Urinary Arsenic Species are Detectable in Urban Underserved Hispanic/Latino Populations: A Pilot Study from the Study of Latinos: Nutrition & Physical Activity Assessment Study (SOLNAS)

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.

Arsenic Exposure, Arsenic Metabolism, and Glycemia: Results from a Clinical Population in New York City

Little information is available regarding the glycemic effects of inorganic arsenic (iAs) exposure in urban populations. We evaluated the association of total arsenic and the relative proportions of arsenic metabolites in urine with glycemia as measured by glycated blood hemoglobin (HbA1c) among 45 participants with prediabetes (HbA1c ≥ 5.7-6.4%), 65 with diabetes (HbA1c ≥ 6.5%), and 36 controls (HbA1c < 5.7%) recruited from an academic medical center in New York City. Each 10% increase in the proportion of urinary dimethylarsinic acid (DMA%) was associated with an odds ratio (OR) of 0.59 (95% confidence interval (CI): 0.28-1.26) for prediabetes, 0.46 (0.22-0.94) for diabetes, and 0.51 (0.26-0.99) for prediabetes and diabetes combined. Each 10% increase in the proportion of urinary monomethylarsonic acid (MMA%) was associated with a 1.13% (0.39, 1.88) increase in HbA1c. In contrast, each 10% increase in DMA% was associated with a 0.76% (0.24, 1.29) decrease in HbA1c. There was no evidence of an association of total urinary arsenic with prediabetes, diabetes, or HbA1c. These data suggest that a lower arsenic methylation capacity indicated by higher MMA% and lower DMA% in urine is associated with worse glycemic control and diabetes. Prospective, longitudinal studies are needed to evaluate the glycemic effects of low-level iAs exposure in urban populations.