The acute systemic toxicity of thallium in rats produces oxidative stress: attenuation by metallothionein and Prussian blue

Predicting the risk of cardiovascular disease in adults exposed to heavy metals: Interpretable machine learning

Machine learning exhibits excellent performance in terms of predictive power. We aimed to construct an interpretable machine learning model utilizing National Health and Nutrition Examination Survey data to investigate the relationship between heavy metal exposure and cardiovascular disease (CVD). A total of 4600 adults were included in the analysis. The Least Absolute Shrinkage and Selection Operator regression method was employed to select relevant feature variables. Subsequently, six machine learning models were constructed, including random forest, decision tree, gradient boosting decision tree, k-nearest neighbor, support vector machine, and AdaBoost algorithms. Feature importance analysis, partial dependence plot, and shapley additive explanations were integrated to enhance the interpretability of the CVD prediction model. Among all models, the random forest exhibited the best performance, with an accuracy of 90 %, an area under the curve of 0.85, and an F1 score of 0.86. Urine cadmium (Cd), blood lead (Pb), urine thallium (Tl), and urine tungsten (W) were identified as the most significant predictors of CVD, with importance scores of 0.062, 0.057, 0.051, and 0.050, respectively. At the overall level, higher levels of urine Cd, blood Pb, and urine W were associated with an increased risk of CVD, whereas a lower level of urine Tl was linked to a reduced CVD risk. Additionally, the analysis of synergistic effects revealed that Cd was the predominant determinant of CVD risk. The random forest-based CVD prediction model demonstrated excellent predictive power and provided valuable insights for personalized patient care and optimal resource allocation in populations exposed to heavy metals.

Dissociation of mitochondrial and ribosomal biogenesis during thallium administration in rat kidney

Thallium (Tl) is a heavy metal with toxicity comparative to other heavy metals such as As, Cd, and Hg. Nevertheless, fewer studies have been reported concerning the molecular mechanism of Tl toxicity as compared to other heavy metals. To obtain insight into Tl toxicity in the kidney, rats were intraperitoneally administered Tl2SO4 (30 mg/kg), and the kidneys were removed 2 or 5 days later to examine the effects of Tl. Transcriptome analysis using DNA microarray of the rat kidney 2 and 5 days after Tl administration showed that cytoplasmic ribosomal proteins are the most upregulated category; many of the genes involved in ribosome biosynthesis were upregulated by Tl administration. This upregulation was associated with the activation of eukaryotic transcription initiation factor 2α (eIF2α), implying that increased ribosome biogenesis was linked to the subsequent activation of protein translation. In contrast, decreased mitochondrial biogenesis was revealed via proteomic analysis. Although we found an increase in Myc, a positive regulator of both ribosomal and mitochondrial biogenesis, decreased levels of NRF1 and TFAM, positive regulators of mitochondrial biogenesis whose gene expression is directory activated by Myc, were paradoxically observed. Taken together, differing responses of ribosomes and mitochondria to Tl toxicity were observed. Failure of transmission of the Myc signal to NRF1/TFAM might be involved in the observed disruption of coordinated responses in mitochondria and ribosomes during Tl administration in rat kidney.

Thallium reabsorption via NKCC2 causes severe acute kidney injury with outer medulla-specific calcium crystal casts in rats

Thallium (Tl) is one of the most toxic heavy metals, associated with accidental poisoning and homicide. It causes acute and chronic systemic diseases, including gastrointestinal and cardiovascular diseases and kidney failure. However, few studies have investigated the mechanism by which Tl induces acute kidney injury (AKI). This study investigated the toxic effects of Tl on the histology and function of rat kidneys using biochemical and histopathological assays after intraperitoneal thallium sulfate administration (30 mg/kg). Five days post-administration, rats exhibited severely compromised kidney function. Low-vacuum scanning electron microscopy revealed excessive calcium (Ca) deposition in the outer medulla of Tl-loaded rats, particularly in the medullary thick ascending limb (mTAL) of the loop of Henle. Tl accumulated in the mTAL, accompanied by mitochondrial dysfunction in this segment. Tl-loaded rats showed reduced expression of kidney transporters and channels responsible for Ca2+ reabsorption in the mTAL. Pre-administration of the Na-K-Cl cotransporter 2 (NKCC2) inhibitor furosemide alleviated Tl accumulation and mitochondrial abnormalities in the mTAL. These findings suggest that Tl nephrotoxicity is associated with preferential Tl reabsorption in the mTAL via NKCC2, leading to mTAL mitochondrial dysfunction and disrupted Ca2+ reabsorption, culminating in mTAL-predominant Ca crystal deposition and AKI. These findings on the mechanism of Tl nephrotoxicity may contribute to the development of novel therapeutic approaches to counter Tl poisoning. Moreover, the observation of characteristic Ca crystal deposition in the outer medulla provides new insights into diagnostic challenges in Tl intoxication.

