Fish consumption, mercury exposure, and the risk of cholesterol profiles: findings from the Korea National Health and Nutrition Examination Survey 2010-2011

Association Between Multiple Heavy Metal Exposures and Cholesterol Levels in Residents Living Near a Smelter Plant in Korea

BACKGROUND

Considering the interactions between heavy metals, a comprehensive evaluation of the effects of exposure to various types of co-interacting heavy metals on health is required. This study assessed the association between dyslipidemia markers and blood mercury, lead, cadmium, iron, zinc, and nickel levels in residents of an abandoned refinery plant.

METHODS

A total of 972 individuals (exposed group: 567, control group: 405) living near the Janghang refinery plant in the Republic of Korea were included. Blood mercury, lead, cadmium, iron, zinc, nickel, cholesterol, and triglyceride levels were measured. The combined effect of the six heavy metals on dyslipidemia markers was evaluated using a Bayesian kernel machine regression (BKMR) model and compared with the results of a linear regression analysis. The BKMR model results were compared using a stratified analysis of the exposed and control groups.

RESULTS

In the BKMR model, the combined effect of the six heavy metals was significantly associated with total cholesterol (TC) levels both below the 45th percentile and above the 55th percentile in the total population. The combined effect range between the 25th and 75th percentiles of the six metals on TC levels was larger in the exposed group than that in the total population. In the control group, the combined effects of the changes in concentration of the six heavy metals on the TC concentration were not statistically significant.

CONCLUSION

These results suggest that the cholesterol levels of residents around the Janghang refinery plant may be elevated owing to exposure to multiple heavy metals.

Association of Blood Total Mercury with Dyslipidemia in a sample of U.S. Adolescents: Results from the National Health and Nutrition Examination Survey Database, 2011-2018

Background

Abnormal lipid profiles in adolescents predict metabolic and cardiovascular diseases in adulthood. While seafood consumption is the primary source of mercury exposure, it also provides beneficial nutrients such as omega-3 fatty acids (O3FA). Prior studies indicate that blood total mercury (TBHg) has endocrine disrupting effects and may be associated with abnormal lipid profiles in adolescents. However, the impact of beneficial nutrients on this relationship has not been examined. Our study investigated the relationship of TBHg with dyslipidemia and lipid profiles and potential confounding and modification of these relationships by sex, body mass index (BMI), selenium and O3FA from seafood consumption.

Methods

We examined 1,390 National Health and Nutrition Examination Survey participants 12-19 years of age from the 2011-2018 cycles. Using logistic and linear regression adjusted for survey design variables and stratified by sex a priori, we estimated the associations of TBHg and methylmercury with dyslipidemia, and with total cholesterol (TC), high (HDL-C) and low-density lipoprotein cholesterol (LDL-C) and triglycerides.

Results

The geometric mean of TBHg in this adolescent population was 0.44 μg/L. After controlling for socio-demographic covariates, BMI, serum selenium, age at menarche (females only) and average daily intake of O3FA; TBHg was significantly associated with higher TC levels (β=3.34, 95% CI: 0.19, 6.50; p<0.05) in females but not males. Methyl Hg was also associated with increased TC, as well as decreased HDL-C in females but not males. We did not find significant associations of Hg exposure with dyslipidemia, LDL-C or triglycerides levels in either male or female adolescents. However, we observed evidence of effect modification by BMI and serum selenium for associations of TBHg with TC levels in male and female adolescents, respectively.

Conclusion

Our findings of elevated TC levels in females but not males necessitates further research to better understand the underlying mechanisms driving these sex-specific associations.

Prediction of hypercholesterolemia using machine learning techniques

Purpose

Hypercholesterolemia is a major risk factor for a wide range of cardiovascular diseases. Developing countries are more susceptible to hypercholesterolemia and its complications due to the increasing prevalence and the lack of adequate resources for conducting screening and/or prevention programs. Using machine learning techniques to identify factors contributing to hypercholesterolemia and developing predictive models can help early detection of hypercholesterolemia, especially in developing countries.

