Associations between urine glyphosate levels and metabolic health risks: insights from a large cross-sectional population-based study

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Jones, David O. Carpenter This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4272811/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background: Prevalence of the Metabolic Syndrome (MetS) in American adults has risen from 37.6% in the 2011-12 period to 41.8% in 2017-2018. Environmental exposure, particularly to common compounds such as glyphosate, has drawn increasing attention as a potential risk factor. Methods: We employ three cycles of data (2013-2018) from the National Health and Nutrition Examination Survey (NHANES) in a cross-sectional study to examine potential associations between urine glyphosate measurements and the MetS. We first created a MetS score using Exploratory Factor Analysis of 6 International Diabetes Federation (IDF) criteria for MetS, with data drawn from the 2013-2018 NHANES cycles, and validated this score independently on an additional associated metric, Albumin to Creatinine Ratio. The score was validated via a machine-learning approach in predicting ACR score via binary classification, then used in multivariable regression to test association between quartile-categorized glyphosate exposure and the MetS score. Results: In adjusted multivariable regressions, quartile regressions between glyphosate exposure and MetS score show a significant inverted U-shaped or saturating dose-response profile, often with largest effect for exposures in quartile 3. Exploration of potential effect modification by sex, race, and age category shows significant differences by race and age, with older people (ages > 65 years) and non-Hispanic African American participants showing larger effect sizes for all exposure quartiles. Conclusions: We find that urinary glyphosate is significantly associated with a statistical score designed to capture the MetS, and that dose-response is nonlinear, with advanced age and non-Hispanic African American and Mexican American and other Hispanic participants showing higher effect sizes. Exploratory Factor Analysis Quantitative Score Metabolic Syndrome MetS NHANES albuminuria Glyphosate Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background The Metabolic Syndrome (MetS) consists of a constellation of conditions—high blood pressure, high blood sugar levels, excess abdominal fat, and elevated cholesterol and triglyceride levels—that collectively amplify the risk of cardiovascular disease, stroke, and type 2 diabetes mellitus (WHO 1999). In the United States, MetS prevalence has risen from 37.6% in the 2011-12 period to 41.8% in 2017-2018 among adults, as seen from an analysis of data from the National Health and Nutrition Examination Survey (NHANES) (Liang et al., 2023). This upward trend underscores the urgency to understand and mitigate the factors contributing to MetS. Risk factors for MetS are broadly categorized into nonmodifiable and modifiable elements. Age, sex, and race/ethnicity are nonmodifiable factors that can influence the likelihood of developing MetS, with variations observed across different demographic groups. Diet, physical activity, weight, and environmental chemical exposure represent modifiable factors. Among these, the role of environmental chemical exposure, particularly to common compounds such as glyphosate, has drawn increasing attention. Glyphosate is the most widely used broadleaf herbicide globally, marketed as Roundup TM in the United States (Benbrook 2016). It blocks a pathway required for synthesis of aromatic amino acids found in plants but not animals (Roberts et al., 2007), although some gut bacteria are sensitive, and is applied to eliminate weeds and grasses. Since its introduction in the 1970’s, usage has increased by a factor of at least 100 due in part to introduction of glyphosate tolerant “Roundup TM -ready” cereal crops (Benbrook 2016). A major use is to kill weeds before harvest of grain crops, and it is also applied directly to grain and pulse crops to help speed desiccation (Parreira et al., 2015). These practices, however, lead to glyphosate residues in foods made of wheat, oats and other grains, as well as beans and lentils, contributing to exposure via dietary intake among the general population (Parreira et al., 2015). Besides dietary exposure, inhalation and dermal contact are significant exposure pathways for farmers and others who apply Roundup TM (Connolly et al., 2019).. While glyphosate was originally thought to have no effect on humans, it is now rated by the International Agency for Research on Cancer (IARC) as a probable human carcinogen (Guyton et al, 2015), it has actions on both female and male fertility (Serra et al., 2021), has neurological effects (Madani and Carpenter 2022) and causes mitochondrial damage (Peixoto 2005). Animal and in vitro studies indicate that glyphosate exposure interferes with glucose uptake into adipocytes (Prasad et al., 2022; de Melo et al., 2018), is associated with liver fibrosis (Mills et al., 2019), increases apoptosis (Chaufan et al., 2014; Gui et al., 2012), induces oxidative stress (Peixoto 2005; Mesnage et al., 2021a), and alters the gut microbiome (Mesnage et al., 2021b; Rueda-Ruzafa et al., 2019; Lehman et al., 2023; Hu et al., 2021; Tang et al., 2020). These mechanisms are linked to the pathogenesis of MetS (Lippert et al., 2017; Cani 2009). Epidemiological research exploring the association between glyphosate and MetS is limited, with only two studies conducted so far. The CHAMACOS Study by Eskenazi et al., 2023, focused on mother-child dyads and found a significant association between glyphosate exposure and MetS risk by young adulthood. Similarly, Glover et al., 2023, observed a positive association between glyphosate levels and MetS among U.S. adults. However both studies have limitations. Eskenazi et al. studied a small farming population with high levels of multiple chemical exposures, leading to poor generalizability to the US population. Glover et al. (2023) adjusts for features intrinsic to the syndrome itself such as hypercholesterolemia, hypertension, and diabetes, potentially biasing estimations of the associations between glyphosate and metabolic syndrome. These studies also employ threshold-based definitions of MetS based on dichotomized continuous variables, leading to loss of information and potentially statistical power. Clinical studies suggest that it is desirable to include continuous variables to create accurate scoring systems for MetS (Cavero-Redondo et al., 2019; Kahn et al., 2005). Given the limitations in the available studies and the problems associated with the use of the threshold-based definitions from a research standpoint, our study aims are two-fold: (1) to create and validate a score that captures the continuous nature of risk factors that make up MetS, and (2), to employ this score in linear regression analyses that explore associations between urine glyphosate concentrations and MetS. We anticipate that our approach will provide a more nuanced understanding of how glyphosate exposure affects risk of MetS. Methods Study Population We employ data from the National Health and Nutrition Examination Survey (NHANES), accessible via the Centers for Disease Control and Prevention (CDC) website (https://www.cdc.gov/nchs/nhanes) in a cross-sectional study to examine potential associations between urine glyphosate measurements and Metabolic Syndrome (MetS). NHANES is a comprehensive research initiative designed to assess the health and nutritional status of adults and children in the United States, surveying approximately 5,000 individuals annually through interviews, physical examinations, and laboratory investigations. The research protocols were sanctioned by the National Center for Health Statistics of the U.S. CDC, and informed consent was required from all participants. Our analysis includes the NHANES datasets from 2013 – 2018 (3 cycles), which report urinary glyphosate measurements. These measurements were selectively obtained from a third of the participants who consented to future analysis of their laboratory samples. Therefore, from the initial pool of 29400 participants, 22333 were excluded due to the lack of urine glyphosate data and incomplete covariate information. Since the earliest symptoms of metabolic syndrome do not occur among the very young, children under the age of 10 years (n=1180) were excluded, yielding a sample size for the primary analysis of 5224 (Figure 1). Age was categorized into four groupings as follows, ages 10 to 19 years, ages 20 to 39 years, 40 to 59 years, and 60 years and above. The reference level was ages 10 to 19. Race was categorized as Non-Hispanic White (White), Non-Hispanic Black (Black), Non-Hispanic Asian (Asian), Mexican-American, Other Hispanic, and Non-Hispanic Multiracial (Multiracial). Exposure: Urine Glyphosate The measurement of urinary glyphosate levels was performed with a 200-microliter urine sample utilizing a 2D online ion chromatography system paired with tandem mass spectrometry (IC-MS/MS), along with isotope dilution for quantification. The results were expressed in nanograms per milliliter (ng/ml), with the assay sensitivity established at a lower limit of detection (LLOD) of 0.2 ng/ml. For our analysis, we categorized urinary glyphosate by quartile (See Table 1 for by-quartile summary of exposure and outcome). Outcome: Metabolic Syndrome Score The Metabolic Syndrome was defined roughly per the International Diabetes Federation (IDF) criteria, which includes the presence of central obesity and any two of four additional risk factors. We include a total of 5 features based on biometric measurements and laboratory assessments reported in NHANES. Following work by Cavero et al. (2019), we include HbA1c as a dysglycemia indicator (Cavero et al. 2019), as well as the composite risk factors triglyceride to HDL ratio and mean arterial pressure (MAP) in our selected features, and extract a single factor score as described below. The mean arterial pressure (MAP) was estimated as SBP + 1/3 (SBP - DBP). Thus selected risk and composite factors include: Waist circumference Fasting glucose glycoheme (HbA1c) triglyceride to HDL ratio Mean Arterial Pressure (MAP) These measures are common to most definitions of MetS (Alberti and Zimmet, 1998; Balkau and Charles, 1999; National Cholesterol Education Program, 2002; Grundy et al., 2005; Zimmet et al., 2005). (See Supplemental Information, Appendix 1). Potential Confounders/Effect Modifiers Potential confounders in this study included sociodemographic indicators, physical examination results, and lifestyle habits. Sociodemographic data included age (categorized into 4 groups), sex, race/ethnicity, and family income-to-poverty ratio, which ranged from 0 to 5. Finally, we included urine creatinine measurements as a covariate to adjust for variations in the concentration of urinary analytes due to dilution effects (Barr et al., 2005). Statistical Analyses Factor analysis to create a MetS index. We used exploratory factor analysis (EFA) to create a single index from the five risk factors described above (waist circumference, triglycerides to HDL ratio, fasting glucose, glycoheme (HbA1c), and MAP). EFA works by capturing common variance in one or more directions in the multivariable space of interest, here among MetS risk factors. Each of these “directions” is a factor. After extracting significant factor(s) from our analysis, we obtain a table of weightings which may be used to create the index or indices. These indices can be considered as weighted sums of the variables of interest. A goal of our study is to obtain a working single-factor model for MetS, and our model is a modification of one of three developed and tested by Cavero-Redondo et al (2019), including both HbAlc and fasting glucose. As missing values in the MetS risk factors ranged from 7.9% to 61.5%, we first imputed, using sorted and grouped hot-deck single imputation, which selects appropriate donors randomly from the existing distributions in specified columns after sorting on age category and grouping by sex. This yields a sample size of 22,258 on the five features selected for the EFA process. Following imputation, an average systolic blood pressure measurement was created by averaging the first three systolic BP measurements. We then computed the MAP, estimated as SBP + 1/3 (SBP - DBP). Since HDL varies inversely with MetS, and we sought a positively weighted score, we computed triglycerides to HDL ratio, a measure of cardiovascular risk that also appears in all three candidate MetS score models tested by Cavero-Redondo et al. (2019). We then standardized the data, and since factor analysis is a linear method that does not respond well to highly correlated data, we checked for excessive multicollinearity by computing a correlogram and confirming that selected metrics all had pairwise correlations less than 0.8 (See supplemental Figure 1 for correlogram). Lastly, metrics were checked for appropriateness for EFA by running the Kaiser–Meyer–Olkin (KMO) Measure of Sampling Adequacy test (Kaiser 1970; Kaiser and Rice, 1974) and a Bartlett’s Sphericity Test (p < 0.00001; Santos et al. 2019). The KMO test measures the proportion of common variance in a group of variables; the higher the statistic (closer to one), the more appropriate the grouping is for EFA (Kaiser 1974). Our KMO values ranged from 0.65 to 0.72 (median 0.67), and confirmed that our selected features were all adequate for factor analysis. The Bartlett’s sphericity test indicates that the correlation matrix of the selected features is suitable for detection of structure. As EFA assumes normality, MetS metrics were first visualized, then log-transformed since all were slightly skewed (Santos et al 2019). Parallel analysis indicates that one factor is sufficient to explain common variance in the five risk features. EFA was run on the standardized metrics and the score for one factor was extracted. Factor loadings of > 0.4 were considered criteria for inclusion in the MetS model, and all five selected features met this criterion for inclusion. The EFA procedure assumed orthogonality, was computed on a correlation matrix, and used a varimax rotation. All imputation and EFA were performed in the R programming language (R 4.3.0), using the “hot-deck” algorithm from the VIM library (Kowarik and Templ 2016), parallel analysis from the paran library, and “factanal” from the psych library, respectively. Validation of MetS Score . While we lacked many IDF metrics for validating the MetS score, NHANES does provide ACR, the ratio of urine albumin to creatinine, available as the “URDACT” variable (See Table 1 for a summary of ACR by quartile). Neither creatinine or albumin measurements are used in the creation of the score, so this provides at least one opportunity to test the proposed MetS score on MetS-associated symptoms, though of three test metrics, ACR score was the least correlated with the MetS scores developed by Cavero-Redondo et al (2019). An ACR score above 30 and below 300 indicates “microalbuminaria,” but this range of transitional effect comprises an order of magnitude. Microalbuminaria is an indicator of kidney disease, is associated with diabetes, and was recently included in a comprehensive definition of MetS proposed by the World Health Organization (Saadi et al 2020). However, while microalbuminaria is more frequent in MetS patients, it is not unique to MetS. We validated the MetS score proposed in this paper using a logistic model as a classifier, and a dataset including only the MetS score, creatinine, and the ACR score (N=20765). Cut off values of ACR > 30 (microalbuminaria), ACR > 100 (microalbuminaria) and ACR > 300 (albuminuria) were selected and a binary ACR variable was created based on these values. The data were divided into training and testing sets using a stratified sampling scheme to ensure that nonzero ACS indicator values appeared in both testing and training sets. Logistic models adjusted for standardized creatinine were fit on the training set and predictions made on the test set, with model error rate, sensitivity, specificity, accuracy and diagnostic odds ratio (Glas et al. 2003) computed. Bivariate and Multivariable Analysis Bivariate analysis was conducted using linear regressions, with t-tests or ANOVA for continuous variables to examine the exposure and outcome across categorical covariate levels. We employed multivariable linear regression models to explore adjusted associations between urinary glyphosate levels and the MetS index from factor analysis. Adjusting confounders were selected using a directed acyclic graph and included age category, sex, BMI, income-poverty ratio, and creatinine to adjust for urine dilution (Barr et al 2005). BMI and creatinine were continuous but were transformed if necessary and standardized before regressions were run. MetS scale was standardized to facilitate comparisons across stratified models, and the exposure was categorized by quartile to allow for potential nonlinearities. Lastly, we assessed for effect modification by age category, race and sex (Shen et al. 2006); stratifying on each variable and repeating the analysis on each stratum. Since this is an exploratory study, we do not adjust for multiplicity. All statistical analyses were performed using the R programming language (R version 4.3.3) and a p-value of less than 0.05 was considered statistically significant. Table 1. Baseline characteristics of complete case study population (N= 5224). Sample size is limited by complete cases for glyphosate (N=5789) and Creatinine. Covariate Level Male (n = 2605) Female ( n = 2619) Sex Male 2605 (100.0) - Female - 2619 (100.0) Age Category Ref: < 19 years 60yrs 681 ( 26.1) 688 ( 26.3) Race-Ethnicity Ref: White White (ref) 1021 ( 39.2) 958 ( 36.6) Mexican American 412 ( 15.8) 421 ( 16.1) Other Hispanic 244 ( 9.4) 279 ( 10.7) Black 519 ( 19.9) 552 ( 21.1) Asian 276 ( 10.6) 293 ( 11.2) Multiracial 133 ( 5.1) 116 ( 4.4) BMI Mean (SD) 27.71 (6.83) 28.78 (8.07) Income-Poverty Ratio Med [IQR] 2.02 [1.06, 3.91] 1.94 [1.03, 3.82] Mean (Median) values by Quartile (N = 5789) Urinary Metric Min Quartile 1 Quartile 2 Quartile 3 Quartile 4 Max Creatinine 0.035 0.40 (0.64) 0.89 (1.12) 1.39 (1.72) 2.39 6.0 Glyphosate 0.071 0.12 (0.14) 0.26 (0.34) 0.45 (0.61) 1.22 8.2 ACR 0.31 3.7 (4.9) 6.1 (7.5) 10.1 (14.3) 157 21152 MetS Score -1.80 -0.89 (-0.56) -0.31 (0.08) 0.17 (0.47) 1.2 4.2 Results Baseline characteristics of the study population (N=5224) are summarized in Table 1 , stratified by sex. Characteristics for all three NHANES cycles utilized, including missing units, are summarized in Supplementary Table 1, and the workflow for data cleaning and sample assembly is shown in Figure 1 . From the initial sample, we first omit children under the age of 10 (n = 7142), leaving a sample size of 22258. We then omit participants lacking urinary glyphosate (74% missing), creatinine (6.7% missing), BMI (5.5% missing) or poverty-income ratio information (10.7% missing). The degree of missing units in the exposure makes imputation of the exposure for inference purposes impossible. However, a comparison of summary information from the complete case sample (Table 1) and the original dataset (Supplementary Table 1) suggests the complete case sample is representative. The sample is roughly balanced by sex, with 2605 males and 2619 females. The largest single ethnic group is Non-Hispanic White (about 38%), followed by Non-Hispanic Black (about 20%). Hispanics as a group (Mexican American and ‘Other Hispanic’) comprised about 25%. Non-Hispanic Asians comprised about 22% and multiracial participants less than 10%. The cohort was older, with 52% of participants age 40 or over, and 26% age 60 or over. Covariates used in EFA (N=22,258) to create the MetS score were imputed using a conservative donor-based single-imputation method before proceeding with EFA (see Figure 1 for details). Features used in the EFA to create the MetS score are summarized in Table 2. Table 2. Covariates used to create Metabolic Syndrome (MetS) score (N=22,258). Feature 1 Min 25% 50% 75% Max Waist circumference 49.5 81.8 94.5 107 177.9 Systolic blood pressure 64.67 108 117.33 130.67 231.33 Diastolic blood pressure 0 60 68 76 135.33 Glycoheme (HbA1c) 3.5 5.2 5.5 5.8 17.5 Fasting Glucose 21 93 100 109 479 HDL 6 42 51 62 226 Triglycerides 10 57 85 129 4233 Triglyceride : HDL Ratio 0.13 1.03 1.65 2.69 103.24 Mean Arterial Pressure (MAP) 71.56 122.89 134.44 150.22 279.56 1 Variables and composite variables (the latter shown below double line at bottom of table) used in score are in boldface. MAP = SBP + 1/3 (SBP - DBP). MetS score is estimated on as many samples as possible to create a score broadly applicable to a general population. Missingness is summarized in Supplementary Table 2. We explored associations between the outcome and exposure, plus patterns of associations between both the outcome and the exposure with covariates identified as confounders or potential effect modifiers (age, sex, race). Exposure and outcome are summarized by Quartile in Table 3A-C. Unadjusted associations between the standardized MetS outcome score and glyphosate, categorized by quartile, are significant for the reference quartile, and quartiles 3 (Q3) and 4 (Q4), with a suggestion of an inverted U-shaped dose-response (maximum at Q3; Table 3A). Figure 1. Work flow and sample sizes showing sample size evolution from the initial raw sample (three NHANES cycles, 2013-2018) to final complete case sample with the created MetS score. Table 3. Bivariate analysis. Note that every level value is relative to reference. a. Bivariate association between MetS scale outcome and glyphosate by exposure quartile . Glyphosate Estimate 95% CI p-value Quartile 1 (ref) -0.072 -0.126 ,-0.018 - Quartile 2 0.071 -0.005, 0.148 0.069 Quartile 3 0.127 0.051, 0.204 0.001 Quartile 4 0.090 0.014, 0.167 0.021 b. Exposure and outcome by Race/ethnicity. Log(Glyphosate) exposure by Race Race/Ethnicity Estimate 95% CI p-value Pr(>F) White 0.530 0.504, 0.555 - F = 8.35 p < 0.0001 Mex - American -0.076 -0.123, -0.029 0.002 Other Hispanic -0.030 -0.086, 0.026 0.291 Black 0.038 -0.005, 0.081 0.083 Asian -0.110 -0.164, -0.055 0.0001 Multiracial 0.037 -0.039, 0.114 0.33 Standardized MetS scale (outcome) by Race White -0.040 -0.084 , 0.004 - F = 19.0 p < 0.0001 Mex American 0.089 0.008, 0.170 0.030 Other Hispanic 0.083 -0.013, 0.180 0.090 Black 0.112 0.038, 0.187 0.003 Asian -0.063 -0.156, 0.031 0.188 Multiracial 0.018 -0.114, 0.149 0.792 c. Exposure and Outcome by Age Category Log(Glyphosate) exposure by Age Grouping Age Category Estimate 95% CI p-value Pr(>F) 10-19yrs 0.586 0.553, 0.62 - F = 22.6 p <0.0001 20-39yrs -0.136 -0.182, -0.09 <0.0001 40-59yrs -0.125 -0.170, -0.08 60yrs -0.025 -0.070, 0.02 0.281 Standardized MetS scale (outcome) by Age Grouping 10-19yrs -0.695 -0.745, -0.645 - F=4113 p < 0.0001 20-39yrs 0.385 0.317, 0.454 <0.0001 40-59yrs 0.966 0.898, 1.034 60yrs 1.308 1.240, 1.376 <0.0001 d. Exposure and outcome by Sex Glyphosate exposure by Sex Sex Estimate 95% CI p-value Male (ref) 0.546 0.524, 0.568 - Female -0.067 -0.099, -0.036 <0.0001 Standardized MetS Scale (outcome) by Sex Male (ref) 0.070 0.031, 0.108 - Female -0.139 -0.193, -0.085 <0.0001 We find two race-ethnicity levels (Mexican-American and NH Asian) with significantly differe t mean estimates from the reference (White) for log-transformed glyphosate from regression (Table 3A; p < 0.0001). For the outcome MetS score, Mexican-American and Black participants have significantly higher scores than reference, and the result from ANOVA is also highly significant (p < 0.0001) (Table 3B). A visualization of differences in exposure by race is shown in Supplementary Figure 2A . Unadjusted regressions of exposure and outcome with age-category are consistent, showing significant differences in exposure between the reference level (ages 10-19) and ages 20-39 and 40-59 years, which are slightly reduced relative to the reference (p = 0.004). Estimates for the age 60 and above level are not significantly different than the reference level, interestingly (Supplementary Figure 2B ). The MetS score is significantly different across all age categories, which is unsurprising (Table 3C). Finally, there are significantly different exposure and outcome levels by sex (reference Male); women have on average slightly but significantly lower exposure levels (p <0.0001) and slightly lower MetS scores than men (p < 0.0001) in unadjusted regressions (Table 3D). Bivariate regressions show a surprising result for age category, in that the highest exposures are among the ages 10 to 19 group and ages above 65, consistent with exposure results from the 2013-14 cycle only by Ospina et al. (2022). Results for MetS score are as expected, not much difference across race/ethnicity, but the score rises with age, and is slightly higher among men than women. Exploratory Factor Analysis resulted in all positive weightings and yielded a single index score. The weightings are shown in a factor analysis diagram ( Figure 2 ): glyphosate and glucose dominate, but just slightly. Biometric measurements waist circumference and MAP are similarly weighted and triglyceride : HDL ratio the least, though well within the range of inclusion. Summary statistics for the index are listed by quartile in Table 1 and for the component covariates, Table 2. Missing units for the component covariates are summarized in Supplemental Table 2. As demonstrated in Cavero-Redondo et al. (2019), we validate our score by using it to predict vascular damage (associated with MetS) according to albumin-to-creatinine ratio (e.g., ACR ); a typical, though not alone sufficient, metric of clinical performance for a MetS score (Shi et al 2020, Saadi et al. 2020). Results from the validation procedure suggest the sensitivity, specificity and accuracy increase with the ACS cutoff (Supplemental Table 3), with an error rate of 12% for an ACR cut-off of 30 and above (microalbuminaria), and 2% for an ACR cut-off of 300 and above (albuminuria). Again, because microalbuminaria is associated with MetS but not completely predictive of it, this is not an ideal test. Figure 2. Factor Analysis Diagram. Variable loadings for the MetS index created via factor analysis on hot-deck imputed risk factor metrics (Table 2). Adjusted associations between standardized metabolic score and glyphosate categorized by quartile, from regressions adjusted for scaled BMI, transformed and standardized creatinine, sex (reference Male), age category (reference 10-19 years), race-ethnicity (reference White) and standardized income to poverty ratio are shown in Figure 3 and Table 4. Results for adjusted quartile models are significant and stronger than unadjusted results (Table 3A) and show the same slight inverted U-shaped dose-response, with peak estimates at the third quartile. In the adjusted model, score estimates increase with age and related estimates are slightly elevated over reference and over other races for Asian and multiracial participants. This reverses in stratified models. Female sex is protective. Figure 3 . Change in MetS score by Quartile Glyphosate, full model with all covariates. covariates (N = 5224). Model is adjusted as shown by sex (reference Male), age category (reference 10-19 years), race-ethnicity (reference Non-Hispanic White), scaled BMI, standardized creatinine, and standardized income-poverty ratio. See Table 4 for estimates, confidence intervals, and p-values. Table 4. Associations between standardized Metabolic Score, glyphosate exposure and covariates (N = 5224). Models are adjusted by sex (ref. Male), age category (ref. 10-19 years), race-ethnicity ( ref. Non-Hispanic White), scaled BMI, standardized creatinine, and standardized income-poverty ratio. Covariate Level/Reference Estimate 95% CI p-value (Intercept) -0.604 -0.681, -0.527 <0.0001 Glyphosate Categorized by Quartile Ref: Quartile 1 Quartile 2 0.092 0.029, 0.155 0.0043 Quartile 3 0.185 0.119, 0.250 <0.0001 Quartile 4 0.177 0.107, 0.248 <0.0001 BMI (standardized) 0.352 0.328, 0.376 <0.0001 Creatinine (sqrt, standardized) -0.067 -0.094, -0.040 <0.0001 Sex (Female) Reference Male -0.233 -0.278, -0.188 <0.0001 Age Category Ref: 10-19 years 20-39 years 0.180 0.114, 0.247 <0.0001 40-59 years 0.702 0.634, 0.770 60 years 1.072 1.004, 1.139 <0.0001 Race/Ethnicity Ref: Non-Hispanic White Mexican American 0.175 0.108, 0.242 <0.0001 Other Hispanic 0.147 0.069, 0.224 0.0002 Black 0.135 0.074, 0.197 0.0002 Asian 0.193 0.117, 0.269 <0.0001 Multi 0.187 0.081, 0.293 0.00055 Income-Poverty Ratio (standardized) -0.049 -0.072, -0.027 0.00002 We explored the possibility of effect modification by age category, race-ethnicity and sex in a series of stratified, adjusted models. Data were stratified on the key variable and models run separately on each stratum. Results for models stratified on age category are shown in Supplemental Table 4. While there are significant differences in quartile estimates by age category (relative to reference level), confidence intervals overlap. MetS scores increase slightly, but inconsistently, with age ( Figure 4 ). Among the 10 to 19 years cohort, only the fourth quartile is significantly different than reference (0.18, 95% CI: 0.035, 0.316, p = 0.015). For the age 20 to 39 cohort, scores rise significantly on quartile 2, drop and are marginal on Q3, and rise slightly to become significant on Q4. Cohorts age 40 and above show the inverted U-shaped dose-response profile. Results are strongest (p = 0.00005 at Q3) for participants 60 years and above. We do not correct for multiplicity. While confidence intervals for the models stratified on race-ethnicity do overlap, there are two to three-fold differences in the effect sizes, relative to White participants, of estimates for Mexican-Americans (Q4 is 2.1 x greater than reference/White Q4), Other Hispanic (Q4 is 3.5 x larger) and Black participants (Q4 is a factor of 2.44 greater). Significant estimates for Mexican Figure 4. Change in MetS score by Quartile Glyphosate Exposure from adjusted regression models stratified on age category. Reference level for age category in full model is ages 10-19 years (<19yrs). See supplemental table 3 for details including sample sizes and p-values. To simplify presentation, only associations for outcome and exposure by quartile are shown for each age grouping. American and Black participants show the inverted U-shaped dose response profile we observe in the study population as a whole ( Figure 5 , Supplemental Table 5). Finally, estimates for models stratified on sex show a significant and inverted U-shaped profile for females and a significant and increasing profile for males. Aside from the patterns of