Paraben exposures and their interactions with ESR1/2 genetic polymorphisms on hypertension

Environmental chemical exposures and a machine learning-based model for predicting hypertension in NHANES 2003-2016

BACKGROUND

Hypertension is a common disease, often overlooked in its early stages due to mild symptoms. And persistent elevated blood pressure can lead to adverse outcomes such as coronary heart disease, stroke, and kidney disease. There are many risk factors that lead to hypertension, including various environmental chemicals that humans are exposed to, which are believed to be modifiable risk factors for hypertension.

OBJECTIVE

To investigate the role of environmental chemical exposures in predicting hypertension.

METHODS

A total of 11,039 eligible participants were obtained from NHANES 2003-2016, and multiple imputation was used to process the missing data, resulting in 5 imputed datasets. 8 Machine learning algorithms were applied to the 5 imputed datasets to establish hypertension prediction models, and the average accuracy score, precision score, recall score, and F1 score were calculated. A generalized linear model was also built to predict the systolic and diastolic blood pressure levels.

RESULTS

All 8 algorithms had good predictions for hypertension, with Support Vector Machine (SVM) being the best, with accuracy, precision, recall, F1 scores and area under the curve (AUC) of 0.751, 0.699, 0.717, 0.708 and 0.822, respectively. The R2 of the linear model on the training and test sets was 0.28, 0.25 for systolic and 0.06, 0.05 for diastolic blood pressure.

CONCLUSIONS

In this study, relatively accurate prediction of hypertension was achieved using environmental chemicals with machine learning algorithms, demonstrating the predictive value of environmental chemicals for hypertension.

Exposure assessment of dairy cows to parabens using hair samples analysis

Parabens (PBs) are used as preservatives in various products. They pollute the environment and penetrate living organisms, showing endocrine disrupting activity. Till now studies on long-term exposure of farm animals to PBs have not been performed. Among matrices using in PBs biomonitoring hair samples are becoming more and more important. During this study concentration levels of methyl paraben (MeP), ethyl paraben (EtP), propyl paraben (PrP) butyl paraben (BuP) and benzyl paraben (BeP) were evaluated using liquid chromatography-mass spectrometry (LC-MS) in hair samples collected from dairy cows bred in the Kyrgyz Republic. MeP was noted in 93.8% of samples (with mean concentration levels 62.2 ± 61.8 pg/mg), PrP in 16.7% of samples (12.4 ± 6.5 pg/mg) and EtP in 8.3% of samples (21.4 ± 11.9 pg/mg). BuP was found only in one sample (2.1%) and BeP was not detected in any sample included in the study. Some differences in MeP concentration levels in the hair samples depending on district, where cows were bred were noted. This study has shown that among PBs, dairy cows are exposed mainly to MeP, and hair samples may be a suitable matrix for research on PBs levels in farm animals.

Building a predictive model for hypertension related to environmental chemicals using machine learning

Hypertension is a chronic cardiovascular disease characterized by elevated blood pressure that can lead to a number of complications. There is evidence that the numerous environmental substances to which humans are exposed facilitate the emergence of diseases. In this work, we sought to investigate the relationship between exposure to environmental contaminants and hypertension as well as the predictive value of such exposures. The National Health and Nutrition Survey (NHANES) provided us with the information we needed (2005-2012). A total of 4492 participants were included in our study, and we incorporated more common environmental chemicals and covariates by feature selection followed by regularized network analysis. Then, we applied various machine learning (ML) methods, such as extreme gradient boosting (XGBoost), random forest classifier (RF), logistic regression (LR), multilayer perceptron (MLP), and support vector machine (SVM), to predict hypertension by chemical exposure. Finally, SHapley Additive exPlanations (SHAP) were further applied to interpret the features. After the initial feature screening, we included a total of 29 variables (including 21 chemicals) for ML. The areas under the curve (AUCs) of the five ML models XGBoost, RF, LR, MLP, and SVM were 0.729, 0.723, 0.721, 0.730, and 0.731, respectively. Butylparaben (BUP), propylparaben (PPB), and 9-hydroxyfluorene (P17) were the three factors in the prediction model with the highest SHAP values. Comparing five ML models, we found that environmental exposure may play an important role in hypertension. The assessment of important chemical exposure parameters lays the groundwork for more targeted therapies, and the optimized ML models are likely to predict hypertension.

Association of sleep problems with urinary concentrations of personal care and consumer product chemicals: a nationally representative, population-based study

Sleep problems are common in modern society and may be related to environmental chemicals. The objective of this study is to investigate the association between exposure to personal care and consumer product chemicals (PCCPCs) and sleep-related disorders. Nationally representative data from the National Health and Nutrition Examination Survey (NHANES) were used in this study (N=2415). Sleep-related variables, including sleep duration, snoring, and self-reported sleep problems, were included as outcome variables to assess sleep quality. Urinary PCCPC concentrations were used to assess the association of PCCPCs with sleep problems and adjusted for variables similar to those used in related studies. PCCPC levels were analysed as quartiles. Multivariate logistic regression and weighted quantile sum (WQS) regression were used to analyse the association of urinary PCCPCs with sleep problems. Nine of the 12 kinds of PCCPCs with a detection rate greater than 50% were included in our study. Specifically, the concentrations of bisphenol A (BPA), bisphenol F (BPF), methyl paraben (MP) and triclosan (TCS) were significantly related to insufficient sleep. Based on the WQS model, combined exposure to PCCPCs was also significantly related to insufficient sleep; TCS, BPA, and MP were the compounds with the greatest impact regarding combined exposure. A variety of PCCPCs were associated with insufficient sleep in participants but were not significantly associated with the other sleep problems reported in the NHANES. As poor quality sleep is associated with multiple adverse health outcomes, our study provides insight into the health risks of PCCPC exposure.