Methods

Data from the nationwide 2016 STEPs study in Iran were used to identify socioeconomic, lifestyle, and metabolic risk factors associated with hypercholesterolemia. Furthermore, the predictive power of the identified risk factors was assessed using five commonly used machine learning algorithms (random forest; gradient boosting; support vector machine; logistic regression; artificial neural network) and 10-fold cross validation in terms of specificity, sensitivity, and the area under the receiver operating characteristic curve.

Results

A total of 14,667 individuals were included in this study, of those 12.8% (n = 1879) had (undiagnosed) hypercholesterolemia. Based on multivariate logistic regression analysis the five most important risk factors for hypercholesterolemia were: older age (for the elderly group: OR = 2.243; for the middle-aged group: OR = 1.869), obesity-related factors including high BMI status (morbidly obese: OR = 1.884; obese: OR = 1.499; overweight: OR = 1.426) and AO (OR = 1.339), raised BP (hypertension: OR = 1.729; prehypertension: OR = 1.577), consuming fish once or twice per week (OR = 1.261), and having risky diet (OR = 1.163). Furthermore, all the five hypercholesterolemia prediction models achieved AUC around 0.62, and models based on random forest (AUC = 0.6282; specificity = 65.14%; sensitivity = 60.51%) and gradient boosting (AUC = 0.6263; specificity = 64.11%; sensitivity = 61.15%) had the optimal performance.

Conclusion

The study shows that socioeconomic inequalities, unhealthy lifestyle, and metabolic syndrome (including obesity and hypertension) are significant predictors of hypercholesterolemia. Therefore controlling these factors is necessary to reduce the burden of hypercholesterolemia. Furthermore, machine learning algorithms such as random forest and gradient boosting can be employed for hypercholesterolemia screening and its timely diagnosis. Applying deep learning algorithms as well as techniques for handling the class overlap problem seems necessary to improve the performance of the models.

Associations between mercury exposure with blood pressure and lipid levels: A cross-sectional study of dental professionals

Mercury (Hg) exposure is a public health problem worldwide that is now being addressed through the Minamata Convention on Mercury. Fish containing methylmercury and dental amalgam containing elemental Hg are the major sources of exposure for most populations. There is some evidence that methylmercury impacts cardiovascular and metabolic health, primarily in populations with high exposure levels. Studies of elemental Hg and these outcomes are relatively rare. We aimed to examine associations between Hg exposure (both elemental and methylmercury) and blood pressure, as well as cholesterol and triglyceride levels. In 2012, we recruited dental professionals attending the Health Screening Program at the American Dental Association (ADA) Annual Session in California. Total Hg levels in hair and blood samples were analyzed as indicators of methylmercury exposure and in urine as an indicator of primarily elemental Hg exposure (n = 386; mean ± sd age 55 ± 11 years). We measured blood pressure (systolic and diastolic) and lipid profiles (total cholesterol, high-density lipoprotein cholesterol [HDL], low-density lipoprotein cholesterol [LDL] and triglycerides). The geometric means (geometric standard deviations) for blood, hair, and urine Hg were 3.64 (2.39) μg/L, 0.60 (2.91) μg/g, and 1.30 (2.44) μg/L, respectively. For every one μg/L increase in specific gravity-adjusted urine Hg, LDL increased by 2.31 mg/dL (95% CI = 0.09, 4.54), in linear regression adjusting for BMI, race, sex, polyunsaturated fatty acid intake from fish consumption, smoking status, and use of cholesterol-lowering medication. No significant associations between Hg biomarkers and blood pressure or hair or blood Hg with lipid levels were observed. Results suggest that elemental Hg exposure may influence LDL concentrations in adults with low-level exposure, and this relationship merits further study in other populations.

