Maternal phthalate exposure and BMI trajectory in children-an 18-year birth cohort follow-up study.

BACKGROUND: Obesity is a major health concern worldwide. Previous studies have suggested that phthalate plasticizers are obesogens. However, the relationship between early-life phthalate exposure and long-term obesity development remains unknown. OBJECTIVE: We investigated the association between prenatal phthalate exposure and children's body mass index (BMI) patterns in an 18-year birth cohort follow-up study in Taiwan. METHODS: Our analytical lab quantified seven phthalate metabolites in maternal urine during pregnancy using quantitative liquid chromatography-tandem mass spectrometry. In addition, we calculated BMI z scores for participated children at each follow-up, utilized trajectory analysis to describe children's BMI z-score patterns at 2-18 years of age, and adopted generalized estimating equations (GEE) and multivariate logistic regression models to assess the association between prenatal phthalate exposure and BMI z scores in children. RESULTS: A total of 208 mother-child pairs were included in the analysis. Maternal urinary diethyl phthalate (DEP) metabolites were associated with the increase of BMI z scores in children aged 2-18 years in the GEE model. Doubled maternal urinary ∑mDEHP (3 mono hexyl-metabolites of di-ethyl-hexyl phthalate (DEHP) increased the risk of children being in the stable-high BMI trajectory group until the age of eighteen. IMPACT STATEMENT: We observed that BMI trajectories of children remained stable after the age of 5 years. During each follow-up, a higher frequency of overweight or obese was observed in children, ranging from 15.9% to 35.6% for girls and 15.2-32.0% for boys, respectively. Prenatal phthalate exposure was associated with increasing BMI z scores in children. Prenatal DEHP exposure was associated with a stable-high BMI trajectory in children up to the age of 18 years.

Prenatal exposure to air pollutants and childhood atopic dermatitis and allergic rhinitis adopting machine learning approaches: 14-year follow-up birth cohort study.

The incidence of childhood atopic dermatitis (AD) and allergic rhinitis (AR) is increasing. This warrants development of measures to predict and prevent these conditions. We aimed to investigate the predictive ability of a spectrum of data mining methods to predict childhood AD and AR using longitudinal birth cohort data. We conducted a 14-year follow-up of infants born to pregnant women who had undergone maternal examinations at nine selected maternity hospitals across Taiwan during 2000-2005. The subjects were interviewed using structured questionnaires to record data on basic demographics, socioeconomic status, lifestyle, medical history, and 24-h dietary recall. Hourly concentrations of air pollutants within 1 year before childbirth were obtained from 76 national air quality monitoring stations in Taiwan. We utilized weighted K-nearest neighbour method (k = 3) to infer the personalized air pollution exposure. Machine learning methods were performed on the heterogeneous attributes set to predict allergic diseases in children. A total of 1439 mother-infant pairs were recruited in machine learning analysis. The prevalence of AD and AR in children up to 14 years of age were 6.8% and 15.9%, respectively. Overall, tree-based models achieved higher sensitivity and specificity than other methods, with areas under receiver operating characteristic curve of 83% for AD and 84% for AR, respectively. Our findings confirmed that prenatal air quality is an important factor affecting the predictive ability. Moreover, different air quality indices were better predicted, in combination than separately. Combining heterogeneous attributes including environmental exposures, demographic information, and allergens is the key to a better prediction of children allergies in the general population. Prenatal exposure to nitrogen dioxide (NO2) and its concatenation changes with time were significant predictors for AD and AR till adolescent.