There were 2372 children and adolescents included in our study. The general characteristics of the participants are presented in Table 1. The prevalence of obesity was 20.53%. It showed that the mean age of obesity and non-obesity is close: approximately 12-and-a-half years old. About half (44.98%) of the participants were 5 grade, and 53.03% had a normal caloric intake. The mean (SD) BMI and waist
circumferences were 30.41 (6.99) and 96.17 (18.05) cm in the obesity group and 19.68 (3.66) and 69.79 (11.36) cm in the non-obesity group, respectively. The mean (SD) BMI z-scores were 2.12 (0.32) in the obesity group and 0.18 (0.94) in the non-obesity group. There were significant differences between the obesity and non-obesity participants in terms of race, family income, caloric intake, urinary creatine, BMI, BMI z-score, and waist circumference.
The LOD and the detection frequency of the chemicals above the LOD are shown in Table 2. The detection frequency of MEP (99.9%) had the highest detection frequency of chemical exposures and the detection frequency of all chemical exposures was above 90%. Table 2 also shows the geometric mean, the mean, and the distribution of the chemical exposures. The highest and the lowest geometric means of the chemical exposures were related to the MEP (87.12) and 2,4,5-TCP (0.09) .
We found significant correlations (P < 0.05) among 9 chemicals (Fig. 1), in addition to the correlation between BP-3 and 2,4-DCP (P=0.69). There was a positive correlation between other compounds, except for a nearly no correlation of BP-3 with 2,5-DCP (r = -0.06). 2,5-DCP was found to have a strongly correlation with 2,4-DCP (r = 0.87). Additionally, a high correlation between MeP and PrP (r = 0.81) was found.
The results from the multivariable logistic and linear regression models adjusted for the covariates are shown in Tables 3 and 4, respectively. The adjusted multivariable logistic regression analysis revealed a statistically significant association between obesity and MeP (OR (95% CI): 0.80 (0.68, 0.94)), 2,5-DCP (OR (95% CI): 1.25 (1.11, 1.40)), MEP (OR (95% CI): 1.28 (1.04, 1.58)), and MiBP (OR (95% CI): 1.42 (1.07, 1.89)), with MeP showing a negative association with dichotomous variable obesity. PrP was found to have a negative association with obesity only when comparing the 4th quartile with the reference quartile (OR (95% CI): 0.69 (0.49, 0.98)). When comparing the 2nd, 3rd, and 4th 2,5-DCP quartiles with the reference quartile, 2,5-DCP had a higher odds ratio (OR (95% CI): 1.49 (1.07, 2.07); 1.80 (1.30, 2.51), and 2.06 (1.47, 2.89), respectively) (Table 3). When comparing the second, third, and fourth quartiles of MEP with the reference quartile, MEP had a higher odds ratio (OR (95% CI): 1.04 (0.75, 1.43); 1.28 (0.92, 1.79), and 1.39 (0.98, 1.98), respectively; Table 3). We used adjusted multivariable linear regression to evaluate the relation between 9 chemical exposures and the BMI z-score (Table 4). We found MeP (second vs. first quartile), 2,5-DCP (third vs. first quartile) and MEP to be positively associated with the BMI z-score (p = 0.044, p = 0.023, p = 0.017, respectively). The second, third, and fourth MEP quartiles had a higher BMI z-score ( (95% CI): 0.02 (-0.12, 0.16); 0.12 (-0.03, 0.27), and 0.14 (-0.02, 0.30), respectively) compared with the lowest reference quartile (Table 4).
