Individual and joint effects of metal exposure on metabolic syndrome among Chinese adults

Association Between Nickel Exposure and Metabolic Syndrome: Data from NHANES 2017-2018

Previous studies have found a possible association between nickel and metabolic syndrome (MetS), but with conflicting results. No studies have determined whether nickel exposure increases the prevalence of MetS in the general U.S. population. Therefore, we used data from the National Health and Nutrition Examination Survey (NHANES) to assess the association between urinary nickel and MetS. Since urinary nickel levels were presented as a skewed distribution, they were normalized using a logarithmic transformation. Weighted multivariate logistic models, restricted cubic spline, threshold effect analysis, and subgroup analyses were used to examine the association between urinary nickel concentration and the risk of MetS and its components. Based on data from 1577 participants, individuals in the second, third, and fourth quartiles of urinary nickel had an adjusted OR for MetS of 1.42 (95% CI: 0.88, 2.28), 2.00 (95% CI: 1.22, 3.28), and 1.68 (95% CI: 1.05, 2.70), respectively, representing an inverted "L"-shaped nonlinear dose-response relationship with an inflection point at 0.2141 ng/L. Patients over the age of 40, males, less educated, and smokers are more susceptible to nickel exposure. In addition, there were significant associations between nickel and most components of the MetS, with the strongest to weakest correlations being high fasting glucose, reduced high-density lipoprotein, abdominal obesity, and elevated blood pressure; however, there was no significant correlation between nickel and hyperlipidemia. In conclusion, environmental nickel exposure increases the prevalence of MetS in U.S. adults, particularly in males over 40 years of age, those with less education, and smokers.

Contextualizing toxic elements in the diet: a case for integration of toxic element data into food databases

Food composition data plays a key role in the practice of nutrition. However, nutrition professionals may currently lack the resources they need to integrate information about toxic elements - such as arsenic, cadmium, and lead - in food into the advice they give consumers. Geographic, sociocultural, and individual factors may impact not only the toxic element content of food, but also how the balance between potentially toxic and health-promoting components of food must be weighed. Better integration and contextualization of toxic element data into key food databases could allow for more nuanced, comprehensive nutrition guidance.

Associations between multiple urinary metals and metabolic syndrome: Exploring the mediating role of liver function in Chinese community-dwelling elderly

BACKGROUND

Multiple metals exposure has been revealed to be related to metabolic syndrome (MetS). However, the associations and interactions between multiple metals exposure and MetS are remains controversial, and the potential mechanism of the above-mentioned is still unclear.

METHODS

The associations between urinary metals and the MetS were analyzed by multivariable logistic regression model and restricted cubic spline (RCS). Bayesian kernel machine regression (BKMR) model and quantile-based g-computation (qgcomp) were applied to explore the mixed exposure and interaction effect of metals. Mediation analysis was used to explore the role of liver function.

RESULTS

In the single metal model, multiple metals were significantly associated with MetS. RCS analysis further verified the associations between 8 metals and MetS. BKMR model and qgcomp showed that zinc (Zn), iron (Fe), and tellurium (Te) were the main factors affecting the overall effect. In addition, mediation analysis indicated that serum alanine aminotransferase (ALT) mediated 21.54% and 13.29% in the associations of vanadium (V) and Zn with the risk of MetS, respectively.

CONCLUSIONS

Elevated urinary concentration of Zn, V, Te, copper (Cu), molybdenum (Mo), and thallium (Tl) were related to the increased risk of MetS. Conversely, Fe and selenium (Se) may be protective factors for MetS in mixed exposure. Liver function may play a key role in the association of V and Zn exposure with MetS.

Multimetal exposure: Challenges in diagnostics, prevention, and treatment

Extensive use of heavy metals has posed a serious concern for ecosystem and human too. Heavy metals are toxic in nature and their accumulation in human body causes serious disorders such as neurological disease, cardiac disease, gastrointestinal problems, skin disorders, reproductive disease, lungs diseases, and so on. Furthermore, heavy metals not only affect the human health but also have a negative impact on the economy. In the current review, we have elaborated the impact of heavy metal exposure on human health and socioeconomics. We have discussed the molecular mechanism involved in the heavy metal-induced human disorders such as oxidative stress, neuroinflammation, and protein misfolding. Finally, we discussed the preventive measure and treatment strategy that could counter the negative effects of heavy metal intoxications. In conclusion, there is a substantial correlation between heavy metals and the onset and advancement of several health issues. Chelation treatment could be a useful tactic to lessen the toxic metal load and the difficulties that come with it.

