Associations Between Serum Multiple Metals Exposures and Metabolic Syndrome: a Longitudinal Cohort Study

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.

Association between multiple-heavy-metal exposures and systemic immune inflammation in a middle-aged and elderly Chinese general population

BACKGROUND

Exposure to heavy metals alone or in combination can promote systemic inflammation. The aim of this study was to investigate potential associations between multiple plasma heavy metals and markers of systemic immune inflammation.

METHODS

Using a cross-sectional study, routine blood tests were performed on 3355 participants in Guangxi, China. Eight heavy metal elements in plasma were determined by inductively coupled plasma mass spectrometry. Immunoinflammatory markers were calculated based on peripheral blood WBC and its subtype counts. A generalised linear regression model was used to analyse the association of each metal with the immunoinflammatory markers, and the association of the metal mixtures with the immunoinflammatory markers was further assessed using weighted quantile sum (WQS) regression.

RESULTS

In the single-metal model, plasma metal Fe (log10) was significantly negatively correlated with the levels of immune-inflammatory markers SII, NLR and PLR, and plasma metal Cu (log10) was significantly positively correlated with the levels of immune-inflammatory markers SII and PLR. In addition, plasma metal Mn (log10 conversion) was positively correlated with the levels of immune inflammatory markers NLR and PLR. The above associations remained after multiple corrections. In the mixed-metal model, after WQS regression analysis, plasma metal Cu was found to have the greatest weight in the positive effects of metal mixtures on SII and PLR, while plasma metals Mn and Fe had the greatest weight in the positive effects of metal mixtures on NLR and LMR, respectively. In addition, blood Fe had the greatest weight in the negative effects of the metal mixtures for SII, PLR and NLR.

CONCLUSION

Plasma metals Cu and Mn were positively correlated with immunoinflammatory markers SII, NLR and PLR. While plasma metal Fe was negatively correlated with immunoinflammatory markers SII, NLR, and PLR.

Association between plasma and dietary trace elements and obesity in a rural Chinese population

Trace elements may play an important role in obesity. This study aimed to assess the plasma and dietary intake levels of four trace elements, Mn, Cu, Zn and Se in a rural Chinese population, and analyse the relationship between trace elements and obesity. A cross-sectional study involving 2587 participants was conducted. Logistic regression models were used to analyse the association between trace elements and obesity; restricted cubic spline (RCS) models were used to assess the dose-response relationship between trace elements and obesity; the weighted quantile sum (WQS) model was used to examine the potential interaction of four plasma trace elements on obesity. Logistic regression analysis showed that plasma Se concentrations in the fourth quartile (Q4) exhibited a lower risk of developing obesity than the first quartile (Q1) (central obesity: OR = 0·634, P = 0·002; general obesity: OR = 0·525, P = 0·005). Plasma Zn concentration in the third quartile (Q3) showed a lower risk of developing obesity in general obesity compared with the first quartile (Q1) (OR = 0·625, P = 0·036). In general obesity, the risk of morbidity was 1·727 and 1·923 times higher for the second and third (Q2, Q3) quartiles of dietary Mn intake than for Q1, respectively. RCS indicated an inverse U-shaped correlation between plasma Se and obesity. WQS revealed the combined effects of four trace elements were negatively associated with central obesity. Plasma Zn and Se were negatively associated with obesity, and dietary Mn was positively associated with obesity. The combined action of the four plasma trace elements had a negative effect on obesity.

Association between multiple metals exposure and sleep disorders in a Chinese population: A mixture-based approach

OBJECTIVE

Previous studies have suggested a possible association between metals and sleep disorders. This study aimed to explore the association between Zn, Cu, Se, Mg and Ca and sleep disorders in single and multi-metal co-exposure models.

METHODS

Logistic regression models, restricted cubic spline model (RCS), Quantile g computation (Q-gcomp), Weighted Quantile Sum (WQS), and Bayesian kernel machine regression (BKMR) models were used to investigate the association between metal levels and sleep disorders.

RESULTS

Logistic regression showed that in the total population, the second, third, and fourth quartile Zn concentration exhibited a lower risk of sleep disorders compared with the first quartile, with odds ratios (ORs) of 0.783, 0.711, and 0.704, respectively. Compared with Zn/Cu and Zn/Se in the first quartile, the third and fourth quartiles showed a lower risk of sleep disorders. In the 30-59 years group, the risk of sleep disorders was 0.699 times greater for the fourth quartile Mg concentration than that for the first quartile. The risk of sleep disorders in Mg/Ca concentration in the third quartile was 0.737 times higher than in the first quartile. Q-gcomp, WQS, and BKMR model analysis showed the negative overall effect of mixtures of the five metals on sleep disorders, with Zn being the largest contributor.