In the multivariable logistic and linear regression models, including all the chemical exposures, adjusting for the confounding effects of other chemicals, 2,5-DCP, 2,4-DCP, and MEP were found to have a significant association with both the dichotomous variable obesity (OR (95% CI): 1.73 (1.35, 2.24), 0.57 (0.40, 0.82), and 1.35 (1.08, 1.69), respectively) and continuous variate BMI z-score ( (95% CI): 0.14 (0.04, 0.24), -0.20 (-0.36, -0.05), and 0.15 (0.05, 0.25), respectively) (see Additional File 1, Tables S1 and S2). We calculated the variance inflation factors (VIFs) (see Additional File 1, Tables S3), and none of them was higher than 10.
circumferences were 30.41 (6.99) and 96.17 (18.05) cm in the obesity group and 19.68 (3.66) and 69.79 (11.36) cm in the non-obesity group, respectively. The mean (SD) BMI z-scores were 2.12 (0.32) in the obesity group and 0.18 (0.94) in the non-obesity group. There were significant differences between the obesity and non-obesity participants in terms of race, family income, caloric intake, urinary creatine, BMI, BMI z-score, and waist circumference.
The LOD and the detection frequency of the chemicals above the LOD are shown in Table 2. The detection frequency of MEP (99.9%) had the highest detection frequency of chemical exposures and the detection frequency of all chemical exposures was above 90%. Table 2 also shows the geometric mean, the mean, and the distribution of the chemical exposures. The highest and the lowest geometric means of the chemical exposures were related to the MEP (87.12) and 2,4,5-TCP (0.09) .
We found significant correlations (P < 0.05) among 9 chemicals (Fig. 1), in addition to the correlation between BP-3 and 2,4-DCP (P=0.69). There was a positive correlation between other compounds, except for a nearly no correlation of BP-3 with 2,5-DCP (r = -0.06). 2,5-DCP was found to have a strongly correlation with 2,4-DCP (r = 0.87). Additionally, a high correlation between MeP and PrP (r = 0.81) was found.
The results from the multivariable logistic and linear regression models adjusted for the covariates are shown in Tables 3 and 4, respectively. The adjusted multivariable logistic regression analysis revealed a statistically significant association between obesity and MeP (OR (95% CI): 0.80 (0.68, 0.94)), 2,5-DCP (OR (95% CI): 1.25 (1.11, 1.40)), MEP (OR (95% CI): 1.28 (1.04, 1.58)), and MiBP (OR (95% CI): 1.42 (1.07, 1.89)), with MeP showing a negative association with dichotomous variable obesity. PrP was found to have a negative association with obesity only when comparing the 4th quartile with the reference quartile (OR (95% CI): 0.69 (0.49, 0.98)). When comparing the 2nd, 3rd, and 4th 2,5-DCP quartiles with the reference quartile, 2,5-DCP had a higher odds ratio (OR (95% CI): 1.49 (1.07, 2.07); 1.80 (1.30, 2.51), and 2.06 (1.47, 2.89), respectively) (Table 3). When comparing the second, third, and fourth quartiles of MEP with the reference quartile, MEP had a higher odds ratio (OR (95% CI): 1.04 (0.75, 1.43); 1.28 (0.92, 1.79), and 1.39 (0.98, 1.98), respectively; Table 3). We used adjusted multivariable linear regression to evaluate the relation between 9 chemical exposures and the BMI z-score (Table 4). We found MeP (second vs. first quartile), 2,5-DCP (third vs. first quartile) and MEP to be positively associated with the BMI z-score (p = 0.044, p = 0.023, p = 0.017, respectively). The second, third, and fourth MEP quartiles had a higher BMI z-score ( (95% CI): 0.02 (-0.12, 0.16); 0.12 (-0.03, 0.27), and 0.14 (-0.02, 0.30), respectively) compared with the lowest reference quartile (Table 4).
In the multivariable logistic and linear regression models, including all the chemical exposures, adjusting for the confounding effects of other chemicals, 2,5-DCP, 2,4-DCP, and MEP were found to have a significant association with both the dichotomous variable obesity (OR (95% CI): 1.73 (1.35, 2.24), 0.57 (0.40, 0.82), and 1.35 (1.08, 1.69), respectively) and continuous variate BMI z-score ( (95% CI): 0.14 (0.04, 0.24), -0.20 (-0.36, -0.05), and 0.15 (0.05, 0.25), respectively) (see Additional File 1, Tables S1 and S2). We calculated the variance inflation factors (VIFs) (see Additional File 1, Tables S3), and none of them was higher than 10.
