Type 2 diabetes occurrence and mercury exposure - From the National Nutrition and Health Survey in Taiwan

Associations of heavy metal exposure with diabetic retinopathy in the U.S. diabetic population: a cross-sectional study

Background

Mounting evidence suggests a correlation between heavy metals exposure and diabetes. Diabetic retinopathy (DR) is a prevalent and irreversible complication of diabetes that can result in blindness. However, studies focusing on the effects of exposure to heavy metals on DR remain scarce. Thus, this study aimed to investigate the potential correlation between heavy metals exposure and DR.

Methods

A total of 1,146 diabetics from the National Health and Nutrition Examination Survey (NHANES) between 2005 and 2018 were included in this study. Heavy metal levels were measured via urine testing. Weighted logistic regression, Bayesian kernel machine regression (BKMR), weighted quantile sum (WQS) regression, and restricted cubic spline (RCS) were utilized to investigate the potential relationships between exposure to 10 heavy metals and DR. Finally, subgroup analysis was conducted based on the glycemic control status.

Results

Among the 1,146 participants, 239 (20.86%) were diagnosed with DR. Those with DR had worse glycemic control and a higher prevalence of chronic kidney disease compared to those without DR. Moreover, both the WQS regression and BKMR models demonstrated a positive relationship between exposure to mixed heavy metals and the risk of DR. The results of weighted logistic regression revealed a positive correlation between cobalt (Co) and antimony (Sb) exposure and the risk of DR (OR = 1.489, 95%CI: 1.064-2.082, p = 0.021; OR = 1.475, 95% CI: 1.084-2.008, p = 0.014), while mercury (Hg) exposure was found to promote DR exclusively in the group with good glycemic control (OR = 1.509, 95% CI: 1.157-1.967, p = 0.003). These findings were corroborated by the results of the RCS analysis.

Conclusion

Heavy metal exposure is associated with an increased risk of DR, especially Sb, Co, and Hg exposure. Nevertheless, well-designed prospective studies are warranted to validate these findings.

Natural Background and the Anthropogenic Enrichment of Mercury in the Southern Florida Environment: A Review with a Discussion on Public Health

Mercury (Hg) is a toxic metal that is easily released into the atmosphere as a gas or a particulate. Since Hg has serious health impacts based on human exposure, it is a major concern where it accumulates. Southern Florida is a region of high Hg deposition in the United States. It has entered the southern Florida environment for over 56 MY. For the past 3000 to 8000 years, Hg has accumulated in the Everglades peatlands, where approximately 42.3 metric tons of Hg was deposited. The pre-industrial source of mercury that was deposited into the Everglades was from the atmosphere, consisting of combined Saharan dust and marine evasion. Drainage and the development of the Everglades for agriculture, and other mixed land uses have caused a 65.7% reduction in the quantity of peat, therefore releasing approximately 28 metric tons of Hg into the southern Florida environment over a period of approximately 133 years. Both natural and man-made fires have facilitated the Hg release. The current range in mercury release into the southern Florida environment lies between 994.9 and 1249 kg/yr. The largest source of Hg currently entering the Florida environment is from combined atmospheric sources, including Saharan dust, aerosols, sea spray, and ocean flux/evasion at 257.1-514.2 kg/yr. The remobilization of Hg from the Everglades peatlands and fires is approximately 215 kg/yr. Other large contributors include waste to energy incinerators (204.1 kg/yr), medical waste and crematory incinerators (159.7+ kg/yr), and cement plant stack discharge (150.6 kg/yr). Minor emissions include fuel emissions from motorized vehicles, gas emissions from landfills, asphalt plants, and possible others. No data are available on controlled fires in the Everglades in sugar farming, which is lumped with the overall peatland loss of Hg to the environment. Hg has impacted wildlife in southern Florida with recorded excess concentrations in fish, birds, and apex predators. This bioaccumulation of Hg in animals led to the adoption of regulations (total maximum loads) to reduce the impacts on wildlife and warnings were given to consumers to avoid the consumption of fish that are considered to be contaminated. The deposition of atmospheric Hg in southern Florida has not been studied sufficiently to ascertain where it has had the greatest impacts. Hg has been found to accumulate on willow tree leaves in a natural environment in one recent study. No significant studies of the potential impacts on human health have been conducted in southern Florida, which should be started based on the high rates of Hg fallout in rainfall and known recycling for organic sediments containing high concentrations of Hg.

