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

Environmental Research 182 (2020) 109017

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

T

Michael W. Dzierlengaa,b,∗, Marjory Moreaua, Gina Songa,1, Pankajini Mallicka, Peyton L. Wardb, Jerry L. Campbellb, Conrad Housanda,2, Miyoung Yoona,c, Bruce C. Allend, Harvey J. Clewell IIIb, Matthew P. Longneckerb a

ScitoVation, LLC, Research Triangle Park, NC, USA Ramboll, Raleigh, NC, USA c ToxStrategies, Research Triangle Park, NC, USA d Independent Consultant, Chapel Hill, NC, USA b

A R T I C LE I N FO

A B S T R A C T

Keywords: PBPK modeling Persistent organic pollutants Perfluoroalkyl substances Subclinical thyroid disease Reverse causality

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.

1. Introduction Perfluoroalkyl substances (PFAS) are synthetic substances used in a variety of industrial processes and consumer products, such as lubricants, textiles, coating additives for carpets and fabrics, non-stick coatings for cookware and food packaging, and fire-fighting foams (Houde et al., 2006; Lau et al., 2007). The carbon-fluoride bonds in PFAS provide high stability, resulting in slow biodegradation and persistence in the environment (Fromme et al., 2009; Lau et al., 2007). Perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) are the two PFAS most frequently detected in humans. For PFOA the half-life in human serum has been reported to be 2.3–3.8 years (Bartell et al., 2010; Olsen et al., 2007) and for PFOS 5.4 years (Olsen et al.,

2007). Despite the phase-out of use of these compounds in many countries (Sunderland et al., 2018), the widespread, global presence of PFOA and PFOS in the environment and human populations has raised concern over persistent low-level exposure to these chemicals and the possible effects they might have on human health. Exposure to PFAS has been reported to be associated with a number of health issues, including lower birthweight (Negri et al., 2017) and higher cholesterol levels (Frisbee et al., 2010; Nelson et al., 2009; Olsen et al., 2000). Studies of measures of exposure to PFAS in relation to thyroid disease or thyroid hormone concentrations in humans have yielded some positive results (Melzer et al., 2010; Wen et al., 2013; Winquist and Steenland, 2014; Kim et al., 2018), and more frequently, null or ambiguous results (Ballesteros et al., 2017). In the large cohort

∗

Corresponding author. 3214 Charles B. Root Wynd Suite 130, Raleigh, NC, 27612, USA. E-mail address: [email protected] (M.W. Dzierlenga). 1 Covance, Madison, Wisconsin, USA. 2 Independent Consultant, Orlando, Florida, USA. https://doi.org/10.1016/j.envres.2019.109017 Received 3 September 2019; Received in revised form 8 November 2019; Accepted 6 December 2019 Available online 09 December 2019 0013-9351/ © 2019 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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study by Winquist and Steenland (2014), potentially important sources of bias were not obvious. But in the cross-sectional study of non-pregnant adults by Wen et al. (2013), the design may have led to a systematic error in the estimate of the association between thyroid disease and PFAS. We hypothesized that variation in chemical excretion among people with different thyroid disease states could have biased the results of Wen et al. (2013). PFAS are excreted mainly through renal clearance (Zhang et al., 2015). Thyroid hormones influence kidney function through their effect on renal blood flow, glomerular filtration rate (GFR), and other mechanisms (Den Hollander et al., 2005; van Hoek and Daminet, 2009). Thus, the thyroid status of an individual alters GFR and excretion of PFAS, and the association between PFAS and thyroid function in the cross-sectional study by Wen et al. (2013) may have been at least in part due to reverse causality rather than a toxicological effect of PFAS. Quantitative bias analysis of epidemiologic associations using physiologically-based pharmacokinetic (PBPK) modeling can be used to evaluate whether an association could be due to residual confounding or reverse causality. This technique has been applied to evaluate potential bias in reported associations between a variety of chemical exposures and health outcomes (Dzierlenga et al., 2019a; Campbell et al., 2018; Ngueta et al., 2017; Ruark et al., 2017; Song et al., 2016; Verner et al., 2015, 2013; Wu et al., 2015). The objective of this study was to test the hypothesis that thyroid effects on PFAS excretion could account for some or all of the association reported between subclinical thyroid disease and serum PFAS concentration in cross-sectional data on non-pregnant adults (Wen et al., 2013). A previously developed PBPK model of PFAS for humans was used to describe PFAS pharmacokinetics for a simulated population. A Markov chain model was used to assign thyroid status over the lifetime of simulated individuals (Dzierlenga et al., 2019b), which was then used as a determinant of kidney function. A Monte Carlo process was implemented to generate a population similar to that in the target study (Wen et al., 2013), and statistical methods were applied to identify the amount of bias present in the study due to thyroid-status induced pharmacokinetic variation in PFAS serum concentration.

Fig. 1. Schematic of the quantitative bias analysis, showing the steps which contributed to the generation of the simulated population which was examined and compared with the real population to identify potential pharmacokinetic bias in observations of the real population.

2.1. PBPK model In order to simulate population distributions of PFOS and PFOA serum concentration, we adapted a life-course PBPK model most recently used by Ruark et al. (2017), and originally constructed by Loccisano et al. (2011). The model has six compartments representing physiological tissues, with additional filtrate and storage compartments to better capture renal clearance and reabsorption of PFOA and PFOS (Fig. 2). This model can be used to calculate the tissue concentration of PFOA or PFOS in a physiologic compartment at any given age. Aggregate daily exposure was treated as a continuous exposure into the plasma, and clearance through renal filtration and menstruation was simulated. The exposure model is described in Section 2.2. GI excretion is a possible route of clearance for PFOA and PFOS but has been shown to be less important than renal excretion in animal models (Butenhoff et al., 2004; Cui et al., 2010; Kudo et al., 2001). This is discussed in more detail in the supplemental material and sensitivity analyses (Section 2.8). Due to renal excretion being a primary route of clearance for PFOA and PFOS, an updated lifecouse GFR (GFRlifecourse) model was implemented using a piecewise linear regression based on the results of Poggio et al. (2009). The GFRlifecourse model gives the GFR for a given age and sex, independent of thyroid status. This regression had a body surface area (BSA) adjusted GFR of 113 mL/min/1.73 m2 at or before age 18, with an age dependent decrease of 3.73 mL/min/1.73 m2 per decade after the age of 18 years and an age-dependent decrease of 7.3 mL/min/1.73 m2 per decade after the age of 45 years. Additionally, we included a small, sex-dependent term such that women at all ages had, on average, a GFR 3.61 mL/min/1.73 m2 higher than men. There is often no sex difference found when GFR is adjusted for BSA, but this small, significant difference found by Poggio et al. (2009) may be due to BSA adjustment not completely accounting for the underlying variable that represents the physiological determinants of GFR. Differences in the calculation of BSA and in study population characteristics and size may play a role in the discrepancies in the literature seen for sex differences in GFR. To model the effect of thyroid function on renal activity we characterized the relationship between glomerular filtration rate (GFR) and thyroid stimulating hormone (TSH) serum levels using a regression on data from the general population as measured in the National Health

