Lead Levels in a Potters Population and Its Association With the Use of Different Glazes: Cross-Sectional Evaluation of the Approved Pottery Program

Rapid Market Screening to assess lead concentrations in consumer products across 25 low- and middle-income countries

Lead exposure can have serious consequences for health and development. The neurological and behavioral effects of lead are considered irreversible. Young children are particularly vulnerable to lead poisoning. In 2020, Pure Earth and UNICEF estimated that one in three children had elevated blood lead levels above 5 µg/dL. The sources of lead exposure vary around the world and can range from household products, such as spices or foodware, to environmental pollution from nearby industries. The aim of this study was to analyze common products from markets in low- and middle-income countries (LMICs) for their lead content to determine whether they are plausible sources of exposure. In 25 LMICs, the research teams systematically collected consumer products (metal foodware, ceramics, cosmetics, paints, toys, spices and other foods). The items were analyzed on site for detectable lead above 2 ppm using an X-ray fluorescence analyzer. For quality control purposes, a subset of the samples was analyzed in the USA using inductively coupled plasma mass spectrometry. The lead concentrations of the individual product types were compared with established regulatory thresholds. Out of 5007 analyzed products, threshold values (TV) were surpassed in 51% for metal foodware (TV 100 ppm), 45% for ceramics (TV 100 ppm), and 41% for paints (TV 90 ppm). Sources of exposure in LMICs can be diverse, and consumers in LMICs lack adequate protection from preventable sources of lead exposure. Rapid Market Screening is an innovative, simple, and useful tool to identify risky products that could be sources of lead exposure.

Development of an interpretable machine learning model associated with heavy metals' exposure to identify coronary heart disease among US adults via SHAP: Findings of the US NHANES from 2003 to 2018

Limited information is available on the links between heavy metals' exposure and coronary heart disease (CHD). We aim to establish an efficient and explainable machine learning (ML) model that associates heavy metals' exposure with CHD identification. Our datasets for investigating the associations between heavy metals and CHD were sourced from the US National Health and Nutrition Examination Survey (US NHANES, 2003-2018). Five ML models were established to identify CHD by heavy metals' exposure. Further, 11 discrimination characteristics were used to test the strength of the models. The optimally performing model was selected for identification. Finally, the SHapley Additive exPlanations (SHAP) tool was used for interpreting the features to visualize the selected model's decision-making capacity. In total, 12,554 participants were eligible for this study. The best performing random forest classifier (RF) based on 13 heavy metals to identify CHD was chosen (AUC: 0.827; 95%CI: 0.777-0.877; accuracy: 95.9%). SHAP values indicated that cesium (1.62), thallium (1.17), antimony (1.63), dimethylarsonic acid (0.91), barium (0.76), arsenous acid (0.79), total arsenic (0.01) in urine, and lead (3.58) and cadmium (4.66) in blood positively contributed to the model, while cobalt (-0.15), cadmium (-2.93), and uranium (-0.13) in urine negatively contributed to the model. The RF model was efficient, accurate, and robust in identifying an association between heavy metals' exposure and CHD among US NHANES 2003-2018 participants. Cesium, thallium, antimony, dimethylarsonic acid, barium, arsenous acid, and total arsenic in urine, and lead and cadmium in blood show positive relationships with CHD, while cobalt, cadmium, and uranium in urine show negative relationships with CHD.