Cross-sectional association of Toxoplasma gondii exposure with BMI and diet in US adults.

Toxoplasmosis gondii exposure has been linked to increased impulsivity and risky behaviors, which has implications for eating behavior. Impulsivity and risk tolerance is known to be related with worse diets and a higher chance of obesity. There is little known, however, about the independent link between Toxoplasma gondii (T. gondii) exposure and diet-related outcomes. Using linear and quantile regression, we estimated the relationship between T. gondii exposure and BMI, total energy intake (kcal), and diet quality as measured by the Health Eating Index-2015 (HEI) among 9,853 adults from the 2009-2014 National Health and Nutrition Examination Survey. Previous studies have shown different behavioral responses to T. gondii infection among males and females, and socioeconomic factors are also likely to be important as both T. gondii and poor diet are more prevalent among U.S. populations in poverty. We therefore measured the associations between T. gondii and diet-related outcomes separately for men and women and for respondents in poverty. Among females <200% of the federal poverty level Toxoplasmosis gondii exposure was associated with a higher BMI by 2.0 units (95% CI [0.22, 3.83]) at median BMI and a lower HEI by 5.05 units (95% CI [-7.87, -2.24]) at the 25th percentile of HEI. Stronger associations were found at higher levels of BMI and worse diet quality among females. No associations were found among males. Through a detailed investigation of mechanisms, we were able to rule out T. gondii exposure from cat ownership, differing amounts of meat, and drinking water source as potential confounding factors; environmental exposure to T. gondii as well as changes in human behavior due to parasitic infection remain primary mechanisms.

Introduction

The protozoon parasite Toxoplasma gondii infects over 10 percent of the US population [1], with low-income populations bearing the greatest burden [2]. Most infections remain undiagnosed and subclinical, but T. gondii can cause serious health problems for some individuals, including infected fetuses and infants [3]. Subclinical chronic T. gondii infection can cause changes in the brain [4], and has been linked to more subtle behavioral changes in humans. T. gondii exposure may decrease cognitive function [5] and may increase the risk of mental health problems such as schizophrenia or psychosis [6-7]. Adults with T. gondii exposure are more likely to engage in risky behavior such as alcohol consumption [8], risky driving [9], and entrepreneurial activities [10] and they may also exhibit higher levels of aggression and impulsivity [11]. Many of these behavioral changes are also known to increase the chance of having poor diet and being obese. In particular, individuals with greater tolerance for risk or who are more impulsive have higher body mass index (BMI) and worse diets [12-17].

Despite these behavioral linkages, little is known about the role of T. gondii exposure in explaining diet or obesity. T. gondii has been shown to cause short-term weight gain in rats [18], and is associated with obesity in German populations [19-20] as well as with type 2 diabetes [21]. However, the relationship with obesity was not found in a sample of Mexican individuals [22], and no link has been found between toxoplasmosis and fatty liver disease [23]. No studies to date have examined whether T. gondii exposure can explain differences in overall diet quality.

Our primary aim in this study was to evaluate whether T. gondii exposure explains body mass index (BMI), total energy consumption, and diet quality in a nationally-representative sample from the United States. Since the available data do not allow us to establish causality, our secondary aim was to investigate potential mechanisms for any relationship between T. gondii exposure and these outcomes.

Methods

Data

We used three waves (2009–2010, 2011–2012, and 2013–2014) of the continuous National Health and Nutrition Examination Survey (NHANES) to examine the relationships between T. gondii exposure, BMI, energy consumed, and diet quality. The NHANES samples U.S. residents and conducts a detailed survey that contains clinical examinations and laboratory tests. From the 2009–2010, 2011–2012, and 2013–2014 waves, a subset of the respondent blood samples was tested for T. gondii IgG antibodies using an enzyme immunoassay. The subset of respondents consisted of all those with surplus serum left after conducting the other regular NHANES laboratory tests, with the exception of pregnant women [1]. We defined T. gondii exposure (i.e. seropositivity) as having T. gondii IgG antibody concentration of ≥33 IU/mL; any amount < 33 IU/mL is classified as no exposure (i.e. seronegative) [1]. We also controlled for poverty in our models as respondents under the poverty level were less likely to have serum tested for T. gondii IgG antibodies. Furthermore, adjusting the NHANES sampling weights to account for income differences leads to the same results on T. gondii exposure prevalence and risk factors as using the original NHANES sampling weights [1]. We therefore used the original NHANES sampling weights in our analyses.

As part of the NHANES clinical examination, respondents’ weight (kg) and height (cm) are measured, and BMI is calculated for each respondent. The clinical examination also includes a 24-hour dietary recall component [24]. NHANES aggregates the foods consumed for each respondent and releases data on total energy consumed (kcal). This allowed us to measure the quantity that a respondent eats. In addition, the nutrient consumption data allow us to examine the quality of the diet. Specifically, the Healthy Eating Index (HEI) evaluates 13 different dietary components and measures on a scale of 0–100 how closely a particular diet adheres to the Dietary Guidelines for Americans. Of the 13 dietary components used to score the HEI, greater amounts of total fruits, whole fruits, total vegetables, green vegetables or beans, whole grains, dairy, total protein, seafood or plant proteins, and fatty acids increase the HEI score. Greater amounts of four components decrease the HEI score: refined grains, sodium, added sugars, and saturated fats. A score of 100 indicates the best diet possible. The HEI has been revised twice since it was first developed in 1995; we use the HEI-2015 [25].

We restricted our sample to adults (18+ years old) whose blood samples were tested for T. gondii antibodies and who have available BMI and dietary data. Previous research has found much higher T. gondii seroprevalence among foreign-born NHANES respondents [1], so we excluded respondents who were not born in the U.S., resulting in a total sample size of 9,853 individuals.