increase across levels, values are consistent between the sexes (Supplemental Table 6), and confidence intervals overlap, suggesting no evidence of effect modification by sex. Figure 5. Change in MetS score by Quartile Glyphosate Exposure from adjusted regression models stratified on race/ethnicity. Reference level for race in full model is Non-Hispanic White. See supplemental Table 4 for details including sample sizes and p-values. To simplify presentation, only associations for outcome and exposure by quartile are shown for each grouping. Discussion The detailed analysis of the association between urinary glyphosate levels and Metabolic Syndrome (MetS) score, using data from the National Health and Nutrition Examination Survey (NHANES) from 2013 to 2018, reveals significant findings that contribute to the growing body of research on environmental chemical exposure and metabolic health risks. We found significant associations between urinary glyphosate levels and the MetS score, often with an inverted U-shaped dose-response relationship across exposure quartiles. This relationship persisted even after adjusting for potential confounders, indicating that higher levels of glyphosate exposure are associated with increased MetS risk, peaking at the third quartile of exposure. The observed inverted U-shaped dose-response relationship aligns with the notion that moderate levels of exposure may have a more pronounced effect on metabolic health than either low or very high exposures; a non-monotonic dose-response relationship (NMDR) suggesting a threshold or a range of exposure within which the deleterious effects of glyphosate on metabolic health are most pronounced. NMDR patterns have been observed in the context of endocrine disruption (Vandenberg et al., 2012). Indeed glyphosate and glyphosate-based herbicides are linked to endocrine disruption in animal models, altering the levels of mRNA and protein expression of insulin receptor (IR), and several other receptors and signaling molecules involved in glucose metabolism such as glucose transporter-2 (GLUT2), JNK, IKKβ, NFkB, IL-6, IL-1β, and TNF-α as well as transcriptional factors like SREBP1c and PPAR-γ (Prasad et al 2022). Glyphosate and glyphosate-based herbicides have also been linked to inflammation and cirrhosis of the liver which led to the development of insulin resistance and type 2 diabetes in animal models (Prasad et al 2022; Jayaraman 2023; Gomes et al., 2022). Our findings suggest need for further investigation into the mechanistic underpinnings of these relationships and their implications for human health, especially considering the widespread use of glyphosate and the prevalence of metabolic syndrome (Lamat et al., 2022). A deeper inquiry could yield additional insight into health effects of glyphosate as well as potential interaction effects with other variables not fully captured in the current study. Stratified analysis suggested potential effect modification by age and race/ethnicity but not by sex, and the strongest exposure-outcome associations were observed in older participants (60 years and above). The latter finding aligns with the broader literature on aging and metabolic health, where older age groups are generally at higher risk for MetS due to various physiological and lifestyle factors (Hirode and Wong, 2020; Alexander et al., 2008). Note that our study omits the age group with the highest levels of documented exposure (ages ~10 years or less), with exposure routes through dietary sources such as sweetened ready-to-eat cereal, or possibly environmental exposure on school or recreational grounds (see Ospina et al., 2022; Table 3). Significant differences in effect sizes by race-ethnicity, with notably higher effect sizes for associations between glyphosate exposure categorized by quartile and the MetS score in Mexican-Americans, Other Hispanics, and Black participants, point to potential disparities in susceptibility or exposure to glyphosate. This finding is particularly important given the existing literature on racial and ethnic disparities in environmental exposures and health outcomes. For instance, Nguyen et al. (2020) found disparities in exposure to various environmental pollutants, including pesticides and herbicides, among racial and ethnic minorities. The present study extends this work by specifically linking these disparities to differential associations with MetS, suggesting that social determinants of health and environmental justice issues are crucial considerations in environmental health research. The observation that the dose-response profile for glyphosate exposure and MetS differs by sex, with a significant and inverted U-shaped profile for females and a linear increase for males, contributes to the growing body of literature on sex-specific health impacts of environmental exposures. This aligns with studies highlighting biological and lifestyle differences between sexes that modulate health risks, but it also underscores the need for further research to elucidate the mechanisms underlying these differences, especially in the context of metabolic health and exposure to pollutants. Unlike many previous studies that have relied on categorical definitions of MetS based on dichotomized risk features, we employ a continuous MetS score derived from via EFA from metrics reliably and reproducibly obtained from a large general population. This approach captures nuanced variations in metabolic risk factors, offering a more detailed and sensitive descriptor of MetS risk. The score is then used to explore change in associations between MetS (represented by the score) and glyphosate by quartile exposure increase. By stratifying the data on key variables such as age, race-ethnicity, and sex, we explore potential effect modification of the association by demographic features, and the results yield insights into how the association between glyphosate and MetS varies across different demographic groups and by age. This approach allows for the identification of potentially vulnerable populations and underscores the complexity of the exposure-outcome relationship. While our study offers significant insights, causal inference is precluded by the cross-sectional design of NHANES. Additional limitations include that urine glyphosate concentration was measured only once per participant per cycle, and because it does not bioaccumulate, the measured concentration may not accurately reflect long-term exposure or account for variations in individual exposure over time. There may be residual confounding in our study, including unmeasured and correlated exposure to other toxins responsible for metabolic dysfunction. We validated the MetS score on only one metric as no other relevant biometric measurements were supplied by NHANES for these cycles. Finally, we present an unweighted study. Conclusions We find that urinary glyphosate is significantly associated with a statistical score designed to capture MetS, and in particular we note the presence of inverted U-shaped dose-response relationships with the most pronounced estimates in the third exposure quartile. This finding suggests a complex, non-linear interaction between glyphosate exposure and MetS risk. Our study findings underscore the complexity of the relationship between environmental exposures like glyphosate and metabolic health, influenced by demographic factors such as age, race-ethnicity, and sex. While our results to not constitute direct evidence, they suggest a need for studies focused on dietary and other glyphosate exposure routes to establish causality, explore mechanisms driving the observed associations, and address the vulnerabilities of specific demographic groups. Abbreviations ACR – Urine Albumin to Creatinine Ratio BMI – Body Mass Index DBP – Diastolic Blood Pressure EFA – Exploratory Factor Analysis GLUT2 – Glucose transporter 2 HbA1c – Hemoglobin A1C HDL – High Density Lipoprotein (Cholesterol) IARC – International Agency for Research on Cancer IDF – International Diabetes Federation IKKβ – Inhibitory Kappa-B kinase beta (regulatory kinase in inflammation) IL-1β – Interleukin-1B, inflammatory cytokine IL-6 – Interleukin-6, inflammatory cytokine IR – Insulin Receptor JNK – a stress-activated protein kinase KMO - Kaiser–Meyer–Olkin (Measure of Sampling Adequacy test) LLOD – Lower Limit of Detection MAP – Mean Arterial Pressure MetS – The Metabolic Syndrome NHANES – National Health and Nutrition Examination Survey NFkB – Nuclear factor kappa b (protein transcription factor) NMDR – Non-monotonic dose-response SBP – Systolic Blood Pressure SREBP1c – Transcriptional Factor TNF-α – Tumor Necrosis Factor-α Declarations Availability of data and materials The datasets are publicly available from the National Health and Nutrition Examination Survey (NHANES) via the Centers for Disease Control and Prevention (CDC) website: https://www.cdc.gov/nchs/nhanes/Default.aspx Competing interests The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: David Carpenter reports paid expert testimony on a Hodgkins Lymphoma Lawsuit (Allen Steward, LLC). 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Journal of atherosclerosis and thrombosis, 12(6), 295-300. https://doi.org/10.5551/jat.12.295 Additional Declarations Competing interest reported. Competing interests The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: David Carpenter reports paid expert testimony on a Hodgkins Lymphoma Lawsuit (Allen Steward, LLC). The remaining authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4272811","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":291689606,"identity":"c77a4459-e4d8-4348-aa12-42b4e52168fb","order_by":0,"name":"Sarah Otaru","email":"","orcid":"","institution":"University at Albany, State University of New York","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Otaru","suffix":""},{"id":291689608,"identity":"644aa111-6503-4bd7-a1f2-0a9e72cc5e4e","order_by":1,"name":"Laura E. Jones","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYBACPgjFBiIYHxOlhQ2JYjaGsJmJ0gJhSxOnhf2M2YcfDHxy5uztz6oLaurs+eX7DzB83FOLWwtPjvHMHgY2Y8ueM2a3Zxw7nDizjZmBccaz43gclmPMwMPAlrjhRg7bbR62AwkGx5gZmHkOHMOthf+NMeMfkJb7z58V8/yrs7cnqEUix5gZYguDGTNvGzPjBjawlho8Wp4VM8sYsBkbnMkxlp7ZdzhxxrFkg4MzDhzAqYWfP3kz45uKY3IGx48//FzwDRhizQcfPvhwoA6nFggwQHM50IrDBLQwYLqckC2jYBSMglEwggAAj6tLYI3iT14AAAAASUVORK5CYII=","orcid":"","institution":"Bassett Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Laura","middleName":"E.","lastName":"Jones","suffix":""},{"id":291689609,"identity":"5d2ca6f3-1360-458d-a867-6efd939d9764","order_by":2,"name":"David O. Carpenter","email":"","orcid":"","institution":"University at Albany, State University of New York","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"O.","lastName":"Carpenter","suffix":""}],"badges":[],"createdAt":"2024-04-16 03:18:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4272811/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4272811/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55250111,"identity":"df55fc2b-4631-4d0b-aa25-76cc238244c6","added_by":"auto","created_at":"2024-04-24 17:32:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":147908,"visible":true,"origin":"","legend":"\u003cp\u003eWork flow and sample sizes showing sample size evolution from the initial raw sample (three NHANES cycles, 2013-2018) to final complete case sample with the created MetS score.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4272811/v1/152f09b590c396bec2127c26.png"},{"id":55251449,"identity":"3a329092-86ec-47d4-944b-dafe473af3e2","added_by":"auto","created_at":"2024-04-24 17:40:12","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":101139,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFactor Analysis Diagram. \u003c/strong\u003eVariable loadings for the MetS index created via factor analysis on hot-deck imputed risk factor metrics (Table 2).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4272811/v1/888fb5bebd0e41b526c00f1c.jpg"},{"id":55250112,"identity":"ab8e56d4-83a6-4c5e-9d36-0fdeadc8baa6","added_by":"auto","created_at":"2024-04-24 17:32:12","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":177670,"visible":true,"origin":"","legend":"\u003cp\u003eChange in MetS score by Quartile Glyphosate, full model with all covariates. covariates (N = 5224). Model is adjusted as shown by sex (reference Male), age category (reference 10-19 years), race-ethnicity (reference Non-Hispanic White), scaled BMI, standardized creatinine, and standardized income-poverty ratio. See Table 4 for estimates, confidence intervals, and p-values.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4272811/v1/3b3255cf1e5050519d5e5423.jpg"},{"id":55250115,"identity":"cd36ea8f-24bc-4631-80ac-7e6554ae05b1","added_by":"auto","created_at":"2024-04-24 17:32:12","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":38723,"visible":true,"origin":"","legend":"\u003cp\u003eChange in MetS score by Quartile Glyphosate Exposure from adjusted regression models stratified on age category. Reference level for age category in full model is ages 10-19 years (\u0026lt;19yrs). See supplemental table 3 for details including sample sizes and p-values. To simplify presentation, only associations for outcome and exposure by quartile are shown for each age grouping.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4272811/v1/3048f51bb2df91cbe2314d87.jpg"},{"id":55251450,"identity":"a5c64444-e926-419c-affd-d22e5e48950c","added_by":"auto","created_at":"2024-04-24 17:40:12","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":38524,"visible":true,"origin":"","legend":"\u003cp\u003eChange in MetS score by Quartile Glyphosate Exposure from adjusted regression models stratified on race/ethnicity. Reference level for race in full model is Non-Hispanic White. See supplemental Table 4 for details including sample sizes and p-values. To simplify presentation, only associations for outcome and exposure by quartile are shown for each grouping.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4272811/v1/d42e16259840a3c8c8587410.jpg"},{"id":55252283,"identity":"5cc58114-154e-4122-8dd8-fcffd53aa517","added_by":"auto","created_at":"2024-04-24 17:48:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":790397,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4272811/v1/2765da56-a1bc-44b8-8f5f-3aba8a4f52ee.pdf"},{"id":55250116,"identity":"407ce424-eb3d-4840-a18b-775ce692a4cb","added_by":"auto","created_at":"2024-04-24 17:32:12","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":20734,"visible":true,"origin":"","legend":"","description":"","filename":"OtaruetalSupplementalCommonDefinitionsMetabolicSyndrome.docx","url":"https://assets-eu.researchsquare.com/files/rs-4272811/v1/70bf57716f9d103815410df1.docx"},{"id":55250114,"identity":"06b93597-9ec5-491c-b158-f80355bdce00","added_by":"auto","created_at":"2024-04-24 17:32:12","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":149799,"visible":true,"origin":"","legend":"","description":"","filename":"OtaruetalSupplementalTablesFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-4272811/v1/67ebee0cc756ecfaddbbb40c.docx"}],"financialInterests":"Competing interest reported. Competing interests\nThe authors declare the following financial interests/personal relationships which may be considered as potential competing interests: David Carpenter reports paid expert testimony on a Hodgkins Lymphoma Lawsuit (Allen Steward, LLC). The remaining authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","formattedTitle":"Associations between urine glyphosate levels and metabolic health risks: insights from a large cross-sectional population-based study","fulltext":[{"header":"Background","content":"\u003cp\u003eThe Metabolic Syndrome (MetS) consists of a constellation of conditions\u0026mdash;high blood pressure, high blood sugar levels, excess abdominal fat, and elevated cholesterol and triglyceride levels\u0026mdash;that collectively amplify the risk of cardiovascular disease, stroke, and type 2 diabetes mellitus (WHO 1999). \u0026nbsp;In the United States, MetS prevalence has risen from 37.6% in the 2011-12 period to 41.8% in 2017-2018 among adults, as seen from an analysis of data from the National Health and Nutrition Examination Survey (NHANES) (Liang et al., 2023). This upward trend underscores the urgency to understand and mitigate the factors contributing to MetS.