Association between environmental mercury exposure and allergic disorders in Korean children: Korean National Environmental Health Survey (KoNEHS) cycles 3-4 (2015-2020)

Although previous studies have suggested potential adverse effects of mercury on a child's immune system, the associations have been inconsistent. We aimed to determine the association between urinary mercury levels and allergic diseases in Korean children with high mercury exposure. Data from 853 and 710 children aged 6-11 years in the Korean National Environmental Health Survey (KoNEHS) cycle 3 (2015-2017) and cycle 4 (2018-2020) were analyzed. We examined the association between mercury exposure and the prevalence of atopic dermatitis (AD), asthma, allergic rhinitis (AR), and allergic multimorbidity. After adjusting for all covariates, the urinary mercury level was positively associated with AD in the 2015-2017 study (OR = 1.34, 95% CI = 1.01, 1.79) and AR in 2018-2020 study (OR = 1.46, 95% CI = 1.01, 2.10). Pooled effects showed OR of 1.34 (95% CI = 1.01, 1.79) for AD and 1.47 (95% CI = 1.01, 2.12) for allergic multimorbidity. The association with allergic multimorbidity was greater in boys (OR = 1.88, 95% CI = 1.01, 3.49) than in girls (OR = 1.25, 95% CI = 0.73, 2.14). These results suggest that environmental mercury exposure may exacerbate symptoms of atopic dermatitis and allergic multimorbidity in children.

Association between Heavy Metals, Metalloids and Metabolic Syndrome: New Insights and Approaches

Metabolic syndrome (MetS) is an important public health issue that affects millions of people around the world and is growing to pandemic-like proportions. This syndrome is defined by the World Health Organization (WHO) as a pathologic condition characterized by abdominal obesity, insulin resistance, hypertension, and hyperlipidemia. Moreover, the etiology of MetS is multifactorial, involving many environmental factors, including toxicant exposures. Several studies have associated MetS with heavy metals exposure, which is the focus of this review. Environmental and/or occupational exposure to heavy metals are a major risk, contributing to the development of chronic diseases. Of particular note, toxic metals such as mercury, lead, and cadmium may contribute to the development of MetS by altering oxidative stress, IL-6 signaling, apoptosis, altered lipoprotein metabolism, fluid shear stress and atherosclerosis, and other mechanisms. In this review, we discuss the known and potential roles of heavy metals in MetS etiology as well as potential targeted pathways that are associated with MetS. Furthermore, we describe how new approaches involving proteomic and transcriptome analysis, as well as bioinformatic tools, may help bring about an understanding of the involvement of heavy metals and metalloids in MetS.

An evaluation of the effects of mixed heavy metals on prediabetes and type 2 diabetes: epidemiological and toxicogenomic analysis

The link between mixed heavy metals (mercury, lead, and cadmium), prediabetes, and type 2 diabetes mellitus (T2DM), especially molecular mechanisms, is poorly understood. Thus, we aimed to identify the association between mixed heavy metals and T2DM and its components using a data set from the Korean National Health and Nutrition Examination Survey. We further analyzed the main molecular mechanisms implicated in T2DM development induced by mixed heavy metals using in-silico analysis. Our findings observed that serum mercury was associated with prediabetes, elevated glucose, and ln2-transformed glucose when using different statistical methods. "AGE-RAGE signaling pathway in diabetic complications", "non-alcoholic fatty liver disease", "metabolic Syndrome X", and three miRNAs (hsa-miR-98-5p, hsa-let-7a-5p, and hsa-miR-34a-5p) were listed as the most important molecular mechanisms related to T2DM development caused by mixed heavy metals. These miRNA sponge structures were created and examined, and they may be beneficial in the treatment of T2DM. The predicted cutoff values for three heavy metal levels linked to T2DM and its components were specifically identified. Our results imply that chronic exposure to heavy metals, particularly mercury, may contribute to the development of T2DM. To understand the changes in the pathophysiology of T2DM brought on by a combination of heavy metals, more research is required.