The NHANES waves used in this study provide the only publicly accessible measures of T. gondii exposure linked to detailed food intake data for the U.S. population. The NHANES dietary recall data, however, have two limitations relevant to examining diet and T. gondii exposure. First, respondents regularly misreport food consumption in self-reported dietary recalls such as the NHANES [26-28] and misreporting is greater for obese respondents [29]. We note though that the dietary recall elicitation method used in the NHANES suffers from substantially less misreporting than other methods [30] and that overall energy intake is underreported by 11% by the method used by NHANES [29]. The second limitation inherent in the NHANES data is the inability to observe food safety and hygiene practices, which may be important in T. gondii transmission [31].

To accomplish our secondary aim of examining potential mechanisms and confounders for our results, we modified our sample and data from the main sample described above. The rationale for each modification is given in the Results section below. One subsample consisted of respondents with valid information on infection with Toxocara canis/cati. This information was available in 2011–2012 and 2013–2014 NHANES waves. Similar to the T. gondii laboratory samples, a subsample of survey respondents’ blood specimens were tested for Toxocara antibodies. Samples were classified as positive for Toxocara if the mean fluorescence intensity was greater than 23.1 and negative otherwise [32]. A total of 6,237 respondents had available Toxocara information and also met our sample restrictions above. A second subsample excluded an additional 1,544 individuals who reported participating in vigorous recreational activities at least three days per week, resulting in a sample size of 8,309. A third subsample included only respondents with valid percent body fat information. NHANES used dual-energy X-ray absorptiometry to measure body composition in the 2011–2012 and 2013–2014 waves. A total of 3,668 respondents meeting our sample restrictions also had valid information on the percent body fat.

A final sample we used to investigate mechanisms consists of respondents in the 2005–2006 NHANES wave. The NHANES waves with T. gondii data described above did not have information on cat ownership, though the 2005–2006 NHANES wave included this information. The 2005–2006 wave captured cat ownership through two questions asking respondents whether a cat lives in their house now, and whether a cat lived at their house at some point in the past 12 months. We combined these two questions and measured cat ownership to be whether an individual either has a cat in the house now or had a cat in the house in the past 12 months. We made the same sample restrictions as our main analysis (U.S.-born adults with valid dietary data), yielding a sample size of 3,545 and 1,395 low-income individuals. We note that though this sample does not have available T. gondii information it does provide the same outcome measures as our primary sample (BMI, total energy consumption, HEI), allowing us to measure the association between cat ownership and diet-related outcomes.

Statistical analysis

We used two analytical methods to examine the relationship between T. gondii exposure and our outcomes of interest. First, we used linear regression of an indicator for T. gondii exposure on continuous BMI, energy consumption, and HEI. We also included an interaction between T. gondii exposure and whether the respondent’s income is over 200% of the federal poverty level. In these regressions, we controlled for whether income is over 200% of poverty, gender, race/ethnicity (non-Hispanic white [reference category], non-Hispanic black, Hispanic, other race), household size, household size squared, age, age squared, whether married, education (less than high school [reference category], high school graduate, some college, college graduate), and whether the interview was conducted between November and April of the particular calendar year (relative to interviews conducted between May-October). We controlled for the part of the year the interview was conducted (November April vs. May-October) to account for possible seasonal differences in diet behavior. To account for differential energy needs we also controlled for the respondent’s BMI in analyses with energy consumption and HEI outcomes. Prior studies found differences between males and females in relationships of behavior with T. gondii exposure [33-34], so we estimated separate regression models for males and females. This decision was made prior to data analysis and was not impacted by the results. Linear regressions used the NHANES examination sampling weight and standard errors take into account the NHANES complex sample design.

Because linear regression results may mask varied relationships between T. gondii exposure and outcomes at different points in the outcome’s distribution [35-36], we estimated a second series of models using quantile regression (QR). QR allows for evaluating relationships between independent variables and the outcome at different quantiles of the outcome’s distribution [37]. We estimated QR models with identical outcome and control variables as our linear regression models. We used the NHANES examination sampling weight in our QR analysis and bootstrapped the standard errors 500 times to account for the complex sample design [38]. QR results are presented in the figures for every fifth percentile from the 5th to the 95th percentile of the outcome distribution. Figures display the coefficient on the indicator for T. gondii exposure and its corresponding 95% confidence interval. Separate figures display the coefficient on the interaction between T. gondii exposure and whether the respondent’s income is >200% of the federal poverty level. The coefficient on the indicator for T. gondii exposure therefore measures the relationship for respondents <200% of poverty and the interaction measures the difference between respondents <200% of poverty and those >200% of poverty. For all modeling approaches we considered p ≤ 0.05 as significant.

We used additional statistical analysis to investigate mechanisms underlying our main results. Rationales are described below. In addition to the control variables above, we separately added the following controls: total protein consumption (grams), pork consumption (kcal) as a percent of total energy consumption (kcal), whether the respondent owns a cat, whether the respondent’s main source of tap water is a well (vs. a municipal source), and whether the respondent is Toxocara seropositive. We also separately examine the association between T. gondii exposure and the following additional outcomes: total protein consumption (grams), pork consumption (kcal) as a percent of total energy consumption (kcal), waist-to-height ratio (WHtR), percent body fat. Since 95.6% of our sample did not eat pork in the 24 hours covered by the dietary recall, quantile regression on the percent of total consumption from pork was unstable. We therefore measured this association using linear regression on mean pork percent as well as indicators for being in different parts of the pork consumption distribution [39].

Results and discussion

T. gondii and outcomes among females and males

Among females <200% of poverty T. gondii exposure was associated with an increased BMI at higher levels of BMI (Fig 1). At the 50th percentile, T. gondii was associated with an increase of 2.0 units (95% CI [0.22, 3.83]), which represents an increase of 7.3% over the BMI of the 50th-percentile female in poverty (BMI = 27.5). The difference between females <200% poverty and females >200% poverty was never statistically significant. T. gondii exposure was not associated with BMI among males <200% poverty, and the difference between males <200% poverty and males >200% poverty was never statistically significant (Fig 2).

