Identification of the hormetic dose-response and regulatory network of multiple metals co-exposure-related hypertension via integration of metallomics and adverse outcome pathways

Association between nickel and multiple metabolic outcomes: The mediating roles of lipid metabolism and inflammation

The relationship between nickel exposure and metabolic disease remains unclear, the study aims to investigate the association between nickel exposure and various metabolic disease outcomes and elucidate potential underlying mechanisms. Using data from the 2017-2018 National Health and Nutrition Examination Survey (NHANES), we evaluated nickel exposure in relation to eight metabolic outcomes: coronary heart disease (CHD), heart failure (HF), heart arrest (HA), angina, stroke, diabetes, hypertension, and chronic kidney disease (CKD). The findings demonstrated that each natural logarithm increase in urinary nickel concentration was significantly associated with higher risk of HF (OR=1.663, 95 % CI: 1.000-2.765), diabetes (OR=1.713, 95 % CI: 1.341-2.187), and CKD (OR=1.347, 95 % CI: 1.078-1.683). Furthermore, individuals with higher nickel exposure demonstrated an elevated risk of CHD (OR=2.439, 95 % CI: 1.008-5.903, P-trend=0.046) and diabetes (OR=2.494, 95 % CI: 1.526-4.075, P-trend<0.001) in a dose-dependent manner. Restricted cubic spline analyses showed no evidence of nonlinear associations between nickel exposure and other outcomes (all P > 0.05). Mediation analyses identified lipid metabolism disruption as a significant mediator for nickel's effects on CHD (Low density lipoprotein cholesterol [LDL-C], 9.76 %, P = 0.040; Total cholesterol [TC], 18.75 %, P < 2 ×10-16) and HF (LDL-C, 16.60 %, P = 0.040). Inflammatory activation mediated the relationship between nickel and CKD (Lymphocyte/White blood cell [Lym/WBC], 10.56 %, P < 2 ×10-16; Monocyte/Lymphocyte [Mono/Lym], 10.19 %, P < 2 ×10-16). Notably, nickel-induced inflammatory responses also appeared to dysregulate lipid metabolism, potentially contributing to diabetes risk (LDL-C, 26.35 %, P < 2 ×10-16; TC, 15.27 %, P < 2 ×10-16; Neutrophils/Lymphocyte [Neu/Lym], 2.42 %, P = 0.040). These results suggest that nickel exposure is associated with increased risks of CHD, HF, CKD, and diabetes, potentially through mechanisms involving lipid metabolism disturbances and inflammatory activation.

Recent advancements in the application of multi-elemental profiling and ionomics in cardiovascular diseases

BACKGROUND

Trace elements and minerals are crucial for human growth and health, whose imbalance is associated with a variety of diseases. Recently, multi-elemental profiling and ionomics have been rapidly developed and widely used to study the distribution, variation, and interactions of various elements in diverse physiological and pathological conditions. By utilizing high-throughput elemental analytical techniques and bioinformatics approaches, researchers can uncover the relationship between the metabolism and balance of different elements and numerous human diseases.

METHODS

The presented work reviews recent advances in multi-elemental and ionomic profiling of human biological samples for several major types of cardiovascular diseases.

RESULTS

Research indicates distinct and dynamic patterns of ion contents in these diseases. Accumulation of copper and environmental toxic metals as well as deficiencies in zinc and selenium appear to be the most significant risk factors for the majority of cardiovascular diseases, suggesting that an imbalance in these elements may play a role in the development of these illnesses. Furthermore, each type of cardiovascular disease exhibits a relatively unique distribution of ions in biofluid and hair samples from patients, potentially serving as indicators for the specific disease.

CONCLUSION

Multi-elemental profiling and ionomics not only enhance our understanding of the association between elemental dyshomeostasis and the development of cardiovascular diseases but also facilitate the discovery of novel diagnostic and prognostic markers for these conditions.