CONCLUSION

Our study showed that plasma Zn, Mg, Zn/Cu, Zn/Se, and Mg/Ca reduced the risk of sleep disorders, and the combined effect of multiple metals was negatively associated with the risk of sleep disorders, with Zn being the largest contributor to this relationship.

Multiple metal exposure and metabolic syndrome in elderly individuals: A case-control study in an active mining district, Northwest China

The prevalence of metabolic syndrome (MetS) is increasing at an alarming rate worldwide, particularly among elderly individuals. Exposure to various metals has been linked to the development of MetS. However, limited studies have focused attention on the elderly population living in active mining districts. Participants with MetS (N = 292) were matched for age (±2 years old) and sex with a healthy subject (N = 292). We measured the serum levels of 14 metals in older people aged 65-85 years. Conditional logistic regression, restricted cubic spline model, multiple linear regression, and Bayesian Kernel Machine Regression (BKMR) were applied to estimate potential associations between multiple metals and the risk of MetS. Serum levels of Sb and Fe were significantly higher than the controls (0.58 μg/L vs 0.46 μg/L, 2167 μg/L vs 2042 μg/L, p < 0.05), while Mg was significantly lower (20035 μg/L vs 20,394 μg/L, p < 0.05). An increased risk of MetS was associated with higher serum Sb levels (adjusted odds ratio (OR) = 1.61 for the highest tertile vs. the lowest tertile, 95% CI = 1.08-2.40, p-trend = 0.018) and serum Fe levels (adjusted OR = 1.55 for the highest tertile, 95% CI = 1.04-2.33, p-trend = 0.032). Higher Mg levels in serum may have potential protective effects on the development of MetS (adjusted OR = 0.61 for the highest tertile, 95% CI = 0.41-0.91, p-trend = 0.013). A joint exposure analysis by the BKMR model revealed that the mixture of 12 metals (except Tl and Cd) was associated with increased risk of MetS. Our results indicated that exposure to Sb and Fe might increase the risk of MetS in an elderly population living in mining-intensive areas. Further work is needed to confirm the protective effect of Mg on MetS.

Association between barium exposed, CYP19A1 and central obesity: A cross-sectional study in rural China

BACKGROUND

obesity is a major risk factor for many metabolic diseases such as diabetes and cardiometabolic diseases. This study aimed to evaluate the association of plasma and urinary barium concentrations, CYP19A1 gene polymorphisms, and their interaction with central obesity in a rural Chinese population.

METHODS

restricted cubic spline model was used to explore the dose-response relationship between barium and the risk of developing central obesity and waist circumference; logistic regression model was used to assess the association between barium, CYP19A1 gene polymorphisms and their interaction with central obesity.

RESULTS

the results of the restricted cubic spline model showed that plasma barium concentration was linearly associated with the risk of developing central obesity and non-linearly associated with waist circumference. Logistic regression analysis showed that participants with Q4 plasma barium concentration exhibited a higher risk of central obesity compared to participants with Q1 barium concentration; participants carrying the rs10046-AA gene exhibited a lower risk of central obesity than those carrying the rs10046-G(GG+GA) gene; participants carrying the rs10046-GA genotype showed 1.754 times higher risk of central obesity than those carrying rs10046-GG+AA genotype. There was a significant interaction between plasma barium and CYP19A1 gene polymorphism on central obesity.

CONCLUSION

the development of central obesity was associated with plasma barium and CYP19A1.

Recent advances in the application of ionomics in metabolic diseases

Trace elements and minerals play a significant role in human health and diseases. In recent years, ionomics has been rapidly and widely applied to explore the distribution, regulation, and crosstalk of different elements in various physiological and pathological processes. On the basis of multi-elemental analytical techniques and bioinformatics methods, it is possible to elucidate the relationship between the metabolism and homeostasis of diverse elements and common diseases. The current review aims to provide an overview of recent advances in the application of ionomics in metabolic disease research. We mainly focuses on the studies about ionomic or multi-elemental profiling of different biological samples for several major types of metabolic diseases, such as diabetes mellitus, obesity, and metabolic syndrome, which reveal distinct and dynamic patterns of ion contents and their potential benefits in the detection and prognosis of these illnesses. Accumulation of copper, selenium, and environmental toxic metals as well as deficiency of zinc and magnesium appear to be the most significant risk factors for the majority of metabolic diseases, suggesting that imbalance of these elements may be involved in the pathogenesis of these diseases. Moreover, each type of metabolic diseases has shown a relatively unique distribution of ions in biofluids and hair/nails from patients, which might serve as potential indicators for the respective disease. Overall, ionomics not only improves our understanding of the association between elemental dyshomeostasis and the development of metabolic disease but also assists in the identification of new potential diagnostic and prognostic markers in translational medicine.