2. Methods This quantitative bias analysis involved a multi-part simulation to generate a population with disease states and physiologies that accurately reflected reality (Fig. 1). The key for this study was describing the incidence and natural history of thyroid disease over time and linking thyroid status to changes in renal function and subsequent pharmacokinetic effects. To do this, each individual was assigned a variable thyroid disease status throughout their lifetime using a Markov chain model (MCM). The disease status of each individual was then used to assign a thyroid-stimulating hormone concentration which represented their overall thyroid activity at a given point. Each individual's GFR was adjusted by TSH to represent the effect of thyroid activity on renal function. Then, a sex-specific, lifetime PBPK model was used, with the modified GFR, to predict each individual's serum concentration of PFOA or PFOS at any given age. We assigned a sex and a study sampling age to each individual to make the sex and age distribution of simulated subjects match that of the target population. Finally, the association between disease status and PFOA/PFOS serum concentration was examined in the simulated population. In this population, disease status and serum concentration were linked only through the effect of the disease on PFOA and PFOS clearance, because none of the possible pathways for PFOA and PFOS exposure to effect thyroid disease were present. Therefore, any association between them in the simulated population would be attributed solely to pharmacokinetic bias. An extended description of each of the individual steps in the workflow follows. 2

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levels obtained from the thyroid model, described briefly in a subsequent section and in detail within Dzierlenga et al. (2019b), for each simulated individual to adjust their GFR predicted from the life-course equations using the relationship obtained from the regression as shown below,

L GFR ⎛ ⎞ = GFRlifecourse + −0.25*(ln(TSH) − 0.4253) + ε, ⎝ hr ⎠ where the error term, ε, is set to 0.18 L/h. An ‘estimated GFR’ (eGFR) was calculated for both sexes in the simulated population by adding a Gaussian noise term to the ‘true’ GFR,

mL mL ⎞ = GFR ⎛ ⎞ + η, eGFR ⎛ ⎝ min*1.73 m2 ⎠ ⎝ min*1.73 m2 ⎠ with the noise term, η, having a mean of −3.5 mL/min/1.73 m2 and a standard deviation of 17.6 mL/min/1.73 m2. The mean of the noise originates from the difference between GFR measured by serum creatinine and by iothalamate clearance in individuals with normal renal function (Levey et al., 2009) and the noise is set to match the estimated GFR distribution in the target population, which was calculated by the authors. This addition of noise corresponds to the experimental error that results from estimating GFR from serum creatinine levels (Botev et al., 2011), which was necessary for comparison with the target population, for which only serum creatinine levels were available. Fig. 2. Schematic of PBPK model of PFOS and PFOA. Boxes represent physiological compartments. Solid lines denote flows, while dotted lines represent chemical entering or leaving the system. The blood flows into and out of compartments are denoted by a parameter name that begins with ‘Q.’ ‘GFR’ is the flow of filtrate from the plasma to the filtrate compartment, and ‘CL’ is the flow of filtrate from the filtrate compartment to the storage compartment. Reabsorption of filtrated PFOS/PFOA is modeled using a Michaelis-Menten equation parameterized by a maximal rate, ‘Tm,’ and a dissociation constant, ‘Kt.’

2.2. Exposure model The exposure model was as in Ruark et al. (2017), except that slightly different multipliers were used to adjust exposure independently for each sex and chemical. These multipliers were chosen using reverse dosimetry (Moreau et al., 2017) such that the median simulated serum concentrations of PFOA and PFOS matched the observed values. This model had age and sex dependent exposure adapted from Noorlander et al. (2011) and modified by a calendar year dependent factor adapted from Wong et al. (2014). The exposure model has peak exposure occurring between 1990 and 1998, with exponential growth and decay before and after the peak exposure period. This time trend represents the increasing use of these chemicals from 1950 to

and Nutrition Examination Survey (NHANES) (Centers for Disease Control and Prevention. National Center for Health Statistics, 2015) as described in the supporting information. We then used the TSH serum

Fig. 3. Diagram of states and transitions within the Markov Chain model. Arrows denote state transitions, which can be between states, or from a state to itself. Circles denote the states and the number inside represents the index of the state. 3

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study, however, the approach was different. First, all subjects who had ever been told by a doctor or other health professional that they had a thyroid problem were excluded. The remaining subjects were classified based on TSH only; all subjects whose TSH was outside the designated normal range were considered to have subclinical disease. This definition included subjects with extreme TSH values (i.e., overt disease) who had not been diagnosed with thyroid disease. The frequency of undiagnosed overt thyroid disease, albeit low, has been well documented (Canaris et al., 2000; Hollowell et al., 2002). Our simulation needed a realistic, complete distribution of TSH among subjects who did not have diagnosed thyroid disease, because TSH was used in the assignment of GFR. In the Markov chain model, simulated subjects were in one of seven thyroid disease states (Fig. 3) at any given point over their lifetime. Each thyroid disease state other than treated was given a specific truncated distribution of plasma TSH concentration (Table 1). The range of TSH associated with each untreated subclinical and overt disease state was selected so that it approximated what would be expected, and so that in the simulated population overall the distribution of TSH was a continuous function, closely matching that of the NHANES population.

