Association between the nickel exposure and lipid profiles in general population from NHANES

Association of metals and bisphenols exposure with lipid profiles and dyslipidemia in Chinese adults: Independent, combined and interactive effects

BACKGROUND

Although studies have assessed the association of metals and bisphenols with lipid metabolism, the observed results have been controversial, and limited knowledge exists about the combined and interactive effects of metals and bisphenols exposure on lipid metabolism.

METHODS

Plasma metals and serum bisphenols concentrations were evaluated in 888 participants. Multiple linear regression and logistic regression models were conducted to assess individual associations of 18 metals and 3 bisphenols with 5 lipid profiles and dyslipidemia risk, respectively. The dose-response relationships of targeted contaminants with lipid profiles and dyslipidemia risk were captured by applying a restriction cubic spline (RCS) function. The bayesian kernel machine regression (BKMR) model was used to assess the overall effects of metals and bisphenols mixture on lipid profiles and dyslipidemia risk. The interactive effects of targeted contaminants on interested outcomes were explored by constructing an interaction model.

RESULTS

Single-contaminant analyses revealed that exposure to iron (Fe), nickel (Ni), copper (Cu), arsenic (As), selenium (Se), strontium (Sr), and tin (Sn) was associated with elevated lipid levels. Cobalt (Co) showed a negative association with high density lipoprotein cholesterol (HDL-C). Bisphenol A (BPA) and bisphenol AF (BPAF) were associated with decreased HDL-C levels, with nonlinear associations observed. Vanadium (V), lead (Pb), and silver (Ag) displayed U-shaped dose-response relationships with most lipid profiles. Multi-contaminant analyses indicated positive trends between contaminants mixture and total cholesterol (TC), triglycerides (TG), low density lipoprotein cholesterol (LDL-C), and non-high-density lipoprotein cholesterol (non-HDL-C). The interaction analyses showed that Se-Fe exhibited synergistic effects on LDL-C and non-HDL-C, and Se-Sn showed a synergistic effect on HDL-C.

CONCLUSIONS

Our study suggested that exposure to metals and bisphenols was associated with changes in lipid levels, and demonstrated their combined and interactive effects.

Association between blood lead, cadmium, selenium levels and hyperlipidemia: A population-based study

BACKGROUND

There are limited epidemiological investigations of blood metal levels related to hyperlipidemia, and results indicating the association between blood lead (Pb), cadmium (Cd) and selenium (Se), and lipid biomarkers have been conflicting.

METHODS

We included populations for which NHANES collected complete data. Multivariate logistic regression and subgroup analyses were conducted to ascertain the relationship between blood Pb, Cd, and Se levels and hyperlipidemia. Nonlinear relationships were characterized by smoothed curve fitting and threshold effect analysis.

RESULTS

5429 participants in all, with a mean age of 53.70 ± 16.63 years, were included; 47.1% of the subjects were male, and 3683 (67.8%) of them had hyperlipidemia. After modifying for variables with confounders in a multivariate logistic regression model, we discovered a positive correlation between blood Pb and Se levels and hyperlipidemia (Pb: OR:2.12, 95% CI:1.56-2.88; Se: OR:1.84, 95% CI:1.38-2.45). Gender, age, smoking status, alcohol use status, hypertension, diabetes, and body mass index were not significantly linked with this positive correlation, according to subgroup analysis and interaction test (P for interaction>0.05). Positive correlations between blood Pb, Cd, and Se levels and the risk of hyperlipidemia have been found using smooth curve fitting.

CONCLUSIONS

This study demonstrates that higher blood levels of Pb, Cd, and selenium are linked to an increased risk of hyperlipidemia.

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.