A machine learning-based diagnosis modelling of type 2 diabetes mellitus with environmental metal exposure

BACKGROUND AND OBJECTIVE

Increasing and compelling evidence has been proved that urinary and dietary metal exposure are underappreciated but potentially modifiable biomarkers for type 2 diabetes mellitus (T2DM). The aims of this study were (1) to identify the key potential biomarkers which contributed to T2DM with effective and parsimonious features and (2) to assess the utility of baseline variables and metal exposure in the diagnosis of T2DM.

METHODS

Based on the National Health and Nutrition Examination Survey (NHANES), we selected 9822 screening records with 82 significant variables covering demographics, lifestyle, anthropometric measures, diet and metal exposure for this study. Combining extreme gradient boosting (XGBoost), random forest and light gradient boosting machine (lightGBM), a soft voting ensemble model was proposed to measure the importance of 82 features. With this soft voting ensemble model and variance inflation factor (VIF), strong multicollinear features with low importance scores were further removed from candidate biomarkers. Then, a soft voting ensemble classifier was adopted to demonstrate the efficiency of the proposed feature selection method.

RESULTS

With the novel feature selection method, 12 baseline variables and 3 metal variables were selected to detect patients at risk for T2DM in our study. For metal variables, the dietary copper (Cu), urinary cadmium (Cd) and urinary mercury (Hg) metals were selected as the most remarkable metal exposure and the corresponding P-values were all less than 0.05. In a classification model of T2DM with 12 baseline biomarkers, the addition of 3 metal exposure improved the classification accuracy of T2DM from a traditional area under the curve (AUC) 0.792 of the receiver operating characteristic (ROC) to an AUC 0.847.

CONCLUSIONS

This was the first demonstration of T2DM classification with machine learning under urinary and dietary metal exposure. Improved prediction precision illustrated the effectiveness of the proposed machine learning-based diagnosis model facilitated lifestyle/dietary intervention for T2DM prevention.

Mercury Exposure and Risk of Type 2 Diabetes: A Systematic Review and Meta-Analysis

METHODS

Scopus and PubMed databases were systematically searched from their inception to November 2021 to obtain pertinent studies. Standardized mean differences (SMDs) with corresponding 95% confidence intervals (CIs) were calculated to evaluate the difference in Hg levels between people with and without T2DM. The association of the Hg exposure with T2DM was assessed using a random-effects model by pooling the odds ratios (ORs) and 95% CIs.

RESULTS

A total of 17 studies, with 42,917 participants, aged ≥18 years, were analyzed. Overall, Hg levels were significantly higher in T2DM patients compared with non-T2DM controls (SMD = 1.07; 95%CI = 0.59 to 1.55, P ≤ 0.001), with significant heterogeneity across studies (I2 = 96.1%; P=≤0.001). No significant association was found between Hg exposure and risk of T2DM in the overall analysis and subgroup analysis based on the source of sample and study design. However, higher exposure to Hg was related to reduced risk of T2DM in men (OR = 0.71; 95%CI = 0.57 to 0.88), but not in women. No significant evidence for publication bias was detected.

CONCLUSIONS

Although the Hg level in T2DM is significantly higher than that of nondiabetics, there was no association between Hg exposure and the overall risk of T2DM. Nevertheless, our study shows that higher exposure to Hg might reduce the risk of T2DM in men.

Selenium and mercury concentrations in biological samples from patients with COVID-19

BACKGROUND

Coronavirus disease 2019 (COVID-19) is a systemic disease affecting multiple organs. Furthermore, viral infection depletes several trace elements and promotes complex biochemical reactions in the body. Smoking has been linked to the incidence of COVID-19 and associated mortality, and it may impact clinical effects, viral and bacterial conversion, and treatment outcomes.

OBJECTIVES

To study the relationship between severe acute respiratory syndrome coronavirus type 2 and the elemental concentrations of selenium (Se) and mercury (Hg) in biological samples from smokers and nonsmokers infected with the virus and in healthy individuals.

