Why Do People Gain Belly Fat in Rural Areas? A Study of Urinary Metal(loid)s and Abdominal Obesity in China

Metals link to diabetes: Insights from a national cross-sectional investigation

Diabetes and impaired fasting glucose (IFG) are significant global health concerns. However, the potential effects of mixed heavy metal exposure on these conditions remain underexplored. This study aims to investigate the combined effects of multiple metals on diabetes risk and explore the mediating role of Body Mass Index (BMI) in rural China. A cross-sectional analysis involved 2313 adults from 12 provinces in rural China. Urinary levels of zinc (Zn), chromium (Cr), nickel (Ni), cadmium (Cd), and lead (Pb) were quantified using inductively coupled plasma mass spectrometry. Fasting blood glucose (FBG) levels were measured with an automatic biochemical analyzer. Logistic regression models and the Bayesian Kernel Machine Regression (BKMR) model were used to examine associations and interactions. Mediation analysis was performed to assess the role of BMI. The results of our study indicate that there is a significant association between urinary Zn (OR = 2.38, 95 % CI: 1.57, 3.60), Cr (OR = 1.24, 95 % CI: 1.31, 1.61), and Ni (OR = 1.51, 95 % CI: 1.05, 2.18) and the diabetes risk. The study revealed that exposure to Ni amplified the associations between Zn, Cr and diabetes/IFG risk. Additionally, BMI was identified as a significant mediator in the relationship between metal exposure, particularly Cr/Cd, and diabetes risk. These findings reveal a complex link between multiple metals, such as Zn, Cr, and Ni, and diabetes risk, and emphasize the potential opposite mediating effects of BMI in different metal-induced diabetes mechanisms. Further investigation of these mechanisms is warranted.

Associations between urinary and blood heavy metal exposure and heart failure in elderly adults: Insights from an interpretable machine learning model based on NHANES (2003-2020)

Background

The relationship between heavy metal exposure and heart failure is complex and poorly understood. This study employs machine learning techniques to model these associations in a population aged 50 years and older from the National Health and Nutrition Examination Survey (NHANES). Our findings emphasize the need for continued investigation into the mechanisms of these associations and highlight the importance of monitoring and regulatory measures to mitigate heavy metal exposure in populations at risk.

Methods

Five machine learning models were evaluated, with Gradient Boosting Decision Trees (GBDT) selected as the optimal model based on accuracy, interpretability, and ability to capture nonlinear relationships. Model performance was assessed through various metrics, and interpretability was enhanced using SHAP (SHapley Additive exPlanations), permuted Feature Importance, Individual Conditional Expectation (ICE), and Partial Dependence Plots (PDP).

Results

The GBDT model achieved an accuracy of 0.78, with a sensitivity of 0.93 and an AUC of 0.92. Our analysis revealed that higher levels of urinary iodine, blood cadmium, urinary cobalt, urinary tungsten, and urinary arsenic acid were significantly associated with heart failure. Synergistic effects involving age and body mass index (BMI) were also observed, further strengthening these associations.

Association of multiple urinary metals/metalloids with obesity defined by body fat percentage: A cross-sectional study among Guangxi Zhuang ethnic in China

BACKGROUND

Previous studies confirmed a link between urinary metals/metalloids and obesity; however, the majority of these studies defined obesity using body mass index (BMI) or waist circumference (WC), and their results were not constantly consistent. Studies defining obesity based on body fat percentage (BFP) is less reported.

METHODS

A total of 5405 participants aged 35-74 from Guangxi Zhuang ethnic group in China were included in the analysis. Inductively coupled plasma mass spectrometry (ICP-MS) was used to detect the concentrations of 22 metals/metalloids in urine. Using a binary logistic regression model, the impact of individual metal/metalloid on the risk of BFP/obesity was analyzed, and the LASSO regression model was employed to choose metals/metalloids independently related with BFP/obesity to construct a multiple-metal models. The quantile g-computation model was used to evaluate the combined impacts of metals/metalloids on BFP/obesity.

RESULTS

In multiple-metal models, compared with the lowest quartile, the highest quartile of urinary concentrations of Mg, Cd, and Ti was significantly associated with a reduced risk of BFP/obesity (Mg: OR=0.66, 95 %CI: 0.51, 0.85; Cd: OR=0.63, 95 %CI: 0.49, 0.82; Ti: OR=0.73, 95 %CI: 0.57, 0.93). Conversely, the highest quartiles of urinary concentrations of Zn, V, and Sb was significantly associated with an increased risk of BFP/obesity (Zn: OR=1.75, 95 %CI: 1.39, 2.22; V: OR=1.63, 95 %CI: 1.25, 2.14; Sb: OR=1.38, 95 %CI: 1.06, 1.79). In quantile g-computation analysis, Mg, Cd, and Sn were the main contributors to negative effects, while Zn, V, and Sb were the main contributors to positive effect, although no significant relationship was observed between the multiple metal/metalloid mixtures and BFP/obesity.