We fitted the WQS regression model to the data to evaluate the relationship between the chemical exposures and the outcome in three models, adjusting for different covariates respectively (Table 5). The WQS index had a significant association with obesity in Model 1 (OR (95% CI): 1.50 (1.19, 1.90)). In Model2, the WQS index had a significant association with obesity (OR (95% CI): 1.51 (1.19, 1.91)). In Model 3, the WQS index also had a significantly positive association with obesity after being adjusted for all covariates (OR (95% CI): 1.48 (1.16, 1.89)). We also calculated the estimated chemical weights of the dichotomous variable obesity in obesity model, which are presented in Fig. 2a. The highest weighted chemical in the fully adjusted obesity model was 2,5-DCP (weighted 0.41), followed by BPA and MEP (weighted 0.17 and 0.16, respectively). We also treated the BMI z-score as a continuous variable and fitted the BMI z-score model (Table 5). However, we did not find any significant association between the exposures and the BMI z-score in all three models. The estimated chemical weights of BMI z-score are presented in Fig. 2b. The highest weighted chemical in the BMI z-score model was 2,5-DCP (weighted 0.30). Next to this were BP-3 and MEP, weighted 0.28 and 0.18, respectively.
We grouped 9 chemical exposures into three groups, according to their source and correlation with each other, and fitted the BKMR model to analyze the simultaneous exposure with obesity and BMI z-score. In the obesity model, the group posterior inclusion probabilities (PIP) of the pesticides group was 0.966, while the group PIP of phenol and phthalates metabolites was higher than 0.5 (Table 6). In the pesticides group, 2,5-DCP seemed to drive the effect of the whole group (CondPIP = 0.978; Table 6). In the phthalate metabolites group, MEP drove the main effect of the whole group (CondPIP: 0.656), while MeP drove the main effect in the phenols group (CondPIP = 0.903) (Table 6). The overall association between the chemical mixtures and the binominal outcome is shown in Fig. 3a. We found a positive tendency between chemical exposures and the outcome, in spite of no statistically significant difference. Fig. 4 a illustrates the positive associations of 2,5-DCP, MEP, and MiBP with obesity in the BKMR models, while controlling all other chemical exposures at their median level. MeP demonstrated an inverse association with obesity, while no other chemical exposures showed a noteworthy change in slope. We also investigated the relationship between the outcome and a unitary predictor in exposures while fixing another predictor in exposures at the 10th, 50th, and 90th quantiles (and holding the remnant predictors to their median level), and the results are shown in Fig. 5 a. Since the slopes were different between 2,5-DCP and obesity, MEP and obesity while fixing MeP at the 10th, 50th, and 90th quantiles, potential interactions might exist between 2,5-DCP and MeP as well as MEP and MeP. In the BMI z-score model, the values of the group PIP in three groups were 0.329, 0.256, and 0.707, respectively. (Table 6). MEP drove the main effect in its group (CondPIP: 0.831). The overall risk of the chemical mixtures on the outcome are presented in Fig. 3b. Although no statistically significant difference was found, they revealed a positive association of the mixed exposures with the BMI z-score, when we compared all the predictors fixed at different levels with their 50th percentiles. 2,5-DCP and MEP had a trend of a positive association with the BMI z-score, while 2,4-DCP had an inverse association (Fig. 4 b). No obvious interaction was found in the BMI z-score model (Fig. 5 b).
To ensure the convergence, we plotted the trace plots, which showed a more or less homogeneously covered space and indicated our model had a good convergence. (see Additional File 1, Fig. 1 and Fig. 2)
For 2,5-DCP and MEP seemed to drive the whole effect in pesticides group (in obesity model) and in phthalate group (in BMI z-score model), we further modeled 2,5-DCP and other groups (phenols group, parabens group, and phthalate group) in obesity model and MEP and other groups (phenols group, parabens group, and pesticides group) in BMI z-score model. The credibility intervals tighten a little (see Additional File 1, Fig. 3 a and b), which meant 2,4-DCP, MiBP and MBzP showed little relevance for the outcome.