Fig 1

Association between T. gondii exposure and BMI, energy consumed, and HEI for females <200% poverty (a-c) and the difference between females <200% poverty and females >200% poverty (“difference”).

Black line is quantile regression coefficient and confidence intervals (95%) are shaded regions. Solid gray line shows the linear regression coefficient and dotted gray lines show the linear regression confidence interval (95%). The dotted line indicates no relationship at that quantile of BMI, energy consumed, and HEI. Straight black line shows value of 0, or no relationship. Controls: income >200% poverty, gender, race/ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, other race), household size, household size squared, age, age squared, whether married, education (less than high school, high school graduate, some college, college graduate), season of interview. Energy and HEI analyses also control for BMI.

Fig 2

Association between T. gondii exposure and BMI, energy consumed, and HEI for males <200% poverty (a-c) and the difference between males <200% poverty and males >200% poverty (“difference”).

Black line is quantile regression coefficient and confidence intervals (95%) are shaded regions. Solid gray line shows the linear regression coefficient and dotted gray lines show the linear regression confidence interval (95%). The dotted line indicates no relationship at that quantile of BMI, energy consumed, and HEI. Straight black line shows value of 0, or no relationship. Controls: income >200% poverty, gender, race/ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, other race), household size, household size squared, age, age squared, whether married, education (less than high school, high school graduate, some college, college graduate), season of interview. Energy and HEI analyses also control for BMI.

T. gondii exposure was not associated with energy (kcal) consumption among females <200% of poverty (Fig 1) and among males <200% of poverty (Fig 2). The difference between both females and males <200% of poverty and respondents >200% of poverty is positive and occasionally statistically significant at above-median levels of energy consumption. The coefficients in our models are noisy, but this suggests that T. gondii exposure is associated with higher energy consumption among respondents >200% of poverty.

T. gondii exposure was associated with lower HEI among females <200% poverty with already-poor diets (i.e. females at the lower end of the HEI distribution) (Fig 1). At the 25th percentile of HEI, T. gondii exposure was associated with a lower HEI by 5.05 units [95% CI [-7.87, -2.24]), which represents an 11.7% decrease in HEI score relative to the 25th percentile HEI among females in poverty (43.1). The difference between females <200% of poverty and females >200% of poverty was positive and occasionally statistically significant at lower levels of HEI. Taking into account the negative relationship among females in poverty, this difference suggests there is no relationship between T. gondii and HEI among females >200% of poverty. T. gondii exposure was not associated with HEI among males <200% of poverty, and the difference between males <200% of poverty and males >200% of poverty is never statistically significant.

Potential mechanisms

While we found that T. gondii exposure is associated with higher BMI and worse diet among females, our data are cross-sectional and do not allow us to make any claim of causality. Therefore, in this section we explore potential mechanisms for our findings among females. We start with potential reasons for a causal relationship and then explore mechanisms that may confound estimation of this causal relationship.

The most direct mechanism explaining our results is that T. gondii influences food preferences to facilitate T. gondii transmission. Parasites have been observed in multiple contexts changing the host’s behavior in order to enhance transmission [40-42], though the extent to which T. gondii influences human behavior is controversial [43]. An alternative mechanism is that T. gondii may have an indirect effect on food preferences through increasing the host’s willingness to undertake risky behavior or by influencing the host’s mental health. T. gondii exposure has been associated with impulsive or risky behavior in humans [8-11]. This impulsive behavior may translate into food choices that prioritize short-term satisfaction over long-term health. On the other hand, less impulsive behavior may contribute to eating healthier but harder-to-prepare foods, increasing risk of T. gondii transmission. In addition to risky behavior, T. gondii exposure has been found to be associated with mental health [7,44], which may in turn influence diet-related outcomes. The above mechanisms would explain a direct or indirect causal relationship between T. gondii exposure and diet-related behavior.

Beyond a direct or indirect impact of T. gondii on diet behavior, our results may be explained by factors that confound estimation of a causal relationship. There are three major sources of confounding factors: food and drink sources, cat and environmental exposure, and coinfections. First, individuals may ingest T. gondii through food or drink sources. If individuals with higher BMI or lower HEI are more likely to eat infected foods or prepare food improperly (i.e. not wash produce, not cook meat to appropriate temperatures), they may have greater exposure to T. gondii. One important food source of T. gondii is infected meat [3]. Higher meat consumption and/or improperly cooking meat could lead to an increased risk of T. gondii exposure. In addition, T. gondii prevalence differs by type of meat. For example, pork is more likely to contain T. gondii than beef [45].

We used multiple methods to test the extent to which meat consumption is a confounding factor. Specifically, we evaluated the association between T. gondii exposure and total protein consumption across the protein consumption distribution (Fig 3). We would expect this association to be positive if T. gondii seropositive individuals have greater exposure to T. gondii through higher levels of meat consumption. Instead, this association was statistically insignificant at all ranges of the protein consumption distribution. Furthermore, our main results were robust to controlling for protein consumption (Fig 4). While total meat consumption does not substantially confound our results, the composition of meat consumed by T. gondii seropositive individuals may be different than seronegative individuals. In particular, T. gondii seropositive individuals may eat more pork and thus be exposed to T. gondii (Table 1). T. gondii exposure had no statistically significant association with the mean percent of energy coming from pork (column 1), whether the individual ate any pork (column 2), or whether pork consumption was in higher ends of the pork consumption distribution (columns 3–5). Our main results were also robust to controlling for the percent of total energy consumption coming from pork (Fig 5). Differential total protein consumption and the composition of current meat consumption thus do not explain our main results. Notably, consideration of protein consumption does not account for potential differences in the individual’s history of protein consumption nor the proper preparation of meat. If food preparation drives our findings, then we would expect higher T. gondii infection among females since U.S. females are more likely than males to cook [46]. However, more males than females have evidence of T. gondii infection in our sample (13.1% of males and 12.1% of females).