Effects of heavy metals on cardiovascular diseases in pre and post-menopausal women: from big data to molecular mechanism involved

To assess the link between a mixed heavy metal (cadmium, lead, and mercury) and the 10-year risk of cardiovascular diseases (CVDs) in pre- and post-menopausal Korean women aged ≥20 years, as well as identify potential molecular mechanisms of mixed heavy metal-induced CVDs. Multivariate linear regression, weighted quantile sum (WQS) regression, quantile g-computation (gqcomp), and Bayesian kernel machine regression (BKMR) models were used to examine the effects of mixed heavy metals and the 10-year risk of CVDs. The Comparative Toxicogenomics Database, MicroRNA ENrichment TURned NETwork, and the microRNA sponge generator and tester were used as the key data-mining approaches. In our BKMR analysis, we found that the overall effect of mixed heavy metals was linked to the 10-year risk of CVDs in postmenopausal women in the upper 20^th percentiles and in premenopausal women in the upper 55^th percentiles. Mercury was identified as the key chemical for the 10-year risk of CVDs in pre- and postmenopausal women. In silico analysis revealed that a heavy metal mixture interacted with six genes associated with CVD development. Physical interactions (77.6%) were found to be the most common among CVD-related genes induced by the heavy metals studied. Several pathways have been identified as the main molecular mechanisms that could be affected by studied heavy metals and are implicated in the development of CVDs (e.g., lipid and lipoprotein metabolism, lipoprotein metabolism, cholesterol metabolism, and cardiovascular disease). ALB, APOE, ATF5, and CREB3L3 were the key genes and transcription factors related to CVDs induced by the mixture of the investigated heavy metals, respectively. The two miRNAs with the highest interaction and expression in the development of CVDs were hsa-miR-199a-5p and hsa-miR-199a-3p. We also designed and tested miRNA sponge sequences for these miRNAs. The cutoff thresholds for each heavy metal level linked with the 10-year risk of CVDs were described. A mixture of heavy metal exposures, especially mercury, was more strongly linked with the 10-year risk of CVDs in postmenopausal women than in premenopausal women. Early interventions in postmenopausal women should be considered to reduce CVD risks.

Association between Heavy Metal Exposure and Dyslipidemia among Korean Adults: From the Korean National Environmental Health Survey, 2015-2017

Cardiovascular disease (CVD) is a leading cause of death in Korea. Dyslipidemia, characterized by the presence of abnormal lipid levels, has been suggested as an early diagnostic and preventable factor for CVD. Recent studies have shown that exposure to lead (Pb), cadmium (Cd), and mercury (Hg) affects lipid metabolism. This study aimed to verify the association between heavy metal concentrations and serum lipid profiles in the general population. A representative sample of 2591 Korean adults from the Korean National Environmental Health Survey (2015−2017) was analyzed. The associations between heavy metals [Blood Pb (BPb), blood Hg (BHg), urinary Hg (UHg), urinary Cd (UCd)] and serum lipid profiles [total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), non-low level of high-density lipoprotein cholesterol (Non-HDL-C)] were assessed using regression analysis. After adjusting for demographic and socioeconomic factors, the proportional changes in serum lipid levels were significantly associated with increases in BPb, BHg, and UHg levels (p for trend < 0.05). Overall, BPb, BHg, and Uhg levels positively correlated with dyslipidemia, whereas UCd levels did not show a significant association. Our results suggest that heavy metal exposure, at low levels, may contribute to an increased prevalence of dyslipidemia in Korean adults.

Buchholzia coriacea seed extract attenuates mercury-induced cerebral and cerebellar oxidative neurotoxicity via NO signaling and suppression of oxidative stress, adenosine deaminase and acetylcholinesterase activities in rats

OBJECTIVE

Mercury (Hg) is a classic cumulative neurotoxicant implicated in neuronal deficit via oxidative damage and inflammatory responses. We sought to investigate whether Buccholzia coriacea seed methanol extract (BCSE) would modulate oxidative neurotoxicity induced by Hg in rats.

MATERIALS AND METHODS

Rats were orally treated with BCSE (200 or 400 mg/kg body weight of rat) for 28 days, while Hg was administered from day 15 to day 28. After sacrifice, antioxidant enzyme activities, reduced glutathione (GSH), nitric oxide (NO), malondialdehyde (MDA), and acetylcholinesterase (AchE) and adenine deaminase (ADA) activities were evaluated in the cerebrum and cerebellum of rats.