1990, followed by a decrease in their use and a transition to more restricted use after 1998 (Wong et al., 2014). 2.3. Model of thyroid disease status over the lifetime A complete description of the model of thyroid disease has been presented elsewhere (Dzierlenga et al., 2019b). The simulation of thyroid disease state over the lifetime was formulated as a Markov chain model (MCM). Briefly, subjects were considered to be in one of 7 states of thyroid disease in any given three-month period of their life (Fig. 3); at the beginning, all started in the normal state, shown at the left in the figure, until age 10 years. The probability of changing state after each three-month period thereafter (arrows in Fig. 3) depended on the current state, and, while in the normal state, also the age and sex. The transition probabilities between states comprised the parameter set. To fit the parameter set for the MCM, the MCM was used to generate sex- and ten-year-age-group specific (10–79 y) prevalences of thyroid disease, which was classified as treated or untreated, with untreated being further classified into subclinical hypothyroidism, overt hypothyroidism, subclinical hyperthyroidism, and overt hyperthyroidism. The parameter set was obtained using a Bayesian approach. The source of a priori parameter values is discussed in the next paragraph. In the Bayesian approach, the MCM parameters were calibrated via maximum likelihood so that the simulated prevalences approximated sex- and age-specific “target table” values calculated from the National Health and Nutrition Examination Survey (NHANES). In the NHANES data, the prevalence of treated thyroid disease was based on the response to the question, “Has a doctor or other health professional ever told you that you had a thyroid problem?” Similarly, the prevalence of untreated thyroid disease was calculated for subjects who did not report a diagnosis of thyroid disease and was based on their measured value of TSH (Table 1). The Bayesian process for fitting a parameter set for the MCM was implemented in two stages. In the first stage, a priori parameter distributions were based on published data, summarized using meta-analysis when possible, and the target tables were from NHANES 2009–2012 (see Fig. S1 in the supplemental material). These a priori parameter distributions were the same as those used earlier (Dzierlenga et al., 2019b). The resulting a posteriori parameter estimates were then re-designated as a priori values for the second stage of the Bayesian process, which had, in its target tables, subjects from NHANES 2007–2008 (see Fig. S1 in supplemental material). The second stage of the process resulted in set of a posteriori parameter values that when used with the MCM approximated prevalence estimates specifically for the Wen et al. (2013) study replication. The MCM parameter values and the MCM model resulted in a realistic distribution of thyroid disease status at any given point and were used to assign a value of TSH throughout the lifetime for each simulated subject.

2.5. Population simulations A simulated population was generated using Monte Carlo methods to produce realistic variability in key physiological characteristics. A table of mean parameter values and the probability distributions from which they were sampled is provided in the supplemental material. While many of the parameter distributions were previously developed for life-course modeling from fits to general population data, the distribution of sampling age was tailored to closely match the target study by assigning selection probabilities to each age based on the distribution of ages in the target study. Each individual was simulated from the age of 2 years to the assigned sampling age, from 20 to 79 years. Due to the high sensitivity of the parameters involved in renal clearance (see below), the variation applied to those parameters was one of the main sources of variation of PFAS concentration in plasma in the total population. The model designed to produce population GFR levels has several components which each contributed to the total variation. The first produced a sex- and age-dependent value and this had additional random variability with a 5% coefficient of variation (CV). This random contribution was chosen (originally by Wu et al., 2015) to match the range of GFR values presented by DeWoskin and Thompson (2008). The model of GFR as a function of TSH also contributed to variation in GFR through an error term, which contributes about 2.6% CV additional variability, reflecting the error in the prediction in the regression model fit to NHANES data. These sources of variation, in addition to the non-random variability associated with sex, age, and thyroid status, results in a CV in GFR (normalized to 1.73 m2 BSA) of 9.5% in the baseline simulated population. A relatively healthy population of potential kidney donors were found to have a CV in GFR of 15.8% (Rule et al., 2004). Another contribution to population variation in renal clearance is

2.4. Translation of disease state into a TSH level Classification of patients according to thyroid disease status normally depends on both TSH and free thyroxine (fT4). In our target Table 1 Range and distribution of TSH assigned to thyroid disease states. State

TSH (mIU/L)a

Overt hyperthyroidism Subclinical hyperthyroidism Normal & reverted to normal Subclinical hypothyroidism Overt hypothyroidism Treated thyroid disease

≤0.035 0.425, > 0.035 – < 0.22 0.425, 0.22–4.00 0.425, > 4.00 – < 18.00 0.425, ≥18.00 0.425, No TSH assigned (excluded from logistic regression)

a b

Mean and standard deviation of lognormal distribution of TSH 1.05 1.05 0.62 and 0.425, 0.56b 0.80 0.80

Rationale for the range of TSH values used for each disease state is given in the supplemental materials. First set of parameters are the values below the median (1.53), second set is for values above. 4

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examination of these data, 200,000 was chosen as the population sufficient for there to be confidence in the odds ratios obtained from a single simulation, while still limiting the computational time required. We also examined the standard error of the ln(OR) in 20 artificial populations with sample size similar to that in the Wen et al. study (n = 1000). As an example of the results, the average standard error of the ln(OR) for the four results on simulated hypothyroidism and PFAS was 0.28, which was of about the same magnitude as the corresponding average ln(OR) calculated using Wen et al.‘s results (0.58).

variation in renal reabsorption. The renal reabsorption parameter describes net flow across the proximal tubule cells, which combines transporter activity into and out of the cells on both the apical and basolateral membranes. The reabsorption parameter has a CV of 30%. This is a default value for parameter variation used in the absence of more detailed information on the population variability in PFOA and PFOS reabsorption (Clewell and Clewell, 2008). 2.6. The target study (Wen et al., 2013) Wen et al. analyzed NHANES data from 2007 to 2008. They selected non-pregnant adults (≥20 years) who were not nursing, and who had serum measures of TSH and PFAS. They excluded subjects with a history of diagnosed thyroid disease, or with missing data on alcohol consumption or urinary iodine, resulting in 1181 participants in the analysis. Logistic regression was used to examine the adjusted odds of having subclinical thyroid disease (based on TSH concentration) in relation to PFAS concentration, by sex.