Fig 3

Association between T. gondii exposure and total protein consumption among U.S. females <200% poverty (a-c) and the difference between females <200% poverty and females >200% poverty (“difference”).

Black line is quantile regression coefficient and confidence intervals (95%) are shaded regions. Solid gray line shows the linear regression coefficient and dotted gray lines show the linear regression confidence interval (95%). The dotted line indicates no relationship at that quantile of BMI, energy consumed, and HEI. Straight black line shows value of 0, or no relationship. Controls: income >200% poverty, gender, race/ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, other race), household size, household size squared, age, age squared, whether married, education (less than high school, high school graduate, some college, college graduate), season of interview.

Fig 4

Association between T. gondii exposure and diet-related outcomes among U.S. females <200% poverty (a-c) and the difference between females <200% poverty and males >200% poverty (“difference”), controlling for total protein consumption.

Black line is quantile regression coefficient and confidence intervals (95%) are shaded regions. Solid gray line shows the linear regression coefficient and dotted gray lines show the linear regression confidence interval (95%). The dotted line indicates no relationship at that quantile of BMI, energy consumed, and HEI. Straight black line shows value of 0, or no relationship. Controls: total protein consumption, income >200% poverty, gender, race/ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, other race), household size, household size squared, age, age squared, whether married, education (less than high school, high school graduate, some college, college graduate), season of interview. Energy and HEI analyses also control for BMI.

Table 1

Association between T. gondii exposure and pork consumption.

	(1)	(2)	(3)	(4)	(5)
	% pork	Any pork	Pork 1–11%	Pork 12–20%	Pork >20%
T. gondii exposure, <200% poverty	0.0002	-0.0111	-0.0139	0.0053	-0.0025
	(0.0023)	(0.0121)	(0.0072)	(0.0078)	(0.0038)
Difference	0.0032	0.0210	0.0095	0.0046	0.0070
	(0.0047)	(0.0224)	(0.0103)	(0.0145)	(0.0106)

Coefficients are from linear regressions of the percent of total energy consumption (kcal) from pork products (column 1), an indicator for whether any pork was consumed (column 2), an indicator for whether pork was 1–11% of energy consumption (column 3), an indicator for whether pork was 12–20% of energy consumption (column 4), or an indicator for whether pork was >20% of energy consumption (column 5) on T. gondii exposure and controls. Cutoffs of 11% and 20% represent the 50th and 75th percentiles of the non-zero pork consumption distribution. Standard errors in parentheses. Bold coefficients are significant at the 95% confidence level. Controls: income >200% poverty, gender, race/ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, other race), household size, household size squared, age, age squared, whether married, education (less than high school, high school graduate, some college, college graduate), season of interview, BMI.

Fig 5

Association between T. gondii exposure and diet-related outcomes among U.S. females <200% poverty (a-c) and the difference between females <200% poverty and males >200% poverty (“difference”), controlling for % of total energy consumption from pork.

Black line is quantile regression coefficient and confidence intervals (95%) are shaded regions. Solid gray line shows the linear regression coefficient and dotted gray lines show the linear regression confidence interval (95%). The dotted line indicates no relationship at that quantile of BMI, energy consumed, and HEI. Straight black line shows value of 0, or no relationship. Controls: % of total energy consumption from pork, income >200% poverty, gender, race/ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, other race), household size, household size squared, age, age squared, whether married, education (less than high school, high school graduate, some college, college graduate), season of interview. Energy and HEI analyses also control for BMI.

In addition to meat, T. gondii has also been found on produce and in drinking water [47-48]. Diets that include significant amounts of produce or contaminated water may therefore have higher T. gondii exposure. Since by construction more produce consumption increases the HEI score, we would expect to find T. gondii infection associated with an increase in HEI and possibly lower BMI. However, we found the opposite relationship, suggesting that the amount of produce consumed may not be an important mechanism. The possibility remains that the produce consumed by individuals with lower HEI scores is also more likely to be contaminated. Different exposure to T. gondii can also come from different drinking water sources, with water from wells is substantially more likely to be contaminated by T. gondii than water from municipal sources [47]. However, controlling whether the respondent uses well water (vs. a municipal source) did not change our main results (Fig 6), suggesting that differences in drinking water sources are unlikely to explain our findings.

Fig 6

Association between T. gondii exposure and diet-related outcomes among U.S. females <200% poverty (a-c) and the difference between females <200% poverty and males >200% poverty (“difference”), controlling for water source.

Black line is quantile regression coefficient and confidence intervals (95%) are shaded regions. Solid gray line shows the linear regression coefficient and dotted gray lines show the linear regression confidence interval (95%). The dotted line indicates no relationship at that quantile of BMI, energy consumed, and HEI. Straight black line shows value of 0, or no relationship. Controls: tap water from well water, income >200% poverty, gender, race/ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, other race), household size, household size squared, age, age squared, whether married, education (less than high school, high school graduate, some college, college graduate), season of interview. Energy and HEI analyses also control for BMI.

Second, individuals can be exposed to T. gondii through sources besides food and water. T. gondii reproduces in cats and spreads into the environment through cat feces. Locations where cats choose to defecate, such as sandboxes and gardens, are at particular risk for having high concentrations of T. gondii and can lead to infection among individuals who live near these locations [48]. Cat ownership information was not available in the NHANES waves that measure T. gondii exposure, so we were unable to test whether previous exposure to cats moderates the relationship between T. gondii exposure and diet. However, cat ownership information was recorded in the 2005–2006 NHANES wave. Fig 7 displays the association between current cat ownership and our diet-related outcomes using the 2005–2006 NHANES wave. These associations were smaller than the main effects of T. gondii exposure and were statistically insignificant.

Fig 7

Association between cat ownership and diet-related outcomes among U.S. females <200% poverty (a-c) and the difference between females <200% poverty and males >200% poverty (“difference”).

