Interaction of biomass fuels use and metals exposure on hypertension: A prospective cohort study in rural areas along the Yangtze River, China

Exploring the relationship between heavy metals and diabetic retinopathy: a machine learning modeling approach

Diabetic retinopathy (DR) is one of the leading causes of adult blindness in the United States. Although studies applying traditional statistical methods have revealed that heavy metals may be essential environmental risk factors for diabetic retinopathy, there is a lack of analyses based on machine learning (ML) methods to adequately explain the complex relationship between heavy metals and DR and the interactions between variables. Based on characteristic variables of participants with and without DR and heavy metal exposure data obtained from the NHANES database (2003-2010), a ML model was developed for effective prediction of DR. The best predictive model for DR was selected from 11 models by receiver operating characteristic curve (ROC) analysis. Further permutation feature importance (PFI) analysis, partial dependence plots (PDP) analysis, and SHapley Additive exPlanations (SHAP) analysis were used to assess the model capability and key influencing factors. A total of 1042 eligible individuals were randomly assigned to two groups for training and testing set of the prediction model. ROC analysis showed that the k-nearest neighbour (KNN) model had the highest prediction performance, achieving close to 100% accuracy in the testing set. Urinary Sb level was identified as the critical heavy metal affecting the predicted risk of DR, with a contribution weight of 1.730632 ± 1.791722, which was much higher than that of other heavy metals and baseline variables. The results of the PDP analysis and the SHAP analysis also indicated that antimony (Sb) had a more significant effect on DR. The interaction between age and Sb was more significant compared to other variables and metal pairs. We found that Sb could serve as a potential predictor of DR and that Sb may influence the development of DR by mediating cellular and systemic senescence. The study revealed that monitoring urinary Sb levels can be useful for early non-invasive screening and intervention in DR development, and also highlighted the important role of constructed ML models in explaining the effects of heavy metal exposure on DR.

Association of household solid fuel use and long-term exposure to ambient air pollution with estimated 10-year high cardiovascular disease risk among postmenopausal women

This study aimed to explore the separate and joint effects of long-term ambient air pollution and household air pollution exposure on 10-year high cardiovascular disease (CVD) risk among postmenopausal women. A total of 4679 postmenopausal women from the China Health and Retirement Longitudinal Study (CHARLS) were included in this study. Information of fuel type was collected by standard questionnaires and use of solid fuel was considered as a proxy for household air pollution. Data of ambient air pollutants (PM1, PM2.5, PM10, SO2, NO2, CO, O3) were obtained from the ChinaHighAirPollutants (CHAP) datasets. Logistic regression models were performed to assess the separate and joint effects of long-term exposure to ambient air pollution and use of solid fuel on 10-year high CVD risk. We found use of solid fuel and its duration and ambient air pollutants (PM1, PM2.5, PM10, SO2, NO2) were all positively associated with 10-year high CVD risk among postmenopausal women (P < 0.05). Compared to those used clean fuel and exposed to low ambient air pollution levels, odds ratios (ORs) and 95% confidence intervals (CIs) for participants using solid fuels and exposed to high ambient air pollution levels (PM1, PM2.5, PM10, SO2, NO2, CO, O3) were 1.66 (1.35, 2.05), 1.66 (1.35, 2.04), 1.49 (1.22, 1.83), 1.28 (1.05, 1.57), 1.67 (1.34, 2.07), 1.28 (1.04, 1.57), 1.46 (1.18, 1.80), respectively. Moreover, significant additive interactions of solid fuel use with PM1 and PM2.5 on 10-year high CVD risk were observed, with approximately 18% and 23% of 10-year high risk of CVD attributable to the interaction. Overall, indoor and outdoor air pollution had separate and joint effects on 10-year high CVD risk among postmenopausal women. Therefore, simultaneously improving indoor and outdoor air quality are of great importance and could have a joint impact on prevention of CVD and improved health among postmenopausal women.

Effects of heavy metal exposure on hypertension: A machine learning modeling approach

Heavy metal exposure is a common risk factor for hypertension. To develop an interpretable predictive machine learning (ML) model for hypertension based on levels of heavy metal exposure, data from the NHANES (2003-2016) were employed. Random forest (RF), support vector machine (SVM), decision tree (DT), multilayer perceptron (MLP), ridge regression (RR), AdaBoost (AB), gradient boosting decision tree (GBDT), voting classifier (VC), and K-nearest neighbour (KNN) algorithms were utilized to generate an optimal predictive model for hypertension. Three interpretable methods, the permutation feature importance analysis, partial dependence plot (PDP), and Shapley additive explanations (SHAP) methods, were integrated into a pipeline and embedded in ML for model interpretation. A total of 9005 eligible individuals were randomly allocated into two distinct sets for predictive model training and validation. The results showed that among the predictive models, the RF model demonstrated the highest performance, achieving an accuracy rate of 77.40% in the validation set. The AUC and F1 score for the model were 0.84 and 0.76, respectively. Blood Pb, urinary Cd, urinary Tl, and urinary Co levels were identified as the main influencers of hypertension, and their contribution weights were 0.0504 ± 0.0482, 0.0389 ± 0.0256, 0.0307 ± 0.0179, and 0.0296 ± 0.0162, respectively. Blood Pb (0.55-2.93 μg/dL) and urinary Cd (0.06-0.15 μg/L) levels exhibited the most pronounced upwards trend with the risk of hypertension within a specific value range, while urinary Tl (0.06-0.26 μg/L) and urinary Co (0.02-0.32 μg/L) levels demonstrated a declining trend with hypertension. The findings on the synergistic effects indicated that Pb and Cd were the primary determinants of hypertension. Our findings underscore the predictive value of heavy metals for hypertension. By utilizing interpretable methods, we discerned that Pb, Cd, Tl, and Co emerged as noteworthy contributors within the predictive model.