Identifying Effects of Urinary Metals on Type 2 Diabetes in U.S. Adults: Cross-Sectional Analysis of National Health and Nutrition Examination Survey 2011-2016

Association Between Serum Concentrations of (Certain) Metals and Type 2 Diabetes Mellitus

The findings regarding trace element concentrations in patients diagnosed with type 2 diabetes and healthy controls are inconsistent, and therefore, we determined to gather them in the form of a review to further indicate the need for more advanced knowledge development. In our study, we reviewed articles and studies that involved the topics of micronutrient and metal associations with the occurrence and development of type 2 diabetes. We mainly included works regarding human-based studies, but with limited research results, animal-based research was also taken into account. With some newer studies, we reached for initial assumptions of previous statements. The results indicated that higher serum levels of lead, cadmium, arsenic, bromine, barium, strontium, nickel, aluminum, calcium, copper, and ferritin are positively associated with diabetic prevalence. Both too-low and too-high levels of zinc, selenium, and magnesium may be connected to the development of diabetes. Chromium has the capability of insulin response modulation, with enhanced insulin-cell binding, and thus, lower serum levels of chromium can be found in diabetic patients. There are contradictory discoveries regarding manganese. Its supplementation can possibly cease the development of insulin resistance and type 2 diabetes. On the contrary, other studies reported that there is no such connection. Our work indicates that, as micronutrients play a significant role in the pathogenesis of metabolic disorders, more research regarding their bodily homeostasis and type 2 diabetes should be conducted.

Accelerated Longitudinal Glycemic Changes in Relation to Urinary Toxic/Essential Metals and Metal Mixtures Among Mexican Americans Living in Starr County, Texas

OBJECTIVE

Metal and metalloid exposures (hereafter "metals") are associated with adverse health outcomes, including type 2 diabetes; however, previous studies were largely cross-sectional or underpowered. Furthermore, underserved racial and ethnic groups are underrepresented in environmental health research despite having higher rates of type 2 diabetes and a greater risk of metal exposures. Consequently, we evaluated continuous glycemic traits in relation to baseline urinary toxic metal, essential metal, and metal mixtures in a cohort of Mexican American adults.

RESEARCH DESIGN AND METHODS

A total of 510 participants were selected based upon self-reported diabetes status and followed over 3 years. Urinary metals were assessed at baseline. Linear mixed-effects models were used to estimate per-month changes in hemoglobin A1c, fasting plasma glucose, and postload glucose in relation to urinary metal levels. Multiple statistical approaches were used to assess the associations between glycemic traits and metal mixtures.

RESULTS

After adjustment, higher urinary levels of arsenic, selenium, copper, molybdenum, nickel, and tin were associated with faster increases in measures of glycemia. The toxic metal mixture composed of arsenic, lead, cadmium, nickel, and tin was associated with faster increases in postload glucose. Using postload glucose criteria, highest versus lowest arsenic was predicted to accelerate conversion of normoglycemia to prediabetes and diabetes by 23 and 65 months, respectively.

CONCLUSIONS

In this underrepresented, high-risk Mexican American population, exposure to toxic metals and alterations in essential metal homeostasis were associated with faster increases in glycemia over time that may accelerate type 2 diabetes development.

Bisphenol mixtures, metal mixtures and type 2 diabetes mellitus: Insights from metabolite profiling

BACKGROUND

Little is known about the combined effect of bisphenol mixtures and metal mixtures on type 2 diabetes mellitus (T2DM) risk, and the mediating roles of metabolites.

METHODS

The study included 606 pairs of T2DM cases and controls matched by age and sex, and information of participants was collected through questionnaires and laboratory tests. Serum bisphenol and plasma metal concentrations were measured using ultra-performance liquid chromatography-mass spectrometry (UPLC-MS/MS) and inductively coupled plasma-mass spectrometry (ICP-MS), respectively. Widely targeted metabolomics was employed to obtain the serum metabolomic profiles. Conditional logistic regression models were used to assess the single associations of bisphenols and metals with T2DM risk after multivariable adjustment. Additionally, the joint effects of bisphenol mixtures and metal mixtures were examined using quantile-based g-computation (QG-C) models. Furthermore, differential metabolites associated with T2DM were identified, and mediation analyses were performed to explore the role of metabolites in the associations of bisphenols and metals with T2DM risk.