Black line is quantile regression coefficient and confidence intervals (95%) are shaded regions. Solid gray line shows the linear regression coefficient and dotted gray lines show the linear regression confidence interval (95%). The dotted line indicates no relationship at that quantile of BMI, energy consumed, and HEI. Straight black line shows value of 0, or no relationship. Controls: income >200% poverty, gender, race/ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, other race), household size, household size squared, age, age squared, whether married, education (less than high school, high school graduate, some college, college graduate), season of interview. Energy and HEI analyses also control for BMI.

Since cats often live outside and defecate in areas where non-owners frequent, many non-owners can have significant exposure to cats as well. Our results may be explained by differential environmental exposure if individuals with worse diets or higher BMI choose to frequent areas with greater exposure to outdoor cats. While it is unlikely that the presence of outdoor cats directly influences an individual’s choice of area, it is unclear what neighborhood characteristics are related to the presence of outdoor cats. Furthermore, if such residential sorting is a factor, it is unclear why we would observe a relationship for females and not for males. We were unable to examine differential environmental exposure using the NHANES.

The final mechanism explaining our results is the possibility of coinfection with other diseases. Coinfection may cause a researcher to attribute effects to one infection that are properly attributed to another infection. In addition, infections may interact and cause behavioral or biological changes different from those of either infection individually. In particular, the parasites Toxocara canis and Toxocara cati cause toxocariasis and transmission of T. canis and T. cati is similar to that of T. gondii [49-50]. Notably, toxocariasis is associated with worse cognitive function among children [51]. Fig 8 displays our main results controlling for Toxocara serostatus. Including this control does not change our main results, indicating that our main results are not driven by coinfection with Toxocara. While we cannot rule out other coinfections, we note that Toxocara is a common infectious disease in the US [2,49].

Fig 8

Association between T. gondii exposure and diet-related outcomes among U.S. females <200% poverty (a-c) and the difference between females <200% poverty and males >200% poverty (“difference”), controlling for Toxocara serostatus.

Black line is quantile regression coefficient and confidence intervals (95%) are shaded regions. Solid gray line shows the linear regression coefficient and dotted gray lines show the linear regression confidence interval (95%). The dotted line indicates no relationship at that quantile of BMI, energy consumed, and HEI. Straight black line shows value of 0, or no relationship. Controls: Toxocara seropositive, income >200% poverty, gender, race/ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, other race), household size, household size squared, age, age squared, whether married, education (less than high school, high school graduate, some college, college graduate), season of interview. Energy and HEI analyses also control for BMI.

This section has discussed potential mechanisms for observing a relationship between T. gondii exposure and diet-related outcomes. Beyond these mechanisms, the potential exists for our results to be driven by sample selection. NHANES examination, dietary, and T. gondii data are recorded for most, but not all, of the NHANES respondents. As noted above, only samples with surplus serum were tested for T. gondii antibodies. BMI data are available for respondents with valid height and weight data, and diet information (total energy consumption and HEI) is available for NHANES respondents who completed the food intake module. If diet-related outcomes among respondents with T. gondii data were systematically different from outcomes among respondents without T. gondii data, our results may be an artifact of this selection. Out of the total 13,082 respondents in our survey waves, 1,916 have missing T. gondii exposure information, 601 respondents have missing BMI data, 1,224 have missing energy consumption information, and 1,224 have missing HEI data. All respondents with missing energy consumption have missing HEI scores and vice versa. Table 2 tests whether T. gondii or our outcomes are systematically missing. We found that none of our main outcomes (BMI, energy consumption, HEI) predict whether a respondent has missing T. gondii data. Likewise, T. gondii exposure does not predict having missing BMI or diet data. These results suggest that patterns of missing values are not driving our results.

Table 2

Determinants of missing T. gondii, BMI, and HEI data.

	Outcome
Variable	T. gondii	BMI	HEI
BMI	-0.0005
	(0.0004)
Energy (kcal)	-0.000003
	(0.000003)
HEI	-0.0003
	(0.0002)
T. gondii exp.		-0.0025	-0.0081
		(0.0034)	(0.0066)
Black	0.0864	-0.0027	0.0203
	(0.0070)	(0.0027)	(0.0053)
Hispanic	0.0123	-0.0054	0.0058
	(0.0093)	(0.0035)	(0.0069)
Other race/ethnicity	0.0185	0.0019	0.0087
	(0.0135)	(0.00512)	(0.0101)
Household size	0.0001	0.0004	0.0047
	(0.0082)	(0.0031)	(0.0061)
HH size squared	0.0002	0.00010	-0.00003
	(0.0011)	(0.0004)	(0.0008)
Age	-0.0010	-0.0011	-0.0012
	(0.0009)	(0.0003)	(0.0007)
Age squared	0.000007	0.00002	0.00002
	(0.000009)	(0.000003)	(0.000007)
>200% of poverty ratio	0.0145	-0.0042	-0.0002
	(0.0062)	(0.0024)	(0.0046)
Married	-0.0151	-0.0051	-0.0188
	(0.0069)	(0.0026)	(0.0051)
Interview between Nov. and Apr.	0.0031	-0.0016	-0.0004
	(0.0058)	(0.0022)	(0.0043)
Education: High school	-0.0111	-0.0051	-0.0045
	(0.0086)	(0.0033)	(0.0064)
Education: Some college	-0.0213	-0.0036	-0.0052
	(0.0081)	(0.0031)	(0.0060)
Education: college or higher	-0.0182	-0.0033	0.0016
	(0.0095)	(0.0036)	(0.0070)
Constant	0.1510	0.0343	0.0532
	(0.0293)	(0.0097)	(0.0189)

Table displays linear probability model coefficients for determinants of an NHANES respondent having missing T. gondii data, BMI data, or HEI data. Standard errors in parentheses. Bold coefficients are significant at the 95% confidence level. Controls: income >200% poverty, gender, race/ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, other race), household size, household size squared, age, age squared, whether married, education (less than high school, high school graduate, some college, college graduate), season of interview.