Associations of mixed urinary metals exposure with metabolic syndrome in the US adult population

BACKGROUND

Metals are harmful to human health in many ways. However, the association between metals and metabolic syndrome (MetS) remains unclear. Aims of this study is to discuss the relationship between urinary metal and MetS.

METHODS

This study included 3419 adult participants from the National Health and Nutrition Examination Survey (NHANES) (2005-2018). Logistic regression analysis, Bayesian kernel machine regression (BKMR), weighted quantile sum (WQS), and restricted cubic spline (RCS) were used to explore the associations of nine urinary metal and MetS.

RESULTS

BKMR and WQS showed the effects of combined nine urinary metal were negatively correlated with MetS. Logistic regression analysis, WQS, and BKMR all suggested that cesium (Cs) and lead (Pb) were negatively correlated with MetS (all PFDCR <0.05). And RCS suggested log2-transformed Cs (χ2 = 20, P < 0.001) and log2-transformed Pb (χ2 = 19.9, P < 0.001) were negatively and linearly associated with MetS.

CONCLUSION

Existing evidence suggests that urine metal content is related to MetS. Cs and Pb are negatively related to MetS. It is still necessary to study and further discuss the causal relationship and mechanism.

The association between urinary metals/metalloids and chronic kidney disease among general adults in Wuhan, China

The relation between exposure to single metal/metalloid and the risk of chronic kidney disease (CKD) remains unclear. We aimed to determine the single and mixed associations of 21 heavy metals/metalloids exposure and the risk of CKD. We performed a cross-sectional study that recruited 4055 participants. Multivariate logistic regression, linear regression and weighted quantile sum (WQS) regression were conducted to explore the possible effects of single and mixed metals/metalloids exposure on the risk of CKD, the risk of albuminuria and changes in the estimated glomerular filtration rate (eGFR). In single-metal models, Cu, Fe, and Zn were positively associated with increased risks of CKD (P-trend < 0.05). Compared to the lowest level, the highest quartiles of Cu (OR = 2.94; 95% CI: 1.70, 5.11; P-trend < 0.05), Fe (OR = 2.39; 95% CI: 1.42, 4.02; P-trend < 0.05), and Zn (OR = 2.35; 95% CI: 1.31, 4.24; P-trend < 0.05) were associated with an increased risk of CKD. After multi-metal adjustment, the association with the risk of CKD remained robust for Cu (P < 0.05). Weighted quantile sum regression revealed a positive association between mixed metals/metalloids and the risk of CKD, and the association was largely driven by Cu (43.7%). Specifically, the mixture of urinary metals/metalloids was positively associated with the risk of albuminuria and negatively associated with eGFR.

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.

Associations of perchlorate, nitrate, and thiocyanate with metabolic syndrome and its components among US adults: A cross-sectional study from NHANES

Perchlorate, nitrate and thiocyanate are common endocrine disruptors. Herein, this study was undertaken to evaluate the associations between perchlorate, nitrate, and thiocyanate exposures (alone or in combination) and risk of metabolic syndrome (MetS) among adults, which has not been explored so far. Analytical data were extracted from different datasets in the National Health and Nutrition Examination Survey (NHANES) database. Multivariate logistic regression models were constructed to investigate the associations between perchlorate, nitrate, and thiocyanate exposures, and the prevalence of MetS. Subsequently, odds ratios (OR) and their corresponding 95 % confidence intervals (CIs) were adopted to represent the magnitude of the effect size. We performed a series of subgroup analyses and sensitivity analyses as well. Moreover, three commonly used mixture modeling strategies [Weighted quantile sum (WQS) regression, quantile-based g-computation (Qgcomp), and Bayesian kernel machine regression (BKMR)] were utilized to evaluate the joint mixture effect on MetS. This study included 12,007 participants in the subsequent analyses. After adjustment for confounding factors, higher levels of perchlorate, and thiocyanate concentrations were significantly associated with the risk of MetS (OR = 1.15, 95%CI:1.00, 1.32; OR = 1.21, 95%CI:1.04, 1.41, respectively). Analyses of WQS and Qgcomp showed that a quartile increase in chemical mixture was correlated with the occurrence of MetS with ORs of 1.07 (95%CI: 0.99, 1.16) and 1.07 (95%CI: 1.00, 1.14), respectively. This positive association was mainly driven by perchlorate and thiocyanate. Analysis of BKMR revealed that perchlorate, nitrate, and thiocyanate mixture was positively associated with the risk of MetS while perchlorate, and thiocyanate were major predictors in the mixture. In summary, our study reveals positive relationships between perchlorate, thiocyanate and MetS. Co-exposure to perchlorate, nitrate and thiocyanate is positively associated with the risk of MetS, with perchlorate and thiocyanate contributing the most to the overall mixture effect.