\u003c/p\u003e\n\u003cp\u003eRisk factors for MetS are broadly categorized into nonmodifiable and modifiable elements. Age, sex, and race/ethnicity are nonmodifiable factors that can influence the likelihood of developing MetS, with variations observed across different demographic groups. Diet, physical activity, weight, and environmental chemical exposure represent modifiable factors. Among these, the role of environmental chemical exposure, particularly to common compounds such as glyphosate, has drawn increasing attention.\u003c/p\u003e\n\u003cp\u003eGlyphosate is the most widely used broadleaf herbicide globally, marketed as Roundup\u003csup\u003eTM\u003c/sup\u003e in the United States (Benbrook 2016). It blocks a pathway required for synthesis of aromatic amino acids found in plants but not animals (Roberts et al., 2007), although some gut bacteria are sensitive, and is applied to eliminate weeds and grasses. \u0026nbsp;Since its introduction in the 1970\u0026rsquo;s, usage has increased by a factor of at least 100 due in part to introduction of glyphosate tolerant \u0026ldquo;Roundup\u003csup\u003eTM\u003c/sup\u003e-ready\u0026rdquo; cereal crops (Benbrook 2016). A major use is to kill weeds before harvest of grain crops, and it is also applied directly to grain and pulse crops to help speed desiccation (Parreira et al., 2015). These practices, however, lead to glyphosate residues in foods made of wheat, oats and other grains, as well as beans and lentils, contributing to exposure via dietary intake among the general population (Parreira et al., 2015). Besides dietary exposure, inhalation and dermal contact are significant exposure pathways for farmers and others who apply Roundup\u003csup\u003eTM\u003c/sup\u003e (Connolly et al., 2019)..\u003c/p\u003e\n\u003cp\u003eWhile glyphosate was originally thought to have no effect on humans, it is now rated by the International Agency for Research on Cancer (IARC) as a probable human carcinogen (Guyton et al, 2015), it has actions on both female and male fertility (Serra et al., 2021), has neurological effects (Madani and Carpenter 2022) and causes mitochondrial damage (Peixoto 2005). \u0026nbsp;Animal and \u003cem\u003ein vitro\u003c/em\u003e studies indicate that glyphosate exposure interferes with glucose uptake into adipocytes (Prasad et al., 2022; de Melo et al., 2018), is associated with liver fibrosis (Mills et al., 2019), increases apoptosis (Chaufan et al., 2014; Gui et al., 2012), induces oxidative stress (Peixoto 2005; Mesnage et al., 2021a), and alters the gut microbiome (Mesnage et al., 2021b; Rueda-Ruzafa et al., 2019; Lehman et al., 2023; Hu et al., 2021; Tang et al., 2020). These mechanisms are linked to the pathogenesis of MetS (Lippert et al., 2017; Cani 2009).\u003c/p\u003e\n\u003cp\u003eEpidemiological research exploring the association between glyphosate and MetS is limited, with only two studies conducted so far. The CHAMACOS Study by Eskenazi et al., 2023, focused on mother-child dyads and found a significant association between glyphosate exposure and MetS risk by young adulthood. Similarly, Glover et al., 2023, observed a positive association between glyphosate levels and MetS among U.S. adults. However both studies have limitations.\u0026nbsp;Eskenazi et al. studied a small farming population with high levels of multiple chemical exposures, leading to poor generalizability to the US population. \u0026nbsp;Glover et al. (2023) adjusts for features intrinsic to the syndrome itself such as hypercholesterolemia, hypertension, and diabetes, \u0026nbsp;potentially biasing estimations of the associations between glyphosate and metabolic syndrome. These studies also employ threshold-based definitions of MetS based on dichotomized continuous variables, leading to loss of information and potentially statistical power. Clinical studies suggest that it is desirable to include continuous variables to create accurate scoring systems for MetS (Cavero-Redondo et al., 2019; Kahn et al., 2005).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGiven the limitations in the available studies and the problems associated with the use of the threshold-based definitions from a research standpoint, our study aims are two-fold: (1) to create and validate a score that captures the continuous nature of risk factors that make up MetS, and (2), to employ this score in linear regression analyses that explore associations between urine glyphosate concentrations and MetS. \u0026nbsp;We anticipate that our approach will provide a more nuanced understanding of how glyphosate exposure affects risk of MetS.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe employ data from the National Health and Nutrition Examination Survey (NHANES), accessible via the Centers for Disease Control and Prevention (CDC) website (https://www.cdc.gov/nchs/nhanes) in a cross-sectional study to examine potential associations between urine glyphosate measurements and Metabolic Syndrome (MetS). NHANES is a comprehensive research initiative designed to assess the health and nutritional status of adults and children in the United States, surveying approximately 5,000 individuals annually through interviews, physical examinations, and laboratory investigations. The research protocols were sanctioned by the National Center for Health Statistics of the U.S. CDC, and informed consent was required from all participants.\u003c/p\u003e\n\u003cp\u003eOur analysis includes the NHANES datasets from 2013 \u0026ndash; 2018 (3 cycles), which report urinary glyphosate measurements. These measurements were selectively obtained from a third of the participants who consented to future analysis of their laboratory samples. Therefore, from the initial pool of 29400 participants, 22333 were excluded due to the lack of urine glyphosate data and incomplete covariate information. \u0026nbsp; Since the earliest symptoms of metabolic syndrome do not occur among the very young, children under the age of 10 years (n=1180) were excluded, yielding a sample size for the primary analysis of 5224 (Figure 1). \u0026nbsp;Age was categorized into four groupings as follows, ages 10 to 19 years, ages 20 to 39 years, 40 to 59 years, and 60 years and above. \u0026nbsp;The reference level was ages 10 to 19. \u0026nbsp;Race was categorized as Non-Hispanic White (White), Non-Hispanic Black (Black), Non-Hispanic Asian (Asian), \u0026nbsp;Mexican-American, Other Hispanic, and Non-Hispanic Multiracial (Multiracial).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExposure: Urine Glyphosate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe measurement of urinary glyphosate levels was performed with a 200-microliter urine sample utilizing a 2D online ion chromatography system paired with tandem mass spectrometry (IC-MS/MS), along with isotope dilution for quantification. The results were expressed in nanograms per milliliter (ng/ml), with the assay sensitivity established at a lower limit of detection (LLOD) of 0.2 ng/ml. For our analysis, we categorized urinary glyphosate by quartile (See Table 1 for by-quartile summary of exposure and outcome). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome: Metabolic Syndrome Score\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Metabolic Syndrome was defined roughly per the International Diabetes Federation \u0026nbsp;(IDF) \u0026nbsp;criteria, which includes the presence of central obesity and any two of four additional risk factors. We include a total of 5 features based on biometric measurements and laboratory assessments reported in NHANES. \u0026nbsp;Following work by Cavero et al. (2019), we include HbA1c as a dysglycemia indicator (Cavero et al. 2019), as well as the composite risk factors triglyceride to HDL ratio and mean arterial pressure (MAP) in our selected features, \u0026nbsp;and extract a single factor score as described below. The mean arterial pressure (MAP) was estimated as SBP + 1/3 (SBP - DBP). \u0026nbsp;Thus selected risk and composite factors include:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eWaist circumference\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eFasting glucose \u0026nbsp;\u003c/li\u003e\n \u003cli\u003eglycoheme (HbA1c)\u003c/li\u003e\n \u003cli\u003etriglyceride to HDL ratio\u003c/li\u003e\n \u003cli\u003eMean Arterial Pressure (MAP)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese measures are common to most definitions of MetS (Alberti and Zimmet, 1998; Balkau and Charles, 1999; National Cholesterol Education Program, 2002; Grundy et al., 2005; Zimmet et al., 2005). \u0026nbsp;(See Supplemental Information, Appendix 1). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePotential Confounders/Effect Modifiers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePotential confounders in this study included sociodemographic indicators, physical examination results, and lifestyle habits. Sociodemographic data included age (categorized into 4 groups), sex, race/ethnicity, and family income-to-poverty ratio, which ranged from 0 to 5. \u0026nbsp;Finally, we included urine creatinine measurements as a covariate to adjust for variations in the concentration of urinary analytes due to dilution effects (Barr et al., 2005).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFactor analysis to create a MetS index.\u0026nbsp;\u003c/strong\u003eWe used exploratory factor analysis (EFA) to create a single index from the five risk factors described above (waist circumference, triglycerides to HDL ratio, \u0026nbsp;fasting glucose, glycoheme (HbA1c), and MAP). \u0026nbsp; EFA works by capturing common variance in one or more directions in the multivariable space of interest, here among MetS risk factors. \u0026nbsp;Each of these \u0026ldquo;directions\u0026rdquo; \u0026nbsp;is a factor. \u0026nbsp; After extracting significant factor(s) from our analysis, we obtain a table of weightings which may be used to create the index or indices. \u0026nbsp;These indices can be considered as weighted sums of the variables of interest. \u0026nbsp;A goal of our study is to obtain a working single-factor model for MetS, and our model is a modification of one of three developed and tested by Cavero-Redondo et al (2019), including both HbAlc and fasting glucose. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs missing values in the MetS risk factors ranged from 7.9% to 61.5%, we first imputed, \u0026nbsp;using sorted and grouped hot-deck single imputation, which selects appropriate donors randomly from the existing distributions in specified columns after sorting on age category and grouping by sex. \u0026nbsp;This yields a sample size of 22,258 on the five features selected for the EFA process. Following imputation, an average systolic blood pressure measurement was created by averaging the first three systolic BP measurements. \u0026nbsp;We then computed the MAP, estimated as SBP + 1/3 (SBP - DBP). \u0026nbsp; Since HDL varies inversely with MetS, and we sought a positively weighted score, we computed triglycerides to HDL ratio, a measure of cardiovascular risk that also appears in all three candidate MetS score models tested by Cavero-Redondo et al. (2019). \u0026nbsp;We then standardized the data, and since factor analysis is a linear method that does not respond well to highly correlated data, we checked for excessive multicollinearity by computing a correlogram and confirming that selected metrics all had pairwise correlations less than 0.8 (See supplemental Figure 1 for correlogram). Lastly, metrics were checked for appropriateness for EFA by running the Kaiser\u0026ndash;Meyer\u0026ndash;Olkin (KMO) Measure of Sampling Adequacy test \u0026nbsp;(Kaiser 1970; Kaiser and Rice, 1974) and a Bartlett\u0026rsquo;s Sphericity Test (p \u0026lt; 0.00001; Santos et al. 2019). The KMO test measures the proportion of common variance in a group of variables; the higher the statistic (closer to one), the more appropriate the grouping is for EFA (Kaiser 1974). \u0026nbsp;Our KMO values ranged from 0.65 to 0.72 (median 0.67), and confirmed that our selected features were all adequate for factor analysis. The Bartlett\u0026rsquo;s sphericity test indicates that the correlation matrix of the selected features is suitable for detection of structure.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs \u0026nbsp;EFA assumes normality, MetS metrics were first visualized, then log-transformed since all were slightly skewed (Santos et al 2019). Parallel analysis indicates that one factor is sufficient to explain common variance in the five risk features. EFA was run on the standardized metrics and the score for one factor was extracted. \u0026nbsp;Factor loadings \u0026nbsp;of \u0026nbsp; \u0026gt; 0.4 were considered criteria for inclusion in the MetS model, and all five selected features met this criterion for inclusion. The EFA procedure assumed orthogonality, was computed on a correlation matrix, and used a varimax rotation. \u0026nbsp; All imputation and EFA were performed in the R programming language (R 4.3.0), using the \u0026ldquo;hot-deck\u0026rdquo; algorithm from the VIM library (Kowarik and Templ 2016), parallel analysis from the paran library, and \u0026ldquo;factanal\u0026rdquo; from the psych library, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of MetS Score\u003c/strong\u003e. \u0026nbsp;While we lacked many IDF metrics for validating the MetS score, NHANES does provide ACR, the ratio of urine albumin to creatinine, available as the \u0026ldquo;URDACT\u0026rdquo; variable (See Table 1 for a summary of ACR by quartile). Neither creatinine or albumin measurements are used in the creation of the score, so this provides at least one opportunity to test the proposed MetS score on MetS-associated symptoms, though of three test metrics, ACR score was the least correlated with the MetS scores developed by Cavero-Redondo et al (2019). \u0026nbsp;An ACR score above 30 and below 300 indicates \u0026ldquo;microalbuminaria,\u0026rdquo; \u0026nbsp;but this range of transitional effect comprises an order of magnitude. Microalbuminaria is an indicator of kidney disease, \u0026nbsp;is associated with diabetes, and was recently included in a comprehensive definition of MetS proposed by the World Health Organization (Saadi et al 2020). However, while microalbuminaria is more frequent in MetS patients, it is not unique to MetS. We validated the MetS score proposed in this paper using a logistic model as a classifier, and a dataset including only the MetS score, creatinine, and the ACR score (N=20765). \u0026nbsp;Cut off values of ACR \u0026gt; 30 (microalbuminaria), ACR \u0026gt; 100 (microalbuminaria) and ACR \u0026gt; 300 (albuminuria) were selected and a binary ACR variable was created based on these values. The data were divided into training and testing sets using a stratified sampling scheme to ensure that nonzero ACS indicator values appeared in both testing and training sets. Logistic models adjusted for standardized creatinine were fit on the training set and predictions made on the test set, with model error rate, \u0026nbsp;sensitivity, specificity, accuracy and diagnostic odds ratio (Glas et al. 2003) computed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBivariate and Multivariable Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBivariate analysis was conducted using linear regressions, with \u0026nbsp;t-tests or ANOVA for continuous variables to examine the exposure and outcome across categorical covariate levels.