Quantitative bias analysis of the association between subclinical thyroid disease and two perfluoroalkyl substances in a single study.

Exposure to perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) has been associated with the occurrence of thyroid disease in some epidemiologic studies. We hypothesized that in a specific epidemiologic study based on the National Health and Nutrition Examination Survey, the association of subclinical thyroid disease with serum concentration of PFOA and PFOS was due to reverse causality. Thyroid hormone affects glomerular filtration, which in turn affects excretion of PFOA and PFOS. We evaluated this by linking a model of thyroid disease status over the lifetime to physiologically based pharmacokinetic models of PFOA and PFOS. Using Monte Carlo methods, we simulated the target study population and analyzed the data using multivariable logistic regression. The target and simulated populations were similar with respect to age, estimated glomerular filtration rate, serum concentrations of PFOA and PFOS, and prevalence of subclinical thyroid disease. Our findings suggest that in the target study the associations with subclinical hypothyroidism were overstated and the results for subclinical hyperthyroidism were, in general, understated. For example, for subclinical hypothyroidism in men, the reported odds ratio per ln(PFOS) increase was 1.98 (95% CI 1.19-3.28), whereas in the simulated data the bias due to reverse causality gave an odds ratio of 1.19 (1.16-1.23). Our results provide evidence of bias due to reverse causality in a specific cross-sectional study of subclinical thyroid disease with exposure to PFOA and PFOS among adults.

A model of functional thyroid disease status over the lifetime.

Mathematical models of the natural history of disease can predict incidence rates based on prevalence data and support simulations of populations where thyroid function affects other aspects of physiology. We developed a Markov chain model of functional thyroid disease status over the lifetime. Subjects were in one of seven thyroid disease states at any given point in their lives [normal, subclinical hypothyroidism, overt hypothyroidism, treated thyroid disease (ever), subclinical hyperthyroidism, overt hyperthyroidism, and reverted to normal thyroid status]. We used a Bayesian approach to fitting model parameters. A priori probabilities of changing from each disease state to another per unit time were based on published data and summarized using meta-analysis, when possible. The probabilities of changing state were fitted to observed prevalence data based on the National Health and Nutrition Examination Survey 2007-2012. The fitted model provided a satisfactory fit to the observed prevalence data for each disease state, by sex and decade of age. For example, for males 50-59 years old, the observed prevalence of ever having treated thyroid disease was 4.4% and the predicted value was 4.6%. Comparing the incidence rates of treated disease predicted from our model with published values revealed that 82% were within a 4-fold difference. The differences seemed to be systematic and were consistent with expectation based on national iodine intakes. The model provided new and comprehensive estimates of functional thyroid disease incidence rates for the U.S. Because the model provides a reasonable fit to national prevalence data and predicts thyroid disease status over the lifetime, it is suitable for simulating populations, thereby making possible quantitative bias analyses of selected epidemiologic data reporting an association of thyroid disease with serum concentrations of environmental contaminants.

Excretion of Di-2-ethylhexyl phthalate (DEHP) metabolites in urine is related to body mass index because of higher energy intake in the overweight and obese.

A higher body mass index (BMI) has been positively associated with the rate of excretion of di-2-ethylhexyl phthalate (DEHP) metabolites in urine in data from the National Health and Nutrition Examination Survey (NHANES), suggesting an association between DEHP exposure and BMI. The association, however, may be due to the association between body mass maintenance and higher energy intake, with higher energy intake being accompanied by a higher intake of DEHP. To examine this hypothesis, we ran a Monte Carlo simulation with a DEHP physiologically-based pharmacokinetic (PBPK) model for adult humans. A realistic exposure sub-model was used, which included the relation of body weight to energy intake and of energy intake to DEHP intake. The model simulation output, when compared with urinary metabolite data from NHANES, supported good model validity. The distribution of BMI in the simulated population closely resembled that in the NHANES population. This indicated that the simulated subjects and DEHP exposure model were closely aligned with the NHANES population of interest. In the simulated population, the ordinary least squares regression coefficient for log(BMI) as a function of log(DEHP nmol/min) was 0.048 (SE 0.001), as compared with the reported value of 0.019 (SE 0.005). In other words, given our model structure, the higher energy intake in the overweight and obese, and the concomitant higher DEHP exposure, describes the reported relationship between BMI and DEHP.