Individual and mixtures of polychlorinated biphenyls and organochlorine pesticides exposure in relation to metabolic syndrome among Chinese adults

Polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) are commonly detected in humans due to their persistence and bioaccumulation, and are suspected risk factors for metabolic syndrome (MetS). However, most studies have focused on individual rather than combined exposure. We explored the associations between individual and combined PCBs/OCPs exposure and MetS to better assess the health effects of PCBs and OCPs. This cross-sectional study included 1996 adults from Wuhan, China. A total of 338 participants fulfilled criteria for MetS. Eight PCBs and OCPs were detected in >50 % of the samples. Most of the hexachlorocyclohexanes (HCHs) in the serum were derived from the recent environmental input of lindane, while the high levels of dichlorodiphenyltrichloroethane (DDTs) were mainly due to historical use. Multivariate linear regression analyses revealed that β-HCH, p,p'-dichlorodiphenyldichloroethylene (p,p'-DDE), PCB-52, PCB-153, and PCB-180 were positively correlated with increased odds of MetS. The profiles of the PCBs and OCPs associated with the different components of MetS were distinct. Furthermore, quantile-based g computation (qgcomp) analyses showed that PCB and OCP mixtures were positively associated with the risk of MetS, and p,p'-DDE was the largest contributor to our model. These findings suggest that PCB and OCP concentrations, both individually and as mixtures, are associated with MetS risk. Prospective studies are needed to confirm these results.

Selenium and Zinc Intakes of Staple Grains and Their Correlation with Urine Selenium and Zinc in the Tibetan Rural Residents along the Yarlung Zangbo River

Grains account for a large proportion of the diet of rural residents in Tibet. The lack of selenium (Se) and zinc (Zn) threatens the population's nutrition and health. However, the intakes of selenium and zinc in grains remains unclear. To clarify the nutritional status of selenium and zinc consumed from staple grains of residents along the Yarlung Zangbo River in Tibet, 341 grain samples and 242 urine samples were collected, and 244 food frequency questionnaires were completed along the Yarlung Zangbo River in 2020-2021. The results showed that the selenium concentrations of 88.5% of self-produced tsampa and 80.8% of self-produced flour were lower than the grain selenium threshold (<25 μg·kg^-1). The intake of selenium and zinc from staple grains (tsampa, flour, and rice) contributed 15.0% and 43.5% to the recommended nutrient intake (RNI) on average, respectively. A geographical detector model analyzed factors affecting urinary selenium and zinc levels. Selenium and zinc intakes in rice and flour, and dietary diversity score (DDS) were the main factors affecting urinary selenium and zinc (p < 0.01). Their interaction effects on urinary selenium and zinc were greater than those of a single factor. The staple grains of rural residents along the Yarlung Zangbo River were in a state of selenium deficiency. The zinc content of the staple grain purchased was lower than that of the main grain produced by rural residents. Changing the grain consumption pattern and adjusting the proportion of exogenous grains can improve selenium and zinc nutrition in residents.

Associations of metals and metals mixture with lipid profiles: A repeated-measures study of older adults in Beijing

Metals inevitably and easily enter into human bodies and can induce a series of pathophysiological changes, such as oxidative stress damage and lipid peroxidation, which then may further induce dyslipidemia. However, the effects of metals and metals mixture on the lipid profiles are still unclear, especially in older adults. A three-visits repeated measurement of 201 older adults in Beijing was conducted from November 2016 to January 2018. Linear Mixed Effects models and Bayesian kernel machine regression models were used to estimate associations of eight blood metals and metals mixture with lipid profiles, including total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), Castelli risk indexes I (CRI-1), Castelli risk indexes II (CRI-2), atherogenic coefficient (AC), and non-HDL cholesterol (NHC). Cesium (Cs) was positively associated with TG (β_Cs = 0.14; 95% CI: 0.02, 0.26) whereas copper (Cu) was inversely related to TG (β_Cu = -0.65; 95%CI: -1.14, -0.17) in adjusted models. Manganese (Mn) was mainly related to higher HDL-C (β_Mn = 0.14; 95% CI: 0.07, 0.21) whereas molybdenum showed opposite association. Metals mixture was marginally positive associated with HDL-C, among which Mn played a crucial role. Our findings suggest that the effects of single metal on lipid profiles may be counteracted in mixtures in the context of multiple metal exposures; however, future studies with large sample size are still needed to focus on the detrimental effects of single metals on lipid profiles as well as to identify key components.