Advancing the quantitative understanding of adverse outcome pathways: current status, methodologies, and future directions

An adverse outcome pathway (AOP) framework maps the sequence of events leading to adverse outcomes from chemical exposures, providing a mechanistic understanding often absent in traditional methods. The quantitative AOP (qAOP) advances AOP by integrating quantitative data and mathematical modeling, thereby providing a more precise comprehension of relationships between molecular initiating events, key events, and adverse outcomes. This review critically examines three primary methodologies: systems toxicology, regression modeling, and Bayesian network modeling, highlighting their strengths, limitations, and specific data requirements within toxicology. Through an analysis of current methodologies and challenges, this review emphasizes the integration of experimental and computational approaches to elucidate key event relationships and proposes strategies for overcoming limitations through standardized protocols and advanced computational tools. By outlining future research directions and the potential of qAOPs to transform chemical risk assessment, this review aims to contribute to the advancement of regulatory science and the protection of public health and the environment.

Combined Effects of Arsenic, Cadmium, and Mercury with Cardiovascular Disease Risk: Insights from the All of Us Research Program

BACKGROUND

Environmental exposures to heavy metals/metalloids such as arsenic, cadmium, and mercury have been implicated in adverse cardiovascular health outcomes. Using data from the All of Us research program, we investigated the associations between these metals/metalloids and six cardiovascular-related biomarkers: systolic blood pressure (SBP), HDL cholesterol, LDL cholesterol, C-reactive protein (CRP), total cholesterol, and triglycerides.

METHODS

This study explored the relationship between outcome cardiovascular variables (SBP, CRP, LDL, HDL, triglycerides, and total cholesterol) and predictor metal/metalloid variables (cadmium, mercury, and arsenic) among 136 participants (53.4 percent women). We initially conducted linear regression to determine the association between variables of interest. Bayesian Kernel Machine Regression (BKMR) analysis was subsequently performed to capture potential non-linear relationships, as well as interactions among metal/metalloid exposures. In the BKMR analysis, posterior inclusion probabilities (PIPs) quantified the contribution of each metal/metalloid to the outcomes, with higher PIP values indicating a greater likelihood of a specific exposure being a key predictor for a given cardiovascular biomarker. Within the BKMR framework, univariate, bivariate, and overall exposure-response analyses provided insights into the individual and combined effects of metal/metalloid exposures. These analyses identified the factors with the strongest associations and highlighted interactions between exposures.

RESULTS

In this study, the average age of male participants was 58.2 years, while female participants had an average age of 55.6 years. The study population included 104 individuals identifying as White (mean age: 57.5 years), 10 as Black or African American (mean age: 63.2 years), 7 as Hispanic (mean age: 48.2), 3 as Asian (mean age: 49.7 years), and 12 as Other race (mean age: 48.8 years). In our study, men exhibited higher levels of SBP, triglycerides, mercury, and arsenic, while women had higher levels of CRP, LDL cholesterol, HDL cholesterol, total cholesterol, and cadmium. Black people exhibited higher levels and greater variability in markers of cardiovascular risk and inflammation (e.g., blood pressure and CRP), Asians consistently showed the lowest levels across most biomarkers, while White people, Hispanics, and the "Other" group demonstrated moderate levels with some variability. In linear regression, we identified significant positive associations between mercury and HDL cholesterol, arsenic and triglycerides, and arsenic and total cholesterol. In BKMR analysis, PIP results revealed that mercury had the highest predictive contribution for SBP, HDL cholesterol, and triglycerides; cadmium for CRP; and arsenic for LDL and total cholesterol. Univariate and bivariate exposure-response analyses in BKMR demonstrated non-linear exposure-response patterns, including U-shaped and inverted U-shaped patterns for cadmium, particularly CRP and total cholesterol. Traditional linear regression techniques would have missed these patterns.

CONCLUSION

Our study results highlight the influence of environmental metal/metalloid exposures on cardiovascular biomarkers, providing evidence of non-linear and interactive effects that warrant further investigation to understand their role in cardiovascular disease risk better.

Associations of Urinary Nickel with NAFLD and Liver Fibrosis in the USA: A Nationwide Cross‑Sectional Study

