Associations of exposure to multiple trace elements with the risk of goiter: A case-control study

A review of common statistical methods for dealing with multiple pollutant mixtures and multiple exposures

Traditional environmental epidemiology has consistently focused on studying the impact of single exposures on specific health outcomes, considering concurrent exposures as variables to be controlled. However, with the continuous changes in environment, humans are increasingly facing more complex exposures to multi-pollutant mixtures. In this context, accurately assessing the impact of multi-pollutant mixtures on health has become a central concern in current environmental research. Simultaneously, the continuous development and optimization of statistical methods offer robust support for handling large datasets, strengthening the capability to conduct in-depth research on the effects of multiple exposures on health. In order to examine complicated exposure mixtures, we introduce commonly used statistical methods and their developments, such as weighted quantile sum, bayesian kernel machine regression, toxic equivalency analysis, and others. Delineating their applications, advantages, weaknesses, and interpretability of results. It also provides guidance for researchers involved in studying multi-pollutant mixtures, aiding them in selecting appropriate statistical methods and utilizing R software for more accurate and comprehensive assessments of the impact of multi-pollutant mixtures on human health.

Effect of exposures to multiple metals on blood pressure and hypertension in the elderly: a community-based study

A chronic disease, hypertension (HTN) is prevalent among the elderly. Exploring the factors that influence HTN and blood pressure (BP) changes is of great public health significance. However, mixed exposure to multiple serum metals has had less research on the effects on BP and HTN for the elderly. From April to August 2019, 2372 people participated in the community physical examination program for the elderly in Tongling City, Anhui Province. We measured BP and serum levels of 10 metals and collected basic demographic information. We analyzed the relationship between metal levels and changes in BP and HTN by the least absolute shrinkage and selection operator regression, Bayesian kernel machine regression model, and generalized linear model. In multiple models, lead (Pb) and cadmium (Cd) were still significantly associated with HTN occurrence after adjusting for potential confounders (Pb: ORquartile 4 VS quartile 1 = 1.20, 95% CI 1.01-1.43; Cd: ORquartile 4 VS quartile 1 = 1.37, 95% CI 1.16-1.62). In the male subgroup, results were similar to those of the general population. In the female group, Cd was positively correlated with HTN and systolic blood pressure, while Pb was not. According to this study, Pb and Cd were correlated with BP and HTN positively, and there was a certain joint effect. To some extent, our findings provide clues for the prevention of hypertension in the elderly.

Effect of trace element mixtures on the outcome of patients with esophageal squamous cell carcinoma: a prospective cohort study in Fujian, China

BACKGROUND

The evidence about the effects of trace elements on overall survival(OS) of patients with esophageal squamous cell carcinoma(ESCC) is limited. This study aims to evaluate mixed effects of plasma trace elements on OS of ESCC.

METHODS

This prospective cohort analysis included 497 ESCC patients with a median follow-up of 52.3 months. The concentrations of 17 trace elements were measured. We fitted Cox's proportional hazards regression, factor analysis and Bayesian kernel machine regression (BKMR) models to estimate the association between trace elements and OS.

RESULTS

Our analysis found that in the single-element model, Co, Ni, and Cd were associated with an increased risk of death, while Ga, Rb, and Ba were associated with a decreased risk. Cd had the strongest risk effect among all elements. As many elements were found to be mutually correlated, we conducted a factor analysis to identify common factors and investigate their associations with survival time. The factor analysis indicated that the factor with high factor loadings in Ga, Ba and B was linked to a decreased risk of death, while the factor with high factor loadings in Co, Ti, Cd and Pb was associated with a borderline significantly increased risk. Using BKMR analysis to disentangle the interaction between elements in significant factors, we discovered that Ga interacted with Ba and both elements had U-shaped effects with OS. Cd, on the other hand, had no interaction with other elements and independently increased the risk of death.

CONCLUSIONS

Our analysis revealed that Ga, Ba and Cd were associated with ESCC outcome, with Ga and Ba demonstrating an interaction. These findings provide new insights into the impact of trace elements on the survival of patients with ESCC.

Machine learning-aided metallomic profiling in serum and urine of thyroid cancer patients and its environmental implications

The incidence rate of thyroid cancer has been growing worldwide. Thyroid health is closely related with multiple trace metals, and the nutrients are essential in maintaining thyroid function while the contaminants can disturb thyroid morphology and homeostasis. In this study, we conducted metallomic analysis in thyroid cancer patients (n = 40) and control subjects (n = 40) recruited in Shenzhen, China with a high incidence of thyroid cancer. We found significant alterations in serumal and urinary metallomic profiling (including Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Sr, Cd, I, Ba, Tl, and Pb) and elemental correlative patterns between thyroid cancer patients and controls. Additionally, we also measured the serum Cu isotopic composition and found a multifaceted disturbance in Cu metabolism in thyroid disease patients. Based on the metallome variations, we built and assessed the thyroid cancer-predictive performance of seven machine learning algorithms. Among them, the Random Forest model performed the best with the accuracy of 1.000, 0.858, and 0.813 on the training, 5-fold cross-validation, and test set, respectively. The high performance of machine learning has demonstrated the great promise of metallomic analysis in the identification of thyroid cancer. Then, the Shapley Additive exPlanations approach was used to further interpret the variable contributions of the model and it showed that serum Pb contributed the most in the identification process. To the best of our knowledge, this is the first study that combines machine learning and metallome data for cancer identification, and it supports the indication of environmental heavy metal-related thyroid cancer etiology.