\u003c/p\u003e\n\u003cp\u003eWe employed multivariable linear regression models to explore adjusted associations between urinary glyphosate levels and the MetS index from factor analysis. Adjusting confounders were selected using a directed acyclic graph and included age category, sex, BMI, \u0026nbsp;income-poverty ratio, and creatinine to adjust for urine dilution (Barr et al 2005). BMI and creatinine were continuous but were transformed if necessary and standardized before regressions were run. \u0026nbsp; MetS scale was standardized to facilitate comparisons across stratified models, \u0026nbsp; and the exposure was categorized by quartile to allow for potential nonlinearities. Lastly, we assessed for effect modification by age category, race \u0026nbsp;and sex (Shen et al. 2006); stratifying on each variable and repeating the analysis on each stratum. \u0026nbsp;Since this is an exploratory study, we do not adjust for multiplicity. All statistical analyses were performed using the R programming language (R version 4.3.3) and a p-value of less than 0.05 was considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eBaseline characteristics of complete case study population (N= 5224). \u0026nbsp; Sample size is limited by complete cases for glyphosate (N=5789) and Creatinine.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"570\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.350877192982455%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCovariate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.017543859649123%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.42105263157895%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(n = 2605)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.210526315789473%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003c/strong\u003en = 2619)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.350877192982455%\" rowspan=\"2\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.017543859649123%\" colspan=\"2\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.42105263157895%\" colspan=\"2\"\u003e\n \u003cp\u003e2605 (100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.210526315789473%\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.92070484581498%\" colspan=\"2\"\u003e\n \u003cp\u003eFemale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.68281938325991%\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.39647577092511%\" colspan=\"2\"\u003e\n \u003cp\u003e2619 (100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.350877192982455%\" rowspan=\"4\"\u003e\n \u003cp\u003eAge Category\u003c/p\u003e\n \u003cp\u003eRef: \u0026nbsp;\u0026lt; 19 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.017543859649123%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;19yrs\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.42105263157895%\" colspan=\"2\"\u003e\n \u003cp\u003e619 ( 23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.210526315789473%\" colspan=\"2\"\u003e\n \u003cp\u003e537 ( 20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.92070484581498%\" colspan=\"2\"\u003e\n \u003cp\u003e20-39yrs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.68281938325991%\" colspan=\"2\"\u003e\n \u003cp\u003e646 ( 24.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.39647577092511%\" colspan=\"2\"\u003e\n \u003cp\u003e678 ( 25.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.92070484581498%\" colspan=\"2\"\u003e\n \u003cp\u003e40-59yrs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.68281938325991%\" colspan=\"2\"\u003e\n \u003cp\u003e659 ( 25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.39647577092511%\" colspan=\"2\"\u003e\n \u003cp\u003e716 ( 27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.92070484581498%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026gt;60yrs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.68281938325991%\" colspan=\"2\"\u003e\n \u003cp\u003e681 ( 26.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.39647577092511%\" colspan=\"2\"\u003e\n \u003cp\u003e688 ( 26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.350877192982455%\" rowspan=\"6\"\u003e\n \u003cp\u003eRace-Ethnicity\u003c/p\u003e\n \u003cp\u003eRef: \u0026nbsp;White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.017543859649123%\" colspan=\"2\"\u003e\n \u003cp\u003eWhite (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.42105263157895%\" colspan=\"2\"\u003e\n \u003cp\u003e1021 ( 39.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.210526315789473%\" colspan=\"2\"\u003e\n \u003cp\u003e958 ( 36.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.92070484581498%\" colspan=\"2\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.68281938325991%\" colspan=\"2\"\u003e\n \u003cp\u003e412 ( 15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.39647577092511%\" colspan=\"2\"\u003e\n \u003cp\u003e421 ( 16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.92070484581498%\" colspan=\"2\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.68281938325991%\" colspan=\"2\"\u003e\n \u003cp\u003e244 ( \u0026nbsp;9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.39647577092511%\" colspan=\"2\"\u003e\n \u003cp\u003e279 ( 10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.92070484581498%\" colspan=\"2\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.68281938325991%\" colspan=\"2\"\u003e\n \u003cp\u003e519 ( 19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.39647577092511%\" colspan=\"2\"\u003e\n \u003cp\u003e552 ( 21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.92070484581498%\" colspan=\"2\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.68281938325991%\" colspan=\"2\"\u003e\n \u003cp\u003e276 ( 10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.39647577092511%\" colspan=\"2\"\u003e\n \u003cp\u003e293 ( 11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.92070484581498%\" colspan=\"2\"\u003e\n \u003cp\u003eMultiracial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.68281938325991%\" colspan=\"2\"\u003e\n \u003cp\u003e133 ( 5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.39647577092511%\" colspan=\"2\"\u003e\n \u003cp\u003e116 ( 4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.36842105263158%\" colspan=\"3\"\u003e\n \u003cp\u003eBMI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.42105263157895%\" colspan=\"2\"\u003e\n \u003cp\u003e27.71 (6.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.210526315789473%\" colspan=\"2\"\u003e\n \u003cp\u003e28.78 (8.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.36842105263158%\" colspan=\"3\"\u003e\n \u003cp\u003eIncome-Poverty Ratio \u0026nbsp; \u0026nbsp; \u0026nbsp; Med [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.42105263157895%\" colspan=\"2\"\u003e\n \u003cp\u003e2.02 [1.06, 3.91]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.210526315789473%\" colspan=\"2\"\u003e\n \u003cp\u003e1.94 [1.03, 3.82]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.526315789473685%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.175438596491228%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"59.824561403508774%\" colspan=\"4\"\u003e\n \u003cp\u003eMean (Median) values by Quartile \u0026nbsp;(N = 5789)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.526315789473685%\" colspan=\"2\"\u003e\n \u003cp\u003eUrinary Metric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.175438596491228%\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eQuartile 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.035087719298245%\"\u003e\n \u003cp\u003eQuartile 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.385964912280702%\"\u003e\n \u003cp\u003eQuartile 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003eQuartile 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.526315789473685%\" colspan=\"2\"\u003e\n \u003cp\u003eCreatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.175438596491228%\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.40 (0.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.035087719298245%\"\u003e\n \u003cp\u003e0.89 (1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.385964912280702%\"\u003e\n \u003cp\u003e1.39 (1.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.526315789473685%\" colspan=\"2\"\u003e\n \u003cp\u003eGlyphosate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.175438596491228%\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.12 (0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.035087719298245%\"\u003e\n \u003cp\u003e0.26 (0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.385964912280702%\"\u003e\n \u003cp\u003e0.45 (0.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.526315789473685%\" colspan=\"2\"\u003e\n \u003cp\u003eACR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.175438596491228%\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e3.7 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.035087719298245%\"\u003e\n \u003cp\u003e6.1 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.385964912280702%\"\u003e\n \u003cp\u003e10.1 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e21152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.526315789473685%\" colspan=\"2\"\u003e\n \u003cp\u003eMetS Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.175438596491228%\"\u003e\n \u003cp\u003e-1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-0.89 (-0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.035087719298245%\"\u003e\n \u003cp\u003e-0.31 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.385964912280702%\"\u003e\n \u003cp\u003e0.17 (0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Results","content":"\u003cp\u003eBaseline characteristics of the study population (N=5224) are summarized in \u003cstrong\u003eTable 1\u003c/strong\u003e, stratified by sex. \u0026nbsp;Characteristics for all three NHANES cycles utilized, including missing units, \u0026nbsp;are summarized in Supplementary Table 1, and the workflow for data cleaning and sample assembly is shown in \u003cstrong\u003eFigure 1\u003c/strong\u003e. \u0026nbsp; \u0026nbsp;From the initial sample, we first omit children under the age of 10 (n = 7142), leaving a sample size of 22258. We then omit participants lacking urinary glyphosate (74% missing), creatinine (6.7% missing), \u0026nbsp;BMI (5.5% missing) or poverty-income ratio information (10.7% missing). \u0026nbsp;The degree of missing units in the exposure makes imputation of the exposure for inference purposes impossible. However, a comparison of summary information from the complete case sample (Table 1) and the original dataset (Supplementary Table 1) suggests the complete case sample is representative. \u0026nbsp; The sample is roughly balanced by sex, with 2605 males and 2619 females. \u0026nbsp;The largest single ethnic group is Non-Hispanic White (about 38%), followed by Non-Hispanic Black (about 20%). \u0026nbsp;Hispanics as a group (Mexican American and \u0026lsquo;Other Hispanic\u0026rsquo;) comprised about 25%. \u0026nbsp;Non-Hispanic Asians comprised about 22% and multiracial participants less than 10%. \u0026nbsp;The cohort was older, with 52% of participants age 40 or over, and 26% age 60 or over. Covariates used in EFA (N=22,258) to create the MetS score were imputed using a conservative donor-based single-imputation method before proceeding with EFA (see \u003cstrong\u003eFigure 1\u003c/strong\u003e for details). Features used in the EFA to create the MetS score are summarized in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. \u0026nbsp;\u003c/strong\u003eCovariates used to create Metabolic Syndrome (MetS) score (N=22,258).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"636\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.9622641509434%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFeature\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.20754716981132%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.320754716981131%\"\u003e\n \u003cp\u003e\u003cstrong\u003e25%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.150943396226415%\"\u003e\n \u003cp\u003e\u003cstrong\u003e50%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.150943396226415%\"\u003e\n \u003cp\u003e\u003cstrong\u003e75%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.20754716981132%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.9622641509434%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaist circumference\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.20754716981132%\"\u003e\n \u003cp\u003e49.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.320754716981131%\"\u003e\n \u003cp\u003e81.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.150943396226415%\"\u003e\n \u003cp\u003e94.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.150943396226415%\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.20754716981132%\"\u003e\n \u003cp\u003e177.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.9622641509434%\"\u003e\n \u003cp\u003eSystolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.20754716981132%\"\u003e\n \u003cp\u003e64.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.320754716981131%\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.150943396226415%\"\u003e\n \u003cp\u003e117.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.150943396226415%\"\u003e\n \u003cp\u003e130.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.20754716981132%\"\u003e\n \u003cp\u003e231.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.9622641509434%\"\u003e\n \u003cp\u003eDiastolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.20754716981132%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.320754716981131%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.150943396226415%\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.150943396226415%\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.20754716981132%\"\u003e\n \u003cp\u003e135.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.9622641509434%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlycoheme (HbA1c)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.20754716981132%\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.320754716981131%\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.150943396226415%\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.150943396226415%\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.20754716981132%\"\u003e\n \u003cp\u003e17.