Despite the robust correlation between metabolic disorders and heavy metals, there has been limited research on the associations between nickel levels and non-alcoholic fatty liver disease (NAFLD) as well as liver fibrosis. This study aimed to examine the associations among urinary nickel, NAFLD, and liver fibrosis. The data utilized in this study were obtained from the National Health and Nutrition Examination Survey 2017-2020. A comprehensive screening process was conducted, resulting in the inclusion of a total of 3169 American adults in the analysis. The measurement of urinary nickel was conducted through inductively coupled-plasma mass spectrometry. Vibration-controlled transient elastography was employed to assess the controlled attenuation parameter and liver stiffness measurement as indicators for NAFLD and liver fibrosis, respectively. Multivariable logistic regression models were employed to evaluate the associations among urinary nickel, NAFLD, and liver fibrosis. Restricted cubic splines were employed to explored the nonlinear associations. After adjusting for all covariates, the correlation between the highest quartile of urinary nickel and NAFLD was found to be significant (OR = 1.65; 95% CI, 1.19-2.27). Subgroup analysis revealed that the correlation was significant only in men. A significant association occurred between the second quartile of urinary nickel and liver fibrosis (OR 1.88; 95% CI, 1.22-2.90). Restricted cubic spline showed that the relationship was linear between urinary nickel and NAFLD and non-monotonic, inverse U-shaped between urinary nickel and liver fibrosis. This cross-sectional study indicated that the risk of NAFLD is associated with urinary nickel, and this correlation was only present among males.

Investigating the relationship of co-exposure to multiple metals with chronic kidney disease: An integrated perspective from epidemiology and adverse outcome pathways

Systematic studies on the associations between co-exposure to multiple metals and chronic kidney disease (CKD), as well as the underlying mechanisms, remain insufficient. This study aimed to provide a comprehensive perspective on the risk of CKD induced by multiple metal co-exposures through the integration of occupational epidemiology and adverse outcome pathway (AOP). The study participants included 401 male mine workers whose blood metal, β2-microglobulin (β2-MG), and cystatin C (Cys-C) levels were measured. Generalized linear models (GLMs), quantile g-computation models (qgcomp), least absolute shrinkage and selection operator (LASSO), and bayesian kernel machine regression (BKMR) were utilized to identify critical nephrotoxic metals. The mean concentrations of lead, cadmium, mercury, arsenic, and manganese were 191.93, 3.92, 4.66, 3.11, 11.35, and 16.33 µg/L, respectively. GLM, LASSO, qgcomp, and BKMR models consistently identified lead, cadmium, mercury, and arsenic as the primary contributors to kidney toxicity. Based on our epidemiological analysis, we used a computational toxicology method to construct a chemical-genetic-phenotype-disease network (CGPDN) from the Comparative Toxicogenomics Database (CTD), DisGeNET, and GeneCard databases, and further linked key events (KEs) related to kidney toxicity from the AOP-Wiki and PubMed databases. Finally, an AOP framework of multiple metals was constructed by integrating the common molecular initiating events (reactive oxygen species) and KEs (MAPK signaling pathway, oxidative stress, mitochondrial dysfunction, DNA damage, inflammation, hypertension, cell death, and kidney toxicity). This is the first AOP network to elucidate the internal association between multiple metal co-exposures and CKD, providing a crucial basis for the risk assessment of multiple metal co-exposures.

Hepatic toxicity prediction of bisphenol analogs by machine learning strategy

Toxicological studies have demonstrated the hepatic toxicity of several bisphenol analogs (BPs), a prevalent type of endocrine disruptor. The development of Adverse Outcome Pathway (AOP) has substantially contributed to the rapid risk assessment for human health. However, the lack of in vitro and in vivo data for the emerging BPs has limited the hazard assessment of these synthetic chemicals. Here, we aimed to develop a new strategy to rapidly predict BPs' hepatotoxicity using network analysis coupled with machine learning models. Considering the structural and functional similarities shared by BPs with Bisphenol A (BPA), we first integrated hepatic disease related genes from multiple databases into BPA-Gene-Phenotype-hepatic toxicity network and subjected it to the computational AOP (cAOP). Through cAOP network and conventional machine learning approaches, we scored the hepatotoxicity of 20 emerging BPs and provided new insights into how BPs' structure features contributed to biologic functions with limited experimental data. Additionally, we assessed the interactions between emerging BPs and ESR1 using molecular docking and proposed an AOP framework wherein ESR1 was a molecular initiating event. Overall, our study provides a computational approach to predict the hepatotoxicity of emerging BPs.