Our main results found that T. gondii exposure was related with increased BMI at higher ranges of the BMI distribution. While BMI is commonly used in public health studies, BMI may mis-identify as obese individuals with greater muscle mass [52]. We investigated the extent to which our results are influenced by this potential bias in three ways. First, we excluded individuals who report participating in vigorous recreational activities at least three days per week. Excluding these highly-active individuals, our results remain robust (Fig 9). Second, we performed our analysis on the waist-to-height ratio (WHtR) instead of BMI. The WHtR has been found to predict health risk and mortality better than BMI [53-54]. Similar to our main results, T. gondii is associated with increased WHtR among females with higher WHtR (Fig 10). Finally, the NHANES recorded the percent body fat for 3,668 respondents out of our total sample. We replicated our analysis with this subsample using percent body fat instead of BMI (Fig 11). We do not emphasize these results because of the small sample size and because fewer respondents with higher levels of BMI have valid percent body fat measures [55]. However, T. gondii exposure is related with increased percent body fat among females with median percent body fat.

Fig 9

Association between T. gondii exposure and diet-related outcomes among U.S. females <200% poverty (a-c) and the difference between females <200% poverty and males >200% poverty (“difference”), excluding highly-active individuals.

Black line is quantile regression coefficient and confidence intervals (95%) are shaded regions. Solid gray line shows the linear regression coefficient and dotted gray lines show the linear regression confidence interval (95%). The dotted line indicates no relationship at that quantile of BMI, energy consumed, and HEI. Straight black line shows value of 0, or no relationship. Controls: income >200% poverty, gender, race/ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, other race), household size, household size squared, age, age squared, whether married, education (less than high school, high school graduate, some college, college graduate), season of interview. Energy and HEI analyses also control for BMI.

Fig 10

Association between T. gondii exposure and waist-to-height ratio (WHtR) among U.S. females <200% poverty (a-c) and the difference between females <200% poverty and males >200% poverty (“difference”).

Black line is quantile regression coefficient and confidence intervals (95%) are shaded regions. Solid gray line shows the linear regression coefficient and dotted gray lines show the linear regression confidence interval (95%). The dotted line indicates no relationship at that quantile of BMI, energy consumed, and HEI. Straight black line shows value of 0, or no relationship. Controls: income >200% poverty, gender, race/ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, other race), household size, household size squared, age, age squared, whether married, education (less than high school, high school graduate, some college, college graduate), season of interview.

Fig 11

Association between T. gondii exposure and percent body fat among U.S. females <200% poverty (a-c) and the difference between females <200% poverty and males >200% poverty (“difference”).

Black line is quantile regression coefficient and confidence intervals (95%) are shaded regions. Solid gray line shows the linear regression coefficient and dotted gray lines show the linear regression confidence interval (95%). The dotted line indicates no relationship at that quantile of BMI, energy consumed, and HEI. Straight black line shows value of 0, or no relationship. Controls: income >200% poverty, gender, race/ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, other race), household size, household size squared, age, age squared, whether married, education (less than high school, high school graduate, some college, college graduate), season of interview.

Conclusions

Our results uncover important relationships between T. gondii exposure and diet-related outcomes for females. Overall, the relationship with BMI and diet quality was strongest for females with high BMIs and worse diets. Other studies have observed similar patterns where obesity determinants have stronger relationships with BMI at increasingly higher levels of BMI [35-36]. On the other hand, we found insignificant relationships between T. gondii exposure and diet/health outcomes among males. Our study suggests that exposure to a largely apathogenic agent could have broader consequences for individual and population health.

We also examined potential mechanisms underlying our findings. Some of these mechanisms represent factors that confound a causal interpretation of our findings. While we were able to provide evidence that our findings were not fully explained by some mechanisms, other mechanisms remained unexplored. This represents one limitation of our study. Data availability does not allow us to examine many sources of exposure to T. gondii. Important potential sources that we cannot observe in our data include contamination of meat or produce and environmental exposure. Since we cannot rule out other mechanisms, we are not able to establish a causal relationship between T. gondii exposure and diet-related measures. This broader limitation is common in cross-sectional observational studies and provides directions for further research. A full investigation of these mechanisms would proceed on two fronts. First, a more detailed understanding of environmental exposure to T. gondii is needed. This analysis would entail examining the extent to which T. gondii exposure is influenced by food preparation practices for both meat and produce. In addition, this analysis would entail an investigation of exposure to outdoor cats and how the prevalence of T. gondii in public spaces differs between neighborhoods. Second, research is needed that elucidates the biological mechanisms for the relationship between T. gondii exposure and diet-related outcomes. Studies using non-human subjects such as mice may investigate the extent to which T. gondii exposure either directly or indirectly leads to changes in eating preferences.

Characterizing how T. gondii exposure influences diet is necessary for formulating a comprehensive public health policy. Unhealthy diets and concurrent obesity increase the risk of mortality and morbidity [56-59]. Recent attention has focused on potential biological bases for diets and obesity [60-62], but little is known about how these biological factors interact with other factors to influence diet and health [63]. To the extent that the differences we find uncovered causal relationships, T. gondii exposure could be exacerbating the current obesity crisis. T. gondii exposure may furthermore work against public health policies that seek to incentivize healthy food consumption. These possibilities suggest the need for making a toxoplasmosis vaccine widely available [64]. Our findings highlight that women with poor diets may benefit most from access to a toxoplasmosis vaccine. The potential effect of T. gondii exposure in exacerbating public health challenges–and working against current public health policy–underscores the importance of understanding the mechanisms underlying the relationships found in this paper.