RESULTS

Mercury induced significant depressions in catalase (CAT) and glutathione peroxidase (GPx) activities and GSH levels, whereas levels of NO and activities of AchE and ADA markedly increased. The histopathology of the brain tissues confirmed these changes. In contrast, BCSE administration prominently modulated the brain NO production and reversed the Hg-induced biochemical alterations comparable to normal control.

CONCLUSION

Methanol extract of B. coriacea seeds protects the cerebrum and cerebellum against Hg-induced brain damage via its antioxidant and NO modulatory actions.

Association between heavy metals, high-sensitivity C-reaction protein and 10-year risk of cardiovascular diseases among adult Korean population

The prevalence of cardiovascular diseases (CVDs) in Korea tends to be increasing. It has worsened during the COVID-19 pandemic. Increasing evidence shows heavy metals are associated with increased CVD risk. We aimed to determine the association between the serum heavy metal levels and 10-year risk of CVDs and to predict risks of CVDs based on marginal effects. Heavy metals were measured by a graphite furnace atomic absorption spectrometry and direct mercury analyzer. The results show a significant relationship between the increase in cadmium, lead, mercury, hs-CRP levels and the 10-year risk of CVD after adjustment for serum cotinine, age group, sex, body mass index, a family history of CVDs, diabetes or hyperlipidemia, high-risk drinking, physical activity, and diabetes. A doubling of serum cadmium, lead, mercury, and hs-CRP was associated with the increase in the 10-year risk of CVD by 0.14%, 0.10%, 0.11% and 0.22%, respectively. Therefore, a special concern should be given to the harmful impacts of heavy metals on the 10-year risk of CVD. It is important to develop a prevention strategy targeting the high-risk population to slow down this progression to risk factors related to heavy metals and reduce prevalence. Remarkedly, hs-CRP is the most validated and widely used inflammatory marker, and could be a potential clinical value in predicting and monitoring CVDs.

An association of blood mercury levels and hypercholesterolemia among Korean adolescents

PURPOSE

A few experimental studies have suggested that mercury exposure might be associated with dyslipidemia, possibly through its interference with the activities of genes and enzymes involved in lipid metabolism. Mercury exposure has been associated with the risk of dyslipidemia and cardiovascular disease in previous adult studies. However, only a few studies have been conducted in pediatric populations. We aimed to assess the associations between total blood mercury concentrations and lipid profiles in Korean adolescents.

METHODS

The study population comprised 1890 adolescents (963 males and 927 females; age: 10-19 years) who participated in the Korea National Health and Nutrition Examination Survey 2010-2013 and 2016 and whose fasting blood samples were obtained to determine their blood mercury concentrations and lipid profiles. We analyzed the distribution of lipid profiles and the prevalence of dyslipidemia based on the total blood mercury concentrations.

RESULTS

The geometric mean of the blood mercury concentration was 1.89 μg/L and was significantly higher in males (1.96 μg/L) than in females (1.83 μg/L). The total cholesterol and low-density lipoprotein (LDL) cholesterol levels significantly increased as the blood total mercury concentration increased in males, not in females. High-density lipoprotein cholesterol and triglyceride levels did not show significant associations with total blood mercury levels. The prevalence of hyper-LDL-cholesterolemia also significantly increased in the highest blood mercury quartile group compared with that in the lowest blood mercury quartile group in males (P-for-trend <0.001). Male adolescents in the highest total blood mercury quartile group were found to be at higher risk of hypercholesterolemia than those in the lowest quartile group after adjusting for the covariates including obesity [odds ratios (95% confidential interval): 3.72 (1.03-13.4)]. Total blood mercury quartile showed a positive linear relationship with the risk of hyper-LDL-cholesterolemia after controlling for the covariates in males.

CONCLUSION

Our results suggest the potential association between mercury exposure and the risk of hyper-LDL-cholesterolemia in male adolescents.