Association of Metallic and Nonmetallic Elements with Fibrin Clot Properties and Ischemic Stroke

Objectives-Metallic elements and fibrin clot properties have been linked to stroke. We examined metallic and nonmetallic elements, fibrin clot lysis time (CLT), and maximum absorbance (Absmax) in relation to ischemic stroke. Design-A case-control study of ischemic stroke patients vs. healthy individuals. Subjects and Methods-Plasma and serum were collected from 260 ischemic stroke patients (45.0% women; age, 68 ± 12 years) and 291 healthy controls (59.7% women; age, 50 ± 17 years). Fibrin CLT and Absmax were measured using a validated turbidimetric assay. Serum elements were quantified by inductively coupled plasma mass spectrometry (ICP-MS) and optical emission spectrometry (ICP-OES). Data were analyzed by bivariate correlations and multiple or logistic regression. Results-In female stroke patients, copper, lithium, and aluminum were significantly lower compared with controls; in male stroke patients, potassium was lower, and beryllium was elevated. In female and male stroke patients, iron, zinc, nickel, calcium, magnesium, sodium, and silicon were significantly lower, while strontium was elevated. Positive correlations between fibrin clot properties and metals, observed in healthy controls, were lost in ischemic stroke patients. In multivariate regression analysis, fibrin CLT and/or Absmax was associated with zinc, calcium, potassium, beryllium, and silicon in stroke patients and with sodium, potassium, beryllium, and aluminum in controls. In logistic regression analysis, stroke was independently associated with lithium, nickel, beryllium, strontium, boron, and silicon and with sodium, potassium, calcium, and aluminum but not with fibrin CLT/Absmax. Conclusions-Various elements were associated with fibrin clot properties and the risk of ischemic stroke. Lithium, sodium, calcium, and aluminum abrogated the association of fibrin clot properties with ischemic stroke.

The relationship of organophosphate flame retardants with hyperuricemia and gout via the inflammatory response: An integrated approach

BACKGROUND

Evidence regarding the relationships between organophosphate flame retardants (OPFRs) and hyperuricemia and gout as well as the underlying mechanisms remains scarce, but some evidence indicates that inflammation might play a key role.

OBJECTIVES

Using an integrated approach, we aim to elucidate the associations of urinary metabolite OPFRs (m-OPFRs) with hyperuricemia and gout.

METHODS

Cross-sectional analyses using data from the National Health and Nutrition Examination Survey were performed to reveal the associations. Adults with complete data on five m-OPFRs with high detection frequencies and outcomes were enrolled. We used multivariate logistic regression, restricted cubic spline (RCS), and Bayesian kernel machine regression (BKMR) methods to account for single, nonlinear, and joint effects. The mediating effect of the inflammatory response was also estimated. Moreover, adverse outcome pathways (AOPs) based on network analysis were further constructed to reveal the underlying mechanism.

RESULTS

Multivariate logistic models revealed that bis(2-chloroethyl) phosphate (BCEP) significantly increased risk of hyperuricemia (OR [95 % CI]: 1.165 [1.047, 1.296]) in the fully adjusted model. Elevated levels of bis(1-chloro-2-propyl) phosphate were associated with gout (OR [95 % CI]: 1.293 [1.015, 1.647]). No nonlinear relationship was observed in RCS. There was a positive association between mixed m-OPFRs and hyperuricemia risk in BMKR, with bis(1,3-dichloro-2-propyl) phosphate and BCEP being the main contributors (PIP > 0.5). We found that the inflammatory response significantly mediated the association between BCEP and hyperuricemia (P < 0.05). Network topology analysis identified seven genes and six phenotypes related to OPFR exposure and hyperuricemia. The AOP framework suggested that the inflammatory response, especially the activation of the TNF pathway, played a core role in the above relationships.

CONCLUSION

Our results first revealed that individual and mixed OPFRs were associated with hyperuricemia, in which the inflammatory response plays an important role. Further longitudinal studies are warranted to consolidate or refute our main findings.

Contaminant Metals and Cardiovascular Health

A growing body of research has begun to link exposure to environmental contaminants, such as heavy metals, with a variety of negative health outcomes. In this paper, we sought to review the current research describing the impact of certain common contaminant metals on cardiovascular (CV) health. We reviewed ten metals: lead, barium, nickel, chromium, cadmium, arsenic, mercury, selenium, zinc, and copper. After a literature review, we briefly summarized the routes of environmental exposure, pathophysiological mechanisms, CV health impacts, and exposure prevention and/or mitigation strategies for each metal. The resulting article discloses a broad spectrum of pathological significance, from relatively benign substances with little to no described effects on CV health, such as chromium and selenium, to substances with a wide-ranging and relatively severe spectrum of CV pathologies, such as arsenic, cadmium, and lead. It is our hope that this article will provide clinicians with a practical overview of the impact of these common environmental contaminants on CV health as well as highlight areas that require further investigation to better understand how these metals impact the incidence and progression of CV diseases.