A Longitudinal Study of Exposure to Manganese and Incidence of Metabolic Syndrome

The association between manganese (Mn) and metabolic syndrome (MetS) is unclear, and no prior study has studied this association longitudinally. The aim of this study was to assess longitudinal associations of Mn exposure with MetS and metabolic outcomes. We used data from the San Luis Valley Diabetes Study (SLVDS), a prospective cohort from rural Colorado with data collected from 1984−1998 (n = 1478). Urinary Mn was measured at baseline (range = 0.20−42.5 µg/L). We assessed the shape of the cross-sectional association between Mn and MetS accounting for effect modification by other metals at baseline using Bayesian kernel machine regression. We assessed longitudinal associations between baseline quartiles of Mn and incident MetS using Fine and Gray competing risks regression models (competing risk = mortality) and between quartiles of Mn and metabolic outcomes using linear mixed effects models. We did not observe evidence that quartiles of Mn were associated with incident MetS (p-value for trend = 0.52). Quartiles of Mn were significantly associated with lower fasting glucose (p-value for trend < 0.01). Lead was found to be a possible effect modifier of the association between Mn and incident MetS. Mn was associated with lower fasting glucose in this rural population. Our results support a possible beneficial effect of Mn on diabetic markers.

Associations Between Plasma Metals and Cognitive Function in People Aged 60 and Above

The objective of the study was to explore the relationship between the plasma levels of 22 metals and cognition status in older adults aged 60 years and above. A cross-sectional survey was conducted between 2018 and 2019. Inductively coupled plasma mass spectrometry (ICP-MS) was used to detect the concentrations of metals, and a mini-mental state examination (MMSE) questionnaire was used to estimate the cognition status of the elderly. Based on the years of education and MMSE scores, the participants were separated into the normal and impaired cognition groups. Lasso regression, logistic regression, and restricted cubic spline models were used to explore the relationship between the metals and cognitive status. A total of 1667 subjects were included in the study, and 333 (19.97%) of the participants had impaired cognition. Then, 12 metals, including Al, Fe, Ni, Cu, As, Se, Rb, Sr, Mo, Cd, Sn, and Sb were selected by lasso regression. Before the multivariate adjustment, Al and Cu were associated with the risk of increasing cognitive impairment (OR = 1.756, 95% CI: 1.166-2.646, P = 0.007; OR = 1.519, 95% CI: 1.050-2.197, P = 0.026, respectively). By contrast, Rb was associated with a decrease in the risk of cognitive impairment (OR = 0.626, 95% CI: 0.427-0.918, P = 0.017), but Cd was significantly associated with an increase in this risk (OR = 1.456, 95% CI: 1.003-2.114, P = 0.048). After multivariate adjustment, only Al (OR = 1.533, 95% CI: 1.000-2.350, P = 0.050) maintained a borderline difference with the risk of cognitive impairment. A significant positive correlation was found between the risk of cognitive impairment and Al, Cu, and Cd, contrary to the negative correlation found with Rb.

Manganese Exposure and Metabolic Syndrome: A Systematic Review and Meta-Analysis

Manganese (Mn) is an essential element acting as a co-factor of superoxide dismutase, and it is potentially beneficial for cardiometabolic health by reducing oxidative stress. Although some studies have examined the relationship between Mn and metabolic syndrome (MetS), no systematic review and meta-analysis has been presented to summarize the evidence. Therefore, the present review examined the association between dietary and environmental Mn exposure, and MetS risk. A total of nine cross-sectional studies and three case-control studies were included, which assessed Mn from diet, serum, urine, and whole blood. The association of the highest Mn level from diet (three studies, odds ratio (OR): 0.83, 95% confidence interval (C.I.) = 0.57, 1.21), serum (two studies, OR: 0.87, 95% C.I. = 0.66, 1.14), urine (two studies, OR: 0.84, 95% C.I. = 0.59, 1.19), and whole blood (two studies, OR: 0.92, 95% C.I. = 0.53, 1.60) were insignificant, but some included studies have suggested a non-linear relationship of urinary and blood Mn with MetS, and higher dietary Mn may associate with a lower MetS risk in some of the included studies. While more evidence from prospective cohorts is needed, future studies should use novel statistical approaches to evaluate relative contribution of Mn on MetS risk along with other inter-related exposures.