Most children are exposed to perfluoroalkyl substances (PFASs) through placental transfer, breastfeeding, and other environmental sources. To date, there are no validated tools to estimate exposure and body burden during infancy and childhood. In this study, we aimed to (i) develop a two-generation pharmacokinetic model of prenatal and postnatal exposure to perfluorooctanoic acid (PFOA), perfluorooctanesulfonate (PFOS), and perfluorohexanesulfonate (PFHxS); and to (ii) evaluate it against measured children's levels in two studies. We developed a pharmacokinetic model consisting of a maternal and a child compartment to simulate lifetime exposure in women and transfer to the child across the placenta and through breastfeeding. To evaluate the model, we performed simulations for each mother-child dyad from two studies in which maternal PFAS levels at delivery and children's PFAS levels were available. Model predictions based on maternal PFAS levels, sex of child, body weight, and duration of breastfeeding explained between 52% and 60% of the variability in measured children's levels at 6 months of age and between 52% and 62% at 36 months. Monte Carlo simulations showed that the daily intake through breastfeeding and resulting internal PFAS levels can be much higher in nursing infants than in mothers. This pharmacokinetic model shows potential for postnatal exposure assessment in the context of epidemiological studies and risk assessment.

BackgroundDrinking water is recognized as a source of lead (Pb) exposure. However, questions remain about the impact of chronic exposure to lead-contaminated water on internal dose.ObjectiveOur goal was to estimate the relation between a cumulative water Pb exposure index (CWLEI) and blood Pb levels (BPb) in children 1–5 years of ages.MethodsBetween 10 September 2009 and 27 March 2010, individual characteristics and water consumption data were obtained from 298 children. Venous blood samples were collected (one per child) and a total of five 1-L samples of water per home were drawn from the kitchen tap. A second round of water collection was performed between 22 June 2011 and 6 September 2011 on a subsample of houses. Pb analyses used inductively coupled plasma mass spectroscopy. Multiple linear regressions were used to estimate the association between CWLEI and BPb.ResultsEach 1-unit increase in CWLEI multiplies the expected value of BPb by 1.10 (95% CI: 1.06, 1.15) after adjustment for confounders. Mean BPb was significantly higher in children in the upper third and fourth quartiles of CWLEI (0.7–1.9 and ≥ 1.9 μg/kg of body weight) compared with the first (< 0.2 μg/kg) after adjusting for confounders (19%; 95% CI: 0, 42% and 39%; 95% CI: 15, 67%, respectively). The trends analysis yielded a p-value < 0.0001 after adjusting for confounders suggesting a dose–response relationship between percentiles of CWLEI and BPb.ConclusionsIn children 1–5 years of age, BPb was significantly associated with water lead concentration with an increase starting at a cumulative lead exposure of ≥ 0.7 μg Pb/kg of body weight. In this age group, an increase of 1 μg/L in water lead would result in an increase of 35% of BPb after 150 days of exposure.CitationNgueta G, Abdous B, Tardif R, St-Laurent J, Levallois P. 2016. Use of a cumulative exposure index to estimate the impact of tap water lead concentration on blood lead levels in 1- to 5-year-old children (Montreal, Canada). Environ Health Perspect 124:388–395; http://dx.doi.org/10.1289/ehp.1409144

Background: Diabetes is a highly prevalent chronic disease that places a large burden on individuals and health care systems. Models predicting the risk (also called predictive models) of other conditions often compare people with and without diabetes, which is of little to no relevance for people already living with diabetes (called patients). This review aims to identify and synthesize findings from existing predictive models of physical and mental health diabetes-related conditions. Methods: We will use the scoping review frameworks developed by the Joanna Briggs Institute and Levac and colleagues. We will perform a comprehensive search for studies from Ovid MEDLINE and Embase databases. Studies involving patients with prediabetes and all types of diabetes will be considered, regardless of age and gender. We will limit the search to studies published between 2000 and 2018. There will be no restriction of studies based on country or publication language. Abstracts, full-text screening, and data extraction will be done independently by two individuals. Data abstraction will be conducted using a standard methodology. We will undertake a narrative synthesis of findings while considering the quality of the selected models according to validated and wellrecognized tools and reporting standards. Discussion: Predictive models are increasingly being recommended for risk assessment in treatment decisionmaking and clinical guidelines. This scoping review will provide an overview of existing predictive models of diabetes complications and how to apply them. By presenting people at higher risk of specific complications, this overview may help to enhance shared decision-making and preventive strategies concerning diabetes complications. Our anticipated limitation is potentially missing models because we will not search grey literature.

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