Thallium Toxicity and its Interference with Potassium Pathways Tested on Various Cell Lines

Thallium (Tl) is a highly toxic heavy metal whose mechanism of toxicity is still not completely understood. The aim of this study was to test Tl cytotoxicity on several cell lines of different tissue origin in order to clarify specific Tl toxicity to a particular organ. In addition, possible interference of Tl with cell potassium (K) transport was examined. Human keratinocytes (HaCaT), human hepatocellular carcinoma (HepG2), porcine kidney epithelial cells (PK15), human neuroblastoma (SH-SY5Y) and Chinese hamster lung fibroblast cells (V79) were treated with thallium (I) acetate in a wide concentration range (3.9-500 µg/mL) for 24 h, 48 and 72 h. To assess competitive interaction between Tl and K, the cells were treated with four Tl concentrations close to IC50 (15.63, 31.25, 62.50, 125 µg/mL) in combination with/or without potassium (I) acetate (500 µg/mL). The cells' morphology was monitored, and cytotoxic effect was assessed by 3-(4, 5-dimethylthiazole-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) test. The most sensitive to Tl exposure were SH-SY5Y cells, while HepG2 were the most resistant. The combined exposure to thallium (I) acetate and potassium (I) acetate for every cell line, except V79 cells, resulted in higher cell viability compared to thallium (I) acetate alone. The results of our study indicate that cell sensitivity to Tl treatment is largely affected by tissue culture origin, its function, and Na+/K+-ATPase activity.

Upregulation of Nrf2 in myocardial infarction and ischemia-reperfusion injury of the heart

Myocardial infarction (MI) is a leading cause of morbidity and mortality in the world and is characterized by ischemic necrosis of an area of the myocardium permanently devoid of blood supply. During reperfusion, reactive oxygen species are released and this causes further insult to the myocardium, resulting in ischemia-reperfusion (IR) injury. Since Nrf2 is a key regulator of redox balance, it is essential to determine its contribution to these two disease processes. Conventionally Nrf2 levels have been shown to rise immediately after ischemia and reperfusion but its contribution to disease process a week after the injury remains uncertain. Mice were divided into MI, IR injury, and sham surgery groups and were sacrificed 1 week after surgery. Infarct was visualized using H&E and trichrome staining and expression of Nrf2 was assessed using immunohistochemistry, Western blot, and ELISA. MI displayed a higher infarct size than the IR group (MI: 31.02 ± 1.45%, IR: 13.03 ± 2.57%; p < 0.01). We observed a significantly higher expression of Nrf2 in the MI group compared to the IR model using immunohistochemistry, spot densitometry of Western blot (MI: 2.22 ± 0.16, IR: 1.81 ± 0.10, Sham: 1.52 ± 0.13; p = 0.001) and ELISA (MI: 80.78 ± 27.08, IR: 31.97 ± 4.35; p < 0.01). There is a significantly higher expression of Nrf2 in MI compared to the IR injury group. Modulation of Nrf2 could be a potential target for therapeutics in the future, and its role in cardioprotection can be further investigated.