2.8. Sensitivity analyses A sensitivity analysis was performed on the model to identify the parameters that had the largest effects on the results. First, a local, oneat-a-time sensitivity analysis was used to determine the sensitivity of each parameter, which is defined as the relative change in model output, in this case serum concentration of PFOS or PFOA, after a 1% change in the PBPK model parameter. This was performed for each sex, for each chemical, and at three ages (20, 50, and 79 years old) which were chosen to create a representative set of individuals. A regression sensitivity analysis was performed to identify the key factors in producing the simulated odds ratios and evaluate the validity of those results. This involved running the simulation and logistic regression after making changes to a parameter or submodel and comparing the odds ratios from those based on the ‘baseline’ simulation. Selected regression sensitivity analyses have sections in the supplemental material due to the detail required to explain their motivation and the change which was enacted in the model. These include the implementation of GI excretion, examination of variability in the free PFAS fraction in plasma, and decreasing renal reabsorption in individuals with kidney failure.

2.7. Statistical analysis Several techniques were implemented to evaluate the similarity of the simulated to the observed population. The median and quartiles of key characteristics were compared, including age, BMI, the BSA adjusted eGFR, and the serum concentrations of PFOA and PFOS in men and women. We compared the prevalence of subclinical hypothyroidism and subclinical hyperthyroidism in the observed and simulated populations. The Pearson correlation coefficients were calculated for TSH and eGFR, PFOA and eGFR, and PFOS and eGFR and compared to the observed population. Because GFR was not measured in the NHANES population, correlations were calculated with eGFR (Levey et al., 2009) rather than GFR. We also examined the correlation of the GFR used in the simulation with the eGFR we estimated from it and compared this with a GFR-eGFR correlation estimated from data in or reported in published studies (Levey et al., 2009; Rule et al., 2004). To examine the amount of pharmacokinetic bias present in the association between subclinical thyroid disease and PFOA/PFOS serum levels, we calculated the odds ratio for subclinical thyroid disease using the simulated population with an approach similar to that used in Wen et al. (2013). The odds ratios of subclinical hypothyroidism or hyperthyroidism were per unit change in the natural logarithm of serum concentration of PFOA or PFOS (ln(PFAS)) and were calculated using a logistic regression controlled for the individual's age. The assignment of subclinical thyroid disease in the simulated population was based on the assignment of Wen et al. (2013), who defined subclinical disease as abnormally high or low TSH levels, with no self-report of doctor diagnosed thyroid problem. To approximate that definition, we included individuals in the subclinical and overt states in the MCM. Individuals who were in the overt disease state of the MCM were classified as having subclinical disease in the target study because they did not report having a thyroid problem. Individuals in the treated category were excluded from the analysis. Reanalysis of the observed population was performed using TSH values for disease diagnosis similar to those used in the simulated population (slightly different that in the original study), and the regression was performed with adjustment for age and race. The supplemental material includes a comparison of our analysis of the NHANES data to Wen et al. (2013). Simulated population size was based on the stability of computed odds ratios, which was evaluated using a bootstrap approach. For this approach, a population of 500,000 was simulated, then that population was sampled with replacement to generate artificial populations of from 10,000 to 500,000 individuals, with 20 artificial populations generated for each sample size. Then the odds ratios were computed for each sample population and the variation in odds ratios between simulated populations of the same size was examined. Based on

2.9. Software The Bayesian optimization of the MCM parameters was performed using Hamiltonian Monte Carlo (Duane et al., 1987) implemented in a Stan model (Stan Development Team, 2017). This optimization was setup, run, and analyzed using scripts written in R (R Core Team, 2018). After parameter optimization, the MCM, as well as the modification of TSH and GFR based on thyroid disease state, were included in the PBPK model, which was implemented in R (R Core Team, 2018). Statistical analysis of the PBPK model results was also performed in R (R Core Team, 2018). Model code is provided as supplemental material. 3. Results 3.1. Exposure and population characteristics An example of the exposure from birth to age 36 years (the age at which the subject's data were collected) for a simulated subject is shown in Fig. 4. The main contributor to the shape of the exposure curve is the calendar year-dependent term in the exposure (Wong et al., 2014), which consists of a phase of increasing production starting in 1950, a phase of peak exposure between 1990 and 1998, and a phase of decreasing exposure around the time of the phase out of PFOA use in the U.S. The distributions of age, BMI, eGFR, and plasma concentration of PFAS in the observed and simulated populations were well matched, although the quartiles of the PFAS distributions for simulated subjects tended to be slightly wider than in the observed population (Table 2). The prevalence of subclinical hypothyroidism in the observed and simulated populations were within one percent of one another (Table 3). For subclinical hyperthyroidism, where prevalences were lower, the observed and simulated prevalences differed by roughly 0.1%. The correlations of GFR with eGFR obtained from the literature 5

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Table 4 Pearson correlation coefficient between GFR and estimated GFR, between estimated GFR and TSH, and between estimated GFR and serum PFAS concentrations. Pearson Correlation Coefficient

GFR and Estimated Estimated GFR and Estimated GFR and Estimated GFR and

GFR TSH Serum PFOA Serum PFOS

Observeda

Simulated

0.35, 0.76 −0.11 −0.07 −0.16

0.51 −0.11 −0.10 −0.13

a Observed correlations were taken from NHANES, except for the correlation between GFR and estimated GFR. The value of 0.35 is from Rule et al. (2004); the value of 0.76 was estimated based on the results in Table 3 of Levey et al. (2009).