CONCLUSIONS

According to our study, urinary Mg, Cd, and Ti levels were negatively associated with BFP/obesity risk, and Zn, V, and Sb levels were positively associated with BFP/obesity risk. However, these associations need to be further verified by longitudinal studies, and the molecular mechanisms need to be further explored by animal and cell experiments.

Associations between individual and mixed urinary metal exposure and dyslipidemia among Chinese adults: Data from the China Multi-Ethnic Cohort Study

The prevalence of dyslipidemia is increasing, and it has become a significant global public health concern. Some studies have demonstrated contradictory relationships between urinary metals and dyslipidemia, and the combined effects of mixed urinary metal exposure on dyslipidemia remain ambiguous. In this study, we examined how individual and combined urinary metal exposure are associated with the occurrence of dyslipidemia. According to the data from the 2018-2019 baseline survey database of the China Multi-Ethnic Cohort (CMEC) Study, a population of 9348 individuals was studied. Inductively coupled plasmamass spectrometry (ICP-MS) was used to measure 21 urinary metal concentrations in the collected adult urinary samples. The associations between urinary metals and dyslipidemia were analyzed by logistic regression, weighted quantile sum regression (WQS), and quantile-based g-computation (qgcomp), controlled for potential confounders to examine single and combined effects. Dyslipidemia was detected in 3231 individuals, which represented approximately 34.6 % of the total population. According to the single-exposure model, Al and Na were inversely associated with the risk of dyslipidemia (OR = 0.95, 95 % CI: 0.93, 0.98; OR = 0.89, 95 % CI: 0.83, 0.95, respectively), whereas Zn, Ca, and P were positively associated (OR = 1.69, 95 % CI: 1.42, 2.01; OR = 1.12, 95 % CI: 1.06, 1.18; OR = 1.21, 95 % CI: 1.09, 1.34, respectively). Moreover, Zn and P were significantly positively associated even after adjusting for these metals, whereas Al and Cr were negatively associated with the risk of dyslipidemia. The results of the WQS and qgcomp analyses showed that urinary metal mixtures were positively associated with the risk of dyslipidemia (OR = 1.26, 95 % CI: 1.15, 1.38; OR = 1.09, 95 % CI: 1.01, 1.19). This positive association was primarily driven by Zn, P, and Ca. In the sensitivity analyses with collinearity diagnosis, interaction, and stratified analysis, the results remained, confirming the reliability of the study findings. In this study, the individual and combined effects of urinary Zn, P, and Ca on dyslipidemia were determined, which provided novel insights into the link between exposure to metals and dyslipidemia.

Perturbation of lipid metabolism in 3T3-L1 at different stages of preadipocyte differentiation and new insights into the association between changed metabolites and adipogenesis promoted by TBBPA or TBBPS

Tetrabromobisphenol A (TBBPA) and tetrabromobisphenol S (TBBPS) are widely distributed brominated flame retardants. While TBBPA has been demonstrated to stimulate adipogenesis, TBBPS is also under suspicion for potentially inducing comparable effects. In this study, we conducted a non-targeted metabolomics to examine the metabolic changes in 3T3-L1 cells exposed to an environmentally relevant dose of TBBPA or TBBPS. Our findings revealed that 0.1 µM of both TBBPA and TBBPS promoted the adipogenesis of 3T3-L1 preadipocytes. Multivariate analysis showed significant increases in glycerophospholipids, sphingolipids, and steroids relative levels in 3T3-L1 cells exposed to TBBPA or TBBPS at the final stage of preadipocyte differentiation. Metabolites set composed of glycerophospholipids was found to be highly effective predictors of adipogenesis in 3T3-L1 cells exposed to TBBPA or TBBPS (revealed from the receiver operating characteristic curve with an area under curve > 0.90). The results from metabolite set enrichment analysis suggested both TBBPA and TBBPS exposures significantly perturbed steroid biosynthesis in adipocytes. Moreover, TBBPS additionally disrupted the sphingolipid metabolism in the adipocytes. Our study presents new insights into the obesogenic effects of TBBPS and provides valuable information about the metabolites associated with adipogenesis induced by TBBPA or TBBPS.