METHOD

We evaluated changes in the concentrations of essential (Se) and toxic (Hg) elements in biological samples (blood, nasal fluid, saliva, sputum, serum, and scalp hair) collected from male smokers and nonsmokers (aged 29-59 years) infected with COVID-19 and from healthy men in the same age group. The patients lived in different cities in Sindh Province, Pakistan. The Se and Hg concentrations were determined using atomic absorption spectrophotometry.

RESULTS

Se concentrations in all types of biological samples from smokers and nonsmokers with COVID-19 were lower than those of healthy smokers and nonsmokers. Hg concentrations were elevated in both smokers and nonsmokers with COVID-19.

CONCLUSIONS

In the current study, persons infected with COVID-19 had higher concentrations of toxic Hg, which could cause physiological disorders, and low concentrations of essential Se, which can also cause weakness. COVID-19 infection showed positive correlations with levels of mercury and selenium. Thus, additional clinical and experimental investigations are essential.

Contamination, exposure, and health risk assessment of Hg in Pakistan: A review

Mercury is a highly toxic and highly mobile heavy metal. It has been regarded as more toxic than other nonessential and toxic nonradioactive heavy metals. Moreover, it has a high tendency of bioaccumulation and biomagnification in the ecosystem. This study aimed to assess the environmental and health risks related to Hg. Seventy studies related to Hg in environmental media, aquatic biota, and food stuffs across Pakistan were reviewed, and their concentrations were used for ecological and human health risk assessments. High concentrations of Hg were reported in the environment, with maximum concentrations of 72 mg L^-1, 144 mg kg^-1, 887 mg kg^-1, and 49,807 ng m^-3 in surface water, surface soil, surface sediments, and urban atmosphere, respectively. The possible non-carcinogenic health risk (hazard quotient) of Hg was assessed in soil, water, and fish. High risks were calculated for seafood and vegetable consumption, while low risks were estimated for soils and groundwater ingestion and exposure. Overall, children showed higher risks than adults. Last, the risk quotient analysis (RQ) revealed significant risks for aquatic species. RQs showed that multiple species, especially those with smaller resilience, could face long-term detrimental impacts. High, medium, and low risks were calculated from 66.66, 16.17, and 16.17% of the reported Hg concentrations.

A preliminary study on health impacts of Mexican mercury mining workers in a context of precarious employment

Mercury mining is one of the main sources of mercury (Hg) release into the environment, causing serious impacts on human health and the environment. Workers in these mines are employed informally and precariously and therefore lack labor rights such as social security. The objective of the study is to make visible the exposure to environmental contaminants and the health of workers in mercury mines. An environmental assessment was conducted to determine workers' exposure to contaminants; urine samples were obtained to measure exposure to mercury and arsenic, and blood samples were obtained for lead and cadmium. Clinical parameters were also evaluated. Concentrations of Hg, As and Pb were determined in soil, 279.4 mg/kg (24.4-788.5), 14.7 mg/kg (9.5-20.3) and 1.4 mg/kg (1-2.8), respectively. The exposure results for mercury were 551 μg/g creatinine, for arsenic 50 μg/L and for lead 4.7 μg/dL. Cd-B was not found. In addition, 17.6 % of the workers had diabetes and 17.6 % had renal disorders. Principal Component Regression was performed obtaining an r2 of 0.86 for glomerular filtration rate and 0.54 for albumin creatinine ratio using clinical, occupational, and metal exposure variables. Exposure to Hg in this type of mine is not exclusive, so there is a cumulative risk of chronic exposure to different environmental pollutants directly impacting the health of workers. It is necessary to implement health strategies and different work opportunities for these workers.