renal health of the participants in the study, with a smaller correlation present in a healthy population with a smaller range of GFR, as in Rule et al. (2004), and a larger correlation present in a more diverse population, as in Levey et al. (2009). 3.2. Odds ratios Our recalculation of the observed odds ratios resulted in values that had 1.0 within the 95% confidence interval for all categories except for the odds ratio of hyperthyroidism is men with PFOA levels and hypothyroidism in men with PFOS levels (Fig. 5). This recalculation was necessary because we excluded individuals of age 80 years or above in our simulation, due to the lack of data and specific age information available for that group, and because we applied our definitions of disease based on TSH. We also adjusted for only the covariates that we felt would be important confounders (age and race). We show in detail how small changes in the NHANES analysis affected the observed odds ratios in the supplemental material. The simulated odds ratios, however, all had confidence intervals separate from 1.0, with values greater than 1.0 for all hypothyroid categories and less than 1.0 for all hyperthyroid categories. The simulated odds ratio can be used to estimate the association that Wen et al. (2013) would have observed in the absence of the pharmacokinetic bias (see SI for details). For example, for subclinical hypothyroidism and PFOA in females, the bias-adjusted odds ratio was 5.9 (95% CI 0.9–38.4), compared with the observed OR of 7.4 (1.1–48.1). For hyperthyroidism and PFOS (both sexes) and hyperthyroidism and PFOA in women, the observed (our analysis) and simulated OR were qualitatively different – OR > 1 for observed and OR < 1 for simulated. In these cases, the implication of the bias analysis is that if the observed OR is truly > 1, then the magnitude of the observed OR is smaller than it should be, due to the bias we identified. The simulated results have a much smaller confidence interval compared to the observed results mainly due to the larger population. While a population of 200,000 individuals would be a massive task for a population survey, a population of simulated individuals of that size only takes about 1 h of computation time. A large simulated population allows us to have a small confidence interval and greater knowledge about the ‘true’ value of the odds ratios but prevents comparison between the size of the confidence intervals in the observed and simulated populations.

Fig. 4. Modeled exposure over a lifetime for a male individual born in 1971 from age 2 to the age of sampling (36) in 2007. Table 2 Characteristics of observed and simulated subjects, where observed subject characteristics were drawn from NHANES using a method similar to that described in the Wen et al. (2013) analysis of NHANES 2007–2008 data.

Age (yr) BMI (kg/m2) eGFR (ml/min per 1.73 m2)c PFOA (μg/L) PFOS (μg/L)

Female Male Female Male

NHANES ‘07-’08a

Simulation

43 (31, 55)b 27.6 (24.0, 31.5) 99.5 (85.0, 112.9) 3.6 (2.5, 5.2) 5.2 (3.7, 7.1) 10.8 (6.9, 17.2) 17.6 (11.9, 24.6)

43 (32, 55) 27.0 (22.9, 32.4) 98.9 (85.6, 112.1) 3.6 (2.2–6.3) 5.2 (3.0–9.5) 10.5 (6.1–17.6) 17.6 (10.4–28.6)

a

Individuals from age 20 to 79. Values are medians and quartiles. c Estimated GFR calculated from serum creatinine in observed data and from the ‘true’ GFR in the simulated data. b

Table 3 Observed and simulated prevalence of subclinical thyroid disease.

Subclinical hypothyroidism prevalence (%) Subclinical hyperthyroidism prevalence (%) a

Female Male Female Male

NHANES ‘07’08a

Simulation

7.52 5.17 0.81 0.22

6.97 4.34 0.92 0.14

Calculated using state definitions in Table 1.

encompassed the correlation based on the simulated data (Table 4). The correlation of eGFR with TSH in the observed population was the same as in the simulated population. The correlation of eGFR with serum PFAS concentrations were similar in the observed and simulated populations. The correlation between GFR and eGFR in population studies appears to be quite variable, with a value of 0.35 presented by Rule et al. (2004) and a value of 0.76 obtained from estimation based on the results presented by Levey et al. (2009). The correlation in our simulated population was between the values from those two studies. A large part of this discrepancy found in published studies depends on the

3.3. Sensitivity analyses The one-at-a-time sensitivity analysis showed that the key determinants of PFAS plasma concentrations were the parameters in the PBPK most closely tied to renal clearance (Fig. S3). These consisted of parameters for GFR, fraction unbound in plasma, and for renal reabsorption and there were not substantial chemical or sex-dependent differences in the sensitivity of these parameters. 6

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Fig. 5. Associations between subclinical thyroid disease and unit increase in natural log-transformed PFAS in logistic regression models in women and men, with observed results weighted for sampling strategy, where observed subjects were from the analysis of NHANES 2007–2008 data using a method similar to that used in Wen et al. (2013). The point represents the odds ratios (OR) and the bars represent the 95% CI.

Table 5 Regression sensitivity analysis showing beta coefficients for the logistic regression model of subclinical thyroid disease status as a function of loge(PFAS) after modification of simulation. Modification of simulation

Subclinical hypothyroidism Females

PFOA None β coefficient for lnTSH decreased 10%a No exposure trendb No sex dependence for exposure adjustmentc Exposure decreased by 50% Increased plasma bindingd Alternate disease cutoffs for TSHe Decreased error term in GFR-lnTSH regressionf Alternate disease model parametersg GI excretion added Decreased variation in plasma bindingh Addition of renal failurei PFOS None β coefficient for lnTSH decreased 10% No exposure trend No sex dependence for exposure adjustment Exposure decreased by 50% Increased plasma binding Alternate disease cutoffs for TSH Decreased error term in GFR-lnTSH regression Alternate disease model parameters GI excretion added Decreased variation in plasma binding Addition of renal failure

Subclinical hyperthyroidism Males

Females

Males

0.23 0.20 0.28 0.21 0.23 0.20 0.27 0.24 0.20 0.25 0.30 0.21

(0.20, (0.17, (0.25, (0.19, (0.21, (0.18, (0.23, (0.22, (0.17, (0.22, (0.28, (0.19,

0.25) 0.22) 0.31) 0.24) 0.25) 0.22) 0.30) 0.27) 0.22) 0.28) 0.33) 0.23)

0.19 0.16 0.23 0.16 0.15 0.16 0.23 0.19 0.21 0.19 0.22 0.17

(0.16, (0.13, (0.19, (0.14, (0.12, (0.13, (0.20, (0.17, (0.18, (0.16, (0.19, (0.14,

0.21) 0.18) 0.27) 0.19) 0.17) 0.18) 0.27) 0.22) 0.24) 0.23) 0.25) 0.19)