Thallium - poisoner's poison: An overview and review of current knowledge on the toxicological effects and mechanisms

Thallium (Tl) is one of the most toxic metals and its historic use in homicides has led it to be known as "the poisoner's poison." This review summarizes the methods for identifying Tl and determining its concentrations in biological samples in recently reported poisoning cases, as well as the toxicokinetics, toxicological effects, toxicity mechanisms, and detoxication methods of Tl. Recent findings regarding Tl neurotoxicological pathways and toxicological effects of Tl during pregnancy are also presented. Confirmation of elevated Tl concentrations in blood, urine, or hair is indispensable for diagnosing Tl poisoning. The kidneys show the highest Tl concentration within 24 h after ingestion, while the brain shows the highest concentration thereafter. Tl has a very slow excretion rate due to its large distribution volume. Following acute exposure, gastrointestinal symptoms are observed at an early stage, and neurological dysfunction is observed later: Tl causes the most severe damage in the central nervous system. Alopecia and Mees' lines in the nails are observed within 1 month after Tl poisoning. The toxicological mechanism of Tl is considered to be interference of vital potassium-dependent processes with Tl+ because its ionic radius is similar to that of K+, as well as inhibition of enzyme reactions by the binding of Tl to -SH groups, which disturbs vital metabolic processes. Tl toxicity is also related to reactive oxygen species generation and mitochondrial dysfunction. Prussian blue is the most effective antidote, and metallothionein alone or in combination with Prussian blue was recently reported to have cytoprotective effects after Tl exposure. Because Tl poisoning cases are still reported, early determination of Tl in biological samples and treatment with an antidote are essential.

Effects of Acute and Subchronic Waterborne Thallium Exposure on Ionoregulatory Enzyme Activity and Oxidative Stress in Rainbow Trout (Oncorhynchus mykiss)

The mechanisms of acute (96-hour) and subchronic (28-day) toxicity of the waterborne trace metal thallium (Tl) to rainbow trout (Oncorhynchus mykiss) were investigated. Specifically, effects on branchial and renal ionoregulatory enzymes (sodium/potassium adenosine triphosphatase [ATPase; NKA] and proton ATPase) and hepatic oxidative stress endpoints (protein carbonylation, glutathione content, and activities of catalase and glutathione peroxidase) were examined. Fish (19-55 g) were acutely exposed to 0 (control), 0.9 (regulatory limit), 2004 (half the acute median lethal concentration), or 4200 (acute median lethal concentration) µg Tl L-1 or subchronically exposed to 0, 0.9, or 141 (an elevated environmental concentration) µg Tl L-1 . The only effect following acute exposure was a stimulation of renal H+ -ATPase activity at the highest Tl exposure concentration. Similarly, the only significant effect of subchronic Tl exposure was an inhibition of branchial NKA activity at 141 µg Tl L-1 , an effect that may reflect the interaction of Tl with potassium ion handling. Despite significant literature evidence for effects of Tl on oxidative stress, there were no effects of Tl on any such endpoint in rainbow trout, regardless of exposure duration or exposure concentration. Elevated basal levels of antioxidant defenses may explain this finding. These data suggest that ionoregulatory perturbance is a more likely mechanism of Tl toxicity than oxidative stress in rainbow trout but is an endpoint of relevance only at elevated environmental Tl concentrations. Environ Toxicol Chem 2024;43:87-96. © 2023 SETAC.

Association between exposure to mixture of heavy metals and hyperlipidemia risk among U.S. adults: A cross-sectional study

Previous studies have suggested that exposure to heavy metals might increase the risk of hyperlipidemia. However, limited research has investigated the association between exposure to mixture of heavy metals and hyperlipidemia risk. To explore the independent and combined effects of heavy metal exposure on hyperlipidemia risk, this study involved 3293 participants from the National Health and Nutrition Examination Survey (NHANES), including 2327 with hyperlipidemia and the remaining without. In the individual metal analysis, the logistic regression model confirmed the positive effects of barium (Ba), cadmium (Cd), mercury (Hg), Lead (Pb), and uranium (U) on hyperlipidemia risk, Ba, Cd, Hg and Pb were further validated in restricted cubic splines (RCS) regression model and identified as positive linear relationships. In the metal mixture analysis, weighted quantile sum (WQS) regression, Bayesian kernel machine regression (BKMR), and quantile-based g computation (qgcomp) models consistently revealed a positive correlation between exposure to metal mixture and hyperlipidemia risk, with Ba, Cd, Hg, Pb, and U having significant positive driving roles in the overall effects. These associations were more prominent in young/middle-aged individuals. Moreover, the BKMR model uncovered some interactions between specific heavy metals. In conclusion, this study offers new evidence supporting the link between combined exposure to multiple heavy metals and hyperlipidemia risk, but considering the limitations of this study, further prospective research is required.

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.