Mixture analysis of associations between exposure to low levels of multiple metals and semen quality and sperm DNA integrity

The objective of this study was to assess the association of exposure to metal mixtures with semen quality and sperm DNA integrity of coke oven workers (n = 96). Urinary six metals (cadmium, lead, arsenic, zinc, selenium, and copper) were quantified using inductively coupled-mass spectrometry. Semen quality parameters included sperm concentration, sperm concentration, sperm motility, sperm morphology, and sperm viability. Sperm DNA fragmentation and 8-oxo-7,8-dihydro-2'-deoxyguanosine (8-oxodGuo) adducts served as biomarkers for assessing sperm DNA integrity. Bayesian kernel machine regression with the hierarchical variable selection process was used for analyzing both individual and joint effects of the metal mixture on the outcomes of semen samples, while adjusting for age, smoking, alcohol consumption, job length, and body mass index. The metal mixture was associated with reduced sperm concentration, motility, viability, and normal morphology. It was novel that a dose-response relationship was observed between exposure of the metal mixture and semen quality. Among the metals tested, cadmium had a reverse relationship with sperm motility, viability, and normal morphology, and a non-linear relationship with sperm viability and sperm motility. The metal mixture and individual metals were not associated with sperm DNA fragmentation and 8-oxodGuo. In conclusion, exposure to metal mixtures and cadmium may exert an association with semen quality and had no association with sperm DNA breakages.

Environmental chemical exposure dynamics and machine learning-based prediction of diabetes mellitus

BACKGROUND

With dramatically increasing prevalence, diabetes mellitus has imposed a tremendous toll on individual well-being. Humans are exposed to various environmental chemicals, which have been postulated as underappreciated but potentially modifiable diabetes risk factors.

OBJECTIVES

To determine the utility of environmental chemical exposure in predicting diabetes mellitus.

METHODS

A total of 8501 eligible participants from NHANES 2005-2016 were randomly assigned to a discovery (N = 5953) set and a validation (N = 2548) set. We applied random forest (RF) and least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation in the discovery set to select features, and built an optimal model to predict diabetes mellitus, blood insulin, fasting plasma glucose (FPG) and 2-h plasma glucose after oral glucose tolerance test (2-h PG after OGTT).

RESULTS

The machine learning model using LASSO regression predicted diabetes with an area under the receiver operating characteristics (AUROC) of 0.80 and 0.78 in the discovery set and validation set, respectively. The linear model predicted blood insulin level with an R2 of 0.42 and 0.40 in the discovery set and validation set, respectively. For FPG, the discovery set and validation set yielded an R2 of 0.16 and 0.15, respectively. For 2-h PG after OGTT, the discovery set and validation set yielded an R2 of 0.18 and 0.17, respectively.

CONCLUSION

We used environmental chemical exposure, constructed machine learning models and achieved relatively accurate prediction for diabetes, emphasizing the predictive value of widespread environmental chemicals for complicated diseases.

Normal concentration range of blood mercury and bone mineral density: a cross-sectional study of National Health and Nutrition Examination Survey (NHANES) 2005-2010

High blood mercury levels could lead to mercury poisoning, undoubtedly causing great harm to human health. However, the impact of the normal concentration of blood mercury on bone mineral density (BMD) is unclear. Therefore, this study explored the relationship between blood mercury levels and BMD and determined whether the relationship between blood mercury and BMD differs by populations. Two researchers extracted data from the 2005-2010 National Health and Nutrition Examination Survey database. Multivariate linear regression models were performed to evaluate the relationship between mercury level and BMD of the femoral regions and spine. Subgroup analysis was used to estimate differences according to population subgroups. Moreover, the nonlinear relationship of blood mercury levels and BMD was assessed using smooth curve fitting and generalized additive models. The results showed increased BMD with increasing mercury levels by multivariable-adjusted linear regression models, especially in the femoral regions. Subgroup analysis showed that the relationship was more likely to be present in non-Hispanic Whites, while a negative correlation between blood mercury levels and spinal BMD was observed in non-Hispanic Blacks. Furthermore, males (aged 20 to 29 years) and females (aged 30 to 39 years) with low blood mercury levels (< 3 ug/L) had increased risks of osteopenia or osteoporosis. This study showed that blood mercury level within the normal reference value of 10 μg/dL may be associated with BMD, especially with a lower blood mercury level, which may suggest an elevated risk of osteopenia or osteoporosis. However, causation could not be established due to the study design.

Exposure to mercury and thyroid function: Is there a connection?