−0.33 −0.39 −0.47 −0.34 −0.31 −0.33 −0.29 −0.31 −0.34 −0.44 −0.44 −0.30

(−0.40, (−0.46, (−0.56, (−0.40, (−0.38, (−0.39, (−0.35, (−0.37, (−0.41, (−0.51, (−0.51, (−0.37,

−0.27) −0.32) −0.38) −0.27) −0.24) −0.26) −0.22) −0.24) −0.28) −0.36) −0.37) −0.24)

−0.27 −0.30 −0.45 −0.15 −0.33 −0.26 −0.23 −0.36 −0.26 −0.34 −0.57 −0.26

(−0.42, (−0.45, (−0.66, (−0.29, (−0.48, (−0.41, (−0.35, (−0.52, (−0.40, (−0.52, (−0.73, (−0.40,

−0.13) −0.16) −0.24) −0.01) −0.18) −0.12) −0.11) −0.21) −0.12) −0.17) −0.40) −0.12)

0.18 0.16 0.20 0.20 0.20 0.17 0.21 0.18 0.19 0.20 0.21 0.17

(0.16, (0.13, (0.17, (0.18, (0.18, (0.15, (0.18, (0.16, (0.17, (0.17, (0.18, (0.15,

0.21) 0.18) 0.23) 0.22) 0.22) 0.20) 0.24) 0.20) 0.21) 0.23) 0.24) 0.19)

0.14 0.14 0.17 0.17 0.16 0.18 0.18 0.15 0.20 0.17 0.19 0.13

(0.11, (0.11, (0.13, (0.14, (0.13, (0.15, (0.14, (0.12, (0.17, (0.14, (0.16, (0.10,

0.17) 0.17) 0.21) 0.20) 0.19) 0.22) 0.23) 0.18) 0.23) 0.20) 0.23) 0.16)

−0.32 −0.26 −0.36 −0.24 −0.27 −0.32 −0.27 −0.33 −0.17 −0.30 −0.39 −0.31

(−0.39, (−0.32, (−0.45, (−0.31, (−0.34, (−0.39, (−0.33, (−0.40, (−0.23, (−0.37, (−0.46, (−0.37,

−0.26) −0.19) −0.27) −0.18) −0.21) −0.26) −0.21) −0.27) −0.10) −0.22) −0.31) −0.24)

−0.31 −0.28 −0.40 −0.35 −0.22 −0.39 −0.23 −0.34 −0.28 −0.27 −0.39 −0.26

(−0.47, (−0.43, (−0.63, (−0.50, (−0.37, (−0.56, (−0.36, (−0.49, (−0.44, (−0.45, (−0.57, (−0.42,

−0.15) −0.12) −0.17) −0.20) −0.06) −0.22) −0.09) −0.19) −0.12) −0.09) −0.21) −0.11)

β coefficient for lnTSH in model of GFR. Constant calendar-year exposure based on model levels for the year 2000. c Similar exposure applied to each sex by averaging the male and female chemical-dependent exposure adjustment. d Plasma binding increased to match published half-life values (2.3 yr for PFOA (Bartell et al., 2010), 5.4 yr for PFOS (Olsen et al., 2007)). Half-lives in the baseline model are shorter than published human values due to suspected continuing exposure in the actual population. e Using TSH cutoffs from Wen to define subclinical disease states. f Error term decreased from 0.18 to 0.16 L/h. The value of 0.16 L/h was derived from the GFR – lnTSH regression, but a slightly higher value was used to better capture variation in GFR. g An alternate set of MCM parameters was used that provided a similar likelihood to the MAP values (change in Bayesian Information Criteria of 4.0) to test the sensitivity of the model to the exact values of the MCM parameters. h Binding of PFAS to plasma proteins (primarily albumin). i 10,000 individuals added to baseline simulation whose GFR and resorption rates are designed to capture the effects of renal failure on PFOA and PFOS pharmacokinetics (see supplemental material). a

b

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positive associations were present between TSH and PFOA among men and among women without diagnosed thyroid disease, but this may have been driven by an association among those with low TSH (hyperthyroid subjects). In NHANES data, Jain (2013) analyzed NHANES data from 2007 to 2008, and after excluding those with current diagnosed thyroid disease found that TSH as a continuous variable was positively associated with PFOA (p < 0.01). Other reports based on the NHANES with TSH represented as a continuous variable, however, do not offer much support for a TSH-PFAS association overall, although positive associations were present in some subgroup analyses (Lewis et al., 2015; Webster et al., 2015; Wen et al., 2013). Our hypothesis about how thyroid disease influences PFAS blood concentration does not explain all the reported cross-sectional associations with thyroid hormones. For example, in the C8 Health Study, which included over 50,000 subjects, an increase in total T4 with concurrently measured serum PFOA and PFOS was reported (Knox et al., 2011). Among highly exposed workers, total T3 increased with higher exposure to PFOS (Olsen et al., 2003). In three studies of occupationally exposed humans, findings have been inconsistent, with one study showing PFOA exposure being associated with decreased free T4 (Olsen et al., 2007) and two showing increased T3 (Olsen et al., 2007, 2003). The C8 Science Panel, with respect to serum PFOA concentration, corroborated the increase in total T4 previously reported by Knox et al. and additionally reported a positive association with an index of free T4. Perturbations in thyroid hormones due to PFAS exposure that do not necessarily reflect pathologic effects on the thyroid are possible (Du et al., 2013; Ren et al., 2015; U.S. Environmental Protection Agency, 2016a, 2016b). Clinically significant alterations in functional thyroid disease status, however, usually coincide with changes in TSH, and the focus of our investigation was TSH and TSHdefined thyroid disease. In the above-mentioned community with a wide range of exposure to PFOA, modeled exposure was associated with increased risk of functional clinical thyroid disease in women (Winquist and Steenland, 2014). In addition, occupational exposure to PFOA estimated with a job exposure matrix showed a suggestive association with thyroid disease in men (Steenland et al., 2015). Because exposure was estimated with a model in these two studies, the results could not have been influenced by an effect of thyroid status on excretion of PFOA. Our reverse causality hypothesis thus does not explain these findings. In several toxicological experiments with rats and monkeys, repeated administration of PFOS did not affect thyroid gland histology or serum TSH level, indicating the maintenance of overall thyroid homeostasis (Butenhoff et al., 2012; Chang et al., 2017, 2009; Curran et al., 2008; Elcombe et al., 2012a, 2012b; Lau et al., 2003; Luebker et al., 2002; Seacat et al., 2003; Yu et al., 2009). Exposure to PFOS in the toxicological studies can indeed result in decreased total T4, with no concomitant changes in free T4—if free T4 is measured by equilibrium dialysis, which avoids an artifact in measurement due to displacement of T4 from plasma binding proteins (Chang et al., 2017, 2008; 2007; Luebker et al., 2005; Seacat et al., 2002). Similar thyroid-related findings are also observed in laboratory rodents with exposure to PFOA (Biegel et al., 2001; Butenhoff et al., 2012). In a small phase 1 clinical trial in cancer patients, high-dose PFOA (resulting in up to approximately 630,000 ng/mL PFOA in plasma) resulted in a slight increase in free T4 (PK/PD statistical analysis) but no change in TSH (Convertino et al., 2018); total T4 was not measured. However, the results of this short-term study are not necessarily applicable to the general population. Furthermore, toxicological evidence exists for biologically plausible effects of PFAS on thyroid function, in some instances even at concentrations not much higher than among background-exposed humans (Chen et al., 2018; Song et al., 2012; Ren et al., 2015; Xin et al., 2018; Yu et al., 2009; Zhang et al., 2016). Finally, toxic effects of PFAS on the thyroid in humans may occur via mechanisms which have not yet been considered. We used TSH as the sole hormonal indicator of thyroid status

