHD diagnoses were established in a tertiary-care setting, enhancing diagnostic accuracy and ensuring greater homogeneity within the clinical group; this strengthened the reliability of the observed associations between chemical exposures and ADHD-related symptoms. The inclusion of an extensive panel of paraben and phthalate metabolites provided a detailed characterization of environmental exposure profiles in adolescents. The combined assessment of product-use habits and biological exposure further elucidated the role of personal care practices and lifestyle behaviors in shaping exposure patterns. Moreover, evaluating ADHD symptoms through both parent- and self-report measures improved measurement reliability and reduced informant bias.
Nonetheless, several limitations warrant consideration. The cross-sectional design precludes causal inference. Reliance on a single spot urine sample limits the ability to capture longerterm exposure dynamics for compounds with short biological half-lives and may introduce exposure misclassification. In addition, product-use behaviors were obtained through adolescent self-report for the 24 hours preceding urine sampling, which may be subject to recall bias or incomplete reporting. Although the short recall window and direct adolescent reporting likely reduced classical parental recall bias, differential reporting may still occur if adolescents with ADHD differ from healthy peers in attention, impulsivity, or reporting accuracy. This could either inflate observed associations (if product use is overreported in the ADHD group) or attenuate true associations (if product use is underreported). The moderate sample size reduced statistical power, particularly for subgroup analyses. Furthermore, because participants were recruited from families presenting to a tertiary-care center— and because the ADHD group had comparatively higher socioeconomic status—the generalizability of the findings to broader or more socioeconomically diverse populations may be restricted. Additionally, the use of LOD/2 substitution for values below the limit of detection may introduce bias, particularly for analytes with low detection frequency, where statistical estimates may be disproportionately influenced by imputed values.
Conclusion
This study shows that adolescents with ADHD exhibit higher levels of cosmetic and personal care product use compared with their healthy peers. Differences in exposure to several parabens and phthalate metabolites were observed; however, these findings should be interpreted with caution, particularly for analytes with low detection frequency, and should be considered preliminary given the influence of potential imputation-related bias. These findings suggest that the link between ADHD and chemical exposure cannot be attributed solely to biological susceptibility; rather, it may reflect a complex interplay among behavioral traits, socioeconomic context, product-use patterns, and the chemical profiles of everyday consumer items. From a clinical perspective, incorporating a brief inquiry on daily product use and awareness of chemical ingredients into ADHD assessments may provide added value. Moreover, psychoeducation focusing on “lower-chemical-content alternatives” could be particularly beneficial for adolescents identified as having elevated exposure risk. Overall, this study represents an important step toward a multidimensional characterization of environmental chemical exposure in clinical ADHD populations. Nevertheless, longitudinal research with larger and more diverse samples is warranted to clarify temporal pathways and strengthen causal inference.
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