The association between lipid biomarkers and osteoarthritis based on the National Health and Nutrition Examination Survey and Mendelian randomization study

To explore the association between lipid markers and osteoarthritis (OA). First, the National Health and Nutrition Examination Survey (NHANES) database was used to screen participants with lipid markers, OA and relevant covariates, and logistic regression was used to analyze the association between lipid markers and OA; Then, under the theoretical framework of Mendelian randomization (MR), two-sample MR was performed using GWAS data of lipid markers and OA to explore the causal association between the two, which was analyzed by inverse variance weighting (IVW) method. Heterogeneity test, sensitivity analysis and pleiotropy analysis were also performed. The NHANES database screened a total of 3706 participants, of whom 836 had OA and 2870 did not have OA. When lipid markers were used as continuous variables, multivariate logistic results showed an association between HDL, LDL and OA (HDL, OR (95%):1.01 (1.00, 1.01); LDL, OR (95%):1.00 (0.99, 1.00)). When lipid markers were used as categorical variables, multivariate logistic results showed the fourth quartile result of 0.713 (0.513, 0.992) for LDL relative to the first quartile. In MR study, the results of the IVW method for TG, TL, HDL and LDL showed OR (95% CI) of 1.06 (0.97-1.16), 0.95 (0.85-1.06), 0.94 (0.86-1.02) and 0.89 (0.80-0.998) with P-values of 0.21, 0.37. 013, 0.046. The heterogeneity tests and multiplicity analyses showed P-values greater than 0.05, and sensitivity analyses showed no abnormal single nucleotide polymorphisms. Through NHANES database and MR analyses, LDL was found to be a protective factor for OA, while HDL still needs further study. Our results provide new biomarkers for preventive and therapeutic strategies for OA.

Association of nickel exposure with body mass index, waist circumference and incidence of obesity in US adults

This study aimed to detect the relationship between nickel exposure and body mass index (BMI), waist circumference and incidence of obesity in the general population of the United States. The National Health and Nutrition Examination Survey (NHANES) 2017-2018 database was utilized, and the sample comprised 1702 participants aged 18 years and above with complete urinary nickel, body mass index, and waist circumference data. Obesity was determined using BMI and waist circumference data. The multivariate linear regression and logistic regression models were utilized to detect the association between urinary nickel concentration and BMI, waist circumference, and incidence of obesity. After multivariable adjustment, the log-transformed urinary nickel concentration was inversely associated with BMI [β = -0.87; 95% confidence interval (CI): (-1.36, -0.38)] and waist circumference [β = -1.51; 95% CI: (-2.93, -0.08)]. Compared with the lowest tertile of urinary nickel, the β value and 95% CI of BMI and waist circumference for the highest tertile were β = -1.65.95% CI: (-2.85, -0.45) and β = -2.78, 95% CI: (-6.17, 0.62), respectively. The log-transformed urinary nickel concentration was also negatively associated with obesity status [adjusted odds ratio (OR) = 0.81, 95% CI: (0.64, 1.01)]. Compared with the lowest tertile of urinary nickel, the adjusted OR and 95% CI of obesity status for the highest tertile were OR = 0.64 and 95% CI: (0.37, 1.12). Smooth curve fitting and the generalized additive model indicated that elevated urinary nickel concentration was associated with decreased BMI, waist circumference, and incidence of obesity. The negative association was consistent and robust in different subgroups, according to stratified analysis. This study found that nickel exposure may be negatively associated with BMI, waist circumference and incidence of obesity in US Adults.

Do blood metals influence lipid profiles? Findings of a cross-sectional population-based survey

Dyslipidemia, an imbalance of lipids and a major risk factor for cardiovascular disease, has been associated with elevated blood and urine levels of several heavy metals. Using data from a Canadian Health Measures Survey (CHMS), we tested associations between blood levels of cadmium, copper, mercury, lead, manganese, molybdenum, nickel, selenium, and zinc, and the lipids triglycerides (TG), total cholesterol (TC), low density lipoproteins (LDL), high density lipoproteins (HDL) and apolipoproteins A1 (APO A1), and B (APO B). All adjusted associations between single metals and lipids were positive and significant, except for APO A1 and HDL. The joint effect of an interquartile range increase in heavy metals was positively associated with percentage increases of TC, LDL and APO B of 8.82% (95%CI: 7.06, 10.57), 7.01% (95%CI: 2.51, 11.51) and 7.15% (95%CI: 0.51, 13.78), respectively. Future studies are warranted to determine if reducing environmental exposure to heavy metals favorably influences lipid profiles and the risk of cardiovascular disease.