After varying key aspects of the simulation, the coefficients from the logistic model (β, where odds ratio = eβ), were qualitatively like the baseline model, and generally within 30% (Table 5). Regarding the coefficients that were 30% or more different than baseline: 1) removing the exposure trend, a rather extreme revision, increased the absolute size of β for PFOA and subclinical hyperthyroidism in females and males, 2) decreasing the error term in the GFR-lnTSH regression increased the absolute size of β for PFOA and subclinical hyperthyroidism in males, 3) use of an alternate lifetime model of thyroid disease status resulted in a larger β for PFOS and subclinical hypothyroidism in males and a smaller β for PFOS and subclinical hyperthyroidism in females, 4) addition of a gastrointestinal excretion route to the PBPK increased the absolute size of β for PFOA and subclinical hyperthyroidism in females, and 5) decreased variation in plasma PFAS binding increased the absolute β for PFOA and subclinical hypothyroidism and subclinical hyperthyroidism in females and PFOA and subclinical hyperthyroidism in males, and PFOS and subclinical hypothyroidism in males. Given the large number of coefficients compared, some random differences in coefficients may have occurred. This is more likely in the categories with smaller prevalence of thyroid disease, such as hyperthyroid men. The effect of decreasing variation in PFAS binding, however, was more consistently important than the other effects evaluated. 4. Discussion Our main findings based on a cross-sectional analysis of data from a simulated population of non-pregnant adults similar to that of the Wen et al. (2013) study, suggest the reported association between PFAS and subclinical hypothyroidism had a moderate positive bias and the association between PFAS and subclinical hyperthyroidism had a modest negative bias due to reverse causality. The simulation contained a population where the only factor affecting the TSH-PFAS association was the interaction between TSH and GFR. This allows the results of the simulation to inform only degree of bias present due to that relationship. In other words, the model predicted that Wen et al.‘s associations with subclinical hypothyroidism were overstated, and that their results for subclinical hyperthyroidism were, in general, understated due to the effect of thyroid function on PFAS clearance. The reverse causality hypothesis stems from two interlinked relations: thyroid function (represented by TSH) influences GFR, and GFR affects PFAS excretion; thus, thyroid function will affect the serum concentration of PFAS. The relation of TSH with GFR is modest, thus we posit that the “effect” of TSH on PFAS is weak. Furthermore, bias due to reverse causality will be operative when concentrations of TSH and PFAS in serum were measured at the same time (Savitz and Wellenius, 2017). Furthermore, the likelihood that the results of an epidemiologic study will clearly reflect the bias will be greater when the study is large. Large studies will include some subjects who have extreme values of TSH—such observations will have greater leverage and coincide with the presence of thyroid disease that has not been diagnosed or adequately treated. Thus, when thyroid status and PFAS concentration were ascertained simultaneously in large studies the bias should be most apparent. The study by Wen et al. (2013) fits the profile of a study where bias due to reverse causality would be in evidence. Assuming that our model is correct, the results should be generalizable to cross-sectional studies of subclinical thyroid disease in nonpregnant adults. In a community with a wide range of exposure to PFOA, the C8 Science Panel (2012) conducted a large cross-sectional study and found statistically significant reduced odds of subclinical hyperthyroidism per interquartile increment in PFOA; for hypothyroidism, however, the association was essentially null. The C8 results are therefore consistent with our prediction for hyperthyroidism, but for hypothyroidism the reason for the lack of association is unclear. One might expect, based on our results, that an association might also be found between PFOA and PFOS and TSH in cross sectional studies of non-pregnant adults. In the C8 study, small, statistically significant 8

