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

Otaru, Sarah; Jones, Laura E.; Carpenter, David O.

doi:10.1186/s12940-024-01098-8

Cited by 2 publications

References 61 publications

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Benchmarking four algorithms for improved classification of agricultural injury cases from free-text analysis of pre-hospital care reports (Preprint)

Jones,

Scott,

Krupa

et al. 2024

Preprint

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BACKGROUND Fatality rates in Agriculture, Forestry, and Fishing (AgFF) industries are historically the highest of any US sector, with a combined rate of 18.6 deaths per 100,000 workers. Despite clear trends for fatal AgFF workplace injuries in federal data, challenges remain in capturing nonfatal agricultural injuries. The Northeast Center for Occupational Health and Safety (NEC) developed a naïve Bayes-based classification strategy to extract non-fatal injury cases from pre-hospital (EMS) free-text records. OBJECTIVE The aim of this paper is to improve retrieval rates, in terms of false positive rate required to obtain a true positive rate of 0.90, by benchmarking naïve Bayes against three other algorithms: elastic net regression, Support Vector Machines, and boosted decision trees (XGBoost). METHODS Using a labeled, fully one-hot coded gold-standard dataset (N=60,143) with substantial (24%) missing data, we benchmark these algorithms on complete case data (N=44,566) and imputed data from two imputation schemes: grouped hot-deck and recoding of missing units to the category “unknown,” using a 75:25 train/test split and stratified sampling. RESULTS All models produced similarly accuracies (~0.98) on complete case data, though necessary False Positive Rates (FPR*) varied from 0.055 (XGBoost) to 0.20 (naïve Bayes) on training data, and on predictions, the range was 0.10 (elastic net) to 0.22 (naïve Bayes). On imputed data, accuracies ranged from 0.96 (Bayes) to 0.98 (XGBoost) for training data, yielding false positive rates from 0.095 (XGBoost) to 0.34 (Bayes). Predictions from imputed data showed FPR* ranging from 0.12 (XGBoost) to 0.41 (Bayes) depending on imputation scheme. CONCLUSIONS While all four models perform well on complete data, missing units are substantial and can result in misclassification and in omissions, both requiring human coding. Reliance on a machine learning method that is robust to missing data and imputation method, such as XGBoost, is a reasonable approach to improving classification rates without omitting data.

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Benchmarking four algorithms for improved classification of agricultural injury cases from free-text analysis of pre-hospital care reports (Preprint)

Jones,

Scott,

Krupa

et al. 2024

Preprint

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Associations between Glyphosate Exposure and Glycemic Disorders: A Focus on the Modifying Effect of Sex Hormones

Dong,

Li,

et al. 2024

Toxics

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Widespread glyphosate contamination in the environment and its endocrine-disrupting potential are concerning. However, evidence of glyphosate’s effects on glycemic health is limited. To examine the association between glyphosate and glucose homeostasis in the general US population, a total of 3038 individuals were enrolled from the 2013-2016 cycles of the National Health and Nutrition Examination Survey (NHANES). Survey-weighted linear regression and restricted cubic spline curves were used to detect the associations between glyphosate and glycemic disorders. The effects of interactions between sex hormones and glyphosate on glycemic outcomes were evaluated. The results showed that glyphosate was significantly linked to increased glycated hemoglobin A1c (HbA1c) levels (β = 0.01; 95%CI, 0.01 to 0.02; p = 0.001) and the compromised homeostatic model assessment of beta-cell function (HOMA-beta) scores (β = −0.09; 95%CI, −0.17 to −0.01; p = 0.024). More importantly, these “glyphosate–glycemic disorder” associations were significantly modified by sex hormone-binding globulin (SHBG; P for interaction < 0.05), with more pronounced relationships being identified in individuals with low SHBG levels. Our findings indicate that glyphosate is correlated with glucose dyshomeostasis. Individuals with low SHBG levels exhibited susceptibility to glyphosate-related glycemic toxicity; therefore, it might be prudent to determine glycemic health in those subjects with glyphosate exposure.

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

Cited by 2 publications

References 61 publications

Benchmarking four algorithms for improved classification of agricultural injury cases from free-text analysis of pre-hospital care reports (Preprint)

Benchmarking four algorithms for improved classification of agricultural injury cases from free-text analysis of pre-hospital care reports (Preprint)

Associations between Glyphosate Exposure and Glycemic Disorders: A Focus on the Modifying Effect of Sex Hormones

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