Selenium and mercury in dolphinfish (Coryphaena hippurus) from the Gulf of California: inter-annual variations and selenium health benefit value

With the aim of determining the benefit from consumption of dolphinfish Coryphaena hippurus from La Paz and Cabo San Lucas in the southern area of the Gulf of California, mercury (Hg) and selenium (Se) were measured in liver and muscle of specimens (n = 362) collected between 2006 and 2013. Mean levels of Hg in muscle (0.61 μg g^-1) and liver (0.42 μg g^-1) of all individuals from La Paz were significantly higher (p < 0.05) than in fish from Cabo San Lucas; in the case of Se, mean concentrations in liver (1.54 μg g^-1) of all individuals from La Paz were significantly higher (p < 0.05) than in fish from Cabo San Lucas. Hg levels in muscle were positively and significantly (p < 0.05) correlated with fork length and weight of fish; in liver, Hg and Se were significantly (p < 0.05) correlated with fork length and weight of specimens. Levels of Hg and Se in muscle and liver among years varied significantly (p < 0.05); although there was not a defined pattern of temporal fluctuations for both elements, the lowest Hg levels occurred in 2009 when surface water temperatures were the highest for the sampled years. With respect to the Se health benefit value (HBV_Se), results were positive and above the unit in all cases; it implies that consumption of dolphinfish in the southern Gulf of California is beneficial.

A Hypothesis and Evidence That Mercury May be an Etiological Factor in Alzheimer's Disease

Mercury is one of the most toxic elements and causes a multitude of health problems. It is ten times more toxic to neurons than lead. This study was created to determine if mercury could be causing Alzheimer's disease (AD) by cross referencing the effects of mercury with 70 factors associated with AD. The results found that all these factors could be attributed to mercury. The hallmark changes in AD include plaques, beta amyloid protein, neurofibrillary tangles, phosphorylated tau protein, and memory loss-all changes that can be caused by mercury. Neurotransmitters such as acetylcholine, serotonin, dopamine, glutamate, and norepinephrine are inhibited in patients with Alzheimer's disease, with the same inhibition occurring in mercury toxicity. Enzyme dysfunction in patients with Alzheimer's disease include BACE 1, gamma secretase, cyclooxygenase-2, cytochrome-c-oxidase, protein kinases, monoamine oxidase, nitric oxide synthetase, acetyl choline transferase, and caspases, all which can be explained by mercury toxicity. Immune and inflammatory responses seen in patients with Alzheimer's disease also occur when cells are exposed to mercury, including complement activation, cytokine expression, production of glial fibrillary acid protein antibodies and interleukin-1, transforming growth factor, beta 2 microglobulins, and phosphodiesterase 4 stimulation. Genetic factors in patients with Alzheimer's disease are also associated with mercury. Apolipoprotein E 4 allele increases the toxicity of mercury. Mercury can inhibit DNA synthesis in the hippocampus, and has been associated with genetic mutations of presenilin 1 and 2, found in AD. The abnormalities of minerals and vitamins, specifically aluminum, calcium, copper, iron, magnesium, selenium, zinc, and vitamins B1, B12, E, and C, that occur in patients with Alzheimer's disease, also occur in mercury toxicity. Aluminum has been found to increase mercury's toxicity. Likewise, similar biochemical factors in AD are affected by mercury, including changes in blood levels of homocysteine, arachidonic acid, DHEA sulfate, glutathione, hydrogen peroxide, glycosamine glycans, acetyl-L carnitine, melatonin, and HDL. Other factors seen in Alzheimer's disease, such as increased platelet activation, poor odor identification, hypertension, depression, increased incidences of herpes virus and chlamydia infections, also occur in mercury exposure. In addition, patients diagnosed with Alzheimer's disease exhibit higher levels of brain mercury, blood mercury, and tissue mercury in some studies. The greatest exogenous sources of brain mercury come from dental amalgams. Conclusion: This review of the literature strongly suggests that mercury can be a cause of Alzheimer's Disease.

The association between heavy metal and serum cholesterol levels in the US population: National Health and Nutrition Examination Survey 2009-2012

BACKGROUND

Few studies have examined the relationship between heavy metal and serum cholesterol levels, and no recent study has examined this relationship in the US population.