Associations of urinary zinc exposure with blood lipid profiles and dyslipidemia: Mediating effect of serum uric acid

The relationship between zinc (Zn) exposure and abnormal blood lipids including dyslipidemia is contentious. Serum uric acid (SUA) has been reported to be correlated to both Zn exposure and dyslipidemia. The underlying mechanisms of Zn exposure associated with blood lipids and the mediating effects of SUA remain unclear. Therefore, this study analyzed the data from Chinese 2110 adults (mean age: 59.0 years old) in rural areas across China to explore the associations of Zn exposure with blood lipid profiles and dyslipidemia, and to further estimate the mediating effects of SUA in these relationships. The study data showed that urinary Zn was associated with increased levels of blood lipid components triglyceride (TG) and low-density lipoprotein cholesterol (LDL-C). Moreover, an increased risk of dyslipidemia was observed in the study participants who had higher urinary Zn levels. Compared with the first quartile, the fourth quartile of urinary Zn concentration corresponded to the increase of TG (β = 0.20, 95 % CI: 0.12, 0.28), LDL-C (β = 0.06, 95 % CI: 0.01, 0.10) and dyslipidemia risk (OR = 2.16, 95 % CI: 1.50, 3.10), respectively. Elevated urinary Zn was also associated with higher levels of SUA and hyperuricemia risk. The SUA levels were positively related to total cholesterol (TC), TG, LDL-C levels and dyslipidemia risk. Mediation analyses revealed that SUA mediated 31.75 %, 46.16 % and 19.25 % of the associations of urinary Zn with TG, LDL-C levels and dyslipidemia risk, respectively. The subgroup and sensitivity analyses confirmed the positive associations between urinary Zn and blood lipid profiles and the mediating effect of SUA. The national population-based study further enhanced our understanding of the associations between Zn exposure and blood lipid profiles and mediating effect of SUA among generally healthy, middle-aged, and elderly individuals.

Associations between metal(loid) exposure with overweight and obesity and abdominal obesity in the general population: A cross-sectional study in China

Previous studies have revealed links between metal(loid)s and health problems; however, the link between metal(loid)s and obesity remains controversial. We evaluated the cross-sectional association between metal(loid) exposure in whole blood and obesity among the general population. Vanadium (V), chromium (Cr), manganese (Mn), cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), selenium (Se), molybdenum (Mo), cadmium (Cd), antimony (Sb), thallium (T1), and lead (Pb) were measured in 3029 subjects in Guangdong Province (China) using ICP-MS. The prevalence of overweight and obesity (OWO) and abdominal obesity (AOB) was calculated according to body mass index (BMI) and waist circumference (WC), respectively. Multivariate analysis showed that elevated blood Cu, Cd, and Pb levels were inversely associated with the risk of OWO, and these associations were confirmed by a linear dose-response relationship. Elevated blood Co concentration was associated with a decreased risk of AOB. A quantile g-computation approach showed a significantly negative mixture-effect of 13 metal(loid)s on OWO (OR: 0.96; 95% CI: 0.92, 0.99). Two metals-Ni and Mo-were inversely associated with the risk of OWO but positively associated with AOB. We cross-grouped the two obesity measurement types and found that the extremes of metal content were present in people with AOB only. In conclusion, blood Cu, Mo, Ni, Cd, and Pb were inversely associated with the risk of OWO. The presence of blood Co may be protective, while Ni and Mo exposure might increase the risk of AOB. The association between metal(loid) exposure and obesity warrants further investigation in longitudinal cohort studies.

Associations between plasma metal/metalloid mixtures and the risk of central obesity: A prospective cohort study of Chinese adults

Central obesity has increased rapidly over the past decade and posed a substantial disease burden worldwide. Exposure to metals/metalloids has been acknowledged to be involved in the development of central obesity through regulation of cortisol, insulin resistance, and glucocorticoid receptor reduction. Despite the importance, it is lack of prospective study which comprehensively evaluate the relations between multiple metals exposure and central obesity. We explored the prospective associations of plasma metal concentrations with central obesity in a prospective study of the Dongfeng-Tongji cohort. The present study included 2127 participants with a 6.87-year mean follow-up duration. We measured 23 plasma metal/metalloid concentrations at baseline. The associations between metals and incident central obesity were examined utilizing the Cox proportional hazard regression in single and multiple metals models. Additionally, we applied elastic net (ENET), Bayesian kernel machine regression (BKMR), plasma metal score (PMS), and quantile-based g-computation (Qgcomp) models to explore the joint associations of metal mixtures with central obesity. After adjusting potential confounders, we found significant associations of plasma manganese (Mn) and thallium (Tl) concentrations with a higher risk of central obesity, whereas plasma rubidium (Rb) concentration was associated with a lower risk of central obesity both in single and multiple metals models (all FDR <0.05). The ENET and Qqcomp models verified similar metals (Mn, Rb, and Tl) as important predictors for central obesity. The results of both BKMR model and PMS suggested cumulative exposure to metal mixtures was associated with a higher risk of central obesity. Our findings suggested that co-exposure to metals was associated with a higher risk of central obesity. This study expands our knowledge that the management of metals/metalloids exposure may be beneficial for the prevention of new-onset central obesity, which may subsequently alleviate the disease burden of late-life health outcomes.