Adults

Background

Laboratory studies have linked nickel with the pathogenesis of cardiovascular disease (CVD); however, few observational studies in humans have confirmed this association.

Objective

This study aimed to use urinary nickel concentrations, as a biomarker of environmental nickel exposure, to evaluate the cross-sectional association between nickel exposure and CVD in a nationally representative sample of U.S. adults.

Methods

Data from a nationally representative sample (n = 2702) in the National Health and Nutrition Examination Survey 2017-20 were used. CVD (n = 326) was defined as self-reported physicians' diagnoses of coronary heart disease, angina, heart attack, or stroke. Urinary nickel concentrations were determined by inductively coupled plasma mass spectrometry. Logistic regression with sample weights was used to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) of CVD.

Results

Urinary nickel concentrations were higher in individuals with CVD (weighted median 1.34 μg/L) compared to those without CVD (1.08 μg/L). After adjustment for demographic, socioeconomic, lifestyle, and other risk factors for CVD, the ORs (95% CIs) for CVD compared with the lowest quartile of urinary nickel were 3.57 (1.73-7.36) for the second quartile, 3.61 (1.83-7.13) for the third quartile, and 2.40 (1.03-5.59) for the fourth quartile. Cubic spline regression revealed a non-monotonic, inverse U-shaped, association between urinary nickel and CVD (Pnonlinearity < 0.001).

Conclusions

Nickel exposure is associated with CVD in a non-monotonic manner among U.S. adults independent of well-known CVD risk factors.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12403-023-00579-4.

Blood pressure mediated the effects of cognitive function impairment related to aluminum exposure in Chinese aluminum smelting workers

OBJECTIVES

The aim of this study is to investigate the effects that the Al on blood pressure and the effect of hypertension in aluminum-induced cognitive impairment in electrolytic aluminum worker.

METHODS

The study was conducted 392 male aluminum electrolytic workers in an aluminum plant of China. The concentration of alumina dust in the air of the electrolytic aluminum workshop is 1.07mg/m3-2.13mg/m3. According to the Permissible concentration-Time Weighted Average of alumina dust is 4mg/ m3, which does not exceed the standard. The blood pressure of the workers was measured. The plasma aluminum concentration of workers was determined by ICP-MS (Inductively Coupled Plasma Mass Spectrometry). Cognitive functions were measured using MMSE (Mini-Mental State Examination), VFT (Verbal Fluency Test), ATIME (Average Reaction Time), FOM (Fuld Object Memory Evaluation), DST (Digit Span Test), CDT (Clock Drawing Test) scales. Modified Poisson regression was used to analyze the risk of hypertension and cognitive impairment with different plasma aluminum concentrations. Generalized linear regression model was used to analyze the relationship between aluminum and cognitive function, blood pressure and cognitive function. Causal Mediation Analysis was used to analyze the mediation effect of blood press in aluminum-induced cognitive impairment.

RESULTS

Plasma aluminum appeared to be a risk factor for hypertension (PR (prevalence ratio) = 1.630, 95%-CI (confidence interval): 1.103 to 2.407), systolic blood pressure (PR = 1.578, 95%-CI: 1.038 to 2.399) and diastolic blood pressure (PR = 1.842, 95%-CI: 1.153 to 2.944). And plasma aluminum increased by e-fold, the scores of MMSE and VFT decreased by 0.630 and 2.231 units respectively and the time of ATIME increased by 0.029 units. In addition, generalized linear regression model showed that blood press was negatively correlated with the scores of MMSE and VFT. Finally, causal Mediation Analysis showed that hypertension was a part of the mediating factors of aluminum-induced decline in MMSE score, and the mediating effects was 16.300% (7.100%, 33.200%). In addition, hypertension was a part of the mediating factors of aluminum-induced decline in VFT score, and the mediating effects was 9.400% (2.600%, 29.000%) CONCLUSION: Occupational aluminum exposure increases the risk of hypertension and cognitive impairment. And hypertension may be a mediating factor of cognitive impairment caused by aluminum exposure.