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.9622641509434%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFasting Glucose\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.20754716981132%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.320754716981131%\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.150943396226415%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.150943396226415%\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.20754716981132%\"\u003e\n \u003cp\u003e479\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.9622641509434%\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.20754716981132%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.320754716981131%\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.150943396226415%\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.150943396226415%\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.20754716981132%\"\u003e\n \u003cp\u003e226\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.9622641509434%\"\u003e\n \u003cp\u003eTriglycerides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.20754716981132%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.320754716981131%\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.150943396226415%\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.150943396226415%\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.20754716981132%\"\u003e\n \u003cp\u003e4233\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.9622641509434%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTriglyceride : HDL Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.20754716981132%\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.320754716981131%\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.150943396226415%\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.150943396226415%\"\u003e\n \u003cp\u003e2.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.20754716981132%\"\u003e\n \u003cp\u003e103.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.9622641509434%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Arterial Pressure (MAP)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.20754716981132%\"\u003e\n \u003cp\u003e71.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.320754716981131%\"\u003e\n \u003cp\u003e122.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.150943396226415%\"\u003e\n \u003cp\u003e134.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.150943396226415%\"\u003e\n \u003cp\u003e150.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.20754716981132%\"\u003e\n \u003cp\u003e279.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eVariables and composite variables (the latter shown below double line at bottom of table) used in score are in boldface. \u0026nbsp;MAP = SBP + 1/3 (SBP - DBP). \u0026nbsp; \u0026nbsp;MetS score is estimated on as many samples as possible to create a score broadly applicable to a general population. Missingness is summarized in Supplementary Table 2.\u003c/p\u003e\n\u003cp\u003eWe explored associations between the outcome and exposure, plus patterns of associations between both the outcome and the exposure with covariates identified as confounders or potential effect modifiers (age, sex, race). Exposure and outcome are summarized by Quartile in \u003cstrong\u003eTable 3A-C.\u003c/strong\u003e\u0026nbsp; \u0026nbsp;Unadjusted associations between the standardized MetS outcome score and glyphosate, categorized by quartile, are significant for the reference quartile, and quartiles 3 (Q3) and 4 (Q4), with a suggestion of an inverted U-shaped dose-response (maximum at Q3; Table 3A). \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1.\u003c/strong\u003e\u0026nbsp; \u0026nbsp;Work flow and sample sizes showing sample size evolution from the initial raw sample (three NHANES cycles, 2013-2018) to final complete case sample with the created MetS score.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e\u0026nbsp; Bivariate analysis. \u0026nbsp;Note that every level value is relative to reference.\u003c/p\u003e\n\u003cp\u003ea. Bivariate association between MetS scale outcome and glyphosate by exposure quartile\u003csup\u003e.\u003c/sup\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.50602409638554%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlyphosate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.89156626506024%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.91566265060241%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.686746987951807%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.50602409638554%\"\u003e\n \u003cp\u003eQuartile 1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.89156626506024%\"\u003e\n \u003cp\u003e-0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.91566265060241%\"\u003e\n \u003cp\u003e-0.126 ,-0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.686746987951807%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.50602409638554%\"\u003e\n \u003cp\u003eQuartile 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.89156626506024%\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.91566265060241%\"\u003e\n \u003cp\u003e-0.005, \u0026nbsp;0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.686746987951807%\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.50602409638554%\"\u003e\n \u003cp\u003eQuartile 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.89156626506024%\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.91566265060241%\"\u003e\n \u003cp\u003e0.051, \u0026nbsp;0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.686746987951807%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.50602409638554%\"\u003e\n \u003cp\u003eQuartile 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.89156626506024%\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.91566265060241%\"\u003e\n \u003cp\u003e0.014, \u0026nbsp;0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.686746987951807%\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;b. Exposure and outcome by Race/ethnicity.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"82.6086956521739%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog(Glyphosate) exposure by Race\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.565217391304348%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace/Ethnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.91304347826087%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.73913043478261%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePr(\u0026gt;F)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.565217391304348%\"\u003e\n \u003cp\u003eWhite\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.91304347826087%\"\u003e\n \u003cp\u003e0.530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.73913043478261%\"\u003e\n \u003cp\u003e0.504, \u0026nbsp;0.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" rowspan=\"6\"\u003e\n \u003cp\u003eF = 8.35\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ep \u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.68421052631579%\"\u003e\n \u003cp\u003eMex - American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.94736842105263%\"\u003e\n \u003cp\u003e-0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003e-0.123, -0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.68421052631579%\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.94736842105263%\"\u003e\n \u003cp\u003e-0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003e-0.086, \u0026nbsp;0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.68421052631579%\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.94736842105263%\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003e-0.005, \u0026nbsp;0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u003cem\u003e0.083\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.68421052631579%\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.94736842105263%\"\u003e\n \u003cp\u003e-0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003e-0.164, -0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.68421052631579%\"\u003e\n \u003cp\u003eMultiracial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.94736842105263%\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003e-0.039, \u0026nbsp;0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"82.6086956521739%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandardized MetS scale (outcome) by Race\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.565217391304348%\"\u003e\n \u003cp\u003eWhite\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.91304347826087%\"\u003e\n \u003cp\u003e-0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.73913043478261%\"\u003e\n \u003cp\u003e-0.084 , 0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\"\u003e\n \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" rowspan=\"6\"\u003e\n \u003cp\u003eF = 19.0\u003c/p\u003e\n \u003cp\u003ep \u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.68421052631579%\"\u003e\n \u003cp\u003eMex American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.94736842105263%\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003e0.008, \u0026nbsp;0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.68421052631579%\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.94736842105263%\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003e-0.013, \u0026nbsp;0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u003cem\u003e0.090\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.68421052631579%\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.94736842105263%\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003e0.038, \u0026nbsp;0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.68421052631579%\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.94736842105263%\"\u003e\n \u003cp\u003e-0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003e-0.156, \u0026nbsp;0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.68421052631579%\"\u003e\n \u003cp\u003eMultiracial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.94736842105263%\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003e-0.114, \u0026nbsp;0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.792\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;c. Exposure and Outcome by Age Category\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"82.6086956521739%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog(Glyphosate) exposure by Age Grouping\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.565217391304348%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.91304347826087%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.73913043478261%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePr(\u0026gt;F)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.565217391304348%\"\u003e\n \u003cp\u003e10-19yrs\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.91304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;0.586 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.73913043478261%\" valign=\"top\"\u003e\n \u003cp\u003e0.553, \u0026nbsp; 0.62 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" rowspan=\"4\"\u003e\n \u003cp\u003eF = 22.6\u003c/p\u003e\n \u003cp\u003ep \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.68421052631579%\"\u003e\n \u003cp\u003e20-39yrs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.94736842105263%\" valign=\"top\"\u003e\n \u003cp\u003e-0.136\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e-0.182, -0.09\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.68421052631579%\"\u003e\n \u003cp\u003e40-59yrs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.94736842105263%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;-0.125\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e-0.170, -0.08 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.68421052631579%\"\u003e\n \u003cp\u003e\u0026gt;60yrs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.94736842105263%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; -0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e-0.070, \u0026nbsp;0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"82.6086956521739%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandardized MetS scale (outcome) by Age Grouping\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.565217391304348%\"\u003e\n \u003cp\u003e10-19yrs\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.91304347826087%\"\u003e\n \u003cp\u003e-0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.73913043478261%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;-0.745, -0.645 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" rowspan=\"4\"\u003e\n \u003cp\u003eF=4113\u003c/p\u003e\n \u003cp\u003ep \u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.68421052631579%\"\u003e\n \u003cp\u003e20-39yrs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.94736842105263%\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e0.317, \u0026nbsp;0.454\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.68421052631579%\"\u003e\n \u003cp\u003e40-59yrs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.94736842105263%\"\u003e\n \u003cp\u003e0.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;0.898, \u0026nbsp; \u0026nbsp; 1.034 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.68421052631579%\"\u003e\n \u003cp\u003e\u0026gt;60yrs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.94736842105263%\"\u003e\n \u003cp\u003e1.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;1.240, \u0026nbsp; \u0026nbsp; 1.376 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;d.\u0026nbsp;\u003c/strong\u003eExposure and outcome by Sex\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlyphosate exposure by Sex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.641025641025642%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.641025641025642%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.05128205128205%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.641025641025642%\"\u003e\n \u003cp\u003eMale (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.641025641025642%\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.05128205128205%\"\u003e\n \u003cp\u003e0.524, 0.568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.641025641025642%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.641025641025642%\"\u003e\n \u003cp\u003e-0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.05128205128205%\"\u003e\n \u003cp\u003e-0.099, -0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandardized MetS Scale (outcome) by Sex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.641025641025642%\"\u003e\n \u003cp\u003eMale (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.641025641025642%\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.05128205128205%\"\u003e\n \u003cp\u003e0.031, \u0026nbsp;0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.641025641025642%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.641025641025642%\"\u003e\n \u003cp\u003e-0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.05128205128205%\"\u003e\n \u003cp\u003e-0.193, -0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe find two race-ethnicity levels \u0026nbsp;(Mexican-American and NH Asian) with significantly differe t mean estimates from the reference (White) for log-transformed glyphosate from regression (Table 3A; p \u0026lt; 0.0001). \u0026nbsp;For the outcome MetS score, \u0026nbsp;Mexican-American and Black participants have significantly higher scores than reference, and the result from ANOVA is also highly significant (p \u0026lt; 0.0001) (Table 3B). \u0026nbsp; A visualization of differences in exposure by race is shown in Supplementary \u003cstrong\u003eFigure 2A\u003c/strong\u003e. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnadjusted regressions of exposure and outcome with age-category are consistent, showing significant differences in exposure between the reference level (ages 10-19) and ages 20-39 and 40-59 years, which are slightly reduced relative to the reference (p = 0.004). \u0026nbsp;Estimates for the age 60 and above level are not significantly different than the reference level, interestingly (Supplementary \u003cstrong\u003eFigure 2B\u003c/strong\u003e). The MetS score is significantly different across all age categories, which is unsurprising (Table 3C). \u0026nbsp;Finally, there are significantly different exposure and outcome levels by sex (reference Male); women have on average slightly but significantly lower exposure levels (p \u0026lt;0.0001) and slightly lower MetS scores than men (p \u0026lt; 0.0001) in unadjusted regressions (Table 3D). \u0026nbsp;Bivariate regressions show a surprising result for age category, in that the highest exposures are among the ages 10 to 19 group and ages above 65, consistent with exposure results from the 2013-14 cycle only by Ospina et al. (2022). \u0026nbsp; Results for MetS score are as expected, not much difference across race/ethnicity, but the score rises with age, and is slightly higher among men than women.\u003c/p\u003e\n\u003cp\u003eExploratory Factor Analysis resulted \u0026nbsp;in all positive weightings and yielded a single index score. \u0026nbsp;The weightings are shown in a factor analysis diagram (\u003cstrong\u003eFigure 2\u003c/strong\u003e): \u0026nbsp;glyphosate and glucose dominate, but just slightly. Biometric measurements waist circumference and MAP are similarly weighted and triglyceride : HDL ratio the least, though well within the range of inclusion. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSummary statistics for the index are listed by quartile in Table 1 and for the component covariates, Table 2. Missing units for the component covariates are summarized in Supplemental Table 2.\u003c/p\u003e\n\u003cp\u003eAs demonstrated in Cavero-Redondo et al. (2019), we validate our score by using it to predict vascular damage (associated with MetS) according to albumin-to-creatinine ratio (e.g., ACR \u003cimg src=\"data:image/png;base64,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\"\u003e); a typical, though not alone sufficient, metric of clinical performance for a MetS score (Shi et al 2020, Saadi et al. 2020). \u0026nbsp;Results from the validation procedure suggest the sensitivity, specificity and accuracy increase with the ACS cutoff (Supplemental Table 3), with an error rate of 12% for an ACR cut-off of 30 and above (microalbuminaria), and 2% for an ACR cut-off of 300 and above (albuminuria). \u0026nbsp;Again, because microalbuminaria is associated with MetS but not completely predictive of it, this is not an ideal test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2. \u0026nbsp;Factor Analysis Diagram.\u0026nbsp;\u003c/strong\u003eVariable loadings for the MetS index created via factor analysis on hot-deck imputed risk factor metrics (Table 2).\u003c/p\u003e\n\u003cp\u003eAdjusted associations \u0026nbsp;between standardized metabolic score and glyphosate categorized by quartile, from regressions adjusted for scaled BMI, transformed and standardized creatinine, sex (reference Male), age category (reference 10-19 years), race-ethnicity (reference White) and standardized income to poverty ratio are shown in \u003cstrong\u003eFigure 3\u003c/strong\u003e and Table 4. \u0026nbsp; Results for adjusted quartile models are significant and stronger than unadjusted results (Table 3A) and show the same slight inverted U-shaped dose-response, with peak estimates at the third quartile. \u0026nbsp; In the adjusted model, score estimates increase with age and related estimates are slightly elevated over \u0026nbsp;reference and over other races for Asian and multiracial participants. \u0026nbsp;This reverses in stratified models. \u0026nbsp;Female sex is protective.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3\u003c/strong\u003e. Change in MetS score by Quartile Glyphosate, full model with all covariates. \u0026nbsp; covariates (N = 5224). Model is adjusted as shown by sex (reference Male), age category (reference 10-19 years), race-ethnicity (reference Non-Hispanic White), \u0026nbsp;scaled BMI, standardized creatinine, and standardized income-poverty ratio. See Table 4 for estimates, confidence intervals, and p-values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. \u0026nbsp; \u0026nbsp;\u003c/strong\u003eAssociations between standardized Metabolic Score, glyphosate exposure and covariates (N = 5224). Models are adjusted by sex (ref. \u0026nbsp;Male), age category (ref. 10-19 years), race-ethnicity ( ref. \u0026nbsp;Non-Hispanic White), \u0026nbsp;scaled BMI, standardized creatinine, and standardized income-poverty ratio.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCovariate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.115384615384617%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel/Reference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.743589743589743%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.07051282051282%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.07051282051282%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.115384615384617%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.743589743589743%\"\u003e\n \u003cp\u003e-0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.07051282051282%\"\u003e\n \u003cp\u003e-0.681, -0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.07051282051282%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" rowspan=\"3\"\u003e\n \u003cp\u003eGlyphosate\u003c/p\u003e\n \u003cp\u003eCategorized by Quartile\u003c/p\u003e\n \u003cp\u003eRef: Quartile 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.115384615384617%\"\u003e\n \u003cp\u003eQuartile 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.743589743589743%\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.07051282051282%\"\u003e\n \u003cp\u003e0.029, \u0026nbsp;0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.07051282051282%\"\u003e\n \u003cp\u003e0.0043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.487179487179485%\"\u003e\n \u003cp\u003eQuartile 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.65811965811966%\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.427350427350426%\"\u003e\n \u003cp\u003e0.119, \u0026nbsp;0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.427350427350426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.487179487179485%\"\u003e\n \u003cp\u003eQuartile 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.65811965811966%\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.427350427350426%\"\u003e\n \u003cp\u003e0.107, \u0026nbsp;0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.427350427350426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.115384615384617%\"\u003e\n \u003cp\u003e(standardized)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.743589743589743%\"\u003e\n \u003cp\u003e0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.07051282051282%\"\u003e\n \u003cp\u003e0.328, \u0026nbsp;0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.07051282051282%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eCreatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.115384615384617%\"\u003e\n \u003cp\u003e(sqrt, standardized)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.743589743589743%\"\u003e\n \u003cp\u003e-0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.07051282051282%\"\u003e\n \u003cp\u003e-0.094, -0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.07051282051282%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eSex (Female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.115384615384617%\"\u003e\n \u003cp\u003eReference Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.743589743589743%\"\u003e\n \u003cp\u003e-0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.07051282051282%\"\u003e\n \u003cp\u003e-0.278, -0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.07051282051282%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" rowspan=\"3\"\u003e\n \u003cp\u003eAge Category\u003c/p\u003e\n \u003cp\u003eRef: 10-19 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.115384615384617%\"\u003e\n \u003cp\u003e20-39 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.743589743589743%\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.07051282051282%\"\u003e\n \u003cp\u003e0.114, \u0026nbsp;0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.07051282051282%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.487179487179485%\"\u003e\n \u003cp\u003e40-59 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.65811965811966%\"\u003e\n \u003cp\u003e0.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.427350427350426%\"\u003e\n \u003cp\u003e0.634, \u0026nbsp;0.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.427350427350426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.487179487179485%\"\u003e\n \u003cp\u003e\u0026gt;60 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.65811965811966%\"\u003e\n \u003cp\u003e1.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.427350427350426%\"\u003e\n \u003cp\u003e1.004, \u0026nbsp;1.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.427350427350426%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" rowspan=\"5\"\u003e\n \u003cp\u003eRace/Ethnicity\u003c/p\u003e\n \u003cp\u003eRef: Non-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.115384615384617%\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.743589743589743%\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.07051282051282%\"\u003e\n \u003cp\u003e0.108, \u0026nbsp;0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.07051282051282%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.487179487179485%\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.65811965811966%\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.427350427350426%\"\u003e\n \u003cp\u003e0.069, \u0026nbsp;0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.427350427350426%\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.487179487179485%\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.65811965811966%\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.427350427350426%\"\u003e\n \u003cp\u003e0.074, \u0026nbsp;0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.427350427350426%\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.487179487179485%\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.65811965811966%\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.427350427350426%\"\u003e\n \u003cp\u003e0.117, \u0026nbsp;0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.427350427350426%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.487179487179485%\"\u003e\n \u003cp\u003eMulti\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.65811965811966%\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.427350427350426%\"\u003e\n \u003cp\u003e0.081, \u0026nbsp;0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.427350427350426%\"\u003e\n \u003cp\u003e0.00055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eIncome-Poverty Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.115384615384617%\"\u003e\n \u003cp\u003e(standardized)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.743589743589743%\"\u003e\n \u003cp\u003e-0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.07051282051282%\"\u003e\n \u003cp\u003e-0.072, -0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.07051282051282%\"\u003e\n \u003cp\u003e0.00002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;We explored the possibility of effect modification by age category, race-ethnicity and sex in a series of stratified, adjusted models. Data were stratified on the key variable and models run separately on each stratum. \u0026nbsp; Results for models stratified on age category are shown in Supplemental Table 4. While there are significant differences in quartile estimates by age category (relative to reference level), confidence intervals overlap. \u0026nbsp;MetS scores increase slightly, but inconsistently, with age (\u003cstrong\u003eFigure 4\u003c/strong\u003e). Among the 10 to 19 years cohort, \u0026nbsp;only the fourth quartile is significantly different than reference (0.18, 95% CI: 0.035, 0.316, p = 0.015). For the age 20 to 39 cohort, scores rise significantly on quartile 2, drop and are marginal on Q3, and rise slightly to become significant on Q4. \u0026nbsp;Cohorts age 40 and above show the inverted U-shaped dose-response profile. \u0026nbsp;Results are strongest (p = 0.00005 at Q3) for participants 60 years and above. We do not correct for multiplicity. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile confidence intervals for the models stratified on race-ethnicity do overlap, there are two to three-fold differences in the effect sizes, relative to White participants, of estimates for Mexican-Americans (Q4 is 2.1 x greater than reference/White Q4), Other Hispanic (Q4 is 3.5 x larger) and Black participants (Q4 is a factor of 2.44 greater). Significant estimates for Mexican\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 4.\u003c/strong\u003e\u0026nbsp; Change in MetS score by Quartile Glyphosate Exposure from adjusted regression models stratified on age category. Reference level for age category in full model is ages 10-19 years (\u0026lt;19yrs). See supplemental table 3 for details including sample sizes and p-values. \u0026nbsp;To simplify presentation, only associations for outcome and exposure by quartile are shown for each age grouping.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmerican and Black participants show the inverted U-shaped dose response profile we observe in the study population as a whole (\u003cstrong\u003eFigure 5\u003c/strong\u003e, Supplemental Table 5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, estimates for models stratified on sex show a significant and inverted U-shaped profile for females and a significant and increasing profile for males. \u0026nbsp;Aside from the patterns of increase across levels, values are consistent between the sexes (Supplemental Table 6), and confidence intervals overlap, suggesting no evidence of effect modification by sex.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 5.\u003c/strong\u003e\u0026nbsp; Change in MetS score by Quartile Glyphosate Exposure from adjusted regression models stratified on race/ethnicity. Reference level for race in full model is Non-Hispanic White. See supplemental Table 4 for details including sample sizes and p-values. \u0026nbsp;To simplify presentation, only associations for outcome and exposure by quartile are shown for each grouping.