RESULTS

The results showed bisphenol mixtures were associated with an increased T2DM risk, with bisphenol A (BPA) identified as the primary contributor. While the association between metal mixtures and T2DM remained inconclusive, cobalt (Co), iron (Fe), and zinc (Zn) showed the highest weight indices for T2DM risk. A total of 154 differential metabolites were screened between the T2DM cases and controls. Mediation analyses indicated that 9 metabolites mediated the association between BPA and T2DM, while L-valine mediated the association between Zn and T2DM risk.

CONCLUSIONS

The study indicated that BPA, Co, Fe, and Zn were the primary contributors to increased T2DM risk, and metabolites played a mediating role in the associations of BPA and Zn with the risk of T2DM. Our findings contribute to a better understanding of the mechanisms underlying the associations of bisphenols and metals with T2DM.

Association between machine learning-assisted heavy metal exposures and diabetic kidney disease: a cross-sectional survey and Mendelian randomization analysis

Background and objective

Heavy metals, ubiquitous in the environment, pose a global public health concern. The correlation between these and diabetic kidney disease (DKD) remains unclear. Our objective was to explore the correlation between heavy metal exposures and the incidence of DKD.

Methods

We analyzed data from the NHANES (2005-2020), using machine learning, and cross-sectional survey. Our study also involved a bidirectional two-sample Mendelian randomization (MR) analysis.

Results

Machine learning reveals correlation coefficients of -0.5059 and - 0.6510 for urinary Ba and urinary Tl with DKD, respectively. Multifactorial logistic regression implicates urinary Ba, urinary Pb, blood Cd, and blood Pb as potential associates of DKD. When adjusted for all covariates, the odds ratios and 95% confidence intervals are 0.87 (0.78, 0.98) (p = 0.023), 0.70 (0.53, 0.92) (p = 0.012), 0.53 (0.34, 0.82) (p = 0.005), and 0.76 (0.64, 0.90) (p = 0.002) in order. Furthermore, multiplicative interactions between urinary Ba and urinary Sb, urinary Cd and urinary Co, urinary Cd and urinary Pb, and blood Cd and blood Hg might be present. Among the diabetic population, the OR of urinary Tl with DKD is a mere 0.10, with a 95%CI of (0.01, 0.74), urinary Co 0.73 (0.54, 0.98) in Model 3, and urinary Pb 0.72 (0.55, 0.95) in Model 2. Restricted Cubic Splines (RCS) indicate a linear linkage between blood Cd in the general population and urinary Co, urinary Pb, and urinary Tl with DKD among diabetics. An observable trend effect is present between urinary Pb and urinary Tl with DKD. MR analysis reveals odds ratios and 95% confidence intervals of 1.16 (1.03, 1.32) (p = 0.018) and 1.17 (1.00, 1.36) (p = 0.044) for blood Cd and blood Mn, respectively.

Conclusion

In the general population, urinary Ba demonstrates a nonlinear inverse association with DKD, whereas in the diabetic population, urinary Tl displays a linear inverse relationship with DKD.

Leukocyte Telomere Length Mediates the Associations between Blood Lead and Cadmium with Hypertension among Adults in the United States: A Cross-Sectional Study

There is evidence to support the links between lead and cadmium exposure with hypertension and also with leukocyte telomere length (LTL). The objective of this study is to investigate the role that LTL may play in the relationship between lead and cadmium exposure and hypertension. This study consisted of 3718 participants from the National Health and Nutrition Examination Survey (NHANES) 1999-2002. Logistic regression was used to analyze the relationship between blood metals with hypertension, and the mediating model was used to evaluate the mediating effect of LTL. In the fully adjusted model, both blood lead and cadmium ln-transformed concentrations were significantly positively associated with hypertension risk, as were all quartiles of blood lead. Additionally, we observed positive linear dose-response relationships with hypertension by restricted cubic spline analysis (both p overall < 0.001, p non-linear = 0.3008 for lead and p non-linear = 0.7611 for cadmium). The ln-transformed blood lead and cadmium concentrations were associated with shorter LTL. LTL was inversely related to hypertension and the OR was 0.65 (95% CI: 0.47 to 0.89). Furthermore, LTL had mediating effects on the associations of blood lead and cadmium with hypertension risk, and the mediation proportions were 2.25% and 4.20%, respectively. Our findings suggested that exposure to lead and cadmium raised the risk of hypertension, while LTL played as a mediating factor.

