Cadmium-induced hypertension is associated with renal myosin light chain phosphatase inhibition via increased T697 phosphorylation and p44 mitogen-activated protein kinase levels

Oxidant-induced disruption of vascular K+ channel function: implications for diabetic vasculopathy

Diabetes in humans a chronic metabolic disorder characterised by hyperglycaemia, it is associated with an increased risk of cardiovascular disease, disruptions to metabolism and vascular functions. It is also linked to oxidative stress and its complications. Its role in vascular dysfunctions is generally reported without detailed impact on the molecular mechanisms. Potassium ion channel (K+ channels) are key regulators of vascular tone, and as membrane proteins, are modifiable by oxidant stress associated with diabetes. This review manuscript examined the impact of oxidant stress on vascular K+ channel functions in diabetes, its implication in vascular complications and metabolic and cardiovascular diseases.

Cadmium induces microcytosis and anisocytosis without anaemia in hypertensive rats

Dietary cadmium (Cd2+) intake is implicated in the pathogenesis of hypertension and anaemia, but there is a paucity of information on the haematological changes in hypertensive conditions. This study, therefore, aims to evaluate the effects of Cd2+ on blood pressure (BP) and haematological indices in the Sprague-Dawley rat model. Three cohorts (n = 10 each) of control and Cd2+-fed male Sprague-Dawley rats were selected. Cd2+-exposed rats received 2.5 or 5 mg/kg b.w. cadmium chloride via gavage thrice-weekly for eight weeks, while control animals received tap water. BP and flow were measured non-invasively from rat tails twice-weekly using a CODA machine, while weights were measured thrice-weekly. Haematological indices were assessed using the Cell-Dyn Emerald Haematology Analyzer. Data were reported as mean ± SEM, and statistically analyzed using One-Way Analysis of Variance. Bonferroni post hoc test was used for multiple comparisons. Cd2+-exposure induced hypertension by significantly (p < 0.05) elevating systolic, diastolic, and mean arterial BPs, pulse pressure, and heart rate (HR), and increased (p < 0.05) blood flow. Mean cell volume (MCV) and haemoglobin (MCH) were significantly (p < 0.05) reduced, and red cell distribution width (RDW) significantly (p < 0.01) increased by exposure to 5 mg/kg b.w. Cd2+. Haemoglobin concentration (MCHC), haematocrit, haemoglobin, red blood cell, platelet, mean platelet volume, and white blood cell counts were unaffected by Cd2+-exposure. Cd2+ induced hypertension, microcytosis, hypochromicity, and anisocytosis without anaemia, which may be precursor to microcytic anaemia and coronary artery disease. This study is important in Cd2+-exposed environments and warrants further investigations.

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.