Exposure to Metal Mixtures and Overweight or Obesity Among Chinese Adults

Previous research has investigated the association between individual metal exposure and overweight/obesity (OW/OB). However, there is limited data about metal mixture exposure and OW/OB. This study aimed to explore the individual and joint effects of 21 metals on OW/OB and its metabolic phenotypes. A total of 4042 participants were enrolled in our study, and 51.0% of them were overweight/obese. We quantified 21 metal levels in the urine sample. OW/OB was defined as BMI ≥ 24 kg/m², while the metabolic phenotypes, including metabolic unhealthy overweight/obesity (MUOW/OB) and metabolic health overweight/obesity (MHOW/OB), were determined by BMI and metabolic state. We used logistic regression to analyze the effect of individual metal exposure on OW/OB and its metabolic phenotypes. Quantile g-computation was applied to evaluate the joint effect of metal exposure on OW/OB and its metabolic phenotypes. In logistic regression, zinc (Zn) was positively associated with OW/OB, with the odds ratio (OR) in the highest quartiles of 2.19 (95% confidence interval (CI), 1.74, 2.77; P trend < 0.001), while arsenic (As) and cadmium (Cd) were negatively associated with OW/OB (OR = 0.70 (0.56, 0.87) and 0.61 (0.48, 0.78), respectively). After adjustment for age, gender, education, cigarette smoking, alcohol drinking, physical activity, meat intake, and vegetable intake, Zn was positively associated with MUOW/OB, while As, Cd, nickel (Ni), and strontium (Sr) were negatively associated with MUOW/OB (all P trend < 0.05). Quantile g-computation showed a significantly negative association between metal mixture exposure and MUOW/OB. Our study suggested that metal mixture exposure might be negatively associated with OW/OB, particularly with MUOW/OB. Zn, As and Cd contributed most to the effect of the mixture. More prospective studies are warranted to confirm these findings and reveal the underlying mechanisms.

Metals and risk of incident metabolic syndrome in a prospective cohort of midlife women in the United States

Exposure to metals may contribute to the development of metabolic syndrome (MetS); however, evidence from midlife women who are at greater risk of cardiometabolic disease is limited. We assessed the associations of 15 urinary metal concentrations with incident MetS in a prospective cohort of midlife women in the United States. The study population included 947 White, Black, Chinese and Japanese women, aged 45-56 years, free of MetS at baseline (1999-2000), who participated in the Study of Women's Health Across the Nation Multi-Pollutant Study. Fifteen metals were detected in almost all participants urine samples using inductively coupled plasma mass spectrometry at the baseline. Incident MetS was identified annually through 2017 as having at least three of the following five components: high blood pressure, impaired fasting glucose, abdominal obesity, high triglycerides, and poor high-density lipoprotein cholesterol. We used the Cox proportional hazards models to investigate the associations between individual metals and MetS incidence. The adjusted hazard ratios (HR) (95% CI) for MetS in associations with each doubling of urinary metal concentration were 1.14 (1.08, 1.23) for arsenic, 1.14 (1.01, 1.29) for cobalt, and 1.20 (1.06, 1.37) for zinc. We further evaluated the associations between metal mixtures and MetS using the elastic net penalized Cox model and summarized the results into the environmental risk score (ERS). Arsenic, barium, cobalt, copper, nickel, antimony, thallium, and zinc had positive weights, and cadmium, cesium, mercury, molybdenum, lead, and tin had negative weights in the construction of the ERS. The adjusted HR of MetS comparing 75th vs. 25th percentiles of the ERS was 1.45 (1.13, 1.87). These findings support the view that arsenic, cobalt, zinc, as well as metal mixtures, might influence the risks of incident MetS in midlife women.