Correlative study on heavy metal-induced oxidative stress and hypertension among the rural population of Malwa Region of Punjab, India

Assessing the impact of arsenic, lead, mercury, and cadmium exposure on glycemic and lipid profile markers: A systematic review and meta-analysis protocol

The toxicity of metals presents a significant threat to human health due to the metabolic changes they induce. Thus, it is crucial to understand the impact of exposure to toxic elements on glycemic and lipid profiles. To this end, we developed a systematic review protocol registered in PROSPERO (CRD42023393681), following PRISMA-P guidelines. This review aims to assess environmental exposure to arsenic, cadmium, mercury, and lead in individuals aged over ten years and elucidate their association with glycemic markers such as fasting plasma glucose, glycated hemoglobin, as well as lipid parameters including total cholesterol, triglycerides, high-density lipoprotein, and low-density lipoprotein cholesterol. Articles published in the MEDLINE (PubMed), EMBASE, Web of Science, LILACS, and Google Scholar databases until March 2024 will be included without language restrictions. The modified Newcastle-Ottawa scale will be employed to assess the quality of the included studies, and the results will be presented through narrative synthesis. If adequate data are available, a meta-analysis will be conducted. This review can help understand the metabolic responses to exposure to toxic elements and the associated health risks.

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.

Spatio-temporal evolution of groundwater quality and its health risk assessment in Punjab (India) during 2000-2020

The state known as the bread basket of India has now been defamed as the cancer capital of the country. The toxicity of groundwater associated with the declining water level is reported in recent years. However, an extensive temporal and spatial analysis is required to identify hotspots. In this study, spatial tools are utilized to understand the evolution of groundwater in Punjab (> 315 sites) for the last two decades (2000-2020) for drinking purposes using the water quality index (WQI). The data for pH, electric conductivity (EC), bicarbonate (HCO3¯), chloride (Cl¯), sulfate (SO42¯), nitrate (NO3¯), fluoride (F¯), calcium (Ca2+), magnesium (Mg2+), sodium (Na+), and potassium (K+) collected from the Central Groundwater Board (CGWB) were analyzed. The results show that the average cation abundance is in declining order of Na > Mg > Ca > K, and anion abundance is in order of HCO3¯ > SO42¯ > Cl¯ > NO3 > F. The ions are compared with water quality standards defined by BIS and WHO. The study shows that in the year 2000, 69.52% of locations are above the acceptable limit for EC, 68.89% for Mg2, 84.76% for Na+, 51.75% for HCO3¯, 38.41% for NO3¯, and 17.20% for F¯. While in the year 2020, 48.89% exceed the acceptable limit for EC, 57.78% for Mg2+, 68.25% for Na+, 34.92% for HCO3¯, 27.30% for NO3¯, and 8.88% for F¯. WQI shows that in the year 2000, 13.01% of sampling locations are categorized as very poor and 20% as unsuitable for drinking. Meanwhile, in 2020, 6.35% of locations are categorized as very poor and 12.38% as unsuitable for drinking in the study area. In addition to the effect on plant growth, consumption of contaminated water can adversely affect human health. The health hazards for F¯ (HQF) and NO3¯ (HQN) and their total health index (THI) are also evaluated that depicts 244 groundwater sampling sites in the year 2000, and 152 sampling sites in the year 2020 show high non-carcinogenic effects on adults, children, and infants. Southwestern Punjab is found to be the worst affected, while north-eastern regions drained by the Himalayan rivers show better quality water. Shifting in agricultural practices in the last two decades and declining water levels due to excess pumping of water from deeper water tables deteriorated the quality of water in the Southern region as observed from the geospatial analysis.