9 Jul 2021

Dear Dr. Cuffey,

Thank you very much for submitting your manuscript "Toxoplasma gondii exposure and risk for poor diets and higher BMI" for consideration at PLOS Neglected Tropical Diseases. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

We would like you to please address carefully the comments made by the three reviewers, and particularly discuss the points that were made regarding data limitations, selection and measurement biases, and overall methodology - and what this implies for the validity and transferrability of the results.

The reviewers have raised some concerns about some statements implying causation when the data & methodology does not allow for causal inference. Please revise the manuscript carefully to remove any ambiguity in that regard that may lead to misleading interpretations of the implications of this research. Possible alternative explanations for the relationship observed should be acknowledged and discussed accordingly in the discussion.

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Laure Saulais

Guest Editor

PLOS Neglected Tropical Diseases

Steven Singer

Deputy Editor

PLOS Neglected Tropical Diseases

***********************

I would like to ask the authors to please address carefully the comments made by the three reviewers, and particularly discuss the points that were made regarding data limitations, selection and measurement biases, and overall methodology - and what this implies for the validity and transferrability of the results.

The reviewers have raised some concerns about some statements implying causation when the data & methodology does not allow for causal inference. Please revise the manuscript carefully to remove any ambiguity in that regard that may lead to misleading interpretations of the implications of this research. Possible alternative explanations for the relationship observed should be acknowledged and discussed accordingly in the discussion.

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #1: The manuscript attempts to study the relationships between diet and Toxoplasma gondii infections, and uses a dataset from the National Health and Nutrition Examination Survey (NHANES) to conduct their analysis. This dataset has limitations, such as:

- biases regarding self-reported eating habits (McKenzie et al., The American Journal of Clinical Nutrition, 2021)

- inability to take into account food safety and hygiene, which are relevant to Toxoplasmosis transmission (Hussain et al., Pathogens, 2017)

- lack of discrimination in the kind of food being consume (for instance, different meats might have different parasite loads (Belluco et al., Plos One, 2016))

- cat ownership data only takes into account current ownership and/or a cat that lived in the same household for the past 12 months. The serological test used in the study detects IgG, which usually last for many years after infection. Additionally, correlate cat ownership and toxoplasma positivity would further strenght the data presented in tables S7 and S8

Reviewer #2: Yes to all those questions.

Reviewer #3: 1- BMI and WHR have high false-positive rates in reference to BFP, which cannot accurately reflect the mass of adipose tissue and leads to obesity misclassification. Pregnant women were excluded from the study, but not athletes and people with high muscle mass who had a false-positive high BMI.

Please explain about this factor and how to eliminate its effect on the results.

2- The level of physical activity is one of the factors affecting the BMI. Please explain about matching the BMi results with it.

--------------------

Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #1: (No Response)

Reviewer #2: Yes to all those questions.

Reviewer #3: 1- Please correct numbers in tables by the following guidelines are usually applicable.

*One decimal place

-Means

-Standard deviations

-Descriptive statistics based on discrete data

*Two decimal places

-Correlation coefficients

-Proportions

-Inferential test statistics such as t values, F values, and chi-squares

*Use two or three decimal places and report exact values for all p values greater than .001. For p values smaller than .001, report them as p < .001.

--------------------

Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #1: Although the extent in which T. gondii affects rodent behavior is still a topic of debate (Worth et al., Advances in Parasitology, 2014), the large body of literature on the subject suggest that these changes increase the likelihood of feline predation (for instance, Vyas et al., PNAS, 2007). These behavioral changes makes sense in light of evolution, as the parasite uses rodents and felines as intermediate and definitive hosts in its life cycle, and contact between the parasite and humans occured very recently (Gazzinelli et al., Cell Host and Microbe, 2014) (Shwab et al., PNAS, 2018). The links between T. gondii and changes in human behavior are controversial (Johnson et al., mBio, 2020) (Sugden et al., Plos One, 2016) and claims linking the parasite to behavioral changes in humans need to be backed by a substantial body of data. The data presented in the article correlating T. gondii infection and bmi/hei cannot be used to imply causation, as a more plausible explanation would be that higher food consuption and/or poor quality of food increases the likelihood of T. gondii infection.

Reviewer #2: Yes to all those questions.

Reviewer #3: Researchers have noted in various articles on toxoplasmosis exposure as causing high-risk and impulsive behaviors. It should be noted that perhaps reversing high-risk, impulsive behaviors may increase the risk of developing or being exposed to toxoplasmosis in these individuals.

please discuss about this issue

--------------------

Editorial and Data Presentation Modifications?

Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”.

Reviewer #1: (No Response)

Reviewer #2: MINOR COMMENTS:

1. Title: I suggest revising to “Cross-sectional association of Toxoplasma gondii exposure with BMI and diet in US adults.” (Some readers may misinterpret the current title as indicating a longitudinal study of the emergence of higher BMI or poor diet over time after T. gondii exposure.)

2. Abstract: Consider reporting 95% CIs for the statistics included in the abstract. Also, consider including more numerical results in the abstract. Finally, consider mentioning the rationale for analyzing women and men separately, and for analyzing low-income people separately, in the abstract, given that results are presented separately by those characteristics.

3. Introduction, paragraph 2: Consider specifying what country was the setting for reference #22 rather than referring to “not … all populations.”

4. Methods, statistical analysis: Why did the authors adjust for season of interview? Consider providing a rationale in the manuscript.

5. Tables/figures: Consider listing the control variables in a footnote below each table or figure so that the reader does not need to refer back to the Methods section to see what was adjusted for.

Reviewer #3: N/A

--------------------

Summary and General Comments

Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed.

Reviewer #1: The study cannot imply causation from correlation, and more robust data would be required to imply that a parasite causes changes in human behavior, specially considering that humans are accidental hosts.