METHODS AND RESULTS

A total of 19,591 individuals aged 0-80 years were included in the National Health and Nutrition Examination Survey 2009-2012; the current study was composed of survey participants for whom some or all low-density lipoprotein cholesterol (LDL-C), serum triglyceride, high-density lipoprotein cholesterol, total cholesterol, blood lead (Pb), total blood mercury (Hg), and blood cadmium (Cd) levels had been measured. Three tertiles (T1-T3) of heavy metals were used to explore dose-response association between heavy metal exposure and serum cholesterol level. Logistic regression was used to explore the relationship between tertiles of blood heavy metal levels and serum cholesterol levels after adjusting for age, sex, and socioeconomic status. We have shown that the groups with the highest levels of Pb, Hg, and Cd had a 56%, 73%, and 41% chance, respectively, of having a high total cholesterol level. In study subjects with the highest Pb levels, there was also a 22% chance of being in the group with the highest LDL-C level. An increase in total cholesterol and LDL-C levels was observed from the first to the third tertiles of the heavy metals studied.

CONCLUSION

Increasing blood Pb, Hg, and Cd levels were associated with significantly increased odds of high total cholesterol after adjusting for age, sex, and socioeconomic status.

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

Lead, mercury, and cadmium are common environmental pollutants in industrialized countries, but their combined impact on hypercholesterolemia (HC) is poorly understood. The aim of this study was to compare the performance of various machine learning (ML) models to predict the prevalence of HC associated with exposure to lead, mercury, and cadmium. A total of 10,089 participants of the Korea National Health and Nutrition Examination Surveys 2008–2013 were selected and their demographic characteristics, blood concentration of metals, and total cholesterol levels were collected for analysis. For prediction, five ML models, including logistic regression (LR), k-nearest neighbors, decision trees, random forests, and support vector machines (SVM) were constructed and their predictive performances were compared. Of the five ML models, the SVM model was the most accurate and the LR model had the highest area under receiver operating characteristic (ROC) curve of 0.718 (95% CI: 0.688–0.748). This study shows the potential of various ML methods to predict HC associated with exposure to metals using population-based survey data.

Identification of hypertriglyceridemia based on bone density, body fat mass, and anthropometry in a Korean population

BACKGROUND

Hypertriglyceridemia is strongly associated with the risks of cardiovascular disease, coronary heart disease, and metabolic syndrome. The relationship between hypertriglyceridemia or high triglyceride levels and bone mineral density remains controversial. Furthermore, to date, no study has simultaneously examined the association among hypertriglyceridemia, bone area, bone mineral content, bone mineral density, body fat mass, and anthropometrics. The present study aimed to evaluate the association among hypertriglyceridemia, anthropometrics and various bone density and body fat composition variables to identify the best indicator of hypertriglyceridemia in a Korean population.

METHODS

The data were obtained from the fifth Korea National Health and Nutrition Examination Survey. In total, 3918 subjects aged 20-80 years participated in this study. In the variable analysis of the waist circumference (WC), trunk fat mass (Trk-Ft), body mass index, etc., a binary logistic regression analysis was performed to examine the significance of the differences between the normal group and hypertriglyceridemia groups.

RESULTS

In both men and women, the WC showed the strongest association with hypertriglyceridemia in the crude analysis (odds ratio (OR) = 1.738 [confidence interval = 1.529-1.976] and OR = 2.075 [1.797-2.397]), but the Trk-Ft was the most strongly associated with the disease after adjusting for age and body mass index (adjusted OR = 1.565 [1.262-1.941] and adjusted OR = 1.730 [1.291-2.319]). In particular, the Pelvis area (Plv-A) was the most significant among the bone variables in women (adjusted OR = 0.641 [0.515-0.796]). In the predictive power analysis, the best indicator of hypertriglyceridemia was WC in women (the area under the receiver operating characteristic curve (AUC) = 0.718 [0.685-0.751]) and Trk-Ft in men (AUC = 0.672 [0.643-0.702]). The WC was also the most predictive among the anthropometric variables in men (AUC = 0.670 [0.641-0.700]). The strength of the association and predictive power was stronger in women than in men.

CONCLUSIONS

The WC in women and Trk-Ft in men exhibited the best predictive power for hypertriglyceridemia. Our findings support the use of basic information for the identification of hypertriglyceridemia or high triglyceride levels in initial health screening efforts.