Associations of metals and metal mixtures with glucose homeostasis: A combined bibliometric and epidemiological study

This study employs a combination of bibliometric and epidemiological methodologies to investigate the relationship between metal exposure and glucose homeostasis. The bibliometric analysis quantitatively assessed this field, focusing on study design, predominant metals, analytical techniques, and citation trends. Furthermore, we analyzed cross-sectional data from Beijing, examining the associations between 14 blood metals and 6 glucose homeostasis markers using generalized linear models (GLM). Key metals were identified using LASSO-PIPs criteria, and Bayesian kernel machine regression (BKMR) was applied to assess metal mixtures, introducing an "Overall Positive/Negative Effect" concept for deeper analysis. Our findings reveal an increasing research interest, particularly in selenium, zinc, cadmium, lead, and manganese. Urine (27.6%), serum (19.0%), and whole blood (19.0%) were the primary sample types, with cross-sectional studies (49.5%) as the dominant design. Epidemiologically, significant associations were found between 9 metals-cobalt, copper, lithium, manganese, nickel, lead, selenium, vanadium, zinc-and glucose homeostasis. Notably, positive-metal mixtures exhibited a significant overall positive effect on insulin levels, and notable interactions involving nickel were identified. These finding not only map the knowledge landscape of research in this domain but also introduces a novel perspective on the analysis strategies for metal mixtures.

Investigating the impact of elevated urinary trace elements on non-alcoholic fatty liver disease using vibration-controlled transient elastography

Introduction

Non-alcoholic fatty liver disease (NAFLD) is a global public health concern. However, limited data are available on urinary trace elements and NAFLD caused by various exposure factors. This study aimed to investigate the relationship between the presence of 16 trace elements in urine and NAFLD using data from the National Health and Nutrition Examination Survey (NHANES).

Methods

By utilizing the NHANES data from 2017 to 2018, 1613 participants who fulfilled the research criteria were identified from the initial pool of 2979 participants with available urine trace element detection data. Among them, 706 individuals had been diagnosed with NAFLD based on a coefficient of attenuation parameter (CAP) value of at least 274 db/m, determined using vibration-controlled transient elastography (VCTE); whereas the remaining 907 participants were classified as non-NAFLD. The data obtained were used to construct univariate and multivariate logistic regression models and restricted cubic spline models (RCS) analyses.

Results

The presence of arsenic, iodine, barium, cesium, molybdenum, lead, tin, and tungsten in the urine of individuals with NAFLD showed a positive correlation with the likelihood of developing NAFLD. The risk of NAFLD had a non-linear dose-dependent relationship with urinary iodine, molybdenum, barium, and cesium. NAFLD was also associated with elevated levels of barium and cesium in urine, which were identified as significant risk factors.

Conclusion

These findings suggest a positive association between exposure to trace elements in the urine and the risk of NAFLD. Specifically, urinary barium and cesium appeared to have the greatest impact on the risk of NAFLD. These results provide novel insights into the diagnosis and treatment of NAFLD.

Associations Between Individual and Combined Metal Exposures in Whole Blood and Kidney Function in U.S. Adults Aged 40 Years and Older

The effects of metal exposure on kidney function have been reported in previous literature. There is limited and inconsistent information on the associations between individual and combined exposures to metals and kidney function among the middle-aged and older population. The aim of this study was to clarify the associations of exposure to individual metals with kidney function while accounting for potential coexposure to metal mixtures and to evaluate the joint and interactive associations of blood metals with kidney function. A total of 1669 adults aged 40 years and older were enrolled in the present cross-sectional study using the 2015-2016 National Health and Nutrition Examination Survey (NHANES). Single-metal and multimetal multivariable logistic regression models, quantile G-computation, and Bayesian kernel machine regression models (BKMR) were fitted to explore the individual and joint associations of whole blood metals [lead (Pb), cadmium (Cd), mercury (Hg), cobalt (Co), manganese (Mn), and selenium (Se)] with the odds of decreased estimated glomerular filtration rate (eGFR) and albuminuria. A decreased eGFR was defined as an eGFR ≤ 60 mL/min per 1.73 m2, and albuminuria was categorized as a urinary albumin-creatinine ratio (UACR) of ≥ 30.0 mg/g. The results from quantile G-computation and BKMR indicated positive associations between exposure to the metal mixture and the prevalence of decreased eGFR and albuminuria (all P values < 0.05). These positive associations were mainly driven by blood Co, Cd, and Pb. Furthermore, blood Mn was identified as an influential element contributing to an inverse correlation with kidney dysfunction within metal mixtures. Increasing blood Se levels were negatively associated with the prevalence of decreased eGFR and positively associated with albuminuria. In addition, a potential pairwise interaction between Mn-Co on decreased eGFR was identified by BKMR analysis. Findings from our study suggested a positive association between exposure to the whole blood metal mixture and decreased kidney function, with blood Co, Pb, and Cd being the main contributors to this association, while Mn demonstrated an inverse relationship with renal dysfunction. However, as our study was cross-sectional in nature, further prospective studies are warranted to better understand the individual and combined effects of metals on kidney function.

