Comparisons among Machine Learning Models for the Prediction of Hypercholestrolemia Associated with Exposure to Lead, Mercury, and Cadmium

Metal mixtures with longitudinal changes in lipid profiles: findings from the manganese-exposed workers healthy cohort

The majority of epidemiological investigations on metal exposures and lipid metabolism employed cross-sectional designs and focused on individual metal. We explored the associations between metal mixture exposures and longitudinal changes in lipid profiles and potential sexual heterogeneity. We recruited 250 men and 73 women, aged 40 years at baseline (2012), and followed them up in 2020, from the manganese-exposed workers healthy cohort. We detected metal concentrations of blood cells at baseline with inductively coupled plasma mass spectrometry. Lipid profiles were repeatedly measured over 8 years of follow-up. We performed sparse partial least squares (sPLS) model to evaluate multi-pollutant associations. Bayesian kernel machine regression was utilized for metal mixtures as well as evaluating their joint impacts on lipid changes. In sPLS models, a positive association was found between manganese and change in total cholesterol (TC) (beta = 0.169), while a negative association was observed between cobalt (beta =  - 0.134) and change in low density lipoprotein cholesterol (LDL-C) (beta =  - 0.178) among overall participants, which were consistent in men. Interestingly, rubidium was positively associated with change in LDL-C (beta = 0.273) in women, while copper was negatively associated with change in TC (beta =  - 0.359) and LDL-C (beta =  - 0.267). Magnesium was negatively associated with change in TC (beta =  - 0.327). We did not observe the significantly cumulative effect of metal mixtures on lipid changes. In comparison to other metals, manganese had a more significant influence on lipid change [group PIP (0.579) and conditional PIP (0.556) for TC change in men]. Furthermore, male rats exposed to manganese (20 mg/kg) had higher levels of LDL-C in plasma and more apparent inflammatory infiltration, vacuolation of liver cells, nuclear pyknosis, and fatty change than the controls. These findings highlight the potential role of metal mixtures in lipid metabolism with sex-dependent heterogeneity. More researches are needed to explore the underlying mechanisms.

Misuse of Cardiac Lipid upon Exposure to Toxic Trace Elements-A Focused Review

Heavy metals and metalloids like cadmium, arsenic, mercury, and lead are frequently found in the soil, water, food, and atmosphere; trace amounts can cause serious health issues to the human organism. These toxic trace elements (TTE) affect almost all the organs, mainly the heart, kidney, liver, lungs, and the nervous system, through increased free radical formation, DNA damage, lipid peroxidation, and protein sulfhydryl depletion. This work aims to advance our understanding of the mechanisms behind lipid accumulation via increased free fatty acid levels in circulation due to TTEs. The increased lipid level in the myocardium worsens the heart function. This dysregulation of the lipid metabolism leads to damage in the structure of the myocardium, inclusive fibrosis in cardiac tissue, myocyte apoptosis, and decreased contractility due to mitochondrial dysfunction. Additionally, it is discussed herein how exposure to cadmium decreases the heart rate, contractile tension, the conductivity of the atrioventricular node, and coronary flow rate. Arsenic may induce atherosclerosis by increasing platelet aggregation and reducing fibrinolysis, as exposure interferes with apolipoprotein (Apo) levels, resulting in the rise of the Apo-B/Apo-A1 ratio and an elevated risk of acute cardiovascular events. Concerning mercury and lead, these toxicants can cause hypertension, myocardial infarction, and carotid atherosclerosis, in association with the generation of free radicals and oxidative stress. This review offers a complete overview of the critical factors and biomarkers of lipid and TTE-induced cardiotoxicity useful for developing future protective interventions.

Machine Learning Methods for Hypercholesterolemia Long-Term Risk Prediction

Cholesterol is a waxy substance found in blood lipids. Its role in the human body is helpful in the process of producing new cells as long as it is at a healthy level. When cholesterol exceeds the permissible limits, it works the opposite, causing serious heart health problems. When a person has high cholesterol (hypercholesterolemia), the blood vessels are blocked by fats, and thus, circulation through the arteries becomes difficult. The heart does not receive the oxygen it needs, and the risk of heart attack increases. Nowadays, machine learning (ML) has gained special interest from physicians, medical centers and healthcare providers due to its key capabilities in health-related issues, such as risk prediction, prognosis, treatment and management of various conditions. In this article, a supervised ML methodology is outlined whose main objective is to create risk prediction tools with high efficiency for hypercholesterolemia occurrence. Specifically, a data understanding analysis is conducted to explore the features association and importance to hypercholesterolemia. These factors are utilized to train and test several ML models to find the most efficient for our purpose. For the evaluation of the ML models, precision, recall, accuracy, F-measure, and AUC metrics have been taken into consideration. The derived results highlighted Soft Voting with Rotation and Random Forest trees as base models, which achieved better performance in comparison to the other models with an AUC of 94.5%, precision of 92%, recall of 91.8%, F-measure of 91.7% and an accuracy equal to 91.75%.

Association of multiple metals with lipid markers against different exposure profiles: A population-based cross-sectional study in China

We sought to evaluate whether essential and toxic metals are cross-sectionally related to blood lipid levels using data among adults from Shimen (n = 564) and Huayuan (n = 637), two counties with different exposure profiles in Hunan province of China. Traditional and grouped weighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR) were performed to assess association between exposure to a mixture of 22 metals measured in urine or plasma, and lipid markers. Most of the exposure levels of metals were significantly higher in Shimen area than those in Huayuan area (all P-values < 0.001). Traditional WQS regression analyses revealed that the WQS index were both significantly associated with lipid markers in two areas, except for the HDL-C. Grouped WQS revealed that essential metals group showed significantly positive associations with lipid markers except for HDL-C in Huayuan area, while toxic metals group showed significantly negative associations except for HDL-C and LDL-C in Huayuan area. There were no significant joint effects, but potential non-linear relationships between metals mixture and TC or LDL-C levels were observed in BKMR analyses. Although consistent significantly associations of zinc and titanium with TG levels were found in both areas, the metals closely related to other lipid markers were varied by sites. Additionally, the BKMR analyses revealed an inverse U shaped association of iron with LDL-C levels and interaction effects of zinc and cadmium on LDL-C in Huayuan area. The relationship between metal exposure and blood lipid were not identical against different exposure profiles.