Mercury (Hg) is one of the most important environmental pollutants with endocrinedisrupting properties. There is little data from epidemiological studies describing the doseresponse relationship between toxic metal levels and hormone levels. The aim of this study was to use the nearest neighbor matching analysis to determine the difference in Hg concentration in healthy/sick subjects with thyroid disease and to use Benchmark modeling to determine the doseresponse relationship between Hg levels in the blood and thyroid-stimulating hormone (TSH) and thyroid hormones in serum. Blood samples were collected and used for Hg measurement using the ICP-MS method, and separated serum was used for hormone analysis. The study showed the existence of a statistically significant difference in Hg levels measured in healthy and sick subjects and the existence of a dose-response relationship between Hg and all measured hormones, with a narrow interval obtained for the Hg-TSH pair. The results of this research support the use of the Benchmark dose approach for the purpose of analyzing data from human studies, and our further research will be focused on examining the impact of low doses on animal models in order to determine more precise effects of low doses on the organism.

Associations of total blood mercury and blood methylmercury concentrations with diabetes in adults: An exposure-response analysis of 2005-2018 NHANES

OBJECTIVE

To examine the exposure-response relationship between mercury exposure and diabetes in adults, and to explore the possible effect modifications by selenium and omega-3 fatty acids.

METHODS

Biomarker data (total blood mercury and blood methylmercury) from individuals ≥20 years of age were obtained from the 2005-2018 NHANES. Diabetes was defined through questionnaires, fasting plasma glucose, 2 -h plasma glucose and hemoglobin A_1c levels. The exposure-response relationship between mercury exposure and diabetes was assessed with logistic regression and restricted cubic splines.

RESULTS

Comparing the highest to lowest quartile of exposure, the multivariable-adjusted odds ratio (95 % CI) of diabetes was 0.76 (0.63-0.92) with total blood mercury and 0.82 (0.66-1.00) with blood methylmercury. The inverse associations between total blood mercury [0.55 (0.40-0.77)] and blood methylmercury [0.61 (0.38-0.97)] and diabetes were observed among individuals having higher intakes of selenium (P_{for interaction}<0.05). Trends toward lower odds of diabetes with mercury exposure were mainly confined to individuals having higher intakes of omega-3 fatty acid, but the interactions were not significant. The inverse associations between total blood mercury and blood methylmercury and diabetes remained in sensitivity analyses after excluding patients with hypertension that may change their dietary intake of fish. Exposure-response analyses showed an initial decrease in odds of diabetes followed by a platform or a weaker decrease beyond 3 μg/L of total blood mercury and methylmercury concentrations, respectively.

CONCLUSIONS

Total blood mercury and blood methylmercury concentrations were inversely associated with diabetes in adults, and the associations were modified by selenium.

Relationship between gestational diabetes and serum trace element levels in pregnant women from Eastern Iran: a multivariate approach

The prevalence of gestational diabetes mellitus (GDM) has increased over the recent decades. Exposure to environmental contaminants may be a risk factor for the development of GDM, but this is heavily dependent on particular circumstances. Studies on various areas linking various factors are therefore needed. We examined the associations between serum trace element levels and incidents of GDM among 102 pregnant women (diabetic n = 60 and healthy n = 42) living in Birjand (Iran). Blood serum samples were analyzed for concentrations of elements linked to particulate matter air pollution such as As, Cd, Cu, Hg, Mn, Ni, V, and Zn. Concentrations of As (8.58 vs. 3.15 μg/L), Cd (6.74 vs. 0.52 μg/L), and Hg (2.60 vs. 0.90 μg/L) were significantly higher in women with GDM. Risk difference (RD) estimation showed that As, 0.516 (0.355, 0.677); Cd, 0.719 (0.534, 0.904); and Hg, 0.505 (0.276, 0.735) increase GDM probability, while V lower that risk, -0.139 (-0.237, -0.042). With the principal component analysis, we were able to separate subjects according to their GDM status based on element levels. Such classification revealed very high efficiency with a true positive rate of 93%, according to linear discriminant analysis. GDM subjects presented higher levels of As, Cd, and Hg, indicating that these elements may disturb insulin metabolism and promote the development of GDM. Therefore, we conclude that systematic monitoring of trace elements followed by multivariate modeling in women planning pregnancy should be carried out to prevent the development of GDM.