Study on the correlation and interaction between metals and dyslipidemia: a case-control study in Chinese community-dwelling elderly

Previous studies on the association between metals and dyslipidemia are not completely consistent. There are few studies investigating the relationship between mixed metal exposure and dyslipidemia as well as the effects of metals on dyslipidemia in community-dwelling elderly. To evaluate the correlations and interaction effect between the urinary concentrations of metals and the risk of dyslipidemia in community-dwelling elderly. We designed a case-control study to assess the correlation between urine metals and dyslipidemia in elderly people in the Yinchuan. The urinary levels of 13 metals, including calcium, vanadium, iron, cobalt, zinc, copper, arsenic, selenium, molybdenum, cadmium, tellurium, and thallium, were measured by inductively coupled plasma-mass spectrometry (ICP-MS), and the blood biochemical analyzer was used to measure the blood lipid levels of 3384 senior individuals from four different areas of Yinchuan city. Logistic regression and restricted cubic splines (RCS) were used to explore the correlation and dose-response relationship between urinary metals and the risk of dyslipidemia. Least absolute shrinkage and selection operator (LASSO) regression was used to select metals, and then weighted quantile sum (WQS) regression was used to explore the weight of each metal in mixed metals. Bayesian kernel machine regression (BKMR) was used to explore the interactions between metals on dyslipidemia risk. (1) After selection by LASSO regression, in the multi-metal model, compared with the lowest quartile, the adjusted ORs (95%CI) of the highest quartiles were 0.47 (0.37-0.60) for Fe, 1.43 (1.13-1.83) for Zn, 1.46 (1.11-1.92) for As, 0.59 (0.44-0.80) for Se, 1.53 (1.18-2.00) for Mo, and 1.36 (1.07-1.73) for Te. (2) In the WQS regression model, Fe and Mo accounted for the largest weight in the negative and positive effects of dyslipidemia, respectively. (3) In the BKMR model, there may be a positive interaction between Te and Se on dyslipidemia. Among the mixed metals, Fe, As, Se, Mo, and Te were associated with the prevalence of dyslipidemia, with Fe and Mo contributing the most. There may be certain interactions between Te and Se.

Association of exposure to multiple serum metals with the risk of chronic kidney disease in the elderly: a population-based case-control study

In the world, chronic kidney disease (CKD) has been recognized as one of the critical public health problems, and the prevalence is higher in the elderly people. However, there are few studies on the association between exposure to multiple serum metal levels and CKD. A case-control study, we established, for elderly people in Anhui Province, China, to explore the effects of different metals and analyze the effect of mixed exposure on CKD. In this study, 287 cases of CKD and 287 controls were selected in the elderly health physical examination project in Tongling City, Anhui Province. Questionnaire survey, physical examination, and blood collection were conducted. Graphite furnace atomic absorption spectrometry (GFAAS) and inductively coupled plasma optical emission spectrometry (ICP-OES) were used to measure the concentration of serum metals. After selecting by least absolute shrinkage and selection operator (LASSO), 5 metals were brought into the multi-metal model. After adjusting all potential covariates additionally, the concentrations of lead (Pb), cadmium (Cd), cobalt (Co), and manganese (Mn) were significantly associated with CKD risk, whereas Pb, Se, and Cd had significant non-linearity with CKD. Besides, patients with highest quartiles of cobalt (Co), lead (Pb), and manganese (Mn) were 1.64, 1.39, and 0.64 times more possible to have CKD, respectively, as compared with the lowest levels. In the Bayesian kernel machine regression (BKMR) model, cadmium (Cd) had a combined effect with lead (Pb) possibly. This study suggested that the CKD risk was associated with exposure of multiple metals in elderly people. The underlying mechanisms of serum metals and CKD need more experimental and prospective studies to elucidate.

Multiple environmental exposures and obesity in eastern China: An individual exposure evaluation model

Obesity has caused a huge burden of disease. Few studies have explored individuals' environmental exposure level and the impact of multiple environmental exposures on obesity. The aim of this study was to explore individual air pollution exposure evaluation, and the association between and multiple environmental factors and obesity among adult residents in rural areas of China. In this study, 8400 residents of 14 districts and counties in eastern of China were selected by multistage stratified cluster sampling, and a total of 8377 residents were included in the final analysis. We adopted BMI (Body Mass Index) > 28 kg/m² as the definition of obesity. First, an individual air pollution evaluation model was established based on the monitoring data of air pollution stations closest to residential address, different demographic characteristics of residents and daily living habits using generalized linear model and random forest model. Then, we used Bayesian Kernel Machine Regression (BKMR) and Quantile g-Computation (QgC) models to explore multiple environmental exposures on obesity. The results showed that six air pollutants were significantly positively associated with obesity, and green space had a significant protective effect on obesity. The BKMR model showed that the effects of different air pollutants on obesity were significantly enhanced by each other, while green space significantly reduced the positive effect of air pollution on obesity. The QgC model showed a significant positive association with obesity when all environmental factors were exposed as a whole, especially in males, higher household incomes and young people. It suggested that relevant authorities should improve regional air quality and green space to reduce the burden of disease caused by obesity.