Single and combined associations of blood lead and essential metals with serum lipid profiles in community-dwelling adults

Background

Although several studies have examined the relationships between lead (Pb) exposure and serum lipid profiles, the associations of the metal mixture, including lead (Pb) and essential metals with lipid profiles, remain unclear.

Objective

To investigate the associations of the metal mixture including Pb and essential metals [magnesium (Mg), manganese (Mn), copper (Cu), iron (Fe), zinc (Zn), and calcium (Ca)] with serum lipid profiles [total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C)], as well as the potential interactions among the metals.

Methods

Nine hundred and ninety-eight Chinese community-dwelling adults completed a questionnaire and underwent checkups of anthropometric parameters, serum lipid profile levels (TC, TG, LDL-C, and HDL-C), and blood metal concentrations (Pb, Mg, Mn, Cu, Fe, Zn, and Ca). The multivariable linear regression, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) were applied to evaluate the single and combined associations of blood Pb and essential metals with serum lipid profiles.

Results

In the multivariable linear regression model, the blood Pb was positively associated with TC, LDL-C, and HDL-C (p < 0.05, all), and the blood Mg were positively associated with serum TC, LDL-C, and Ln TG (p < 0.05, all). In the WQS regression and BKMR models, the metal mixture of blood Pb and the essential metals was positively associated with all of the serum lipid profiles. In addition, an inverse U-shaped association of Pb with Ln TG and the positive interactive effect between blood Pb and Mg levels on TC and LDL-C were found.

Conclusion

The levels of blood Pb, together with the essential metals, especially Mg levels, are suggested to be considered when assessing dyslipidemia risk. However, more evidence is still needed to validate the conclusions.

Cross-Sectional Association of Blood Selenium with Glycemic Biomarkers among U.S. Adults with Normoglycemia in the National Health and Nutrition Examination Survey 2013–2016

Selenium (Se) remains to have an inconsistent relationship with glycemic biomarkers and the risk of developing type 2 diabetes (T2D). Few studies have investigated the relationship between blood Se and glycemic biomarkers among people with normoglycemia. We conducted a cross-sectional analysis using the U.S. National Health and Nutrition Examination Survey 2013–2016. Multivariable linear regression models were developed to examine the associations of blood Se with glycemic biomarkers, namely, fasting plasma glucose (FPG), hemoglobin A1c (HbA1c), insulin, and the oral glucose tolerance test (OGTT). Blood Se was treated as continuous (per log-10 increment) and categorical exposure (in quartiles) in separate regression models. We assessed the dose–response relationships by restricted cubic spline analysis. After excluding the participants with T2D or incomplete data, 2706 participants were analyzed. The highest quartile of blood Se was associated with increased FPG [adjusted β = 0.12, 95% Confidence Intervals (CI) = 0.04, 0.20], OGTT (adjusted β = 0.29, 95% CI = 0.02, 0.56), HbA1c (adjusted β = 0.04, 95% CI = 0.00, 0.07), and insulin (adjusted β = 2.50, 95% CI = 1.05, 3.95) compared with the lowest quartile. Positive associations were also observed between every log-10 increment of blood Se level and glycemic biomarkers, except for OGTT. A positive linear dose–response relationship existed between blood Se and FPG (P_overall = 0.003, P_nonlinear = 0.073) and insulin (P_overall = 0.004, P_nonlinear =0.060). BMI, age, and smoking status modified the associations of the highest quartile of Se (compared with the lowest quartile) with glycemic biomarkers. Overall, positive associations of blood Se with glycemic biomarkers were observed among U.S. adults with normoglycemia. These findings implied that people with normoglycemia need to be aware of the level of Se and other mineral intakes from diet and supplements. Further research is required to identify the mechanisms of excess Se in the progression of diabetes.