Reviewer #2: The authors studied the associations of Toxoplasma gondii exposure with BMI, total energy intake, and Healthy Eating Index (HEI) in a cross-sectional study of 9,853 American-born women and men in NHANES. They found that T. gondii exposure was associated with higher BMI in women, not in men, and with lower HEI in low-income women, not in all women or in men. Investigation of some potential biological mechanisms including protein consumption, water source, cat ownership, and Toxocara coinfection suggested that such mechanisms do not explain the observed associations.

In my opinion, the authors based their study on a strong rationale, they expressed their research questions clearly, their approach was methodologically strong in most respects, their reporting of results was thorough and well organized, and their conclusions were justified by the results. The manuscript was well written.

I have a few comments and questions for the authors to consider:

MAJOR COMMENTS:

1. Methods: Regarding the potential for selection bias, I have four questions: (1) How many people from the total 2009-2014 sample were excluded from analysis due to missing T. gondii exposure? (2) How many were excluded due to missing BMI or diet data? (3) To what degree did exclusion for missing T. gondii measure differ across BMI, total energy, or HEI values? (4) To what degree did exclusion for missing BMI or dietary data differ by T. gondii exposure? Reporting this information in the manuscript would help some readers better assess the potential for selection bias affecting the results, and could also allow the authors to comment more about that possibility in the discussion.

2. Methods/Results: In the investigation of heterogeneity by low-income status, consider reporting results for those above 200% of the poverty line in addition to those below 200% of the poverty line, so that readers will be able to see both sides. Also, consider investigating the T. gondii by low-income interaction in a single model with an interaction term and calculating the excess association due to interaction. Such an analysis would better support the authors’ interest in the low-income population by determining the degree to which the joint effect of T. gondii plus low-income is stronger than would be expected if the two exposures didn’t interact.

3. Methods, statistical analysis: Why did the authors analyze females and males separately, with no models combining females and males? Consider providing a rationale in the manuscript. Was this decision made a priori or influenced by the data analysis?

4. Tables: Consider reporting 95% CI for each estimate instead of standard error. Also consider showing the results for the group that is above 200% of the poverty line.

5. Figures: Consider using the same Y-axis scales in the figures for women and for men, in order to make the visual comparison easier. Also consider showing the graphs for the group that is above 200% of the poverty line.

6. Results, potential mechanisms: I found this section easy to understand and logically organized, however the organizational scheme was unusual in the context of the overall IMRaD (Intro-Methods-Results-and-Discussion) structure. A lot of sentences in this section of Results sounded more like Intro, Methods, and Discussion. Consider moving sentences into the appropriate sections of the article. However, you may want to consult with the journal’s editorial team on this.

7. Supplemental tables and text corresponding with Results, potential mechanisms: First, I recommend incorporating all supplemental text into the main body of the manuscript in appropriate places (some may fit better in Intro, Methods, or Discussion, than in Results, similar to above comment). Second, I recommend replacing all Supplemental Tables with figures similar to the figures shown in the main manuscript. I think all the points the authors want to make in this section about potential mechanisms will be made more clear with visual figures instead of lots of tables. Finally, if the authors accept my suggestion to replace the supplemental tables with figures, consider incorporating some (or all) of the new figures into the main manuscript instead of a supplement. The exploration of potential mechanisms was one of the most interesting parts of the article, and in my view deserves greater emphasis and visibility. To make room for more figures in the main manuscript, consider moving Tables 1 and 2 into a supplement. The figures really showcase the findings better than the tables, in my opinion.

8. Discussion: The cross-sectional study design is not the strongest for this type of research question, where the hypothesis is that T. gondii exposure LEADS TO increased BMI or deterioration in diet quality, but the data structure do not permit knowing which came first, the T. gondii exposure versus the BMI and diet quality levels. Although the authors mention the limitations of cross-sectional studies, I suggest stating more clearly and strongly the difficulty in drawing causal inference from this particular study on this particular question. To what extent is reverse-causality a possible explanation for the results?

MINOR COMMENTS -- See above in the section "Editorial and Data Presentation Modifications?"

I prefer to sign my review – Evan Thacker, PhD, Brigham Young University

Reviewer #3: The goals and methodology of this study is noteworthy, although the authors need to address some minor problems in the methodology and reporting data as well as discussion section.

--------------------

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Reviewer #1: No

Reviewer #2: Yes: Evan L. Thacker

Reviewer #3: Yes: Reza HabibiSaravi, MD, PhD

Mazandaran University of Medical Sciences

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1 Sep 2021

Submitted filename: responses.docx

Click here for additional data file.

20 Sep 2021

Dear Dr. Cuffey,

We are pleased to inform you that your manuscript 'Cross-sectional association of Toxoplasma gondii exposure with BMI and diet in US adults' has been provisionally accepted for publication in PLOS Neglected Tropical Diseases.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

Best regards,

Laure Saulais

Guest Editor

PLOS Neglected Tropical Diseases

Steven Singer

Deputy Editor

PLOS Neglected Tropical Diseases

***********************************************************

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #1: (No Response)

Reviewer #3: Yes

**********

Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #1: (No Response)

Reviewer #3: Yes

**********

Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #1: (No Response)

Reviewer #3: Yes

**********

Editorial and Data Presentation Modifications?

Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”.

Reviewer #1: (No Response)

Reviewer #3: Accept

**********

Summary and General Comments

Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed.

Reviewer #1: I believe the modifications greatly improved the manuscript. The revised manuscript is very clear and well written.

Reviewer #3: This study is now as good as for publication.

**********

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Reviewer #1: No

Reviewer #3: Yes: Reza HabibiSaravi, MD, PhD

Mazandaran University of Medical Sciences

27 Sep 2021

Dear Dr. Cuffey,

We are delighted to inform you that your manuscript, "Cross-sectional association of Toxoplasma gondii exposure with BMI and diet in US adults," has been formally accepted for publication in PLOS Neglected Tropical Diseases.

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Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

Best regards,

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co-Editor-in-Chief

PLOS Neglected Tropical Diseases

Paul Brindley

co-Editor-in-Chief

PLOS Neglected Tropical Diseases