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possible and may be computationally tractable—but may not add much to the overall result given the added uncertainties in and assumptions of such a model. Our simulation ignores the small positive relation of plasma albumin to TSH (Morris et al., 2001). Hypothyroid subjects, for example, might be expected to have higher plasma PFAS because of increased binding. However, ignoring this relation may not have had an important effect on our results. In support of this was that when we repeated our analysis of the NHANES data on subclinical hypothyroidism and PFOS, adjustment for albumin moved the odds ratio from 1.65 to 1.60, a relatively small change compared with what we estimated in the simulation as due to TSH-GFR relation. A further limitation of our study is that it addresses only subclinical, and not clinical thyroid disease. Although our model did not include gestational or lactational exposure, these would be reflected by the initial plasma PFAS concentrations assigned at age 2, and the plasma concentrations simulated in the teenage years and later were not sensitive to the plasma PFAS values initially assigned (not shown). The relation between GFR and thyroid hormone concentrations has been established qualitatively (Coura-Filho et al., 2015; Duranton et al., 2013) and quantitatively (Lippi et al., 2008; Sun et al., 2012; Tsuda et al., 2013). However, the quantitative studies were of non-U.S. populations and had features that limited their usefulness to us – for example, no regression model was fit (Lippi et al., 2008; Tsuda et al., 2013), or those with abnormal thyroid function were excluded (Sun et al., 2012; Tsuda et al., 2013). For those reasons we fitted our own model of the GFR-TSH relation to NHANES data (see supplemental material). The regression sensitivity analysis (Table 5, second row of results) showed that for subclinical hypothyroidism, where the confidence limits for the simulation were relatively narrow, the effect of decreasing the GFR-TSH regression coefficient was as expected – the coefficient for calculating the odds ratio decreased slightly. We chose 10% as the amount to attenuate the GFR-TSH coefficient in the regression sensitivity analysis on the basis that 10% was the amount we used to deattenuate the GFR-TSH coefficient to compensate for the effect of measurement error in TSH due to day to day physiologic variation (see supplemental material). The general approach we used to evaluate reverse causality in the thyroid state-PFAS association might be useful for assessing other associations between serum or plasma concentrations of environmental contaminants or metals and thyroid disease status in cross-sectional studies (similar to Jain and Choi, 2016), though careful consideration would be needed of whether the requirements of the procedure would be met (see the supplemental material for a list). If the relation of thyroid disease status to the serum concentration of the agent met the criteria, application of the approach we used here might also be useful as a method for validating it.

because we were simulating a study in which the outcome was defined by TSH, the primary clinical indicator used for diagnosing human thyroid hormone status (Melmed et al., 2011). In the simulated study (Wen et al., 2013), defining subclinical disease on the basis of TSH only was reasonable because: 1) people with a diagnosis of thyroid disease were excluded, and 2) TSH is extremely sensitive to small changes in fT4 and is the primary test for thyroid dysfunction (Garber et al., 2012). However, the definition of “subclinical” thyroid disease used here is unusual. Wen et al. (2013) used “subclinical” to refer to all who were undiagnosed and had abnormal concentrations of TSH, regardless of their thyroxine concentration. In general, subclinical thyroid disease refers to those with an abnormal TSH but normal thyroxine (Bahn et al., 2011; Garber et al., 2012). Our analysis may be biased by not including the effects of active renal secretion of PFOA and PFOS. Active secretion is important in the rat, with organic anion transporters (OAT) 1 and 3 identified as the primary transporters for PFOA on the basolateral surface (Kudo et al., 2002; Nakagawa et al., 2007). These transporters are sex-hormone dependent and this seems to be the cause of the large difference in halflife between male and female rats (Kudo et al., 2002). Human isoforms of the transporters identified in rat have shown similar activity in vitro (Nakagawa et al., 2007) and have relatively high prevalence in the proximal tubule cells (Hilgendorf et al., 2007; Motohashi et al., 2002). However, despite the similar in vitro affinities, humans have a much longer half-life than rats and do not show any difference in half-life between sexes (Harada et al., 2005). This may be due, in part, to the activity of OAT4, which functions as a reabsorption transporter on the apical surface and is not present in the rat (Nakagawa et al., 2009). Viewing secretion and reabsorption as an aggregate flow across the proximal tubule and comparing that with filtration, rats have a net secretion, while humans have a net reabsorption (Han et al., 2011). Our PBPK model includes this aggregate view of the proximal tubule, with a single equation describing saturable reabsorption. While the passage of PFOA and PFOS into the filtrate may include GFR and active transport, it is the aggregate view of the filtration/reuptake and its representation that is important. The uncertainty that we had about many of the assumptions we made to conduct this quantitative bias analysis was reflected by the large number of regression sensitivity analyses we conducted (Table 5). The informativeness of our simulation depended on the accuracy of the component models: the PBPK model, the model of exposure, and the models of lifetime thyroid disease status and its relation to TSH. The examination of the effect of reducing the variation in plasma protein binding of PFAS indicated that our default assumption of a 30% variation for this parameter may have attenuated the size of the bias we estimated. Uncertainty about the extent of gastrointestinal excretion prevented us from including it in our “baseline” simulation; however, the regression sensitivity analysis suggested that the association had a relatively minor influence on our results, and the adequacy of our model was supported by the realistic correlation of PFAS with GFR. Our MCM predicted the prevalence of thyroid disease fairly well; however, for practical reasons, most of the parameters in the MCM did not vary by sex or age. Because the clinical course of thyroid disease may vary by sex and age, this simplification may have introduced inaccuracies. This and other potential weaknesses of the MCM have been discussed in detail elsewhere (Dzierlenga et al., 2019b). In our simulation, for a given thyroid disease state, the probability of changing to another state was independent of the value of TSH assigned to the subject. Several reports indicate that for subjects in a given thyroid disease state, their probability of changing to another state depends on their TSH concentration (Åsvold et al., 2012; Rosario, 2010; Vanderpump et al., 1995). Our simplifying assumption that TSH concentrations were independent from transition probabilities would cause the model to underestimate the association of thyroid state with GFR, and hence with PFAS concentration. An elaboration of our MCM, e.g., to accommodate disease “substates” defined by TSH concentration is theoretically

5. Conclusions Given our model structure, this simulation indicated that in a crosssectional study of subclinical thyroid disease and serum PFAS concentration among non-pregnant adults, associations (e.g., OR for subclinical hypothyroidism per 1 natural log unit increase in PFOA = 1.26 for females and 1.21 for males) would be expected based on pharmacokinetics in the absence of a toxic effect. This bias would cause an overestimation of the size of the underlying association, if any, for subclinical hypothyroidism and, in general, an underestimation of the size of the underlying association, if any, for subclinical hyperthyroidism.

Funding This study was supported by a grant from 3M.

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Declaration of competing interest

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