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe detailed analysis of the association between urinary glyphosate levels and Metabolic Syndrome (MetS) score, using data from the National Health and Nutrition Examination Survey (NHANES) from 2013 to 2018, reveals significant findings that contribute to the growing body of research on environmental chemical exposure and metabolic health risks. \u0026nbsp;We found significant associations between urinary glyphosate levels and the MetS score, often with an inverted U-shaped dose-response relationship across exposure quartiles. This relationship persisted even after adjusting for potential confounders, indicating that higher levels of glyphosate exposure are associated with increased MetS risk, peaking at the third quartile of exposure. The observed inverted U-shaped dose-response relationship aligns with the notion that moderate levels of exposure may have a more pronounced effect on metabolic health than either low or very high exposures; \u0026nbsp; a non-monotonic dose-response relationship (NMDR) suggesting a threshold or a range of exposure within which the deleterious effects of glyphosate on metabolic health are most pronounced. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNMDR patterns have been observed in the context of endocrine disruption (Vandenberg et al., 2012). \u0026nbsp;Indeed glyphosate and glyphosate-based herbicides are linked to endocrine disruption in animal models, altering the levels of mRNA and protein expression of insulin receptor (IR), and several other receptors and signaling molecules involved in glucose metabolism such as glucose transporter-2 (GLUT2), JNK, IKK\u0026beta;, NFkB, IL-6, IL-1\u0026beta;, and TNF-\u0026alpha; as well as transcriptional factors like SREBP1c and PPAR-\u0026gamma; (Prasad et al 2022). Glyphosate and glyphosate-based herbicides have also been linked to inflammation and cirrhosis of the liver which led to the development of insulin resistance and type 2 diabetes in animal models (Prasad et al 2022; Jayaraman 2023; Gomes et al., 2022). \u0026nbsp;Our findings suggest need for further investigation into the mechanistic underpinnings of these relationships and their implications for human health, especially considering the widespread use of glyphosate and the prevalence of metabolic syndrome (Lamat et al., 2022). A deeper inquiry could yield additional insight into health effects of glyphosate as well as potential interaction effects with other variables not fully captured in the current study.\u003c/p\u003e\n\u003cp\u003eStratified analysis suggested potential effect modification by age and race/ethnicity but not by sex, and the strongest exposure-outcome associations were observed in older participants (60 years and above). The latter finding aligns with the broader literature on aging and metabolic health, where older age groups are generally at higher risk for MetS due to various physiological and lifestyle factors (Hirode and Wong, 2020; Alexander et al., 2008). \u0026nbsp;Note that our study omits the age group with the highest levels of documented exposure (ages ~10 years or less), with exposure routes through dietary sources such as sweetened ready-to-eat cereal, or possibly environmental exposure on school or recreational grounds (see Ospina et al., 2022; Table 3). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSignificant differences in effect sizes by race-ethnicity, with notably higher effect sizes for associations between glyphosate exposure categorized by quartile and the MetS score in Mexican-Americans, Other Hispanics, and Black participants, point to potential disparities in susceptibility or exposure to glyphosate. This finding is particularly important given the existing literature on racial and ethnic disparities in environmental exposures and health outcomes. For instance, Nguyen et al. (2020) found disparities in exposure to various environmental pollutants, including pesticides and herbicides, among racial and ethnic minorities. The present study extends this work by specifically linking these disparities to differential associations with MetS, suggesting that social determinants of health and environmental justice issues are crucial considerations in environmental health research.\u003c/p\u003e\n\u003cp\u003eThe observation that the dose-response profile for glyphosate exposure and MetS differs by sex, with a significant and inverted U-shaped profile for females and a linear increase for males, contributes to the growing body of literature on sex-specific health impacts of environmental exposures. This aligns with studies highlighting biological and lifestyle differences between sexes that modulate health risks, but it also underscores the need for further research to elucidate the mechanisms underlying these differences, especially in the context of metabolic health and exposure to pollutants.\u003c/p\u003e\n\u003cp\u003eUnlike many previous studies that have relied on categorical definitions of MetS based on dichotomized risk features, we employ a continuous MetS score derived from via EFA from metrics reliably and reproducibly obtained from a large general population. This approach captures nuanced variations in metabolic risk factors, offering a more detailed and sensitive descriptor of MetS risk. \u0026nbsp;The score is then used to explore change in associations between MetS (represented by the score) and glyphosate by quartile exposure increase. By stratifying the data on key variables such as age, race-ethnicity, and sex, we explore potential effect modification of the association by demographic features, and the results yield insights into how the association between glyphosate and MetS varies across different demographic groups and by age. This approach allows for the identification of potentially vulnerable populations and underscores the complexity of the exposure-outcome relationship.\u003c/p\u003e\n\u003cp\u003eWhile our study offers significant insights, \u0026nbsp;causal inference is precluded by the cross-sectional design of NHANES. \u0026nbsp;Additional limitations include that urine glyphosate concentration was measured only once per participant per cycle, and because it does not bioaccumulate, the measured concentration may not accurately reflect long-term exposure or account for variations in individual exposure over time. \u0026nbsp;There may be residual confounding in our study, including unmeasured and correlated exposure to other toxins responsible for metabolic dysfunction. We validated the MetS score on only one metric as no other relevant biometric measurements were supplied by NHANES for these cycles. \u0026nbsp;Finally, we present an unweighted study.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe find that urinary glyphosate is significantly associated with a statistical score designed to capture MetS, and in particular we note the presence of inverted U-shaped dose-response relationships with the most pronounced estimates in the third exposure quartile. This finding suggests a complex, non-linear interaction between glyphosate exposure and MetS risk. Our study findings underscore the complexity of the relationship between environmental exposures like glyphosate and metabolic health, influenced by demographic factors such as age, race-ethnicity, and sex. While our results to not constitute direct evidence, they suggest a need for studies focused on dietary and other glyphosate exposure routes to establish causality, explore mechanisms driving the observed associations, and address the vulnerabilities of specific demographic groups.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eACR \u0026nbsp;\u0026ndash; Urine Albumin to Creatinine Ratio\u003c/p\u003e\n\u003cp\u003eBMI \u0026nbsp;\u0026ndash; \u0026nbsp;Body Mass Index\u003c/p\u003e\n\u003cp\u003eDBP \u0026ndash; Diastolic Blood Pressure\u003c/p\u003e\n\u003cp\u003eEFA \u0026ndash; Exploratory Factor Analysis\u003c/p\u003e\n\u003cp\u003eGLUT2 \u0026ndash; Glucose transporter 2\u003c/p\u003e\n\u003cp\u003eHbA1c \u0026ndash; Hemoglobin A1C\u003c/p\u003e\n\u003cp\u003eHDL \u0026ndash; High Density Lipoprotein (Cholesterol)\u003c/p\u003e\n\u003cp\u003eIARC \u0026ndash; \u0026nbsp;International Agency for Research on Cancer\u003c/p\u003e\n\u003cp\u003eIDF\u0026nbsp;\u0026ndash; \u0026nbsp;International Diabetes Federation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIKK\u0026beta; \u0026ndash; Inhibitory Kappa-B kinase beta (regulatory\u0026nbsp;kinase in inflammation)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIL-1\u0026beta; \u0026ndash; Interleukin-1B, inflammatory cytokine\u003c/p\u003e\n\u003cp\u003eIL-6 \u0026ndash; Interleukin-6, inflammatory cytokine\u003c/p\u003e\n\u003cp\u003eIR \u0026ndash; Insulin Receptor\u003c/p\u003e\n\u003cp\u003eJNK \u0026ndash; a stress-activated protein kinase\u003c/p\u003e\n\u003cp\u003eKMO - Kaiser\u0026ndash;Meyer\u0026ndash;Olkin (Measure of Sampling Adequacy test) \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLLOD \u0026ndash; Lower Limit of Detection\u003c/p\u003e\n\u003cp\u003eMAP \u0026ndash; Mean Arterial Pressure\u003c/p\u003e\n\u003cp\u003eMetS \u0026ndash; The Metabolic Syndrome\u003c/p\u003e\n\u003cp\u003eNHANES \u0026ndash; National Health and Nutrition Examination Survey\u003c/p\u003e\n\u003cp\u003eNFkB \u0026ndash; Nuclear factor kappa b (protein transcription factor)\u003c/p\u003e\n\u003cp\u003eNMDR \u0026ndash; Non-monotonic dose-response\u003c/p\u003e\n\u003cp\u003eSBP \u0026ndash; Systolic Blood Pressure\u003c/p\u003e\n\u003cp\u003eSREBP1c \u0026ndash; Transcriptional Factor\u003c/p\u003e\n\u003cp\u003eTNF-\u0026alpha; \u0026ndash; Tumor Necrosis Factor-\u0026alpha;\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets are publicly available\u0026nbsp;from the National Health and Nutrition Examination Survey (NHANES) via the Centers for Disease Control and Prevention (CDC) website: https://www.cdc.gov/nchs/nhanes/Default.aspx\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare the following financial interests/personal relationships which may be considered as potential competing interests: David Carpenter reports paid expert testimony on a Hodgkins Lymphoma Lawsuit (Allen Steward, LLC). The remaining authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSO participated in study design, compiled data, and drafted/revised the manuscript. LEJ participated in study design, conducted all statistical analyses, and drafted/ revised the manuscript. DOC provided financial support through the IHE, participated in study design and manuscript revision. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Megan Kern of the Center for Biostatistics at Bassett Research Institute for assistance with preparing our graphical abstract. \u0026nbsp;This work was partially supported by internal funds from the Institute for Health and the Environment (IHE), University at Albany.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAitbali, Y., Ba-M\u0026rsquo;hamed, S., Elhidar, N., Nafis, A., Soraa, N., Bennis, M., 2018.\u003c/li\u003e\n\u003cli\u003eGlyphosate based- herbicide exposure affects gut microbiota, anxiety and depression-like behaviors in mice. Neurotoxicol. Teratol. 67, 44\u0026ndash;49. https://doi.org/10.1016/j.ntt.2018.04.002.\u003c/li\u003e\n\u003cli\u003eAlberti, K. G. M. M., \u0026amp; Zimmet, P. Z. (1998). 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Journal of atherosclerosis and thrombosis, 12(6), 295-300. https://doi.org/10.5551/jat.12.295\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enhe","sideBox":"Learn more about [Environmental Health](http://ehjournal.biomedcentral.com)","snPcode":"12940","submissionUrl":"https://submission.nature.com/new-submission/12940/3","title":"Environmental Health","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Exploratory Factor Analysis, Quantitative Score, Metabolic Syndrome, MetS, NHANES, albuminuria, Glyphosate","lastPublishedDoi":"10.21203/rs.3.rs-4272811/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4272811/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003ePrevalence of the Metabolic Syndrome (MetS) in American adults has risen from 37.6% in the 2011-12 period to 41.8% in 2017-2018. Environmental exposure, particularly to common compounds such as glyphosate, has drawn increasing attention as a potential risk factor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe employ three cycles of data (2013-2018) from the National Health and Nutrition Examination Survey (NHANES) in a cross-sectional study to examine potential associations between urine glyphosate measurements and the MetS. \u0026nbsp;\u0026nbsp;We first created a MetS score using Exploratory Factor Analysis of 6 International Diabetes Federation (IDF) criteria for MetS, with data drawn from the 2013-2018 NHANES cycles, and validated this score independently on an additional associated metric, Albumin to Creatinine Ratio. The score was validated via a machine-learning approach in predicting ACR score via binary classification, then used in multivariable regression to test association between quartile-categorized glyphosate exposure and the MetS score.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e In adjusted multivariable regressions, quartile regressions between glyphosate exposure and MetS score show a significant inverted U-shaped or saturating dose-response profile, often with largest effect for exposures in quartile 3. \u0026nbsp;Exploration of potential effect modification by sex, race, and age category shows significant differences by race and age, with older people (ages \u0026gt; 65 years) and non-Hispanic African American participants showing larger effect sizes for all exposure quartiles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eWe find that urinary glyphosate is significantly associated with a statistical score designed to capture the MetS, and that dose-response is nonlinear, with advanced age and non-Hispanic African American and Mexican American and other Hispanic participants showing higher effect sizes.\u003c/p\u003e","manuscriptTitle":"Associations between urine glyphosate levels and metabolic health risks: insights from a large cross-sectional population-based study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-24 17:32:07","doi":"10.21203/rs.3.rs-4272811/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-31T14:14:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-15T08:24:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"350d63fe-df26-4418-b72d-b9d2f5c95b04","date":"2024-04-24T10:34:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-24T10:21:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-16T05:37:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-16T05:37:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Health","date":"2024-04-16T03:17:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enhe","sideBox":"Learn more about [Environmental Health](http://ehjournal.biomedcentral.com)","snPcode":"12940","submissionUrl":"https://submission.nature.com/new-submission/12940/3","title":"Environmental Health","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3c3455a0-68a1-4aa6-b7d2-db2bc45c6f0d","owner":[],"postedDate":"April 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-06-18T12:36:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-24 17:32:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4272811","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4272811","identity":"rs-4272811","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}