Intakes of Fish and Long-chain n-3 Polyunsaturated Fatty Acid Supplements During Pregnancy and Subsequent Risk of Type 2 Diabetes in a Large Prospective Cohort Study of Norwegian Women

OBJECTIVE

To investigate associations between intakes of total fish, lean fish, fatty fish, and long-chain n-3 polyunsaturated fatty acid (LCn-3PUFA) supplements and risk of type 2 diabetes in women after pregnancy. Furthermore, we sought to compare the estimated intakes of methylmercury (MeHg) and sum of dioxins and dioxin-like polychlorinated biphenyls (dl-PCBs) with tolerable weekly intakes (TWI).

RESEARCH DESIGN AND METHODS

Women free of diabetes at baseline (n = 60,831) who participated in the population-based Norwegian Mother, Father and Child Cohort Study (MoBa) were prospectively evaluated for incident type 2 diabetes, identified on the basis of medication usage >90 days after delivery, ascertained through the Norwegian Prescription Database. Dietary intake data were obtained with a validated 255-item food-frequency questionnaire (FFQ), which assessed habitual diet during the first 4-5 months of pregnancy. Intakes of MeHg and sum of dioxins and dl-PCBs were derived with use of a contaminant database and the FFQ.

RESULTS

Median age was 31 years (interquartile range 27, 34) at time of delivery, and follow-up time was 7.5 years (6.5, 8.5). Type 2 diabetes occurred in 683 (1.1%) participants. Multivariable Cox regression analyses identified lower risk of type 2 diabetes with increasing energy-adjusted lean fish intake, 25 g/1,000 kcal (25 g/1,000 kcal: hazard ratio 0.71, 95% CI 0.53-0.95, P = 0.022). However, in stratified analyses, a lower risk was found only in women with prepregnancy BMI ≥25 kg/m2. There were no associations between intake of total fish, fatty fish, or LCn-3PUFA supplements and type 2 diabetes. MeHg intake was low, but the intake of the sum of dioxins and dl-PCBs (picograms of toxic equivalents/kilograms of body weight/week) exceeded the TWI set by the European Food Safety Authority (EFSA) for the majority of participants.

CONCLUSIONS

Intake of lean fish, but not fatty fish or LCn-3PUFA supplements, was associated with lower risk of pharmacologically treated type 2 diabetes in Norwegian women who were overweight or obese. Fatty fish, which contain dioxins and dl-PCBs, did not increase the risk of type 2 diabetes, but the exceedance of the EFSA TWI for dioxins and dl-PCBs is a health concern.

Pollutants and nutrition: Are methylmercury effects on blood pressure and lipoprotein profile comparable to high-fat diet in mice?

Human exposure to methylmercury (MeHg) due to contaminated fish intake as part of a high-fat (HFD), high-carbohydrate diets is a reality today for many populations. HFD is associated with hypertension and hyperlipidemia, primary cardiovascular disease (CVD) risk factors. Some studies suggest that MeHg induces those risk factors. We evaluated the effect of MeHg exposure in mice fed with HFD or control diet for eight weeks. In the last experimental 15 days, the half group received a MeHg solution (20 mg/L) replacing water. Blood pressure (BP), heart rate, lipoprotein concentrations, and paraoxonase activity were evaluated. Liver cholesterol, triacylglycerol, and IBA-1+ cells, as well as transcriptional levels of genes related to lipid metabolism and inflammatory response, were also assessed. HFD and both MeHg groups presented increased BP and total cholesterol (TC). In the liver, HFD but not MeHg was related to an increase in TC. Also, MeHg intoxication reduced paraoxonase activity regardless of diet. MeHg intoxication and HFD increased steatosis and the number of IBA-1+ cells and modified some gene transcripts associated with lipid metabolism. In conclusion, we demonstrated that MeHg effects on CVD risk factors resemble those caused by HFD.

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.