Joint Associations of Food Groups with All-Cause and Cause-Specific Mortality in the Mr. OS and Ms. OS Study: A Prospective Cohort

Despite continuous growth in dietary pattern research, the relative importance of each dietary component in the overall pattern and their joint effects on mortality risk have not been examined adequately. We explored the individual and joint associations of multiple food groups with all-cause and cause-specific mortality (cardiovascular disease (CVD) or cancer), by analyzing data from a cohort of 3995 Hong Kong Chinese older adults in the Mr. Osteoporosis (OS) and Ms. OS Study. Cox proportional hazards models were used to examine the associations of food groups with mortality risk. The individual and joint contribution of food groups to mortality risk has been quantified by a machine learning approach, i.e., the Quantile G-Computation. When comparing the highest with the lowest quartile of intake, dark green and leafy vegetables (hazard ratio (HR) = 0.82, 95% confidence interval (CI) = 0.70 to 0.96, Ptrend = 0.049), fruit (HR = 0.79, 95% CI = 0.68 to 0.93, Ptrend = 0.006), legumes (HR = 0.75, 95% CI = 0.63 to 0.87, Ptrend = 0.052), mushroom and fungi (HR = 0.76, 95% CI = 0.65 to 0.88, Ptrend = 0.023), soy and soy products (HR = 0.77, 95% CI = 0.66 to 0.90, Ptrend = 0.143), and whole grains (HR = 0.76, 95% CI = 0.65 to 0.89, Ptrend = 0.008) were inversely associated with all-cause mortality. Legume intake was associated with a lower risk of CVD mortality, while fruit, nuts, soy and soy products were associated with a lower risk of cancer mortality. From the Quantile G-Computation, whole grains, legumes, fruits, mushroom and fungi, soy and soy products had a higher relative weighting on mortality risk, and the joint effect of food groups was inversely associated with the mortality risk due to all-causes (HR = 0.39, 95% CI = 0.27 to 0.55), CVD (HR = 0.78, 95% CI = 0.67 to 0.91), and cancer (HR = 0.31, 95% CI = 0.15 to 0.65). From a sex-stratified analysis, most associations between food groups (whole grains, legumes, fruits, mushroom and fungi, soy and soy products) and mortality risk remained significant among men. In conclusion, whole grains, legumes, fruits, mushroom and fungi, soy and soy products were the main contributors to a reduction in mortality risk, and their joint effects were stronger than individual food groups. Moreover, the sex-specific association of sweets and desserts with cancer mortality may be worth further investigation.

Metal Exposure, Smoking, and the Risk of COPD: A Nested Case-Control Study in a Chinese Occupational Population

Chronic obstructive pulmonary disease (COPD) was the third leading cause of death worldwide in 2019, with a significant disease burden. We conducted a nested case-control study using data from the China Metal-Exposed Workers Cohort Study (Jinchang Cohort) and assessed the associations of exposure to metals and tobacco smoking with the risk of COPD. We used the logistic regression model and the interaction multiplication model to assess the independent and combined effects of heavy metal and smoke exposure on COPD. The cumulative incidence of COPD was 1.04% in 21,560 participants during a median of two years of follow-up. The risk of COPD was significantly elevated with an increase in the amount of tobacco smoked daily (p < 0.05), the number of years of smoking (p_trend < 0.05), and the number of packs of cigarettes smoked per year (p_trend < 0.01). Compared with the low metal exposure group, the adjusted OR was 1.22 (95% CI: 0.85-1.76) in the medium exposure group (mining/production workers) and 1.50 (95% CI: 1.03-2.18) in the high exposure group; smoking and metal exposure had a combined effect on the incidence of COPD (p_interaction < 0.01), with an OR of 4.60 for those with >40 pack-years of smoking who also had the highest metal exposures. Both exposures to metals and smoking were associated with the risk of COPD, and there was an interaction between the two exposures for the risk of COPD.